ADMM-based full-waveform inversion for microseismic imaging

TL;DR

This paper introduces an ADMM-based full-waveform inversion method for microseismic imaging that accurately locates and characterizes microseismic events without prior knowledge of their number, even in noisy conditions.

Contribution

It adapts the WRI method with ADMM to jointly locate microseismic events and reconstruct wavefields and source signatures without needing prior event count information.

Findings

Accurately locates microseismic events in complex models.

Recovers source signatures effectively even with noise.

Works with different initial velocity models.

Abstract

Full waveform inversion (FWI) is beginning to be used to characterize weak seismic events at different scales, an example of which is microseismic event (MSE) characterization. However, FWI with unknown sources is a severely underdetermined optimization problem, and hence requires strong prior information about the sources and/or the velocity model. The frequency-domain wavefield reconstruction inversion method (WRI) has shown promising results to mitigate the nonlinearity of the FWI objective function that is generated by cycle-skipping. WRI relies on the reconstruction of data-assimilated wavefields, which approach the true wavefields near the receivers, a helpful feature when the source is added as an additional optimization variable. We present an adaptation of a recently proposed version of WRI based on the alternating direction method of multipliers (ADMM) that first finds the location of the MSEs and then reconstructs the wavefields and the source signatures jointly. Finally, the subsurface model is updated to focus the MSEs at their true location. The method does not require prior knowledge of the number of MSEs. The inversion is stabilized by sparsifying regularizations separately tailored to the source location and velocity model subproblems. The method is tested on the Marmousi model using one MSE and two clusters of MSEs with two different initial velocity models, an accurate one and a rough one, as well as with added noise. In all cases, the method accurately locates the MSEs and recovers their source signatures.