近日，浙江大学杜震洪团队研究了地质约束数据驱动的矿产远景填图建模。相关论文于2025年12月22日发表在《地质学》杂志上。

矿产资源勘探与开发对于维持现代能源需求至关重要。然而，控制成矿作用的地质过程具有内在复杂性，其引发的显著空间变异性为预测建模带来巨大挑战。尽管机器学习方法已日益应用于矿产远景预测，但多数模型未能明确纳入关键地质约束条件，导致其难以有效解析成矿系统的非线性与方向依赖性特征。

研究组提出一种地质约束数据驱动方法，可明确表征成矿过程中的空间非平稳性与各向异性。在加拿大的基准案例研究中，该方法相比现有模型的查全率性能提升7.4%。在科迪勒拉山脉南段的应用中同样观察到稳健性能。此外，该方法阐明了区域成矿控制因素，并量化了斑岩铜矿系统的空间各向异性。研究组表明，将地质约束融入数据驱动模型可同步提升矿产远景预测的准确性与可解释性，为资源勘探提供了稳健的发展路径。

附：英文原文

Title: Geologically constrained data-driven modeling for mineral prospectivity mapping

Author: Luoqi Wang, Tianyi Li, Sensen Wu, Jie Yang, Yanhua Hu, Linshu Hu, Yijun Chen, YunZhao Ge, Yunfeng Chen, Can Rao, Zhenhong Du

Issue&Volume: 2025-12-22

Abstract: The discovery and development of mineral resources are critical for sustaining modern energy demands. However, the geological processes that control mineralization are inherently complex, introducing considerable spatial variability that presents significant challenges for predictive modeling. While machine learning approaches have been increasingly applied to mineral prospectivity, many fail to explicitly incorporate key geological constraints, limiting their capacity to resolve the nonlinear and directionally dependent nature of mineralizing systems. Here we present a geologically constrained data-driven method that explicitly accounts for the spatial non-stationarity and anisotropy in ore-forming processes. In the benchmark case study from Canada, our method demonstrates a 7.4% improvement in recall performance compared with existing models. This robust performance is also observed in applications to the southern Cordillera region. Furthermore, the method elucidates regional ore-forming controls and quantifies spatial anisotropy in porphyry copper systems. Our findings demonstrate that incorporating geological constraints into data-driven models enhances both the accuracy and interpretability of mineral prospectivity assessments, offering a robust path forward in resource exploration.

DOI: 10.1130/G53947.1

Source: https://pubs.geoscienceworld.org/gsa/geology/article-abstract/doi/10.1130/G53947.1/723699/Geologically-constrained-data-driven-modeling-forredirectedFrom=fulltext