Relationships Between Seismic Velocity Structures and Seismogenic Zone Decoded by Interpretable Machine Learning

Seismic velocities, particularly P-wave (Vp) and S-wave (Vs) are critical for imaging the Earth's interior and understanding geodynamic processes. While the basic spatial relationships between seismic velocity structures and seismogenic zones have been extensively discussed, they are often described in a simplistic and coarse manner. Systematic and statistical investigations of these relationships particularly within the shallow crust and uppermost mantle, remain scarce. Significant challenges for such analyses are that the shallow crust and upper mantle are geologically complex, characterized by heterogeneous lithologies, intricate thermal and mechanical properties, and variable fluid distributions. These factors lead to nonlinear and highly heterogeneous spatial distributions of the Vp and Vs, complicating the interpretation and modeling of their relationship with seismogenic zones. Moreover, multiple interpretive paths for the same phenomenon often introduce subjective biases, making objective quantification of these relationships challenging. This study aims to address these challenges by leveraging machine learning (ML) techniques to explore and quantify the non-linear and complex relationships between seismic velocity structures and seismogenic zones across the Japan Arc. Using two distinct three-dimensional seismic velocity datasets, we employed various ML models to enhance the robustness and reliability of our analyses. Our results demonstrate that variations in the spatial distribution of Vp and Vs—especially the Vp/Vs ratios, vertical gradients, and variance of Vp, Vs—serve as reliable indicators for distinguishing seismogenic zones from non-seismogenic zones across both depth and geographic space even though the tectonic settings vary significantly. To interpret the complex nonlinear patterns revealed by ML models, we employed Shapley Additive Explanations (SHAP), which elucidated the spatial relationship between seismic velocities and seismogenic zones. By examining local seismogenic zones, The results by SHAP found factors influencing seismogenic zones differ with depth: at shallow depths, Vp, Vs, Vp/Vs ratio, and variance of Vp, Vs are dominant, while at greater depths, gradient changes are primary. It may relate to the thermal structure and indicate different triggering mechanisms for earthquakes at various depths. These findings can provide deeper insights into the spatial coupling between seismic velocities and seismicity, thereby advancing our understanding of the factors controlling earthquake generation.