PINNPStomo: Simultaneous P- and S-wave seismic traveltime tomography using physics-informed neural networks with a new factored eikonal equation

TL;DR

This paper introduces PINNPStomo, a neural network-based seismic tomography method that jointly inverts P- and S-wave velocities using a new factored eikonal equation, improving convergence and accuracy over previous approaches.

Contribution

The paper extends PINNtomo to perform simultaneous P- and S-wave velocity inversion with a novel factored eikonal equation, enhancing convergence speed and accuracy.

Findings

Demonstrates superior convergence speed with the new method.

Achieves higher inversion accuracy for P- and S-wave velocities.

Validates improvements on 2D and 3D seismic models.

Abstract

Seismic tomography has long been an effective tool for constructing reliable subsurface structures. However, simultaneous inversion of P- and S-wave velocities presents a significant challenge for conventional seismic tomography methods, which depend on numerical algorithms to calculate traveltimes. A physics informed neural network (PINN)-based seismic tomography method (PINNtomo) has been proposed to solve the eikonal equation and construct the velocity model. Leveraging the powerful approximation capabilities of neural networks, we propose extending PINNtomo to perform multiparameter inversion of P- and S-wave velocities jointly, which we refer to as PINNPStomo. In PINNPStomo, we employ two neural networks: one for the P- and S-wave traveltimes, and another for the P- and S-wave velocities. By optimizing the misfits of P- and S-wave first-arrival traveltimes calculated from the eikonal equations, we can obtain the predicted P- and S-wave velocities that determine these traveltimes. Recognizing that the original PINNtomo utilizes a multiplicative factored eikonal equation, which depends on background traveltimes corresponding to a homogeneous velocity at the source location. We propose a new factored eikonal equation for PINNPStomo to eliminate this dependency. The proposed PINNPStomo, incorporating the new factored eikonal equation, demonstrates superior convergence speed and multiparameter inversion accuracy. We validate these improvements using 2D Marmousi and 2D/3D Overthrust elastic velocity models across three different seismic data acquisition geometries.