Evolutionary full-waveform inversion

We present a new approach to full waveform inversion (FWI) that enables the assimilation of datasets that expand over time without the need to re-invert all data. This evolutionary inversion rests on a re-interpretation of stochastic L-BFGS, which randomly exploits redundancies to achieve convergence without ever considering the dataset 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 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.