This repository serves as a comprehensive, curated list of resources for mineral exploration machine learning, targeting geoscientists, data scientists, and researchers. It aims to consolidate useful code, datasets, papers, and tools to facilitate the application of ML in prospectivity mapping, geological analysis, and resource discovery.

How It Works

The repository is structured thematically, covering areas such as prospectivity, geology, natural language processing, remote sensing, data quality, and cloud computing. It links to a wide array of libraries, frameworks, and research papers, often with direct code examples or datasets, providing a practical guide for implementing ML techniques in mineral exploration. The curator actively maintains and updates the list, encouraging community contributions.

Quick Start & Requirements

This repository is a curated list of links and resources, not a single installable package. Requirements vary based on the linked projects, but common dependencies include Python, common ML libraries (e.g., TensorFlow, PyTorch, scikit-learn), and geospatial libraries (e.g., GDAL, Rasterio, Geopandas).

Highlighted Details

• Extensive coverage of global datasets and case studies across various geological domains and mineral deposit types.
• Detailed sections on Natural Language Processing (NLP) and Remote Sensing, including foundation models and specific libraries for geoscience applications.
• A broad collection of tools and libraries for data manipulation, visualization, and machine learning, with a focus on Python.
• Numerous links to research papers with associated code and data, enabling reproducibility.

Maintenance & Community

The repository is maintained by RichardScottOZ, with an open invitation for suggestions, issues, and pull requests to expand and improve the resource list.

Licensing & Compatibility

The repository itself is not licensed as a software package. The licenses of linked projects and resources vary widely, from permissive open-source licenses (MIT, Apache) to more restrictive ones. Users must consult the individual licenses of any software or data they choose to use.

Limitations & Caveats

This is a curated list, not a unified framework. Users will need to navigate and integrate various tools and datasets independently. The sheer volume of links may require significant effort to filter and evaluate for specific use cases.