Located in eastern California, Owens Valley is a rift basin in the western Basin and Range Province, bounded by the Sierra Nevada and White Mountains. While the basin has been the site of extensive geologic study, most interpretations of the subsurface structure depend on non-unique interpretation of gravity data. Under these constraints, past researchers have inferred the presence of the North Bishop Block, a block slumped from the White Mountains, roughly 100 square kilometers in area. A section of optical fiber near the town of Bishop, CA, in Owens Valley has been converted to a seismic array, and records apparent Ps converted phases above the proposed location of the North Bishop Block. We assume that the Ps phase is produced by the slumped block and jointly invert for a velocity structure through ambient noise cross-correlation and from the observed Ps phases, advancing the use of converted phases in fiber-optic seismic data. We replicate the proposed depth of the North Bishop Block and observe a deeper phase-converting interface to the south. We interpret this interface as an unmapped slumped block, located at roughly 1 kilometer depth. This result has important implications for the tectonic history and subsurface hydrology of Bishop, CA.

The use of fiber-optic sensing systems in seismology has exploded in the past decade. Despite an ever-growing library of ground-breaking studies, questions remain about the potential of fiber-optic sensing technologies as tools for advancing if not revolutionizing earthquake-hazards-related research, monitoring, and early warning systems. A working group convened to explore these topics; we comprehensively examined the application of fiber optics in various aspects of earthquake hazards, encompassing earthquake source processes, crustal imaging, data archiving, and technological challenges. There is great potential for fiber-optic systems to advance earthquake monitoring and understanding, but to fully unlock their capabilities requires continued progress in key areas of research and development, including instrument testing and validation, increased dynamic range for applications focused on larger earthquakes, and continued improvement in subsurface and source imaging methods. A key current stumbling block results from the lack of clear data archiving requirements, and we propose an initial strategy that balances data volume requirements with preserving key data for a broad range of future studies. In addition, we demonstrate the potential for fiber-optic sensing to impact monitoring efforts by documenting the data completeness in a number of long-term experiments. Finally, we outline the features of a instrument testing facility that would enable progress toward reliable and standardized distributed acoustic sensing data. Overcoming these current obstacles would facilitate progress in fiber-optic sensing and unlock its potential application to a broad range of earthquake hazard problems.

We present a microwave-frequency optical time-domain reflectometry (MF-OTDR) scheme for distributed acoustic sensing (DAS) of large strain-rate mechanical perturbations, supported by both theoretical analysis and experimental demonstration. The system employs microwave-modulated optical probe pulses injected into the fiber, where large external mechanical perturbations induce localized phase changes on the backscattered signal at the microwave frequency, and the resulting beat encodes group delay variations from which strain signals are efficiently recovered through phase demodulation, even under large strain-rate conditions.The performance is validated by both theoretical analysis and proof-of-concept experiments, demonstrating high-fidelity recovery of strain amplitudes up to the microstrain level, reaching 1.8 microstrains, and dynamic frequencies up to the kilohertz range over multi-kilometer distances, currently demonstrated over 4 km. Compared with conventional phase-demodulation DAS, the proposed scheme extends the measurable strain range by up to four orders of magnitude at the same spatial resolution. In particular, direct detection enables linear strain quantification up to 1000-fold the saturation limit of conventional DAS under identical performance conditions. Meanwhile, the proposed scheme has a system architecture and hardware requirements highly similar to existing phase-sensitive OTDR configurations, making the two approaches complementary for constructing a sensing system that simultaneously offers high sensitivity and high dynamic range.Such an experimentally verified increase in saturation level is especially important for earthquake early warning (EEW), where near-field strong motions may generate extremely large strain-rate signals. Peak ground strain rate is known to scale approximately exponentially with earthquake magnitude, implying that a three- to four-order-of-magnitude increase in measurable strain-rate amplitude can correspond to an increase of several magnitude units. In practical terms, while conventional DAS systems may saturate for events on the order of M3 at distances of ~10 km, the enhanced dynamic range demonstrated here could in principle extend measurable conditions toward much larger-magnitude events, thereby substantially reducing signal saturation in near-field strong-motion scenarios. The proposed MF-OTDR scheme is therefore a promising solution for distributed sensing of large strain-rate dynamic events, including strong ground motions in EEW scenarios.To enable such performance under large strain-rate conditions, a key challenge must also be addressed: direct detection of the microwave beat introduces phase distortion within the perturbed region, as well as amplitude fluctuations and phase deviations after the perturbation, including irregular π phase jumps. The underlying mechanism of this issue is theoretically analyzed, and a corresponding data processing strategy is developed, which is experimentally implemented and addressed by a dedicated processing procedure involving phase-jump correction and smoothing to suppress amplitude anomalies. This ensures accurate phase demodulation and strain reconstruction.

