Mitigating cycle skipping in full waveform inversion using max-pooling-based approximate envelope and shot patching

TL;DR

This paper introduces a max-pooling-based approximate envelope and shot patching strategy to improve full waveform inversion, especially when initial models are inaccurate, by enhancing low-frequency information and balancing gradients.

Contribution

It proposes a novel MPBAE method for better low-frequency representation and combines it with shot patching and normalization to accelerate and improve FWI convergence.

Findings

MPBAE-FWI outperforms HTE-FWI with poor initial models.

MPBAEP-FWI further improves inversion accuracy.

Numerical experiments confirm the effectiveness of the proposed methods.

Abstract

Full waveform inversion (FWI) can produce accurate subsurface velocity models. However, the lack of sufficiently low-frequency content in field data often causes cycle skipping and traps the inversion in local minima. The Hilbert-transform envelope (HTE) provides a low-frequency representation that helps mitigate cycle skipping, but it may be insufficient when the initial velocity model is highly inaccurate. To further enhance low-frequency information and reduce dependence on the initial model, we compute an approximate envelope using a sequence of 2D max-pooling operations. Compared with HTE, the resulting max-pooling-based approximate envelope (MPBAE) contains richer low-frequency components and better mitigates cycle skipping. We further combine the MPBAE loss with a shot patching strategy and exploit the inherent normalization property of the Euclidean loss to formulate the MPBAEP loss, in which each shot gather is divided into localized patches for misfit evaluation. This introduces local adjoint-source energy balancing, as the adjoint source associated with the Euclidean loss exhibits a normalization effect within each local region, thereby improving gradient balance and accelerating convergence. Numerical experiments on synthetic and field data demonstrate that MPBAE-FWI significantly outperforms HTE-FWI when the initial model is poor, while MPBAEP-FWI further improves inversion accuracy.