Application of Concentration-Area fractal modeling and artificial neural network to identify Cu, Zn±Pb geochemical anomalies in Hashtjin area, NW of Iran - Ali Imamalipour; Hamed Ebrahimi; Amir reza Abdollahpur (Journal of Economic Geology)
Identification of geochemical anomalies plays an essential role in mineral exploration. Recent research investigations have shown that Machine Learning (ML) algorithms can identify geochemical anomalies associated with mineralization that represent targets for mineral exploration. Machine Learning algorithms are widely used in various fields due to their strong capability to extract and display high-level features of training samples. Autoencoder networks show a high ability to identify geochemical anomalies. In this study, the combined method of autoencoder network with the fractal concentration-area method was used to identify geochemical anomalies. First, using multivariate factor analysis, the elements barium, lead, zinc, copper, gold, iron, gold and arsenic were selected as indicators. Subsequently, the uni-element geochemical maps of these elements were prepared, and to standardize the maps in terms of minimum and maximum values, all maps were fuzzified and scaled. Using the fuzzy gamma operator, uni-element geochemical maps were combined. Then the resulting map applied the deep autoencoder method with eight layers of encoder and decoder were reconstructed. Finally, a mineral prospectively map was prepared for the potential area using the concentration-area fractal method. The mixed model proposed in this study introduces the region with high mineralization potential northeast of the studied area.