Acoustic Full Waveform Inversion with Hamiltonian Monte Carlo Method

TL;DR

This paper explores the application of Hamiltonian Monte Carlo to acoustic full waveform inversion, proposing a new tuning strategy that enhances model reconstruction accuracy and computational efficiency in noisy data scenarios.

Contribution

It introduces a novel mass matrix tuning strategy for HMC in FWI, improving model reconstruction and computational feasibility.

Findings

HMC can effectively reconstruct seismic models in noisy conditions.

The new mass matrix tuning strategy enhances sampling efficiency.

The method reduces computational costs compared to traditional approaches.

Abstract

Full-Waveform Inversion (FWI) is a high-resolution technique used in geophysics to evaluate the physical parameters and construct subsurface models in a noisy and limited data scenario. The ill-posed nature of the FWI turns this a challenging problem since more than one model can match the observations. In a probabilistic way, solving the FWI problem demands efficient sampling techniques to infer information on parameters and to estimate the uncertainties in high-dimensional model spaces. We investigate the feasibility of applying the Hamiltonian Monte Carlo (HMC) method in the acoustic FWI by a reflection setup containing different noise level data. We propose a new strategy for tuning the mass matrix based on the acquisition geometry of the seismic survey. Our methodology significantly improves the ability of the HMC method in reconstructing reasonable seismic models with affordable computational efforts.