Mineral Exploration ML Resources

This GitHub repository, maintained by Richard Scott, serves as an invaluable compilation of resources for professionals and researchers interested in the application of machine learning within mineral exploration. It provides a structured list of tools, libraries, and methodologies, often accompanied by practical code and illustrative examples. This makes it an excellent starting point for those looking to implement data-driven approaches in geological surveys and resource discovery.

The repository covers various aspects of machine learning relevant to the geosciences, from data processing and analysis to predictive modeling. Users can explore different techniques and their applications, gaining insights into how modern computational methods can enhance traditional mineral exploration workflows. It fosters learning and collaboration by centralizing high-quality, actionable information.

By focusing on code and practical examples, the resource aims to bridge the gap between theoretical machine learning concepts and their real-world application in the mining and geology sectors. It is designed to be a living document, evolving with new contributions and advancements in the field, making it a go-to reference for cutting-edge methodologies.