While the benefits of fiber-optic sensing for conventional monitoring are becoming clear, real-time processing of these data could further enhance their societal impact, with promising implications for the early warning of hazardous phenomena. In volcanic settings, real-time analysis of low-frequency strain can provide valuable information on ongoing eruptive activity, while fiber-optic cables deployed near seismogenic sources could increase lead times for earthquake early-warning systems. At the same time, the growing frequency of extreme meteorological events associated with climate change—such as flash floods or avalanches—calls for monitoring systems capable of rapidly detecting potentially devastating phenomena. Real-time urban monitoring may also contribute to improving the viability and efficiency of smart cities, where fiber-optic sensing could play a key role.
We encourage contributions on operative or potential applications of fiber optic technologies for real-time monitoring in various natural, urban or industrial environments. Contributions highlighting the integration of conventional sensors into fiber-optic networks (e.g. SMART cables), as well as focusing on the challenges of real time processing are welcome. We also strongly encourage contributions that leverage artificial intelligence and machine learning for real-time monitoring, including automated analysis and decision-support systems for early warning applications.
The tsunami generated by the 2025 Mw 8.8 Kamchatka earthquake was recorded by a 450-km-long distributed acoustic sensing (DAS) array leveraging telecom cables offshore Chilean coasts. Tsunami waves of ~2 cm amplitude induced measurable strain on the cable despite the low-frequency sensitivity limitations of DAS. From the conversion of the cable distributed strain-rate to water elevations considering compliance and Poisson effects, we evaluate the potential contribution to tsunami warning systems. We estimate coastal tsunami arrival times and amplitudes based on assimilation alone of the DAS data recorded at distances ranging from 10 to 30 km off the coast. We find that warning can potentially provide 3 to 15 minutes of lead time before the tsunami waves reach the coast. We also show through synthetic waveform tests that the accuracy of both arrival-time and amplitude estimates improves as longer portions of the DAS record become available. This DAS-based approach highlights the potential of leveraging existing telecommunication cables as a cost-effective and complementary tsunami warning system in many exposed regions of the world.
How to cite: Rivet, D., Xie, Y., Sladen, A., van den Ende, M., Alister, T., Jean-Paul, A., and Barrientos, S.: DAS Recording of a Transoceanic Tsunami on Submarine Cables in Chile and its Implications for Tsunami Early Warning, Galileo conference: Fibre Optic Sensing in Geosciences, Aussois, France, 31 Aug–4 Sep 2026, GC14-FibreOptic-37, https://doi.org/10.5194/egusphere-gc14-fibreoptic-37, 2026.
Although observations of tsunami waves using Distributed Acoustic Sensing (DAS) remain relatively scarce, examples do exist, confirming the feasibility of direct detection of tsunami-induced signals on submarine cables (e.g., Xiao et al., 2024; Tonegawa & Araki, 2024, 2026). These observations, however, also highlight practical limitations including low signal-to-noise ratios at long periods, challenges in discriminating tsunami signals from environmental and oceanographic background noise, directional sensitivity of the recorded strain field, and complexities introduced by bathymetry and seafloor coupling. These studies emphasize that while direct detection is feasible, robust and reliable operational deployment requires further refinement in both instrumentation and signal processing.
Beyond observational evidence, an analytical framework has been developed to quantify the coupling between tsunami-induced pressure fields and the strain recorded by submarine cables. Becerril et al. (2026) demonstrate that hydrostatic pressure perturbations associated with tsunami waves induce measurable horizontal strain through both, the effects of cable elasticity (via Poisson’s response) and seafloor compliance, with additional contributions from shear stresses induced by horizontal fluid motion in shallow depths. This formulation provides a quantitative basis for interpreting DAS observations and assessing expected signal amplitudes relative to instrumental noise levels.
Taken together, these analytical and observational advances underscore the potential of DAS as an ancillary sensor for next-generation Tsunami Early-Warning Systems (TEWS). Addressing the identified technical challenges is therefore a prerequisite for operational adoption. In this context, current efforts will be outlined focused on integrating these insights into the development of improved DAS system designs, including enhanced low-frequency sensitivity, optimized deployment strategies, and application-specific processing methodologies, with the objective of enabling reliable, real-time tsunami detection in future operational settings.
How to cite: Becerril, C., Sladen, A., Ampuero, J.-P., Gonzalez-Herraez, M., Kutschera, F., and Agnes-Gabriel, A.: Developing Operational Tsunami Detection with DAS: Bridging Theory and Observation, Galileo conference: Fibre Optic Sensing in Geosciences, Aussois, France, 31 Aug–4 Sep 2026, GC14-FibreOptic-27, https://doi.org/10.5194/egusphere-gc14-fibreoptic-27, 2026.
Indonesia is one of the world's most exposed tsunami-prone regions, yet it still lacks a fully operational early warning system capable of addressing non-tectonic sources such as submarine landslides and volcanic events — precisely the scenarios that can threaten coastlines with minimal reaction time. SAMUDRA (Submarine Alert Monitoring Using Dedicated Remote Advanced sensors) is a Franco-Indonesian project designed to address this critical gap by deploying an integrated multi-sensor fiber-optic observatory leveraging existing submarine telecom cable infrastructure.
The project combines three complementary optical sensing technologies: the LOKI optical interrogator (MAAGM), which enables all-optical, electronics-free remote sensing of point sensors including seismometers, pressiometers, tiltmeters, strainmeters and hydrophones; the CANOPUS system (Exail) for in-situ oceanographic measurements; and a Distributed Acoustic Sensing (DAS) system (FEBUS Optics) for continuous distributed measurements of strain, pressure, and temperature along the full cable length. All electronics remain onshore, ensuring robustness, low maintenance, and long-term reliability in harsh deep-sea environments — a key requirement for sustained operational monitoring.
This architecture directly addresses the core challenge of real-time monitoring in poorly instrumented offshore environments. The simultaneous acquisition of spatially distributed DAS measurements and high-precision point observations creates a multi-observable dataset enabling rapid discrimination between seismogenic, landslide-induced, and volcanic tsunami sources. Data processing pipelines, AI-supported event detection and scenario-based modelling are being co-developed with BMKG (Indonesia's national meteorological and geophysical agency) to produce operationally usable warning-ready products compatible with the existing InaTEWS warning chain — not a parallel system.
