The use of distributed acoustic sensing (DAS) presents unique advantages for earthquake monitoring compared with standard seismic networks: spatially dense measurements adapted for harsh environments and designed for remote operation. However, the ability to determine earthquake source parameters using DAS is yet to be fully established. In particular, resolving the magnitude and stress drop is a fundamental objective for seismic monitoring and earthquake early warning. To apply existing methods for source parameter estimation to DAS signals, they must first be converted from strain to ground motions. This conversion can be achieved using the waves' apparent phase velocity, which varies for different seismic phases ranging from fast body waves to slow surface and scattered waves. To facilitate this conversion and improve its reliability, an algorithm for slowness determination is presented, based on the local slant-stack transform. This approach yields a unique slowness value at each time instance of a DAS time series. The ability to convert strain-rate signals to ground accelerations is validated using simulated data and applied to several earthquakes recorded by dark fibers of three ocean-bottom telecommunication cables in the Mediterranean Sea. The conversion emphasizes fast body waves compared to slow scattered waves and ambient noise and is robust even in the presence of correlated noise and varying wave propagation directions. Good agreement is found between source parameters determined using converted DAS waveforms and on-land seismometers for both P and S wave records. The demonstrated ability to resolve source parameters using P waves on horizontal ocean-bottom fibers is key for the implementation of DAS-based earthquake early warning, which will significantly improve hazard mitigation capabilities for offshore earthquakes, including those capable of generating tsunami.

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.

The north Ligurian margin is a complex geological area in many ways. It has witnessed several phases of highly contrasting deformation styles, at both crustal scale and that of shallower cover tectonics, simultaneously or in quick succession, and with significant spatial variability. This complex interplay is mirrored in the resulting intricate structures that make it hard to identify active faults responsible for both, the significant seismicity observed, and the tectonic inversion undergone by the margin, identified at longer time scales on morphostructural grounds. We present here the first preliminary results of the leg 1 of SEFASILS cruise, conducted in 2018 offshore Monaco, in an effort to answer these questions by means of modern deep seismic acquisitions, using multichannel reflection and wide-angle sea-bottom records. Some first interpretations are provided and point towards an active basement deformation that focuses at the limits between main crustal domains.

SUMMARY: We revisit the hypocentre–velocity problem, which is of interest in different fields as for example microseismics and seismology. We develop a formulation based on kinematic migration of two picked kinematic attributes in the 2-D case, the traveltime and the slope (horizontal component of the slowness vector), from which we are able to retrieve the location and subsequently the origin time correction and the subsurface parameters mainly velocity. We show how, through a variable projection, the optimization problem boils down to a physically consistent and parsimonious form where the location estimation is projected into the subsurface parameter problem. We present in this study a proof of concept validated by a toy test in two dimensions and a synthetic case study on the Marmousi model. The method presented in this study is extendible to three dimensions by incorporating the crossline slope or the backazimuth as a supplementary attribute.

SUMMARY: This study focuses on the hydrophone component of a dense ocean bottom cable data set from the North Sea. This data had already been used in the past to illustrate the high resolution power of full waveform inversion based strategies. We have developed a highly scalable implementation of a visco-acoustic full waveform inversion engine making it possible to double the frequency content of the inverted data compared to previous studies, using simultaneously up to almost 50 000 CPU. This results in a multiparameter reconstruction of the subsurface, where the P-wave velocity, the density and the quality factor are reliably reconstructed down to 2 km depth, with a resolution of about 10 m.