Frequency-domain reflection-based full waveform inversion for short-offset seismic data

Abstract

The full waveform inversion (FWI) is a high-resolution algorithm used to invert accurate subsurface velocity models. However, inverting accurate velocity models from field data using the conventional FWI without an accurate long-wavelength starting velocity model is difficult. This difficulty occurs because of the band-limited frequency of the field seismic data and the acquisition geometry of the field seismic exploration. The low-frequency components and long-offset seismic data are essential for the inversion of the long-wavelength velocity model using the FWI. However, low-frequency signals are difficult to record from the field seismic exploration, and the maximum offset of the streamer is usually not sufficient in length. Therefore, the conventional FWI cannot easily invert the long-wavelength velocity from field seismic data but can invert the migration-like short-wavelength velocity, and it is subject to the problem of severe local minima. To invert the long-wavelength velocity from reflection-dominant, short-offset field seismic data, reflection-based full waveform inversion (RFWI) which decomposes the FWI gradient into high- and low-wavenumber components, is suggested. However, the conventional RFWI also contains high-wavenumber components, which obstruct long-wavelength velocity updates in the deep part of the model. Moreover, true amplitude migration and preprocessing to extract reflection signals from observed data are necessary for the conventional RFWI. In this study, a new frequency-domain RFWI algorithm, which uses wavefield separation and a two-step approach, is proposed. The wavefield separation divides the wavefield into up/down-going waves to remove the high-wavenumber component of the gradient and the two-step approach alternately updates the short- and long-wavelength velocities to reduce the computational cost. The effectiveness of the proposed algorithm is verified using the reflection-dominant, short-offset Marmousi synthetic seismic data and Tonga field seismic data. (C) 2019 Elsevier B.V. All rights reserved.