Machine Learning (ML) for Mineral Prospectivity Mapping (MPM) remains a challenging problem as it requires the analysis of associations between large-scale multi-modal geospatial data and few historical mineral commodity observations (positive labels). Recent MPM works have explored Deep Learning (DL) as a modeling tool with more representation capacity. However, these overparameterized methods may be more prone to overfitting due to their reliance on scarce labeled data. While a large quantity of unlabeled geospatial data exists, no prior MPM works have considered using such information in a self-supervised manner. Our MPM approach uses a masked image modeling framework to pretrain a backbone neural network in a self-supervised manner using unlabeled geospatial data alone. After pretraining, the backbone network provides feature extraction for downstream MPM tasks. We evaluated our approach alongside existing methods to assess mineral prospectivity of Mississippi Valley Type (MVT) and Clastic-Dominated (CD) Lead-Zinc deposits in North America and Australia. Our results demonstrate that self-supervision promotes robustness in learned features, improving prospectivity predictions. Additionally, we leverage explainable artificial intelligence techniques to demonstrate that individual predictions can be interpreted from a geological perspective.

翻译：矿产资源潜力预测（MPM）中的机器学习（ML）仍然是一个具有挑战性的问题，因为它需要分析大规模多模态地理空间数据与少量历史矿产观测（正样本）之间的关联关系。近期的MPM研究探索了使用具有更强表征能力的深度学习（DL）作为建模工具。然而，这些过参数化方法因其对稀缺标注数据的依赖，可能更容易出现过拟合。尽管存在大量未标注的地理空间数据，但此前尚无MPM研究考虑以自监督的方式利用此类信息。我们的MPM方法采用掩码图像建模框架，仅使用未标注的地理空间数据以自监督方式预训练一个骨干神经网络。预训练完成后，该骨干网络为下游MPM任务提供特征提取功能。我们评估了我们的方法以及现有方法，以评估北美和澳大利亚密西西比河谷型（MVT）和碎屑岩型（CD）铅锌矿床的矿产资源潜力。我们的结果表明，自监督增强了学习特征的鲁棒性，从而改善了潜力预测。此外，我们利用可解释人工智能技术证明，单个预测可以从地质学角度进行解释。