Progress of Remote Sensing Geological Prospecting Domestic and Abroad in Recent Years
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摘要:
近年来，随着卫星、航空和地面遥感数据源及处理技术的快速发展，国内外遥感地质找矿在技术方法与应用领域方面取得了显著进展。笔者系统梳理了目前主要的卫星遥感数据、航空高光谱遥感数据和地面数据。其中，Landsat-8、ASTER和Sentinel-2等多光谱影像应用最为广泛，GF-5、ZY1-02D等国产卫星高光谱遥感数据已覆盖全球大部分陆地范围，可满足全球矿产资源勘查数据需求，展现出巨大的应用潜力与社会经济效益；Headwall、HySpex和SSMAP等无人机高光谱传感器在矿区尺度岩性及矿物识别中潜力巨大。遥感技术在岩性分类、矿化蚀变信息提取、构造提取及遥感找矿模型方面均取得了良好的应用效果，随着人工智能技术的发展，其在遥感地质找矿中必将发挥更大的作用。目前，遥感地质找矿仍面临植被覆盖区等复杂地貌景观区示矿弱信息提取及遥感数据的尺度差异等问题，未来还需在多源遥感数据融合技术、更广阔的应用拓展及人工智能找矿应用方面进一步探索。
Abstract:In recent years, with the rapid development of satellite, aerial and ground remote sensing data sources and processing technology, significant progress has been made in the technical methods and applications of remote sensing geological prospecting at home and abroad. In this paper, major satellite remote sensing data, aerial hyperspectral remote sensing data and ground data are systematically reviewed. Among them, multi-spectral images such as Landsat-8, ASTER and Sentinel-2 are the most widely used, and hyperspectral remote sensing data of domestic satellites such as GF-5 and ZY1-02D have covered most of the world's land area. It can meet the demand of global mineral resources exploration data and show great application potential and social and economic benefits. UAV hyperspectral sensors such as Headwall, HySpex and SSMAP have great potential in lithology and mineral identification at mining area scale. Remote sensing technology has achieved good application results in lithology classification, mineralization alteration information extraction, structure extraction and remote sensing prospecting model. With the development of artificial intelligence technology, it will play a greater role in remote sensing geological prospecting. At present, remote sensing geological prospecting still faces problems such as weak ore information extraction and scale difference of remote sensing data in complex landscape areas such as vegetated areas. In the future, further exploration is needed in multi-source remote sensing data fusion technology, broader application expansion and artificial intelligence prospecting application.
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图 7 新疆白杨河铀矿床蚀变矿物图（Zhang et al.，2022）
Figure 7.
图 8 巴基斯坦查盖斑岩铜矿带Al-OH吸收波长图（Liu et al.，2024）
Figure 8.
表 1 国内外主要多光谱遥感卫星参数一览表
Table 1. Parameters of major multispectral remote sensing satellites worldwide
卫星名称 卫星发射国家 光谱范围（μm） 空间分辨率（m） 重访时间（d） 波段数量 Landsat-1～3 美国 0.50～1.10 80 18 4 Landsat-4～5 0.45～2.35 30 16 6 10.40～12.50 120 16 1 Landsat7 0.45～2.35 30 16 6 10.40～12.50 60 16 1 Landsat-8～9 0.43～2.29 30 16 7 10.60～12.51 100 16 2 Terra ASTER 0.52～0.86 15 16 3 1.60～2.43 30 16 6 8.125～11.65 90 16 5 Sentinel-2 欧洲 0.43～2.30 10/20/60 5 12 ZY1-02C 中国 0.52～0.89 10 3 4 ZY1-02D 0.45～1.047 10 3 9 ZY-3 0.45～0.89 6 5 4 GF-1 0.45～0.89 8/16 4 4 GF-2 0.45～0.89 4 5 4 GF-4 0.45～0.90 50 20 s 5 3.50～4.10 400 20 s 1 GF-5 0.45～2.35 20 5 6 3.50～12.5 40 5 6 GF-6 0.45～0.90 8 4 4 0.45～0.90 16 2 8 GF-7 0.45～0.89 3.2 5 4 HJ1A/1B，HJ2A/2B 0.43～0.90 30 4 4 SDGSAT-1 0.374～0.911 10 1 7 8～12.50 30 1 3 
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表 2 国内外主要高光谱遥感卫星参数一览表
Table 2. Parameters of major hyperspectral remote sensing satellites worldwide
卫星 卫星发射国家 光谱范围（μm） 光谱分辨率（nm） 空间分辨率（m） 重访时间（d） 波段数量 EO-1 美国 0.357～2.567 10 30 16 242 PRISMA 意大利 0.40～2.50 6.5/10 30 29 239 EnMAP 德国 0.42～2.45 10 30 27 224 ZY1-02D、ZY1-02E 中国 0.40～2.50 10/20 30 3 166 GF-5、GF-5 01A、GF-5B 0.40～2.50 5/10 30 5 330 HJ1A/1B，HJ2A/2B 0.45～0.95 5 100 4 110～128
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