A geologically-constrained deep learning algorithm for recognizing geochemical anomalies

Abstract

The effective identification of geochemical anomalies is essential in mineral exploration. Recently, data-driven deep learning algorithms have gained popularity for recognizing the geochemical patterns linked to mineralization. While purely data-driven deep learning algorithms can exploit geochemical patterns well, but the predicted and extracted results may be inconsistent with the geologic knowledge. In this study, a geologically-constrained deep learning algorithm was proposed to extract multivariate geochemical anomalies associated with W polymetallic mineralization in the south Jiangxi Province, China. The construction of the proposed algorithm involved two steps: (1) quantifying the spatial distribution of the known mineral deposits via fractal analysis, and (2) using prior knowledge obtained by the fractal analysis as a geological constraint to restrain an adversarial autoencoder network for delineating geochemical anomalies associated with mineralization. We conducted a comparative study of geologically-constrained and purely data-driven deep learning algorithms. We found that the former obtained more reasonable and interpretable geochemical anomalies linked to W mineralization. The results obtained by a geologically-constrained deep learning algorithm were more consistent with the regional metallogenic law. Therefore, this geological constraint can improve the generalization ability of the deep learning algorithm and enhance the interpretation of the obtained results in geosciences.