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
- UNCOVER-ML Framework
- 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.
- scikit-map
- TorchGeo -> Pytorch library for remote sensing style models
- Geo Deep Learning -> Simple deep learning framework based on RGB
- CAST -> Caret Applications for Spatio-Temporal models
- paper
- geotargts -> Extension of targts to terra and stars
- Machine learning for geological mapping : algorithms and applications -> PhD thesis with code and data
- Prospectivity Mapping of Ni-Co Laterites
- Transform 2022 Tutorial -> Random forest example
- Tin-Tungsten
- Porphyry Copper Spatio-Temporal Exploration
- paper
- minpot-toolkit -> Example of Hoggard et al Lab Boundary analysis with Sedimentary copper
- MPM-WofE -> Mineral Potential Mapping - Weights of Evidence
- Explorer Challenge -> OZ Minerals run competition with Data Science introduction
- Gawler_MPM -> Cobalt, Chromium, Nickel
- Paper
- Winners -> SARIG data information
- Caldera -> Caldera Analytics analysis
- IncertoData
- Butterworth and Barnett -> Butterworth and Barnett entry
- Data Driven Mineralisation Mapping
- Mapa Preditivo -> Brazil student project
- Course_Predictive_Mapping_USP -> Course Project
- Mineral Prospectivity Mapping
- 3D Weights of Evidence
- Geological Complexity SMOTE -> includes fractal analysis
- 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
- 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
- Geological mapping in the age of artificial intelligence -> Geological mapping in the age of artificial intelligence
- mapeamento_litologico_preditivo
- Mapping Global Lithospheric Mantle Pressure-Temperature Conditions by Machine-Learning Thermobarometry
- paper
- Neural Rock Typing
- West Musgraves Geology Uncertainty -> Uncertainty map prediction with entropy analysis: highly useful
- Non Stationarity Mitigation Transformer
- Collab -> Notebook
- paper
- Bedrock-vs-sediment
- paper
- autoencoders_remotesensing
- paper -> Remote sensing framework for geological mapping via stacked autoencoders and clustering
- Into the Noddyverse -> a massive data store of 3D geological models for machine learning and inversion applications
- 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-Classifcation -> Volcanic facies Classification using Random Forest
- SedNet
- 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
- Predicatops -> Stratigraphic predication designed for hydrocarbon
- Seismic Foundation Model -> "a new generation deep learning model in geophysics"
- Machine Learning and Geophysical Inversion -> reconstruct paper from Y. Kim and N. Nakata (The Leading Edge, Volume 37, Issue 12, Dec 2018)
- Lineament Learning -> Fault prediction and mapping via potential field deep learning and clustering
- [Recovering 3D Basement Relief Using Gravity Data Through Convolutional Neural Networks]
- 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
- ML4Rocks -> Some intro work
- Colab notebook -> Google Colab input file for benchmark results of ML-SEISMIC publication
- paper
- 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
- LewisML -> Analysis of the Lewis Formation
- Global geochemistry
- indicator_minerals -> Can PCA can tell the story of the origin of tourmaline?
- QMineral Modeller -> Mineral Chemistry virtual assistant from the Brazilian geological survey
- Journal of Geochemical Exploration - Manifold
- MICA -> Chemical composition, in Shiny
- Dash Geochemical Prospection -> Web-app classifying stream sediments with K-means
- CODAinPractice -> Compositional Data Analysis in Practice
- GeoCoDa
- 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] - https://www.researchgate.net/publication/352009328_Chinese_Word_Segmentation_Based_on_Self-Learning_Model_and_Geological_Knowledge_for_the_Geoscience_Domain
- Automated Extraction of Mining Company Drillhole Results
- Conference Paper
- 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
- GeoVec Model -> OSF Storage for GeoVec 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
- PetroVec -> Portuguese Word Embeddings for the Oil and Gas Industry: development and evaluation
- wordembeddingsOG -> Portuguese Oil and Gas word embeddings
- Portuguese Word Embeddings
- Portuguese models
- Spanish Word Embeddings
- Multilingual alignment
- Overview of approaches
- Geo NER Model -> Named entity recognition
- GeoBERT - hugging face repo for model in https://www.researchgate.net/publication/359186219_Few-shot_learning_for_name_entity_recognition_in_geological_text_based_on_GeoBERT
- 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
- Ontology CWS
- GAKG -> A Multimodal Geoscience Academic Knowledge Graph (Chinese)
- GeoERE-Net -> Understanding geological reports based on knowledge graphs using a deep learning approach
- [paper] https://www.researchgate.net/publication/363408251_Understanding_geological_reports_based_on_knowledge_graphs_using_a_deep_learning_approach
