Using geostatistics and maximum entropy model to identify geochemical anomalies: A case study in Mila Mountain region, southern Tibet

Highlights: • Geochemical anomalies identified by direct sampling algorithm and maximum entropy model. • Direct sampling algorithm tackles smoothing effect and uncertainty of missing points. • Maximum entropy model effective in fusing multiple geochemical anomalies information. Separating geochemical anomalies from background values is crucial for the processing of geochemical data. In the present study, a workflow for identifying geochemical anomalies was constructed by using the direct sampling algorithm of multi-point geostatistics, the maximum entropy model, and local singularity analysis. The smoothing effect and the uncertainty of the unsampled point value in the traditional interpolation method were taken into consideration in this workflow. Based on the statistic of singular exponential distribution of each element with equal probability, the geochemical anomaly probability distribution of each element was obtained (AgCdCuPbZn). Based on the five anomaly probability distributions, the maximum entropy model was used to establish a comprehensive perspective of geochemical anomaly uncertainty evaluation. The validity of the method was verified by analyzing geochemical data of stream sediment samples from the Mila Mountain region in Tibet. The results showed that the prospectivity map of copper deposits generated by the maximum entropy model can effectively link the probability of multivariate geochemical anomalies with the known positions of copper deposits and greatly increase the precision of the potential exploration areas for copper deposits.