The project follows a staged deployment strategy: DAS measurements on the existing Rokatenda submarine cable (Flores) provide immediate data from an active seismogenic zone, while the main multi-instrument demonstrator is progressively built in Ambon Bay. Preliminary basin and dive tests (2026) will validate instrument-cable compatibility before full offshore deployment (Q1 2027, co-funded by ANR). The project is co-designed with BMKG and BRIN, ensuring local ownership and long-term operational sustainability — lessons drawn directly from the failure of post-2004 international aid deployments.
SAMUDRA exemplifies the new paradigm of exploiting existing fiber-optic infrastructure as a real-time geophysical observatory, with direct societal impact for tsunami early warning. Its scalable architecture is designed for replication across up to 50 priority sites in Indonesia, with broader applicability to other tsunami-exposed coastal regions worldwide.
How to cite: Guattari, F., Leray, V., Bercy, A., Daddi Hammou, I., Menard, P., Feuilloy, M., Pagès, G., Ménigot, S., Bernard, P., and Boudin, F.: SAMUDRA: A Multi-Sensor Fiber-Optic Observatory for Real-Time Tsunami Early Warning in Indonesia, Galileo conference: Fibre Optic Sensing in Geosciences, Aussois, France, 31 Aug–4 Sep 2026, GC14-FibreOptic-117, https://doi.org/10.5194/egusphere-gc14-fibreoptic-117, 2026.
In recent years, fibre optic sensing methods, in particular Distributed Acoustic Sensing (DAS), have been experimentally demonstrated to be suitable for monitoring Earth System parameters in submarine cables. The SUBMERSE project (SUBMarinE cables for ReSearch and Exploration) aims to develop blueprints for using telecommunication fibre optic cables as sensors by attaching fibre optic interrogators at selected landing stations, also building a data infrastructure for both temporary storage of full resolution data and permanent archival of reduced data sets.
We analyse data from interrogating the East/West oriented Ionian Submarine System cable from both end points, i.e., Preveza, Greece, and Crotone, Italy, along the same fibre. This cable is operated by Islalink and located to the north of the Kefalonia Transform Zone. This fault zone marks the western termination of the Hellenic subduction system and is one of the most active seismic zones in Greece, with large damaging earthquakes above M > 6 occurring every few years on average.
In addition to acquiring the full-resolution dataset, decimated channels (~100 in each case) acted as virtual seismic stations offshore, acquired at the NOA datacenter for real-time monitoring purposes. We explored various approaches to automated phase picking and magnitude determination on a reduced data set as well as the full-resolution data. We also consider other test sites on the Ellalink cable branches extending from Sines in southern Portugal and from Madeira.
In order to support these and other acquisitions, we have developed a range of tools that can be deployed at future sites. We have enabled real-time streaming of DAS data following the standard Seedlink protocol, which allows straightforward integration into existing workflows at earthquake observatories. We have developed an automated, machine-learning-based algorithm for analysing earthquake waveforms and assembled a benchmark data set of earthquake recordings from DAS cables worldwide with labels of P and S arrival times that can serve to further refine machine learning and other automated analysis approaches. Finally, leveraging the Xdas platform (Trabattoni et al. 2025), we have extended the popular SeisBench platform (Woollam et al, 2022) for machine learning in seismology with the ability to efficiently process dense DAS datasets with algorithms/machine learning models operating across either single or multiple channels.
How to cite: Tilmann, F., Evangelidis, C., Fountoulakis, I., Xiao, H., Münchmeyer, J., Heinloo, A., Strollo, A., Hillmann, L., Morten, J.-P., Poggi, V., Parolai, S., Oikonomakos, M., Martínez, S., Loureiro, A., Custódio, S., and Atherton, C.: Establishing continuous seismic monitoring by interrogation of submarine telecommunication cables in Europe with the SUBMERSE project: tools and the Ionian use case, Galileo conference: Fibre Optic Sensing in Geosciences, Aussois, France, 31 Aug–4 Sep 2026, GC14-FibreOptic-92, https://doi.org/10.5194/egusphere-gc14-fibreoptic-92, 2026.
Distributed Acoustic Sensing (DAS) applied to existing fiber optic telecommunication cables provide dense spatial measurements and enables seismic monitoring in regions where conventional instrumentation is limited. In this study, we evaluate the performance of a submarine DAS system deployed along a ~60 km fiber-optic telecommunication cable for earthquake detection and monitoring in the Marmara Sea, Türkiye.
The DAS interrogator, installed at Tavşantepe Metro Station in İstanbul, continuously records strain-rate data with 10 m channel spacing and a sampling rate of 1500 Hz. The cable extends between the Istanbul mainland and the Princes’ Islands, crossing the Marmara Sea in close proximity to the North Anatolian Fault, thereby enabling continuous offshore seismic observations in one of the most seismically critical regions of Türkiye.
Since early 2023, the system has recorded more than 1,500 seismic events with magnitudes ranging from Mw 1.7 to 7.8, as well as teleseismic events up to Mw 8.8. In addition to cataloged earthquakes, the DAS data reveal smaller local events that are not clearly detected by traditional seismic networks. This highlights the high sensitivity of the system, enabled by its dense spatial sampling.
We implement a simple real-time detection approach based on characteristic functions applied to selected DAS channels, showing that earthquake signals can be detected reliably under operational conditions. The continuous spatial sampling along the cable also allows following the wavefield propagation over tens of kilometers.
The dataset also reveals several important limitations of the current system. During the April 23, 2025 Silivri, İstanbul earthquake (Mw 6.2), the DAS recordings exhibit clear signal saturation, indicating that the current interrogator dynamic range is insufficient for strong ground motion. The distance of the nearest channel to the source zone of the Silivri earthquakes was less than 40 km. Magnitude estimates derived from DAS data agree well with national catalogs for moderate events, but show increasing deviations for larger magnitudes (approximately Mw ≥ 5.0), likely due to this saturation effect. In addition, the linear geometry of a single cable limits the accuracy of standalone event location.
Overall, the study demonstrates the operational feasibility and long-term stability of submarine DAS systems for real-time earthquake monitoring in the Marmara region, while also highlighting current instrumental and geometrical limitations that must be addressed for future earthquake early warning and rapid response applications.
