Repository files navigation

This page lists resources for mineral exploration and machine learning, generally with useful code and examples.ML and Data Science is a huge field, these are resources I have found useful and/or interesting to me in practice.Links currently to a fork of a repository are because I have changed something to use and put in a list for reference.Resources are also given for data analysis, transformation and visualisation as that is most of the work.

Suggestions welcome: open a discussion, issue or pull request.

• Prospectivity
• Geology
• Natural Language Processing
• Remote Sensing
• Data Quality
• Community
• Cloud providers
• Domains
• Overview
• Web Services
• Data Portals
• Tools
• Ontologies
• Books
• Datasets
• Papers
• Other
• General Interest

• Map of Github
• DeepWiki -> automagic wiki style analysis of a repo via llm

• UNCOVER-ML Framework
  • Geo-Wavelets
  • ML-Preprocessing
  • GIS ML Workflow
• EIS Toolkit -> Python library for mineral prospectivity mapping from EIS Horizon EU Project
• PySpatialML -> Library that facilitates prediction and handling for raster machine learning automatically to geotiff, etc.
• DARPA Criticalmaas
  • competition info
• scikit-map
• TorchGeo -> Pytorch library for remote sensing style models
• terratorch -> Flexible fine-tuning framework for Geospatial Foundation Models
• TorchSpatial
  • paper
• geodl
• Geo Deep Learning -> Simple deep learning framework based on RGB
• AIDE: Artificial Intelligence for Disentangling Extremes
• ExPloRA -> ExPLoRA: Parameter-Efficient Extended Pre-training to Adapt Vision Transformers under Domain Shifts
  • Website
  • paper
• (https://www.researchgate.net/profile/Miguel-Angel-Fernandez-Torres/publication/381917888_The_AIDE_Toolbox_Artificial_intelligence_for_disentangling_extreme_events/links/66846648714e0b03153f38ae/The-AIDE-Toolbox-Artificial-intelligence-for-disentangling-extreme-events.pdf)
• pyClusterwise
  • [paper] ->https://www.sciencedirect.com/science/article/pii/S0169136825001519?via%3Dihub -> Clustering in geo-data science: Navigating uncertainty to select the most reliable method
• GeoStat Framework -> Group of repositories with kriging and other

• CAST -> Caret Applications for Spatio-Temporal models
• geodl -> semantic segmentation of geospatial data using convolutional neural network-based deep learning
  • paper

• geotargts -> Extension of targets to terra and stars

• Iron oxide copper-gold mineral potential maps
• [Lateritic Ni-Co prospectivity modeling in eastern Australia using an enhanced generative adversarial network and positive-unlabeled bagging] -> (https://zenodo.org/records/14037494)
  • paper
• Machine learning for geological mapping : algorithms and applications -> PhD thesis with code and data
• minpot-toolkit -> Example of Hoggard et al Lab Boundary analysis with Sedimentary copper
• MPM-WofE -> Mineral Potential Mapping - Weights of Evidence
• Porphyry Copper Spatio-Temporal Exploration
  • paper
• Prospectivity Mapping of Ni-Co Laterites
• Transform 2022 Tutorial -> Random forest example
  • Video
• Tin-Tungsten
  • Collab

• Explorer Challenge -> OZ Minerals run competition with Data Science introduction

• Gawler_MPM -> Cobalt, Chromium, Nickel
  • Paper
• Geophysical Data Clustering in the Gawler Craton
  • [Zenodo Data](Automated detection of mineralization-related craton structures using geophysical data and unsupervised machine learning)

• Winners -> SARIG data information
• Caldera -> Caldera Analytics analysis
• IncertoData
• Butterworth and Barnett -> Butterworth and Barnett entry
• Data Driven Mineralisation Mapping

• SpatioTemporAl Mineral Prospectivity (STAMP) Modelling
  • Zenodo Data

• Transfer Prospectivity Learnnig
  • paper -> Porphyry-type mineral prospectivity mapping with imbalanced data via prior geological transfer learning

• Machine learning to classify ore deposits from tectonomagmatic properties

• Mapa Preditivo -> Brazil student project
• Course_Predictive_Mapping_USP -> Course Project
• Mineral Prospectivity Mapping
• 3D Weights of Evidence
• Geological Complexity SMOTE -> includes fractal analysis
  • paper
• MPM Jurena -> Jurena Mineral Province

• MPM by ensemble learning -> Qingchengzi Pb-Zn-Ag-Au polymetallic district China
• Mineral Prospectivity Prediction Convolutional Neural Networks -> CNN Example with a few architectures [a paper by this author uses GoogleNet]
• Mineral Prospectivity Prediction by CSAE
• Mineral Prospectivity Prediction by CAE
  • paper

• Mineral Prospectivity Mapping ML

• A machine learning–based approach to regional-scale mapping of sensitive glaciomarine clay combining airborne electromagnetics and geotechnical data
  • paper

• Brazil Predictive Geology Maps -> Work by the Brazil geological survey
• depth to bedrock(Evaluating spatially enabled machine learning approaches for depth to bedrock mapping)
  • paper
• DL-RMD -> A geophysically constrained electromagnetic resistivity model database for deep learning applications
• Earthscape -> EarthScape: A Multimodal Dataset for Surficial Geologic Mapping and Earth Surface Analysis
  • paper
• Geological Image Classifier
• Geological mapping in the age of artificial intelligence -> Geological mapping in the age of artificial intelligence
• GeolNR -> Geological Implicit Neural Representation for three-dimensional structural geological modelling applications
  • paper
• mapeamento_litologico_preditivo
• Mapping Global Lithospheric Mantle Pressure-Temperature Conditions by Machine-Learning Thermobarometry
  • paper
• Neural Rock Typing
• PEACE -> Empowering Geologic Map Holistic Understanding with MLLMs)*paper -> GeoMap-Bench
• SimCLR Core Disturbance
  • paper
• West Musgraves Geology Uncertainty -> Uncertainty map prediction with entropy analysis: highly useful
• Non Stationarity Mitigation Transformer
  • Collab -> Notebook
    • paper
• Bedrock-vs-sediment
  • [paper](https://www.researchgate.net/publication/370929945_Where_are_the_outcrops_Automatic_delineation_of_bedrock_from_sediments_using_Deep-Learning_techniques
• autoencoders_remotesensing
  • paper -> Remote sensing framework for geological mapping via stacked autoencoders and clustering
• Geological Section Generated based on per-pixel linked lists
  • paper

• Into the Noddyverse -> a massive data store of 3D geological models for machine learning and inversion applications
  • Original Zenondo repository
  • website
  • New Geonetwork Location
• Geo-Bench

• Deep Learning Lithology
• Rock Protolith Predictor
• SA Geology Lithology Predictions
• Automated Well Log Correlation
• dawson-facies-2022 -> Transfer learning for geological images
  • paper - > Impact of dataset size and convolutional neural network architecture on transfer learning for carbonate rock classification
• Litho-Classification -> Volcanic facies Classification using Random Forest
• Multi-view ensemble machine learning approach for 3D modeling using geological and geophysical data
  • paper
• SedNet
  • paper
• SCB-Net -> Lithological mapping using Spatially Constrained Bayesian Network
  • paper

• Heterogenous Drilling - Nicta/Data61 project report for looking at modelling using drillholes that don't go far enough
• corel -> smart computer vision model that identifies facies and performs rock typing on core images

• Sub3DNet1.0: a deep-learning model for regional-scale 3D subsurface structure mapping

• Predicatops -> Stratigraphic predication designed for hydrocarbon
• stratal-geometries -> Predicting Stratigraphic Geometries from Subsurface Well Logs

• APGS -> Structural geology package
• Assessing plate reconstruction models using plate driving force consistency tests -> Jupyter notebook and data
  • paper
• gplately
• [structural geology cookbook](https://github.com/gcmatos/structural-geology-cookbook]
• GEOMAPLEARN 1.0 -> Detecting geological structures from geological maps with machine learning
• Lineament Learning -> Fault prediction and mapping via potential field deep learning and clustering
• LitMod3D -> 3D integrated geophysical-petrological interactive modelling of the lithosphere and underlying upper mantle
• Machine Learning Based Mohometry -> Paleo Crustal Thickness Estimation and Evolution Visualization
  • paper
• others

• GebPy -> generation of geological data for rocks and minerals
• OpenGeoSys -> development of numerical methods for the simulation of thermo-hydro-mechanical-chemical (THMC) processes in porous and fractured media
• Stratigraphics.jl -> Creating 3D stratigraphy from 2D geostatistical processes

• Badlands -> Basin and Landscape Dynamics
• CitcomS -> finite element code designed to solve compressible thermochemical convection problems relevant to Earth's mantle.
• LaMEM -> simulate various thermo-mechanical geodynamical processes such as mantle-lithosphere interaction
• PTatin3D -> studying long time-scale processes relevant to geodynamics [original motivation :toolkit capable of studying high-resolution, three-dimensional models of lithospheric deformation]
• underworld -> Finite element modelling of geodynamics
• Discern detachment of the subducting slab in an ancient subduction zone using machine learning
  • paper

• Feature fusion-enhanced t-SNE image atlas for geophysical features discovery
  • paper

• Structural Geophysics Tools -> QGIS aimed

• Cross-Domain Foundation Model Adaptation: Pioneering Computer Vision Models for Geophysical Data Analysis -> some of code to come
  • paper
• Seismic Foundation Model -> "a new generation deep learning model in geophysics"

• Regolith Depth -> Model
• Complete Radiometrics Grid of Australia with modelled infill

• High resolution conductivity mapping using regional AEM survey
  • Abstract

• TEM-NLnet: A Deep Denoising Network for Transient Electromagnetic Signal with Noise Learning

• Machine Learning and Geophysical Inversion -> reconstruct paper from Y. Kim and N. Nakata (The Leading Edge, Volume 37, Issue 12, Dec 2018)

• https://legacy.fatiando.org/gallery/gravmag/euler_moving_window.html
  • Harmonica version eventually?https://hackmd.io/@fatiando/development-calls-2024?utm_source=preview-mode&utm_medium=rec
• https://notebook.community/joferkington/tutorials/1404_Euler_deconvolution/euler-deconvolution-examples
• https://github.com/ffigura/Euler-deconvolution-plateau

• [Recovering 3D Basement Relief Using Gravity Data Through Convolutional Neural Networks]
  • Data
  • paper
• Stable downward continuation of the gravity potential field implemented using deep learning
  • paper
• Fast imaging for the 3D density structures by machine learning approach
  • paper

• High-resolution aeromagnetic map through Adapted-SRGAN
  • paper
• MagImage2Geo3D
  • paper

• StorSeismic -> An approach to pre-train a neural network to store seismic data features
• PINNtomo -> Seismic tomography using physics-informed neural networks
  • paper

• obspy -> framework for processing seismological

• ML4Rocks -> Some intro work

• Discern detachment of the subducting slab in an ancient subduction zone using machine learning -> Notebook
  • figshare
• Colab notebook -> Google Colab input file for benchmark results of ML-SEISMIC publication
  • paper
• Unleashing the power of MachineLearning in Geodynamics
  • Honours Thesisrelated
• Physics-Infomred Neural Networks for fault slip simulation with rate and state friction law
  • simulation and frictional paramter estimation on slow slip events
  • paper -> Physics-Informed Deep Learning for Estimating the Spatial Distribution of Frictional Parameters in Slow Slip Regions

• CODAinPractice -> Compositional Data Analysis in Practice
• GeoCoDa
• DAN-GRF -> Deep autoencoder network connected to geographical random forest for spatially aware geochemical anomaly detection
  • paper
• Dash Geochemical Prospection -> Web-app classifying stream sediments with K-means
• Enhancing machine learning thermobarometry for clinopyroxene-bearing magmas
  • paper -> Enhancing-ML-Thermobarometry-for-Clinopyroxene-Bearing-Magmas
• Zircon fertility models -> Decision trees to predict fertile zircon from porphyry copper deposits
  • paper
• Machine Learning Zircon Trace Element Tool to Predict Porphyry Deposit Type and Resource Size
  • zenodo archive
  • paper
• geology_class0 -> A machine learning approach to discrimination of igneous rocks and ore deposits by zircon trace elements
  • paper
  • Demo application
  • https://colab.research.google.com/drive/1-bOZgG6Nxt2Rp1ueO1SYmzIqCRiyyYcT
• GeochemPrint
  • paper
• Global geochemistry
• ICBMS Jacobina -> Analysis of pyrite chemistry from a gold deposit
• Interpretation of Trace Element Chemistry of Zircons from Bor and Cukaru Peki: Conventional Approach and Random Forest Classification
• indicator_minerals -> Can PCA can tell the story of the origin of tourmaline?
• Journal of Geochemical Exploration - Manifold
• LewisML -> Analysis of the Lewis Formation
• MICA -> Chemical composition, in Shiny
• Multivariate statistical analysis and bespoke deviation network modeling for geochemical anomaly detection of rare earth elements
  • paper
• Prospectivity mapping of rare earth elements through geochemical data analysis -> Prospectivity mapping of rare earth elements through geochemical data analysis
• QMineral Modeller -> Mineral Chemistry virtual assistant from the Brazilian geological survey
• Secular Changes in the Occurrence of Subduction During the Archean -> Zenodo code archive
  • [paper]https://www.researchgate.net/publication/380289934_Secular_Changes_in_the_Occurrence_of_Subduction_During_the_ArcheanA machine learning approach to discrimination of igneous rocks and ore deposits by zircon trace elements
• geochemical anomaly detection -> Multivariate Outlier Detection in Geochemical Datasets

• DKNN: deep kriging neural network for interpretable geospatial interpolation
  • paper

• spacy -> NLP Library

• Text Extraction -> Text extraction from documents : paid ML as a service, but works very well, can extract tables efficiently
  • Large Scale -> Large scale version
• NASA Concept Tagging -> Keyword prediction
  • API -> API web service
  • Presentation
• Petrography Report Data Extractor
• SA Exploration Topic Modelling -> Topic modelling from exploration reports
• Stratigraph
• Geocorpus
• Portuguese BERT
• BERT CWS
  • paper
• Automated Extraction of Mining Company Drillhole Results
  • Conference Paper

• pdfminer
• pdfplumber -> pdf table extraction
• pikepdf -> pdf image extraction
• PyMuPDF -> pdf parser

• camelot -> pdf text table extraction
• layoutparser -> deep learning layhout detection
• messytables -> find headers and datatypes

• GeoTopicParser

• GIS Metadata parsing -> extract data from xml etc.

• crssuggest -> coordinate reference system suggerstions
• tidyxl

• Apache Tika -> OCR, content analysis
• Parsee PDF Reader - PDF Reading/OCR
• Tesseract -> OCR

• Geoscience Language Models -> processing code pipeline and models [Glove, BERT) retrained on geoscience documents from Canada
  • Datasets -> Data to support models
  • paper -> Geoscience language models and their intrinsic evaluation
  • paper -> Applications of Natural Language Processing to Geoscience Text Data and Prospectivity Modeling
• GeoVec -> Word embedding model trained on 300K geoscience papers
  • GeoVec Model -> OSF Storage for GeoVec model
  • paper
  • GeoVecto Litho -> 3D Models interpolation from word embeddings
  • GeoVEC Playground -> Working with the Padarian GeoVec glove word embeddings model
• GloVe -> Standford library for producing word embeddings
  • gloVE python glove, glove-python highly problematic on windows: here Binary version for Windows installs:
• Mittens -> In memory vectorized glove implementation
• PetroNLP -> Organisation
  • PetroVec -> Portuguese Word Embeddings for the Oil and Gas Industry: development and evaluation
  • Petro KGraph -> Ontological work with petrovec
  • paper -> [UNSEEN]
• wordembeddingsOG -> Portuguese Oil and Gas word embeddings
• Portuguese Word Embeddings
  • Portuguese models
• Spanish Word Embeddings
• Multilingual alignment
  • Overview of approaches
• Application-of-natural-language-processing-for-finding-semantic-relation-of-elusive-natural-resource
  • paper -> Geological Inference from Textual Data using Word Embeddings

• Geo NER Model -> Named entity recognition
• GeoBERT - hugging face repo for model in
  • [paper]https://www.researchgate.net/publication/359186219_Few-shot_learning_for_name_entity_recognition_in_geological_text_based_on_GeoBERT
• GLiNER -> Few shot deep learning NER
• INDUS -> NASA science tailored LLM suite
  • paper
• How to find key geoscience terms in text without mastering NLP using Amazon Comprehend
• OzRock - OzRock: A labeled dataset for entity recognition in geological (mineral exploration) domain

• GAKG -> A Multimodal Geoscience Academic Knowledge Graph (Chinese)
• GeoERE-Net -> Understanding geological reports based on knowledge graphs using a deep learning approach
  • paper
• GeoFault Ontology
• geosim -> Semantically Triggered Qualitative Simulation of a Geological Process
  • [https://www.duo.uio.no/handle/10852/111467](Knowledge Modelling for Digital Geology) -> PhD thesis with two papers
  • SIRIUS GeoAnnotator -> Website example from above
• Ontology CWS
• Stratigraphic Knowledge Graph (StraKG)
  • paper

• pyenchant -> spelling checker

• JiuZhou -> Open Foundation Language Models for Geoscience
  • paper
• Large Language Model for Geoscience
  • Learning Foundation Language Models for Geoscience Knowledge Understanding and Utilization paper
• GeoGalactica -> A Larger foundation language model in Geoscience
  • technical report
• GeoChat -> grounded Large Vision Language Model for Remote Sensing
• LAGDAL -> LLM Matching geology map information to location experiments
• OmniGeo -> Towards a Multimodal Large Language Models for Geospatial Artificial Intelligence

• GeoGPT -> Deep Time Digital Earth Research Group from China project
• GeoGPT -> Deep Time Digital Earth Research Group from China project
• GeoGPT-Research-Project

• GeoAgent -> An LLM Agent for Automatic Geospatial Data Analysis
  • paper

• CNN Sentinel -> Overview about land-use classification from satellite data with CNNs based on an open dataset
• DEA notebooks -> Scalable machine learning example but lots of useful things here
• EASI cookbook notebooks -> CSIRO Earth Analytics platform introductions for ODC style analysis
• DS_UNet -> Unet fusing Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 Multispectral Imager
• Multi Pretext Masked Autoencoder (MP-MAE)
• data
• segment-geospatial -> Segment anything for geospatial uses
• SamGIS -> Segment Anything applied to GIS
• SatMAE++ -> Rethinking Transformers Pre-training for Multi-Spectral Satellite Imagery
• grid-mae -> Investigate using multiscale grids in a Vision Transformer Masked Autoencoder
• ScaleMae
  • paper
• CIMAE -> CIMAE - Channel Independent Masked Autoencoder
• fork -> to give it the name for reference
• [Self-Supervised Representation Learning for Remote Sensing] -> Master's thesis includes the above and comparisons of several models
• U Barn
  • paper
• earthnets
• GeoTorchAI -> GeoTorchAI: A Spatiotemporal Deep Learning Framework
• [pytorcheo](https://github.com/earthpulse/pytorchEO -> Deep Learning for Earth Observation applications and research
• pytorch cloud geotiff optimization
  • paper -> Optimizing Cloud-to-GPU Throughput for Deep Learning With EarthObservation Data
• AiTLAS -> an open-source benchmark suite for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation
• Segmentation Gym -> Gym is designed to be a "one stop shop" for image segmentation on "N-D" - any number of coincident bands in a multispectral image
• deep_learning_alteration_zones
• awesome mining band ratio collection -> collection of simple band ratio uses for highlight various minerals

• awesome remote sensing foundation models
  • ChatEarthNet
  • Zenodo
    • [paper] -> a global-scale image–text dataset empowering vision–language geo-foundation models
• Clay -> An open source AI model and interface for Earth
• [GeoDINO]A Vision Foundation Model for Earth Observation Leveraging DINO Architecture and Sentinel-2 Multi-Spectral Data
• IBM-NASA-GEOSPATIAL Prithvi
• Image segmentation by foundation model finetuning -> For Prithvi
• AM-RADIO: Agglomerative Vision Foundation Model
  • paper -> - Reduce All Domains Into One
• RemoteCLIP -> A Vision Language Foundation Model for Remote Sensing
• SpectralGPT
• zenodo) -> remote sensing foundation model customized for spectral data
  • paper -> Unseen
• [Terramind] (https://github.com/IBM/terramind) -> any-to-any generative foundation model for Earth Observation
  • paper -> Terramind paper

• ASTER Conversion -> Conversion from ASTER hd5 to geotiff NASA github
• HLS Data Resources -> Harmonized Landsat Sentinel wrangling
• sarsen -> xarray based SAR image processing and correction
• openEO -> openEO develops an open API to connect R, Python, JavaScript and other clients to EO cloud back-ends

• Conventional-to-Transformer-for-Hyperspectral-Image-Classification-Survey-2024
  • paper
• Hyperspectral Deep Learning Review
• Hyperspectral Autoencoders
• Deeplearn HSI
• 3DCAE-hyperspectral-classification
• DeHIC
• Rev-Net
  • paper -> A Reversible Generative Network for Hyperspectral Unmixing With Spectral Variability
• Pysptools -> also has useful heuristic algorithms
• Spectral Python
• Spectral Dataset RockSL -> Open spectral dataset
• Unmixing
• Unmamba -> Cascaded Spatial–Spectral Mamba for Blind Hyperspectral Unmixing
  • paper

A Joint Multi-Scale Graph Attention and Classify-Driven Autoencoder Framework for Hyperspectral Unmixing

• CasFormer: Cascaded Transformers for Fusion-aware Computational Hyperspectral Imaging
• Spectral Normalization for Keras
  • paper
• S^2HM^2 -> S2HM2: A Spectral-Spatial Hierarchical Masked Modeling Framework for Self-Supervised Feature Learning and Classification of Large-Scale Hyperspectral Image
  • paper

• Deep Colormap Extraction from Visualizations
  • paper
• Semantic Segmentation for Extracting Historic Surface Mining Disturbance from Topographic Maps -> Example is for coal mines
  • paper
• International Chronostratigraphic Color Codes -> RGB codes and others in spreadsheet and other formats
• LithClass -> USGS version of lithology color codes
• color version
• SeisWiz -> Lightweight python SEG-Y viewer

• Mineral Texture Classification Using Deep Convolutional Neural Networks: An Application to Zircons From Porphyry Copper Deposits
  • paper

• Intelligent Prospector -> Sequential data acquisition planning
• Zenodo
  • paper

• Deep Angle -> Fast calculation of contact angles in tomography images using deep learning

• Network Analysis of Mineralogical Systems
  • Data -> Data from paper here
• Geoanalytics and machine learning
• Machine Learning Subsurface
• ML Geoscience
• Be a Geoscience Detective
• Earth ML -> Some basic tutorials in PyData approaches
• GeoMLA -> Machine Learning algorithms for spatial and spatiotemporal data
• open-geospatial -> Install multiple common packages at once

• Geospatial CLI - List of geospatial command line tools
• Satellite Image Deep Learning
• Earth Observation
• Earth Artificial Intelligence
• Open Source GIS -> Comprehensive overview of the ecosystem

• Geoscience Data Quality for Machine Learning -> Geoscience Data Quality for Machine Learning
• Australian Gravity Data -> Overview and analysis of gravity station data
• Geodiff -> Comparison of vector data
• Redflag -> Analysis of data and an overview to detect problems

• CuML -> CUDA accelerated
  • scikit-learn acceleration notebook
• Dask-ml -> Distributed versions of some common ML algorithms
  • paper
• geospatial-rf -> Functions and wrappers to assist with random forest applications in a spatial context
• Geospatial-ml -> Install multiple common packages at once

• Nested Fusion
  • paper -> Nested Fusion: Dimensionality Reduction and Latent Structure Analysis of Multi-Scale Nested Data for M2020 PIXL RGBU and XRF Data

• scores -> Verifying and evaluating models and predictions with xarray

• NG Boost -> probabilistic regression
• Probabilistic ML
• Bagging PU with BO -> Positive Unlabeled Bagging with Bayesian Optimisation

• GisSOM -> Geospatial centred Self Organising Maps from Finland Geological Survey
  • paper -> example of GisSOM example
• SimpSOM -> Self Organising Maps

• hdbscan
• kmedoids
  • kmedoids
• Picasso

• Bayseg -> Spatial segmentation

• InterpretML -> Interpreting models of tabular data
• InterpretML -> Community addition

• Deep Colormap Extraction -> Trying to extract a data scale from pictures
• Extract and Classify Images from Geoscience Documents

• Xbatcher -> Xarray based data reading for deep learning
  • Cloud native data loaders for machine learning using Zarr and Xarray
• zen3geo -> Xbatcher style data science with pytorch

• Shap Values
• Weight Watcher -> Analyse how well networks are trained
• weightwatcher.ai
• weightwatcher-ai.com -> Professional web version

• Self Supervised -> Pytorch lightning implementations of multiple algorithms
• Simclr
• Awesome self-supervised learning -> Curated list

• Hyperopt
• TPOT Automated ML

• DEA Sandbox
• Cube In A Box

• Software Underground - Community of people interested in exploring the intersection of the subsurface and code
  • Chat Signup - SWUNG community chat signup
  • Mattermost- Community chat service
    • Old Slack Channel(deprecated, see mattermost above)
  • Geoscience Open Source Tie-In
  • Videos
    • Transform 2022
  • Awesome Open Geoscience[note Oil and Gas Biased]
  • Transform 2021 Hacking Examples
  • Segysak 2021 Tutorial
  • T21 Seismic Notebook
  • Practical Seismic with Python
  • Transform 2021 Simpeg
• Pangeo
  • Forum
  • COG Best Practices
• Digital Earth Australia
  • Slack Channel
• Open Source Geospatial Foundation
• OSGeoLive -> Bootable DVD/USB with lots of open source geospatial software
• ASEG -> videos from Australia Society of Exploration Geoscientists
• AI for Geological Modelling and Mapping -> videos from the conference day
• conference

• ec2 Spot Labs -> Making automatically working sith Spot instances easier
• Sagemaker Geospatial ML
• Sagemaker -> ML Managed Service
  • SDK
  • Entrypoint Utilities
  • Workshop 101
  • Training Toolkit

• Shepard -> Automated cloud formation setup of AWS Batch Pipelines: this is great

• Mlmax - Start fast library
• Smallmatter
• Pyutil

• Deep Learning Containers
• Loguru -> Logging library
• AWS GDAL Robot -> Lambda and batch processing of geotiffs
• Serverless Seismic Processing
• LIthops -> multi-cloud distributed computing framework

• Mineral Exploration

• Geology
  • Geologic Ages
  • Lithology
  • Stratigraphy
• Geochemistry
• Geophysics
• Remote Sensing

If listed it is assumed they are generally data, if just pictures like WMS it will say so.

