Evolutionary full-waveform inversion. Issue 1 (26th September 2020)

• Record Type: Journal Article
• Authors: van Herwaarden, Dirk Philip; Afanasiev, Michael; Thrastarson, Solvi; Fichtner, Andreas
• Is Part Of: Geophysical journal international. Volume 224:Issue 1(2021)
• Page Start: 306
• Page End: 311
• Publication Date: 2020-09-26
• Subjects: Inverse theory -- Waveform inversion -- Computational seismology -- Seismic tomography; Geophysics -- Periodicals; 550
• DOI: 10.1093/gji/ggaa459
• Languages: English
• ISSNs: 0956-540X

Abstract

SUMMARY: We present a new approach to full-waveform inversion (FWI) that enables the assimilation of data sets that expand over time without the need to reinvert all data. This evolutionary inversion rests on a reinterpretation of stochastic Limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS), which randomly exploits redundancies to achieve convergence without ever considering the data set as a whole. Specifically for seismological applications, we consider a dynamic mini-batch stochastic L-BFGS, where the size of mini-batches adapts to the number of sources needed to approximate the complete gradient. As an illustration we present an evolutionary FWI for upper-mantle structure beneath Africa. Starting from a 1-D model and data recorded until 1995, we sequentially add contemporary data into an ongoing inversion, showing how (i) new events can be added without compromising convergence, (ii) a consistent measure of misfit can be maintained and (iii) the model evolves over times as a function of data coverage. Though applied retrospectively in this example, our method constitutes a possible approach to the continuous assimilation of seismic data volumes that often tend to grow exponentially.