Applications of physics-informed neural networks in geosciences: From basic seismology to comprehensive environmental studies

Abstract

Physics-informed neural networks (PINNs) have emerged as a powerful tool in the intersection of machine learning and physical sciences, offering novel approaches to solve complex differential equations inherent in geoscientific phenomena. Despite their growing application, a review of their applications and potential within geosciences remains missing. This review systematically examines the utilization of PINNs in various geosciences such as hydrology, seismology, atmospheric sciences, geophysics, and others, highlighting their ability to integrate physical laws into neural network training processes. It describes the potential of PINNs to improve predictive modeling accuracy, reduce computational costs, and overcome the limitations of traditional numerical methods. The importance of this research lies in its assessment of PINNs' contributions to geosciences, offering valuable insights for researchers and practitioners seeking to use these advanced methodologies. The findings underscore the versatility and efficiency of PINNs, enhancing a deeper understanding of their role in advancing geoscientific research and applications. Ultimately, this review aims to bridge the current knowledge gap, promote the wider adoption and development of PINNs in geosciences, drive innovation, and enhance the accuracy and reliability of geoscientific models and predictions.