Detection of Geochemical Element Assemblage Anomalies Using a Local Correlation Approach

Xianchuan Yu; Shicheng Wang; Hao Wang; Yuchen Liang; Siying Chen; Kang Wu; Zhaoying Yang; Chongyang Li; Yunzhen Chang; Ying Zhan; Wang Yao; Dan Hu

doi:10.1007/s12583-021-1444-9

Volume 32 Issue 2

Apr 2021

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Journal of Earth Science > 2021 > 32(2): 408-414.

Xianchuan Yu, Shicheng Wang, Hao Wang, Yuchen Liang, Siying Chen, Kang Wu, Zhaoying Yang, Chongyang Li, Yunzhen Chang, Ying Zhan, Wang Yao, Dan Hu. Detection of Geochemical Element Assemblage Anomalies Using a Local Correlation Approach. Journal of Earth Science, 2021, 32(2): 408-414. doi: 10.1007/s12583-021-1444-9

Citation:

Xianchuan Yu, Shicheng Wang, Hao Wang, Yuchen Liang, Siying Chen, Kang Wu, Zhaoying Yang, Chongyang Li, Yunzhen Chang, Ying Zhan, Wang Yao, Dan Hu. Detection of Geochemical Element Assemblage Anomalies Using a Local Correlation Approach. Journal of Earth Science, 2021, 32(2): 408-414. doi: 10.1007/s12583-021-1444-9

Citation:

Xianchuan Yu, Shicheng Wang, Hao Wang, Yuchen Liang, Siying Chen, Kang Wu, Zhaoying Yang, Chongyang Li, Yunzhen Chang, Ying Zhan, Wang Yao, Dan Hu. Detection of Geochemical Element Assemblage Anomalies Using a Local Correlation Approach. Journal of Earth Science, 2021, 32(2): 408-414. doi: 10.1007/s12583-021-1444-9

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Detection of Geochemical Element Assemblage Anomalies Using a Local Correlation Approach

doi: 10.1007/s12583-021-1444-9

1.
School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China
2.
Mineral Resources Institute of Comprehensive Information Prediction, Jilin University, Changchun 130012, China
3.
China Aero Geophysical Survey and Remote Sensing Center for Land and Resources, Beijing 100083, China
4.
No.; 1 Institute of Geological and Mineral Resources Survey of Henan, Nanyang 471000, China
5.
Key Laboratory of Au-Ag-Polymetallic Deposit Series and Deep-Seated Metallogenic Prognosis of Henan Province, Luoyang 471000, China
6.
Biomedical Research Imaging Center, University North Carolina Chapel Hill, Chapel Hill NC 27599, USA
7.
Department of Radiology, University North Carolina Chapel Hill, Chapel Hill NC 27599, USA

More Information

Corresponding author: Yu Xianchuan, yuxianchuan@163.com
Received Date: 12 Aug 2020
Accepted Date: 27 Feb 2021
Publish Date: 01 Apr 2021

Abstract

Abstract

As direct prospecting data, geochemical data play an important role in modelling prospect potential. Geochemical element assemblage anomalies are usually reflected by the correlation between elements. Correlation coefficients are computed from the values of two elements, which reflect only the correlation at a global level. Thus, the spatial details of the correlation structure are ignored. In fact, an element combination anomaly often exists in geological backgrounds, such as on a fault zone or within a lithological unit. This anomaly may cause some combination of anomalies that are submerged inside the overall area and thus cannot be effectively extracted. To address this problem, we propose a local correlation coefficient based on spatial neighbourhoods to reflect the global distribution of elements. In this method, the sampling area is first divided into a set of uniform grid cells. A moving window with a size of 3×3 is defined with an integer of 3 to represent the sampling unit. The local correlation in each unit is expressed by the Pearson correlation coefficient. The whole area is scanned by the moving window, which produces a correlation coefficient matrix, and the result is portrayed with a thermal diagram. The local correlation approach was tested on two selected geochemical soil survey sites in Xiao Mountain, Henan Province. The results show that the areas of high correlation are mainly distributed in the fault zone or the known mineral spots. Therefore, the local correlation method is effective in extracting geochemical element combination anomalies.
- local correlation coefficients,
- geochemical prospecting,
- geochemical soil survey,
- assemblage anomalies,
- geodata mining

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References(31)

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