Manganese mineral prospectivity based on deep convolutional neural networks in Songtao of northeastern Guizhou

The world has moved into an era of hidden ore body exploration, necessitating the development of new prospecting and exploration methods. One promising approach is to use the deep convolutional neural network (DCNN) algorithm to extract spatial and correlation characteristics of multiple two-dimensional elements related to hidden ores. This paper explores this method on Datangpo manganese (Mn), constructing prediction datasets that includes geological, geochemical, geophysical and aeromagnetic features. Analyzing metallogenic conditions and control factors of Mn ores, we construct a Mn ore prediction model (Geo-DCNN) based on multiple geographical knowledge and DCNN. The Geo-DCNN model reaches ore-bearing accuracy of 79.11%, non-ore-bearing accuracy of 99.01%, overall accuracy of 95.35%, and loss value of 0.0227 after training. Based on analysis of ROC curve, P-R curve, field investigation, and target area verification, we discover that the prediction results of the Geo-DCNN model in northeast Guizhou have a high correspondence rate with known manganese deposits. This provides valuable insight for further ore exploration in the area. Additionally, the results indicate that the Geo-DCNN model is robust and portable, suggesting that it can be applied to metallogenic prediction practices for manganese ore in similar regions.