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Continuous monitoring of volcanic gas emissions is crucial for understanding volcanic activity and potential eruptions. However, emissions of volcanic gases underwater are infrequently studied or quantified. This study explores the potential of Distributed Acoustic Sensing (DAS) technology to monitor underwater volcanic degassing. DAS converts fiber-optic cables into high-resolution vibration recording arrays, providing measurements at unprecedented spatio-temporal resolution. We conducted an experiment at Laacher See volcano in Germany, immersing a fiber-optic cable in the lake and interrogating it with a DAS system. We detected and analyzed numerous acoustic signals that we associated with bubble emissions in different lake areas. Three types of text-book bubbles exhibiting characteristic waveforms are all found from our detections, indicating different nucleation processes and bubble sizes. Using clustering algorithms, we classified bubble events into four distinct clusters based on their temporal and spectral characteristics. The temporal distribution of the events provided insights into the evolution of gas seepage patterns. This technology has the potential to revolutionize underwater degassing monitoring and provide valuable information for studying volcanic processes and estimating gas emissions. Furthermore, DAS can be applied to other applications, such as monitoring underwater carbon capture and storage operations or methane leaks associated with climate change. 

SUMMARY Ocean bottom distributed acoustic sensing (OBDAS) is emerging as a new measurement method providing dense, high-fidelity and broad-band seismic observations from fibre-optic cables deployed offshore. In this study, we focus on 35.7 km of a linear telecommunication cable located offshore the Sanriku region, Japan, and apply seismic interferometry to obtain a high-resolution 2-D shear wave velocity (VS) model below the cable. We first show that the processing steps applied to 13 d of continuous data prior to computing cross-correlation functions (CCFs) impact the modal content of surface waves. Continuous data pre-processed with 1-bit normalization allow us to retrieve dispersion images with high Scholte-wave energy between 0.5 and 5 Hz, whereas spatial aliasing dominates dispersion images above 3 Hz for non-1-bit CCFs. Moreover, the number of receiver channels considered to compute dispersion images also greatly affects the resolution of extracted surface-wave modes. To better understand the remarkably rich modal nature of OBDAS data (i.e. up to 30 higher modes in some regions), we simulate Scholte-wave dispersion curves for stepwise linear VS gradient media. For soft marine sediments, simulations confirm that a large number of modes can be generated in gradient media. Based on pre-processing and theoretical considerations, we extract surface wave dispersion curves from 1-bit CCFs spanning over 400 channels (i.e. ∼2 km) along the array and invert them to image the subsurface. The 2-D velocity profile generally exhibits slow shear wave velocities near the ocean floor that gradually increase with depth. Lateral variations are also observed. Flat bathymetry regions, where sediments tend to accumulate, reveal a larger number of Scholte-wave modes and lower shallow velocity layers than regions with steeper bathymetry. We also compare and discuss the velocity model with that from a previous study and finally discuss the combined effect of bathymetry and shallow VS layers on earthquake wavefields. Our results provide new constraints on the shallow submarine structure in the area and further demonstrate the potential of OBDAS for high-resolution offshore geophysical prospecting. 

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.

 

Abstract During the past few years, distributed acoustic sensing (DAS) has become an invaluable tool for recording high-fidelity seismic wavefields with great spatiotemporal resolutions. However, the considerable amount of data generated during DAS experiments limits their distribution with the broader scientific community. Such a bottleneck inherently slows down the pursuit of new scientific discoveries in geosciences. Here, we introduce PubDAS—the first large-scale open-source repository where several DAS datasets from multiple experiments are publicly shared. PubDAS currently hosts eight datasets covering a variety of geological settings (e.g., urban centers, underground mines, and seafloor), spanning from several days to several years, offering both continuous and triggered active source recordings, and totaling up to ∼90 TB of data. This article describes these datasets, their metadata, and how to access and download them. Some of these datasets have only been shallowly explored, leaving the door open for new discoveries in Earth sciences and beyond. 