How to cite: Coşkun, Z., Koç, B., Özgür, H. G., Aslan, K., Tunç, S., and Pınar, A.: Real-Time Earthquake Detection with a Submarine DAS Array in the Marmara Sea, Galileo conference: Fibre Optic Sensing in Geosciences, Aussois, France, 31 Aug–4 Sep 2026, GC14-FibreOptic-81, https://doi.org/10.5194/egusphere-gc14-fibreoptic-81, 2026.
Distributed Acoustic Sensing (DAS) deployed on submarine telecommunication cables provides continuous and spatially dense measurements in offshore environments where conventional instrumentation is sparse. In this study, we analyze DAS data recorded along a submarine fiber-optic cable in the eastern Marmara Sea (Türkiye), located in close proximity to the North Anatolian Fault.
The dataset consists of continuous strain-rate recordings along a ~34 km-long cable connecting the Istanbul mainland to the Princes’ Islands, which is a component of the integration of fiber-optic sensing into GONAF (Geophysical Observatory of the Northern Anatolian Fault) operated by GFZ in collaboration with the Turkish Disaster and Emergency Management Authority (AFAD). Since May 2024, passive DAS data have been continuously recorded along the marine cable in the Marmara Sea. As an initial step, we focus on understanding the cable geometry and data characteristics, including channel selection, spatial variability, and waveform behavior along the fiber.
Clear and coherent wavefields are observed for multiple events, allowing the tracking of seismic wave propagation along the cable over tens of kilometers. Variations between cable segments indicate differences in coupling conditions and local recording characteristics.
Possible saturation effects are currently being investigated in the analyzed recordings. So far, no obvious signal clipping has been observed within the current data range, although further quantitative analysis and recordings of stronger ground motion are required to better evaluate the dynamic response of the system.
These first observations highlight the potential of submarine DAS for offshore seismic monitoring and provide a basis for future studies focusing on earthquake detection, characterization, and integration with existing seismic networks.
How to cite: Coşkun, Z., Martínez-Garzón, P., Rodríguez Tribaldos, V., Pinzon-Rincon, L., Hillmann, L., Feyiz Kartal, R., Kılıç, T., Kadirioğlu, F. T., Krawczyk, C., and Bohnhoff, M.: Offshore DAS Observations from the Marmara Sea: First Insights from a Submarine Fiber-Optic Cable, Galileo conference: Fibre Optic Sensing in Geosciences, Aussois, France, 31 Aug–4 Sep 2026, GC14-FibreOptic-80, https://doi.org/10.5194/egusphere-gc14-fibreoptic-80, 2026.
Distributed Acoustic Sensing (DAS) is emerging as a powerful tool for seismic monitoring, enabling dense spatial sampling of seismic wavefields in offshore environments where conventional networks are sparse. This study focuses on the THETIS submarine fiber-optic cable operated by Vodafone, connecting the islands of Santorini and Kos across a tectonically and volcanically active sector of the South Aegean. The cable spans ~150 km, mostly parallel with major offshore fault systems and the Santorini volcanic complex. The study is particularly relevant in light of the ongoing volcano-seismic crisis in the Santorini–Amorgos region, reflecting coupled tectonic and magmatic processes. Since November 2025, continuous DAS recordings have captured abundant local seismicity, including numerous events originating from the Anydros–Anafi basin and from offshore and onshore Santorini island. The fiber geometry intersects several submarine faults, which produce distinct and coherent signatures in DAS earthquake record sections.
In addition to retrospective analysis, the DAS system is used operationally for real-time monitoring through edge computing at the remote interrogator site, combining ML-based earthquake detection with fiber-optic geodesy based on low-frequency distributed acoustic sensing (LFDAS). This enables continuous on-site detection and tracking of earthquakes and possible strain-induced intrusion activity with minimal latency during evolving volcano-tectonic crises. A subset of decimated channels (~100 virtual stations) is streamed in real time to the National Observatory of Athens to support operational monitoring. We present preliminary results on data quality, earthquake detectability, machine-learning-based phase picking and location, and real-time DAS monitoring performance, highlighting the potential of DAS to improve monitoring capabilities and to provide new insights into fault structures and ongoing geodynamic processes in the region.
How to cite: Evangelidis, C. P., Li, J., Fountoulakis, I., Liao, H., Kamalov, V., and Skyvalos, N.: Monitoring Earthquakes and Magmatic Intrusions in the Santorini–Amorgos Region Using Offshore DAS, Galileo conference: Fibre Optic Sensing in Geosciences, Aussois, France, 31 Aug–4 Sep 2026, GC14-FibreOptic-88, https://doi.org/10.5194/egusphere-gc14-fibreoptic-88, 2026.
Recent volcanic activity in Iceland has attracted significant attention, threatening populations on the Reykjanes Peninsula and necessitating enhanced monitoring efforts. In this context, the use of Distributed Acoustic Sensing (DAS) in Iceland has significantly increased.
In this work, we focus our attention on a DAS dataset collected during the 2021 Geldingadalir eruption, which took place in the Fagradalsfjall volcanic system. Our goal is to explore how DAS can be integrated into a seismic network to detect and locate volcanic tremor. To do so, we analyze the spatial coherence of both DAS and seismic stations records using CovSeisNet (Seydoux et al. 2016, Soubestre et al. 2019).
First, we investigate the integration of DAS data into a seismic network for tremor source location. To this end, we quantify how differences between instruments and their associated measured physical quantities may affect the recorded phase. Specifically, when comparing a broadband seismometer measuring velocity with a DAS system measuring strain rate, the spatial derivative along the fiber axis induces a phase lag of pi/2 between the two sensors. We find that this is not the primary source of uncertainty in our current network configuration. We also observe that combining a dense DAS array (8 channels) located on one side of the volcano with only a few stations surrounding it results in a suboptimal network geometry. To address this, we explore scaling schemes designed to balance the relative contributions of each sensor and/or sensor pair. These approaches prove highly effective in reducing location uncertainty.
Across the experiment, we observe an increase in spatial coherence at low frequency (0.1–1 Hz) that could have been interpreted as a signal. However, we could not find it in the seismic network and after changing the interrogator parameter it disappeared. We show that this band of more coherent energy can be explained by an interplay between array geometry artifacts in the coherence calculation (array aperture vs. wavelength) and the DAS self-noise.
Overall, our work demonstrates that DAS technology can be effectively integrated into seismic networks, and provides approaches to manage the high measurement density inherent to DAS while distinguishing coherency caused by the interrogator.
