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Remote Sensing for Land & Resources    2019, Vol. 31 Issue (3) : 193-200     DOI: 10.6046/gtzyyg.2019.03.24
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Prediction of polymetallic metallogenic favorable area based on ASTER data
Junbin DUAN, Peng PENG, Zhi YANG, Le LIU
Geological Survey of Anhui Province, Heifei 230001, China
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Abstract  

In recent years, China has paid more and more attention to the mineral geology in the northwest frontier area. However, due to natural geography and other reasons, it is difficult to carry out large-scale manual investigation. By collecting and sorting the available data, the authors have found that there are gold, silver, copper, lead and other minerals in the vicinity of Shumu campsite of the northwest frontier area, and hence it is an important metallogenic prospective area of China’s mineral resources. In order to give full play to the advantages and leading role of remote sensing in prospecting in difficult and dangerous areas of Western China, the authors used ASTER remote sensing image data to extract alteration anomalies and controlling factors. On such a basis, various thematic factors which were used to evaluate the metallogenic favorable areas were obtained, and the correlation and usability between thematic factors were investigated. The establishment of remote sensing geological prospecting model and verification through known deposit point information have obtained good evaluation results, which can provide reference for similar study areas in the future.
Keywords ASTER      alteration anomalies      information model      metallogenic prediction     
:  TP79  
Issue Date: 30 August 2019
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Junbin DUAN
Peng PENG
Zhi YANG
Le LIU
Cite this article:   
Junbin DUAN,Peng PENG,Zhi YANG, et al. Prediction of polymetallic metallogenic favorable area based on ASTER data[J]. Remote Sensing for Land & Resources, 2019, 31(3): 193-200.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.03.24     OR     https://www.gtzyyg.com/EN/Y2019/V31/I3/193
Fig.1  Image of study area
Fig.2  Flow chart of information extraction
Fig.3  Spectrum curve of mineral contain aluminum and hydroxyl
特征向量 B1 B3 B4 B6
PC1 0.770 753 0.626 731 0.109 583 0.033 756
PC2 0.632 877 -0.773 870 -0.023 653 -0.005 706
PC3 0.073 474 0.091 289 -0.955 632 -0.270 250
PC4 -0.002 650 -0.000 936 -0.272 396 0.962 181
Tab.1  Eigenvector of PCA of ASTER B1,B3,B4,B6
Fig.4  Alteration anomalies of mineral contain aluminum and hydroxyl
Fig.5  Spectrum curve of mineral contain magnesium and hydroxyl
特征向量 B1 B4 B6 B8
PC1 0.998 683 -0.048 257 -0.014 705 -0.009 387
PC2 0.051 308 0.943 723 0.271 521 0.181 744
PC3 0.000 757 -0.242 232 0.955 284 -0.169 574
PC4 0.000 186 -0.219 958 0.116 157 0.968 569
Tab.2  Eigenvector of PCA of ASTER B1,B4,B6,B8
Fig.6  Alteration anomalies of mineral contain magnesium and hydroxyl
Fig.7  Spectrum curve of mineral contain carbonate
特征向量 B1 B3 B4 B8
PC1 0.770 994 0.626 924 0.109 595 0.022 889
PC2 0.632 640 -0.774 114 -0.022 634 0.001 293
PC3 0.072 924 0.087 735 -0.973 640 -0.197 509
PC4 -0.004 145 -0.004 061 -0.198 750 0.980 033
Tab.3  Eigenvector of PCA of ASTER B1,B3,B4,B8
Fig.8  Alteration anomalies of mineral contain carbonate
Fig.9  Spectrum curve of mineral contain iron
特征向量 B1 B2 B3 B4
PC1 0.604 968 0.621 052 0.490 718 0.086 503
PC2 0.534 382 0.139 250 -0.833 656 -0.007 917
PC3 -0.301 563 0.290 230 -0.153 328 0.895 163
PC4 0.507 432 -0.714 612 0.201 752 0.437 193
Tab.4  Eigenvector of PCA of ASTER B1,B2,B3,B4
Fig.10  Alteration anomalies of iron stain
Fig.11  Linear structure of study area
Fig.12  Circular structure of study area
1 2 3 4 5 6
1 1.000 00 0.095 01 0.172 63 0.188 92 0.208 32 -0.130 34
2 0.095 01 1.000 00 -0.017 95 0.245 76 -0.031 24 -0.124 97
3 0.172 63 -0.017 95 1.000 00 0.211 96 0.957 93 -0.022 92
4 0.188 92 0.245 76 0.211 96 1.000 00 0.231 30 -0.447 48
5 0.208 32 -0.031 24 0.957 93 0.231 30 1.000 00 -0.084 32
6 -0.130 34 -0.124 97 -0.022 92 -0.447 48 -0.084 32 1.000 00
Tab.5  Correlation of factors
因子图层 分类值
铝羟基蚀变 一级异常 二级异常 三级异常 无异常
碳酸盐岩蚀变 一级异常 二级异常 三级异常 无异常
铁染蚀变 一级异常 二级异常 三级异常 无异常
线性构造长度/km ≥3 [2,3) [1,2) <1
环形构造长度/km ≥3 [2,3) [1,2) <1
Tab.6  Classification standard of factors
Fig.13  Mineral foreground area classification of study area
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