GeoCFM: Positive-Only Conditional Flow Matching for Mineral Occurrence Sampling

Abstract:Critical mineral discovery is a positive-only problem: deposits are observed as sparse locations, while unlabeled regions are not reliable negatives, and similar geophysical signatures can arise from different subsurface states. We therefore model mineral targeting as learning a conditional spatial distribution over occurrence locations, $\pi(p\mid d)$, given geo-images $d$, rather than predicting a deterministic per-pixel score map. We introduce GeoCFM, a conditional flow-matching model that generates mineral occurrence point sets conditioned on multi-channel geo-images; GeoCFM learns a point-wise transport field in $\mathbb{R}^2$, using UNet features with point-conditioned velocity prediction to bridge dense rasters and sparse supervision without pseudo-negatives. On a synthetic magnetics--geochemistry benchmark with latent activation and on USGS Earth MRI data with a spatially disjoint tile split, GeoCFM improves geometric agreement with observed occurrences over score-map and non-conditional baselines, while representing epistemic uncertainty through conditional sampling.

Machine Learning (cs.LG)

arXiv:2609.34398 [cs.LG]

(or arXiv:2609.34398v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.34398 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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