Geochemical sampling scheme optimization on mine wastes based on hyperspectral data

Abstract

Spatial sampling optimization is an important issue for both geo-chemists and geo-statisticians. Many spatial sampling optimization methods have been previously developed. In this paper, we present a spatial simulated annealing method is presented using hyperspectral data.This sampling method was applied in a project concerning environment assessment of the Dexing Copper Mine. Mine waste contains high concentrations of metals, mostly of a non-economic value. Most of them are discharged without any decontamination, for example, acid-generating minerals. Acid rock drainage can adversely have an impact on the quality of drinking water and the health of riparian ecosystems. To assess or monitor environmental impact of mining, sampling of mine waste is required. Optimal geochemical sampling schemes, which focus on ground verification of mine wastes extracted from hyperspectral data, was derived automatic from a JAVA program. Hyperspectral data help to identify ground objects by a larger spectral range. Spectral angle mapper classification technique is carried out to obtain rule images. A rule image provides weights that are utilized in defining the objective function for the sampling scheme. These are optimized by means of simulated annealing. The simulated annealing uses the Weighted Means Shortest Distance (WMSD) criterion between sampling points. The scaled weight function intensively samples areas where an abundance of weathering mine waste occurs. A threshold is defined to constrain the sampling points to certain areas of interest