• Critical Minerals and Deposits

• AusGIN
• Geoscience Australia
• Mineral Potential -> WMS
• Geoscience Australia Catalogue Service

• AUSLAMP - > Tennant Creek - MtIsa
• Field Geology
• Deep Lithosphere -> Deep Lithospheric Mineral Potential
• Geochronology -> Geochronology
• Geological Provinces
• WMS -> WMS picture
• EGGS -> Estimates of Geological and Geophysical Surfaces
• Proterozoic Alkaline Rocks - Proterozoic Alkaline Rocks Dataset WFS {also has WMS}
• Cenozoic
• Mesozoic
• Paleozoic
• Archaean
• Stratigraphy -> Stratigraphic Units

• Geophysics Surveys
• Seismic Surveys -> Onshore seismic surveys
• Magnetotelluric -> Northern Australia AUSLAMP Stations

• Ni-Cu-PEGE -> Intrusion hosted Nickel Copper PGE Deposits
• EFTF Area -> Exploring for the future areas
• Temperature -> Interpreted temperature
• DEA -> Digital Earth Australia
• Land Cover
• Waterbodies
• BOM -> Bureau of Meteorology Hydrogeochemistry

• NSW
• WCS
• WFS Mineral Drillholes
• WFS Petroleum Drillholes
• WFS Coal Drillholes
• Seismic -> Seismic and others

• Queensland
• Geoscientific -> Geophysics and Report Index
• Geology
• Regional
• State
• Tenements
• Roads
• Watercourse

• SARIG
• Drillholes
• Geology
• Geophysics
• Prospectivity
• Minerals and Mines
• Remote Sensing
• Seismic
• Tenements

• NTGS -> Northern Territory Geological Survey

• Tasmania WFS
• Tasmania REST
• Boreholes

• Victoria Geonetwork

• Western Australia
• Rest

• GNS -> List of web services

• Brazil Geoportal
• Brazil CPRM

• Ingement
• Mineral Occurrences
• Environmental

• GeoInfo -> Rest services

• SIGAM

• Rest
• Deposits 2018

• Rest

• SIG Andes -> Andes geology

EGDI -> EGDI Minerals

• BGS -> British Geological Survey
• Geoindex -> mineral occurrence example
• Rest -> BGS Rest services
• Inspire 625

• Rest

• deus -> Greenland WMS/WFS
• Deus - Update?

web map

• Rest
• Mineral Locations

• GTK -> Geological Survey of Finland
  • Finland
    • Bedrock Geology
    • Geophysics
    • Ground Surveys
• Arctic Minerals -> Arctic 1M Mineral Occurrences

• BRG

• MBFZ OGC
• MBFZ Rest

• Rest example -> Many more mapservers

• Portugal Geology
• Mineral Occurrences -> WMS
• Cities and Town

• IGR -> WMS only
• IGR minres -> WMS only

• Rest

• Spain
• Geology -> 200K
• 1M -> 1M
• 50K -> 50K
• IGME Geode
• Geophysics
• Copper - Copper
• GeoFPI - > Geology and Minerals South Portuguese Zone
• Water

• SGU Magnetics WMS
• SGU Uranium
• SGU Borehole
• Geophysics metadata

• Geoinform -> [currently suspended]

• USGS Boreholes

• Manitoba
• Newfoundland & Labrador
• NWT
  • Rest
  • References
• Quebec
• Yukon

• USGS World Mineral
• USGS MRDS
• Minnesota

• China -> WMS mineral deposit wap
• orefield -> Mineral occurrence points
• India Mineral -> WMS
• India Geophysics
• Korea
• ASEAN wms -> no data, just picture

• Africa Geoportal -> Rest services
• Africa 10M -> Africa 10M Mineral Occurrenceshttps://pubs.usgs.gov/of/2005/1294/e/OF05-1294-E.pdf
• IPIS Artisanal Mines - > There is a WMS version too
• github
• Uganda -> GMIS WMS

• Mineral Exploration Web Services -> QGIS plugin with access to many relevant web services

• Open Street Map -> useful general tile service

• Open Data API -> GSQ Open Data Portal API
  • Geochemistry parsing
• CORE -> Open Research Texts
  • API Notebook -> Example and fucntions
• SHARE -> Open Science API
• USGS Publications
• CROSSREF
• xDD -> former GeoDeepDive
• ADEPT -> GUI to xDD to search 15M harvested papers
• OpenAlex
  • api
  • diophila Python Library
  • Python Library
    • paper
• Macrostrat
  • paper
• OpenMinData -> facilitate querying and retrieving data on minerals and geomaterials from the Mindat API
• Iceland Geological Society

• Earth Model Collaboration -> access to various Earth models, visualization tools for model preview, facilities to extract model data/metadata and access to the contributed processing software and scripts.
• ISC Bulletin -> Earthquake focal mechanism search
• Magnetics Information Consortium -> paleomagnetic, geomagnetic, rock magnetic
• Earthchem -> Community-driven preservation, discovery, access, and visualization of geochemical, geochronological, and petrological data

• Geoscience Australia Data Catalogue
• AusAEM
• Geoscience Australia Portal
• Exploring for the Future Portal -> Geoscience Australia web portal with download information
• AusAEM
• AusLAMP
• Geochronology and Isotopes
• Hydrogeology Catchments -> search for catchments layer
• Critical Minerals Mapping Initiative
• Australian Stratigraphic Units
• Australian Borehole Stratigraphic Units -> Compilation for groundwater of sedimentary units
• Geoscience Australia Geophysics thredds -> OpendDAP and https access
• MORPH gdb -> Officer Musgrave drilling data

• CSIRO Data Access Portal
• Regolith Depth
• TWI -> Topographic Wetness Index
• ASTER Geoscience Maps -> Website
  • FTP -> CSIRO ftp site
  • ASTER Maps notes -> Notes for the above

• 3D Geology -> Models from multiple areas

• Enhanced Bare Earth Covariates for Soil and Lithological Modelling

• Groundwater Explorer -> Bureau of Meteorology

• Elvis

• SARIG -> South Australia Geological Survey geospatial map based search
• SARIG Catalogue -> data catalogue
  • 3D Models
    • SA Wide
    • s3 location
  • Data Packages - Annual update
  • s3 Reports -> Reports and textracted versions in s3 bucket with web interface)
  • Reports
  • Seismic
    • Seismic downloads -> One page of links

• STRIKE -> Northern Territory Geological Survey
• GEMIS
  • McArthur Basin -> 3D Model
  • Geophysical Surveys
  • Geophysics -> reference
  • Drilling and Geochemistry -> reference
    • Data Package -> data package

• Geological Survey of Queensland
• Geophysical Surveys
• Drilling and geochemistry

• GEOVIEW -> Western Australia Geological Survey
• DMIRS -> DMIRS Data and Software Centre
• Download URLS -> dataset of download links
• Drilling and Geochemistry
• Download package - improvement?
• Geochemistry
• Petroleum Wells with depths
• data WA subset

• MINVIEW -> New South Wales Geological Survey
• DiGS -> Publications and Geotechnical collections

• MRT
• MRT Maps -> Webmap

• Earth Resources
  • Geophysics
• GeoVIC -> Webmaps needs registration to be more useful

• Exploration Database -> Online
• GERM -> Geological Resource Map of New Zealand
• Geology -> Web Map
  • https://maps.gns.cri.nz/gns/wfs

• SIGAM -> Argentina Geological Survey
• SIGAM

• CPRM -> Brazil Geological Survey
• Downloads -> Brazil Geological Survey Downloads
• Rigeo -> Institutional Repository of Geosciences

• Portalgeomin

• Geportal

• GeoInfo

• Geology of Paraguay

• Ingemmet GeoPROMINE -> Geological Survey of Peru
• GeoMAPE

• Dimage

• EGDI -> Europe geoscience
• WFS
• Promine
• Inspire -> Inspire Geoportal

• Danish Subsurface Data

• Minerals4EU
• GTK -> Geological Survey of Finland
• Geochemical Maps -> pdf only!

• SGU -> Swedish Geological Survey

• IGME -> Spanish Geological Survey

• Geoportal
• Mineral Occurences

• GSI -> Geological Survey of Ireland
• GSI - Map viewer
• Goldmine -> Map and document search
• data.gov.ie -> National portal view
• isde -> Irish Spatial Data Exchange

• NGU -> Norway Geological Survey
• database -> Mineral resources and stratigraphy lookups
• github
• API
• Geoporta -> Geophysics
• GEONORGE -> Data catalogue with download

• Britain
• mapserver
• github

• Mineral Resources

• Russian Geological Research Institute -> Inaccessible currently
• RGU -> GIS project of deposits

• Geoportal
• Geomap -> M
• Atom -> Atom data feed
• GDI -> 3D Models Germany

• Infoterre -> French Geological Survey

• Geoportal -> Croatia Geological Survey
• Geology

• GS -> Czech Geological Survey

• Data Catalogue

• Data Catalogue
• Geoportapi api

• MBFSZ Maps

• IGR -> Romania Geological Survey
• Mineral Resources

• Geoportal

• UK Onshore Geophysical Library
• OS Data Hub British Geology
• Geology 625

• Natural Resources Canada
• github
• Geoscience Data Repository -> DAP Server
• Mining Web Map Portal
• DEM -> Canada DEM in COG format
• CDEM -> Digital Elevation Model (2011)
• Ontario
  • Geology Ontario
  • Drilling
• Quebec
• SIGEOM Database
• Drilling
• British Columbia
• Mineral occcurrence database
• Yukon
  • ftp
• Nova Scotia
• provincial
• Prince Edward Island
• Saskatchewan
• Mineral occurrence database
• Newfoundland -> didn't work in Chrome, tried it in Edge
• New Brunswick
  • Exploration drillholes
• Alberta
• Interactive Mapping Application
• Northwest Territories
• Mineral Tenure
• Nunavut Geoscience

• USGS -> Map database
• MRDS -> Mineral Resources Data Systems
• Earth Explorer -> USGS Remote Sensing Data Portal
• National Map Database
• National Map Database
• Alaska
• ReSci -> Registry of Scientific Collections of the National Geological and Geophysical Data Preservation Program
• Michigan

• Cadastre
• Hydrogeology -> Hydrogeology and geology from groundwater atlas
• West Africa -> Mineral deposits
• Namibia
  • (https://portal.mme.gov.na/page/MapPublic)
• Mineral Occurrences
• Miners
• South Africa -> South Africa geological survey
• Mineral Occurrences -> Example where you need to log in to download
• Uganda -> GMIS portal
• Metallic minerals
• Tanzania
• Mineral Occurrences
• Mines
• SIGM -> Tunisia Geology and Mining
• Zambia -> Zambia tenements here
  • Kobold geophysics grids

• Mineral Occurrences

• Geoscientific Data
• Mineral Occurrences
• National Mineral Deposit Database

• Sedimentary Basins

• Bhukosh -> India Geological Survey
• Note Rajasthan geology doesn't work except piecemeal which is painful - if you want it, let me

• [IGG Data] (https://unit.aist.go.jp/igg/en/database/index.html)

• geoporta

• National Geological Database Portal

• GIS Portal

• StratDB
• GEM Global Active Faults
• RRuff Mineral Properties
• article -> Evolutionary system of mineralogy
• OneGeology
• catalog

• OGC BGS 1M Bedrock

• OSF -> Open Science Foundation
• Sediment Hosted Base Metals -> Sediment Hosted Base Metals
• Lithosphere Athenosphere Boundary -> LAB Hoggard/Czarnota
• Geological Survey list

• Northern Territory GEMIS
  • search
  • example
• South Australia SARIG
• Western Australia WAMEX
  • search
  • api
    • example
• Queensland
• NSW Digs
  • NSW Digs open
  • API not public
• PorterGEO -> World mineral deposits databases with summary overviews
• Sustainable Minerals Institute -> Queensland organisation of university affiliated researchers producing datasets and knowledge

• British Columbia
  • Search -> Mineral Assessment Reports
  • Publications -> Publications
• Ontario -> Mineral Asssessment Reports
  • Ontario Publications
• Alberta
  • Publications
• Yukon
• Footprint
• Manitoba
  • Geosciencetific Maps
  • Publications
• Newfoundland and Labrador
• Northwest Territories
• Nova Scotia
• Quebec
• Saskatchewan
  • Search
  • iMaQs -> Integrated Mining and Quarrying System

• Arizona
• Montana
• Nevada
• New Mexico
• Minnesota
• Michigan
• json
• Alaska
• Washington

• British Geological Survey NERC
  • Mineral Potential
  • Search
  • API example
  • Publications
  • MEIGA -> MEIGA 600 BGS mineral exploration project reports
• GeoLagret -> Sweden
• MinData -> Compilation of rock locations from around the world
• Mineral Database -> Exportable list of minerals with scientific properties and ages
• NASA
• ResearchGate -> Researcher and professional network

• QGIS -> GIS Data Visualisation and Analysis Open Source desktop application, has some ML tools : Indispensible for some quick and easy viewing
  • 2D Geology in QGIS -> Workshop for QGIS NA 2020 introducing geologic maps and cross-sections for students and hobbyists
  • OpenLog -> Drillhole plugin beta
  • Geo-SAM -> QGIS plugin for Segment Anything with rasters
  • QGIS Project Packager
  • Weights-of-Evidence
  • plugin
• GRASS
• saga -> mirror of sourceforge

• PyVista -> VTK wrapping api for great data visualisation and analysis

  • PVGeo
  • Pyvista-Xarray -> Transforming xarray data to VTK 3D painlessly: a great library!
  • OMFVista -> Pyvista for Open Mining Format
  • Scipy 2022 Tutorial

• PyMeshLab -> Mesh transformation

• Open Mining Format

• Whitebox Tools

  • GUI -> Desktop version

• Subsurface

• Geolambda -> AWS Lambda setup

• Geoscience Analyst

  • geoh5py -> getting data to and from geoh5 projects
  • geoapps -> notebook based applications for geophysics via geoh5py
  • geoh5vista
  • gams -> magnetic data analysis
    • paper - A Framework for Mineral Geoscience Data and Model Portability - geoh5

• Rayshader

• Vdeo

• Python resources for earth science
• geoutils -> geospatial analysis and foster inter-operability between other Python GIS packages.

• Geopandas
  • Dask-geopandas
    • Tutorial
  • geofileops -> Increased speed spatial joins via database functions and geopackage
• Kart -> Distributed version control for daata
• PyESRIDump -> Library to grab data at scale from ESRI Rest
• DuckDB Spatial Extension -> Can also use standalone no install necessary executable

• SF
• terra -> terra provides methods to manipulate geographic (spatial) data in "raster" and "vector" form.

• exactextract -> command line zonal stats in C

• Rasters.jl -> reading and writing common raster data types

• Rasterio -> python base library for raster data handling
• georeader -> process raster data from different satellite missions
• Rasterstats -> summarising geospatial raster datasets based on vector geometries
• Xarray -> Multidimensional Labelled array handling and analysis
  • Rioxarray -> Fabulous high level api for xarray handling of raster data
  • Geocube -> Rasterisation of vector data api
  • ODC-GEO -> Tools for remote sensing based raster handling with many extremely handy tools like colorisation, grid workflows
  • Rasterix -> Raster tricks for xarray
  • COG Validator -> checking format of cloud optimised geotiffs
  • Griffine -> utilities for working with affine grids
  • serverless-datacube-demo -> xarray via lithops / Coiled / Modal
  • Xarray Spatial -> Statistical analysis of raster data such as classification like natural breaks
  • xarray-einstats -> Stats, linear algebra and einops for xarray
  • xdggs -> Other types of grids
  • xgcm -> Histograms with labels
  • xrft -> Xarray based Fourier Transforms
  • xvec -> Vector data cubes for Xarray

• Raster -> R library
• terra -> provides methods to manipulate geographic (spatial) data in "raster" and "vector" form.
• stars -> spatiotemporal Arrays: Raster and Vector Datacubes
• exactextracr -> raster zonal statistics for R

• raster-benchmark -> Benchmarking some raster libaries in python and R

• Whitebox Tools -> python api, gui, etc. have used for topographical wetness index calculation

• PiAutoStage -> 'An Open-Source 3D Printed Tool for the Automatic Collection of High-Resolution Microscope Imagery;' designed for mineral samples.

• AEM to seg-y
• ASEG GDF2
• CGG Outfile reader
• Geosoft Grid to Raster
• Loop Geosoft Grid
• Harmonica Geosoft Grid -> Pull request in progress on conversion to xarray
• AuScope -> Data from binary GOCAD models
• GOCAD SG Grid Reader
  • geomodel-2-3dweb -> In here they have a method to extract data from binary GOCAD SG Grids
• Leapfrog Mesh Reader
• OMF -> Open Mining Format for conversion between things
• PDF Miner
• VTK to DXF

• Pygeochemtools -> library and command line to enable rapid QC and plotting of geochemical data
• SA Geochemical Maps -> Data Analysis and plotting of South Australia geochemistry data from the Geological Survey of SA
• Geochemical levenning
• Scott Halley's geochemistry tutorial
• Periodic Table

• Geostatspy
• PyInterpolate

• Geologic Time Scale -> Code to produce, but also has a nice regular csv of the Ages

• Gempy -> Implicit Modellinggeo-lm -> GemPy models via Llama-4

• Gemgis -> Geospatial Data Analysis assistance

• LoopStructural -> Implicity Modelling

• Manual python geologia -> Analysis of geology data

• Map2Loop -> 3D Modelling Automation

  • Loop3D -> GUI for Map2Loop

• Pybedforms

• SA Stratigraphy -> Stratigraphy database editor webapp

• Striplog

• Analise_de_Dados_Estruturais_Altamira

• Global Tectonics -> Open source dataset to build on, plates, margins etc.

  • paper

• zenodo additions

• Litholog

• pyGPlates

• Tutorial data

  • paper

• GplatesReconstructionModel

• Geoscience Australia Utilities
• Geophysics for Practicing Geoscientists
• Potential Field Toolbox -> Some xarray based Fast Fourier Transform filters - derivatives, pseudogravity, rpg etc.
  • Notebook -> Class with some examples [Vertical derivative, Pseudogravity, Upward Continuation etc)
• Computation geophysics sandbox
• RIS Basement Sediment -> Depth to Magnetic Basement in Antarctica
• Signal Image Processing

• Geoscience Australia AEM
• UH Electromagnetics -> Coursework notebooks on understanding this domain
• AEM Interpretation
• EMag Py -> FDEM
• ResIPy -> DC / IP

• Harmonica
• Filter examples -> Fast Fourier transform based processing via xarray
• Australian Gravity Data
• Worms
• Worms update <- potential fields worm creation with some minor updates to handle new networkx api*github mirror
• Osborne Magnetic -> Survey data processing example

• Segyio
• Segysak -> Xarray based seg-y data handling and analysis
• Geophysical notes -> Seismic data processing

• MtPy
• Tutorials
• MtPy -> update of the above to make things easier
• Mineral Stats Toolkit -> Distance to MT features analysis
• Lithospheric conductors paper
• mtwaffle -> MT data analysis examples
• pyMT
• resistics
• MECMUS -> tools to read Electrical Conductivity model of the USA
• model

• GMT
  • PyGMT
• Verde
• Grid_aeromag -> Brazilian gridding example
• pyinterp -> Multidimensional gridding via Boost
• Pseudogravity -> From Blakely, 95

• SimPEG
  • Mira Geoscience Fork -> Used for geoapps
  • SimPEG fork
  • Transform 2020 SimPEG
  • Transform 2021 SimPEG
  • SimPEG scripts
  • Astic Joint Inversion example
• Gimli
• Tomofast-x
• USGS anonymous ftp
• USGS Software -> longer list of older useful stuff: dosbox, anyone?
• Geophysics Subroutines -> Fortran code
• 2020 Aachen Inversion problems -> Overview of gravity inversion theory

• Pyrolite
  • gs2021 Pyrolite
• Levelling
• Pygeochem tools
• Geoquimica
• Geochemistrypi
  • paper

• dh2loop -> Drilling Interval assistance
  • paper
• drilldown -> Drilling visualisation in notebooks via geoh5py -> note desurveying
• PyGSLib -> Downhole surveying and interval normalising
• pyborehole -> Processing and visualizing borehole data
• dhcomp -> composites geophysical data to a set of intervals

• Awesome spectral indices -> Guide to spectral index creation
• Open Data Cube
  • DEA Notebooks -> Code for use in ODC style workflows
  • Datacube-stats -> Statistical analysis library for ODC
  • Geo Notebooks -> Code examples from Element 84
• Raster4ML -> A large number of vegetation indices
• Lefa -> Fracture analysis, lineaments

• Kerchunk -> Serverless access to cloud based data via Zarr
  • Kerchunk geoh5 -> Access to Geoscient Analyst/geoh5 projects serverlessly via kerchunk
• Tifffile variant
• Virtuallizar -> Similar idea to kerchunk
  • icehunk -> Transactional storage engine for tensor / ND-array data designed for use on cloud object storage.

• DEA Stackstac -> Examples of working with Digital Earth Australia data
• Intake-stac
• ML AOI Extension
• ML Model Extension Specification -> Machine Learning Model Specification for CatalogingSpatio-Temporal Models
  • paper
• ODC-Stac -> Database free Open Data Cube
• Pystac
• Sat-search
• Stackstac -> Metadata speeded up dask and xarray timeseries

• Orange -> Data Mining Gui
• Hdstats -> Algorithmic basis of geometric medians
• Hdmedians

• TV -> view satellite imagery in a terminal
• Titiler
• Sits
• Hsdar
• Stars
• Peru Gold Mining SAR

• Nickel Mineral Potential Mapping -> ESRI Based analysis
• Prospectivity Online Tool

• Bluecap -> Framework from Monash University for assessing mine viability
• Zipfs Law -> Curve fitting the distribution of Mineral Depositions
• PyASX -> ASX Data Feed scraping
• Metal Price API -> Containerised Microservice

• Napari -> Multidimensional image viewer
• Holoviews -> Large scale data visualisation
• Graphviz -> Graph plotting/viewing assistance windows installation info
• Spatial-kde

• CET Perceptually Uniform Colormaps
• PU Colormaps -> Formatted for user in Geoscience Analyst
• Colormap distortions -> A Panel app to demonstrate distortions created by non-perceptual colormaps on geophysical data
• Ripping Data from Colormpas
• Open Geoscience Code Projects

• Geospatial >- installs multiple common python packages
• Geospatial python -> Curated list

• GDAL -> Absolutely crucial data transformation and analysis framework
  • Tools -> Note has many command line tools that are very useful as well

• Julia Earth -> Fostering geospatial data science and geostatistical modeling in Earth sciences
• Julia Geodynamics -> computational geodynamics code
• Introduction to Julia for Geoscience

• Anaconda -> Get lots installed already with this package manager.
  • GDAL et al -> Take the pain out of GDAL and Tensorflow installs here
  • Git Bash -> Getting conda to work in Git Bash
• Numpy Multidimensional arrays
• Pandas Tabular data analysis
• Matplotlib visualisation
• Zarr -> Compressed, chunked distributed arrays
• Dask -> Parallel, distributed computing
  • Dask Cloud Provider -> Automatically start dask clusters on the cloud
  • Dask Median -> Notebook giving a Dask median function prototype
• Python Geospatial Ecosystem -> Curated information

• GeoRust -> Collection of geospatial utilities in rust

• DuckDB -> In process OLAP DB at speed - has some geospatial and array capabilities
• ibis + Duckdb geopsatial -> scipy2024 talk

• Python Data Science Template -> Project package setup
• Awesome python data science -> Curated guide

• distfit -> Probability density fitting

• Python resources for earth sciences
• Awesome scientific computing

• AWS Deep Learning Containers
• Spatial Docker
• DL Docker Geospatial
• Rocker
• Docker Lambda
• Geobase
• DL Docker Geospatial

• Geological Society of Queensland vocabularies
  • Geological Properties Database
  • Geofeatures
• Geological Society of Western Australia
• Stratigraphic
• Geoscience Knowledge Manager
• GeoSciML Vocabularies

• Python geospatial analysis cookbook
• Geoprocessing with Python -> Manning livebook

• Textbook
• Machine Learning in the Oil and Gas industry
• Geocomputation with R
• Earthdata Cloud Cookbook -> How to access NASA resources
• Data Cleaner's Cookbook -> Putting unix tools to good use for data wrangling and cleaning
• Encyclopedia of Mathematical Geosciences
• Handbook of Mathematical Geosciences

• GXPy -> Geosoft Python API
• EarthArxiv -> Download papers from the preprint archive
• Essoar -> Preprint paper archive

• Deep Ocean

• GOGI -> GOGI Oil and Gas wells
  • report
• OGIM -> Oil and Gas Infrastructure Mapping [including wells]
  • paper -> OGIM paper
  • supplement -> Permian Basin ML check
    • paper -> Wellpad detection from space

• Critical Minerals in Ores

• Bedrock -> Generalised geology of the world
• GLIM -> Global lithology map
• Paleogeology An Atlas of Phanerozoic Paleogeographic Maps
• Sedimentary Layers -> Global 1-km Gridded Thickness of Soil, Regolith, and Sedimentary Deposit Layers
• World Stress Map -> Global compilation of information on the crustal present-day stress field
• GMBA -> Global mountain inventory

• Curvature -> Global curvature analysis from gravity gradient data
• WGM 2012

• EAMG2V3 _> Earth Magnetic Anomaly Grid
• WDMAM -> World Digital Magnetic Anomaly Map

• EMC -> global 3D inverse model of electrical conductivity

• LAB SLNAAFSA
• LAB CAM2016
• Moho -> GEMMA Data
• Moho -> Szwillus Data
• Seismic Velocity - > Debayle et al
• LithoRef18 -> A global reference model of the lithosphere and upper mantle from joint inversion and analysis of multiple data sets
• CRUST1.0 -> global crustal model netcdf
• Overview homepage

• Heat Flow

• Deep Time Digital Earth -> Data and visualisation for a variety of data sources and models
• EarthChem -> Community-driven preservation, discovery, access, and visualization of geochemical, geochronological, and petrological data
• GEOROC -> Geochemical composition of rocks
  • georoc-data
• global geology -> A short recipe to make a global geology map in GIS format (e.g. shapefile), with age ranges mapped to the GTS2020 timescale
• Large Igenous Provinces Commission
• Mantle Plumes
• Sediment Thickness -> Map
• spatialreference.org -> repository for the website

• Common Earth Model
• Heavy Mineral Map
• Heavy MIneral Map of Australia Pilot
• Shiny App

• Predictive grids of major oxide concentrations in surface rock and regolith over the Australian continent -> Various oxides

• Alkaline Rocks Atlas
• Cenozoic
• Mesozoic
• Paleozoic
• Archaean
• search
• Proterozoic Alkaline Rocks -> Proterozoic alkaline and related igneous rocks of Australia GIS
• Cenozoic
• Mesozoic
• Paleozoic
• Archaean
  • paperhttps://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search#/metadata/147963
• Hydrogeology -> Hydrogeology Map of Australia
• Hydrogeology -> 5M
• Layered Geology -> 1M
• Surface Geology -> 1M Scale
• The Australian Mafic-Ultramafic Magmatic Events GIS Dataset

• Gravity -> 2019 Australian National Gravity Grids

• TMI -> Magnetic Anomaly Map of Australia, Seventh Edition, 2019 TMI
• 40m -> 40m version
• VRTP -> Total Magnetic Intensity (TMI) Grid of Australia with Variable Reduction to Pole (VRTP) 2019
• 1VD -> Total Magnetic Intensity Grid of Australia 2019 - First Vertical Derivative (1VD)

