Subsurface sedimentary structure identification using deep learning: A review

• Published in: Earth-science reviews 2023-04, Vol.239, p.104370, Article 104370
• Main Authors: Zhan, Chuanjun, Dai, Zhenxue, Yang, Zhijie, Zhang, Xiaoying, Ma, Ziqi, Thanh, Hung Vo, Soltanian, Mohamad Reza
• Format: Article
• Language: English
• Subjects: Data assimilation; Deep learning; Heterogeneity; Inversion; Subsurface sedimentary structure
• ISSN: 0012-8252; 1872-6828
• DOI: 10.1016/j.earscirev.2023.104370

The reliable identification of subsurface sedimentary structures (i.e., geologic heterogeneity) is critical in various earth and environmental sciences, petroleum reservoir engineering, and other porous media-related application. The application includes some important and societally relevant problems such as contaminated aquifer remediation, enhanced oil recovery, geological carbon storage, geological hydrogen storage, radioactive waste disposal, and contaminant fate and transport modeling. An inaccurately estimated subsurface sedimentary structure may introduce a larger bias into simulation results than inappropriate model parameters. Research on the development of subsurface sedimentary structure identification methods has recently witnessed increasing interest in deep learning (DL)-based methods. Such methods allow structure identification in a considerably different manner compared to traditional methods (e.g., covariance-based (co)kriging, multi-point statistics). The DL-based methods achieve significantly higher efficiency and accuracy. This review describes how DL-based methods have been utilized for subsurface sedimentary structure identification from the viewpoint of different identification approaches (direct and data assimilation-based modeling). Differences between DL-based and traditional methods are discussed. Furthermore, the limitations and challenges of existing DL-based methods are summarized. This includes training data acquisition, comparison of different algorithms, and limitations on accuracy and efficiency. Finally, future research directions are explored, including coupling DL-based and traditional methods, development of benchmark databases, DL-based methods driven by both data and theory, and applications of meta- and transfer learning. Effective solutions to these problems can provide numerous opportunities for DL-based methods to realize advances in subsurface sedimentary structure identification, thereby enabling a deeper scientific understanding of subsurface sedimentary structures. •Reliably identifying the subsurface sedimentary structures is critical in various earth sciences.•This review highlights deep learning for subsurface sedimentary structure identification.•Limitations, challenges, and future directions of deep learning methods are discussed.