发表机构
Ben-Gurion University of the Negev; University of British Columbia（内盖夫本-古里安大学; 不列颠哥伦比亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对矿产发现中仅正例的稀疏监督问题，提出条件流匹配模型GeoCFM，学习赋存位置的条件分布，在合成与真实数据上优于基线并表征不确定性。
AI 中文摘要
关键矿产发现是一个仅正例问题：矿床以稀疏位置的形式被观测到，而未标记区域并非可靠的负例，且相似的地球物理特征可能源自不同的地下状态。因此，我们将矿产靶区建模为学习给定地理图像 $d$ 下赋存位置的条件空间分布 $\pi(p\mid d)$，而非预测确定性的逐像素得分图。我们提出GeoCFM，一种条件流匹配模型，其以多通道地理图像为条件生成矿产赋存点集；GeoCFM在$\mathbb{R}^2$中学习逐点输运场，利用UNet特征与点条件速度预测，在无需伪负例的情况下桥接稠密栅格与稀疏监督。在具有潜在激活的合成磁法-地球化学基准以及采用空间不相交瓦片划分的USGS Earth MRI数据上，GeoCFM相较于得分图和非条件基线，提高了与观测赋存点的几何一致性，同时通过条件采样表征认知不确定性。
英文摘要
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, $π(p\mid d)$, 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.
CommentsECCV 2026
