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The firn layer covers 98% of Antarctica's ice sheets, protecting underlying glacial ice from the external environment. Accurate measurement of firn properties is essential for assessing cryosphere mass balance and climate change impacts. Characterizing firn structure through core sampling is expensive and logistically challenging. Seismic surveys, which translate seismic velocities into firn densities, offer an efficient alternative. This study employs Distributed Acoustic Sensing technology to transform an existing fiber‐optic cable near the South Pole into a multichannel, low‐maintenance, continuously interrogated seismic array. The data resolve 16 seismic wave propagation modes at frequencies up to 100 Hz that constrain P and S wave velocities as functions of depth. Using co‐located geophones for ambient noise interferometry, we resolve very weak radial anisotropy. Leveraging nearby SPICEcore firn density data, we find prior empirical density‐velocity relationships underestimate firn air content by over 15%. We present a new empirical relationship for the South Pole region.

 

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.

 

Seismic ocean thermometry uses sound waves generated by repeating earthquakes to measure temperature change in the deep ocean. In this study, waves generated by earthquakes along the Japan Trench and received at Wake Island are used to constrain temperature variations in the Kuroshio Extension region. This region is characterized by energetic mesoscale eddies and large decadal variability, posing a challenging sampling problem for conventional ocean observations. The seismic measurements are obtained from a hydrophone station off and a seismic station on Wake Island, with the seismic station's digital record reaching back to 1997. These measurements are combined in an inversion for the time and azimuth dependence of the range‐averaged deep temperatures, revealing lateral and temporal variations due to Kuroshio Extension meanders, mesoscale eddies, and decadal water mass displacements. These results highlight the potential of seismic ocean thermometry for better constraining the variability and trends in deep‐ocean temperatures. By overcoming the aliasing problem of point measurements, these measurements complement existing ship‐ and float‐based hydrographic measurements. 

Monitoring seismic activity on the ocean floor is a critical yet challenging task, largely due to the difficulties of physical deployment and maintenance of sensors in these remote areas. Optical fiber sensing techniques are well-suited for this task, given the presence of existing transoceanic telecommunication cables. However, current techniques capable of interrogating the entire length of transoceanic fibers are either incompatible with conventional telecommunication lasers or are limited in their ability to identify the position of the seismic wave. In this work, we propose and demonstrate a method to measure and localize seismic waves in transoceanic cables using only conventional polarization optics, by launching pulses of changing polarization. We demonstrate our technique by measuring and localizing seismic waves from a magnitudeM_w6.0 earthquake (Guerrero, Mexico) using a submarine cable connecting Los Angeles, California and Valparaiso, Chile. Our approach introduces a cost-effective and practical solution that can potentially increase the density of geophysical measurements in hard-to-reach regions, improving disaster preparedness and response, with minimal additional demands on existing infrastructure.

 

Geophysical characterization of calderas is fundamental in assessing their potential for future catastrophic volcanic eruptions. The mechanism behind the unrest of Long Valley Caldera in California remains highly debated, with recent periods of uplift and seismicity driven either by the release of aqueous fluids from the magma chamber or by the intrusion of magma into the upper crust. We use distributed acoustic sensing data recorded along a 100-kilometer fiber-optic cable traversing the caldera to image its subsurface structure. Our images highlight a definite separation between the shallow hydrothermal system and the large magma chamber located at ~12-kilometer depth. The combination of the geological evidence with our results shows how fluids exsolved through second boiling provide the source of the observed uplift and seismicity.

 

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.