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Large earthquakes can trigger smaller seismic events, even at significant distances. The process of earthquake triggering offers valuable insights into the evolution of local stress states, deepening our understanding of the mechanisms of earthquake nucleation. However, our ability to detect these triggered events is limited by the quality and spatial density of local seismometers, posing significant challenges if the triggered event is hidden in the signal of a nearby larger earthquake. Distributed acoustic sensing (DAS) has the potential to enhance the monitoring capability of triggered earthquakes through its high spatial sampling and large spatial coverage. Here, we report on an uncatalogued magnitude (M) 5.1 event in northeast Turkey, which was likely dynamically and instantaneously triggered by the 2023 M7.8 earthquake in southeast Turkey, located 400 km away. This event was initially discovered on ∼1,100 km of active DAS recordings that are part of an 1,850‐km linear array. Subsequent validation using local seismometers confirmed the event's precise time, location, and magnitude. Interestingly, this dynamically triggered event exhibited precursory signals preceding its P arrivals on the nearby seismometers. It can be interpreted as the signal from other nearby, uncatalogued, smaller triggered events. Our results highlight the potential of high‐spatial‐density DAS in enhancing the local‐scale detection and the detailed analysis of earthquake triggering.

Underwater Distributed Acoustic Sensing (DAS) utilizes optical fiber as a continuous sensor array. It enables high‐resolution data collection over long distances and holds promise to enhance tsunami early warning capabilities. This research focuses on detecting infragravity and tsunami waves associated with earthquakes and understanding their origin and dispersion characteristics through frequency‐wavenumber domain transformations and beamforming techniques. We propose a velocity correction method based on adjusting the apparent channel spacing according to water depth to overcome the challenge of detecting long‐wavelength and long‐period tsunami signals. Experimental results demonstrate the successful retrieval of infragravity and tsunami waves using a subsea optical fiber in offshore Oregon. These findings underscore the potential of DAS technology to complement existing infragravity waves detection systems, enhance preparedness, and improve response efforts in coastal communities. Further research and development in this field are crucial to fully utilize the capabilities of DAS for enhanced tsunami monitoring and warning systems.

Distributed Acoustic Sensing (DAS) is an emerging technology for earthquake monitoring and subsurface imaging. However, its distinct characteristics, such as unknown ground coupling and high noise level, pose challenges to signal processing. Existing machine learning models optimized for conventional seismic data struggle with DAS data due to its ultra-dense spatial sampling and limited manual labels. We introduce a semi-supervised learning approach to address the phase-picking task of DAS data. We use the pre-trained PhaseNet model to generate noisy labels of P/S arrivals in DAS data and apply the Gaussian mixture model phase association (GaMMA) method to refine these noisy labels and build training datasets. We develop PhaseNet-DAS, a deep learning model designed to process 2D spatio-temporal DAS data to achieve accurate phase picking and efficient earthquake detection. Our study demonstrates a method to develop deep learning models for DAS data, unlocking the potential of integrating DAS in enhancing earthquake monitoring.

We present a real-data test for offshore earthquake early warning (EEW) with distributed acoustic sensing (DAS) by transforming submarine fiber-optic cable into a dense seismic array. First, we constrain earthquake locations using the arrival-time information recorded by the DAS array. Second, with site effects along the cable calibrated using an independent earthquake, we estimate earthquake magnitudes directly from strain rate amplitudes by applying a scaling relation transferred from onshore DAS arrays. Our results indicate that using this single 50 km offshore DAS array can offer ∼3 s improvement in the alert time of EEW compared to onshore seismic stations. Furthermore, we simulate and demonstrate that multiple DAS arrays extending toward the trench placed along the coast can uniformly improve alert times along a subduction zone by more than 5 s.

Earthquake focal mechanisms provide critical in-situ insights about the subsurface faulting geometry and stress state. For frequent small earthquakes (magnitude< 3.5), their focal mechanisms are routinely determined using first-arrival polarities picked on the vertical component of seismometers. Nevertheless, their quality is usually limited by the azimuthal coverage of the local seismic network. The emerging distributed acoustic sensing (DAS) technology, which can convert pre-existing telecommunication cables into arrays of strain/strain-rate meters, can potentially fill the azimuthal gap and enhance constraints on the nodal plane orientation through its long sensing range and dense spatial sampling. However, determining first-arrival polarities on DAS is challenging due to its single-component sensing and low signal-to-noise ratio for direct body waves. Here, we present a data-driven method that measures P-wave polarities on a DAS array based on cross-correlations between earthquake pairs. We validate the inferred polarities using the regional network catalog on two DAS arrays, deployed in California and each comprising ~ 5000 channels. We demonstrate that a joint focal mechanism inversion combining conventional and DAS polarity picks improves the accuracy and reduces the uncertainty in the focal plane orientation. Our results highlight the significant potential of integrating DAS with conventional networks for investigating high-resolution earthquake source mechanisms.

