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Abstract In the nearshore area, ocean current display intricate complexities due to interactions among tide, river, and coastline, which makes accurate current modeling challenging. Continuous in situ observation with high spatial and temporal resolution helps to better understand the dynamics of these currents. In this study, we used a 10‐km long submarine fiber‐optic cable with distributed acoustic sensing technology to record seismic signals associated with ocean waves. The current velocity and water depth were obtained from the velocity dispersion using frequency‐wave number analysis matched against theoretical ocean wave propagation equations. The results show remarkable agreement with observation of a nearby current meter, confirming the dominance of tidal currents as well as a small‐scale residual current. The temporal variation of water depth is consistent with observation by a nearby tidal gauge. This study demonstrates the potential of using submarine fiber‐optic cable for long‐term, high‐resolution, near real‐time nearshore current monitoring.

Abstract We provide high‐resolution seismic imaging of the central Garlock fault using data recorded by two dense seismic arrays that cross the Ridgecrest rupture zone (B4) and the Garlock fault (A5). Analyses of fault zone head waves and P‐wave delay times at array A5 show that the Garlock fault is a sharp bimaterial interface with P waves traveling ∼5% faster in the northern crustal block. The across‐fault velocity contrast agrees with regional tomography models and generates clear P‐wave reflections in waveforms recorded by array B4. Kirchhoff migration of the reflected waves indicates a near‐vertical fault between 2 and 6 km depth. The P‐wave delay times imply a ∼300‐m‐wide transition zone near the Garlock fault surface trace beneath array A5, offset to the side with faster velocities. The results provide important constraints for derivations of earthquake properties, simulations of ruptures and ground motion, and future imaging studies associated with the Garlock fault.

The importance of railway safety cannot be overemphasized; hence it requires reliable traffic monitoring systems. Widespread trackside telecommunication fiber-optic cables can be suitably deployed in the form of dense vibration sensors using Distributed Acoustic Sensing technology (DAS). Train-induced ground motion signals are recorded as continuous “footprints” in the DAS recordings. As the DAS system records huge datasets, it is thus imperative to develop optimized/stable algorithms which can be used for accurate tracking of train position, speed, and the number of trains traversing the position of the DAS system. In this study, we transform a 6-days continuous DAS data sensed by a 2-km cable into time-velocity domain using beamforming on phase-squeezed signals and automatically extract the position and velocity information from the time-beampower curve. The results are manually checked and the types of the trains are identified by counting the peaks of the signals. By reducing the array aperture and moving subarrays, the train speed-curve/motion track is obtained with acceptable computational performance. Therefore, the efficiency and robustness of our approach, to continuously collect data, can play a supplementary role with conventional periodic and time-discrete monitoring systems, for instance, magnetic beacons, in railway traffic monitoring. In addition, our method can also be used to automatically slice time windows containing train-induced signals for seismic interferometry.

Distributed acoustic sensing (DAS) is a novel seismological observation technology based on the fiber-optic sensing method, and can transform existing urban fiber-optic cables into ultra-dense array for urban seismological researches, thus opening abundant opportunities for resolving fine details of near surface structures. While high frequency ambient noise recorded on DAS has been applied in surface wave tomography, it is often difficult to extract a clear dispersion curve for the data recorded by urban internet cable because of the effect of precursor signals on noise correlation functions due to uneven distribution of noise sources, and weak coupling between the cable and the solid earth. In this study, we investigate the performance of the three-station interferometry method for improving the noise cross-correlation functions of the linear array. We applied this method to a DAS dataset acquired in an urban area, suppressed the precursor signal, improved the measurement of the dispersion curve, and constructed a 2D S-wave profile that reveals the hidden fault beneath the city. We also observed that the convergence of noise cross-correlation functions with weak coupling was significantly accelerated using this method. We employed this method to improve the signal quality of surface waves at far offset for the long segment, thus obtaining a more accurate dispersion curve. In conclusion, the three-station interferometry is an effective method to enhance the surface wave signal and suppress the precursor signal retrieved from the data recorded by urban internet cable, which could help in providing high resolution images of shallow structures in built-up areas.

