Integrating sequential indicator simulation and singularity analysis to analyze uncertainty of geochemical anomaly for exploration targeting of tungsten polymetallic mineralization, Nanling belt, South China

Abstract

Abstract The Nanling belt (South China) is one of the most important metallogenic region in the world in regard to tungsten polymetallic resources. In this belt and surrounding area, the most important elements accompanying tungsten deposits are Sn, Bi, and Mo. Modelling the spatial variability and uncertainty of W, Sn, Bi, and Mo anomalies is critical for tungsten polymetallic exploration risk assessment. However, traditional interpolation methods (e.g., kriging, polynomial trend surface and inverse distance weighted method) for modelling continuous geochemical fields based on sparse sampling data provide smoothed representations of element concentrations, which often result in unreliable decisions. Here, uncertainty analysis of geochemical anomaly for tungsten polymetallic exploration targeting was investigated through combined sequential indicator simulation and local singularity analysis. Anomalous thresholds of E-type singularity indices for the selected elements (W, Sn, Mo and Bi) were determined by singularity-quantile plot analysis. The distribution pattern of probability of not exceeding a threshold singularity index was obtained from a series of equally probable representations of singularity indices of individual elements (W, Sn, Mo and Bi) based on local and spatial uncertainty algorithms. Based on the four probabilistic models, one per element, a synthetic probability map was produced for uncertainty assessment of geochemical anomaly for exploration targeting. The results indicate that zones with very high probabilities are significantly correlated with known tungsten polymetallic deposits and are closely associated with lithostratigraphic contacts. These results provide for important decision-making for risk evaluation of tungsten polymetallic exploration targeting based on spare stream sediment geochemical data in the Nanling belt.