How to cite: Fontaine, O., Fichtner, A., Seydoux, L., Klaasen, S., Soubestre, J., Jónsdóttir, K., and Caudron, C.: FagraDASfjall: Toward the integration of DAS into seismic networks for volcano monitoring, Galileo conference: Fibre Optic Sensing in Geosciences, Aussois, France, 31 Aug–4 Sep 2026, GC14-FibreOptic-47, https://doi.org/10.5194/egusphere-gc14-fibreoptic-47, 2026.
Distributed Acoustic Sensing (DAS) systems generate continuous high-resolution measurements along fibre-optic cables, enabling persistent monitoring of marine environments, vessel traffic, and subsea infrastructure. However, the extreme data volumes produced by modern DAS interrogators, sometimes several terabytes per day, pose major challenges for long-term storage, data transmission, and real-time analysis. This study investigates the use of a convolutional autoencoder (CAE) for near-real-time anomaly detection and intelligent data reduction on submarine DAS cables, with a focus on on-edge deployment for operational monitoring systems.
Using data from the Great Belt submarine fibre-optic cable (7250 channels sampled at 800 Hz and a spatial resolution of 4.085m), we trained a lightweight convolutional autoencoders on normal background behaviour to identify anomalous acoustic events through reconstruction error analysis. The idea is that the model learns the normal representation of the data, i.e. background noise and uses that knowledge to detect signals not normally present in the data – transient or non-stationary signals. Two preprocessing approaches were evaluated: a signed logarithmic compression (log1p) and a robust per-channel z-score normalization based on the median absolute deviation (MAD). While both approaches successfully detected signals generated by vessel activity, vehicles and transient acoustic events, they exhibited complementary behaviour. The log1p normalization produced highly sensitive automated detections with stable thresholds, whereas the robust z-score normalization preserved stronger visual contrast for human interpretation of anomalies.
The proposed framework is specifically designed for deployment directly at the acquisition site or on-edge hardware at the DAS interrogator. Instead of storing continuous raw strain-rate data, only anomaly signals, metadata, and selected event segments are stored and saved. This enables a substantial reduction in storage requirements from several terabytes per day to approximately 50 GB/day, depending on cable environment and vessel traffic density. Heavily trafficked marine areas naturally produce larger event volumes, whereas quieter cables yield significantly lower storage demands. The signals are further fused through morphological processing, making the signals more coherent and easier to extract.
The results demonstrate that CAE-based AI models can provide reliable near-real-time detection of signals of interest while dramatically reducing data storage demand. Such approaches represent an important step toward scalable operational DAS systems capable of continuous long-duration monitoring without the need for extensive storage infrastructure. The work highlights the potential of combining unsupervised learning, adaptive thresholding, and on-edge computing to enable practical real-time DAS monitoring for marine surveillance, infrastructure monitoring, and environmental sensing applications.
However, some signal types, particularly low-frequency seismic events and ocean wakes, remain difficult to detect and extract using the proposed method, as the CAE is less sensitive to slowly varying temporal features and long-duration low-frequency signals. A possible improvement would be to incorporate explicit frequency-domain information or multi-scale temporal feature extraction into the AI model. This is left for future work
How to cite: Bülow Pedersen, H., Hylsberg Jacobsen, G., Heiselberg, P., Heiselberg, H., and Aalling Sørensen, K.: Towards Near-Real-Time Anomaly Detection in Distributed Acoustic Sensing Using Convolutional Autoencoders, Galileo conference: Fibre Optic Sensing in Geosciences, Aussois, France, 31 Aug–4 Sep 2026, GC14-FibreOptic-98, https://doi.org/10.5194/egusphere-gc14-fibreoptic-98, 2026.
This work introduces a deep learning framework based on recurrent neural networks (RNNs) developed for real-time recognition of volcano-seismic signals from distributed acoustic sensing (DAS) data. The model was developed using a large dataset of volcano-tectonic events associated with the 2021 La Palma eruption, captured by a high-resolution submarine DAS array deployed close to the volcanic source. For training phase, we employed features derived from the signal energy across different frequency bands and spatial points, allowing the model to effectively exploit both spatial and temporal patterns inherent in seismo-volcanic signals. The proposed approach is capable not only of detecting volcano-tectonic events but also of characterizing their temporal behavior, identifying and classifying complete waveforms with an accuracy close to 97%. In addition, the model exhibits strong generalization capabilities across different time periods and volcanic settings. The results showed fast and automatic analysis with relatively low computational cost and limited retraining, enabling continuous real-time seismic monitoring and supporting the automatic generation of labeled seismic catalogs directly from DAS data, representing a significant step forward in the application of DAS technology for studying active volcanoes and their seismic activity.
How to cite: Fernández-Caravamtes, J., Titos Luzón, M. M., D'Auria, L., García, J., García, L., and Benítez, C.: A Recurrent Neural Network Approach for Real-Time Detection and Monitoring of Volcano-Tectonic Events Using DAS, Galileo conference: Fibre Optic Sensing in Geosciences, Aussois, France, 31 Aug–4 Sep 2026, GC14-FibreOptic-20, https://doi.org/10.5194/egusphere-gc14-fibreoptic-20, 2026.
Distributed Acoustic Sensing (DAS) provides several benefits over conventional seismic station sensing. DAS offers long spatial coverage at high resolution: approximately every 10 meters of a kilometric optical fiber can act as a seismic sensor. Furthermore, the elements necessary for DAS sensing require very little maintenance. This sort of sensing generates large volumes of data with high spatial correlation, bringing opportunities for more precise exploratory analysis, monitoring and knowledge generation; as well as new data analysis challenges.
In previous works [1], we have studied the data collected by conventional seismic stations in the Canary Islands, Spain; including data from the volcanic eruption of La Palma of 2021 (see Fig. 1). These studies employed data fusion techniques as well as Multivariate Statistic (MS) methods like oMEDA [2] and PCA-MSPC [3] to understand the combined data of several spatially distributed stations. This approach let us develop an interpretable real time monitoring system able to not only detect anomalies, but also trace them to their source in the original data, locating them in time and space
This archipelago also bears a submarine DAS fibre infrastructure deployed between the islands of La Palma and Tenerife (see Fig. 2). In this contribution, we expand the MS framework developed for seismic station data to a DAS data framework.
