A Convolutional Neural Network of GoogLeNet Applied in Mineral Prospectivity Prediction Based on Multi-source Geoinformation

The traditional convolutional neural networks applied in mineral prospectivity mapping usually extract features from only one scale at each iteration, resulting in plain features. To combat this limitation, this study utilized a convolutional neural network based on GoogLeNet to predict prospectivity for gold deposits in the Fengxian study area, China. The GoogLeNet adopted four groups of convolution kernels to extract and integrate features from multiple scales, obtaining abundant and comprehensive features related to mineralization. According to a multi-source geoinformation analysis, we selected 11 exploration criteria, including three geological factors (NW-trending brittle-ductile faults, NE-trending brittle faults, and anticline axes) and eight geochemical exploration data layers (Au, Ag, As, Hg, Pb, Zn, Cu, and Sb). Then, we created predictor samples to train the models to mine evidential features. Following to a comprehensive analysis, we formed a fusion model of GoogLeNet for mineral prospectivity modeling. The results demonstrated that the fusion model achieved an optimized predictive accuracy of 93.1% and an area under curve of 0.968. This fusion model outperformed the other models with superior success rate and prediction area rate performances, capturing 72% of the known gold deposits in just 27.3% of the research area. The results indicate the effectiveness of GoogLeNet in mineral prospectivity mapping. Finally, we classified the Fengxian district into three areas according to their different mineral prospectivity. The high-prospectivity areas provide significant implications for further exploration of gold deposits in the study area.