Mapping Crustal Vp/Vs in North America With a Machine Learning Approach

Abstract Vp/Vs (Poisson's ratio) provides critical information for constraining the bulk crustal composition, stress state, and tectonic evolution of the Earth. The receiver function technique has been extensively utilized to constrain the crustal Vp/Vs, yet the reliability of measurements can be affected by complex structures and uneven distribution of seismic stations. Consequently, the interpolated Vp/Vs maps can often be biased by unreliable observations, especially in data‐sparse regions. We tackle these issues by proposing a machine learning model that integrates multiple geophysical data sets to estimate Vp/Vs, leveraging the physical and structural properties of the crust. We train the model by compiling an extensive data set of global Vp/Vs measurements at 13,314 seismic stations and employ XGBoost to map Vp/Vs with other key crustal properties. Experiments using data from the (a) United States and (b) United States and Canada demonstrate superior prediction accuracy, achieving an overall value of 0.84 in both cases. Feature importance analysis indicates that crustal tectonic type, geographic coordinates, mid‐crust shear‐wave velocity, and crustal thickness primarily capture Vp/Vs variations, together explaining over 70% of reduction in the normalized root‐mean‐square error. The inclusion of other features further refines small‐scale Vp/Vs variation. Compared to cubic and Kriging interpolations, the predicted Vp/Vs map from machine learning exhibits less local extremes and a better alignment with the first‐order crustal structure across the continent. This study highlights the capability of machine learning to uncover complex geophysical relationships for reliable Vp/Vs estimates and its potential to constrain crustal composition at a continental scale.