New Indices for Characterizing Spatial Models of Ore Deposits by the Use of a Sensitivity Vector and an Influence Factor

Precise spatial estimation of ore grades and impurity contents from sample data limited in amount and location is indispensable to metallic and nonmetallic resource exploration. One of the advantages of using geostatistics for this purpose is that it can incorporate multivariate data into spatial estimation of one variable. However, there are two weak points concerning technical and post-processing problems. First is the difficulty in application to geologic data in which spatial correlations are not clear because of intrinsic nonlinear behavior. Second is the absence of indices to interpret the mechanisms and factors which govern the spatial distribution. To address these problems, a spatial method of modeling based on a feedforward neural network, SLANS, which recognizes the relationship between the data value and location by considering supplementary attributes such as lithology and biostratigraphy, and a sensitivity analysis using this network were developed. These methods were applied to two case studies, genetic mechanisms of kuroko deposits and quality assessment of a limestone mine. The first case study is a spatial analysis of principal metals of kuroko deposits (volcanogenic massive sulfide deposits) in the Hokuroku district, northern Japan. It was clarified that upward and downward sensitivity vectors were distinguished near the deposits inside and outside the tectonic basin, respectively. Sensitivity analysis for the second case study showed a strong effect of crystalline limestone on the important impurity, P2O5 contents. Hydrothermal alteration, which could cause leaching and secondary concentration of phosphorus, is considered to have produced this effect.