用地图填空法预测地下矿产分布，提升找矿效率。
Masked Mineral Modeling: Continent-Scale Mineral Prospecting via Geospatial Infilling
- 通过掩码与补全地理数据，学习矿产空间分布模式。
- 在1英里²分辨率下，最佳模型召回率达22%，骰子系数0.31。
- 可融合地质、土壤等辅助数据，适用于无记录矿区。
矿产对实现能源脱碳至关重要，但地下矿藏的勘探成本高、难度大。受生成建模进展启发，我们提出一种基于地理空间数据掩码与补全的方法，用于推断矿产位置。以美国本土地区矿产数据为例，训练出高性能模型，最佳结果在1×1英里²空间分辨率下，测试集的骰子系数为0.31±0.01，召回率为0.22±0.02。该方法一大优势是能灵活融入辅助数据源（如地球物理信息和原为农业用途的全国土壤调查数据），显著提升预测性能，并支持在无已知矿点区域进行模型评估。
原文摘要 · Abstract (English)
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.
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