Fuzzy inference systems for mineral prospectivity modeling-optimized using Monte Carlo simulations

Abstract

This paper uses Monte Carlo simulations to estimate the parameters of rule-based fuzzy inference systems (FISs) designed for mineral prospectivity modeling. The targeted process for the case study is gold mineralization in the Rajapalot project area in northern Finland. Mamdani-type FISs are developed and implemented for the predictive modeling of favorable structural settings and favorable chemical traps causing gold enrichment in host rocks from ore-bearing hydrothermal fluids. The parameters of the fuzzification functions control the output fuzzy membership values. Traditionally these parameters are chosen subjectively based on the expert's domain knowledge. This study uses drill core data statistics to define the distribution of the parameters. Subsequently, Monte Carlo simulations are used to simulate the corresponding fuzzy membership values and optimize the FISs.•Capturing the complexities of the multi-processes geodynamic systems and the possible interplay mineralization-related geological aspects using ‘If-Then’ rule-based fuzzy inference systems.•Implementation of Monte Carlo simulations for quantification of uncertainties related to a Mamdani-type FIS-based prospectivity modeling.•Reporting prospectivity modeling results at different confidence levels for informed decision making on selection of exploration targets.