Evolutionary full-waveform inversion
- van Herwaarden, Dirk Philip Department of Earth Sciences, Institute of Geophysics, ETH Zurich, 8092 Zurich, Switzerland
- Afanasiev, Michael ORCID Department of Earth Sciences, Institute of Geophysics, ETH Zurich, 8092 Zurich, Switzerland
- Thrastarson, Solvi ORCID Department of Earth Sciences, Institute of Geophysics, ETH Zurich, 8092 Zurich, Switzerland
- Fichtner, Andreas ORCID Department of Earth Sciences, Institute of Geophysics, ETH Zurich, 8092 Zurich, Switzerland
- 2020-9-26
Published in:
- Geophysical Journal International. - Oxford University Press (OUP). - 2020, vol. 224, no. 1, p. 306-311
English
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.
- Language
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- English
- Open access status
- hybrid
- Identifiers
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- DOI 10.1093/gji/ggaa459
- ISSN 0956-540X
- Persistent URL
- https://sonar.ch/global/documents/220122
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