Machine learning for rock mechanics problems; an insight - Yu, Hao; Taleghani, Arash Dahi; Al Balushi, Faras; Wang, Hao (Frontiers in Mechanical Engineering, 2022)
Machine learning techniques have become very popular in different disciplines of mechanics and material science nowadays. The timeliness of this effort is supported by several recent technological advances. Machine learning (ML), data analytics (DA), and data management (DM) have expanded rapidly in many commercial sectors, providing an array of resources that can be leveraged for subsurface applications. In the last 15 years, deep learning in the form of deep neural networks (NNs), has been used very effectively in diverse applications, such as computer vision, seismic inversion, and natural language processing. Despite the remarkable success in these and related areas, deep learning has not yet been widely used in the field of scientific computing specially when it comes to subsurface applications due to the lack of large amount of data to train algorithms. In this paper, we review such efforts and try to envision future game-changing advances that may impact this field.