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SUMMARY We explore the potential of utilizing distributed acoustic sensing (DAS) for back-projection (BP) to image earthquake rupture processes. Synthetic tests indicate that sensor geometry, azimuthal coverage and velocity model are key factors controlling the quality of DAS-based BP images. We show that mitigation strategies and data processing modifications effectively stabilize the BP image in less optimal scenarios, such as asymmetric geometry, narrow azimuthal coverage and poorly constrained velocity structures. We apply our method to the $$M_w7.6$$ 2022 Michoacán earthquake recorded by a DAS array in Mexico City. We also conduct a BP analysis with teleseismic data for a reference. We identify three subevents from the DAS-based BP image, which exhibit a consistent rupture direction with the teleseismic results despite minor differences caused by uncertainties of BP with DAS data. We analyse the sources of the associated uncertainties and propose a transferable analysis scheme to understand the feasibility of BP with known source–receiver geometries preliminarily. Our findings demonstrate that integrating DAS recordings into BP can help with earthquake rupture process imaging for a broad magnitude range at regional distances. It can enhance seismic hazard assessment, especially in regions with limited conventional seismic coverage. 

SUMMARY The high cost of active surveys and the scarcity of underwater instruments hinder the availability of seismic imaging in oceanic environments. Ocean-bottom Distributed Acoustic Sensing (OBDAS) utilizing existing telecommunicational infrastructure is an alternative and economical approach to illuminate the subsurface at unprecedented resolution and over distances of tens of kilometers. In this study, we utilize OBDAS data along a 60-km cable perpendicular to the coast of Oregon to image the continental shelf subsurface. We extract landward and seaward surface waves via cross-correlation and coherent stacking of the ambient seismic field. To stably measure dispersions of both the fundamental modes and higher overtones, we apply a double-beamforming (DBF) workflow across different array subsections in the 0.2–3 Hz band with a spatial averaging technique. We perform a perturbational-based inversion scheme to reliably invert for S-wave velocities over the first 2000 m of the subsurface underlying the fiber-optic cable. By comparing our results with the 1-D slant-stack approach, we demonstrate the applicability of the DBF method on the OBDAS data set and the enhanced spatial resolution of this approach. From our single-mode DBF results, we observe a coherent layering feature in the Florence shelf-sea region, while multimode DBF results reveal possibly smaller-scale heterogeneity in the study region. 

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. 

Abstract Distributed Acoustic Sensing (DAS) is a promising technique to improve the rapid detection and characterization of earthquakes. Previous DAS studies mainly focus on the phase information but less on the amplitude information. In this study, we compile earthquake data from two DAS arrays in California, USA, and one submarine array in Sanriku, Japan. We develop a data‐driven method to obtain the first scaling relation between DAS amplitude and earthquake magnitude. Our results reveal that the earthquake amplitudes recorded by DAS in different regions follow a similar scaling relation. The scaling relation can provide a rapid earthquake magnitude estimation and effectively avoid uncertainties caused by the conversion to ground motions. Our results show that the scaling relation appears transferable to new regions with calibrations. The scaling relation highlights the great potential of DAS in earthquake source characterization and early warning.