Elastic full waveform inversion of multicomponent ocean-bottom cable seismic data: Application to Alba Field, U. K. North Sea

Abstract

Elastic full waveform inversion of multichannel seismic data represents a data-driven form of analysis leading to direct quantification of the subsurface elastic parameters in the depth domain. Previous studies have focused on marine streamer data using acoustic or elastic inversion schemes for the inversion of P-wave data. In this paper, P- and S-wave velocities are inverted for using wide-angle multicomponent ocean-bottom cable (OBC) seismic data. Inversion is undertaken using a two-dimensional elastic algorithm operating in the time domain, which allows accurate modeling and inversion of the full elastic wavefield, including P- and mode-converted PS-waves and their respective amplitude variation with offset (AVO) responses. Results are presented from the application of this technique to an OBC seismic data set from the Alba Field, North Sea. After building an initial velocity model and extracting a seismic wavelet, the data are inverted instages. In the first stage, the intermediate wavelength P-wave velocity structure is recovered from the wide-angle data and then the short-scale detail from near-offset data using P-wave data on the [Formula: see text] (vertical geophone) component. In the second stage, intermediate wavelengths of S-wave velocity are inverted for, which exploits the information captured in the P-wave’s elastic AVO response. In the third stage, the earlier models are built on to invert mode-converted PS-wave events on the [Formula: see text] (horizontal geophone) component for S-wave velocity, targeting first shallow and then deeper structure. Inversion of [Formula: see text] alone has been able to delineate the Alba Field in P- and S-wave velocity, with the main field and outlier sands visible on the 2D results. Inversion of PS-wave data has demonstrated the potential of using converted waves to resolve shorter wavelength detail. Even at the low frequencies [Formula: see text] inverted here, improved spatial resolution was obtained by inverting S-wave data compared with P-wave data inversion results.

Elastic least-squares reverse time migration

“…Acoustic LSRTM, although kinematically correct, fails to invert for a reflectivity image that best predicts the amplitude of pressure data generated in an elastic medium, as shown in Figure 11. This is because the acoustic-wave equation incorrectly models the P-wave wavefield by ignoring the AVO effect (Virieux and Operto, 2009;Sears et al, 2010). This P-wave amplitude error will generate artifacts in acoustic least-squares migration.…”

“…In our previous synthetic example, we used a P-wave source, which is common in elastic migration or inversion of marine (ocean-bottom) data, including synthetic data (Lu et al, 2009;Guasch et al, 2012;Raknes and Arntsen, 2014) and field data (Sears et al, 2008(Sears et al, , 2010Jiao et al, 2012). The recorded wavefield thus contains only PP-and converted PS-reflections.…”

“…The results can be validated by comparing the observed traces to the synthetic pressure data generated by an acoustic-or viscoacoustic-wave equation. Such pressure data have neither a correct P-wave amplitudevariation-with-offset (AVO) effect (Virieux and Operto, 2009;Sears et al, 2010) nor S-wave events as generated in the elastic modeling or collected in the field. Recently, Stanton and Sacchi (2015) combine wavefield separation and split-step wave-equation migration to improve the ability of least-squares migration to fit elastic data.…”

“…The S-waves are commonly recorded by multicomponent receivers in land or marine (ocean bottom) seismic experiments. For acoustic migration, the elastic characteristics of the wavefield, such as P-wave radiation patterns and mode-converted events, are treated as coherent noise rather than an additional source of information of the subsurface parameters (Sears et al, 2010).…”

Double-difference waveform inversion: Feasibility and robustness study with pressure data

“…Many successful real-data applications have also been published (Sears et al, 2010;Prieux et al, 2013;Warner et al, 2013), which establish FWI as a tool for quantitative subsurface property estimation.…”