Long-Wavelength Earth Model via Accelerated Full-Waveform Inversion

As the volume of available seismic waveform data increases, the responsibility to use the data in an effective way emerges. This requires computational efficiency as well as maximizing the exploitation of the information associated with the data.

In this contribution, we present a long-wavelength Earth model, created by using the data recorded from over a thousand earthquakes, starting from a simple one-dimensional background (PREM). The model is constructed with an accelerated full-waveform inversion (FWI) method which can seamlessly include large data volumes with a significantly reduced computational overhead. Although we present a long-wavelength model, the approach has the potential to go to much higher frequencies, while maintaining a reasonable cost.

Our approach combines two novel FWI variants. (1) The dynamic mini-batch approach which uses adaptively defined subsets of the full dataset in each iteration, detaching the direct scaling of inversion cost from the number of earthquakes included. (2) Wavefield-adapted meshes which utilize the azimuthal smoothness of the wavefield to design meshes optimized for each individual source location. Using wavefield adapted meshes can drastically reduce the cost of both forward and adjoint simulations as well as it makes the scaling of the computing cost to modelled frequencies more favourable.