This work delivers an in-depth bibliometric analysis of Distributed Acoustic Sensing (DAS) research within the realm of geophysics, covering the period from 2012 to 2022 and drawing on data from the Web of Science. By employing bibliographic and structured network analysis methods, including the use of VOSviewer®, the study highlights the most influential scholars, leading institutions, and pivotal research contributions that have significantly shaped the field of DAS in geophysics. The research delves into key collaborative dynamics, unraveling them through co-authorship network analysis, and delves into thematic developments and trajectories via comprehensive co-citation and keyword co-occurrence network analyses. These analyses elucidate the most robust and prominent areas within DAS research. A critical insight gained from this study is the rise of 'Photonic Seismology' as an emerging interdisciplinary domain, exemplifying the fusion of photonic sensing techniques with seismic science. The paper also discuss certain limitations inherent in the study, and concludes with implications for future research.

How to cite: Cheng, F., Zhao, K., Zhao, L., and Ajo-Franklin, J.: Photonic Seismology: A New Decade of Distributed Acoustic Sensing in Geophysics from 2012 to 2022, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2514, https://doi.org/10.5194/egusphere-egu24-2514, 2024.

The reliable estimation of earthquake magnitude and stress drop are key in seismology. The novel technology of distributed acoustic sensing (DAS) holds great promise for source parameter inversion owing to the measurements' high spatial density. Here, I demonstrate the robustness of DAS for magnitude and stress drop estimation using the empirical Green's function deconvolution method. This method is applied to nine co-located earthquakes recorded in Israel following the 2023 Turkey earthquakes. Spectral ratios were stacked along the fiber, and fitted with a relative Boatwright source spectral model. Excellent fits were obtained even for similar sized earthquakes. Stable seismic moments and stress drops were calculated assuming the moment of one earthquake is known. DAS derived estimates were found to be more stable and reliable than those obtained using a dense accelerometer network. The results demonstrate the great potential of DAS for source studies.

How to cite: Lior, I.: Accurate Magnitude and Stress Drop Using the Spectral Ratios Method Applied to Distributed Acoustic Sensing, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3214, https://doi.org/10.5194/egusphere-egu24-3214, 2024.

Distributed Acoustic Sensing (DAS) is establishing as a promising technique in Seismology. This novel system turns a fibre optic cable into a continuous single component array with very dense spatial sampling. Simplicity of installation and availability of telecommunication cables (dark fibres) make the technique very advantageous for investigating harsh environments like seafloors, sensing up to hundreds of kilometres of fibre with fine spatial resolution. Given the high potential, the technology has been successfully tested in recent years for several earthquake monitoring tasks, such as location, subsurface characterization, focal mechanism determination, tomography, or source back projection. 
The transferability of standard seismological tools to DAS data is straightforward when working with time picking, while analysis of the amplitude content of the signal demands further research. This is the case of earthquake source characterization, where standard approaches require conversion of strain rate data into more classical kinematic quantities (i.e. acceleration or velocity). In this work we develop a new formulation that allows to estimate source parameters without the need for conversion. We start from the description of the far-field strain radiation emitted from a circular seismic rupture, showing that the time integral of the strain is related to the Source Time Function. Using this quantity, we develop the spectral modelling allowing for frequency domain inversion of DAS data for estimation of moment magnitude and corner frequency. The formulation accounts for the unique azimuthal sensitivity of the cable in the radiation pattern average, and explicitly shows DAS enhanced sensitivity to slow scattered waves propagating beneath the fibre.
We validated the proposed approach on two case-studies, for events in local magnitude range  0.4 - 4.3, comparing the results with estimates from standard seismic instruments. Earthquakes recorded on a 150km long cable offshore the coast of central Chile during a 1-month DAS survey exhibit scale invariant stress drops, with an average of Δσ=(0.8±0.6)MPa. Also, moment magnitude estimates agree with results from standard seismic instruments. The analysis of small magnitude events (M_L<2.5) recorded on a 1km long fiber during a 5-month DAS survey in the Italian southern Appenines shows agreement of moment magnitude estimates when compared with local seismic network estimations. Nevertheless, site effects dominate the high frequency domain resulting in an apparent corner frequency around 5Hz and masking the actual event size. An appropriate modelling of site effects using a parametric EGF approach was thus adopted to estimate corner frequencies for the highest magnitude events in the catalogue. 
This study shows the possibility to work with raw DAS data to retrieve information about earthquake source and highlights the high potential of these systems in characterizing the seismic moment and the size of earthquakes in a wide magnitude range.

