CRFA-CRBM: a hybrid technique for anomaly recognition in regional geochemical exploration; case study: Dehsalm area, east of Iran

Identification of geochemical anomalies is a crucial step in regional geochemical explorations. In this regard, new techniques have been developed based on deep learning networks. These simple-structure-networks act as human brains in processing the data by simulating deep layers of thinking. In this paper, a hybrid compositional-deep learning technique was applied to identify anomalous zones in the Dehsalm area located in 90 km of SW-Nehbandan, a town in South Khorasan province, Iran. The compositional robust factor analysis (CRFA) was applied as a tool to select a meaningful subset as an input to Continuous Restricted Boltzmann Machine (CRBM). The dataset consists of 635 stream sediment geochemical samples analyzed for 21 elements. Using CRFA, the 3rd factor (i.e. Pb, Zn, Cu, Ag, Sb, Sr, Ba, Hg, and W), which indicates the occurrence of epithermal mineralization in the area, was considered as an input set to CRBM. The best-performed CRBM with 80 hidden units and stabilized parameters at 150 iterations was finalized and trained on all the geochemical samples of the study area. The average square contribution (ASC) and average square error (ASE) values were determined as anomaly identifiers on the reconstructed error of the trained CRBM. A statistical threshold was applied to the values of the criteria (ASC & ASE), and the resulting outputs were mapped to delineate the anomalous samples. The maps indicated that ASC and ASE had the same performance in multivariate geochemical anomaly recognition. The anomalies were confirmed spatially using mineral prospects of Pb, Zn, Cu, and Sb, as well as several active lead and copper mines in the study area.