Continuous geodetic measurements near volcanic systems can image magma transport dynamics, yet resolving dike intrusions with high spatiotemporal resolution remains challenging. We introduce fiber-optic geodesy, leveraging low-frequency distributed acoustic sensing (LFDAS) recordings along a telecommunication fiber-optic cable, to track dike intrusions near Grindavík, Iceland, on a minute timescale. LFDAS revealed distinct strain responses from nine intrusive events, six resulting in fissure eruptions. Geodetic inversion of LFDAS strain reveals detailed magmatic intrusions, with inferred dike volume rate peaking systematically 15 to 22 min before the onset of each eruption. Our results demonstrate DAS's potential for a dense strainmeter array, enabling high-resolution, nearly real-time imaging of subsurface quasistatic deformations. In active volcanic regions, LFDAS recordings can offer critical insights into magmatic evolution, eruption forecasting, and hazard assessment.

Submarine optical fiber cables have become a vital component of global communication infrastructure, handling more than 99% of intercontinental data traffic. While vital for our daily life, these cables are also vulnerable and can be damaged or disrupted by natural events such as earthquakes or landslides, unintentionally by fishing activities, and also by deliberate acts of sabotage or terrorism. In this paper, we show that Distributed Acoustic Sensing (DAS) on these cables, in combination with suitable machine learning models, offers significant potential to anticipate third party aggressions to the cable. Moreover, we also show that DAS in this scenario can become a valuable new way for obtaining geophysical information at the bottom of the ocean. We discuss some of the applications of DAS in understanding ocean dynamics, including precise observations of surface waves, currents, tides, and the nonlinear phenomena driving water mixing, all of which are crucial for climate change predictions.

In light of the global shift toward deep-water offshore wind projects, including recent leases off California's central and northern coasts, this study leverages distributed acoustic sensing (DAS) technology on existing dark fiber-optic cables to address environmental and engineering challenges. The primary objective is to identify seafloor geohazards-such as faults, landslides, and turbidites-necessary for the design of floating platform foundations and power cable routes while concurrently monitoring the impact on marine life, specifically whale migrations. To address these two issues simultaneously, we have turned an existing seafloor fiber-optic cable off the central California coast to a DAS array to both image the near-surface conditions of the seafloor and track whale migration through the surrounding area. Passive recordings of ocean waves and ambient seismic noise on DAS were used to image the upper 3000-4000 m of the seafloor sediments, with particularly high resolution on the top sediment layer of geotechnical interest. Fault-scattered seismic waves from local earthquakes also provided fault identification and location within the offshore Los Osos and Hosgri fault zones that the fiber crosses. With the very same recordings, whale calls were used to track the locations of the whales as they passed along and over the fiber. This study shows that DAS can improve both seafloor geophysics and whale monitoring, which are two key issues for emerging energy generation in deep-water locations.