• Radiometrics -> Complete Radiometric Grid of Australia (Radmap) v4 2019 with modelled infill
• K -> Radiometric Grid of Australia (Radmap) v4 2019 filtered pct potassium grid
• U -> Radiometric Grid of Australia (Radmap) v4 2019 filtered ppm uranium
• Th -> Radiometric Grid of Australia (Radmap) v4 2019 filtered ppm thorium
• Th/K -> Radiometric Grid of Australia (Radmap) v4 2019 ratio thorium over potassium
• U/K -> Radiometric Grid of Australia (Radmap) v4 2019 ratio uranium over potassium
• U/Th -> Radiometric Grid of Australia (Radmap) v4 2019 ratio uranium over thorium
• U squared/Th -> Radiometric Grid of Australia (Radmap) v4 2019 ratio uranium squared over thorium
• Dose Rate-> Radiometric Grid of Australia (Radmap) v4 2019 filtered terrestrial dose rate
• Ternary Picture -> Radiometric grid of Australia (Radmap) v4 2019 - Ternary image (K, Th, U)

• AusAEM 1 -> AusAEM Year 1 NT/QLD Airborne Electromagnetic Survey; GA Layered Earth Inversion Products
• AusAEM 1 -> AusAEM Year 1 NT/QLD: TEMPEST® airborne electromagnetic data and Em Flow® conductivity estimates
• AusAEM 1 -> AusAEM1 Interpretation Data Package
• AusAEM 2 -> AusAEM 02 WA/NT 2019-20 Airborne Electromagnetic Survey
• AusAEM–WA -> AusAEM–WA, Murchison Airborne Electromagnetic Survey Blocks
• AusAEM–WA -> AusAEM-WA, Southwest-Albany Airborne Electromagnetic Survey Blocks
• AusAEM–WA -> AusAEM WA 2020-21, Eastern Goldfields & East Yilgarn Airborne
• AusAEM–WA -> AusAEM (WA) 2020-21, Earaheedy & Desert Strip
• AusAEM ERC -> AusAEM Eastern Resources Corridor
• AusAEM WRC -> AusAEM Western Resources Corridor
• interp overview
• National surface and near-surface conductivity grids -> National ML interpolation for AusEM in similar fashion to Northern Australia

• AusLAMP SEA -> Resistivity model of the southeast Australian mainland from AusLAMP magnetotelluric data
• Victoria Data
• NSW Data
• AusLAMP TISA -> Resistivity model derived from magnetotellurics: AusLAMP-TISA project
• AusLAMP Delamerian -> Lithospheric resistivity model of the Delamerian Orogen from AusLAMP magnetotelluric data
• AusLAMP NE SA
• AusLAMP Gawler
• AusLAMP Stations -> circa 2017
• Tasmanides Paper

• Moho 2019 and 2023
  • paper

• Geological setting, age and endowment of major Australian mineral deposits
• A Comprehensive dataset for Australian mine production 1799 to 2021
  • paper

• Overview - Geoscience Australia -> Overview of publications and datasets
• Sediment Hosted Zinc
• Report
• Sediment Hosted Copper
• Report
• Abstract
• Carbonatite Rare Earth Elements
  • paper

• Australian Mine Waste

• National Native Title Tribunal

• Landsat Bare Earth - Bare earth median from Landsat
• Enhanced barest earth Landsat imagery for soil and lithological modelling: Dataset -> Details of an enhancement
• Global mining footprint mapped from high-resolution satellite imagery**Paper
• DEM -> Australia 1 sec SRTM DEM of various varieties

• Major Crustal Boundaries of Australia - 2024 Edition

• AU Tomo -> Next-generation velocity model of the Australian crust from synchronous and asynchronous ambient noise imaging

• Multiscale Topographic Position - RGB
• Info
• Topographic Wetness Index - 1 and 3 arc seconds
• Info
• Topographic Position Index - 1 and 3 arc seconds
• Info
• Weathering Intensity Model
  • paper
• Info
• {Info](https://researchdata.edu.au/weathering-intensity-model-australia/1361069)

• Cover thickness TISA -> Cover thickness points for Tennant Creek Mt Isa with interpolated grids
• High resolution conductivity mapping using regional AEM survey and machine learning -> ML conductivity interpolation for AusAEM
• Extended abstract
• Solid Geology -> Solid Geology of the North Australian Craton
• Inversion Models -> The North Australian Craton 3D Gravity and Magnetic Inversion Models
• Ni-Cu-PGE -> Potential for intrusion-hosted Ni-Cu-PGE sulfide deposits in Australia: A continental-scale analysis of mineral system prospectivity
• TISA IOCG -> Iron oxide copper-gold (IOCG) mineral potential assessment for the Tennant Creek – Mt Isa region: geospatial data
• TISA Alteration -> Producing Magnetite and Hematite Alteration Proxies using 3D Gravity and Magnetic Inversion

• Bedrock Geology
• Crystalline Basement -> Crystalline basement intersecting drillholes
• Mines and Mineral Deposits
• Mineral Drillholes
• Solid Geology 3D
• 100K Faults
• Archaean
• Archaean Faults
• Mesoproterozoic -> Middle
• Mesoproterozoic -> Middle faults
• Mesoproterozoic - > Late
• Mesoproterozoic Faults -> Late faults
• Neoproterozoic
• Neoproterozoic faults
• Stuart Shelf Sedimentary Copper 3D Model
• Surface Geology

• AusLAMP 3D -> Magnetotelluric inversions
• GCAS -> Gawler Craton Airborne Survey
• Gravity -> Gravity grids
• Stations -> Gravity stations
• Magnetics -> Magnetics
• Seismic Lines -> Seismic lines

• Gawler MPP -> Gawler Mineral Promotion Project - Data

• Overview
• Deep Mining Queensland-> Deep Mining Queensland
• Deposit Atlas -> Northwest Mineral Province Deposit Atlas
• Geology -> Geology series overview
• Mineral and Energy Report -> NORTH-WEST QUEENSLAND MINERAL AND ENERGY PROVINCE REPORT 2011 - NWQMEP
• Vectoring -> Mineral geochemistry vectoring
• Petroleum Wells
• Coal Seam Gas Wells
• Drillholes

• Toolkit -> Multielement toolkit and laboratory

• Arunta IOCG -> Iron oxide-copper-gold potential of the southern Arunta Region
• South Uranium -> Southern Northern Territory uranium and geothermal energy systems assessment digil data package
• Tennant Creek -> Conductivity Model Derived from Magnetotelluric Data in the East Tennant Region, Northern Territory

• Seamless Geology -> NSW Seamless Geology Data Package (older version also on this page)

• Curnamona
• Eastern Lachlan
• Central Lachlan
• Southern New England

• Mafic/Ultramafic

• 100K Bedrock
• 100K mapsheets for surface you have to download individually and combine - they aren't consistent
• 250K mapsheets for surface you have to download individually and combine - they aren't consistent
• 500K Bedrock
• Abandoned Mines
• Mineral Occurrences

• Komatiite-hosted nickel
• Report

• Capricorn-> Prospectivity analysis using a mineral systems approach - Capricorn case study project
• King Leopold -> Mineral prospectivity of the King Leopold Orogen and Lennard Shelf: analysis of potential field data in the west Kimberley region
• Yilgarn Gold
• Yilgarn 2 -> Predictive mineral discovery in the eastern Yilgarn Craton: an example of district-scale targeting of an orogenic gold mineral system
• [Shop note] -> WA has a few prospectivity packages available to purchase on USB drive for 50-60AU type prices - see in geospaital maps section

• 250k
• 500k
• 25K
• Mineral Occurrences
• 3D Model

• Seamless Geology

• Mineral Data Pack -> Mineral Exploration Data Pack

• National-Scale Geophysical, Geologic, and Mineral Resource Data and Grids -> Also has some Australia data
• Groundwater wells -> Database
• Maximum horizontal stress orientation and relative stress magnitude (faulting regime) data throughout North America

• Database of Canadian surveys -> some standardisation done here

• Map
• Geology -> Updated Bedrock geology map
• Geology -> Bedrock geology compilation and regional synthesis of south Rae and parts of Hearne domains, Churchill Province, Northwest Territories, Saskatchewan, Nunavut, Manitoba and Alberta
• Moho -> National database of Moho depth estimates estimates from seismic refraction and teleseismic surveys

• Dap Search -> Geoportal search - note annoyingly these are in Geosoft grids - see elsewere for conversion possibilties
• [Gravity, Magnetics, Radiometrics] -> Mostly country scale

• FODD -> Fennoscandian Mineral Deposits

• MPM -> Mineral Potentinal Mapping project

• https://www.sciencedirect.com/science/article/pii/S2590197422000064?via%3Dihub#bib20- -> Geoscience language models and their intrinsic evaluation -> NRCan code above [includes model]
• https://www.researchgate.net/publication/334507958_Word_embeddings_for_application_in_geosciences_development_evaluation_and_examples_of_soil-related_concepts -> GeoVec [includes model]
• https://www.researchgate.net/publication/347902344_Portuguese_word_embeddings_for_the_oil_and_gas_industry_Development_and_evaluation -> PetroVec [includes model]
• A resource for automated search and collation of geochemical datasets from journal supplements

• https://www.researchgate.net/publication/365758387_A_resource_for_automated_search_and_collation_of_geochemical_datasets_from_journal_supplements
  • https://github.com/erinlmartin/figshare_geoscrape?s=09

• https://github.com/sydney-machine-learning/autoencoders_remotesensing -> Stacked Autoencoders for Lithological Mapping

• https://www.researchgate.net/publication/318839364_Network_analysis_of_mineralogical_systems

• These you can reproduce the output geospatially from the data given.

• https://www.sciencedirect.com/science/article/pii/S016913682100010X#s0135 -> Prospectivity modelling of Canadian magmatic Ni (±Cu ± Co ± PGE) sulphide mineral systems [well worth reading]
• https://www.sciencedirect.com/science/article/pii/S0169136821006612#b0510 -> Data–driven prospectivity modelling of sediment–hosted Zn–Pb mineral systems and their critical raw materials [well worth reading]
• https://www.researchgate.net/publication/358956673_Towards_a_fully_data-driven_prospectivity_mapping_methodology_A_case_study_of_the_Southeastern_Churchill_Province_Quebec_and_Labrador

• https://www.researchgate.net/publication/358083076_Machine_learning_for_geochemical_exploration_classifying_metallogenic_fertility_in_arc_magmas_and_insights_into_porphyry_copper_deposit_formation

• https://www.researchgate.net/publication/361076789_Automated_machine_learning_pipeline_for_geochemical_analysis

• https://eprints.utas.edu.au/32368/ -> Machine-assisted modelling of lithology and metasomatism

• https://github.com/TomasNaprstek/Aeromagnetic_CNN - Aeromagnetic CNN
• Paperhttps://www.researchgate.net/publication/354772176_Convolution_Neural_Networks_Applied_to_the_Interpretation_of_Lineaments_in_Aeromagnetic_Data
• PhD -> New Methods for the Interpolation and Interpretation of Lineaments in Aeromagnetic Data
• Paperhttps://www.researchgate.net/publication/354772176_Convolution_Neural_Networks_Applied_to_the_Interpretation_of_Lineaments_in_Aeromagnetic_Data -> Convolution Neural Networks Applied to the Interpretation of Lineaments in Aeromagnetic Data

• https://geoscience.data.qld.gov.au/report/cr113697 -> NWMP Data-Driven Mineral Exploration And Geological Mapping [CSIRO too]

• https://www.sciencedirect.com/journal/artificial-intelligence-in-geosciences -> Artificial Intelligence in Geosciences

• Generally Not ML, or no Code/Data and sometimes no availability at all
• Eventually will separate out into things that have data packages or not like NSW Zone studies.
• However, if interested in an area you can often georeference a picture if nothing else as a rough guide.
• Generally these are not reproducible - a few like the NSW prospectivity zone studies and NWQMP are with some work.
• The occasional paper in this section may be listed above

• https://www.researchgate.net/publication/337650865_A_combinative_knowledge-driven_integration_method_for_integrating_geophysical_layers_with_geological_and_geochemical_datasets
• https://link.springer.com/article/10.1007/s11053-023-10237-w - A New Generation of Artificial Intelligence Algorithms for Mineral Prospectivity Mapping
• https://www.researchgate.net/publication/235443297_Addressing_challenges_with_exploration_datasets_to_generate_usable_mineral_potential_maps
• https://www.researchgate.net/publication/330077321_An_Improved_Data-Driven_Multiple_Criteria_Decision-Making_Procedure_for_Spatial_Modeling_of_Mineral_Prospectivity_Adaption_of_Prediction-Area_Plot_and_Logistic_Functions
• Artificial intelligence and machine learning to enhance critical mineral deposit discovery ->https://www.sciencedirect.com/science/article/pii/S2772883825000111?via%3Dihub
• Artificial intelligence for mineral exploration: A review and perspectives on future directions from data science ->https://www.sciencedirect.com/science/article/pii/S0012825224002691
• https://www.researchgate.net/project/Bayesian-Machine-Learning-for-Geological-Modeling-and-Geophysical-Segmentation
• https://www.researchgate.net/publication/229714681_Classifiers_for_Modeling_of_Mineral_Potential
• https://www.researchgate.net/publication/352251078_Data_Analysis_Methods_for_Prospectivity_Modelling_as_applied_to_Mineral_Exploration_Targeting_State-of-the-Art_and_Outlook
• https://www.researchgate.net/publication/267927728_Data-Driven_Evidential_Belief_Modeling_of_Mineral_Potential_Using_Few_Prospects_and_Evidence_with_Missing_Values
• https://www.linkedin.com/pulse/deep-learning-meets-downward-continuation-caldera-analytics/?trackingId=Ybkv3ukNI7ygH3irCHZdGw%3D%3D
• https://www.researchgate.net/publication/382560010_DINOv2_Rocks_Geological_Image_Analysis_Classification_Segmentation_and_Interpretability
• https://www.researchgate.net/publication/368489689_Discrimination_of_Pb-Zn_deposit_types_using_sphalerite_geochemistry_New_insights_from_machine_learning_algorithm
• https://link.springer.com/article/10.1007/s11430-024-1309-9 -> Explainable artificial intelligence models for mineral prospectivity mapping
• https://www.researchgate.net/publication/229792860_From_Predictive_Mapping_of_Mineral_Prospectivity_to_Quantitative_Estimation_of_Number_of_Undiscovered_Prospects
• https://www.researchgate.net/publication/339997675_Fully_reversible_neural_networks_for_large-scale_surface_and_sub-surface_characterization_via_remote_sensing
  • arxiv
  • presentation
  • conference
  • juliaCon
• https://www.researchgate.net/publication/220164488_Geocomputation_of_mineral_exploration_targets
• https://www.researchgate.net/publication/272494576_Geological_knowledge_discovery_and_minerals_targeting_from_regolith_using_a_machine_learning_approach
• https://www.researchgate.net/publication/280013864_Geometric_average_of_spatial_evidence_data_layers_A_GIS-based_multi-criteria_decision-making_approach_to_mineral_prospectivity_mapping
• https://www.researchgate.net/publication/355467413_Harnessing_the_Power_of_Artificial_Intelligence_and_Machine_Learning_in_Mineral_Exploration-Opportunities_and_Cautionary_Notes
• https://www.researchgate.net/publication/335819474_Importance_of_spatial_predictor_variable_selection_in_machine_learning_applications_-Moving_from_data_reproduction_to_spatial_prediction
• https://www.researchgate.net/publication/337003268_Improved_supervised_classification_of_bedrock_in_areas_of_transported_overburden_Applying_domain_expertise_at_Kerkasha_Eritrea - Gazley/Hood
• https://www.researchgate.net/publication/360660467_Lithospheric_conductors_reveal_source_regions_of_convergent_margin_mineral_systems
• https://api.research-repository.uwa.edu.au/portalfiles/portal/5263287/Lysytsyn_Volodymyr_2015.pdf (PhD thesis) GIS-based epithermal copper prospectivity mapping of the Mt Isa Inlier, Australia: Implications for exploration targeting
• https://www.researchgate.net/publication/374972769_Knowledge_and_technology_transfer_in_and_beyond_mineral_exploration -> Knowledge and technology transfer in and beyond mineral exploration
• https://www.researchgate.net/publication/331946100_Machine_learning_for_data-driven_discovery_in_solid_Earth_geoscience
• https://theses.hal.science/tel-04107211/document - Machine Learning Approaches for Sub-surface Geological Heterogeneous Sources
• https://www.researchgate.net/publication/309715081_Magmato-hydrothermal_space_A_new_metric_for_geochemical_characterisation_of_metallic_ore_deposits - Magmato-hydrothermal space: A new metric for geochemical characterisation of metallic ore deposits
• https://www.researchgate.net/publication/220164234_Mapping_complexity_of_spatial_distribution_of_faults_using_fractal_and_multifractal_models_Vectoring_towards_exploration_targets
• https://www.researchgate.net/publication/220163838_Objective_selection_of_suitable_unit_cell_size_in_data-driven_modeling_of_mineral_prospectivity
• https://www.researchgate.net/publication/273500012_Prediction-area_P-A_plot_and_C-A_fractal_analysis_to_classify_and_evaluate_evidential_maps_for_mineral_prospectivity_modeling
• https://www.researchgate.net/publication/354925136_Soil-sample_geochemistry_normalised_by_class_membership_from_machine-learnt_clusters_of_satellite_and_geophysics_data [Gazley/Hood]
• https://link.springer.com/article/10.1007/s12665-024-11870-1 -> Quantification of the uncertainty of geoscientific maps relying on human sensory engagement
• https://www.researchgate.net/publication/235443294_The_effect_of_map-scale_on_geological_complexity
• https://www.researchgate.net/publication/235443305_The_effect_of_map_scale_on_geological_complexity_for_computer-aided_exploration_targeting
• https://link.springer.com/article/10.1007/s11053-024-10322-8 -> Workflow-Induced Uncertainty in Data-Driven Mineral Prospectivity Mapping

• https://www.mdpi.com/2072-4292/15/16/4074 -> A Spatial Data-Driven Approach for Mineral Prospectivity Mapping
• https://www.researchgate.net/publication/353253570_A_Truly_Spatial_Random_Forests_Algorithm_for_Geoscience_Data_Analysis_and_Modelling
• https://www.researchgate.net/publication/253217016_Advanced_methodologies_for_the_analysis_of_databases_of_mineral_deposits_and_major_faults
• https://www.researchgate.net/publication/362260616_Assessing_the_impact_of_conceptual_mineral_systems_uncertainty_on_prospectivity_predictions
• https://www.researchgate.net/publication/352310314_Central_Lachlan_Mineral_Potential_Study
• https://meg.resourcesregulator.nsw.gov.au/sites/default/files/2024-05/EITH%202024%20Muller_Exploration_in_the_House_keynote.pdf -> Critical minerals – prospectivity mapping using generative AI
• https://www.tandfonline.com/doi/pdf/10.1080/22020586.2019.12073159?needAccess=true - > Integrating a Minerals Systems Approach with Machine Learning: A Case Study of ‘Modern Minerals Exploration’ in the Mt Woods Inlier – northern Gawler Craton, South Australia
• https://www.researchgate.net/publication/365697240_Mineral_potential_modelling_of_orogenic_gold_systems_in_the_Granites-Tanami_Orogen_Northern_Territory_Australia_A_multi-technique_approach
• https://publications.csiro.au/publications/publication/PIcsiro:EP2022-0483 -> Signatures of Key Mineral Systems in the Eastern Mount Isa Province, Queensland: New Perspectives from Data Analytics
• https://link.springer.com/article/10.1007/s11004-021-09989-z -> Stochastic Modelling of Mineral Exploration Targets
• https://www.researchgate.net/publication/276171631_Supervised_Neural_Network_Targeting_and_Classification_Analysis_of_Airborne_EM_Magnetic_and_Gamma-ray_Spectrometry_Data_for_Mineral_Exploration
• https://www.researchgate.net/publication/353058758_Using_Machine_Learning_to_Map_Western_Australian_Landscapes_for_Mineral_Exploration
• https://www.researchgate.net/publication/264535019_Weights-of-evidence_and_logistic_regression_modeling_of_magmatic_nickel_sulfide_prospectivity_in_the_Yilgarn_Craton_Western_Australia

• https://www.researchgate.net/publication/263542691_ANALYSIS_OF_SPATIAL_DISTRIBUTION_OF_EPITHERMAL_GOLD_DEPOSITS_IN_THE_DESEADO_MASSIF_SANTA_CRUZ_PROVINCE
• https://www.researchgate.net/publication/263542560_EVIDENTIAL_BELIEF_MAPPING_OF_EPITHERMAL_GOLD_POTENTIAL_IN_THE_DESEADO_MASSIF_SANTA_CRUZ_PROVINCE_ARGENTINA
• https://www.researchgate.net/publication/277940917_Porphyry_epithermal_and_orogenic_gold_prospectivity_of_Argentina
• https://www.researchgate.net/publication/269518805_Prospectivity_for_epithermal_gold-silver_deposits_in_the_Deseado_Massif_Argentina
• https://www.researchgate.net/publication/235443303_Prospectivity_mapping_for_multi-stage_epithermal_gold_mineralization_in_Argentina

• https://www.researchgate.net/publication/367245252_Geochemical_multifractal_modeling_of_soil_and_stream_sediment_data_applied_to_gold_prospectivity_mapping_of_the_Pitangui_Greenstone_Belt_northwest_of_Quadrilatero_Ferrifero_Brazil
• https://www.researchgate.net/publication/381880769_How_do_non-deposit_sites_influence_the_performance_of_machine_learning-based_gold_prospectivity_mapping_A_study_case_in_the_Pitangui_Greenstone_Belt_Brazil
• https://www.researchsquare.com/article/rs-5066453/v1 -> Enhancing Lithium Exploration in the Borborema Province, Northeast Brazil: Integrating Airborne Geophysics, Low-Density Geochemistry, and Machine Learning Algorithms
• https://www.researchgate.net/publication/362263694_Machine_Learning_Methods_for_Quantifying_Uncertainty_in_Prospectivity_Mapping_of_Magmatic-Hydrothermal_Gold_Deposits_A_Case_Study_from_Juruena_Mineral_Province_Northern_Mato_Grosso_Brazil
• https://www.researchgate.net/publication/360055592_Predicting_mineralization_and_targeting_exploration_criteria_based_on_machine-learning_in_the_Serra_de_Jacobina_quartz-pebble-metaconglomerate_Au-U_deposits_Sao_Francisco_Craton_Brazil

• https://www.researchgate.net/publication/272170968_A_Comparative_Analysis_of_Weights_of_Evidence_Evidential_Belief_Functions_and_Fuzzy_Logic_for_Mineral_Potential_Mapping_Using_Incomplete_Data_at_the_Scale_of_Investigation A Comparative Analysis of Weights of Evidence, Evidential Belief Functions, and Fuzzy Logic for Mineral Potential Mapping Using Incomplete Data at the Scale of Investigation
• https://www.researchgate.net/publication/360386350_Application_of_Fuzzy_Gamma_Operator_to_Generate_Mineral_Prospectivity_Mapping_for_Cu-Mo_Porphyry_Deposits_Case_Study_Kighal-Bourmolk_Area_Northwestern_Iran
• https://www.researchgate.net/publication/348823482_Combining_fuzzy_analytic_hierarchy_process_with_concentration-area_fractal_for_mineral_prospectivity_mapping_A_case_study_involving_Qinling_orogenic_belt_in_central_China
• https://tupa.gtk.fi/raportti/arkisto/m60_2003_1.pdf -> Conceptual Fuzzy Logic Prospectivity Analysis of the Kuusamo Area
• https://www.researchgate.net/publication/356508827_Geophysical-spatial_Data_Modeling_using_Fuzzy_Logic_Applied_to_Nova_Aurora_Iron_District_Northern_Minas_Gerais_State_Southeastern_Brazil
• https://www.researchgate.net/publication/356937528_Mineral_prospectivity_mapping_a_potential_technique_for_sustainable_mineral_exploration_and_mining_activities_-_a_case_study_using_the_copper_deposits_of_the_Tagmout_basin_Morocco

• https://eartharxiv.org/repository/view/8898/ -> Geoscientific Input Feature Selection for CNN-driven Mineral Prospectivity Mapping
• http://www.geosciencebc.com/i/pdf/SummaryofActivities2015/SoA2015_Granek.pdf -> Advanced Geoscience Targeting via Focused Machine Learning Applied to the QUEST Project Dataset, British Columbia
• https://open.library.ubc.ca/soa/cIRcle/collections/ubctheses/24/items/1.0340340 -> Application of machine learning algorithms to mineral prospectivity mapping
• https://www.researchgate.net/publication/369599705_A_study_of_faults_in_the_Superior_province_of_Ontario_and_Quebec_using_the_random_forest_machine_learning_algorithm_spatial_relationship_to_gold_mines
• A balanced mineral prospectivity model of Canadian magmatic Ni (± Cu ± Co ± PGE) sulphide mineral systems using conditional variational autoencoders ->https://www.sciencedirect.com/science/article/pii/S0169136824004621
• https://link.springer.com/article/10.1007/s11053-025-10468-z -> Class Label Representativeness in Machine Learning-Based Mineral Prospectivity Mapping
• https://www.researchgate.net/publication/273176257_Data-_and_Knowledge_driven_mineral_prospectivity_maps_for_Canada's_North
• https://www.researchgate.net/publication/300153215_Data_mining_for_real_mining_A_robust_algorithm_for_prospectivity_mapping_with_uncertainties
• https://www.sciencedirect.com/science/article/pii/S1674987123002268 -> Development and application of feature engineered geological layers for ranking magmatic, volcanogenic, and orogenic system components in Archean greenstone belts
• https://qspace.library.queensu.ca/bitstream/handle/1974/28138/Cevik_Ilkay_S_202009_MASc.pdf?sequence=3&isAllowed=y -> MACHINE LEARNING ENHANCEMENTS FOR KNOWLEDGE DISCOVERY IN MINERAL EXPLORATION AND IMPROVED MINERAL RESOURCE CLASSIFICATION
• https://www.researchgate.net/publication/343511849_Identification_of_intrusive_lithologies_in_volcanic_terrains_in_British_Columbia_by_machine_learning_using_Random_Forests_the_value_of_using_a_soft_classifier
• https://www.researchgate.net/publication/365782501_Improving_Mineral_Prospectivity_Model_Generalization_An_Example_from_Orogenic_Gold_Mineralization_of_the_Sturgeon_Lake_Transect_Ontario_Canada
• https://www.researchgate.net/publication/348983384_Mineral_prospectivity_mapping_using_a_VNet_convolutional_neural_network
• corporate link
• https://www.researchgate.net/publication/369048379_Mineral_Prospectivity_Mapping_Using_Machine_Learning_Techniques_for_Gold_Exploration_in_the_Larder_Lake_Area_Ontario_Canada
• https://link.springer.com/article/10.1007/s11053-024-10451-0 -> Mineral Prospectivity Modeling of Graphite Deposits and Occurrences in Canada
• https://www.researchgate.net/publication/337167506_Orogenic_gold_prospectivity_mapping_using_machine_learning
• https://www.researchgate.net/publication/290509352_Precursors_predicted_by_artificial_neural_networks_for_mass_balance_calculations_Quantifying_hydrothermal_alteration_in_volcanic_rocks
• https://link.springer.com/article/10.1007/s11053-024-10438-x#Sec9 -> Pan-Canadian Predictive Modeling of Lithium–Cesium–Tantalum Pegmatites with Deep Learning and Natural Language Processing
• https://link.springer.com/article/10.1007/s11053-024-10369-7 -> Predictive Modeling of Canadian Carbonatite-Hosted REE +/− Nb Deposits
• https://www.sciencedirect.com/science/article/pii/S0098300422001406 -> Preliminary geological mapping with convolution neural network using statistical data augmentation on a 3D model
• https://www.researchgate.net/publication/352046255_Study_of_the_Influence_of_Non-Deposit_Locations_in_Data-Driven_Mineral_Prospectivity_Mapping_A_Case_Study_on_the_Iskut_Project_in_Northwestern_British_Columbia_Canada
• https://www.researchgate.net/publication/220164155_Support_vector_machine_A_tool_for_mapping_mineral_prospectivity
• https://www.researchgate.net/publication/348111963_Support_Vector_Machine_and_Artificial_Neural_Network_Modelling_of_Orogenic_Gold_Prospectivity_Mapping_in_the_Swayze_greenstone_belt_Ontario_Canada
• PhD thesis ->https://zone.biblio.laurentian.ca/bitstream/10219/3736/1/PhD%20Thesis%20Maepa_20210603.%281%29.pdf -> Exploration targeting for gold deposits using spatial data analytics, machine learning and deep transfer learning in the Swayze and Matheson greenstone belts, Ontario, Canada
• https://data.geology.gov.yk.ca/Reference/95936#InfoTab -> Updates to the Yukon Geological Survey’s mineral potential mapping methodology
• http://www.geosciencebc.com/i/pdf/SummaryofActivities2015/SoA2015_Granek.pdf -> Advanced Geoscience Targeting via Focused Machine Learning Applied to the QUEST Project Dataset, British Columbia

