The impact of acquisition geometry on full-waveform inversion updates

Abstract

Full-waveform inversion (FWI) is a high-resolution model-building technique that uses the entire recorded seismic data content to build the earth model. Conventional FWI usually utilizes diving and refracted waves to update the low-wavenumber components of the velocity model. However, updates are often depth limited due to the limited offset range of the acquisition design. To extend conventional FWI beyond the limits imposed by using only transmitted energy, we must utilize the full acquired wavefield. Analyzing FWI kernels for a given geology and acquisition geometry can provide information on how to optimize the acquisition so that FWI is able to update the velocity model for targets as deep as basement level. Recent long-offset ocean-bottom node acquisition helped FWI succeed, but we would also like to be able to utilize the shorter-offset data from wide-azimuth data acquisitions to improve imaging of these data sets by developing the velocity field with FWI. FWI models are heading toward higher and higher wavenumbers, which allows us to extract pseudoreflectivity directly from the developed velocity model built with the acoustic full wavefield. This is an extremely early start to obtaining a depth image that one would usually produce in much later processing stages.

Localized adaptive waveform inversion: regularizations for Gabor deconvolution and 3-D field data application

“…These provided velocities as initial models may not make FWI free of local minima issues. On the other hand, high-fidelity low-frequency data require lowfrequency source injection and high-quality geophones, together with a long-offset and full-azimuth acquisition (Dellinger et al 2016 ;Vigh et al 2021 ). Such an acquisition system is often costly, Robust localized adaptive waveform inversion 449 especially for high-density acquisition.…”

Localized adaptive waveform inversion: theory and numerical verification

“…The non-convexity issue of the misfit function can be alleviated by lowering the frequency generated by the source (Plessix et al 2010;Ten Kroode et al 2013;Dellinger et al 2016): however it relies on long-offset and full-azimuth acquisitions (Plessix & Krupovnickas 2021;Vigh et al 2021), as first-arrival phases are essentially driving the optimization workflow. Late low-frequency reflection phases, quite important for high-resolution contribution, are likely to interfere, increasing the non-linearity of the inverse problem if the current model is not precise enough (Ten Kroode et al 2013).…”