The recorded seismic waveform is a convolution of event source term, path term, and station term. Removing high-frequency attenuation due to path effect is a challenging problem. Empirical Green's function (EGF) method uses nearly collocated small earthquakes to correct the path and station terms for larger events recorded at the same station. However, this method is subject to variability due to many factors. We focus on three events that were well recorded by the seismic network and a rapid response distributed acoustic sensing (DAS) array. Using a suite of high-quality EGF events, we assess the influence of time window, spectral measurement options, and types of data on the spectral ratio and relative source time function (RSTF) results. Increased number of tapers (from 2 to 16) tends to increase the measured corner frequency and reduce the source complexity. Extended long time window (e.g., 30 s) tends to produce larger variability of corner frequency. The multi-taper algorithm that simultaneously optimizes both target and EGF spectra produces the most stable corner-frequency measurements. The stacked spectral ratio and RSTF from the DAS array are more stable than two nearby seismic stations, and are comparable to stacked results from the seismic network, suggesting that DAS array has strong potential in source characterization.

We introduce a modular software framework designed to integrate distributed acoustic sensing (DAS) data into operational earthquake monitoring systems. Building on the infrastructure of the Advanced National Seismic System (ANSS) and the Southern California Seismic Network (SCSN), which employs the ANSS Quake Monitoring Software (AQMS), our solution supports real-time DAS waveform streaming and machine-learning-based traveltime picking to leverage the dense spatial sampling of DAS arrays. To enable seamless compatibility with the AQMS, our approach uses standardized seismic data formats to incorporate predetermined DAS channels. We demonstrate the integration of data from a 100-km-long DAS array deployed in Ridgecrest, California, and provide a detailed description of the software components and deployment strategy. This work represents a step toward incorporating DAS into routine seismic monitoring and opens new possibilities for real-time hazard assessment using fiber-optic networks.

High-resolution tomographic imaging of the subsurface structures beneath volcanic systems is fundamental to better assess their hazard and potentially estimate the amount of eruptive materials. However, obtaining such images represents a major challenge for multiple reasons. The significant velocity contrasts commonly present within volcanic systems require accurate wave simulations or traveltime modeling to correctly account for wavefield triplications and ray bending. Moreover, station coverage and temporary deployments usually cannot achieve the requirements needed to meet high-resolution imaging targets.We demonstrate how distributed acoustic sensing (DAS) data recorded on existing telecommunication fiber cables and employed within an accurate and efficient matrix-free Eikonal tomography workflow can overcome these limitations. Specifically, we produce high-resolution tomographic images of the Long Valley caldera system in California, which in recent years has been undergoing significant inflation and seismic unrest. Our results reveal a distinct separation between the large magma chamber at approximately 10 km depth and the shallow crust. We interpret this separation as an upper-crust lid confining the pressurized volcanic fluid released through the crystallization of the magma reservoir over time. Our study highlights the potential of DAS for advancing volcano science; from providing insights into subsurface structures to monitoring dynamic processing due to fluid and magma movements through these complex systems.

Geophysical sensing in the open ocean is both costly and technically challenging. Here we developed a novel distributed fiber optic sensing technique that employs microwave modulation for phase measurement in signals returned from submarine repeaters. We transformed a trans‐Atlantic telecom cable into an 81‐sensor array and measured sub‐millihertz strains. The strains correlate with ocean tide height variations in phase, suggesting a dominant factor of the cable's Poisson's effect. Large strains observed at fiber spans located in the shallow water match the strong variations of simulated seafloor temperature. This study presents the first experimental confirmation of detecting sub‐millihertz signals using trans‐oceanic distributed sensing with submarine cables at span‐wise spatial resolution (∼80 km), opening the potential for cost‐efficient tsunami early warning and long‐term ocean temperature monitoring compatible with active data‐carrying fibers.

