LLNL‐G3Dv3: Global P wave tomography model for improved regional and teleseismic travel time prediction

Abstract

We develop a global‐scale P wave velocity model (LLNL‐G3Dv3) designed to accurately predict seismic travel times at regional and teleseismic distances simultaneously. The model provides a new image of Earth's interior, but the underlying practical purpose of the model is to provide enhanced seismic event location capabilities. The LLNL‐G3Dv3 model is based on ∼2.8 million P and Pn arrivals that are re‐processed using our global multiple‐event locator called Bayesloc. We construct LLNL‐G3Dv3 within a spherical tessellation based framework, allowing for explicit representation of undulating and discontinuous layers including the crust and transition zone layers. Using a multiscale inversion technique, regional trends as well as fine details are captured where the data allow. LLNL‐G3Dv3 exhibits large‐scale structures including cratons and superplumes as well numerous complex details in the upper mantle including within the transition zone. Particularly, the model reveals new details of a vast network of subducted slabs trapped within the transition beneath much of Eurasia, including beneath the Tibetan Plateau. We demonstrate the impact of Bayesloc multiple‐event location on the resulting tomographic images through comparison with images produced without the benefit of multiple‐event constraints (single‐event locations). We find that the multiple‐event locations allow for better reconciliation of the large set of direct P phases recorded at 0–97° distance and yield a smoother and more continuous image relative to the single‐event locations. Travel times predicted from a 3‐D model are also found to be strongly influenced by the initial locations of the input data, even when an iterative inversion/relocation technique is employed.