Infomax-based deep autoencoder network for recognition of multi-element geochemical anomalies linked to mineralization

Abstract

In recent years, deep autoencoder networks (DANs) have shown enormous potential to achieve state-of-the-art performance for recognizing multi-element geochemical anomalies related to mineralization. By training a DAN, multi-element signatures of geochemical background are learned by higher-level representations of input signals, providing key references to quantify reconstruction errors linked to complex patterns of metal-vectoring geochemical anomalies in non-linear Earth systems. However, the learning of geochemical background representations may be suppressed by redundant mutual information from inter-element correlations and by mixed information of elemental concentration data caused by multiplicative cascade geo-processes. To deal with these issues, we conceptualized an idea of a new deep learning architecture called Info‒DAN, chaining the information maximization (Infomax) processor to the training network of stacked autoencoders. Infomax is an adaptive learning algorithm from information theory paradigms which aims at maximizing the information flow (joint entropy) passed through a feed-forward neural network processor. It was adopted to encode original multi-element data into independent source signals associated with different geochemical sub-populations and to prevent the dilution of background representations caused by inter-element information redundancy. The recovered source signals were then fed into a DAN processor to assist in modeling the improved representations of geochemical background populations and in enhancing complex anomaly patterns. The Info‒DAN technique was applied to stream sediment geochemical data pertaining to the Moalleman district, NE Iran, for performance appraisal in recognition of metal-vectoring geochemical anomalies. Evaluation tools comprising success-rate curves and prediction-area plots indicated that anomaly patterns derived from Info‒DAN, compared to those from a stand-alone DAN, reveal a stronger spatial correlation between ore-controlling fractures/faults and locations of known metal occurrences. The findings suggest that, thanks to the proposed algorithm, complex patterns of geochemical anomalies can be quantified with improved generalization accuracy as well as practical insights for vectoring towards metal exploration targets.