- Stratigraphic Knowledge Graph (StraKG)
- 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
- GeoGPT -> Deep Time Digital Earth Research Group from China project
- 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
- segment-geospatial -> Segment anything for geospatial uses
- SatMAE++ -> Rethinking Transformers Pre-training for Multi-Spectral Satellite Imagery
- 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
- AiTLAS -> an open-source benchmark suite for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation
- Segementation 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
- awesome remote sensing foundation models
- Clay -> An open source AI model and interface for Earth
- IBM-NASA-GEOSPATIAL Prithvi
- Image segmentation by foundation model finetuning -> For Prithvi
- RemoteCLIP -> A Vision Language Foundation Model for Remote Sensing
- SpectralGPT
- zenodo) -> remote sensing foundation model customized for spectral data
- paper -> Unseen
- 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
- Hyperspectral Deep Learning Review
- Hyperspectral Autoencoders
- Deeplearn HSI
- 3DCAE-hyperspectral-classification
- DeHIC
- Pysptools -> also has useful heuristic algorithms
- Spectral Python
- Spectral Dataset RockSL -> Open spectral dataset
- Unmixing
- CasFormer: Cascaded Transformers for Fusion-aware Computational Hyperspectral Imaging
- Spectral Normalization for Keras
- 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
- Microsoft Planetary Computer -> Computing platform connected to data sources
- 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
- Geospatial-ml -> Install multiple common packages at once
- Dask-ml -> Distributed versions of some common ML algorithms
- 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
- SimpSOM -> Self Organising Maps
- hdbscan
- 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
- 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
- Software Underground - Community of people interested in exploring the intersection of the subsurface and code
- [Chat Singup](https://softwareunderground.org/mattermost - SWUNG community chat signup
- Mattermost- Community chat service
- Old Slack Channel(deprecated, see mattermost above)
- Geoscience Open Source Tie-In
- Videos
- 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
- Digital Earth Australia
- Open Source Geospatial Foundation
- OSGeoLive -> Bootable DVD/USB with lots of open source geospatial software
- ASEG -> videos from Australia Society of Exploration Geoscientists
- ec2 Spot Labs -> Making automatically working sith Spot instances easier
- Sagemaker Geospatial ML
- Mlmax - Start fast library
- Sagemaker -> ML Managed Service
- Shepard -> Automated cloud formation setup of AWS Batch Pipelines: this is great
- Smallmatter
- Pyutil
- Deep Learning Containers
- Loguru -> Logging library
- AWS GDAL Robot -> Lambda and batch processing of geotiffs
- Serverless Seismic Processing
If listed it is assumed they are generally data, if just pictures like WMS it will say so.
- 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
- GNS -> List of web services
- GeoInfo -> Rest services
- SIG Andes -> Andes geology
EGDI -> EGDI Minerals
- GTK -> Geological Survey of Finland
- Arctic Minerals -> Arctic 1M Mineral Occurrences
- Spain
- Geology -> 200K
- 1M -> 1M
- 50K -> 50K
- IGME Geode
- Geophysics
- Copper - Copper
- GeoFPI - > Geology and Minerals South Portuguese Zone
- Water
- Geoinform -> [currently suspended]
- IGR -> WMS only
- IGR minres -> WMS only
- Rest example -> Many more mapservers
- China -> WMS mineral deposit wap
- orefield -> Mineral occurrence points
- India Mineral -> WMS
- India Geophysics
- Africa Geoportal -> Rest services
- Africa 10M -> Africa 10M Mineral Occurrences https://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
- 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
- Macrostrat
- 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.
- 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 ftp://ftp.arrc.csiro.au/arrc/Australian_ASTER_Geoscience_Map/
- ASTER Maps notes -> Notes for the above
- 3D Geology -> Models from multiple areas
- Groundwater Explorer -> Bureau of Meteorology
- SARIG -> South Australia Geological Survey geospatial map based search
- SARIG Catalogue -> data catalogue
- 3D Models
- 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
- 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
- Earth Resources
- GeoVIC -> Webmaps needs registration to be more useful
- Exploration Database -> Online
- GERM -> Geological Resource Map of New Zealand
- Geology -> Web Map
- CPRM -> Brazil Geological Survey
- Downloads -> Brazil Geological Survey Downloads
- Rigeo -> Institutional Repository of Geosciences
- Ingemmet GeoPROMINE -> Geological Survey of Peru
- GeoMAPE
- Minerals4EU
- GTK -> Geological Survey of Finland
- Geochemical Maps -> pdf only!