• https://www.researchgate.net/publication/323452014_The_Utility_of_Machine_Learning_in_Identification_of_Key_Geophysical_and_Geochemical_Datasets_A_Case_Study_in_Lithological_Mapping_in_the_Central_African_Copper_Belt
• https://www.researchgate.net/publication/334436808_Lithological_Mapping_in_the_Central_African_Copper_Belt_using_Random_Forests_and_Clustering_Strategies_for_Optimised_Results

• https://www.researchgate.net/publication/341485750_Evaluation_of_random_forest-based_analysis_for_the_gypsum_distribution_in_the_Atacama_desert

• https://www.researchgate.net/publication/374968979_3D_cooperative_inversion_of_airborne_magnetic_and_gravity_gradient_data_using_deep_learning_techniques - 3D cooperative inversion of airborne magnetic and gravity gradient data using deep learning techniques [UNSEEN]
• https://www.researchgate.net/publication/369919958_3D_mineral_exploration_Cu-Zn_targeting_with_multi-source_geoscience_datasets_in_the_Weilasituo-bairendaba_district_Inner_Mongolia_China
• https://www.researchgate.net/publication/350817136_3D_Mineral_Prospectivity_Mapping_Based_on_Deep_Metallogenic_Prediction_Theory_A_Case_Study_of_the_Lala_Copper_Mine_Sichuan_China
• https://www.researchgate.net/publication/336771580_3D_Mineral_Prospectivity_Mapping_with_Random_Forests_A_Case_Study_of_Tongling_Anhui_China
• https://www.sciencedirect.com/science/article/pii/S0169136823005772 -> 3D mineral prospectivity modeling in the Sanshandao goldfield, China using the convolutional neural network with attention mechanism
• https://www.sciencedirect.com/science/article/pii/S0009281924001144 -> 3D mineral prospectivity modeling using deep adaptation network transfer learning: A case study of the Xiadian gold deposit, Eastern China
• https://www.sciencedirect.com/science/article/pii/S0009281924000497 -> 3D mineral prospectivity modeling using multi-scale 3D convolution neural network and spatial attention approaches
• https://www.researchgate.net/publication/366201930_3D_Quantitative_Metallogenic_Prediction_of_Indium-Rich_Ore_Bodies_in_the_Dulong_Sn-Zn_Polymetallic_Deposit_Yunnan_Province_SW_China
• https://www.researchgate.net/publication/329600793_A_combined_approach_using_spatially-weighted_principal_components_analysis_and_wavelet_transformation_for_geochemical_anomaly_mapping_in_the_Dashui_ore-concentration_district_Central_China
• https://www.researchgate.net/publication/349034539_A_Comparative_Study_of_Machine_Learning_Models_with_Hyperparameter_Optimization_Algorithm_for_Mapping_Mineral_Prospectivity
• https://www.researchgate.net/publication/354132594_A_Convolutional_Neural_Network_of_GoogLeNet_Applied_in_Mineral_Prospectivity_Prediction_Based_on_Multi-source_Geoinformation
• https://www.researchgate.net/publication/369865076_A_deep-learning-based_mineral_prospectivity_modeling_framework_and_workflow_in_prediction_of_porphyry-epithermal_mineralization_in_the_Duolong_Ore_District_Tibet
• https://www.researchgate.net/publication/374982967_A_Framework_for_Data-Driven_Mineral_Prospectivity_Mapping_with_Interpretable_Machine_Learning_and_Modulated_Predictive_Modeling
• https://www.sciencedirect.com/science/article/pii/S0169136824002026 -> A Global-Local collaborative approach to quantifying spatial non-stationarity in three-dimensional mineral prospectivity modeling
• https://link.springer.com/article/10.1007/s11053-024-10344-2 -> A Heterogeneous Graph Construction Method for Mineral Prospectivity Mapping [UNSEEN]
• https://www.researchgate.net/publication/353421842_A_hybrid_logistic_regression_gene_expression_programming_model_and_its_application_to_mineral_prospectivity_mapping
• https://www.researchgate.net/publication/375764940_A_lightweight_convolutional_neural_network_with_end-to-end_learning_for_three-dimensional_mineral_prospectivity_modeling_A_case_study_of_the_Sanhetun_Area_Heilongjiang_Province_Northeastern_China
• https://www.researchgate.net/publication/339821823_A_Monte_Carlo-based_framework_for_risk-return_analysis_in_mineral_prospectivity_mapping
• https://www.researchgate.net/publication/373715610_A_Multimodal_Learning_Framework_for_Comprehensive_3D_Mineral_Prospectivity_Modeling_with_Jointly_Learned_Structure-Fluid_Relationships
• https://www.sciencedirect.com/science/article/pii/S0169136824001343 -> A novel hybrid ensemble model for mineral prospectivity prediction: A case study in the Malipo W-Sn mineral district, Yunnan Province, China
• https://www.researchgate.net/publication/347344551_A_positive_and_unlabeled_learning_algorithm_for_mineral_prospectivity_mapping
• https://www.researchgate.net/publication/335036019_An_Autoencoder-Based_Dimensionality_Reduction_Algorithm_for_Intelligent_Clustering_of_Mineral_Deposit_Data
• https://www.researchgate.net/publication/363696083_An_Integrated_Framework_for_Data-Driven_Mineral_Prospectivity_Mapping_Using_Bagging-Based_Positive_Unlabeled_Learning_and_Bayesian_Cost-Sensitive_Logistic_Regression
• https://link.springer.com/article/10.1007/s11053-024-10349-x -> An Uncertainty-Quantification Machine Learning Framework for Data-Driven Three-Dimensional Mineral Prospectivity Mapping
• https://link.springer.com/article/10.1007/s11004-023-10076-8 - An Interpretable Graph Attention Network for Mineral Prospectivity Mapping
• https://www.researchgate.net/publication/332751556_Application_of_hierarchical_clustering_singularity_mapping_and_Kohonen_neural_network_to_identify_Ag-Au-Pb-Zn_polymetallic_mineralization_associated_geochemical_anomaly_in_Pangxidong_district
• https://www.mdpi.com/2075-163X/14/9/945 -> Application of Machine Learning to Characterize Metallogenic Potential Based on Trace Elements of Zircon: A Case Study of the Tethyan Domain
• https://www.researchgate.net/publication/339096362_Application_of_nonconventional_mineral_exploration_techniques_case_studies
• https://www.researchgate.net/publication/325702993_Assessment_of_Geochemical_Anomaly_Uncertainty_Through_Geostatistical_Simulation_and_Singularity_Analysis
• https://www.researchgate.net/publication/368586826_Bagging-based_Positive-Unlabeled_Data_Learning_Algorithm_with_Base_Learners_Random_Forest_and_XGBoost_for_3D_Exploration_Targeting_in_the_Kalatongke_District_Xinjiang_China
• https://link.springer.com/article/10.1007/s11004-024-10153-6 -> Causal Discovery and Deep Learning Algorithms for Detecting Geochemical Patterns Associated with Gold-Polymetallic Mineralization: A Case Study of the Edongnan Region [UNSEEN]
• https://www.sciencedirect.com/science/article/pii/S0169136824001409 -> CNN-Transformers for mineral prospectivity mapping in the Maodeng–Baiyinchagan area, Southern Great Xing'an Range
• https://www.researchgate.net/publication/347079505_Convolutional_neural_network_and_transfer_learning_based_mineral_prospectivity_modeling_for_geochemical_exploration_of_Au_mineralization_within_the_Guandian-Zhangbaling_area_Anhui_Province_China
• https://www.researchgate.net/publication/352703015_Data-driven_based_logistic_function_and_prediction-area_plot_for_mineral_prospectivity_mapping_a_case_study_from_the_eastern_margin_of_Qinling_orogenic_belt_central_China
• https://www.sciencedirect.com/science/article/abs/pii/S0012825218306123 -> Deep learning and its application in geochemical mapping
• https://www.frontiersin.org/articles/10.3389/feart.2024.1308426/full -> Deep gold prospectivity modeling in the Jiaojia gold belt, Jiaodong Peninsula, eastern China using machine learning of geometric and geodynamic variables
• https://www.researchgate.net/publication/352893038_Detection_of_geochemical_anomalies_related_to_mineralization_using_the_GANomaly_network
• https://www.researchgate.net/publication/357685352_Determination_of_Predictive_Variables_in_Mineral_Prospectivity_Mapping_Using_Supervised_and_Unsupervised_Methods
• https://www.sciencedirect.com/science/article/abs/pii/S0375674221001370 -> Distinguishing IOCG and IOA deposits via random forest algorithm based on magnetite composition
• https://www.researchgate.net/publication/340401748_Effects_of_Random_Negative_Training_Samples_on_Mineral_Prospectivity_Mapping
• https://www.researchgate.net/publication/360333702_Ensemble_learning_models_with_a_Bayesian_optimization_algorithm_for_mineral_prospectivity_mapping
• https://www.researchgate.net/publication/267927676_Evaluation_of_uncertainty_in_mineral_prospectivity_mapping_due_to_missing_evidence_A_case_study_with_skarn-type_Fe_deposits_in_Southwestern_Fujian_Province_China
• https://www.mdpi.com/2075-163X/14/5/492 ->Exploration Vectors and Indicators Extracted by Factor Analysis and Association Rule Algorithms at the Lintan Carlin-Type Gold Deposit, Youjiang Basin, China
• https://www.researchgate.net/publication/379852209_Fractal-Based_Multi-Criteria_Feature_Selection_to_Enhance_Predictive_Capability_of_AI-Driven_Mineral_Prospectivity_Mapping
• https://www.researchgate.net/publication/338789096_From_2D_to_3D_Modeling_of_Mineral_Prospectivity_Using_Multi-source_Geoscience_Datasets_Wulong_Gold_District_China
• https://www.researchgate.net/publication/359714254_Geochemical_characterization_of_the_Central_Mineral_Belt_U_Cu_Mo_V_mineralization_Labrador_Canada_Application_of_unsupervised_machine-learning_for_evaluation_of_IOCG_and_affiliated_mineral_potential
• https://www.researchgate.net/publication/350788828_Geochemically_Constrained_Prospectivity_Mapping_Aided_by_Unsupervised_Cluster_Analysis
• https://www.researchgate.net/publication/267927506_GIS-based_mineral_potential_modeling_by_advanced_spatial_analytical_methods_in_the_southeastern_Yunnan_mineral_district_China
• https://www.researchgate.net/publication/380190183_Geologically_Constrained_Convolutional_Neural_Network_for_Mineral_Prospectivity_Mapping
• https://www.researchgate.net/publication/332997161_GNER_A_Generative_Model_for_Geological_Named_Entity_Recognition_Without_Labeled_Data_Using_Deep_Learning
• https://www.researchgate.net/publication/307011381_Identification_and_mapping_of_geochemical_patterns_and_their_significance_for_regional_metallogeny_in_the_southern_Sanjiang_China
• https://link.springer.com/article/10.1007/s11053-024-10334-4 -> Identification of Geochemical Anomalies Using an End-to-End Transformer
• https://www.researchgate.net/publication/359627130_Identification_of_ore-finding_targets_using_the_anomaly_components_of_ore-forming_element_associations_extracted_by_SVD_and_PCA_in_the_Jiaodong_gold_cluster_area_Eastern_China
• https://www.researchgate.net/publication/282621670_Identifying_geochemical_anomalies_associated_with_Au-Cu_mineralization_using_multifractal_and_artificial_neural_network_models_in_the_Ningqiang_district_Shaanxi_China
• https://www.sciencedirect.com/science/article/abs/pii/S0375674224000943 -> Integrate physics-driven dynamics simulation with data-driven machine learning to predict potential targets in maturely explored orefields: A case study in Tongguangshan orefield, Tongling, China
• https://www.researchgate.net/publication/329299202_Integrating_sequential_indicator_simulation_and_singularity_analysis_to_analyze_uncertainty_of_geochemical_anomaly_for_exploration_targeting_of_tungsten_polymetallic_mineralization_Nanling_belt_South_
• https://www.sciencedirect.com/science/article/abs/pii/S0883292724001987 -> Integrating soil geochemistry and machine learning for enhanced mineral exploration at the dayu gold deposit, south China block
• https://www.mdpi.com/2071-1050/15/13/10269 -> Intelligent Identification and Prediction Mineral Resources Deposit Based on Deep Learning
• https://link.springer.com/article/10.1007/s11053-024-10396-4 -> Interpretable SHAP Model Combining Meta-learning and Vision Transformer for Lithology Classification Using Limited and Unbalanced Drilling Data in Well Logging
• https://www.researchgate.net/publication/358555996_Learning_3D_mineral_prospectivity_from_3D_geological_models_using_convolutional_neural_networks_Application_to_a_structure-controlled_hydrothermal_gold_deposit
• https://www.researchgate.net/publication/352476625_Machine_Learning-Based_3D_Modeling_of_Mineral_Prospectivity_Mapping_in_the_Anqing_Orefield_Eastern_China
• https://www.mdpi.com/2076-3417/15/8/4082 -> Machine-Learning-Based Integrated Mining Big Data and Multi-Dimensional Ore-Forming Prediction: A Case Study of Yanshan Iron Mine, Hebei, China
• https://www.researchgate.net/publication/331575655_Mapping_Geochemical_Anomalies_Through_Integrating_Random_Forest_and_Metric_Learning_Methods
• https://www.researchgate.net/publication/229399579_Mapping_geochemical_singularity_using_multifractal_analysis_Application_to_anomaly_definition_on_stream_sediments_data_from_Funin_Sheet_Yunnan_China
• https://www.researchgate.net/publication/328255422_Mapping_mineral_prospectivity_through_big_data_analytics_and_a_deep_learning_algorithm
• https://www.researchgate.net/publication/334106787_Mapping_Mineral_Prospectivity_via_Semi-supervised_Random_Forest
• https://www.researchgate.net/publication/236270466_Mapping_of_district-scale_potential_targets_using_fractal_models
• https://www.researchgate.net/publication/357584076_Mapping_prospectivity_for_regolith-hosted_REE_deposits_via_convolutional_neural_network_with_generative_adversarial_network_augmented_data
• https://www.researchgate.net/publication/328623280_Maximum_Entropy_and_Random_Forest_Modeling_of_Mineral_Potential_Analysis_of_Gold_Prospectivity_in_the_Hezuo-Meiwu_District_West_Qinling_Orogen_China
• https://www.sciencedirect.com/science/article/pii/S016913682400163X -> Metallogenic prediction based on fractal theory and machine learning in Duobaoshan Area, Heilongjiang Province
• https://www.sciencedirect.com/science/article/pii/S0169136824003810 -> Mineral prospectivity mapping susceptibility evaluation based on interpretable ensemble learning
• https://link.springer.com/article/10.1007/s11053-024-10386-6 -> Mineral Prospectivity Mapping Based on Spatial Feature Classification with Geological Map Knowledge Graph Embedding: Case Study of Gold Ore Prediction at Wulonggou, Qinghai Provinc
• https://www.researchgate.net/publication/235443301_Mineral_potential_mapping_in_a_frontier_region
• https://www.researchgate.net/publication/235443302_Mineral_potential_mapping_in_frontier_regions_A_Mongolian_case_study
• https://www.researchgate.net/publication/369104190_Mineral_Prospectivity_Mapping_Using_Attention-based_Convolutional_Neural_Network
• https://www.nature.com/articles/s41598-024-73357-0 -> Mineral prospectivity prediction based on convolutional neural network and ensemble learning
• https://www.researchgate.net/publication/329037175_Mineral_prospectivity_analysis_for_BIF_iron_deposits_A_case_study_in_the_Anshan-Benxi_area_Liaoning_province_North-East_China
• https://link.springer.com/article/10.1007/s11053-024-10335-3 -> Mineral Prospectivity Prediction Based on Self-Supervised Contrastive Learning and Geochemical Data: A Case Study of the Gold Deposit in the Malanyu District, Hebei Province, China [USEEN]
• https://www.researchgate.net/publication/377694139_Manganese_mineral_prospectivity_based_on_deep_convolutional_neural_networks_in_Songtao_of_northeastern_Guizhou
• https://www.researchgate.net/publication/ 351649498_Mineral_Prospectivity_Mapping_based_on_Isolation_Forest_and_Random_Forest_Implication_for_the_Existence_of_Spatial_Signature_of_Mineralization_in_Outliers
• https://www.researchgate.net/publication/358528670_Mineral_Prospectivity_Mapping_Based_on_Wavelet_Neural_Network_and_Monte_Carlo_Simulations_in_the_Nanling_W-Sn_Metallogenic_Province
• https://www.researchgate.net/publication/352983697_Mineral_prospectivity_mapping_by_deep_learning_method_in_Yawan-Daqiao_area_Gansu
• https://www.researchgate.net/publication/367106018_Mineral_Prospectivity_Mapping_of_Porphyry_Copper_Deposits_Based_on_Remote_Sensing_Imagery_and_Geochemical_Data_in_the_Duolong_Ore_District_Tibet - Mineral Prospectivity Mapping of Porphyry Copper Deposits Based on Remote Sensing Imagery and Geochemical Data in the Duolong Ore District, Tibet
• https://www.researchgate.net/publication/355749736_Mineral_prospectivity_mapping_using_a_joint_singularity-based_weighting_method_and_long_short-term_memory_network
• https://www.researchgate.net/publication/369104190_Mineral_Prospectivity_Mapping_Using_Attention-based_Convolutional_Neural_Network
• https://www.researchgate.net/publication/365434839_Mineral_Prospectivity_Mapping_Using_Deep_Self-Attention_Model
• https://www.researchgate.net/publication/379674196_Mineral_prospectivity_mapping_using_knowledge_embedding_and_explainable_ensemble_learning_A_case_study_of_the_Keeryin_ore_concentration_in_Sichuan_China
• Mineral Prospectivity Mapping Using Semi-supervised Machine Learning ->https://link.springer.com/article/10.1007/s11004-024-10161-6
• https://www.researchgate.net/publication/350817877_Mineral_Prospectivity_Prediction_via_Convolutional_Neural_Networks_Based_on_Geological_Big_Data
• https://www.researchgate.net/publication/338871759_Modeling-based_mineral_system_approach_to_prospectivity_mapping_of_stratabound_hydrothermal_deposits_A_case_study_of_MVT_Pb-Zn_deposits_in_the_Huayuan_area_northwestern_Hunan_Province_China
• https://www.sciencedirect.com/science/article/pii/S0169136824003172 -> New insights into the metallogenic genesis of the Xiadian Au deposit, Jiaodong Peninsula, Eastern China: Constraints from integrated rutile in-situ geochemical analysis and machine learning discrimination
• https://www.researchgate.net/publication/332547136_Prospectivity_Mapping_for_Porphyry_Cu-Mo_Mineralization_in_the_Eastern_Tianshan_Xinjiang_Northwestern_China
• https://www.sciencedirect.com/science/article/pii/S0169136824001823 -> Quantitative prediction methods and applications of digital ore deposit models
• https://www.researchgate.net/publication/344303914_Random-Drop_Data_Augmentation_of_Deep_Convolutional_Neural_Network_for_Mineral_Prospectivity_Mapping
• https://www.researchgate.net/publication/371044606_Supervised_Mineral_Prospectivity_Mapping_via_Class-Balanced_Focal_Loss_Function_on_Imbalanced_Geoscience_DatasetsSupervised Mineral Prospectivity Mapping via Class-Balanced Focal Loss Function on Imbalanced Geoscience Datasets
• https://www.researchgate.net/publication/361520562_Recognizing_Multivariate_Geochemical_Anomalies_Related_to_Mineralization_by_Using_Deep_Unsupervised_Graph_Learning
• https://www.sciencedirect.com/science/article/pii/S0169136824003937 -> Semi-supervised graph convolutional networks for integrating continuous and binary evidential layers for mineral exploration targeting
• https://www.sciencedirect.com/science/article/pii/S0009281924001375 -> Spatial weighting — An effective incorporation of geological expertise into deep learning models
• https://www.researchgate.net/publication/371044606_Supervised_Mineral_Prospectivity_Mapping_via_Class-Balanced_Focal_Loss_Function_on_Imbalanced_Geoscience_Datasets
• https://www.researchgate.net/publication/360028637_Three-Dimensional_Mineral_Prospectivity_Mapping_by_XGBoost_Modeling_A_Case_Study_of_the_Lannigou_Gold_Deposit_China
• https://link.springer.com/article/10.1007/s11053-024-10387-5 - Toward Data-Driven Mineral Prospectivity Mapping from Remote Sensing Data Using Deep Forest Predictive Model
• https://www.researchgate.net/publication/361589587_Unlabeled_Sample_Selection_for_Mineral_Prospectivity_Mapping_by_Semi-supervised_Support_Vector_Machine
• https://www.researchgate.net/publication/343515866_Using_deep_variational_autoencoder_networks_for_recognizing_geochemical_anomalies
• https://link.springer.com/article/10.1007/s11004-024-10151-8 -> Using Three-dimensional Modeling and Random Forests to Predict Deep Ore Potentials: A Case Study on Xiongcun Porphyry Copper–Gold Deposit in Tibet, China
• https://www.researchgate.net/publication/361194407_Visual_Interpretable_Deep_Learning_Algorithm_for_Geochemical_Anomaly_Recognition

• https://www.researchgate.net/publication/340084035_Reliability_of_using_ASTER_data_in_lithologic_mapping_and_alteration_mineral_detection_of_the_basement_complex_of_West_Berenice_Southeastern_Desert_Egypt

• https://www.researchgate.net/publication/342339753_A_machine_learning_approach_to_tungsten_prospectivity_modelling_using_knowledge-driven_feature_extraction_and_model_confidence
• https://www.researchgate.net/project/Enhancing-the-Geological-Understanding-of-SW-England-Using-Machine-Learning-Algorithms

• https://www.researchgate.net/publication/349158008_Mapping_gold_mineral_prospectivity_based_on_weights_of_evidence_method_in_southeast_Asmara_Eritrea

• https://www.researchgate.net/publication/360661926_Target-scale_prospectivity_modeling_for_gold_mineralization_within_the_Rajapalot_Au-Co_project_area_in_northern_Fennoscandian_Shield_Finland_Part_2_Application_of_self-organizing_maps_and_artificial_n
• https://www.sciencedirect.com/science/article/pii/S0169136824004037 -> Addressing imbalanced data for machine learning based mineral prospectivity mapping
• https://publications.csiro.au/publications/#publication/PIcsiro:EP146125/SQmineral%20prospectivity/RP1/RS50/RORECENT/STsearch-by-keyword/LISEA/RI12/RT26 -> A novel spatial analysis approach for assessing regional-scale mineral prospectivity In Northern Finland
• https://www.researchgate.net/publication/332352805_Boosting_for_Mineral_Prospectivity_Modeling_A_New_GIS_Toolbox
• https://www.researchgate.net/publication/324517415_Can_boosting_boost_minimal_invasive_exploration_targeting
• https://www.researchgate.net/publication/248955109_Combined_conceptualempirical_prospectivity_mapping_for_orogenic_gold_in_the_northern_Fennoscandian_Shield_Finland
• https://www.researchgate.net/publication/283451958_Data-driven_logistic-based_weighting_of_geochemical_and_geological_evidence_layers_in_mineral_prospectivity_mapping
• https://www.researchgate.net/publication/320280611_Evaluation_of_boosting_algorithms_for_prospectivity_mapping
• https://www.researchgate.net/publication/298297988_Fuzzy_logic_data_integration_technique_used_as_a_nickel_exploration_tool
• https://www.researchgate.net/publication/259372191_Gravity_data_in_regional_scale_3D_and_gold_prospectivity_modelling_-_example_from_the_Central_Lapland_greenstone_belt_northern_Finland
• https://www.researchgate.net/publication/315381587_Introduction_to_the_special_issue_GIS-based_mineral_potential_targeting
• https://www.researchgate.net/publication/320709733_Knowledge-driven_prospectivity_model_for_Iron_oxide-Cu-Au_IOCG_deposits_in_northern_Finland
• https://tupa.gtk.fi/raportti/arkisto/57_2021.pdf -> Mineral Prospectivity and Exploration Targeting MinProXT 2021 Webinar - paper compilation
• https://tupa.gtk.fi/raportti/arkisto/29_2023.pdf -> Mineral Prospectivity and Exploration Targeting MinProXT 2022 Webinar - paper compilation
• https://www.researchgate.net/publication/312180531_Optimizing_a_Knowledge-driven_Prospectivity_Model_for_Gold_Deposits_Within_Perapohja_Belt_Northern_Finland
• https://www.researchgate.net/publication/320703774_Prospectivity_Models_for_Volcanogenic_Massive_Sulfide_Deposits_VMS_in_Northern_Finland
• https://www.researchgate.net/publication/280875727_Receiver_operating_characteristics_ROC_as_validation_tool_for_prospectivity_models_-_A_magmatic_Ni-Cu_case_study_from_the_Central_Lapland_Greenstone_Belt_Northern_Finland
• https://www.researchgate.net/publication/332298116_Scalability_of_the_Mineral_Prospectivity_Modelling_-_An_orogenic_gold_case_study_from_northern_Finland
• https://www.researchgate.net/publication/251786465_Spatial_data_analysis_as_a_tool_for_mineral_prospectivity_mapping
• https://www.researchgate.net/publication/331006924_Unsupervised_clustering_and_empirical_fuzzy_memberships_for_mineral_prospectivity_modelling