During February 2023, a total of 32 individual distributed acoustic sensing (DAS) systems acted jointly as a global seismic monitoring network. The aim of this Global DAS Month campaign was to coordinate a diverse network of organizations, instruments, and file formats to gain knowledge and move toward the next generation of earthquake monitoring networks. During this campaign, 156 earthquakes of magnitude 5 or larger were reported by the U.S. Geological Survey and contributors shared data for 60 min after each event’s origin time. Participating systems represent a variety of manufacturers, a range of recording parameters, and varying cable emplacement settings (e.g., shallow burial, borehole, subaqueous, and dark fiber). Monitored cable lengths vary between 152 and 120,129 m, with channel spacing between 1 and 49 m. The data has a total size of 6.8 TB, and are available for free download. Organizing and executing the Global DAS Month has produced a unique dataset for further exploration and highlighted areas of further development for the seismological community to address.

 

Seismicity during explosive volcanic eruptions remains challenging to observe through the eruptive noise, leaving first‐order questions unanswered. How do earthquake rates change as eruptions progress, and what is their relationship to the opening and closing of the eruptive vent? To address these questions for the Okmok Volcano 2008 explosive eruption, Volcano Explosivity Index 4, we utilized modern detection methods to enhance the existing earthquake catalog. Our enhanced catalog detected significantly more earthquakes than traditional methods. We located, relocated, determined magnitudes and classified all events within this catalog. Our analysis reveals distinct behaviors for long‐period (LP) and volcano‐tectonic (VT) earthquakes, providing insights into the opening and closing cycle. LP earthquakes occur as bursts beneath the eruptive vent and do not coincide in time with the plumes, indicating their relationship to an eruptive process that occurs at a high pressurization state, that is, partially closed conduit. In contrast, VT earthquakes maintain a steadier rate over a broader region, do not track the caldera deflation and have a largerb‐value during the eruption than before or after. The closing sequence is marked by a burst of LPs followed by small VTs south of the volcano. The opening sequence differs as only VTs extend to depth and migrate within minutes of the eruption onset. Our high‐resolution catalog offers valuable insights, demonstrating that volcanic conduits can transition between partially closed (clogged) and open (cracked) states during an eruption. Utilizing modern earthquake processing techniques enables clearer understanding of eruptions and holds promise for studying other volcanic events.

 

By providing unrivaled resolution in both time and space, volcano seismicity helps to chronicle and interpret eruptions. Standard earthquake detection methods are often insufficient as the eruption itself produces continuous seismic waves that obscure earthquake signals. We address this problem by developing an earthquake processing workflow specific to a high‐noise volcanic environment and applying it to the explosive 2008 Okmok Volcano eruption. This process includes applying single‐channel template matching combined with machine‐learning and fingerprint‐based techniques to expand the existing earthquake catalog of the eruption. We detected an order of magnitude more earthquakes, then located, relocated, determined locally calibrated magnitudes, and classified the events in the enhanced catalog. This new high‐resolution earthquake catalog increases the number of observations by about a factor of 10 and enables the detailed spatiotemporal seismic analysis during a large eruption.

 

Soft sediment layers can significantly amplify seismic waves from earthquakes. Large dynamic strains can trigger a nonlinear response of shallow soils with low strength, which is characterized by a shift of resonance frequencies, ground motion deamplification, and in some cases, soil liquefaction. We investigate the response of marine sediments during earthquake ground motions recorded along a fiber‐optic cable offshore the Tohoku region, Japan, with distributed acoustic sensing (DAS). We compute AutoCorrelation Functions (ACFs) of the ground motions from 105 earthquakes in different frequency bands. We detect time delays in the ACF waveforms that are converted to relative velocity changes (dv/v).dv/vdrops, which characterize soil nonlinearity, are observed during the strongest ground motions and exhibit a large variability along the cable. This study demonstrates that DAS can be used to infer the dynamic properties of the shallow Earth with an unprecedented spatial resolution.

 

Although microseisms have been observed for more than 100 years, the precise locations of their excitation sources in the oceans are still elusive. Underwater Distributed Acoustic Sensing (DAS) brings new opportunities to study microseism generation mechanisms. Using DAS data off the coast of Valencia, Spain, and applying a cross‐correlation approach, we show that the sources of high‐frequency microseisms (0.5–2 Hz) are confined between 7 and 27 km from the shore, where the water depth varies from 25 to 100 m. Over time, we observe that these sources move quickly along narrow areas, sometimes within a few kilometers. Our methodology applied to DAS data allows us to characterize microseisms with a high spatiotemporal resolution, providing a new way of understanding these global and complex seismic phenomena happening in the oceans.