Distributed acoustic sensing (DAS) is proving to be an effective technology for seismological applications. Its success is due to the ability to deploy DAS instrumentation on the existing ever-growing telecommunication fiber networks across the globe. However, the benefits of DAS are hindered by the sheer volume of data commonly recorded from single-instrument deployments, which can easily reach tens of TBs. Additionally, since DAS measures along fiber strain, new data analysis paradigms are necessary to exhaustively exploit all the information contained within these large datasets. We showcase successful applications of DAS experiments using existing fiber cables located in different scenarios, from volcanic systems to densely populated urban environments. To harness the information within these novel datasets, we combine machine-learning tools with efficient algorithms running on high-performance computing architectures. For example, we showcase how the arrival times obtained from PhaseNet-DAS can provide real-time earthquake detection and localization, allowing for the inclusion of DAS data within earthquake early warning systems. Moreover, we demonstrate the capability of integrating real-time streamed DAS channels within seismic network operations. Our processing paradigm is proving to be an effective ground for discoveries and for creating the next generation of seismic monitoring frameworks.

Distributed Acoustic Sensing (DAS) is an emerging technology that converts optical fibers into dense arrays of strainmeters, significantly enhancing our understanding of earthquake physics and Earth's structure. While most past DAS studies have focused primarily on seismic wave phase information, accurate measurements of true ground motion amplitudes are crucial for comprehensive future analyses. However, amplitudes in DAS recordings, especially for pre‐existing telecommunication cables with uncertain fiber‐ground coupling, have not been fully quantified. By calibrating three DAS arrays with co‐located seismometers, we systematically evaluate DAS amplitudes. Our results indicate that the average DAS amplitude of earthquake signals closely matches that of co‐located seismometer data across frequencies from 0.01 to 10 Hz. The noise floor of DAS is comparable to that of strong‐motion stations but higher than that of broadband stations. The saturation amplitude of DAS is adjustable by modifying the pulse repetition rate and gauge length. We also demonstrate how our findings enhance the understanding of fiber‐optic seismology and its implications for natural hazard mitigation and Earth structure imaging and monitoring. Specifically, our results suggest that with proper settings, DAS can detect P ‐waves from an M6+ earthquake occurring 10 km from the cable without saturation, indicating its viability for earthquake early warning. Through quantitative comparison and analysis, we also find that local ambient traffic noise levels strongly affect the quality of seismic interferometry measurement, which is a powerful tool for near‐surface imaging and monitoring. Our methodology and findings are valuable for future DAS experiments that require precise seismic amplitude measurements.

We demonstrate how distributed acoustic sensing arrays deployed on existing urban telecommunication fiber can be used to perform high-resolution subsurface imaging of the Los Angeles basin by leveraging local and regional natural seismicity present in Southern California.

Distributed acoustic sensing (DAS) systems have attracted significant attention from seismology thanks to their ability to perform spatially resolved measurements of the seismic wavefield over tens of kilometers. Unfortunately, conventional DAS systems are limited to measuring small strain rates (typically 10s of nanostrains shot-to-shot), which limits their use to the measurement of relatively small events (M<4 at 10 km distance from the source). This makes DAS systems appealing for scientific studies but not for early warning systems which are intended for alerting the population. In this paper, a novel DAS system based on microwave frequency OTDR with high saturation strain sensing ability is formulated and demonstrated. The system employs radiofrequency modulation on the probe pulse injected into the fiber. Large external mechanical perturbations cause localized phase changes on the backscatter signal beating at the microwave frequency component. From the demodulated phases, the strain signals are recovered efficiently, even for large strain-rate signals. A proof of principle experiment with an external perturbation of a few microstrain amplitude and 400 Hz frequency over a distance of 60 meter is well recovered over 4 km fiber. Hence, the new system is proved to broaden significantly the upper measurement limit of typical DAS, bringing a significant application prospect of extensive, real-time large strain sensing.

Abstract 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.

Geophysical phenomena and associated hazards unfold over various scales, from a few kilometers near faults or eruptive fissures to thousands of kilometers across ocean basins. The advent of fiber sensing technologies like Distributed Acoustic Sensing (DAS) and trans-oceanic long-range sensing has revolutionized the deployment of dense seismic arrays on land, ocean floors, and in volcanic regions. These technologies' broad reach and continuous monitoring capabilities enable the study of geophysical processes across multiple spatial and temporal scales. In my presentation, I will provide recent case studies where fiber sensing has enhanced our understanding of earthquakes, volcanic activities, and tsunami, and I will discuss their growing potential in early warning systems.

