A spatially weighted singularity mapping method applied to identify epithermal Ag and Pb-Zn polymetallic mineralization associated geochemical anomaly in Northwest Zhejiang, China
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The ordinary singularity mapping (OSM) approach utilizing regular square window and ignoring the representativeness of geochemical samples may not characterize the anisotropic geochemical anomaly features. To overcome the abovementioned drawbacks, a spatially weighted singularity mapping (SWSM) method was developed. The SWSM approach focuses more on the spatial anisotropy and the representativeness of samples considering the ore-controlling factors in the mineralization process. In this case study, the stream sediment geochemical data of Ag, As, Pb and Zn, which are the main ore forming elements of epithermal Ag and Pb-Zn polymetallic mineralization in northwestern Zhejiang distinct, China, have been used to illustrate implementation of the SWSM method. Using the spatial weighting factor associated with NE-SW ore-controlling faults system, anisotropic singularity index values for each of the four elements were estimated. Principal component analysis (PCA) was then applied to the anisotropic singularity index to obtain a comprehensive geochemical anomaly. A strong spatial correlation between the integrated anomalies and the known Ag and Pb-Zn polymetallic deposits have been detected through weights-of-evidence modeling. Finally, a comparison of the hybrid geochemical anomaly identification methods including SWSM-PCA (PCA applied to the estimated anisotropic singularity index), OSM-SWPCA (spatially weighted PCA applied to the estimated ordinary singularity index), and OSM-PCA (PCA applied to the estimated ordinary singularity index), has been discussed. The main conclusions are: (1) the SWSM method is applicable for identifying the geochemical anomaly incorporating both singularity and anisotropy that are two significant properties inherent from a nonlinear mineralization system; and (2) compared with the other two hybrid methods, the SWSM-PCA approach provides a better tool to delineate geochemical anomalies for predicting locations of potential mineral deposits.
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