Spatial extrapolation of downscaled geochemical data using conditional GAN

Abstract

Multi-scale geochemical data play an important guiding role in mineral exploration and environmental assessment. However, due to the influence of certain factors such as terrain, sampling cost and time, and instrument detection limit, geochemical data obtained from a study area are often incomplete or sparse. In this study, a conditional generative adversarial network (GAN) model was developed as a stochastic simulation technique for spatial extrapolation of downscaled geochemical data by learning nonlinear relationships between two soil geochemical datasets with different sampling densities, one set called fine data and the other coarse data. Taking Qianjiang City, Hubei Province, China as an example, first the fine data and coarse data were divided separately into training area and verification area for simulation in the GAN model. Then, exploratory data analysis, error analysis and variogram analysis were used to evaluate the realizations. Finally, Ni anomalies related to soil heavy metal pollution were extracted by combined concentration–area (C–A) fractal model and uncertainty analysis, in the verification area. Our studies indicate that the proposed conditional GAN model is efficient in extrapolating downscaled geochemical data, since both the realizations and fine data maintain similar spatial autocorrelation and statistical distribution.