- SGU -> Swedish Geological Survey
- IGME -> Spanish Geological Survey
- 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
- Russian Geological Research Institute -> Inaccessible currently
- RGU -> GIS project of deposits
- Infoterre -> French Geological Survey
- GS -> Czech Geological Survey
- IGR -> Romania Geological Survey
- Mineral Resources
- [UK Onshore Geophysical Library] (https://ukogl.org.uk/)
- 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
- Quebec
- SIGEOM Database
- British Columbia
- Mineral occcurrence database
- Yukon
- Nova Scotia
- provincial
- Prince Edward Island
- Saskatchewan
- Mineral occurrence database
- Newfoundland -> didn't work in Chrome, tried it in Edge
- Alberta
- Interactive Mapping Application
- Northwest Territories
- Mineral Tenure
- 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
- 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
- Bhukosh -> India Geological Survey
- Note Rajasthan geology doesn't work except piecemeal which is painful - if you want it, let me know
- StratDB
- GEM Global Active Faults
- RRuff Mineral Properties
- article -> Evolutionary system of mineralogy
- OneGeology
- catalog
- OSF -> Open Science Foundation
- Sediment Hosted Base Metals -> Sediment Hosted Base Metals
- Lithosphere Athenosphere Boundary -> LAB Hoggard/Czarnota
- Geological Survey list
- Northern Territory GEMIS
- South Australia SARIG
- Western Australia WAMEX
- Queensland
- NSW Digs
- 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
- Alberta
- Yukon
- Footprint
- Manitoba
- Publications
- Newfoundland and Labrador
- Northwest Territories
- Nova Scotia
- Quebec
- Saskatchewan
- British Geological Survey NERC
- GeoLagret -> Sweden
- MinData -> Compilation of rock locations from around the world
- Mineral Databse -> 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
- 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
- Rayshader
- Vdeo
- Python resources for earth science
- geoutils -> geospatial analysis and foster inter-operability between other Python GIS packages.
- Geopandas
- Dask-geopandas
- 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 Servers
- SF
- terra -> terra provides methods to manipulate geographic (spatial) data in "raster" and "vector" form.
- exactextract -> command line zonal stats in C
- Raster -> R library
- terra -> terra provides methods to manipulate geographic (spatial) data in "raster" and "vector" form.
- stars -> stars: spatiotemporal Arrays: Raster and Vector Datacubes
- Rasterio -> python base library for raster data handling
- 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 tools like colorisation, grid workflows
- COG Validator -> checking format of cloud optimised geotiffs
- serverless-datacube-demo -> xarray via lithops / Coiled / Modal
- Xarray Spatial -> Statistical analysis of raster data such as classification like natural breaks
- xdggs -> Other types of grids
- xgcm -> Histograms with labels
- xrft -> Xarray based Fourier Transforms
- xvec -> Vector data cubes for Xarray
- xarray-einstats -> Stats, linear algebra and einops for xarray
- 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
- Geologic Time Scale -> Code to produce, but also has a nice regular csv of the Ages
-
Gempy -> Implicit Modelling
-
Gemgis -> Geospatial Data Analysis assistance
-
LoopStructural -> Implicity Modelling
-
Manual python geologia -> Analysis of geology data
-
Map2Loop -> 3D Modelling Automation
- Loop3D -> GUI for Map2Loop
-
SA Stratigraphy -> Stratigraphy database editor webapp
-
Global Tectonics -> Open source dataset to build on, plates, margins etc.