The development of this framework has the potential to significantly increase the risk monitoring capabilities of DAS infrastructures in areas with high seismic and volcanic activity, which can be instrumental for public safety during times of crisis. Thanks to the interpretability provided by MS methods, specialists would be able to assess the anomalies detected by a monitoring system, distinguishing real threats from false alarms. This approach leaves the agency and decision making to the specialists, who don’t need to trust a warning system blindly.
As a result of this work, we will generate an expertise in understanding DAS data structures for seismic data, its particular challenges, related to data size, spatial correlation and computational challenges in real time applications. Results are compared to those of conventional seismometers analyzed in previous works.
[1] García Sánchez, J., García, L., D'Auria, L., Fernández-Carabantes, J., Benítez Ortúzar, C., Camacho, J. Volcanic eruption forecast using PCA. IEEE International Geoscience and Remote Sensing Symposium 2025, Brisbane (Australia), 2025.
[2] Camacho, J. Observation-based missing data methods for exploratory data analysis to unveil the connection between observations and variables in latent subspace models. Journal of Chemometrics, 2011, 25 (11) : 592 - 600.
[3] Fuentes-García, N.M., Maciá-Fernández, G., Camacho, J. Evaluation of diagnosis methods in PCA-based Multivariate Statistical Process Control. Chemometrics and Intelligent Laboratory Systems, 2018, 172 : 194 - 210.
This work is part of project Multi-scale Spatio-Temporal Analysis of Research Data (MuSTARD,https://codas.ugr.es/mustard/en/), supported by grant no. PID2023-1523010B-IOO funded by the Agencia Estatal de Investigación in Spain, call no. MICIU/AEI/10.13039/501100011033, and by the European Regional Development Fund.
How to cite: García Sánchez, J., Benítez, C., D'Auria, L., García, L., and Camacho, J.: Multivariate Statistical analysis of DAS seismic data, Galileo conference: Fibre Optic Sensing in Geosciences, Aussois, France, 31 Aug–4 Sep 2026, GC14-FibreOptic-12, https://doi.org/10.5194/egusphere-gc14-fibreoptic-12, 2026.
Distributed Acoustic Sensing (DAS) has recently emerged in seismological monitoring, converting standard telecommunication fiber optic cables into dense linear arrays of virtual seismic sensors. This capability is particularly relevant in underground environments, where existing infrastructure can be reused.
This study presents results from a continuous experiment, conducted at the Low-Noise Underground Laboratory (LSBB, Laboratoire Souterrain à Bas Bruit, https://www.lsbb.eu/ located in Rustrel, southern France). Multiple fiber optic cables with varying characteristics and installation configurations were deployed throughout the gallery network, including cables laid directly on the ground, weighted with sandbags, sealed in concrete trenches, or structurally attached to gallery walls. These fibers were interrogated by a Febus Optics DAS A1 interrogator. A dense array of seismometers is already in place at the LSBB site, inside the galleries and also at the surface. The geometry of the seismic and the DAS networks is detailed in the Figure 1.
Figure 1 : Map of the LSBB galleries with seismic stations (red stations inside the galleries, green stations at the surface). On the right, map of the fiber optic cable deployment. We deployed two different cable types Telecom and MultiSens.
A key objective of the study is to develop and evaluate automated workflows for the detection and characterization of teleseismic and regional events recorded on DAS arrays. For development we used the Xdas[i] python library. Two complementary detection strategies were applied. The first relies on the classical STA/LTA algorithm. The detection is based on statistics of STA/LTA among all channels (see Figure 2).
The second detection strategy leverages deep-learning phase pickers. The standard PhaseNet[ii] model, applied channel by channel, and the PhaseNet-DAS[iii] model, designed to exploit the multi-channels geometry, were evaluated. The Figure 2, shows a representative regional event, a magnitude 2.6 earthquake at an epicentral distance of 147 km. PhaseNet-DAS, exploiting the spatial continuity of the array, found coherent picks collocated with the predicted P and S arrival times. Both approaches confirmed the capability of the DAS system to detect regional events at the LSBB, even at moderate magnitudes. The goal is to create a catalog detection and to develop an automatic detection workflow.
Figure 2 : Strain-rate data with PhaseNet-DAS picks (top). PhaseNet-DAS detection (middle). Seismic detection using STA/LTA (bottom). The PhaseNet-DAS detection enlightening the better detection of P waves for AI algorithms.
The results demonstrate that DAS fiber networks deployed in underground galleries can reliably detect regional seismic events. Machine-learning phase pickers improve the detection of P waves and offer a better phase discrimination compared to energy-based STA/LTA detectors.
[i] Trabattoni, A., Baillet, M., Ende, M. van den, Rivet, D., Stutzman, E., Strumia, C., & Biagioli, F. (2025). Xdas: A Python Framework for Distributed Acoustic Sensing. Seismological Research Letters. https://doi.org/10.1785/0220240366
[ii] Weiqiang Zhu, Gregory C Beroza, PhaseNet: a deep-neural-network-based seismic arrival-time picking method, Geophysical Journal International, Volume 216, Issue 1, January 2019, Pages 261–273, https://doi.org/10.1093/gji/ggy423
[iii] Zhu, W., Biondi, E., Li, J. et al. Seismic arrival-time picking on distributed acoustic sensing data using semi-supervised learning. Nat Commun 14, 8192 (2023). https://doi.org/10.1038/s41467-023-43355-3
How to cite: Brémaud, V., Sèbe, O., Lallemand, C., Chauvet, R., Nikolov, S., Decitre, J.-B., Rouillé, G., and Goyer, F.: Continuous DAS Acquisition for Seismic Event Detection in an Underground Environment, Galileo conference: Fibre Optic Sensing in Geosciences, Aussois, France, 31 Aug–4 Sep 2026, GC14-FibreOptic-28, https://doi.org/10.5194/egusphere-gc14-fibreoptic-28, 2026.
Seismic monitoring in active volcanic areas requires systems capable of providing high spatial and temporal resolution, in order to capture the complex and rapidly evolving dynamics of such environments. In this context, the reuse of existing telecommunications infrastructure through distributed acoustic sensing (DAS) technology represents a promising solution, as it transforms standard optical fibres into dense arrays of virtual sensors distributed over large distances. This approach enables continuous, high-sensitivity monitoring and significantly improves the detection capability of local seismic activity, even for low-magnitude events.
