Masked Mineral Modeling: Continent-Scale Mineral Prospecting via Geospatial Infilling

TL;DR

This paper introduces a geospatial infilling method using masked mineral modeling to predict underground mineral deposits at a continental scale, leveraging auxiliary geophysical data for improved accuracy.

Contribution

It develops a novel generative modeling approach for mineral prospecting that incorporates auxiliary data sources, enhancing prediction in regions lacking mineral records.

Findings

Achieved Dice coefficient of 0.31 on test data.

Incorporating geophysical data improves inference performance.

Model enables mineral prediction in data-scarce regions.

Abstract

Minerals play a critical role in the advanced energy technologies necessary for decarbonization, but characterizing mineral deposits hidden underground remains costly and challenging. Inspired by recent progress in generative modeling, we develop a learning method which infers the locations of minerals by masking and infilling geospatial maps of resource availability. We demonstrate this technique using mineral data for the conterminous United States, and train performant models, with the best achieving Dice coefficients of $0.31 \pm 0.01$ and recalls of $0.22 \pm 0.02$ on test data at 1$\times$1 mi$^2$ spatial resolution. One major advantage of our approach is that it can easily incorporate auxiliary data sources for prediction which may be more abundant than mineral data. We highlight the capabilities of our model by adding input layers derived from geophysical sources, along with a nation-wide ground survey of soils originally intended for agronomic purposes. We find that employing such auxiliary features can improve inference performance, while also enabling model evaluation in regions with no recorded minerals.