Moonquakes can provide valuable insights into the lunar interior and its geophysical processes. However, extreme scattering of the lunar seismic waves makes seismic phase identification and source characterization difficult. In recent years, Distributed Acoustic Sensing (DAS) technology has emerged as a promising tool for seismic monitoring on Earth by turning a fiber optic cable into a dense array of strainmeters. DAS array can detect the full wavefield even in highly scattering environments and track scattered phases that were previously aliased on the standard sparse seismic networks. This study assesses the feasibility of DAS for moonquake detection. We present synthetic DAS recordings demonstrating its suitability for capturing moonquake signals in environments with significant scattering and low seismic velocities. By comparing Apollo moonquake signals with DAS's current minimum noise floor observed in Antarctica's quiet conditions, we find that existing DAS technology can detect more than 60 % of moonquakes previously recorded by Apollo seismic sensors. With expected and achievable improvements in DAS equipment, detection rates could surpass 90 %. Our findings suggest that DAS could, on average, detect around 15 moonquakes daily, with large fluctuations depending on recording during lunar sunrise/sunset for thermal moonquakes and the moon's distance from perigee/apogee for deep moonquakes. The deployment of DAS on the Moon could mark a revolutionary step in lunar seismology, significantly enhancing our understanding of the Moon's internal structure.

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 similar to 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. Large earthquakes can trigger smaller ones even far away. This helps us understand more about how earthquakes start and develop. However, finding these smaller earthquakes can be difficult as sometimes they are hidden in the chaos of a bigger, nearby earthquake. There is a novel technology called Distributed Acoustic Sensing (DAS) that might help. DAS can listen for earthquakes over large areas and gives a more detailed picture of what's happening underground. In this study, we discovered a moderate-size earthquake with a magnitude of 5.1 in Northeast Turkey that was triggered by a large earthquake with a magnitude of 7.8 in Southeast Turkey, using a DAS system that stretched over a thousand kilometers. We then checked this finding with conventional seismometers to be sure. Interestingly, this triggered earthquake showed precursory signals before its main shaking started, which could be signs of other smaller earthquakes happening nearby. Our findings deepen our understanding of how earthquakes interact with each other and offer insights into how earthquakes start. Our results also suggest that DAS could help us find and understand these triggered earthquakes, providing us with invaluable information for future seismology studies. We find an uncatalogued M5.1 earthquake in NE Turkey dynamically and instantaneously triggered by the 2023 Mw 7.8 SE Turkey earthquake We uncover this event on a thousand-kilometer linear distributed acoustic sensing array, then confirm its precise time, location, and magnitude with seismometers Precursory signals of the M5.1 event are observed and are most likely resulting from other nearby events rather than its nucleation phase

The structure of fault zones and the ruptures they host are inextricably linked. Fault zones are narrow, which has made imaging their structure at seismogenic depths a persistent problem. Fiber‐optic seismology allows for low‐maintenance, long‐term deployments of dense seismic arrays, which present new opportunities to address this problem. We use a fiber array that crosses the Garlock Fault to explore its structure. With a multifaceted imaging approach, we peel back the shallow structure around the fault to see how the fault changes with depth in the crust. We first generate a shallow velocity model across the fault with a joint inversion of active source and ambient noise data. Subsequently, we investigate the fault at deeper depths using travel‐time observations from local earthquakes. By comparing the shallow velocity model and the earthquake travel‐time observations, we find that the fault's low‐velocity zone below the top few hundred meters is at most unexpectedly narrow, potentially indicating fault zone healing. Using differential travel‐time measurements from earthquake pairs, we resolve a sharp bimaterial contrast at depth that suggests preferred westward rupture directivity.