In this study, we present an integrated seismic monitoring system for the Campi Flegrei area based on the combined use of artificial intelligence (AI) and DAS technology. The system has been implemented along the 22 km Bagnoli–Bacoli route by leveraging an existing optical fibre link. The continuous data stream acquired by the DAS system is processed in real-time by a cascade of AI models, specifically designed for the automatic detection of seismic phases (P and S waves), event association and localization, as well as magnitude estimation. This multi-stage architecture allows robust and efficient analysis of large volumes of data, enabling near real-time detection and characterization of local seismicity.
The processing results are stored in a structured database and made accessible through web services, including real-time visualization interfaces for monitoring and analysis. This architecture demonstrates how the integration of existing telecommunications infrastructure, distributed sensing technologies, and advanced AI methods can provide an effective, scalable, and low-cost solution for continuous seismic monitoring in complex volcanic areas. Such systems have the potential to enhance early warning capabilities and support risk mitigation strategies in densely populated regions exposed to volcanic hazards.
How to cite: Corsaro, M., Currenti, G., Cannavò, F., Allegra, M., Prestifilippo, M., Jousset, P., Spampinato, C., and Palazzo, S.: Real-Time Seismic Monitoring Using DAS and AI, Galileo conference: Fibre Optic Sensing in Geosciences, Aussois, France, 31 Aug–4 Sep 2026, GC14-FibreOptic-93, https://doi.org/10.5194/egusphere-gc14-fibreoptic-93, 2026.
Distributed Acoustic Sensing (DAS) converts fiber-optic cables into continuous seismic sensors, producing measurements at rates exceeding hundreds of megabytes per second. Conventional workflows designed for periodic analysis introduce a latency incompatible with real-time monitoring: events remain invisible until the next scheduled processing window completes, often hours or days after occurrence. Furthermore, this process is brittle for monitoring contexts as data is processed with predefined parameters, and changing those requires manual effort. Additionally, reprocessing historic data of those volumes requires a lot of time and compute, either of which may not be readily available when the need occurs. To address those challenges, we present a modular, real-time system architecture consisting of interconnected services designed for high throughput data ingestion and processing. The system will be deployed within the POSEIMON‑I project, which focuses on extending induced seismicity monitoring in the vicinity of the Porthos CO₂ storage site. It is built around two core components - Apache Kafka for data streaming and Apache Flink for stateful, real-time processing of live data from multiple sources. Additionally, further services allow for the storage, querying and visualization of operational, processed or derived event data. Additionally, algorithms can be deployed or removed from a running system through task-based orchestration enabling the continuous development, refinement and evaluation of algorithms that can adapt to a changing environment. This system design bridges the gap between research and industry by enabling the use of industry-grade systems in scientific contexts and bringing experimentation closer to real-world deployments. Finally, the system is fully built on open-source components ensuring data and system sovereignty and albeit designed to deal with high-throughput data, characteristic for DAS, is agnostic to the domain and can be applied in any context with live data-streams.
How to cite: Valev, H. V., Visser, W. F. J., Diab‑Montero, H. A., Zhou, W., Boullenger, B., and Buijze, L.: Modular real-time streaming architecture for high-throughput DAS monitoring, Galileo conference: Fibre Optic Sensing in Geosciences, Aussois, France, 31 Aug–4 Sep 2026, GC14-FibreOptic-54, https://doi.org/10.5194/egusphere-gc14-fibreoptic-54, 2026.
Distributed Acoustic Sensing (DAS) is of critical value for the offshore expansion of seismological networks. The work presented here is part of the 5-years ERC ABYSS project, which aims at building a permanent seafloor seismic observatory leveraging offshore telecommunication cables along the central coast of Chile.
The ABYSS project near-real time data collection started the 30th of September 2023 using three ASN (Alcatel Submarine Networks) OptoDAS units to sense three segments over two offshore telecommunications cables connecting the cities of Concón to La Serena and La Serena to Caldera. The DAS data covers over 500 km of cable, comprising 26,664 virtual sensors sampled at 62.5 Hz and 100 Hz. These data are synchronized once a day with a storage server located in France, the volume of which is anticipated to reach an estimated 608 TB by the end of the project. We developed an automatic workflow to detect an average of 100 daily local, regional and teleseismic events with magnitudes down to ML = 0.5, over 59 GB of data per day after compression.
As a first step, we perform automatic seismic phase arrival picking using PhaseNet pretrained on conventional seismological stations, followed by phase association with GaMMA. We then apply a correction of the phase picks to account for shallow sedimentary layers and invert for the event hypocenters with NonLinLoc software. Finally, we estimate the Richter local magnitude based on peak ground displacements. The results show that DAS data combined with data from the national onland seismic network greatly increases the accuracy of the earthquake hypocenters. Once the earthquake catalogs are built, we can perform a relative relocation of the earthquakes with HypoDD software using cross-correlation and/or the catalog results. With this workflow we show that conventional tools used in seismology can be used on DAS data with few adjustments. Furthermore, the size of our catalog, enriched with numerous undetected offshore events is a significant improvement over the existing regional catalogs, which may aid future studies of the Chilean margin subduction zone seismicity.
How to cite: Baillet, M., Trabattoni, A., Ambrois, D., Chèze, J., Peix, F., van den Ende, M., Vernet, C., and Rivet, D.: A workflow for building an automatic earthquake catalog from DAS data recorded on offshore telecommunications cable in central Chile, Galileo conference: Fibre Optic Sensing in Geosciences, Aussois, France, 31 Aug–4 Sep 2026, GC14-FibreOptic-66, https://doi.org/10.5194/egusphere-gc14-fibreoptic-66, 2026.
Distributed Acoustic Sensing (DAS) transforms fibre-optic cables into densely spaced arrays of strain sensors, providing metre-scale resolution over distances of up to hundreds of kilometres. By analysing interferometric backscatter from laser pulses, DAS measures deformation rate along the fibre, yielding single-component recordings of longitudinal strain.
In seismology, DAS supports both high-resolution imaging and seismic monitoring. Its dense sampling enables regional tomography and detailed characterization of shallow structures, while coherent travel-time data can be integrated into standard processing workflows. Phase picking can be performed using adapted artificial intelligence methods for continuous data, although earthquake location remains challenging due to cable geometry. Nevertheless, DAS observations allow for source characterization, including magnitude and source parameter estimation, and in some cases focal mechanism determination.
