Multi-parameter Analysis of Local Singularity Mapping and Its Application to Identify Geochemical Anomalies in the Xisha
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Original Paper
Multi-parameter Analysis of Local Singularity Mapping and Its Application to Identify Geochemical Anomalies in the Xishan Gold Deposit, North China Shan Xu,1 Xiangyun Hu,1,2,5 Emmanuel John M. Carranza,3 and Gongwen Wang4 Received 27 December 2019; accepted 25 March 2020
Identification of geochemical anomalies from geological background is of great significance in the exploration of complex mineralization systems. For a 2D problem, the window-based local singularity mapping has been used widely to identify the distribution patterns of geochemical data. However, the optimal window parameters for calculating the singularity index are hard to determine. Previous studies commonly use the default parameters when applying singularity mapping. In this study, window-based local singularity mapping was performed and improved by comprehensive analysis of multiple parameters to explore geochemical anomalies associated with gold mineralization in the Xishan deposit, North China, with the aim of revealing undiscovered mineralization. By using Au anomalies as an example, the parameters that may influence the result of window-based local singularity processing have been analyzed and discussed to improve the mapping result. The parameters include the average concentration calculation algorithm, the shape of the sliding windows, the window size increment and the number of windows. Success-rate curves and area under the success-rate curve have been used to assess the spatial correlation of the singularity map with the known mineral occurrences. While square sliding window is the most regularly used window shape, circular and elliptical windows can be alternative choices. We found that the directions of major axis of the ellipses parallel or quasi-parallel to geological strike fit the locations of ore deposits better if the mineralization system is controlled by regional faults. After taking the influence of different parameters into account, geochemical anomalies were successfully separated from background and have been enhanced compared to anomalies identified solely from concentration values. Singularity–quantile analysis has been applied to recognize and separate multiple geochemical anomaly populations based on the singularity map in frequency and spatial domain. While the Au concentration map shows quite scattered strong and weak geochemical anomalies, the linear regions of positive singularity resolved by singularity–quantile analysis coincide well with the location of regional faults and alteration zones, which might indicate footprints of ore-forming fluids. Based on the
1
Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan, China. 2 State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan, China. 3 Geological Sciences, School of Agriculture, Engineering and Sciences, University of KwaZulu-Natal, Westville, South Africa. 4 School of Earth Sciences and Resources, China University of Geosciences, Beijing, China. 5 To whom corresponde
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