Abstract The sparsity of permanent seismic instrumentation in marine environments often limits the availability of subsea information on geohazards, including active fault systems, in both time and space. One sensing resource that provides observational access to the seafloor environment are existing networks of ocean bottom fiber optic cables; these cables, coupled to modern distributed acoustic sensing (DAS) systems, can provide dense arrays of broadband seismic observations capable of recording both seismic events and the ambient noise wavefield. Here, we report a marine DAS application which demonstrates the strength and limitation of this new technique on submarine structural characterization. Based on ambient noise DAS records on a 20 km section of a fiber optic cable offshore of Moss Landing, CA, in Monterey Bay, we extract Scholte waves from DAS ambient noise records using interferometry techniques and invert the resulting multimodal dispersion curves to recover a high resolution 2D shear-wave velocity image of the near seafloor sediments. We show for the first time that the migration of coherently scattered Scholte waves observed on DAS records can provide an approach for resolving sharp lateral contrasts in subsurface properties, particularly shallow faults and depositional features near the seafloor. Our results provide improved constraints on shallow submarine features in Monterey Bay, including fault zones and paleo-channel deposits, thus highlighting one of many possible geophysical uses of the marine cable network.

The study of seismic sources and wave propagation in ice plate is helpful to understand the structure, migration, 10 fracture mechanics, mass balance and other processes. However, due to extreme environment, in-situ dense seismic observations are rare and the dynamic changes of the ice plate remain poorly understood. We conduct a seismic experiment with distributed acoustic sensing array on a frozen lake. We excite water vibrations by under-water airgun shots. With an artificial intelligence method, we detected seismic signals including high frequency icequakes and low frequency events. Icequakes cluster along the fractures and correlate with the local temperature variation. The flexural-gravity wave reveals the 15 property of the ice plate. Our study demonstrates the utility of DAS array as an in-situ dense seismic network in illuminating the internal failure process and dynamic deformation of ice plate such as ice shelf, which contributes to understanding and prediction of disintegrations of ice shelves. [ABSTRACT FROM AUTHOR]

This paper proposes an improvement on the scattering-integral (SI) approach for acoustic frequency-domain full waveform inversion (FWI) based on the individual Born kernels. The main development is a method for calculating the steepest-descent direction and the pseudo- Newton direction by vector operations with definite physical meaning, without needing to store the huge Fréchet kernels in memory beforehand. The Gauss-Newton descent direction can therefore be iteratively constructed without needing to store the huge approximate Hessian matrix or to calculate its inverse. The banded pseudo-Hessian and its inverse can be obtained in this way as well. This approach is efficient and makes the traditional SI approach more practical. At the same time, it keeps the advantages of the SI approach which can generate exact gradient of the objective function, enables separation of forward and inverse computation and provides straightforward access to Gauss-Newton iteration. This approach is called Born kernel full waveforminversion (BKFWI). Its effectiveness using the steepest-descent direction has been proved through 2-D numerical experiments. More fully resolved results and faster convergence are obtained when the Gauss-Newton direction is used. Because no additional forward simulations are needed for the Gauss-Newton direction, BKFWI is a highly valid alternative to traditional adjoint-state full waveform inversion (ADFWI) when the number of source stations is more than a few percent of the number of receiver stations. Such conditions are common in controlled source exploration, OBS (Ocean Bottom Seismometer) exploration, earthquake seismology, and even certain traditional seismic explorations. This method is also shown to be a convenient way to obtain accurate Gauss-Newton directions for multiparameter FWI because kernel coupling in the Hessian matrix can be easily handled. [ABSTRACT FROM AUTHOR]

Because modeling for full-waveform inversion (FWI) cannot produce reflections unless the velocity model has the scattering potential (high wavenumbers), using a migration/demigration process to generate modeling data, which is a key step in what is now known as reflection FWI (RFWI), is a credible alternative to tackle the reflection nonlinearity associated with FWI. However, because RFWI depends on a conventional data residual or zero-lag correlation objective function, high nonlinearity can still exist when the true amplitude migration is not used, as well as at far offsets due to cycle skipping. To avoid the cycle skipping and the need for a true amplitude migration, we have developed a correlation-based reflection full-waveform inversion method to update the low-wavenumber components of the velocity model. The success of this method relies on a sensitivity kernel decomposition and a correlation-based objective function. The sensitivity kernel decomposition makes it possible to separate out the contributions of different subkernels and to smear the reflected wave residuals along the "rabbit-ear" wavepath to obtain middle and deep background model estimates. The correlation-based objective function measures differences in kinematic information and behaves in a more linear way than the traditional waveform residual misfit. Moreover, our approach is less sensitive to the frequency content and amplitude information of the seismic data, enabling reliable background velocity estimates to be obtained without the need for low frequencies and full-physics modeling. Because the kinematic features of reflected waves are described correctly, the inversion result of the proposed method can be used as a migration model or an initial model for conventional FWI to achieve a correct high-wavenumber model update. [ABSTRACT FROM AUTHOR]