Exploring Luorite Veins Using Multi-source Remote Sensing Astellite Data: A Case Study from the Shuitou Fluorite Deposit in Inner Mongolia, China
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摘要:
萤石是一种战略性非金属矿产，脉状萤石矿床是全球萤石产量的主要来源，应用遥感技术开展脉状萤石矿床的勘查找矿研究具有重要意义。笔者选择内蒙古中东部水头萤石矿床为研究区，在野外地质调查和前期研究的基础上，综合应用Landsat-8、ASTER、Sentinel-2和WorldView-2多源遥感影像进行成矿预测。首先，基于光谱协同理论将Landsat-8与WorldView-2融合生成协同数据，对研究区地层岩性和控矿构造进行遥感解译；利用Sentinel-2和ASTER影像进行了羟基、铁染和硅化蚀变信息提取。基于已知萤石矿点的遥感解译特征建立研究区萤石矿的遥感解译标志，在此基础上，应用GIS平台对提取特征信息进行加权叠加分析，开展研究区内萤石矿的综合预测。研究结果表明：Landsat-8与WorldView-2数据融合的假彩色合成影像可有效区分研究区萤石矿化点；由于脉状萤石矿体具有明显的垂向分带特征，在研究区地表露头中多发育硅质顶盖，因此硅化蚀变异常与羟基异常组合可作为萤石矿化的重要特征依据。GIS综合预测结果与已知矿点吻合度高，证实了应用多源遥感数据在脉状萤石矿床勘查找矿中的有效性，并预测了三处新的靶区，相关结果可为后续勘查部署提供依据，也可为其他地区的萤石遥感找矿勘查提供参考。
Abstract:Fluorite is a strategic nonmetallic mineral. Vein fluorite deposits represent the primary source of global fluorite production. The application of remote sensing technology to the exploration and mineral searches of vein-type fluorite deposits is of great significance. In this paper, the Shuitou fluorite deposit in the east-central part of Inner Mongolia is selected as the study area. Based on a comprehensive field geological survey and preliminary research, the authors apply a multi-source remote sensing approach using Landsat-8, ASTER, Sentinel-2, and WorldView-2 images to make mineralization predictions. First, spectral synergy theory was employed to fuse Landsat-8 and WorldView-2 data, thereby generating synergistic data for remote sensing interpretation of stratigraphic lithology and ore-controlling tectonic information in the study area. Additionally, hydroxyl, iron-stained, and silicified alteration information was extracted from Sentinel-2 and ASTER images. Based on the remote sensing interpretation features of known fluorite mining sites, a remote sensing interpretation flag of fluorite mining in the study area was established. This was then applied to the GIS platform, which was used to analyze the extracted feature information with weighted superposition and to carry out a comprehensive prediction of fluorite mining in the study area. The results demonstrate that the false-color composite image, which has been fused with data from both Landsat-8 and WorldView-2, is an effective tool for distinguishing fluorite mineralization points. In vein-type fluorite deposits, the ore body exhibits distinct vertical zonation, while in surface outcrops, more siliceous tops develop. The combination of silica and hydroxyl alteration anomalies can be used as the basis for identifying the key characteristics of fluorite mineralization. The results of this comprehensive GIS prediction and the known ore points align well, thereby corroborating the efficacy of the utilization of multisource remote sensing data in vein-type fluorite deposits for the purposes of exploration and the search for minerals, as well as the prediction of three new target areas. The results are suitable for use as a basis for subsequent surveys and as a reference for the remote sensing of fluorite vein systems exploration in other areas.
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图 1 研究区位置图（a）与水头萤石矿床地质简图（b） （据Pei et al., 2017修）
Figure 1.
表 1 研究区线性构造解译标志
Table 1. Linear structural deciphering signs in the study area
走向 NNE NE NS NW NS 影像特征 线性影像两侧具有明显的色调和纹理差异，右侧被第四系覆盖地势平坦，左侧地势起伏较大 山脊沿走向错断明显，形成延伸较短山脊 平行且直线延伸的
沟谷区域地表破碎，冲沟发育 呈直线延伸的断层三角面，线性特征两侧色调和纹理差异
较大最佳波段组合影像 
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表 2 Sentinel-2波段（2, 4, 8A, 11）特征向量值
Table 2. Sentinel-2 band （2, 4, 8A, 11） eigenvector values
特征向量 Band 2 Band 4 Band 8A Band11 PCA 1 0.228008 0.485797 0.529531 0.656971 PCA 2 −0.248006 −0.465049 −0.398096 0.750827 PCA 3 0.837512 0.106257 −0.532612 0.060057 PCA 4 0.430207 −0.732421 0.526726 −0.032271
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表 3 ASTER 波段（1, 2, 3, 4）特征向量值
Table 3. ASTER band (1, 2, 3, 4) eigenvector values
特征向量 Band 1 Band 2 Band 3 Band 4 PCA 1 0.366091 0.501476 0.544108 0.564309 PCA 2 0.358618 0.385511 0.247074 −0.813467 PCA 3 −0.531336 −0.300564 0.779598 −0.139893 PCA 4 −0.674576 0.713838 −0.187421 −0.016018
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表 4 Sentinel-2波段（2, 8A, 11, 12）特征向量值
Table 4. Sentinel-2 band (2, 8A, 11, 12) eigenvector values
特征向量 Band 2 Band 8A Band 11 Band 12 PCA 1 0.214945 0.462860 0.623654 0.592127 PCA 2 0.076734 −0.741924 −0.100916 0.658389 PCA 3 0.775354 0.257379 −0.565524 0.112987 PCA 4 0.588844 −0.411175 0.530146 −0.450714
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表 5 ASTER波段（1, 3, 4, 8）特征向量值
Table 5. Eigenvector values for ASTER bands (1, 3, 4, 8)
特征向量 Band 1 Band 3 Band 4 Band 8 PCA 1 0.301105 0.461674 0.561204 0.617449 PCA 2 0.501821 0.596173 −0.075151 −0.622178 PCA 3 0.313634 0.135309 −0.807917 0.480203 PCA 4 0.74776 −0.642749 0.163316 −0.032501
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