Rock Classification in a Vanadiferous Titanomagnetite Deposit Based on Supervised Machine Learning

Abstract

As the potential locations of undiscovered ore deposits become deeper, a technique for predicting promising areas in the subsurface media has become necessary. Geoscience data on a wide range of underground media can be obtained through geophysical field exploration, but integration and interpretation of multi-geophysical data are difficult because of differences in spatial resolution. We developed a rock classifier that can predict promising vanadiferous titanomagnetite deposits from multi-geophysical data using supervised machine learning. Vanadiferous titanomagnetite ores are the main source of vanadium, which can be used as a large-scale energy storage system. Model training was conducted using rock samples from drilling cores, and the density of rock samples was used as a criterion for data labeling. We employed the support vector machine, random forest, extreme gradient boosting, LightGBM, and deep neural network for supervised learning, and the accuracy of all methods was 0.95 or greater. We applied trained models to three-dimensional geophysical field data to predict ore body locations. These candidate regions were distributed in the northeast of the geophysical survey area, and some classified areas were verified using a geological map.