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FastICA and total variation algorithm for geochemical anomaly extraction
Earth Science Informatics ( IF 2.8 ) Pub Date : 2019-09-09 , DOI: 10.1007/s12145-019-00412-0 Bin Liu , Zhongli Zhou , Qilin Dai , Wei Tong
Earth Science Informatics ( IF 2.8 ) Pub Date : 2019-09-09 , DOI: 10.1007/s12145-019-00412-0 Bin Liu , Zhongli Zhou , Qilin Dai , Wei Tong
Because the ore-forming system in the crust of the Earth is a highly nonlinear system, geochemical anomaly classification is very important for improving the accuracy of metallogenic prediction. Further, since the distribution of the geochemical elements usually presents nonlinear characteristics due to the complexity and uncertainty of geology factors, the traditional linear data processing method has limited applications for an ore-forming system. The FastICA algorithm is applied to preprocessed geochemical data to reduce the interference information between elements. On the basis of obtaining the separated geochemical elements, the continuity of the spatial distribution of geochemical elements is considered, and combined with the application of the total variation (TV) in image processing; thus the total variation is introduced when processing geochemical data for anomaly analysis to eliminate the influence of singular geochemical data values. To measure the spatial distribution of geochemical elements, assays of the 1:10000 soil geochemical data in the area of Dachaidan in the Qinghai province of China are processed. The elemental anomaly zoning sequences are divided into three levels of anomaly: 85%, 90% and 95%. The anomaly isograms of Au and Cu processed by FastICA and the total variation algorithm predict the geological background of the study area better than the traditional cumulative frequency method. These results indicate that the application of the FastICA algorithm and the total variation algorithm to process geochemical data processing is valid and effective.
更新日期：2019-09-09
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