Beyond offline applications, DAS shows strong potential for real-time monitoring, particularly for Earthquake Early Warning (EEW). Its integration into operational systems requires tailored strategies to exploit dense spatial sampling and to address system-specific features such as directional sensitivity and strain-rate saturation.
Here, we evaluate the applicability of EEW methodologies to DAS data using three interrogators from the ABYSS network in central Chile that sense 450km of offshore cables running parallel to the subduction trench. The region, characterized by frequent moderate-to-large earthquakes, provides an ideal testbed for assessing EEW performance and the potential for rapid alerting.
We develop a real-time magnitude estimation approach suited to offshore DAS conditions, where direct P-wave signals are often weak and followed in the first seconds by stronger arrivals of secondary phases, and integrate it into the QuakeUp algorithm. Performance is assessed using M≥4 events and 60 days of continuous data to evaluate both source characterization capability and robustness to false alerts and missed detections. Our results demonstrate the feasibility of a prototype DAS-based EEW system and highlight its potential to improve response times in high-seismicity regions.
Part of this work has been performed by the Transnational Access to the GeoAzur laboratory MAREA (Magnitude estimAtion in Real TimE using DAS) supported by the EU project Geo-INQUIRE. Geo-INQUIRE is funded by the European Commission under project number 101058518 within the HORIZON-INFRA-2021-SERV-01 call.
How to cite: Strumia, C., Rivet, D., Baillet, M., Trabattoni, A., Colombelli, S., Elia, L., and Festa, G.: A Prototypal DAS-Based Earthquake Early Warning System Offshore Chile, Galileo conference: Fibre Optic Sensing in Geosciences, Aussois, France, 31 Aug–4 Sep 2026, GC14-FibreOptic-29, https://doi.org/10.5194/egusphere-gc14-fibreoptic-29, 2026.
Seismic monitoring in volcanic environments is crucial for hazard assessment and eruption forecasting. In this context, the use of Distributed Acoustic Sensing (DAS) as a complementary acquisition system to conventional seismometers represents a promising approach for seismo-volcanic event analysis and classification.
During the summer of 2019, Etna exhibited a variety of seismo-volcanic signals that were simultaneously recorded by two different acquisition systems: a broadband array of 26 sensors deployed at Piano delle Concazze and a 1.5 km-long fiber-optic cable interrogated by a DAS device installed in the Pizzi Deneri Observatory. The recorded dataset includes different types of events, such as volcano-tectonic (VT) earthquakes, as well as long-period (LP), very-long-period (VLP) events, and volcanic explosions.
In this study, we present a comparative analysis between conventional seismic data and DAS recordings, with the aim of evaluating the consistency and reliability of event classification between the two systems. While traditional seismometers offer greater accuracy in signal fidelity, DAS measurements provide high spatial resolution, allowing for detailed observation of signal variability along the fiber.
The analysis focuses on the comparison of waveform characteristics and frequency content across the two datasets.
Event classification provides key insights into underlying physical processes, such as rock fracturing and fluid migration within the volcano edifice, and enables tracking of the temporal evolution of volcanic activity, basically contributing to improved hazard assessment and monitoring strategies.
How to cite: Altana, G. C., Currenti, G., Cassisi, C., Aliotta, M., Prestifilippo, M., Pulvirenti, A., Corsaro, M., and Allegra, M.: Complementary Use of DAS and Seismometers for Seismo-Volcanic Event Classification at Mt.Etna, Galileo conference: Fibre Optic Sensing in Geosciences, Aussois, France, 31 Aug–4 Sep 2026, GC14-FibreOptic-55, https://doi.org/10.5194/egusphere-gc14-fibreoptic-55, 2026.
Distributed Acoustic Sensing (DAS) has emerged as a transformative technology in the field of geophysics. Among its notable advantages stands out the ability to leverage existing fibre-optic telecommunications infrastructure to obtain high-quality seismic recordings with unprecedented spatial and temporal resolution. This feature makes the DAS system particularly well-suited to densely populated urban areas, where the deployment of traditional seismic arrays is often hindered by prohibitive costs and logistical complexities. However, the proximity of commercial cables to human activity introduces significant challenges, as anthropogenic noise—arising from transportation, industrial, and construction activities—frequently masks target seismic-volcanic signals, severely degrading the signal-to-noise ratio.
With the purpose of cleaning the DAS signal, we propose a deep learning-based approach for denoising of DAS data. We have developed a specialized neural network architecture and an ad-hoc training strategy designed in order to remove man-induced interference while preserving the essential characteristics of the seismic-volcanic signal waveforms.
The findings demonstrate the efficacy of the proposed model in enhancing signal clarity, underscoring its potential as a robust pre-processing tool to facilitate and refine subsequent DAS signal analysis in complex, noise-rich environments.
How to cite: Allegra, M., Cannavò, F., Currenti, G., Corsaro, M., Jousset, P., Spampinato, C., and Palazzo, S.: Leveraging deep learning for denoising DAS recordings in urban volcanic areas, Galileo conference: Fibre Optic Sensing in Geosciences, Aussois, France, 31 Aug–4 Sep 2026, GC14-FibreOptic-59, https://doi.org/10.5194/egusphere-gc14-fibreoptic-59, 2026.
The Sakurajima volcano in Japan is one of the most active volcanoes in the world. The volcano has erupted in Vulcanian style since the 1950s from three craters at the summit. We started continuous DAS observations in September 2025. Two fiber cables are embedded in FEP pipes at a depth of approximately 50 cm below the road surface: one cable is deployed along the Nojiri River (ca. 4.4 km long) and another one is deployed under the road that circles the volcano island (ca. 38 km long). We record many explosion earthquakes accompanied by Vulcanian eruptions, eruption tremors associated with continuous volcanic ash emissions, harmonic tremors characterized by multiple spectral peaks, volcano-tectonic earthquakes (VTs), and regional tectonic earthquakes. In addition, we record the debris flows occurring along the Nojiri River. Because most of these volcanic earthquakes, except VTs, are characterized by unclear onsets of P- and S-waves, their source locations are determined from phase differences between nearby channels of DAS data using cross-correlation function (CCF) and complex principal component analyses. The amplitude source location (ASL) is also applied to the DAS data. As a result, explosion earthquakes, eruption tremors, and harmonic tremors were inferred to be generated at shallow depths of the active craters from the slowness determined from DAS data along the Nojiri River. Debris flows are tracked using ASL and/or phase difference data. Template matching using explosion earthquakes was applied to automatically detect Vulcanian eruptions from very small to large scales. These data analyses of the detection and source location codes are planned to be mounted on an edge computer connected to an interrogator (ONYX, SINTELA), and the results are transferred to our laboratory for real-time monitoring.
