Deep Learning in Geophysics: Current Status, Challenges, and Future Directions

Abstract

With the rapid development of artificial intelligence technology, machine learning and deep learning techniques have been actively applied in the field of exploration geophysics. In this study, we extensively investigated the application of deep learning in geophysical techniques including seismic, gravity, magnetic, electromagnetics, ground penetrating radar, and joint inversion. Recent research has confirmed that deep learning techniques optimized for geophysical problems are being developed that transcend traditional theory-based approaches, suggesting that deep learning is becoming a new paradigm in the field of geophysics. Despite these technological advances, there are still challenges to be overcome, such as a lack of training data, model opacity, and heterogeneity between training data and field data, and this study suggests future research directions to address these aspects.