Mineral Potential Modeling Using Deep Learning Auto-Encoder Network in the Dehsalm District, Eastern Iran

Abstract

Summary Identification of promising areas associated with mineralization and integration of exploratory multi-resource data-sets are essential in mineral potential modeling. In this research, big data analysis method and an unsupervised deep auto-encoder network algorithm were used to identify the exploratory targets areas associated with porphyry copper-gold mineralization in the Dehsalm strict of Iran. The results show that the identified exploratory target areas have strong spatial relationships with known mineral indices in the study area. The Prediction-Area (P_A) plot analysis shows that the generated model performs well. The result of this study demonstrates that big data analytics supported by deep learning methods is a potential technique to be considered for use in mineral prospectivity mapping. Introduction New theories and analytical methods are required for mapping, interpreting and integrating diverse geo-information to increase the success rate and reduce the costs of mineral exploration. Particularly, as the amounts of high-quality data from multiple sources covering a broad range of scales have recently become readily available. The massive collection of earth observation data presents an unprecedented opportunity to apply big data approaches to solving problems in the geosciences. The significance of applying big data approaches to mineral exploration is not only to generate a variety of anomaly maps using different kinds of big data, but also to identify the statistical and spatial characteristics of distribution, enrichment and depletion of metallogenic elements. The core function of big data analytics is a prediction, making it an ideal approach in mineral potential modeling. Methodology and Approaches In this study, big data analytics and a deep auto-encoder network were used to learn and mine meaningful patterns from massive amounts of input data for mapping mineral prospectivity in the Dehsalm strict of eastern Iran. This study aims to demonstrate the effectiveness of big data analytics and deep learning methods for mapping mineral prospectivity in this area. Results and Conclusions The case study of mapping porphyry Cu-Au mineralization in the Dehsalm strict of Iran demonstrates the effectiveness of big data analytics and deep learning algorithms for mineral prospectivity mapping. The output of the generated model predicted new exploratory target areas in the north, west and southwest parts of the study area.