Modeling spatial uncertainty of geochemical anomalies using fractal and sequential indicator simulation methods

Abstract

Mapping of geochemical anomalies is crucial to exploration and environmental geochemistry. The complex geochemical landscape, multiple geological sources and various secondary surficial processes impose a certain degree of spatial uncertainty in mapping of geochemical anomalies. Quantifying such uncertainty is significant for improving the efficiency of environmental monitoring or mineral prospecting. In this paper, sequential indicator simulation (SISIM) was used to assess local and spatial uncertainties of geochemical anomalies, and the concentration–area (C–A) fractal model was employed to determine geochemical threshold prior to SISIM analysis. To illustrate uncertainty of Ag geochemical anomalies, Ag concentration data of 1,880 soil samples collected from northeast of Dong Ujimqin Banner district of Inner Mongolia, North China, was used in this study. Based on a set of simulation realizations of Ag concentrations, it was concluded that areas with low local (i.e., single location) uncertainty of Ag concentrations have low risk for mineral exploration. However, the spatial uncertainty for multi-locations showed that the joint probability statistics were stricter than local uncertainty. Therefore, combining local probability and spatial joint probability for delineating geochemical anomalies of Ag is more acceptable and reliable. The hybrid approach using C–A fractal model and SISIM provides a new way to delineate anomalous areas considering uncertainty of spatial distributions of geochemical elements. Thematic collection: This article is part of the Applications of Innovations in Geochemical Data Analysis collection available at: https://www.lyellcollection.org/topic/collections/applications-of-innovations-in-geochemical-data-analysis