Crustal‐scale imaging by the full‐waveform inversion (FWI) of long‐offset seismic data is inherently difficult because the large number of wavelengths propagating through the crust makes the inversion prone to cycle skipping. Therefore, efficient crustal‐scale FWI requires an accurate starting model and a stable workflow minimizing the nonlinearity of the inversion. Here we attempt to reprocess a challenging 2‐D ocean‐bottom seismometer (OBS) data set from the eastern Nankai Trough. The starting model is built by first‐arrival traveltime tomography (FAT), which is FWI assisted for tracking cycle skipping. We iteratively refine the picked traveltimes and then reiterate the FAT until the traveltime residuals remain below the cycle‐skipping limit. Subsequently, we apply Laplace‐Fourier FWI, in which progressive relaxation of time damping is nested within frequency continuation to hierarchically inject more data into the inversion. These two multiscale levels are complemented by a layer‐stripping approach implemented through offset continuation. The reliability of the FWI velocity model is assessed by means of source wavelet estimation, synthetic seismogram modeling, ray tracing modeling, dynamic warping, and checkerboard tests. Although the viscoacoustic approximation is used for wave modeling, the synthetic seismograms reproduce most of the complexity of the data with a high traveltime accuracy. The revised FWI scheme produces a high‐resolution velocity model of the entire crust that can be jointly interpreted with migrated images derived from multichannel seismic data. This study opens a new perspective on the design of OBS crustal‐scale experiments amenable to FWI; however, a further assessment of the optimal OBS spacing is required for reliable FWI.

Detailed reconstruction of deep structures with full‐waveform inversion (FWI) of wide‐angle ocean‐bottom seismometer (OBS) data remains challenging and unconventional. The complexity of the long‐offset waveforms increases the nonlinearity of the inverse problem, while the sparsity of the OBS deployments leads to a poorly constrained model reconstruction. Consequently, for such a FWI setting it is difficult to derive an initial model that satisfies the cycle‐skipping criterion. Searching for a remedy to this issue, we investigate the graph‐space optimal transport (GSOT) technique, which can potentially overcome the cycle‐skipping problem at the initial FWI stage. The key feature of the GSOT cost function is the convexity with respect to the patterns in the two seismograms, which allows for correct matching of the arrivals shifted in time for more than half of the wavelet. This in turn shall allow FWI to handle the large kinematic errors of the starting model. We test this hypothesis by applying the time‐domain acoustic FWI to the synthetic and field data from the subduction zone environment. We show that despite the complexity of the geological structure, the GSOT misfit function is able to guide the FWI toward the precise velocity model reconstruction and data fitting starting from a simple 1D model. The improved convexity of the GSOT misfit function allows FWI to converge even when mismatches between the observed and synthetic signals reach a few cycles. This ability reduces the constraint on the kinematic accuracy of the initial model and makes the FWI from the OBS data more feasible.

SUMMARY: First-arrival traveltime tomography is one of the most used velocity model building techniques especially in sparse wide-angle acquisitions for deep crustal seismic imaging cases. Relying on the inversion of a picked attribute, the absolute traveltimes, the approach is ill-posed in terms of non-uniqueness of the solution. The latter is remedied by proper regularization or the introduction of prior information. Indeed, since traveltime kernels are vulnerable to the velocity–depth ambiguity, the inversion is stabilized by the introduction of complementary data like reflections and explicit reflectors in the velocity models. Here, we propose to supplement first-arrival traveltimes by their slopes, in other words the horizontal component of the slowness vectors at the sources and/or receivers. Slopes are a crucial attribute in state of the art scattering-based or reflection-based tomographic methods like slope tomography or wavefront tomography where the differential information is needed in order to locate the scattering events position or to parametrize the wavefront. The optional but valuable injection of slopes as an objective measure in first-arrival traveltime tomography stabilizes the problem by constraining the emergence angle or in turn implicitly the turning point depth of the rays. We explain why slopes have a tremendous added value in such a tomographic problem and highlight its remedial effect in cases where the medium is unevenly illuminated. We also show that the contribution of slopes become even more significant when the acquisition is sparse as it is generally the case with ocean-bottom seismometer surveys. The inferred models from such an extended time-attributes tomography will be used as initial guesses in a full-waveform inversion workflow context. The proposed strategy is benchmarked in 2-D media against a dip section of the SEG/EAGE overthrust model and then followed by a revisit of ocean bottom seismometers data from the eastern-Nankai subduction margin as a real deep crustal case study.