- 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
- 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
- Verde
- Grid_aeromag -> Brazilian gridding example
- pyinterp -> Multidimensional gridding via Boost
- Pseudogravity -> From Blakely, 95
- SimPEG
- 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
- 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
- ODC-Stac -> Database free Open Data Cube
- Intake-stac
- Sat-search
- Pystac
- Stackstac -> Metadata speeded up dask and xarray timeseries
- DEA Stackstac -> Examples of working with Digital Earth Australia data
- 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
- 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
- Python Data Science Template -> Project package setup
- Awesome python data science -> Curated guide
- AWS Deep Learning Containers
- Spatial Docker
- DL Docker Geospatial
- Rocker
- Docker Lambda
- Geobase
- DL Docker Geospatial
- Geological Society of Queensland vocabularies
- Geological Society of Western Australia
- Stratigraphic
- Geoscience Knowledge Manager
- GeoSciML Vocabularies
- Python geospatial analysis cookbook
- [Geoprocessing with Python(https://livebook.manning.com/book/geoprocessing-with-python/about-this-book/31) -> 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
- Bedrock -> Generalised geology of the world
- Sedimentary Layers -> Global 1-km Gridded Thickness of Soil, Regolith, and Sedimentary Deposit Layers
- Paleogeology An Atlas of Phanerozoic Paleogeographic Maps
- 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
- 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
- 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
- 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
- paper https://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search#/metadata/147963
- Hydrogeology -> Hydrogeology Map of Australia
- Hydrogeology -> 5M
- 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
- 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
- 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
- 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)
- 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
- 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
- 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
- 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://github.com/sydney-machine-learning/autoencoders_remotesensing -> Stacked Autoencoders for Lithological Mapping
- 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://eprints.utas.edu.au/32368/ -> Machine-assisted modelling of lithology and metasomatism
- https://github.com/TomasNaprstek/Aeromagnetic_CNN - Aeromagnetic CNN
- Paper https://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
- Paper https://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://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/229714681_Classifiers_for_Modeling_of_Mineral_Potential
-
https://www.researchgate.net/publication/220164488_Geocomputation_of_mineral_exploration_targets
-
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://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/235443294_The_effect_of_map-scale_on_geological_complexity
- 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://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/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
- 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
- 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://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://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/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/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
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- 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/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.mdpi.com/2075-163X/9/2/131/htm - Prospectivity Mapping of Mineral Deposits in Northern Norway Using Radial Basis Function Neural Networks
- 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/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://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://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.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/338663292_A_Predictive_Geospatial_Exploration_Model_for_Mississippi_Valley_Type_Pb-Zn_Mineralization_in_the_Southeast_Missouri_Lead_District
- 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
- Video https://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/353530416_A_Systematic_Review_on_the_Application_of_Machine_Learning_in_Exploiting_Mineralogical_Data_in_Mining_and_Mineral_Industry
- https://www.researchgate.net/publication/352104303_Deep_Learning_for_Geophysics_Current_and_Future_Trends
- 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://pubs.er.usgs.gov/publication/ofr20211049 -> Deposit Classification Scheme for the Critical Minerals Mapping Initiative Global Geochemical Database
- 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.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://link.springer.com/article/10.1007/s11053-024-10317-5 -> 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/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.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://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/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.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://pubmed.ncbi.nlm.nih.gov/35776744/ - Deep learning based lithology classification of drill core images
- 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.researchgate.net/publication/335104674_Does_shallow_geological_knowledge_help_neural-networks_to_predict_deep_units
- 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/370175012_GeoPDNN_A_Semisupervised_Deep_Learning_Neural_Network_Using_Pseudolabels_for_Three-dimensional_Urban_Geological_Modelling_and_Uncertainty_Analysis_from_Borehole_Data
- https://www.sciencedirect.com/science/article/pii/S0169136824000921 -> Machine learning-based field geological mapping: A new exploration of geological survey data acquisition strategy https://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://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://agu.confex.com/agu/fm18/mediafile/Handout/Paper427843/Landforms%20Poster.pdf -> Using machine learning to classify landforms for minerals exploration
- 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/373714604_Seismic_Foundation_Model_SFM_a_new_generation_deep_learning_model_in_geophysics
- 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://www.researchgate.net/publication/351507441_A_Neural_Network-Based_Hybrid_Framework_for_Least-Squares_Inversion_of_Transient_Electromagnetic_Data
- https://www.researchgate.net/publication/325980016_Agglomerative_hierarchical_clustering_of_airborne_electromagnetic_data_for_multi-scale_geological_studies
- 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://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.researchgate.net/publication/365142017_3D_gravity_inversion_based_on_deep_learning
- 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://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] -### Magnetics
- 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/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://www.researchgate.net/publication/367504269_Flexible_and_accurate_prior_model_construction_based_on_deep_learning_for_2D_magnetotelluric_data_inversion
- 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://www.researchgate.net/publication/361741409_Physics-Driven_Deep_Learning_Inversion_with_Application_to_Magnetotelluric
- https://arxiv.org/abs/2403.15095 -> End-to-End Mineral Exploration with Artificial Intelligence and Ambient Noise Tomography
- 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/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.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
Last edited: 29/09/2020 The 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
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https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022EA002626 -> Comparative Study on Three Autoencoder-Based Deep Learning Algorithms for Geochemical Anomaly Identification
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https://www.researchgate.net/publication/ 259716832_Supervised_and_unsupervised_classification_of_near-mine_soil_Geochemistry_and_Geophysics_data
- 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/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/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/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/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/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://www.researchgate.net/publication/356393038_Generalized_Unsupervised_Clustering_of_Hyperspectral_Images_of_Geological_Targets_in_the_Near_Infrared
- https://www.researchgate.net/publication/333301728_Hyperspectral_Image_Classification_Method_Based_on_CNN_Architecture_Embedding_With_Hashing_Semantic_Feature
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