Remote sensing interpretation and metallogenic prediction in the Bairendaba-Weilasituo silver-tin polymetallic deposit, Inner Mongolia
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摘要:
内蒙古拜仁达坝—维拉斯托地区成矿条件优越，银、锡等矿产资源富集，矿化蚀变广泛发育，且地质构造控矿作用明显。文章利用ASTER、Landsat-8 OLI和DEM等数据，采用DEM山体阴影渲染、坡度分析、高通滤波和方向滤波等方法，结合主成分分析等数字图像处理技术，在拜仁达坝—维拉斯托地区解译出329条线性构造和4个环形构造。同时，利用ASTER数据，采用主成分分析方法，提取了Fe3+、Al-OH、Mg-OH、碳酸盐化、硅酸盐化和泥化蚀变信息，依据蚀变信息与构造信息圈出3处找矿有利区。结果表明：采用基于DEM、Landsat-8 OLI和ASTER数据的构造解译和蚀变信息提取方法进行找矿预测，可以准确指示构造和蚀变的关键找矿标志，在内蒙古中部地区具有一定的适用性，是快速有效找矿勘查的重要手段，也可为区带内银锡多金属矿床研究提供借鉴。
Abstract:The Bairendaba-Weilasituo area in Inner Mongolia is characterized with favorable metallogenic conditions, where the silver and tin mineral resources are enriched, mineralization alterations extensively developed and prominent geo-structures control ore deposits. By utilizing ASTER, Landsat-8 OLI, and DEM data, applying methods such as DEM hill shading, slope analysis, high-pass filtering, and directional filtering, in combination with principal component analysis and other digital image processing methods, this study finally identified a total of 329 linear structures and 4 circular structures of the Bairendaba-Weilasituo area. Additionally, ASTER data and the principal component analysis method were employed to extract alteration information related to Fe3+, Al-OH, Mg-OH, carbonation, silicification, and clay alteration. Based on alteration and structural data, three prospects favorable for mineral exploration were delineated. The results indicate that structural interpretation and alteration information extraction based on DEM, Landsat-8 OLI, and ASTER data can accurately identify key structural and alteration-related exploration markers. This approach is applicable in central Inner Mongolia, providing a rapid and effective method for mineral exploration and offering valuable insights for the study of silver-tin polymetallic deposits within the region.
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表 1 ASTER、Landsat-8 OLI和ASTER DEM数据参数表
Table 1. The data parameter for ASTER, Landsat-8 OLI and ASTER DEM
卫星名称 最大空间
分辨率/m波谱范
围/μm波段数 采集时间 ASTER 15 520～2 430 14 2004.04.09 Landsat-8 OLI 15 433～1 390 9 2013.10.03 ASTER DEM 30 
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表 2 ASTER数据各类干扰地物去除方法（唐超等，2013）
Table 2. The removal of various interfering ground objects in ASTER data（Tang et al.,2013）
干扰因素 去除方法 干扰因素 去除方法 冰雪 band9或band1高端剪切 阴影 band9/band1 植被 （band3-band2）/（band3+band2） 盐碱地 band9/band1或band7、9高端剪切 云 band7、9高端剪切 水体 band9/band1
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表 3 蚀变提取主成分特征向量矩阵
Table 3. Eigenvector matrix of the principal components by alteration mineral extraction
蚀变类别 特征向量 Band2 Band3 Band4 Band5 蚀变类别 特征向量 Band1 Band3 Band4 Band5 Fe3+ eig1 0.37 0.49 0.52 0.59 CO32− eig1 0.35 0.51 0.67 0.41 eig2 0.39 0.38 0.25 −0.80 eig2 0.57 0.54 −0.54 −0.29 eig3 0.55 0.25 −0.79 0.14 eig3 0.71 −0.65 −0.04 0.27 eig4 0.64 −0.74 0.22 0.03 eig4 −0.20 0.15 −0.51 0.82 蚀变类别 特征向量 Band1 Band3 Band4 Band6 蚀变类别 特征向量 Band1 Band4 Band5 Band8 Al-OH eig1 −0.35 −0.51 −0.67 −0.42 SiO42− eig1 −0.33 −0.73 −0.45 −0.39 eig2 −0.58 −0.55 0.52 0.31 eig2 −0.94 0.30 0.11 0.09 eig3 −0.73 0.66 0.00 −0.19 eig3 0.07 0.60 −0.49 −0.62 eig4 −0.13 0.10 −0.53 0.83 eig4 0.00 0.09 −0.74 0.67 蚀变类别 特征向量 Band1 Band3 Band4 Band8 蚀变类别 特征向量 Band1 Band4 Band6 Band7 Mg-OH eig1 0.36 0.53 0.68 0.36 高岭土-绢云母 eig1 −0.32 −0.73 −0.46 −0.40 eig2 0.57 0.53 −0.57 −0.26 eig2 −0.94 0.29 0.14 0.08 eig3 0.71 −0.65 −0.02 0.27 eig3 0.06 0.60 −0.40 −0.69 eig4 −0.20 0.14 −0.46 0.85 eig4 −0.01 0.17 −0.78 0.60
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