Using Fast Marching Eikonal Solver to Compute 3‐D Pds Traveltime for Deep Receiver‐Function Imaging
In common‐conversion‐point stacking of receiver functions, most current studies compute 3‐D relative Pds traveltime corrections by integrating traveltime anomalies along 1‐D raypaths. This ray tracing approach is generally time‐consuming and less accurate when prominent velocity anomalies exist and effects of 3‐D raypaths become significant. In this study we introduce a new scheme that utilizes a fast‐marching method eikonal solver to improve both the efficiency and accuracy of 3‐D Pds traveltime computation. We first employ a 1‐D raytracing method and the iasp91 model to calibrate the accuracy of the new scheme and optimize the parameters of the numerical solver. We then apply the new scheme to compute a massive number of Pds traveltimes using two sets of 3‐D synthetic models, one set with a high‐velocity slab and another set with a low‐velocity plume, and compare these 3‐D traveltimes with those computed with the raypath integrating approach. We find 2.7% and 11.8% raypaths in the two slab models and 7.8% and 12.0% raypaths in the two plume models show a 3‐D traveltime difference of ≥0.5 s. We apply the proposed scheme to a subset of transportable array receiver functions that sample the transition zone beneath the Yellowstone hotspot and find that a common‐conversion‐point stack using 3‐D Pds traveltimes computed by the eikonal solver method has the best focused P660s. Finally, we illustrate that computational times can be reduced by 1 to 2 orders of magnitude with the new scheme to compute 3‐D Pds traveltimes of 20,000–200,000 receiver functions.