How to cite: Nishimura, T., Hirose, T., Emoto, K., Taguchi, K., and Nakamichi, H.: Monitoring of volcanic earthquakes and tremor using DAS at Sakurajima volcano, Japan, Galileo conference: Fibre Optic Sensing in Geosciences, Aussois, France, 31 Aug–4 Sep 2026, GC14-FibreOptic-60, https://doi.org/10.5194/egusphere-gc14-fibreoptic-60, 2026.
The Vega Baja del Segura region (southeastern Iberian Peninsula) is one of the areas with the highest seismic risk in Spain, as demonstrated by the destructive 1829 Torrevieja earthquake and recurrent seismic activity associated with the Eastern Betic Shear Zone. Despite this hazard, the spatial and temporal evolution of active deformation and microseismicity in the region remains poorly constrained due to the limited density of conventional seismic instrumentation. Here we present SISMOVEGA: Seismic Analysis of the Vega Baja del Segura using Fiber Optics and Laser Technology, a new initiative aimed at transforming existing telecommunication fiber-optic infrastructure into a large-scale seismic observatory using Distributed Acoustic Sensing (DAS). The project will deploy DAS technology along tens of kilometers of existing fiber-optic cables to continuously monitor seismic wavefields with high spatial resolution in a densely populated and tectonically active region. SISMOVEGA seeks to investigate whether dense DAS observations can reveal subtle spatiotemporal patterns associated with active crustal deformation and potential earthquake preparatory processes, including microseismicity, transient deformation-related signals, fault-zone responses, and changes in ambient seismic noise. One of the long-term goals of the initiative is to evaluate the potential of fiber-optic monitoring to contribute to future real-time seismic monitoring and operational earthquake forecasting frameworks capable of detecting evolving seismic unrest with unprecedented spatial detail. The project also explores the integration of DAS observations with complementary geophysical and environmental datasets to better characterize seismic signals in urban and agricultural environments. Beyond its scientific objectives, SISMOVEGA incorporates a strong immersive outreach and science communication component designed to use fiber-optic sensing and earthquake monitoring as a platform to engage local communities, schools, and young students with Earth science and natural hazards. The initiative includes the development of interactive educational activities and field-based experiences aimed at increasing awareness of seismic risk while illustrating how emerging sensing technologies can help better understand and monitor the dynamic Earth. By combining cutting-edge DAS monitoring with community-oriented engagement, SISMOVEGA aims to establish a long-term open platform for collaborative fiber-optic seismology in southeastern Spain while strengthening the connection between geophysical research, education, and society.
How to cite: Girona, T., Expósito, L., Geyer Traver, A., González Hernández, M. Á., Liboreiro Rodríguez, A., Manzanera Quiles, S., Pic Cuartero, J. Á., Schamuells, N., Sánchez Pardo, N., and Vedia García, V.: SISMOVEGA: From the 1829 Torrevieja Earthquake to Real-Time Fiber-Optic Monitoring in the Vega Baja del Segura Region (SE Spain), Galileo conference: Fibre Optic Sensing in Geosciences, Aussois, France, 31 Aug–4 Sep 2026, GC14-FibreOptic-83, https://doi.org/10.5194/egusphere-gc14-fibreoptic-83, 2026.
Indonesia faces a unique combination of exposure to tsunamis, with dense coastal populations, intense seismic and volcanic activity, and multiple tsunami-generation mechanisms that can threaten shorelines with very little time for reaction.
BMKG which is in charge of monitoring tsunamis faces 2 limitations: First, the operational system still relies mainly on land-based seismology, coastal sea-level observations, and precomputed modelling, while the availability and operability of deep-ocean buoy observations have remained an ongoing issue. Second, the existing system was designed for tectonic earthquake-generated tsunamis and is not sufficient on its own for non-tectonic sources such as volcanic or landslide-triggered events.
To address these limitations, BPPT (subsequently integrated into BRIN in 2021), initiated the planning, design, and deployment of two prototype Ocean Bottom Unit (OBU) sensors between 2020 and 2022. The deployed units integrate high-sensitivity accelerometers and static pressure sensors to enable real-time monitoring of seismic activity and tsunami wave propagation in the northern waters of Labuan Bajo, Flores.
The first OBU was installed approximately 25 km offshore at a depth of 2,110 m, whereas the second unit, positioned at the terminal section of the submarine cable, was deployed approximately 55 km offshore at a depth of 4,122 m. Both installations were strategically designed to monitor seismic activity associated with the Flores Thrust, located roughly 100 km north of Flores Island. Historical earthquakes along this tectonic structure, together with the submarine landslides they triggered, generated destructive tsunamis in 1982 and 1992, resulting in significant casualties and coastal run-up heights exceeding 20 m.
The deployed submarine cable system has a total length of 55 km and incorporates 12 optical fibres, providing the necessary infrastructure for integrated sensing and real-time data transmission. At the end of April 2026, we connected a Febus Optics A1 DAS interrogator to one of the fibres for a preliminary study of local and regional seismicity. Our objectives with this initial deployment are to catalogue and characterise local seismicity, locate sources, and investigate improvements in the location of local earthquakes by using a combination of land-based seismic stations and the undersea optical cable.
How to cite: Metaxian, J.-P., Purwoadi, M., Rahardjo, S., Riama, N. F., Gunawan, I., Dewi, M. F., Trabattoni, A., Biagioli, F., Rasuanta, M. P., Firdaus, Y., Heryandoko, N., Chauvet, R., and Duret, F.: Distributed Acoustic Sensing for Seismic Monitoring of the Flores Thrust, Indonesia, Galileo conference: Fibre Optic Sensing in Geosciences, Aussois, France, 31 Aug–4 Sep 2026, GC14-FibreOptic-102, https://doi.org/10.5194/egusphere-gc14-fibreoptic-102, 2026.
