Machine Learning‐Based Seismic Subsurface Characterization: The State of the Art and Future Perspectives

Abstract Seismic subsurface characterization involves interpreting seismic records to delineate geological features and estimate the physical properties of subsurface rocks (such as lithofacies, porosity, and fluid saturation). As an effective remote sensing technology to characterize the large‐scale spatial distribution of subsurface properties, seismic surveys play a crucial role in many fields of Earth, Energy, and Environmental Sciences, including hydrocarbon exploration and development, geological sequestration of CO 2 , underground water management, and geothermal energy exploitation. With the growing need for characterizing geological heterogeneity, the application of machine learning (ML)—capable of nonlinear mapping and feature extraction—has become essential for effective seismic subsurface characterization. To clarify the current state of ML‐based subsurface characterization and promote its application to complex geological formations, we review conventional and machine learning workflows, along with the challenges they face. Subsequently, we delve into ML and deep learning concepts, summarizi