基于多源数据融合的喜马拉雅淡色花岗岩识别  

Mapping Himalayan leucogranites by machine learning using multi-source data

作　　者：王子烨 左仁广[1] WANG Ziye;ZUO Renguang(State Key Laboratory of Geological Processes and Mineral Resources,China University of Geosciences(Wuhan),Wuhan 430074,China;School of Earth Resources,China University of Geosciences(Wuhan),Wuhan 430074,China)

机构地区：[1]中国地质大学(武汉)地质过程与矿产资源国家重点实验室,湖北武汉430074 [2]中国地质大学(武汉)资源学院,湖北武汉430074

出　　处：《地学前缘》2023年第5期216-226,共11页Earth Science Frontiers

基　　金：国家自然科学基金项目(41972303,42102332)。

摘　　要：稀有金属在新材料、新能源和信息技术等新兴产业中具有不可替代性,已成为全球争夺的关键战略性矿产资源。喜马拉雅地区广泛发育呈东西向分布的淡色花岗岩带,延绵上千千米,已被证实具有较大的稀有金属找矿潜力,有望成为我国重要的稀有金属成矿带。以往喜马拉雅淡色花岗岩识别主要依靠野外地质填图,然而该地区自然条件恶劣,地质研究工作程度较低,使得圈定的淡色花岗岩空间分布范围具有不确定性,制约了该地区进一步找寻稀有金属矿。本文围绕喜马拉雅淡色花岗岩的识别,探讨了如何利用地球化学、地球物理和遥感等多源数据,基于机器学习技术在区域尺度和矿区尺度上圈定淡色花岗岩的空间分布范围,为该区进一步稀有金属找矿工作提供参考依据。研究发现:(1)区域勘查地球化学、地球物理和遥感数据可从不同的角度为高效识别淡色花岗岩提供有效的信息;(2)多源数据融合技术通过结合同一目标的不同特征,可以吸收各种数据源的优点,实现不同类型数据的优势互补;(3)深度学习较传统的浅层机器学习算法在深层次地学数据挖掘与集成方面具有显著优势,可深入挖掘多源地学数据间的相关信息,提取与淡色花岗岩有关的高级特征,从而提高岩体识别精度。Rare-metal elements are irreplaceable in the advanced materials,new energy and information technology industries,making them key strategic mineral resources in global competition.The N-E trending Himalayan leucogranite belt,over 1000 km long,with proven rare-metal resource potential,is expected to become an important rare-metal metallogenic belt in China.The identification of Himalayan leucogranites has mainly relied on geological field mapping;however,the mapping results have high uncertainty due to poor natural conditions,difficult working conditions and lack of detailed geological research—which has hindered rare-metal prospecting in this area.This paper investigates how to delineate the spatial distribution of Himalayan leucogranites by machine learning using geochemical,geophysical and remote sensing data.Results show that(1)regional geochemical,geophysical and remote sensing data provide significant information for leucogranite mapping in a variety of ways.(2)Multi-source data fusion captures the complementarity advantage of using various types of datasets and provides additional diagnostic information for leucogranite mapping.(3)Deep learning algorithms can effectively mine multi-source geoscience data and significantly improve the identification accuracy of leucogranites than traditional machine learning.

关 键 词：喜马拉雅淡色花岗岩 多源数据融合 机器学习 

分 类 号：P59[天文地球—地球化学] P588.121[天文地球—地质学]