• https://www.researchgate.net/publication/227256267_Application_of_Data-Driven_Evidential_Belief_Functions_to_Prospectivity_Mapping_for_Aquamarine-Bearing_Pegmatites_Lundazi_District_Zambia
• https://www.researchgate.net/publication/226842511_Mapping_of_prospectivity_and_estimation_of_number_of_undiscovered_prospects_for_lode_gold_southwestern_Ashanti_Belt_Ghana
• https://www.researchgate.net/publication/233791624_Spatial_association_of_gold_deposits_with_remotely_-_sensed_faults_South_Ashanti_belt_Ghana

• https://www.researchgate.net/publication/360970965_Identification_of_Radioactive_Mineralized_Lithology_and_Mineral_Prospectivity_Mapping_Based_on_Remote_Sensing_in_High-Latitude_Regions_A_Case_Study_on_the_Narsaq_Region_of_Greenland

• https://www.researchgate.net/publication/226092981_A_Hybrid_Neuro-Fuzzy_Model_for_Mineral_Potential_Mapping
• https://www.researchgate.net/publication/225328359_A_Hybrid_Fuzzy_Weights-of-Evidence_Model_for_Mineral_Potential_Mapping
• https://www.researchgate.net/publication/227221497_Artificial_Neural_Networks_for_Mineral-Potential_Mapping_A_Case_Study_from_Aravalli_Province_Western_India
• https://www.researchgate.net/publication/222050039_Bayesian_network_classifiers_for_mineral_potential_mapping
• https://www.researchgate.net/publication/355397149_Gold_Prospectivity_Mapping_in_the_Sonakhan_Greenstone_Belt_Central_India_A_Knowledge-Driven_Guide_for_Target_Delineation_in_a_Region_of_Low_Exploration_Maturity
• https://www.researchgate.net/publication/272092276_Extended_Weights-of-Evidence_Modelling_for_Predictive_Mapping_of_Base_Metal_Deposit_Potential_in_Aravalli_Province_Western_India
• https://www.researchgate.net/publication/226193283_Knowledge-Driven_and_Data-Driven_Fuzzy_Models_for_Predictive_Mineral_Potential_Mapping
• https://www.researchgate.net/publication/238027981_SVM-based_base-metal_prospectivity_modeling_of_the_Aravalli_Orogen_Northwestern_India
• https://www.researchgate.net/publication/372636338_Unsupervised_machine_learning_based_prospectivity_analysis_of_NW_and_NE_India_for_carbonatite-alkaline_complex-related_REE_deposits

• https://www.researchgate.net/publication/263542819_Regional-Scale_Geothermal_Prospectivity_Mapping_in_West_Java_Indonesia_by_Data-driven_Evidential_Belief_Functions

• https://www.researchgate.net/publication/390072341_Enhancing_regional-scale_Pb-Zn_prospectivity_mapping_through_data_augmentation_Joint_application_of_unsupervised_random_forests_and_convolutional_neural_network
• https://www.researchgate.net/publication/325697373_A_comparative_analysis_of_artificial_neural_network_ANN_wavelet_neural_network_WNN_and_support_vector_machine_SVM_data-driven_models_to_mineral_potential_mapping_for_copper_mineralizations_in_the_Shah
• https://www.researchgate.net/publication/358507255_A_Comparative_Study_of_Convolutional_Neural_Networks_and_Conventional_Machine_Learning_Models_for_Lithological_Mapping_Using_Remote_Sensing_Data
• https://www.researchgate.net/publication/351750324_A_data_augmentation_approach_to_XGboost-based_mineral_potential_mapping_An_example_of_carbonate-hosted_Zn_Pb_mineral_systems_of_Western_Iran
• https://www.researchgate.net/publication/336471932_A_knowledge-guided_fuzzy_inference_approach_for_integrating_geophysics_geochemistry_and_geology_data_in_deposit-scale_porphyry_copper_targeting_Saveh-Iran
• https://www.researchgate.net/publication/348500913_A_new_strategy_for_spatial_predictive_mapping_of_mineral_prospectivity
• https://www.researchgate.net/publication/348482539_A_new_strategy_for_spatial_predictive_mapping_of_mineral_prospectivity_Automated_hyperparameter_tuning_of_random_forest_approach
• https://www.researchgate.net/publication/352251016_A_simulation-based_framework_for_modulating_the_effects_of_subjectivity_in_greenfield_Mineral_Prospectivity_Mapping_with_geochemical_and_geological_data
• https://www.researchgate.net/publication/296638839_An_AHP-TOPSIS_Predictive_Model_for_District-Scale_Mapping_of_Porphyry_Cu-Au_Potential_A_Case_Study_from_Salafchegan_Area_Central_Iran
• https://www.researchgate.net/publication/278029106_Application_of_Discriminant_Analysis_and_Support_Vector_Machine_in_Mapping_Gold_Potential_Areas_for_Further_Drilling_in_the_Sari-Gunay_Gold_Deposit_NW_Iran
• https://www.researchgate.net/publication/220164381_Application_of_geochemical_zonality_coefficients_in_mineral_prospectivity_mapping
• https://www.researchgate.net/publication/330359897_Application_of_hybrid_AHP-TOPSIS_method_for_prospectivity_modeling_of_Cu_porphyry_in_Varzaghan_district_Iran
• https://www.researchgate.net/publication/356872819_Application_of_self-organizing_map_SOM_and_K-means_clustering_algorithms_for_portraying_geochemical_anomaly_patterns_in_Moalleman_district_NE_Iran
• https://www.researchgate.net/publication/258505300_Application_of_staged_factor_analysis_and_logistic_function_to_create_a_fuzzy_stream_sediment_geochemical_evidence_layer_for_mineral_prospectivity_mapping
• https://www.researchgate.net/publication/358567148_Applications_of_data_augmentation_in_mineral_prospectivity_prediction_based_on_convolutional_neural_networks
• https://www.researchgate.net/publication/353761696_Assessing_the_effects_of_mineral_systems-derived_exploration_targeting_criteria_for_Random_Forests-based_predictive_mapping_of_mineral_prospectivity_in_Ahar-Arasbaran_area_Iran
• https://www.sciencedirect.com/science/article/abs/pii/S0375674225000871 -> A semi-supervised learning framework for intelligent mineral prospectivity mapping: Incorporation of the CatBoost and Gaussian mixture model algorithms [UNSEEN]
• https://www.researchgate.net/publication/270586282_Data-Driven_Index_Overlay_and_Boolean_Logic_Mineral_Prospectivity_Modeling_in_Greenfields_Exploration
• https://www.researchgate.net/publication/356660905_Deep_GMDH_Neural_Networks_for_Predictive_Mapping_of_Mineral_Prospectivity_in_Terrains_Hosting_Few_but_Large_Mineral_Deposits
• https://link.springer.com/article/10.1007/s12145-025-01708-0 Density based spatial clustering of applications with noise and fuzzy C-means algorithms for unsupervised mineral prospectivity mapping [UNSEEN]
• https://www.researchgate.net/publication/317240761_Enhancement_and_Mapping_of_Weak_Multivariate_Stream_Sediment_Geochemical_Anomalies_in_Ahar_Area_NW_Iran
• https://www.sciencedirect.com/science/article/pii/S0009281924001223 -> Enhancing training performance of convolutional neural network algorithm through an autoencoder-based unsupervised labeling framework for mineral exploration targeting
• https://www.researchgate.net/publication/356580903_Evidential_data_integration_to_produce_porphyry_Cu_prospectivity_map_using_a_combination_of_knowledge_and_data_driven_methods
• https://research-repository.uwa.edu.au/en/publications/exploration-feature-selection-applied-to-hybrid-data-integration-Exploration feature selection applied to hybrid data integrationmodeling: Targeting copper-gold potential in central
• https://www.researchgate.net/publication/333199619_Incorporation_of_principal_component_analysis_geostatistical_interpolation_approaches_and_frequency-space-based_models_for_portraying_the_Cu-Au_geochemical_prospects_in_the_Feizabad_district_NW_Iran
• https://www.researchgate.net/publication/351965039_Intelligent_geochemical_exploration_modeling_using_multiclass_support_vector_machine_and_integration_it_with_continuous_genetic_algorithm_in_Gonabad_region_Khorasan_Razavi_Iran
• https://www.researchgate.net/publication/310658663_Multifractal_interpolation_and_spectrum-area_fractal_modeling_of_stream_sediment_geochemical_data_Implications_for_mapping_exploration_targets
• https://www.researchgate.net/publication/267635150_Multivariate_regression_analysis_of_lithogeochemical_data_to_model_subsurface_mineralization_A_case_study_from_the_Sari_Gunay_epithermal_gold_deposit_NW_Iran
• https://www.researchgate.net/publication/330129457_Performance_evaluation_of_RBF-_and_SVM-based_machine_learning_algorithms_for_predictive_mineral_prospectivity_modeling_integration_of_S-A_multifractal_model_and_mineralization_controls
• https://www.researchgate.net/publication/353982380_Porphyry_Cu-Au_prospectivity_modelling_using_semi-supervised_learning_algorithm_in_Dehsalm_district_eastern_Iran_In_Farsi_with_extended_English_abstract
• https://www.researchgate.net/publication/320886789_Prospectivity_analysis_of_orogenic_gold_deposits_in_Saqez-Sardasht_Goldfield_Zagros_Orogen_Iran
• https://www.researchgate.net/publication/361529867_Prospectivity_mapping_of_orogenic_lode_gold_deposits_using_fuzzy_models_a_case_study_of_Saqqez_area_NW_of_Iran
• https://www.researchgate.net/publication/361717490_Quantifying_Uncertainties_Linked_to_the_Diversity_of_Mathematical_Frameworks_in_Knowledge-Driven_Mineral_Prospectivity_Mapping
• https://www.researchgate.net/publication/374730424_Recognition_of_mineralization-related_anomaly_patterns_through_an_autoencoder_neural_network_for_mineral_exploration_targeting
• https://www.researchgate.net/publication/349957803_Regional-Scale_Mineral_Prospectivity_Mapping_Support_Vector_Machines_and_an_Improved_Data-Driven_Multi-criteria_Decision-Making_Technique
• https://www.researchgate.net/publication/339153591_Sensitivity_analysis_of_prospectivity_modeling_to_evidence_maps_Enhancing_success_of_targeting_for_epithermal_gold_Takab_district_NW_Iran
• https://www.researchgate.net/publication/321076980_Spatial_analyses_of_exploration_evidence_data_to_model_skarn-type_copper_prospectivity_in_the_Varzaghan_district_NW_Iran
• https://www.researchgate.net/publication/304904242_Stepwise_regression_for_recognition_of_geochemical_anomalies_Case_study_in_Takab_area_NW_Iran
• https://www.researchgate.net/publication/350423220_Supervised_mineral_exploration_targeting_and_the_challenges_with_the_selection_of_deposit_and_non-deposit_sites_thereof
• https://www.sciencedirect.com/science/article/pii/S0009281924000801 -> Targeting porphyry Cu deposits in the Chahargonbad region of Iran: A joint application of deep belief networks and random forest techniques
• https://www.researchgate.net/publication/307874730_The_use_of_decision_tree_induction_and_artificial_neural_networks_for_recognizing_the_geochemical_distribution_patterns_of_LREE_in_the_Choghart_deposit_Central_Iran
• https://www.researchsquare.com/article/rs-4760956/v1 -> Uncertainty reduction with Hyperparameter Optimization in mineral prospectivity mapping: A Regularized Artificial Neural Network approach[UNSEEN]

• https://www.gsi.ie/en-ie/programmes-and-projects/tellus/activities/tellus-product-development/mineral-prospectivity/Pages/default.aspx - > NW Midlands Mineral Prospectivity Mapping

• https://www.researchgate.net/publication/390597293_Assessment_of_Mineral_Deposits_in_Part_of_North_Senatorial_Zone_Adamawa_State_Nigeria_Using_Remote_Sensing_Geographic_Information_Systems_and_Machine_Learning

• https://www.mdpi.com/2075-163X/9/2/131/htm - Prospectivity Mapping of Mineral Deposits in Northern Norway Using Radial Basis Function Neural Networks

• https://onlinelibrary.wiley.com/doi/full/10.1002/eng2.13031 _> Advanced Mineral Deposit Mapping via Deep Learning and SVM Integration With Remote Sensing Imaging Data

• https://www.researchgate.net/publication/221911782_Application_of_Artificial_Neural_Network_for_Mineral_Potential_Mapping
• https://www.researchgate.net/publication/359861043_Rock_Classification_in_a_Vanadiferous_Titanomagnetite_Deposit_Based_on_Supervised_Machine_Learning#fullTextFileContent Rock Classification in a Vanadiferous Titanomagnetite Deposit Based on Supervised Machine Learning
• https://www.researchgate.net/publication/382131746_Domain_Adaptation_from_Drilling_to_Geophysical_Data_for_Mineral_Exploration

• https://www.researchgate.net/publication/359632307_A_Geologically_Constrained_Variational_Autoencoder_for_Mineral_Prospectivity_Mapping
• https://www.researchgate.net/publication/263174923_Application_of_Mineral_Exploration_Models_and_GIS_to_Generate_Mineral_Potential_Maps_as_Input_for_Optimum_Land-Use_Planning_in_the_Philippines
• https://www.researchgate.net/publication/267927677_Data-driven_predictive_mapping_of_gold_prospectivity_Baguio_district_Philippines_Application_of_Random_Forests_algorithm
• https://www.researchgate.net/publication/276271833_Data-Driven_Predictive_Modeling_of_Mineral_Prospectivity_Using_Random_Forests_A_Case_Study_in_Catanduanes_Island_Philippines
• https://www.researchgate.net/publication/209803275_Evidential_belief_functions_for_data-driven_geologically_constrained_mapping_of_gold_potential_Baguio_district_Philippines
• https://www.researchgate.net/publication/241001432_Geologically_Constrained_Probabilistic_Mapping_of_Gold_Potential_Baguio_District_Philippines
• https://www.researchgate.net/publication/263724277_Geologically_Constrained_Fuzzy_Mapping_of_Gold_Mineralization_Potential_Baguio_District_Philippines
• https://www.researchgate.net/publication/229641286_Improved_Wildcat_Modelling_of_Mineral_Prospectivity
• https://www.researchgate.net/publication/238447208_Logistic_Regression_for_Geologically_Constrained_Mapping_of_Gold_Potential_Baguio_District_Philippines
• https://www.researchgate.net/publication/248977334_Mineral_imaging_with_Landsat_TM_data_for_hydrothermal_alteration_mapping_in_heavily-vegetated_terrane​​​​​​
• https://www.researchgate.net/publication/356546133_Mineral_Prospectivity_Mapping_via_Gated_Recurrent_Unit_Model
• https://www.researchgate.net/publication/267640864_Random_forest_predictive_modeling_of_mineral_prospectivity_with_small_number_of_prospects_and_data_with_missing_values_in_Abra_Philippines
• https://www.researchgate.net/publication/3931975_Remote_detection_of_vegetation_stress_for_mineral_exploration
• https://www.researchgate.net/publication/263422015_Where_Are_Porphyry_Copper_Deposits_Spatially_Localized_A_Case_Study_in_Benguet_Province_Philippines
• https://www.researchgate.net/publication/233488614_Wildcat_mapping_of_gold_potential_Baguio_District_Philippines
• https://www.researchgate.net/publication/226982180_Weights_of_Evidence_Modeling_of_Mineral_Potential_A_Case_Study_Using_Small_Number_of_Prospects_Abra_Philippines

• https://www.researchgate.net/publication/358431343_Application_of_Maximum_Entropy_for_Mineral_Prospectivity_Mapping_in_Heavily_Vegetated_Areas_of_Greater_Kurile_Chain_with_Landsat_8_Data
• https://www.researchgate.net/publication/354000754_Mineral_Prospectivity_Mapping_for_Forecasting_Gold_Deposits_in_the_Central_Kolyma_Region_North-East_Russia

• https://www.researchgate.net/publication/359294267_Data-driven_multi-index_overlay_gold_prospectivity_mapping_using_geophysical_and_remote_sensing_datasets
• https://link.springer.com/article/10.1007/s11053-024-10390-w -> Mineral Reconnaissance Through Scientific Consensus: First National Prospectivity Maps for PGE–Ni–Cu–Cr and Witwatersrand-type Au Deposits in South Africa
• https://www.researchgate.net/publication/361526053_Mineral_prospectivity_mapping_of_gold-base_metal_mineralisation_in_the_Sabie-Pilgrim%27s_Rest_area_Mpumalanga_Province_South_Africa
• https://www.researchgate.net/publication/264296137_PREDICTIVE_BEDROCK_AND_MINERAL_PROSPECTIVITY_MAPPING_IN_THE_GIYANI_GREENSTONE_BELT_SOUTH_AFRICA
• https://www.researchgate.net/publication/268196204_Predictive_mapping_of_prospectivity_for_orogenic_gold_Giyani_greenstone_belt_South_Africa

• https://www.researchgate.net/publication/225656353_Deriving_Optimal_Exploration_Target_Zones_on_Mineral_Prospectivity_Maps
• https://www.researchgate.net/publication/222198648_Knowledge-guided_data-driven_evidential_belief_modeling_of_mineral_prospectivity_in_Cabo_de_Gata_SE_Spain
• https://www.researchgate.net/publication/356639977_Machine_learning_models_for_Hg_prospecting_in_the_Almaden_mining_district
• https://www.researchgate.net/publication/43165602_Methodology_for_deriving_optimal_exploration_target_zones
• https://www.researchgate.net/publication/263542579_Optimal_Exploration_Target_Zones
• https://www.researchgate.net/publication/222892103_Optimal_field_sampling_for_targeting_minerals_using_hyperspectral_data
• https://www.researchgate.net/publication/271671416_Predictive_modelling_of_gold_potential_with_the_integration_of_multisource_information_based_on_random_forest_a_case_study_on_the_Rodalquilar_area_Southern_Spain

• https://link.springer.com/article/10.1007/s11053-024-10387-5 -> Toward Data-Driven Mineral Prospectivity Mapping from Remote Sensing Data Using Deep Forest Predictive Model [UNSEEN]

• https://www.researchgate.net/publication/259128115_Biogeochemical_expression_of_rare_earth_element_and_zirconium_mineralization_at_Norra_Karr_Southern_Sweden
• https://www.researchgate.net/publication/260086862_COMPARISION_OF_VMS_PROSPECTIVITY_MAPPING_BY_EBF_AND_WOFE_MODELING_THE_SKELLEFTE_DISTRICT_SWEDEN
• https://www.researchgate.net/publication/336086368_GIS-based_mineral_system_approach_for_prospectivity_mapping_of_iron-oxide_apatite-bearing_mineralisation_in_Bergslagen_Sweden
• https://www.researchgate.net/publication/229347041_Predictive_mapping_of_prospectivity_and_quantitative_estimation_of_undiscovered_VMS_deposits_in_Skellefte_district_Sweden
• https://www.researchgate.net/publication/260086947_PRELIMINARY_GIS-BASED_ANALYSIS_OF_REGIONAL-SCALE_VMS_PROSPECTIVITY_IN_THE_SKELLEFTE_REGION_SWEDEN

• https://www.sciencedirect.com/science/article/pii/S2666261224000270 -> Machine learning based prospect targeting: A case of gold occurrence in central parts of Tanzania, East Africa

• https://www.researchgate.net/publication/242339962_Predictive_mapping_for_orogenic_gold_prospectivity_in_Uganda
• https://www.researchgate.net/publication/262566098_Predictive_Mapping_of_Prospectivity_for_Orogenic_Gold_in_Uganda
• https://www.researchgate.net/publication/381219015_Machine_Learning_Application_in_Predictive_Mineral_Mapping_of_Southwestern_Uganda_Leveraging_Airborne_Magnetic_Radiometric_and_Electromagnetic_Data

• https://www.researchgate.net/publication/383580839_Improved_mineral_prospectivity_mapping_using_graph_neural_networks

• https://www.researchgate.net/publication/338663292_A_Predictive_Geospatial_Exploration_Model_for_Mississippi_Valley_Type_Pb-Zn_Mineralization_in_the_Southeast_Missouri_Lead_District
• Machine Learning and Plate Tectonic Analysis for Mantle Heterogeneity, Paleoclimate, and Critical Minerals ->https://repository.arizona.edu/handle/10150/675507?show=full
• https://www.sciencedirect.com/science/article/abs/pii/S0375674218300396?via%3Dihub -> Machine learning strategies for classification and prediction of alteration facies: Examples from the Rosemont Cu-Mo-Ag skarn deposit, SE Tucson Arizona
• [presentation of the above!]https://www.slideshare.net/JuanCarlosOrdezCalde/geology-chemostratigraphy-and-alteration-geochemistry-of-the-rosemont-cumoag-skarn-deposit-southern-arizona
• https://github.com/rohitash-chandra/research/blob/master/presentations/CSIRO%20Minerals-Seminar-September2022.pdf -> Machine Learning for Mineral Exploration: A Data Odyssey
  • Videohttps://www.youtube.com/watch?v=zhXuPQy7mk8&t=561s -> Talks about using plate subduction and associated statistics via GPlates

• https://www.researchgate.net/publication/263542565_APPLICATION_OF_REMOTE_SENSING_AND_SPATIAL_DATA_INTEGRATION_TO_PREDICT_POTENTIAL_ZONES_FOR_AQUAMARINE-BEARING_PEGMATITES_LUNDAZI_AREA_NORTHEAST_ZAMBIA
• https://www.researchgate.net/publication/264041472_Geological_and_Mineral_Potential_Mapping_by_Geoscience_Data_Integration

• https://www.researchgate.net/publication/260792212_Nickel_Sulphide_Deposits_in_Archaean_Greenstone_Belts_in_Zimbabwe_Review_and_Prospectivity_Analysis

• https://www.sciencedirect.com/science/article/pii/S2772883824000347 -> A review on the applications of airborne geophysical and remote sensing datasets in epithermal gold mineralisation mapping
• https://www.researchgate.net/publication/353530416_A_Systematic_Review_on_the_Application_of_Machine_Learning_in_Exploiting_Mineralogical_Data_in_Mining_and_Mineral_Industry
• https://www.researchgate.net/publication/365777421_Computer_Vision_and_Pattern_Recognition_for_the_Analysis_of_2D3D_Remote_Sensing_Data_in_Geoscience_A_Survey - Computer Vision and Pattern Recognition for the Analysis of 2D/3D Remote Sensing Data in Geoscience: A Survey
• https://www.researchgate.net/publication/352104303_Deep_Learning_for_Geophysics_Current_and_Future_Trends
• https://www.proquest.com/openview/e7bec6c8ee50183b5049516b000d4f5c/1?pq-origsite=gscholar&cbl=18750&diss=y -> Probabilistic Knowledge-Guided Machine Learning in Engineering and Geoscience Systems
• KGMLPrescribedFires repository for one paper / part of above dissertation

• https://pubs.er.usgs.gov/publication/ofr20211049 -> Deposit Classification Scheme for the Critical Minerals Mapping Initiative Global Geochemical Database

• https://www.escubed.org/journals/earth-science-systems-and-society/articles/10.3389/esss.2024.10109/full -> Geospatial Data and Deep Learning Expose ESG Risks to Critical Raw Materials Supply: The Case of Lithium

Causal Discovery and Deep Learning Algorithms for Detecting Geochemical Patterns Associated with Gold-Polymetallic Mineralization: A Case Study of the Edongnan Region

• https://link.springer.com/article/10.1007/s11053-024-10408-3 -> A New Sphalerite Thermometer Based on Machine Learning with Trace Element Geochemistry
• https://www.researchgate.net/publication/378150628_A_SMOTified_extreme_learning_machine_for_identifying_mineralization_anomalies_from_geochemical_exploration_data_a_case_study_from_the_Yeniugou_area_Xinjiang_China A SMOTified extreme learning machine for identifying mineralization anomalies from geochemical exploration data
• https://ui.adsabs.harvard.edu/abs/2018EGUGA..20.4169R/abstract -> Accelerating minerals exploration with in-field characterisation, sample tracking and active machine learning
• https://www.researchgate.net/publication/375509344_Alteration_assemblage_characterization_using_machine_learning_applied_to_high_resolution_drill-core_images_hyperspectral_data_and_geochemistry
• https://qspace.library.queensu.ca/items/38f52d19-609d-4916-bcd0-3ce20675dee3/full - > Application of Computational Methods to Data Integration and Geoscientific Problems in Mineral Exploration and Mining
• https://www.sciencedirect.com/science/article/pii/S0169136822005509?dgcid=rss_sd_all -> Applying neural networks-based modelling to the prediction of mineralization: A case-study using the Western Australian Geochemistry (WACHEM) database
• https://www.sciencedirect.com/science/article/pii/S0169136824002099 -> Development of a machine learning model to classify mineral deposits using sphalerite chemistry and mineral assemblages
• https://www.sciencedirect.com/science/article/pii/S0169136824002403 -> Discrimination of deposit types using magnetite geochemistry based on machine learning
• https://www.researchgate.net/publication/302595237_A_machine_learning_approach_to_geochemical_mapping
• https://www.researchgate.net/publication/369300132_DEEP-LEARNING_IDENTIFICATION_OF_ANOMALOUS_DATA_IN_GEOCHEMICAL_DATASETS_DEEP-LEARNING_IDENTIFICATION_OF_ANOMALOUS_DATA_IN_GEOCHEMICAL_DATASETS
• https://www.researchgate.net/publication/378549920_Denoising_of_geochemical_data_using_deep_learning-Implications_for_regional_surveys -> Denoising of Geochemical Data using Deep Learning–Implications for Regional Surveys]
• https://www.researchgate.net/publication/368489689_Discrimination_of_Pb-Zn_deposit_types_using_sphalerite_geochemistry_New_insights_from_machine_learning_algorithm
• https://www.researchgate.net/publication/381369176_Effectiveness_of_LOF_iForest_and_OCSVM_in_detecting_anomalies_in_stream_sediment_geochemical_data#:~:text=LOF%20outperformed%20iForest%20and%20OCSVM,patterns%20in%20the%20iForest%20map
• Fusion of Geochemical Data and Remote SensingData Based on Convolutional Neural Network ->https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10758357
• https://dzkjqb.cug.edu.cn/en/article/doi/10.19509/j.cnki.dzkq.tb20220423 -> Gaussian mixture model in geochemical anomaly delineation of stream sediments: A case study of Xupu, Hunan Province [UNSEEN]
• https://www.sciencedirect.com/science/article/pii/S0883292724002427 -> Geologically constrained unsupervised dual-branch deep learning algorithm for geochemical anomalies identification
• https://www.researchgate.net/publication/365953549_Incorporating_the_genetic_and_firefly_optimization_algorithms_into_K-means_clustering_method_for_detection_of_porphyry_and_skarn_Cu-related_geochemical_footprints_in_Baft_district_Kerman_Iran
• https://www.researchgate.net/publication/369768936_Infomax-based_deep_autoencoder_network_for_recognition_of_multi-element_geochemical_anomalies_linked_to_mineralization -> Paywalled
• https://www.sciencedirect.com/science/article/abs/pii/S0098300424001626 -> Local phase-constrained convolutional autoencoder network for identifying multivariate geochemical anomalies
• https://www.researchgate.net/publication/354564681_Machine_Learning_for_Identification_of_Primary_Water_Concentrations_in_Mantle_Pyroxene
• https://www.researchgate.net/publication/366210211_Machine_Learning_Prediction_of_Ore_Deposit_Genetic_Type_Using_Magnetite_Geochemistry
• https://link.springer.com/article/10.1007/s42461-024-01013-2 -> NIR-Spectroscopy and Machine Learning Models to Pre-concentrate Copper Hosted Within Sedimentary Rocks[UNSEEN]
• https://www.researchsquare.com/article/rs-4106957/v1 -> Multi-element geochemical anomaly recognition applying geologically-constrained convolutional deep learning algorithm with Butterworth filtering
• https://www.researchgate.net/publication/369241349_Quantifying_continental_crust_thickness_using_the_machine_learning_method
• https://link.springer.com/article/10.1007/s11004-024-10158-1 -> Spatial-Spectrum Two-Branch Model Based on a Superpixel Graph Convolutional Network and 1DCNN for Geochemical Anomaly Identification
• https://www.researchgate.net/publication/334651800_Using_machine_learning_to_estimate_a_key_missing_geochemical_variable_in_mining_exploration_Application_of_the_Random_Forest_algorithm_to_multi-sensor_core_logging_data

