Multi-model full-waveform inversion

TL;DR

This paper introduces a multi-model approach to full-waveform inversion, inspired by image decomposition techniques, enabling more effective seismic model recovery from noisy data using unconstrained multi-norm optimization.

Contribution

It presents a novel multi-model formulation for full-waveform inversion that leverages image decomposition concepts and can be solved with standard iterative methods.

Findings

Successfully recovered blocky seismic models from noisy data

Applicable to both time-lapse and single-model inversion

Demonstrates improved robustness to noise

Abstract

We propose a multi-model formulation of full-waveform inversion that is similar to image decomposition into a "cartoon" and "texture" used in image processing. Inversion problem is formulated as unconstrained multi-norm optimization that can be solved using conventional iterative solvers. We demonstrate the proposed model decomposition approach by recovering a blocky subsurface seismic model from noisy data in time-lapse and single-model full-waveform inversion problems.