地电化学勘查技术与机器学习的融合

Integration of Geoelectrochemical Exploration Technology and Machine Learning

摘要: 随着浅表矿产资源日益减少，深部与隐伏矿产勘查成为全球资源勘探的核心挑战。传统地球化学方法在覆盖层厚、地形复杂区域难以提取深部微弱信息。地电化学勘查技术通过外加电场驱动离子迁移，可强化获取深部矿化信息，已成为深部找矿的重要手段。与此同时，大数据与人工智能技术为处理地球化学数据的高维、非线性、小样本等特性提供了新方法。本文系统阐述地电化学技术原理与发展，探讨其与机器学习融合的技术路径，并以川西木绒锂矿为例，详细分析随机森林与概率神经网络在异常识别与靶区预测中的应用。文章总结该融合技术的优势与挑战，并对未来发展进行展望，旨在为智能矿产资源勘查体系构建提供参考。

Abstract: As shallow mineral resources are increasingly depleted, the exploration of deep and concealed mineral deposits has become a central challenge in global resource prospecting. Traditional geochemical methods struggle to extract weak deep-seated information in areas with thick overburden or complex terrain. Electrogeochemical exploration technology, which employs an external electric field to drive ion migration, enhances the acquisition of deep mineralization information and has emerged as a critical tool for deep mineral exploration. Concurrently, big data and artificial intelligence technologies offer novel approaches for handling the high-dimensional, nonlinear, and small-sample characteristics of geochemical data. This article systematically elaborates on the principles and development of electrogeochemical technology, explores the technical pathways for its integration with machine learning, and provides a detailed analysis of the application of random forest and probabilistic neural networks in anomaly identification and target prediction, using the Muzrong lithium deposit in western Sichuan as a case study. The article summarizes the advantages and challenges of this integrated technology, offers insights into future developments, and aims to provide a reference for constructing an intelligent mineral resource exploration system.