Over a period of less than a decade, distributed acoustic sensing (DAS) has become a well-established technology in seismology. For historical and practical reasons, DAS manufacturers usually provide instruments that natively record strain (rate) as the principal measurement. While at first glance strain recordings seem related to ground motion waveforms (displacement, velocity and acceleration), not all the seismological tools developed over the past century (e.g. magnitude estimation, seismic beamforming, etc.) can be readily applied to strain data. Notably, the directional sensitivity of DAS is more limited than conventional particle motion sensors, and DAS experiences an increased sensitivity to slow waves, often highly scattered by the subsurface structure and challenging to analyse. To address these issues, several strategies have been already proposed to convert strain rate measurements to particle motion. In this study, we focus on strategies based on a quantity we refer to as ‘deformation’. Deformation is defined as the change in length of the cable and is closely related to displacement, yet both quantities differ from one another: deformation is a relative displacement measurement along a curvilinear path. We show that if the geometry of the DAS deployment is made of sufficiently long rectilinear sections, deformation can be used to recover the displacement without the need of additional instruments. We validate this theoretical result using full-waveform simulations and by comparing, on a real data set, the seismic velocity recovered from DAS with that recorded by collocated seismometers. The limitations of this approach are discussed, and two applications are shown: enhancing direct P-wave arrivals and simplifying the magnitude estimation of seismic events. Converted displacement provides better sensitivity to high velocity phases, improves broadside response and permits the direct application of conventional seismological tools that are less effective when applied to strain (rate) data.

In the past two decades, the central portion of Campi Flegrei caldera has experienced ground uplift up to 15 mm/month, with an increase of rate, magnitude and extent of the seismicity. In this work, we perform multi‐scale precise earthquake relocation of the 2014–2024 seismicity, mapping in detail activated fault zones. We relate the geometry, extent, and depth of these zones with up‐to‐date structural reconstructions of the caldera. The current seismicity is mainly driven by ground‐uplift‐induced stress concentration on pre‐existing, weaker fault zones, some of which identified for the first time. These structures are not only related to the inner caldera and dome resurgence but also to volcano‐tectonic events of the last 10 ka. The extent of imaged fault segments suggests they can accommodate ruptures up to a moment magnitude 5.1, significantly increasing seismic hazard in the area.

Campi Flegrei is a densely populated volcanic area in Italy. Its inner caldera periodically experiences uplift and subsidence, known as bradyseism, also accompanied by seismic activity. In the last decade, with uplift rates up to 2 cm/month, about nine-thousand earthquakes were recorded. Upon request of the local administration, the most updated data were collected and analyzed to evaluate the risk management strategy consisting of structural retrofitting according to the building code. Here it is shown that the reference moment magnitude is in the range \(\left({{\mathrm{4.4,5.1}}}\right)\), based on fault mapping, geomorphological inference, earthquake relocation, stress-drop analysis, and ground motion modelling. Earthquake forecasting enabled computing the exceedance probabilities of these magnitudes. Earthquake engineering showed that the minimum magnitudes expected to cause exceedance of design ground motion, are larger than the reference magnitudes. Finally, the risk reduction implied by the safety levels of new constructions was assessed for reinforced concrete buildings.

Distributed Acoustic Sensing (DAS) is becoming a powerful tool for earthquake monitoring, providing continuous strain‐rate records of seismic events along fiber optic cables. However, the use of standard seismological techniques for earthquake source characterization requires the conversion of data in ground motion quantities. In this study we provide a new formulation for far‐field strain radiation emitted by a seismic rupture, which allows to directly analyze DAS data in their native physical quantity. This formulation naturally accounts for the complex directional sensitivity of the fiber to body waves and to the shallow layering beneath the cable. In this domain, we show that the spectral amplitude of the strain integral is related to the Fourier transform of the source time function, and its modeling allows to determine the source parameters. We demonstrate the validity of the technique on two case‐studies, where source parameters are consistent with estimates from standard seismic instruments in magnitude range 2.0–4.3. When analyzing events from a 1‐month DAS survey in Chile, moment‐corner frequency distribution shows scale invariant stress drop estimates, with an average of Δ σ = (0.8 ± 0.6) MPa. Analysis of DAS data acquired in the Southern Apennines shows a dominance of the local attenuation that masks the effective corner frequency of the events. After estimating the local attenuation coefficient, we were able to retrieve the corner frequencies for the largest magnitude events in the catalog. Overall, this approach shows the capability of DAS technology to depict the characteristic scales of seismic sources and the released moment.

Applying Distributed Acoustic Sensing (DAS) to sense offshore telecom cables offers a unique opportunity to expand permanent seismological networks far offshore with an unprecedented instrumentation density but presents challenges in managing and processing data. This study introduces a workflow for automating earthquake catalog generation by combining DAS arrays with regional on‐land seismic networks. We apply it to the ABYSS offshore permanent observatory in central Chile, which began continuous recording in October 2023 on three 150‐km segments of a regional dark‐fiber telecommunication network. Our workflow includes state‐of‐the‐art components originally developed for conventional seismic sensor data. We first show that PhaseNet, pretrained on conventional seismic stations, can perform automatic phase picking on DAS strain‐rate data converted to velocity. In a second step, we demonstrate that the GaMMA associator, tuned with parameters adapted to our dense data set, can cluster picked arrivals of small and moderate‐sized earthquakes. We then adapt NonLinLoc to compute hypocenter locations using numerous phase picks, combining DAS and onland seismic data for improved accuracy. Finally, we show that reliable local magnitudes can be estimated on DAS recordings using the Richter scale. The earthquake catalog is stored in an SQL database using QuakeML formalism and is accessible via a user‐friendly webpage. By applying the workflow to onshore station recordings, we evaluate the benefits of adding permanent seafloor DAS instrumentation, demonstrating that offshore DAS significantly improves the detection of small‐magnitude earthquakes and reduces the offshore magnitude of completeness to 1.3.

Xdas is a Python library designed to manipulate distributed acoustic sensing (DAS) data. It provides a unified abstraction for reading any DAS file format into a standardized Python object, streamlining data handling across different acquisition systems. To address the challenge of massive, multifile data sets, Xdas aggregates data chunks into virtually contiguous arrays organized by instrument and acquisition. This structure allows for efficient spatial and temporal slicing while minimizing overhead. To enable scalable offline processing of massive DAS data sets, Xdas processes data in manageable chunks. To ensure processing continuity, Xdas uses a stateful pipes-and-filters architecture. Most Xdas operations are multithreaded by default to take full advantage of multicore systems. This approach also enables real-time data processing. Its built-in network streaming capabilities allow Xdas to be deployed on DAS instruments for custom, real-time workflows at the point of data generation. At its core, Xdas uses a labeled ND (N-dimensional) array structure that encapsulates both data values and coordinate metadata and can be used to handle any kind of data set (not just time–space DAS records). This data model adheres to the established standards provided by the NetCDF4/HDF5 formats and the Climate and Forecast conventions. Designed to mirror the application programming interfaces (APIs) of popular libraries such as NumPy, SciPy, and Xarray, Xdas minimizes the learning curve for new users. Its modular and extensible design means that adding support for a new file format or integrating a processing function typically requires less than 10 lines of code.