• https://www.researchgate.net/publication/377892369_Apatite_trace_element_composition_as_an_indicator_of_ore_deposit_types_A_machine_learning_approachApatite trace element composition as an indicator of ore deposit types: A machine learning approach
• https://www.researchgate.net/publication/369729999_Visual_Interpretation_of_Machine_Learning_Genetical_Classification_of_Apatite_from_Various_Ore_Sources

• https://ieeexplore.ieee.org/abstract/document/10544529 -> Remote sensing data processing using convolutional neural networks for mapping alteration zones [UNSEEN]

• https://www.researchgate.net/publication/332263305_A_speedy_update_on_machine_learning_applied_to_bedrock_mapping_using_geochemistry_or_geophysics_examples_from_the_Pacific_Rim_and_nearby
• https://eprints.utas.edu.au/32368/ - thesis paper update
• https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2024.1407173/full -> Deep learning for geological mapping in the overburden area
• https://www.researchgate.net/publication/280038632_Estimating_the_fill_thickness_and_bedrock_topography_in_intermontane_valleys_using_artificial_neural_networks_-_Supporting_Information
• https://www.researchgate.net/publication/311783770_Mapping_the_global_depth_to_bedrock_for_land_surface_modeling
• https://www.researchgate.net/publication/379813337_Contribution_to_advancing_aquifer_geometric_mapping_using_machine_learning_and_deep_learning_techniques_a_case_study_of_the_AL_Haouz-Mejjate_aquifer_Marrakech_Morocco
• https://www.linkedin.com/pulse/depth-basement-modelling-machine-learning-perspective-n5gyc/?trackingId=qFSktvVPUiSa2V2nlmXVoQ%3D%3D

• https://pubmed.ncbi.nlm.nih.gov/35776744/ - Deep learning based lithology classification of drill core images
• https://www.researchgate.net/publication/381445417_Machine_Learning_for_Lithology_Analysis_using_a_Multi-Modal_Approach_of_Integrating_XRF_and_XCT_data
• https://www.researchgate.net/publication/379760986_A_machine_vision_approach_for_detecting_changes_in_drill_core_textures_using_optical_images
• https://www.sciencedirect.com/science/article/pii/S2949891024002112 -> Sensitivity analysis of similarity learning models for well-intervals based on logging data
• https://www.sciencedirect.com/science/article/pii/S2949891024003828 -> CoreViT: a new vision transformer model for lithology identification in cores

• https://www.researchgate.net/publication/390084932_A_Deep_Learning_Method_for_3D_Geological_Modeling_Using_ET4DD_with_Offset-Attention_Mechanism -> A deep learning method for 3D geological modeling using ET4DD with offset-attention mechanism [UNSEEN - repo listed in paper but not available]
• https://www.sciencedirect.com/science/article/pii/S0034425724002323 -> Deep learning-based geological map generation using geological routes
• https://www.researchgate.net/publication/354781583_Deep_learning_framework_for_geological_symbol_detection_on_geological_maps
• https://www.researchgate.net/publication/335104674_Does_shallow_geological_knowledge_help_neural-networks_to_predict_deep_units
• https://www.researchgate.net/publication/379939974_Graph_convolutional_network_for_lithological_classification_and_mapping_using_stream_sediment_geochemical_data_and_geophysical_data
• https://www.sciencedirect.com/science/article/abs/pii/S0098300424001493-> FlexLogNet: A flexible deep learning-based well-log completion method of adaptively using what you have to predict what you are missing
• https://ieeexplore.ieee.org/abstract/document/10493129 -> Geological Background Prototype Learning Enhanced Network for Remote Sensing-Based Engineering Geological Lithology Interpretation in Highly Vegetated Areas [Unseen]
• https://www.sciencedirect.com/science/article/pii/S2096249524000619 -> Generating extremely low-dimensional representation of subsurface earth models using vector quantization and deep Autoencoder
• https://www.researchgate.net/publication/370175012_GeoPDNN_A_Semisupervised_Deep_Learning_Neural_Network_Using_Pseudolabels_for_Three-dimensional_Urban_Geological_Modelling_and_Uncertainty_Analysis_from_Borehole_Data
• https://www.researchsquare.com/article/rs-4805227/v1 -> Synergizing AI with Geology: Exploring VisionTransformers for Rock Classification
• https://www.researchgate.net/publication/343511849_Identification_of_intrusive_lithologies_in_volcanic_terrains_in_British_Columbia_by_machine_learning_using_Random_Forests_the_value_of_using_a_soft_classifier
• https://www.sciencedirect.com/science/article/pii/S0169136824000921 -> Machine learning-based field geological mapping: A new exploration of geological survey data acquisition strategyhttps://www.researchgate.net/publication/324411647_Predicting_rock_type_and_detecting_hydrothermal_alteration_using_machine_learning_and_petrophysical_properties_of_the_Canadian_Malartic_ore_and_host_rocks_Pontiac_Subprovince_Quebec_Canada
• https://www.sciencedirect.com/science/article/abs/pii/S0895981124001743 -> Utilizing Random Forest algorithm for identifying mafic and ultramafic rocks in the Gameleira Suite, Archean-Paleoproterozoic basement of the Brasília Belt, Brazil
• https://arxiv.org/pdf/2407.18100 -> DINOv2 Rocks Geological Image Analysis: Classification
• https://jgsb.sgb.gov.br/index.php/journal/article/view/252 -> Unveiling geological complexity in the Serra Dourada Granite using self-organizing maps and hierarchical clustering: Insights for REE prospecting in the Goiás Tin Province, Brasília Belt, Central Brazil

• https://www.researchgate.net/publication/379077847_Tracing_Andean_Origins_A_Machine_Learning_Framework_for_Lead_Isotopes

• https://agu.confex.com/agu/fm18/mediafile/Handout/Paper427843/Landforms%20Poster.pdf -> Using machine learning to classify landforms for minerals exploration
• https://www.tandfonline.com/doi/abs/10.1080/13658816.2024.2414409 -> GeomorPM: a geomorphic pretrained model integrating convolution and Transformer architectures based on DEM data

• https://www.researchgate.net/publication/389767586_Machine_Learning_for_Characterizing_Magma_Fertility_in_Porphyry_Copper_Deposits_A_Case_Study_of_Southeastern_Tibet
• https://www.sciencedirect.com/science/article/abs/pii/S0926985125000692 -> Identifying ultramafic rocks using artificial neural network method based on aeromagnetic data [UNSEEN]
• https://link.springer.com/article/10.1007/s11053-024-10396-4 -> Interpretable SHAP Model Combining Meta-learning and Vision Transformer for Lithology Classification Using Limited and Unbalanced Drilling Data in Well Logging [UNSEN]
• https://www.nature.com/articles/s41598-024-66199-3 -> Machine learning and remote sensing-based lithological mapping of the Duwi Shear-Belt area, Central Eastern Desert, Egypt
• https://link.springer.com/article/10.1007/s11053-024-10375-9 - SsL-VGMM: A Semisupervised Machine Learning Model of Multisource Data Fusion for Lithology Prediction [UNSEEN]
• https://www.researchgate.net/publication/380719080_An_integrated_machine_learning_framework_with_uncertainty_quantification_for_three-dimensional_lithological_modeling_from_multi-source_geophysical_data_and_drilling_data
• https://www.bio-conferences.org/articles/bioconf/pdf/2024/34/bioconf_rena23_01005.pdf -> Lithological Mapping using Artificial Intelligence and Remote Sensing data: A Case Study of Bab Boudir region Morocco

• https://pubs.geoscienceworld.org/msa/ammin/article-abstract/doi/10.2138/am-2023-9092/636861/The-application-of-transfer-learning-in-optical -> The application of “transfer learning” in optical microscopy: the petrographic classification of metallic minerals
• https://www.researchgate.net/publication/385074584_Deep_Learning-Based_Mineral_Classification_Using_Pre-Trained_VGG16_Model_with_Data_Augmentation_Challenges_and_Future_Directions

• https://link.springer.com/article/10.1007/s11053-025-10485-y -> Uncertainty Quantification of Microblock-Based Resource Models and Sequencing of Sampling

• https://www.researchgate.net/publication/335486001_A_Stratigraphic_Prediction_Method_Based_on_Machine_Learning
• https://www.researchgate.net/publication/346641320_Classifying_basin-scale_stratigraphic_geometries_from_subsurface_formation_tops_with_machine_learning

• https://www.sciencedirect.com/science/article/pii/S0098300421000285 -> A machine learning model for structural trend fields
• https://onlinelibrary.wiley.com/doi/full/10.1111/1365-2478.13589 -> Inferring fault structures and overburden depth in 3D from geophysical data using machine learning algorithms – A case study on the Fenelon gold deposit, Quebec, Canada
• https://www.sciencedirect.com/science/article/pii/S019181412400138X -> Mapping paleostress trajectories by means of the clustering of reduced stress tensors determined from homogeneous and heterogeneous data sets
• https://www.researchgate.net/publication/332267249_Seismic_fault_detection_using_an_encoder-decoder_convolutional_neural_network_with_a_small_training_set
• https://www.researchgate.net/publication/377168034_Unsupervised_machine_learning_and_depth_clusters_of_Euler_deconvolution_of_magnetic_data_a_new_approach_to_imaging_geological_structures
• https://academic.oup.com/gji/advance-article/doi/10.1093/gji/ggae226/7701418 -> Use of Decision Tree Ensembles for Crustal Structure Imaging from Receiver Functions

• https://www.researchgate.net/publication/371594975_Assessing_plate_reconstruction_models_using_plate_driving_force_consistency_tests
• https://www.researchgate.net/publication/333182666_Decoding_Earth's_plate_tectonic_history_using_sparse_geochemical_data
• https://www.researchgate.net/publication/376519740_Machine_learning_and_tectonic_setting_determination_Bridging_the_gap_between_Earth_scientists_and_data_scientists
• https://pubs.geoscienceworld.org/gsa/geology/article-abstract/doi/10.1130/G52466.1/648458/Prediction-of-CO2-content-in-mid-ocean-ridge -> Prediction of CO2 content in mid-ocean ridge basalts via a machine learning approach

• https://www.researchgate.net/publication/373714604_Seismic_Foundation_Model_SFM_a_new_generation_deep_learning_model_in_geophysics

• https://essopenarchive.org/users/841077/articles/1231187-bayesian-inference-in-geophysics-with-ai-enhanced-markov-chain-monte-carlo -> Bayesian Inference in Geophysics with AI-enhanced Markov chain Monte Carlo
• https://www.researchgate.net/publication/353789276_Geology_differentiation_by_applying_unsupervised_machine_learning_to_multiple_independent_geophysical_inversions
• https://www.sciencedirect.com/science/article/pii/S001379522100137X - Joint interpretation of geophysical data: Applying machine learning to the modeling of an evaporitic sequence in Villar de Cañas (Spain)
• https://www.sciencedirect.com/science/article/pii/S2666544121000253 - Microleveling aerogeophysical data using deep convolutional network and MoG-RPCA
• https://www.researchgate.net/publication/368550674_Objective_classification_of_high-resolution_geophysical_data_Empowering_the_next_generation_of_mineral_exploration_in_Sierra_Leone
• https://datarock.com.au/blog/transfer-learning-with-seismic-attributes -> Transfer Learning with Seismic Attributes

• https://api.research-repository.uwa.edu.au/ws/portalfiles/portal/390212334/THESIS_-_DOCTOR_OF_PHILOSOPHY_-_SMITH_Luke_Thomas_-_2023_.pdf -> Potential Field Geophysics Enhancement Using Conteporary Deep Learning
• https://ieeexplore.ieee.org/abstract/document/10767251 -> A Stable Method for Estimating the Derivatives of Potential Field Data Based on Deep Learning -> [UNSEEN]https://www.researchgate.net/publication/389575997_Pole_Transformation_of_Magnetic_Data_Using_CNN-Based_Deep_Learning_Models
• https://www.mdpi.com/2073-8994/17/4/523 -> Inversion of Gravity Anomalies Based on U-Net Network

• https://d197for5662m48.cloudfront.net/documents/publicationstatus/206704/preprint_pdf/59681a0a2c571bc2a9006f37517bc6ef.pdf -> A Fast Three-dimensional Imaging Scheme of Airborne Time Domain Electromagnetic Data using Deep Learning
• https://www.researchgate.net/publication/351507441_A_Neural_Network-Based_Hybrid_Framework_for_Least-Squares_Inversion_of_Transient_Electromagnetic_Data
• https://www.researchgate.net/profile/Yunhe-Liu/publication/382196526_An_Efficient_Bayesian_Inference_for_Geo-electromagnetic_Data_Inversion_based_on_Surrogate_Modeling_with_Adaptive_Sampling_DNN
• https://www.researchgate.net/publication/325980016_Agglomerative_hierarchical_clustering_of_airborne_electromagnetic_data_for_multi-scale_geological_studies
• https://www.earthdoc.org/content/papers/10.3997/2214-4609.202410980 -> Deep Learning Assisted 2-D Current Density Modelling of Very Low Frequency Electromagnetic Data
• https://npg.copernicus.org/articles/26/13/2019/ -> Denoising stacked autoencoders for transient electromagnetic signal denoising
• https://www.researchgate.net/publication/373836226_An_information_theoretic_Bayesian_uncertainty_analysis_of_AEM_systems_over_Menindee_Lake_Australia -> An information theoretic Bayesian uncertainty analysis of AEM systems over Menindee Lake, Australia
• https://www.researchgate.net/publication/348850484_Effect_of_Data_Normalization_on_Neural_Networks_for_the_Forward_Modelling_of_Transient_Electromagnetic_Data
• https://www.researchgate.net/publication/342153377_Fast_imaging_of_time-domain_airborne_EM_data_using_deep_learning_technology
• https://library.seg.org/doi/10.4133/JEEG4.2.93 -> Neural Network Interpretation of High Frequency Electromagnetic Ellipticity Data Part I: Understanding the Half-Space and Layered Earth Response
• https://arxiv.org/abs/2207.12607 -> Physics Embedded Machine Learning for Electromagnetic Data Imaging
• https://academic.oup.com/gji/advance-article/doi/10.1093/gji/ggae244/7713480 -> Physics-guided deep learning-based inversion for airborne electromagnetic data
• https://library.seg.org/doi/abs/10.1190/geo2024-0282.1 -> Comparative Analysis of Deep Learning and Traditional Airborne Electromagnetic Data Processing: A Case Study [UNSEEN]
• https://www.researchgate.net/publication/359441000_Surface_parameters_and_bedrock_properties_covary_across_a_mountainous_watershed_Insights_from_machine_learning_and_geophysics
• https://www.researchgate.net/publication/337166479_Using_machine_learning_to_interpret_3D_airborne_electromagnetic_inversions
• https://www.researchgate.net/publication/344397798_TEMDnet_A_Novel_Deep_Denoising_Network_for_Transient_Electromagnetic_Signal_With_Signal-to-Image_Transformation
• https://www.researchgate.net/publication/366391168_Two-dimensional_fast_imaging_of_airborne_EM_data_based_on_U-net

• https://www.sciencedirect.com/science/article/pii/S0013795224001893 -> Geo-constrained clustering of resistivity data revealing the heterogeneous lithological architectures and the distinctive geoelectrical signature of shallow deposits

• https://ieeexplore.ieee.org/abstract/document/10597585 -> 3D Basement Relief and Density Inversion Based on EfficientNetV2 Deep Learning Network [UNSEEN]
• https://link.springer.com/article/10.1007/s11770-024-1096-5 -> 3D gravity inversion using cycle-consistent generative adversarial network [UNSEEN]
• https://www.researchgate.net/publication/365142017_3D_gravity_inversion_based_on_deep_learning
• https://www.researchgate.net/publication/378930477_A_Deep_Learning_Gravity_Inversion_Method_Based_on_a_Self-Constrained_Network_and_Its_Application
• https://www.researchgate.net/publication/362276214_DecNet_Decomposition_network_for_3D_gravity_inversion -> Olympic Dam example here
• https://www.researchgate.net/publication/368448190_Deep_Learning_to_estimate_the_basement_depth_by_gravity_data_using_Feedforward_neural_network
• https://www.researchgate.net/publication/326231731_Depth_and_Lineament_Maps_Derived_from_North_Cameroon_Gravity_Data_Computed_by_Artificial_Neural_Network_International_Journal_of_Geophysics_vol_2018_Article_ID_1298087_13_pages_2018
• https://www.researchgate.net/publication/366922016_Fast_imaging_for_the_3D_density_structures_by_machine_learning_approach
• https://www.researchgate.net/publication/370230217_Inversion_of_the_Gravity_Gradiometry_Data_by_ResUet_Network_An_Application_in_Nordkapp_Basin_Barents_Sea
• https://library.seg.org/doi/abs/10.1190/geo2024-0150.1 -> Integration of PSPU-Net gravity inversion neural network with gravity data for enhanced 3D basement relief estimation
• https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2022.897055/full -> High-precision downward continuation of the potential field based on the D-Unet network
• https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10672527 -> RTM Gravity Forward Modeling Using Improved Fully Connected Deep Neural Networks

• https://www.researchgate.net/publication/380391736_A_review_on_hyperspectral_imagery_application_for_lithological_mapping_and_mineral_prospecting_Machine_learning_techniques_and_future_prospects
• https://www.researchgate.net/publication/372876863_Ore-Grade_Estimation_from_Hyperspectral_Data_Using_Convolutional_Neural_Networks_A_Case_Study_at_the_Olympic_Dam_Iron_Oxide_Copper-Gold_Deposit_Australia [UNSEEN]

• https://www.researchgate.net/publication/383454185_Deep_joint_inversion_of_electromagnetic_seismic_and_gravity_data
• https://ieeexplore.ieee.org/abstract/document/10677418 -> Joint Inversion of Seismic and Resistivity Data Powered by Deep-learning [UNSEEN]
• Deep learning-based geophysical joint inversion using partial channel drop method ->https://www.sciencedirect.com/science/article/abs/pii/S0926985124002702
• https://www.mdpi.com/2076-3417/15/6/3125 -> A Hybrid Deep Learning Approach for Integrating Transient Electromagnetic and Magnetic Data to Enhance Subsurface Anomaly Detection

• https://www.researchgate.net/publication/348697645_3D_geological_structure_inversion_from_Noddy-generated_magnetic_data_using_deep_learning_methods
• https://www.researchgate.net/publication/360288249_3D_Inversion_of_Magnetic_Gradient_Tensor_Data_Based_on_Convolutional_Neural_Networks
• https://www.researchgate.net/publication/295902270_Artificial_neural_network_inversion_of_magnetic_anomalies_caused_by_2D_fault_structures
• https://www.researchgate.net/publication/354002966_Convolutional_neural_networks_for_the_characterization_of_magnetic_anomalies
• https://www.researchgate.net/publication/354772176_Convolution_Neural_Networks_Applied_to_the_Interpretation_of_Lineaments_in_Aeromagnetic_Data
• https://www.researchgate.net/publication/363550362_High-precision_downward_continuation_of_the_potential_field_based_on_the_D-Unet_network
• https://www.sciencedirect.com/science/article/pii/S0169136822004279?via%3Dihub -> Magnetic grid resolution enhancement using machine learning: A case study from the Eastern Goldfields Superterrane
• https://www.researchgate.net/publication/347173621_Predicting_Magnetization_Directions_Using_Convolutional_Neural_Networks -> Paywalled
• https://www.researchgate.net/publication/361114986_Reseaux_de_Neurones_Convolutifs_pour_la_Caracterisation_d'Anomalies_Magnetiques -> French original of the above

• https://advancesincontinuousanddiscretemodels.springeropen.com/articles/10.1186/s13662-024-03842-3 -> 2D magnetotelluric imaging method based on visionary self-attention mechanism and data science
• https://ieeexplore.ieee.org/abstract/document/10955415- -> 3DInception-U: Lightweight Network for 3-D Magnetotelluric Inversion Based on Inception Module [UNSEE]
• https://ieeexplore.ieee.org/abstract/document/10530937 -> A Magnetotelluric Data Denoising Method Based on Lightweight Ensemble Learning [UNSEEN]
• https://academic.oup.com/gji/advance-article/doi/10.1093/gji/ggae166/7674890 -> Deep basin conductor characterization using machine learning-assisted magnetotelluric Bayesian inversion in the SW Barents Sea
• http://en.dzkx.org/article/doi/10.6038/cjg2024R0580 -> Fast inversion method of apparent resistivity based on deep learning
• https://www.researchgate.net/publication/367504269_Flexible_and_accurate_prior_model_construction_based_on_deep_learning_for_2D_magnetotelluric_data_inversion
• https://www.sciencedirect.com/science/article/pii/S2214579624000510 -> Intelligent Geological Interpretation of AMT Data Based on Machine Learning
• https://ieeexplore.ieee.org/abstract/document/10551853 -> Magnetotelluric Data Inversion Based on Deep Learning with the Self-attention Mechanism
• https://www.researchgate.net/publication/361741409_Physics-Driven_Deep_Learning_Inversion_with_Application_to_Magnetotelluric
• https://www.researchgate.net/publication/355568465_Stochastic_inversion_of_magnetotelluric_data_using_deep_reinforcement_learning
• https://www.researchgate.net/publication/354360079_Two-dimensional_deep_learning_inversion_of_magnetotelluric_sounding_data
• https://ieeexplore.ieee.org/abstract/document/10530923 -> Three Dimensional Magnetotelluric Forward Modeling Through Deep Learning

• https://nature.com/articles/s41467-020-17841-x -> Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning
• https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022GL099053 -> Eikonal Tomography With Physics-Informed Neural Networks: Rayleigh Wave Phase Velocity in the Northeastern Margin of the Tibetan Plateau
• https://arxiv.org/abs/2403.15095 -> End-to-End Mineral Exploration with Artificial Intelligence and Ambient Noise Tomography
• https://www.nature.com/articles/s41598-019-50381-z -> High-resolution seismic tomography of Long Beach, CA using machine learning

• https://www.sciencedirect.com/science/article/pii/S0040195124002166 -> Reprocessing and interpretation of legacy seismic data using machine learning from the Granada Basin, Spain
• https://ojs.uni-miskolc.hu/index.php/geosciences/article/view/3313 -> EDGE DETECTION OF TOMOGRAPHIC IMAGES USING TRADITIONAL AND DEEP LEARNING TOOLS
• https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2024JH000432 -> One‐Fit‐All Transformer for Multimodal Geophysical Inversion: Method and Application

• https://www.researchgate.net/publication/367606119_Deriving_Surface_Resistivity_from_Polarimetric_SAR_Data_Using_Dual-Input_UNet

• https://library.seg.org/doi/abs/10.1190/GEM2024-084.1 -> Quantifying uncertainty in 3D geophysical inverse problems: Advancing from deterministic to Bayesian and deep generative models [UNSEEN]

• https://www.osti.gov/biblio/2335471 - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada [adjacent but interesting]
• https://gdr.openei.org/submissions/1402 - Associated code
• https://catalog.data.gov/dataset/python-codebase-and-jupyter-notebooks-applications-of-machine-learning-techniques-to-geoth
• https://www.researchgate.net/publication/341418586_Preliminary_Report_on_Applications_of_Machine_Learning_Techniques_to_the_Nevada_Geothermal_Play_Fairway_Analysis

• https://www.researchgate.net/publication/347786302_Semantic_Segmentation_Deep_Learning_for_Extracting_Surface_Mine_Extents_from_Historic_Topographic_Maps

• https://www.researchgate.net/publication/357942198_Mineral_classification_of_lithium-bearing_pegmatites_based_on_laser-induced_breakdown_spectroscopy_Application_of_semi-supervised_learning_to_detect_known_minerals_and_unknown_material
• https://iopscience.iop.org/article/10.1088/1755-1315/1032/1/012046 -> Classifying Minerals using Deep Learning Algorithms
• https://www.researchgate.net/publication/370835450_Predicting_new_mineral_occurrences_and_planetary_analog_environments_via_mineral_association_analysis
• https://www.researchgate.net/publication/361230503_What_is_Mineral_Informatics

