A Spatially Constrained Multi-Autoencoder approach for multivariate geochemical anomaly recognition

The spatial heterogeneity of geochemical background is often ignored in geochemical anomaly recognition, leading to ineffective recognition of valuable anomalies for geochemical prospecting. In this paper, a Spatially Constrained Multi-Autoencoder (SCMA) approach is proposed to deal with such an issue in multivariate geochemical anomaly recognition, which includes two unique steps: (1) with the consideration of both chemical similarity and spatial continuity of geochemical samples, a region is divided into multiple sub-domains to discriminate the various backgrounds over space, through multivariate clustering, spatial filtering, and spatial fusion; and (2) the geochemical background of each sub-domain is learned and reconstructed by a multi-autoencoder structure, which is designed to reduce the effects of random initialization of weights in an autoencoder neural network. Finally, the anomaly score is calculated as the difference between the observed geochemical features and the reconstructed features. The performance of SCMA was demonstrated by a case study involving Cu, Mn, Pb, Zn and Fe2O3 in stream sediment samples from the Chinese National Geochemical Mapping Project, in the southwestern Fujian province of China. The results showed that the spatial domain constraining greatly improved the quality of anomaly recognition, and SCMA outperformed several existing methods in all aspects. In particular, the anomalies from SCMA were the most consistent with the known Fe deposits in the area, achieving an AUC of 0.89.