In this paper, we present a dual-drive multi-mineral-species anomaly detection system which involves the combined use of Bayesian maximum entropy, a spectral separable module, high-order factor analysis, a geologically constrained loss function, as well as a mixed Gaussian distribution-based thresholding algorithm, attaching to a deep convolutional autoencoder. We have achieved several firsts rarely considered in the existing literature, for example: previous works focus mainly on separating single-mineral-species rather than multi-elemental anomalies, while we have attempted to recognize multi-mineral-species anomalies; previous works pay more attention to data, while we suggest how to discover the ore-related correlations hidden within the input data; previous works fail to integrate soft data in a quantitative fashion, while we have achieved that by capitalizing on Bayesian maximum entropy and the stratigraphic combination entropy. A series of comparative experiments have demonstrated the advantages over other state-of-the-art approaches. Finally, we obtained a mineral-occurrence identification rate (δ) ranging from 36.87 to 61.46% v. the anomaly area ranging from 33.75 to 55.38% for each metalliferous anomaly division.

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