• https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2025.1530004/full#B23 -> Assessing named entity recognition by using geoscience domain schemas: the case of mineral systems
• https://www.researchgate.net/publication/358616133_Chinese_Named_Entity_Recognition_in_the_Geoscience_Domain_Based_on_BERT
• https://www.researchgate.net/publication/339394395_Dictionary-Based_Automated_Information_Extraction_From_Geological_Documents_Using_a_Deep_Learning_Algorithm
• https://www.aclweb.org/anthology/2020.lrec-1.568/ -> Embeddings for Named Entity Recognition in Geoscience Portuguese Literature
• https://www.researchgate.net/publication/359186219_Few-shot_learning_for_name_entity_recognition_in_geological_text_based_on_GeoBERT
• https://www.researchgate.net/publication/333464862_GeoDocA_-_Fast_Analysis_of_Geological_Content_in_Mineral_Exploration_Reports_A_Text_Mining_Approach
• https://www.researchgate.net/publication/366710921_Geological_profile-text_information_association_model_of_mineral_exploration_reports_for_fast_analysis_of_geological_content
• https://www.researchgate.net/publication/330835955_Geoscience_Keyphrase_Extraction_Algorithm_Using_Enhanced_Word_Embedding [UNSEEN]
• https://www.researchgate.net/publication/332997161_GNER_A_Generative_Model_for_Geological_Named_Entity_Recognition_Without_Labeled_Data_Using_Deep_Learning
• https://www.researchgate.net/publication/321850315_Information_extraction_and_knowledge_graph_construction_from_geoscience_literature
• https://www.researchgate.net/publication/365929623_Named_Entity_Annotation_Schema_for_Geological_Literature_Mining_in_the_Domain_of_Porphyry_Copper_Deposits
• https://www.researchgate.net/publication/329621358_Ontology-Based_Enhanced_Word_Embedding_for_Automated_Information_Extraction_from_Geoscience_Reports
• https://www.researchgate.net/publication/379808469_Ontology-driven_relational_data_mapping_for_constructing_a_knowledge_graph_of_porphyry_copper_deposits -> Ontology-driven relational data mapping for constructing a knowledge graph of porphyry copper deposits
• https://www.researchgate.net/publication/327709479_Prospecting_Information_Extraction_by_Text_Mining_Based_on_Convolutional_Neural_Networks-A_Case_Study_of_the_Lala_Copper_Deposit_China
• https://ieeexplore.ieee.org/document/8711400 -> Research and Application on Geoscience Literature Knowledge Discovery Technology
• https://www.researchgate.net/publication/332328315_Text_Mining_to_Facilitate_Domain_Knowledge_Discovery
• https://www.researchgate.net/publication/351238658_Understanding_Ore-Forming_Conditions_using_Machine_Reading_of_Text
• https://www.researchgate.net/publication/359089763_Visual_analytics_and_information_extraction_of_geological_content_for_text-based_mineral_exploration_reports
• https://www.researchgate.net/publication/354754114_What_is_this_article_about_Generative_summarization_with_the_BERT_model_in_the_geosciences_domain
• https://www.slideshare.net/phcleverley/where-text-analytics-meets-geoscience -> Where text analytics meets geoscience

• https://www.researchgate.net/publication/335226326_Digital_petrography_Mineralogy_and_porosity_identification_using_machine_learning_algorithms_in_petrographic_thin_section_images

Last edited: 29/09/2020The below are a collection of works from when I was doing a review

• https://www.researchgate.net/publication/331852267_Applying_Spatial_Prospectivity_Mapping_to_Exploration_Targeting_Fundamental_Practical_issues_and_Suggested_Solutions_for_the_Future
• https://www.researchgate.net/publication/284890591_Geochemical_Anomaly_and_Mineral_Prospectivity_Mapping_in_GIS
• https://www.researchgate.net/publication/341472154_Geodata_Science-Based_Mineral_Prospectivity_Mapping_A_Review
• https://www.researchgate.net/publication/275338029_Introduction_to_the_Special_Issue_GIS-based_mineral_potential_modelling_and_geological_data_analyses_for_mineral_exploration
• https://www.researchgate.net/publication/339074334_Introduction_to_the_special_issue_on_spatial_modelling_and_analysis_of_ore-forming_processes_in_mineral_exploration_targeting
• https://www.researchgate.net/publication/317319129_Natural_Resources_Research_Publications_on_Geochemical_Anomaly_and_Mineral_Potential_Mapping_and_Introduction_to_the_Special_Issue_of_Papers_in_These_Fields
• https://www.researchgate.net/publication/46696293_Selection_of_coherent_deposit-type_locations_and_their_application_in_data-driven_mineral_prospectivity_mapping

• https://www.researchgate.net/publication/375926319_A_paradigm_shift_in_Precambrian_research_driven_by_big_data

• https://www.researchgate.net/publication/359447201_A_review_of_machine_learning_in_geochemistry_and_cosmochemistry_Method_improvements_and_applications

  • https://jaywen.com/files/He_2022_Applied_Geochemistry.pdf

• https://www.researchgate.net/publication/220164381_Application_of_geochemical_zonality_coefficients_in_mineral_prospectivity_mapping

• https://www.researchgate.net/publication/238505045_Analysis_and_mapping_of_geochemical_anomalies_using_logratio-transformed_stream_sediment_data_with_censored_values

• https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022EA002626 -> Comparative Study on Three Autoencoder-Based Deep Learning Algorithms for Geochemical Anomaly Identification

• https://www.researchgate.net/publication/373758047_Decision-making_within_geochemical_exploration_data_based_on_spatial_uncertainty_-A_new_insight_and_a_futuristic_review

• https://www.researchgate.net/publication/331505001_Deep_learning_and_its_application_in_geochemical_mapping

• https://www.researchgate.net/publication/380262759_Factor_analysis_in_residual_soils_of_the_Iberian_Pyrite_Belt_Spain_Comparison_between_raw_data_log_transformation_data_and_compositional_data [UNSEEN]

• https://www.researchgate.net/publication/272091723_Geochemical_characteristics_of_mineral_deposits_Implications_for_ore_genesis

• https://www.researchgate.net/publication/257189047_Geochemical_mineralization_probability_index_GMPI_A_new_approach_to_generate_enhanced_stream_sediment_geochemical_evidential_map_for_increasing_probability_of_success_in_mineral_potential_mapping

• https://www.researchgate.net/publication/333497470_Integration_of_auto-encoder_network_with_density-based_spatial_clustering_for_geochemical_anomaly_detection_for_mineral_exploration

• https://www.researchgate.net/publication/319303831_Introduction_to_the_thematic_issue_Analysis_of_exploration_geochemical_data_for_mapping_of_anomalies

• https://www.researchgate.net/publication/356722687_Machine_learning-based_prediction_of_trace_element_concentrations_using_data_from_the_Karoo_large_igneous_province_and_its_application_in_prospectivity_mapping#fullTextFileContent

• https://www.degruyter.com/document/doi/10.2138/am-2023-9115/html -> Machine learning applied to apatite compositions for determining mineralization potential [UNSEEN]

• https://www.researchgate.net/publication/257026525_Primary_geochemical_characteristics_of_mineral_deposits_-_Implications_for_exploration

• https://www.researchgate.net/publication/283554338_Recognition_of_geochemical_anomalies_using_a_deep_autoencoder_network

  • https://zarmesh.com/wp-content/uploads/2017/04/Recognition-of-geochemical-anomalies-using-a-deep-autoencoder-network.pdf

• https://www.researchgate.net/publication/349606557_Robust_Feature_Extraction_for_Geochemical_Anomaly_Recognition_Using_a_Stacked_Convolutional_Denoising_Autoencoder [UNSEEN]

• https://www.researchgate.net/publication/375911531_Spatial_Interpolation_Using_Machine_Learning_From_Patterns_and_Regularities_to_Block_Models#fullTextFileContent

• https://www.researchgate.net/publication/259716832_Supervised_and_unsupervised_classification_of_near-mine_soil_Geochemistry_and_Geophysics_data

• https://www.researchgate.net/publication/277813662_Supervised_Geochemical_Anomaly_Detection_by_Pattern_Recognition

• https://www.researchgate.net/publication/249544991_Usefulness_of_stream_order_to_detect_stream_sediment_geochemical_anomalies

• https://www.researchgate.net/publication/321275541_Weighting_stream_sediment_geochemical_samples_as_exploration_indicator_of_deposit_-_type

• https://www.researchgate.net/publication/272170968_A_Comparative_Analysis_of_Weights_of_Evidence_Evidential_Belief_Functions_and_Fuzzy_Logic_for_Mineral_Potential_Mapping_Using_Incomplete_Data_at_the_Scale_of_Investigation
• https://www.researchgate.net/publication/267816279_Fuzzification_of_continuous-value_spatial_evidence_for_mineral_prospectivity_mapping
• https://www.researchgate.net/publication/301635716_Union_score_and_fuzzy_logic_mineral_prospectivity_mapping_using_discretized_and_continuous_spatial_evidence_values

• https://deliverypdf.ssrn.com/delivery.php?ID=555064031119110002088087068121000096050036019060022069010050000053011056029076002067121000064004002088113115000107115017083105004026015092089005123065040099024112018026013043065104094012124120126039100033055018066074125089104115090100009064122122019003015085069021024027072126106082092110&EXT=pdf&INDEX=TRUE -> Estimating uncertainties in 3-D models of complex fold-and-thrust 2 belts: a case study of the Eastern Alps triangle zone
• https://www.researchgate.net/publication/333339659_Incorporating_conceptual_and_interpretation_uncertainty_to_mineral_prospectivity_modelling
• https://www.researchgate.net/publication/235443307_Managing_uncertainty_in_exploration_targeting
• https://www.researchgate.net/publication/255909185_The_upside_of_uncertainty_Identification_of_lithology_contact_zones_from_airborne_geophysics_and_satellite_data_using_random_forests_and_support_vector_machines

• https://www.researchgate.net/publication/334440382_Mapping_iron_oxide_Cu-Au_IOCG_mineral_potential_in_Australia_using_a_knowledge-driven_mineral_systems-based_approach

• https://www.researchgate.net/publication/335313790_Prospectivity_modelling_of_the_Olympic_Cu-Au_Province -https://services.sarig.sa.gov.au/raster/ProspectivityModelling/wms?service=wms&version=1.1.1&REQUEST=GetCapabilities
• An assessment of the uranium and geothermal prospectivity of east-central South Australia -https://d28rz98at9flks.cloudfront.net/72666/Rec2011_034.pdf

• https://www.researchgate.net/publication/285235798_An_assessment_of_the_uranium_and_geothermal_prospectivity_of_the_southern_Northern_Territory

• https://www.researchgate.net/publication/273073675_Building_a_machine_learning_classifier_for_iron_ore_prospectivity_in_the_Yilgarn_Craton
• http://dmpbookshop.eruditetechnologies.com.au/product/district-scale-targeting-for-gold-in-the-yilgarn-craton-part-2-of-the-yilgarn-gold-exploration-targeting-atlas.do$55 purchase
• http://dmpbookshop.eruditetechnologies.com.au/product/mineral-prospectivity-of-the-king-leopold-orogen-and-lennard-shelf-analysis-of-potential-field-data-in-the-west-kimberley-region-geographical-product-n14bnzp.do
• http://dmpbookshop.eruditetechnologies.com.au/product/mineral-systems-analysis-of-the-west-musgrave-province-regional-structure-and-prospectivity-modelling-geographical-product-n12dzp.do
• http://dmpbookshop.eruditetechnologies.com.au/product/mineral-systems-analysis-of-the-west-musgrave-province-regional-structure-and-prospectivity-modelling.do $22 purchase
• https://researchdata.edu.au/predictive-mineral-discovery-gold-mineral/1209568?source=suggested_datasets - Predictive mineral discovery in the eastern Yilgarn Craton: an example of district-scale targeting of an orogenic gold mineral system -https://d28rz98at9flks.cloudfront.net/82617/Y4_Gold_Targeting.zip
• http://dmpbookshop.eruditetechnologies.com.au/product/prospectivity-analysis-of-the-halls-creek-orogen-western-australia-using-a-mineral-systems-approach-geographical-product-n15af3zp.do
• https://researchdata.edu.au/prospectivity-analysis-using-063-m436/1424743 - Prospectivity analysis using a mineral systems approach - Capricorn case study project CSIRO Prospectivity analysis using a mineral systems approach - Capricorn case study project (13.5 GB Download)
• http://dmpbookshop.eruditetechnologies.com.au/product/regional-scale-targeting-for-gold-in-the-yilgarn-craton-part-1-of-the-yilgarn-gold-exploration-targeting-atlas.do $55 purchase
• https://www.researchgate.net/publication/263928515_Towards_Australian_metallogenic_maps_through_space_and_time
• https://www.sciencedirect.com/science/article/abs/pii/S0301926810002111 - Yilgarn

• https://www.researchgate.net/publication/340633563_CATALOG_OF_PROSPECTIVITY_MAPS_OF_SELECTED_AREAS_FROM_BRAZIL
• https://www.researchgate.net/publication/341936771_Modeling_of_Cu-Au_Prospectivity_in_the_Carajas_mineral_province_Brazil_through_Machine_Learning_Dealing_with_Imbalanced_Training_Data
• https://www.researchgate.net/publication/287270273_Nickel_prospective_modelling_using_fuzzy_logic_on_nova_Brasilandia_metasedimentary_belt_Rondonia_Brazil
• https://www.scielo.br/scielo.php?script=sci_arttext&pid=S2317-48892016000200261 - Sao Francisco Craton Nickel

• https://www.researchgate.net/publication/248211737_A_continent-wide_study_of_Australia's_uranium_potential
• https://www.researchgate.net/publication/334440382_Mapping_iron_oxide_Cu-Au_IOCG_mineral_potential_in_Australia_using_a_knowledge-driven_mineral_systems-based_approach
• https://researchdata.edu.au/predictive-model-opal-mining-approach/673159/?refer_q=rows=15/sort=score%20desc/class=collection/p=2/q=mineral%20prospectivity%20map/ - Opal

• https://data.gov.au/dataset/ds-ga-a8619169-1c2a-6697-e044-00144fdd4fa6/details?q= -> An assessment of the uranium and geothermal prospectivity of east central South Australia
• https://d28rz98at9flks.cloudfront.net/72666/Rec2011_034.pdf -> An assessment of the uranium and geothermal prospectivity of east-central South Australia
• https://www.pir.sa.gov.au/__data/assets/pdf_file/0011/239636/204581-001_wise_high.pdf - Eastern Gawler - WPA
• http://www.energymining.sa.gov.au/minerals/knowledge_centre/mesa_journal/previous_feature_articles/new_prospectivity_map
• https://catalog.sarig.sa.gov.au/geonetwork/srv/eng/catalog.search#/metadata/e59cd4ba-1a0a-4911-9e6a-58d80576678d - Olympic Domain IOCG Prospectivity model
• https://www.researchgate.net/publication/335313790_Prospectivity_modelling_of_the_Olympic_Cu-Au_Province -https://services.sarig.sa.gov.au/raster/ProspectivityModelling/wms?service=wms&version=1.1.1&REQUEST=GetCapabilities

• https://www.sciencedirect.com/science/article/abs/pii/S0301926810002111 - Yilgarn Karol Czarnota
• https://www.researchgate.net/publication/229333177_Prospectivity_analysis_of_the_Plutonic_Marymia_Greenstone_Belt_Western_Australia
• https://www.researchgate.net/publication/280039091_Mineral_systems_approach_applied_to_GIS-based_2D-prospectivity_modelling_of_geological_regions_Insights_from_Western_Australia
• https://www.researchgate.net/publication/351238658_Understanding_Ore-Forming_Conditions_using_Machine_Reading_of_Text

• https://www.researchgate.net/publication/285235798_An_assessment_of_the_uranium_and_geothermal_prospectivity_of_the_southern_Northern_Territory
• https://www.researchgate.net/publication/342352173_Modelling_gold_potential_in_the_Granites-Tanami_Orogen_NT_Australia_A_comparative_study_using_continuous_and_data-driven_techniques

• https://www.resourcesandgeoscience.nsw.gov.au/miners-and-explorers/geoscience-information/projects/mineral-potential-mapping#_southern-_new-_england-_orogen-mineral-potential
• https://www.smedg.org.au/GSNSW_2019_Blevin.pdf - Eastern Lachlan Orogen
• https://www.researchgate.net/publication/265915602_Comparing_prospectivity_modelling_results_and_past_exploration_data_A_case_study_of_porphyry_Cu-Au_mineral_systems_in_the_Macquarie_Arc_Lachlan_Fold_Belt_New_South_Wales

• https://www.researchgate.net/publication/340633563_CATALOG_OF_PROSPECTIVITY_MAPS_OF_SELECTED_AREAS_FROM_BRAZIL
• https://www.researchgate.net/publication/340633739_MINERAL_POTENTIAL_AND_OPORTUNITIES_FOR_THE_EXPLORATION_OF_NEW_GEOLOGICAL_GROUNDS_IN_BRAZIL
• https://www.semanticscholar.org/paper/Mineral-Potential-Mapping-for-Orogenic-Gold-in-the-Silva-Silva/a23a9ce4da48863da876758afa9e1d2723088853
• https://www.scielo.br/scielo.php?script=sci_arttext&pid=S2317-48892016000200261 - Supergene nickel deposits in outhwestern Sao Francisco Carton, Brazil

• https://www.researchgate.net/publication/258466504_Self-Organizing_Maps_A_Data_Mining_Tool_for_the_Analysis_of_Airborne_Geophysical_Data_Collected_over_the_Brazilian_Amazon
• https://www.researchgate.net/publication/258647519_Semiautomated_geologic_mapping_using_self-organizing_maps_and_airborne_geophysics_in_the_Brazilian_Amazon
• https://www.researchgate.net/publication/235443304_GIS-Based_prospectivity_mapping_for_orogenic_gold_A_case_study_from_the_Andorinhas_region_Brasil
• https://www.researchgate.net/publication/341936771_Modeling_of_Cu-Au_Prospectivity_in_the_Carajas_mineral_province_Brazil_through_Machine_Learning_Dealing_with_Imbalanced_Training_Data
• https://www.researchgate.net/publication/332031621_Predictive_lithological_mapping_through_machine_learning_methods_a_case_study_in_the_Cinzento_Lineament_Carajas_Province_Brazil
• https://www.researchgate.net/publication/340633659_Copper-gold_favorability_in_the_Cinzento_Shear_Zone_Carajas_Mineral_Province
• https://www.researchgate.net/publication/329477409_Favorability_potential_for_IOCG_type_deposits_in_the_Riacho_do_Pontal_Belt_New_insights_for_identifying_prospects_of_IOCG-type_deposits_in_NE_Brazil
• https://www.researchgate.net/publication/339453836_Uranium_anomalies_detection_through_Random_Forest_regression
• https://d1wqtxts1xzle7.cloudfront.net/48145419/Artificial_neural_networks_applied_to_mi20160818-5365-odv4na.pdf?1471522188=&response-content-disposition=inline%3B+filename%3DArtificial_neural_networks_applied_to_mi.pdf&Expires=1593477539&Signature=DNmSxKogrD54dE4LX~8DT4K7vV0ZGcf8Q2RRfXEPsCc8PGiBrbeBpy4NVQdCiENLz-YfSzVGk6LI8k5MEGxR~qwnUn9ISLHDuIau6VqBFSEA29jMixCbvQM6hbkUJKQlli-AuSPUV23TsSk76kB6amDYtwNHmBnUPzTQGZLj2XkzJza9PA-7W2-VrPQKHNPxJp3z8J0mPq4rhmHZLaFMMSL6QMpK5qpvSqi6Znx-kIhCprlyYfODisq0unOIwnEQstiMf2RnB6gPmGOodhNlLsSr01e7TvtvFDBOQvhhooeDeQrvkINN4DJjAIIrbrcQ8B2b-ATQS0a3QQe93h-VFA__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA - Leite, E.P.L.; de Souza Filho, C.R. Artificial neural networks applied to mineral potential mapping for copper-gold mineralizations in the Carajás Mineral Province, Brazil. Geoph. Prosp. 2009, 57, 1049–1065.
• https://link-springer-com.access.library.unisa.edu.au/content/pdf/10.1007/s11053-015-9263-2.pdf - A Comparative Analysis of Weights of Evidence, Evidential Belief Functions, and Fuzzy Logic for Mineral Potential Mapping Using Incomplete Data at the Scale of Investigation
• https://library.seg.org/doi/abs/10.1190/sbgf2011-245 - Gold Prospectivity Mapping of Andorinhas Greenstone Belt, Para

• https://www.researchgate.net/publication/312220651_Predictive_Mapping_of_Prospectivity_in_the_Gurupi_Orogenic_Gold_Belt_North-Northeast_Brazil_An_Example_of_District-Scale_Mineral_System_Approach_to_Exploration_Targeting

• https://www.researchgate.net/publication/260107484_Unsupervised_clustering_of_continental-scale_geophysical_and_geochemical_data_using_Self-Organising_Maps
• https://www.researchgate.net/publication/332263305_A_speedy_update_on_machine_learning_applied_to_bedrock_mapping_using_geochemistry_or_geophysics_examples_from_the_Pacific_Rim_and_nearby
• https://www.researchgate.net/publication/317312520_Catchment-based_gold_prospectivity_analysis_combining_geochemical_geophysical_and_geological_data_across_northern_Australia
• https://www.researchgate.net/publication/326571155_Continental-scale_mineral_prospectivity_assessment_using_the_National_Geochemical_Survey_of_Australia_NGSA_dataset
• https://www.researchgate.net/publication/334440382_Mapping_iron_oxide_Cu-Au_IOCG_mineral_potential_in_Australia_using_a_knowledge-driven_mineral_systems-based_approach
• https://www.researchgate.net/publication/282189370_Uranium_Prospectivity_Mapping_Across_the_Australian_Continent_via_Unsupervised_Cluster_Analysis_of_Integrated_Remote_Sensing_Data

• https://www.researchgate.net/publication/335313790_Prospectivity_modelling_of_the_Olympic_Cu-Au_Province -https://services.sarig.sa.gov.au/raster/ProspectivityModelling/wms?service=wms&version=1.1.1&REQUEST=GetCapabilities

• https://www.researchgate.net/publication/317312520_Catchment-based_gold_prospectivity_analysis_combining_geochemical_geophysical_and_geological_data_across_northern_Australia
• https://www.researchgate.net/publication/252707107_GIS-based_epithermal_copper_prospectivity_mapping_of_the_Mt_Isa_Inlier_Australia_Implications_for_exploration_targeting
• https://www.researchgate.net/publication/222211452_Predictive_modelling_of_prospectivity_for_Pb-Zn_deposits_in_the_Lawn_Hill_Region_Queensland_Australia

• https://www.researchgate.net/publication/336349643_MINERAL_POTENTIAL_MAPPING_AS_A_STRATEGIC_PLANNING_TOOL_IN_THE_EASTERN_LACHLAN_OROGEN_NSW
• https://www.publish.csiro.au/ex/pdf/ASEG2013ab236 - Mineral prospectivity analysis of the Wagga–Omeo belt in NSW
• https://www.researchgate.net/publication/329761040_NSW_Zone_54_Mineral_Systems_Mineral_Potential_Report
• https://www.researchgate.net/publication/337569823_Practical_Implementation_of_Random_Forest-Based_Mineral_Potential_Mapping_for_Porphyry_Cu-Au_Mineralization_in_the_Eastern_Lachlan_Orogen_NSW_Australia
• https://www.researchgate.net/publication/333551776_Translating_expressions_of_intrusion-related_mineral_systems_into_mappable_spatial_proxies_for_mineral_potential_mapping_Case_studies_from_the_Southern_New_England_Orogen_Australia

• https://www.researchgate.net/publication/262380025_Mapping_geology_and_volcanic-hosted_massive_sulfide_alteration_in_the_Hellyer-Mt_Charter_region_Tasmania_using_Random_Forests_TM_and_Self-Organising_Maps

• https://www.researchgate.net/publication/323856713_Lithological_mapping_using_Random_Forests_applied_to_geophysical_and_remote_sensing_data_a_demonstration_study_from_the_Eastern_Goldfields_of_Australia
• https://publications.csiro.au/publications/#publication/PIcsiro:EP123339/SQmineral%20prospectivity/RP1/RS50/RORECENT/STsearch-by-keyword/LISEA/RI16/RT26 [nickel]
• https://www.researchgate.net/publication/257026553_Regional_prospectivity_analysis_for_hydrothermal-remobilised_nickel_mineral_systems_in_western_Victoria_Australia

• https://www.researchgate.net/publication/274714146_Reducing_subjectivity_in_multi-commodity_mineral_prospectivity_analyses_Modelling_the_west_Kimberley_Australia
• https://www.researchgate.net/publication/319013132_Identifying_mineral_prospectivity_using_3D_magnetotelluric_potential_field_and_geological_data_in_the_east_Kimberley_Australia
• https://www.researchgate.net/publication/280930127_Regional-scale_targeting_for_gold_in_the_Yilgarn_Craton_Part_1_of_the_Yilgarn_Gold_Exploration_Targeting_Atlas
• https://www.researchgate.net/publication/279533541_District-scale_targeting_for_gold_in_the_Yilgarn_Craton_Part_2_of_the_Yilgarn_Gold_Exploration_Targeting_Atlas
• https://www.researchgate.net/publication/257026568_Exploration_targeting_for_orogenic_gold_deposits_in_the_Granites-Tanami_Orogen_Mineral_system_analysis_targeting_model_and_prospectivity_analysis
• https://www.researchgate.net/publication/280039091_Mineral_systems_approach_applied_to_GIS-based_2D-prospectivity_modelling_of_geological_regions_Insights_from_Western_Australia (the West Arunta Orogen, West Musgrave Orogen and Gascoyne Province -http://dmpbookshop.eruditetechnologies.com.au/product/mineral-systems-analysis-of-the-west-musgrave-province-regional-structure-and-prospectivity-modelling.do
• https://reader.elsevier.com/reader/sd/pii/S0169136810000417? - token=9FD1C06A25E7ECC0C384C0ECF976E4BC9C36047C53CEED08066811979A640E89DD94C49510D1B500C6FF5E69982E018E Prospectivity analysis of the Plutonic Marymia Greenstone Belt, Western Australia
• https://research-repository.uwa.edu.au/en/publications/exploration-targeting-for-orogenic-gold-deposits-in-the-granites- - Tanami orogen
• https://www.researchgate.net/publication/332631130_Fuzzy_inference_systems_for_prospectivity_modeling_of_mineral_systems_and_a_case-study_for_prospectivity_mapping_of_surficial_Uranium_in_Yeelirrie_Area_Western_Australia_Ore_Geology_Reviews_71_839-852Tasmania
• https://publications.csiro.au/rpr/download?pid=csiro:EP102133&dsid=DS3 [nickel]

• https://www.researchgate.net/publication/248211962_A_new_method_for_spatial_centrographic_analysis_of_mineral_deposit_clusters
• https://www.researchgate.net/publication/275620329_A_Time-Series_Audit_of_Zipf's_Law_as_a_Measure_of_Terrane_Endowment_and_Maturity_in_Mineral_Exploration
• https://www.researchgate.net/publication/341087909_Assessing_the_variability_of_expert_estimates_in_the_USGS_Three-part_Mineral_Resource_Assessment_Methodology_A_call_for_increased_skill_diversity_and_scenario-based_training
• https://github.com/iagoslc/ZipfsLaw_Quadrilatero_Ferrifero
• https://www.researchgate.net/publication/222834436_Controls_on_mineral_deposit_occurrence_inferred_from_analysis_of_their_spatial_pattern_and_spatial_association_with_geological_features
• https://www.researchgate.net/publication/229792860_From_Predictive_Mapping_of_Mineral_Prospectivity_to_Quantitative_Estimation_of_Number_of_Undiscovered_Prospects
• https://www.researchgate.net/publication/330994502_Global_Grade-and-Tonnage_Modeling_of_Uranium_deposits
• https://pubs.geoscienceworld.org/segweb/economicgeology/article-abstract/103/4/829/127993/Linking-Mineral-Deposit-Models-to-Quantitative?redirectedFrom=fulltext
• https://www.researchgate.net/publication/238365283_Metal_endowment_of_cratons_terranes_and_districts_Insights_from_a_quantitative_analysis_of_regions_with_giant_and_super-giant_deposits
• https://www.researchgate.net/publication/308778798_Spatial_analysis_of_mineral_deposit_distribution_A_review_of_methods_and_implications_for_structural_controls_on_iron_oxide-copper-gold_mineralization_in_Carajas_Brazil
• https://www.researchgate.net/publication/229347041_Predictive_mapping_of_prospectivity_and_quantitative_estimation_of_undiscovered_VMS_deposits_in_Skellefte_district_Sweden
• https://www.researchgate.net/publication/342405763_Predicting_grade-tonnage_characteristics_of_undiscovered_mineralisation_application_of_the_USGS_Three-part_Undiscovered_Mineral_Resource_Assessment_to_the_Sandstone_Greenstone_Belt_of_the_Yilgarn_Bloc
• https://www.sciencedirect.com/science/article/pii/S0169136810000685
• https://www.researchgate.net/publication/240301743_Spatial_statistical_analysis_of_the_distribution_of_komatiite-hosted_nickel_sulfide_deposits_in_the_Kalgoorlie_terrane_Western_Australia_Clustered_or_Not

• https://www.researchgate.net/publication/331283650_Archean_crust_and_metallogenic_zones_in_the_Amazonian_Craton_sensed_by_satellite_gravity_data
• https://eartharxiv.org/2kjvc/ -> Global distribution of sediment-hosted metals controlled by craton edge stability
• https://www.researchgate.net/post/Is_it_possible_to_derive_free_air_anomaly_or_bouguer_anomaly_from_gravity_disturbance_data
• https://www.researchgate.net/publication/325344128_The_role_of_basement_control_in_Iron_Oxide-Copper-Gold_mineral_systems_revealed_by_satellite_gravity_models
• https://www.researchgate.net/publication/331428028_Supplementary_Material_for_the_paper_Archean_crust_and_metallogenic_zones_in_the_Amazonian_Craton_sensed_by_satellite_gravity_data
• https://www.leouieda.com/pdf/use-the-disturbance.pdf
• https://www.leouieda.com/papers/use-the-disturbance.html

• https://www.researchgate.net/publication/317137060_Forecasting_copper_prices_by_decision_tree_learning
• https://www.researchgate.net/publication/4874824_Mine_Size_and_the_Structure_of_Costs

• https://mpra.ub.uni-muenchen.de/62159/ -> Mineral exploration as a game of chance [Agent Based Modelling]

• Overviews and examples, with some focus on neural network approaches.

