Random-Drop Data Augmentation of Deep Convolutional Neural Network for Mineral Prospectivity Mapping

Convolutional neural network (CNN) has demonstrated promising performance in classification and prediction in various fields. In this study, a CNN is used for mineral prospectivity mapping (MPM) in the southwestern Fujian Province, China. Two limitations of applying CNNs in MPM are addressed: insufficient labeled samples and difficulty of applying CNNs to geological prospecting big data for MPM, which are characterized by massive size, multiple sources, multiple types, multi-temporality, multiple scales, non-stationarity, and heterogeneity. The random-drop data augmentation method, which repeatedly takes dropouts from data, is adopted in this study for generating sufficient training samples. Various experiments are conducted to determine a suitable CNN architecture for MPM. The mapped areas obtained by the constructed CNN are strongly spatially correlated with the locations of known mineralization, and most of the known Fe polymetallic deposits are located in areas with high probabilities. Our findings indicate that such a random-drop data augmentation method is suitable and effective for constructing training datasets to predict the locations of rare geological events. Additionally, CNN appears as a promising tool for integrating multi-source geoscience data, thereby supporting further mineral exploration.

Automated summary

The study utilized a CNN for mineral prospectivity mapping in southwestern Fujian Province, China, addressing limitations in applying CNN to geological prospecting big data. A random-drop data augmentation method was used to generate sufficient training samples, determining a suitable CNN architecture for MPM. The findings suggest that CNN is a promising tool for integrating multi-source geoscience data for mineral exploration.