Bayesian model based on Markov chain Monte Carlo for identifying mine water sources in Submarine Gold Mining

Abstract

Based on the Bayesian theory, the Bayesian water source discrimination model and model prediction method were established by using the adaptive differential evolution Metropolis algorithm of Markov chain Monte Carlo simulation as the parameter posterior distribution sampling calculation method. Taking the mine water samples of Sanshandao Gold Mine as an example, according to the chemical composition analysis of the water samples from different monitoring points, six indexes of Mg2+, Na+ + K+, Ca2+, SO42−, Cl− and HCO3− were selected as the discrimination factors. According to the water characteristics of each aquifer and the actual needs of discrimination, the water inrush sources in the mining area were divided into four categories: sea water is class I, Quaternary pore water is class II, bedrock fissure water is class III, and fresh water is class IV. Taking 40 typical water inrush samples as training samples, using SPSS statistics and Bayes method based on Markov Chain Monte Carlo to build Bayes discriminant analysis model, the posterior distribution of algorithm estimation based on water sample information was obtained, and the analysis method of mine water damage source was obtained. The results showed that the Bayes discriminant model based on Markov Chain Monte Carlo has high accuracy, good applicability and discriminant ability, and has certain guiding significance for the prevention and control of water damage and the related field work.