• https://www.researchgate.net/publication/388546200_A_Joint_Multi-Scale_Graph_Attention_and_Classify-Driven_Autoencoder_Framework_for_Hyperspectral_Unmixing
• https://www.researchgate.net/publication/224180646_A_neural_network_approach_for_pixel_unmixing_in_hyperspectral_data
• https://www.researchgate.net/publication/340690859_A_Supervised_Nonlinear_Spectral_Unmixing_Method_by_Means_of_Neural_Networks
• https://www.researchgate.net/publication/326205017_Classification_of_Hyperspectral_Data_Using_a_Multi-Channel_Convolutional_Neural_Network
• https://www.researchgate.net/publication/339062151_Classification_of_small-scale_hyperspectral_images_with_multi-source_deep_transfer_learning
• https://www.researchgate.net/publication/331824337_Comparative_Analysis_of_Unmixing_Algorithms_Using_Synthetic_Hyperspectral_Data
• https://www.researchgate.net/publication/335501086_Convolutional_Autoencoder_For_Spatial-Spectral_Hyperspectral_Unmixing
• https://www.researchgate.net/publication/341501560_Convolutional_Autoencoder_for_Spectral-Spatial_Hyperspectral_Unmixing
• https://www.researchgate.net/publication/333906204_Deep_convolutional_neural_networks_for_land-cover_classification_with_Sentinel-2_images
• https://www.researchgate.net/publication/356711693_Deep-learning-based_latent_space_encoding_for_spectral_unmixing_of_geological_materials
• https://www.researchgate.net/publication/331505001_Deep_learning_and_its_application_in_geochemical_mapping
• https://www.researchgate.net/publication/332696102_Deep_Learning_for_Classification_of_Hyperspectral_Data_A_Comparative_Review
• https://www.researchgate.net/publication/336889271_Deep_Learning_for_Hyperspectral_Image_Classification_An_Overview
• https://www.researchgate.net/publication/327995228_Deep_Spectral_Convolution_Network_for_Hyperspectral_Unmixing
• https://ieeexplore.ieee.org/abstract/document/10580951 -> Exploring Hybrid Contrastive Learning and Scene-to-Label Information for Multilabel Remote Sensing Image Classification [UNSEEN]
• https://www.researchgate.net/publication/356393038_Generalized_Unsupervised_Clustering_of_Hyperspectral_Images_of_Geological_Targets_in_the_Near_Infrared
• https://ieeexplore.ieee.org/abstract/document/10588073 -> Hyperspectral Image Classification Using Spatial and Spectral Features Based on Deep Learning [UNSEEN]
• https://www.researchgate.net/publication/333301728_Hyperspectral_Image_Classification_Method_Based_on_CNN_Architecture_Embedding_With_Hashing_Semantic_Feature
• https://www.researchgate.net/publication/323950012_Hyperspectral_Unmixing_Using_A_Neural_Network_Autoencoder
• https://www.researchgate.net/publication/339657313_Hyperspectral_unmixing_using_deep_convolutional_autoencoder
• https://www.researchgate.net/publication/339066136_Hyperspectral_Unmixing_Using_Deep_Convolutional_Autoencoders_in_a_Supervised_Scenario
• https://www.researchgate.net/publication/335878933_LITHOLOGICAL_CLASSIFICATION_USING_MULTI-SENSOR_DATA_AND_CONVOLUTIONAL_NEURAL_NETWORKS
• https://ieeexplore.ieee.org/abstract/document/10551851 -> MSNet: Self-Supervised Multiscale Network With Enhanced Separation Training for Hyperspectral Anomaly Detection
• https://www.researchgate.net/publication/331794887_Nonlinear_Unmixing_of_Hyperspectral_Data_via_Deep_Autoencoder_Networks
• https://ieeexplore.ieee.org/abstract/document/10534107 -> ReSC-net: Hyperspectral Image Classification Based on Attention-Enhanced Residual Module and Spatial-Channel Attention
• https://www.researchgate.net/publication/340961027_Recent_Advances_in_Hyperspectral_Unmixing_Using_Sparse_Techniques_and_Deep_Learning
• https://www.researchgate.net/publication/330272600_Semisupervised_Stacked_Autoencoder_With_Cotraining_for_Hyperspectral_Image_Classification
• https://www.researchgate.net/publication/336097421_Spatial-Spectral_Hyperspectral_Unmixing_Using_Multitask_Learning
• https://www.researchgate.net/publication/312355586_Spectral-Spatial_Classification_of_Hyperspectral_Imagery_with_3D_Convolutional_Neural_Network
• https://meetingorganizer.copernicus.org/EGU2020/EGU2020-10719.html -> Sentinel-2 as a tool for mapping iron-bearing alteration minerals: a case study from the Iberian Pyrite Belt (Southern Spain)
• https://www.researchgate.net/publication/334058881_SSDC-DenseNet_A_Cost-Effective_End-to-End_Spectral-Spatial_Dual-Channel_Dense_Network_for_Hyperspectral_Image_Classification
• https://www.researchgate.net/publication/333497470_Integration_of_auto-encoder_network_with_density-based_spatial_clustering_for_geochemical_anomaly_detection_for_mineral_exploration
• https://www.sciencedirect.com/science/article/pii/S0009281924000473 -> Geochemical characteristics and mapping of Reşadiye (Tokat-Türkiye) bentonite deposits using machine learning and sub-pixel mixture algorithms

• https://www.sciencedirect.com/science/article/pii/S0273117724004861?dgcid=rss_sd_all -> Optimization of machine learning algorithms for remote alteration mapping
• https://www.researchgate.net/publication/337841253_A_solar_optical_hyperspectral_library_of_rare_earth-bearing_minerals_rare_earth_oxides_copper-bearing_minerals_and_Apliki_mine_surface_samples
• https://ieeexplore.ieee.org/document/10536904 -> A Reversible Generative Network for Hyperspectral Unmixing With Spectral Variability
• https://www.researchgate.net/publication/3204295_Abundance_Estimation_of_Spectrally_Similar_Minerals_by_Using_Derivative_Spectra_in_Simulated_Annealing
• https://www.researchgate.net/publication/338371376_Accuracy_assessment_of_hydrothermal_mineral_maps_derived_from_ASTER_images
• https://www.researchgate.net/publication/337790490_Analysis_of_Most_Significant_Bands_and_Band_Ratios_for_Discrimination_of_Hydrothermal_Alteration_Minerals
• https://www.researchgate.net/project/Deep-Learning-for-Remote-Sensing-2
• https://ieeexplore.ieee.org/abstract/document/10589462 -> Deep Spectral Spatial Feature Enhancement through Transformer for Hyperspectral Image Classification
• https://www.researchgate.net/publication/331876006_Fusion_of_Landsat_and_Worldview_Images
• https://www.researchgate.net/publication/259096595_Geological_mapping_using_remote_sensing_data_A_comparison_of_five_machine_learning_algorithms_their_response_to_variations_in_the_spatial_distribution_of_training_data_and_the_use_of_explicit_spatial_
• https://www.researchgate.net/publication/341802637_Improved_k-means_and_spectral_matching_for_hyperspectral_mineral_mapping
• https://www.researchgate.net/publication/272565561_Integration_and_Analysis_of_ASTER_and_IKONOS_Images_for_the_Identification_of_Hydrothermally-_Altered_Mineral_Exploration_Sites
• https://www.researchgate.net/publication/236271149_Multi-_and_hyperspectral_geologic_remote_sensing_A_review_GRSG_Member_News
• https://www.researchgate.net/publication/220492175_Multi-and_Hyperspectral_geologic_remote_sensing_A_review
• https://www.sciencedirect.com/science/article/pii/S1574954124001572 -> Rapid estimation of soil Mn content by machine learning and soil spectra in large-scale
• https://www.researchgate.net/publication/342184377_remotesensing-12-01239-v2_1
• https://www.researchgate.net/project/Remote-sensing-exploration-of-critical-mineral-deposits
• https://www.researchgate.net/project/Sentinel-2-MSI-for-geological-remote-sensing
• https://www.researchgate.net/publication/323808118_Thermal_infrared_multispectral_remote_sensing_of_lithology_and_mineralogy_based_on_spectral_properties_of_materials
• https://www.researchgate.net/publication/340505978_Unsupervised_and_Supervised_Feature_Extraction_Methods_for_Hyperspectral_Images_Based_on_Mixtures_of_Factor_Analyzers

• https://www.researchgate.net/publication/235443308_Application_of_remote_sensing_and_GIS_mapping_to_Quaternary_to_recent_surficial_sediments_of_the_Central_Uranium_district_Namibia
• https://www.researchgate.net/publication/342373512_Geological_mapping_using_Random_Forests_applied_to_Remote_Sensing_data_a_demonstration_study_from_Msaidira-Souk_Al_Had_Sidi_Ifni_inlier_Western_Anti-Atlas_Morocco
• https://www.researchgate.net/publication/340534611_Identifying_high_potential_zones_of_gold_mineralization_in_a_sub-tropical_region_using_Landsat-8_and_ASTER_remote_sensing_data_a_case_study_of_the_Ngoura-Colomines_goldfield_Eastern_Cameroon
• https://www.researchgate.net/publication/342162988_Lithological_and_alteration_mineral_mapping_for_alluvial_gold_exploration_in_the_south_east_of_Birao_area_Central_African_Republic_using_Landsat-8_Operational_Land_Imager_OLI_data
• https://www.researchgate.net/publication/329193841_Mapping_Copper_Mineralisation_using_EO-1_Hyperion_Data_Fusion_with_Landsat_8_OLI_and_Sentinel-2A_in_Moroccan_Anti_Atlas
• https://www.researchgate.net/publication/230918249_SPECTRAL_REMOTE_SENSING_OF_HYDROTHERMAL_ALTERATION_ASSOCIATED_WITH_VOLCANOGENIC_MASSIVE_SULPHIDE_DEPOSITS_GOROB-HOPE_AREA_NAMIBIA
• https://www.researchgate.net/publication/337304180_The_application_of_day_and_night_time_ASTER_satellite_imagery_for_geothermal_and_mineral_mapping_in_East_Africa
• https://www.researchgate.net/publication/336823002_Towards_Multiscale_and_Multisource_Remote_Sensing_Mineral_Exploration_Using_RPAS_A_Case_Study_in_the_Lofdal_Carbonatite-Hosted_REE_Deposit_Namibia
• https://www.researchgate.net/publication/338296843_Use_of_the_Sentinel-2A_Multispectral_Image_for_Litho-Structural_and_Alteration_Mapping_in_Al_Glo'a_Map_Sheet_150000_Bou_Azzer-El_Graara_Inlier_Central_Anti-Atlas_Morocco

• https://www.researchgate.net/publication/287950835_Altimetric_and_aeromagnetometric_data_fusion_as_a_tool_of_geological_interpretation_the_example_of_the_Carajas_Mineral_Province_PA
• https://www.researchgate.net/publication/237222985_Analise_e_integracao_de_dados_do_SAR-R99B_com_dados_de_sensoriamento_remoto_optico_e_dados_aerogeofisicos_na_regiao_dos_depositos_de_oxido_de_Fe-Cu-Au_tipo_Sossego_e_118_na_Provincia_Mineral_de_Caraja
• https://www.researchgate.net/publication/327503453_Comparison_of_Altered_Mineral_Information_Extracted_from_ETM_ASTER_and_Hyperion_data_in_Aguas_Claras_Iron_Ore_Brazil
• https://www.researchgate.net/publication/251743903_Enhancement_Of_Landsat_Thematic_Mapper_Imagery_For_Mineral_Prospecting_In_Weathered_And_Vegetated_Terrain_In_SE_Brazil
• https://www.researchgate.net/publication/228854234_Hyperspectral_Data_Processing_For_Mineral_Mapping_Using_AVIRIS_1995_Data_in_Alto_Paraiso_de_Goias_Central_Brazil
• https://www.researchgate.net/publication/326612136_Mapping_Mining_Areas_in_the_Brazilian_Amazon_Using_MSISentinel-2_Imagery_2017
• https://www.researchgate.net/publication/242188704_MINERALOGICAL_CHARACTERIZATION_AND_MAPPING_USING_REFLECTANCE_SPECTROSCOPY_AN_EXPERIMENT_AT_ALTO_DO_GIZ_PEGMATITE_IN_THE_SOUTH_PORTION_OF_BORBOREMA_PEGMATITE_PROVINCE_BPP_NORTHEASTERN_BRAZIL

• https://www.researchgate.net/publication/338355143_A_comprehensive_scheme_for_lithological_mapping_using_Sentinel-2A_and_ASTER_GDEM_in_weathered_and_vegetated_coastal_zone_Southern_China
• https://www.researchgate.net/publication/332957713_Data_mining_of_the_best_spectral_indices_for_geochemical_anomalies_of_copper_A_study_in_the_northwestern_Junggar_region_Xinjiang
• https://www.researchgate.net/publication/380287318_Machine_learning_model_for_deep_exploration_Utilizing_short_wavelength_infrared_SWIR_of_hydrothermal_alteration_minerals_in_the_Qianchen_gold_deposit_Jiaodong_Peninsula_Eastern_China
• https://www.researchgate.net/publication/304906898_Remote_sensing_and_GIS_prospectivity_mapping_for_magmatic-hydrothermal_base-_and_precious-metal_deposits_in_the_Honghai_district_China

• https://www.researchgate.net/publication/326655551_Application_of_Multi-Sensor_Satellite_Data_for_Exploration_of_Zn-Pb_Sulfide_Mineralization_in_the_Franklinian_Basin_North_Greenland
• https://www.researchgate.net/publication/337512735_Fusion_of_DPCA_and_ICA_algorithms_for_mineral_detection_using_Landsat-8_spectral_bands
• https://www.researchgate.net/publication/336684298_Landsat-8_Advanced_Spaceborne_Thermal_Emission_and_Reflection_Radiometer_and_WorldView-3_Multispectral_Satellite_Imagery_for_Prospecting_Copper-Gold_Mineralization_in_the_Northeastern_Inglefield_Mobil

• https://www.researchgate.net/publication/337649256_Automated_lithological_mapping_by_integrating_spectral_enhancement_techniques_and_machine_learning_algorithms_using_AVIRIS-NG_hyperspectral_data_in_Gold-bearing_granite-greenstone_rocks_in_Hutti_India
• https://www.researchgate.net/publication/333816841_Integrated_application_of_AVIRIS-NG_and_Sentinel-2A_dataset_in_altered_mineral_abundance_mapping_A_case_study_from_Jahazpur_area_Rajasthan
• https://www.researchgate.net/publication/339631389_Identification_and_characterization_of_hydrothermally_altered_minerals_using_surface_and_space-based_reflectance_spectroscopy_in_parts_of_south-eastern_Rajasthan_India
• https://www.researchgate.net/publication/338116272_Potential_Use_of_ASTER_Derived_Emissivity_Thermal_Inertia_and_Albedo_Image_for_Discriminating_Different_Rock_Types_of_Aravalli_Group_of_Rocks_Rajasthan

• https://www.researchgate.net/publication/338336181_A_Remote_Sensing-Based_Application_of_Bayesian_Networks_for_Epithermal_Gold_Potential_Mapping_in_Ahar-Arasbaran_Area_NW_Iran
• https://www.researchgate.net/publication/338371376_Accuracy_assessment_of_hydrothermal_mineral_maps_derived_from_ASTER_images
• https://www.researchgate.net/publication/340606566_Application_of_Landsat-8_Sentinel-2_ASTER_and_WorldView-3_Spectral_Imagery_for_Exploration_of_Carbonate-Hosted_Pb-Zn_Deposits_in_the_Central_Iranian_Terrane_CIT
• https://www.researchgate.net/publication/331428927_Comparison_of_Different_Algorithms_to_Map_Hydrothermal_Alteration_Zones_Using_ASTER_Remote_Sensing_Data_for_Polymetallic_Vein-Type_Ore_Exploration_Toroud-Chahshirin_Magmatic_Belt_TCMB_North_Iran
• https://www.researchgate.net/publication/327832371_Band_Ratios_Matrix_Transformation_BRMT_A_Sedimentary_Lithology_Mapping_Approach_Using_ASTER_Satellite_Sensor
• https://www.researchgate.net/publication/331314687_Lithological_mapping_in_Sangan_region_in_Northeast_Iran_using_ASTER_satellite_data_and_image_processing_methods
• https://www.researchgate.net/publication/330774780_Mapping_hydrothermal_alteration_zones_and_lineaments_associated_with_orogenic_gold_mineralization_using_ASTER_data_A_case_study_from_the_Sanandaj-Sirjan_Zone_Iran
• https://www.researchgate.net/publication/380812370_Optimization_of_machine_learning_algorithms_for_remote_alteration_mapping
• https://www.researchgate.net/publication/362620968_Spatial_mapping_of_hydrothermal_alterations_and_structural_features_for_gold_and_cassiterite_exploration

• https://www.researchgate.net/publication/271714561_Geology_and_Hydrothermal_Alteration_of_the_Chapi_Chiara_Prospect_and_Nearby_Targets_Southern_Peru_Using_ASTER_Data_and_Reflectance_Spectroscopy
• https://www.researchgate.net/publication/317141295_Hyperspectral_remote_sensing_applied_to_mineral_exploration_in_southern_Peru_A_multiple_data_integration_approach_in_the_Chapi_Chiara_gold_prospect

• https://www.researchgate.net/publication/233039694_Geological_mapping_using_Landsat_Thematic_Mapper_imagery_in_Almeria_Province_south-east_Spain
• https://www.researchgate.net/publication/263542786_WEIGHTS_DERIVED_FROM_HYPERSPECTRAL_DATA_TO_FACILITATE_AN_OPTIMAL_FIELD_SAMPLING_SCHEME_FOR_POTENTIAL_MINERALS

• https://www.researchgate.net/publication/341611032_ASTER_spectral_band_ratios_for_lithological_mapping_A_case_study_for_measuring_geological_offset_along_the_Erkenek_Segment_of_the_East_Anatolian_Fault_Zone_Turkey
• https://www.researchgate.net/publication/379960654_From_sensor_fusion_to_knowledge_distillation_in_collaborative_LIBS_and_hyperspectral_imaging_for_mineral_identification
• https://www.researchgate.net/publication/229383008_Hydrothermal_Alteration_Mapping_at_Bodie_California_using_AVIRIS_Hyperspectral_Data
• https://www.researchgate.net/publication/332737573_Identification_of_alteration_zones_using_a_Landsat_8_image_of_densely_vegetated_areas_of_the_Wayang_Windu_Geothermal_field_West_Java_Indonesia
• https://www.researchgate.net/publication/325137721_Interpretation_of_surface_geochemical_data_and_integration_with_geological_maps_and_Landsat-TM_images_for_mineral_exploration_from_a_portion_of_the_precambrian_of_Uruguay
• https://www.researchgate.net/publication/336684298_Landsat-8_Advanced_Spaceborne_Thermal_Emission_and_Reflection_Radiometer_and_WorldView-3_Multispectral_Satellite_Imagery_for_Prospecting_Copper-Gold_Mineralization_in_the_Northeastern_Inglefield_Mobil
• https://www.researchgate.net/publication/304036250_Mineral_Exploration_for_Epithermal_Gold_in_Northern_Patagonia_Argentina_From_Regional-_to_Deposit-Scale_Prospecting_Using_Landsat_TM_and_Terra_ASTER
• https://www.researchgate.net/publication/340652300_New_logical_operator_algorithms_for_mapping_of_hydrothermally_altered_rocks_using_ASTER_data_A_case_study_from_central_Turkey
• https://www.researchgate.net/publication/324938267_Regional_geology_mapping_using_satellite-based_remote_sensing_approach_in_Northern_Victoria_Land_Antarctica
• https://www.mdpi.com/2072-4292/17/11/1878 -> Scalable Hyperspectral Enhancement via Patch-Wise Sparse Residual Learning: Insights from Super-Resolved EnMAP Data

• https://www.researchgate.net/publication/390874026_Assessing_named_entity_recognition_by_using_geoscience_domain_schemas_the_case_of_mineral_systems
• https://link.springer.com/article/10.1007/s12371-024-01011-2 -> Can AI Get a Degree in Geoscience? Performance Analysis of a GPT-Based Artificial Intelligence System Trained for Earth Science (GeologyOracle)
• https://www.researchgate.net/publication/376671309_Enhancing_knowledge_discovery_from_unstructured_data_using_a_deep_learning_approach_to_support_subsurface_modeling_predictions
• https://www.mdpi.com/2220-9964/13/7/260 -> Extracting Geoscientific Dataset Names from the Literature Based on the Hierarchical Temporal Memory Model
• Knowledge-Infused LLM Application in Data Analytics: Using Mindat as an Example
• https://www.sciencedirect.com/science/article/pii/S0169136824002154 -> Three-dimensional mineral prospectivity mapping based on natural language processing and random forests: A case study of the Xiyu diamond deposit, China

• https://arxiv.org/pdf/2401.16822 - EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing Domain
• https://link.springer.com/article/10.1007/s12371-024-01011-2 -> Can AI Get a Degree in Geoscience? Performance Analysis of a GPT-Based Artificial Intelligence System Trained for Earth Science (GeologyOracle)
  • Geology Oracle web prototype -https://geologyoracle.com/ask-the-geologyoracle/
  • Knowledge-Infused LLM Application in Data Analytics: Using Mindat as an Example ->https://www.proquest.com/openview/38854958cb460de71484a93584fe0ff4/1?cbl=18750&diss=y&pq-origsite=gscholar [UNSEEN PAST THIS]
• https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5242692 -> GeoProspect: a domain specific geological large language model

• https://arxiv.org/abs/2404.05746v1 -> Causality for Earth Science -> A Review on Time-series and Spatiotemporal Causality Methods
• https://link.springer.com/article/10.1007/s11831-025-10244-5 -> Deep Learning for Time Series Forecasting: Review and Applications in Geotechnics and Geosciences
• https://ieeexplore.ieee.org/abstract/document/10825956 -> Enabling Scalable Mineral Exploration:Self-Supervision and Explainability
• https://www.researchgate.net/publication/384137154_Guidelines_for_Sensitivity_Analyses_in_Process_Simulations_for_Solid_Earth_Geosciences
• https://www.mdpi.com/1660-4601/18/18/9752 -> Learning and Expertise in Mineral Exploration Decision-Making: An Ecological Dynamics Perspective
• https://www.sciencedirect.com/science/article/pii/S2214629624001476 -> Mapping critical minerals projects and their intersection with Indigenous peoples' land rights in Australia
• https://www.sciencedirect.com/science/article/pii/S0169136824003470 -> Overcoming survival bias in targeting mineral deposits of the future: Towards null and negative tests of the exploration search space, accounting for lack of visibility
• https://www.sciencedirect.com/science/article/pii/S088329272400115X -> Ranking Mineral Exploration Targets in Support of Commercial Decision Making: A Key Component for Inclusion in an Exploration Information System
• https://earthmover.io/blog/tensors-vs-tables -> Tensors vs tables - why array native data structures are needed for performance

• https://www.researchgate.net/publication/390346411_A_Biological-Inspired_Deep_Learning_Framework_for_Big_Data_Mining_and_Automatic_Classification_in_Geosciences
• https://arxiv.org/abs/2408.11804 -> Approaching Deep Learning through the Spectral Dynamics of Weights
• https://www.researchgate.net/publication/384833877_A_Review_of_Mineral_Prospectivity_Mapping_Using_Deep_Learning
• https://arxiv.org/pdf/2310.19909.pdf -> Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks
• https://pure.mpg.de/rest/items/item_3029184_8/component/file_3282959/content -> Deep learning and process understanding for data-driven Earth system science
• https://www.tandfonline.com/doi/pdf/10.1080/17538947.2024.2391952 -> Deep learning for spatiotemporal forecasting in Earth system science: a review
• https://wires.onlinelibrary.wiley.com/doi/pdfdirect/10.1002/widm.1554 -> From 3D point-cloud data to explainable geometric deep learning: State-of-the-art and future challenges
• https://arxiv.org/pdf/2410.16602 -> Foundation Models for Remote Sensing and Earth Observation: A Survey
• https://www.researchgate.net/publication/383460665_Poly2Vec_Polymorphic_Encoding_of_Geospatial_Objects_for_Spatial_Reasoning_with_Deep_Neural_Networks
• https://www.nature.com/articles/s41467-021-24025-8 -> Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data-https://arxiv.org/html/2401.10825v3 -> Recent Advances in Named Entity Recognition: A Comprehensive Survey and Comparative Study
• https://arxiv.org/abs/2404.07738 ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models
• https://ieeexplore.ieee.org/abstract/document/10605826 -> Swin-CDSA: The Semantic Segmentation of Remote Sensing Images Based on Cascaded Depthwise Convolution and Spatial Attention Mechanism
• https://www.sciencedirect.com/science/article/abs/pii/S0098300424000839#sec6 -> Leveraging automated deep learning (AutoDL) in geosciences

• Paul Bourke GOCAD formatshttps://paulbourke.net/dataformats/gocad/gocad.pdf