Using geostatistics and maximum entropy model to identify geochemical anomalies: A case study in Mila Mountain region, southern Tibet
作者： Binbin Li , Bingli Liu , Guxi Wang , Ling Chen , Ke Guo
DOI: 10.1016/J.APGEOCHEM.2020.104843
关键词: Soil science 、 Prospectivity mapping 、 Geology 、 Geostatistics 、 Probability distribution 、 Exponential distribution 、 Principle of maximum entropy 、 Smoothing 、 Anomaly (natural sciences) 、 Multivariate statistics
摘要: Abstract Separating geochemical anomalies from background values is crucial for the processing of data. In present study, a workflow identifying was constructed by using direct sampling algorithm multi-point geostatistics, maximum entropy model, and local singularity analysis. The smoothing effect uncertainty unsampled point value in traditional interpolation method were taken into consideration this workflow. Based on statistic singular exponential distribution each element with equal probability, anomaly probability obtained (Ag、Cd、Cu、Pb、Zn). five distributions, model used to establish comprehensive perspective evaluation. validity verified analyzing data stream sediment samples Mila Mountain region Tibet. results showed that prospectivity map copper deposits generated can effectively link multivariate known positions greatly increase precision potential exploration areas deposits.
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