Although typically used to measure dynamic strain from seismic and acoustic waves, Rayleigh‐based distributed acoustic sensing (DAS) is also sensitive to temperature, offering longer range and higher sensitivity to small temperature perturbations than conventional Raman‐based distributed temperature sensing. Here, we demonstrate that ocean‐bottom DAS can be employed to study internal wave and tide dynamics in the bottom boundary layer, a region of enhanced ocean mixing but scarce observations. First, we show temperature transients up to about 4 K from a power cable in the Strait of Gibraltar south of Spain, associated with passing trains of internal solitary waves in water depth <200 m. Second, we show the propagation of thermal fronts associated with the nonlinear internal tide on the near‐critical slope of the island of Gran Canaria, off the coast of West Africa, with perturbations up to about 2 K at 1‐km depth and 0.2 K at 2.5‐km depth. With spatial averaging, we also recover a signal proportional to the barotropic tidal pressure, including the lunar fortnightly variation. In addition to applications in observational physical oceanography, our results suggest that contemporary chirped‐pulse DAS possesses sufficient long‐period sensitivity for seafloor geodesy and tsunami monitoring if ocean temperature variations can be separated.

Fault zone structures at many scales largely dictate earthquake ruptures and are controlled by the geologic setting and slip history. Characterizations of these structures at diverse scales inform better understandings of earthquake hazards and earthquake phenomenology. However, characterizing fault zones at sub‐kilometer scales has historically been challenging, and these challenges are exacerbated in urban areas, where locating and characterizing faults is critical for hazard assessment. We present a new procedure for characterizing fault zones at sub‐kilometer scales using distributed acoustic sensing (DAS). This technique involves the backprojection of the DAS‐measured scattered wavefield generated by natural earthquakes. This framework provides a measure of the strength of scattering along a DAS array and thus constrains the positions and properties of local scatterers. The high spatial sampling of DAS arrays makes possible the resolution of these scatterers at the scale of tens of meters over distances of kilometers. We test this methodology using a DAS array in Ridgecrest, CA which recorded much of the 2019 M_w7.1 Ridgecrest earthquake aftershock sequence. We show that peaks in scattering along the DAS array are spatially correlated with mapped faults in the region and that the strength of scattering is frequency‐dependent. We present a model of these scatterers as shallow, low‐velocity zones that is consistent with how we may expect faults to perturb the local velocity structure. We show that the fault zone geometry can be constrained by comparing our observations with synthetic tests.

Fault zone complexities contain important information about factors controlling earthquake dynamic rupture. High‐resolution fault zone imaging requires high‐quality data from dense arrays and new seismic imaging techniques that can utilize large portions of recorded waveforms. Recently, the emerging Distributed Acoustic Sensing (DAS) technique has enabled near‐surface imaging by utilizing existing telecommunication infrastructure and anthropogenic noise sources. With dense sensors at several meters' spacing, the unaliased wavefield can provide unprecedented details for fault zones. In this work, we use a DAS array converted from a 10‐km underground fiber‐optic cable across Ridgecrest City, California. We report clear acausal and coda signals in ambient noise cross‐correlations caused by surface‐to‐surface wave scattering. We use these scattering‐related waves to locate and characterize potential faults. The mapped fault locations are generally consistent with those in the United States Geological Survey Quaternary Fault database of the United States but are more accurate than the extrapolated ones. We also use waveform modeling to infer that a 35 m wide, 90 m deep fault with 30% velocity reduction can best fit the observed scattered coda waves for one of the identified fault zones. These findings demonstrate the potential of DAS for passive imaging of fine‐scale faults in an urban environment.

The cross‐correlation of a diffuse or random wavefield at two points has been demonstrated to recover an empirical estimate of the Green's function under a wide variety of source conditions. Over the past two decades, the practical development of this principle, termed ambient noise interferometry, has revolutionized the fields of seismology and acoustics. Yet, because of the spatial sparsity of conventional water column and seafloor instrumentation, such array‐based processing approaches have not been widely utilized in oceanography. Ocean‐bottom distributed acoustic sensing (OBDAS) repurposes pre‐existing optical fibers laid in seafloor cables as dense arrays of broadband strain sensors, which observe both seismic waves and ocean waves. The thousands of sensors in an OBDAS array make ambient noise interferometry of ocean waves straightforward for the first time. Here, we demonstrate the application of ambient noise interferometry to surface gravity waves observed on an OBDAS array near the Strait of Gibraltar. We focus particularly on a 3‐km segment of the array on the continental shelf, containing 300 channels at 10‐m spacing. By cross‐correlating the raw strain records, we compute empirical ocean surface gravity wave Green's functions for each pair of stations. We first apply beamforming to measure the time‐averaged dispersion relation along the cable. Then, we exploit the non‐reciprocity of waves propagating in a flow to recover the depth‐averaged current velocity as a function of time using a waveform stretching method. The result is a spatially continuous matrix of current velocity measurements with resolution <100 m and <1 hr.

The COVID-19 lockdown has unprecedently affected the dynamics of our society. As traffic flow is a good proxy for societal activity, traffic monitoring becomes a useful tool to assess the lockdown’s impacts. Here we turned two strands of unused telecommunication fibers in Pasadena, California into a seismic array of ~5,000 sensors and detected ground vibrations caused by moving vehicles along the streets above the cable. We monitor the number of vehicles and their mean speed between December 2019 and August 2020 in high spatial and temporal resolution, and then analyze the traffic patterns change due to the COVID-19 lockdown. Our results show a city-wide decline in traffic volume and an increase in speed due to the lockdown, although the level of impact varies substantially by streets. This study demonstrates the feasibility of using telecommunication fiber optic cables in traffic monitoring, which has implications for public health, economy, and transportation safety.