van Herwaarden, Dirk Philip Afanasiev, Michael Thrastarson, Solvi Fichtner, Andreas 2020 <jats:title>SUMMARY</jats:title> <jats:p>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.</jats:p> application/pdf https://sonar.ch/global/documents/220122 eng info:eu-repo/semantics/altIdentifier/doi/10.1093/gji/ggaa459 info:eu-repo/semantics/altIdentifier/issn/0956-540X info:eu-repo/semantics/openAccess Geophysical Journal International. - Oxford University Press (OUP). - 2020, vol. 224, no. 1, p. 306-311 Geochemistry and Petrology Geophysics Evolutionary full-waveform inversion http://purl.org/coar/resource_type/c_6501