Extraction of weak geochemical anomalies based on multiple-point statistics and local singularity analysis

Traditional interpolations might cause smoothing effect on geochemical anomaly detection due to the moving weighted average properties. Since Multiple-Point Statistics (MPS) is a kind of stochastic simulation based on regional variables statistical patterns in a certain space, it can reduce the smoothing effect and quantify the element distribution uncertainties effectively. However, due to the insufficient Training Images (TIs) in geochemical exploration fields, simulation processes cannot be directly applied on the original data. Meanwhile, element spatial distribution patterns cannot be finely characterized under single scale, with uncertainty exists during the attribute information prediction in some regions. In addition, due to the stochastic properties, it is difficult to identify geochemical anomalous information accurately based on various simulation results. Therefore, a hybrid framework combined MPS and Local Singularity Analysis (LSA) are mainly introduced in this paper. Firstly, rasterization algorithms are used to construct geochemical TI to ensure the MPS simulation processes. Then, two-step simulation, including large-scale and small-scale simulation, is applied to finely represent the geochemical element distribution patterns. Based on various simulation results, LSA and information fusion are finally introduced to construct the probability map of geochemical anomalies. The stream sediment geochemical data was mainly used in this paper to verify the feasibility of proposed methods. Results show that comparing with the Kriging-based ones, smoothing effect of different geochemical anomalous fields is significantly reduced, which shows a closer spatial correlation with the known deposits according to the ROC curve analysis. Based on the anomaly identification results, some mineralization indices can be preliminarily determined to offer some theoretical supports for further mineral exploration.