遥感技术找矿预测研究进展与展望

Advances and Perspectives of Remote Sensing Technology in Mineral Exploration

摘要: 随着遥感技术的发展，其宏观、高效、低成本及可周期性观测的独特优势，在区域成矿预测中发挥着不可替代的作用。然而，面对复杂地质背景，当前遥感找矿仍面临单一信息源提取精度有限、“异物同谱”导致的伪异常干扰严重、以及多元异构地质信息融合机制不完善等关键技术瓶颈。本文系统综述了基于遥感数据的线性构造分形定量表征、围岩蚀变信息提取及多源信息协同预测方法的研究进展。综述表明，基于自动算法的线性构造分维统计能够有效量化区域构造的复杂性与活动性；集成主成分分析、混合像元分解与高分辨率影像验证的蚀变信息提取流程，可显著提升矿化异常识别的可靠性；而“构造控矿”与“蚀变示矿”信息的协同融合，能有效聚焦找矿目标区域，提高预测结果的可靠性。最后，本文探讨了当前技术体系在探测深度与识别精度方面的局限，并对高光谱遥感、人工智能算法及“空–地–深”多尺度勘查融合的未来发展方向进行了展望。

Abstract: With the advancement of remote sensing technology, its unique advantages of macroscopic coverage, high efficiency, low cost, and periodic observation have rendered it indispensable in regional metallogenic prediction. However, in complex geological settings, current remote sensing-based mineral exploration still faces critical technical bottlenecks, including limited extraction accuracy from single information sources, severe pseudo-anomaly interference caused by the “same spectrum for different objects” phenomenon, and imperfect fusion mechanisms for multi-source heterogeneous geological data. This paper systematically reviews recent progress in fractal quantitative characterization of linear structures, alteration information extraction, and multi-source collaborative prediction methods based on remote sensing data. The review indicates that automatic algorithm-based fractal statistics of linear structures can effectively quantify the complexity and activity of regional tectonics. An integrated alteration extraction workflow—combining principal component analysis, mixed pixel decomposition, and high-resolution image validation—significantly enhances the reliability of mineralization anomaly identification. Furthermore, the synergistic integration of “tectonic-controlling” and “alteration-indicating” information effectively focuses target areas and improves the credibility of prediction results. Finally, this paper discusses the limitations of the current technical system regarding detection depth and identification accuracy, and provides an outlook on future development directions involving hyperspectral remote sensing, artificial intelligence algorithms, and “space-ground-subsurface” multi-scale exploration integration.