Spatial modelling of hydrothermal mineralization-related geochemical patterns using INLA+SPDE and local singularity analysis

Abstract

The complexity of geochemical patterns in surficial media makes it necessary to consider the uncertainty when identifying geochemical anomalies in geochemical prospecting. In this contribution, the type of uncertainty related to spatial modelling of geochemical element distribution based on a limited number of observations is considered. A hybrid method combining the integrated nested Laplace approximation (INLA) and the stochastic partial differential equation (SPDE), commonly termed “INLA + SPDE”, is employed to simulate the spatial distribution of a geochemical variable. The local singularity analysis (LSA) is then performed on each realization simulated by INLA + SPDE. Based on the ensemble of local singularity exponent maps, geochemical anomalies can be evaluated by considering both the intensity and uncertainty of local singularity. A case study of processing 1:50, 000 stream sediment samples collected from the Fanshan district, Fujian province of China, further illustrates and validates the procedure, and enhance the knowledge of hydrothermal mineralization-related geochemical patterns in this region. The findings indicate that (1) INLA + SPDE offers a viable alternative to existing methods for simulating spatial distribution of geochemical element distributions, with advantages in quantifying uncertainties of model responses as well as model parameters, computational efficiency due to INLA, etc.; (2) it is an effective way to combine INLA + SPDE and LSA to facilitate identifying geochemical anomalies by considering both the intensity and uncertainty of geochemical patterns. The delineated geochemical anomalies based on the mean of local singularity exponents should be paid attention in further geochemical exploration, but those associated with high uncertainty should be taken care of and further verified by other evidences. The workflow that combines INLA + SPDE and LSA can also be used to obtain information from other types of geoscientific data, and can hence enrich the toolbox for further mineral exploration.