Application of PCA and Bayesian MCMC to discriminate between water sources in seabed gold mines

Water hazards in submarine gold mines pose a great threat to mine production, construction personnel, and mining equipment, and represent one of the natural disasters that occur in mining. To prevent and control accidents, it is critical to quickly and effectively identify water sources. Cracks in the rocks surrounding the roadway in the Sanshandao Gold Mine are a widespread and long-term water gushing phenomenon. The main sources of mine water hazards in mining areas are seawater, Quaternary water, bedrock fissure water, and groundwater. To accurately and quickly identify mine water sources and effectively prevent inrushes of mine water and water-hazard threats, the hydrogeological conditions and chemical composition of water samples from different monitoring points were analyzed and six indicators, i.e., Mg²⁺, Na⁺+K⁺, Ca²⁺, SO₄ ²⁻, Cl⁻, and HCO₃ ⁻, were selected as discriminant factors. Based on the analysis principle of the Bayesian algorithm, the Markov chain Monte Carlo (MCMC) approach was introduced into the Bayesian method. A Bayesian discriminant analysis model was then constructed using SPSS Statistics and the MCMC Bayesian method. The posterior distribution estimated by the algorithm is based on water-sample information, which enables the analysis of the mine water source. Based on the water-sample data from a water intake point at the Sanshandao Gold Mine, detailed analysis and verification were performed, and a water-source model for the inrush of mine water was established. An analysis of different water samples was then performed. Through the selection of variables, variables with a strong discriminant ability and high degree of correlation were introduced into the discriminant function to obtain the Bayesian statistical function, thus enabling a discriminatory analysis of the water sources. The accuracy and practicability of the proposed Bayesian mine-water-source identification model were verified. This model has certain significance for guiding future field work and water-hazard prevention and control efforts.