How to cite: Strumia, C., Trabattoni, A., Supino, M., Baillet, M., Rivet, D., and Festa, G.: A new formulation for source parameters estimation from DAS native strain data., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9611, https://doi.org/10.5194/egusphere-egu24-9611, 2024.

Distributed Acoustic Sensing (DAS) along seafloor fiber optic cables offers high-density and wide-aperture seismic data close to seismic sources, at a lower cost than conventional cabled ocean bottom seismic networks. It is thus a very promising approach to develop offshore monitoring systems for hazard mitigation and to obtain deeper insights into earthquake mechanics. We introduce a workflow for back-projection earthquake rupture imaging based on ocean bottom DAS data off the Chilean coast, taking full advantage of DAS data features to greatly refine the resolution and accuracy of source parameter estimation of local earthquakes. 

The workflow includes a number of steps that improve the back-projection performance. To reduce the negative effects of wave scattering on waveform coherence, we apply spatial integration to convert DAS strains into displacements. We refine travel time accuracy through shallow-sediment time corrections. We apply array processing on multiple overlapping cable segments (sub-arrays) to get the apparent slowness. The information from all sub-arrays is used jointly to locate the earthquakes using a 1D local velocity model.

Through systematic synthetic tests, utilizing the 120-km-long cable configuration off the coast of Chile, we identified a ‘high-precision, high-resolution source region”, which is also less sensitive to uncertainties of the velocity structure. This region extends to about 80 km laterally from the cable and reaches depths of up to 15 km, a range likely attributable to optimal signal focusing from various angles and that can be extended by increasing the cable length. We apply our method to data of roughly 50 local earthquakes with magnitudes from 1.5 to 3. We consistently obtain sharp back-projection images with high spatial accuracy, within 1 to 4 km, for earthquakes occurring within this defined region. Such precision is comparable to the location uncertainties of the seismic catalog.

The true strength of our approach is its potential for imaging the rupture process of larger earthquakes. We apply our method to the synthetic waveforms of a magnitude 7 earthquake constructed from multiple empirical Green's functions. We demonstrate that strong coda waves do not compromise the precise detection and location of subsequent sub-sources, if we apply a travel time calibration. The rupture speeds and locations of sub-sources are accurately recovered, even for concurrent multiple sources. We are currently improving the calibration of travel times to increase the location accuracy and resolution. These include waveform alignment with static calibration, 3D velocity model travel time tables, and slowness bias measurements and calibrations for each source-subarray pair. Collectively, these methods will increase the resolution and accuracy of our method, along with more sophisticated back-propagation methods for individual arrays. Our work holds promise for the development of earthquake and tsunami early warning, provided that we can effectively address the issue of amplitude saturation of DAS data.

How to cite: Xie, Y., Ampuero, J.-P., van den Ende, M., Trabattoni, A., Baillet, M., and Rivet, D.: Towards back-projection earthquake rupture imaging with ocean bottom distributed acoustic sensing data , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6610, https://doi.org/10.5194/egusphere-egu24-6610, 2024.

The use of optical fibre cables to sense ground motion is one of the most researched topics in seismology at present day. By using the technique of Distributed Acoustic Sensing (DAS), a single fibre can be turned into thousands of seismic sensors, providing unprecedented spatial resolution. The instrument response of optical fibre cables, however, is largely unknown and difficult to separate from source, path, and directivity effects on seismic records, preventing us from using the information from the full seismic waveform.

Here we present a full-waveform simulation scheme developed to model the DAS instrument response using a particle-based Elastic Lattice Model (ELM-DAS). The scheme allows us to simulate a virtual cable embedded in the medium and made of a string of connected particles. By measuring the strain along these particles, we are able to replicate the axial strain natively measured by DAS as well as the effects of irregular cable geometries. The scheme allows us to easily simulate complex properties of the material around the cable (e.g., unconsolidated sediments, nonlinear materials) as well as different degrees of cable-ground coupling, both of which are believed to be the key factors controlling the DAS instrument response.

By simulating DAS cables in 2D, we observe that at the meter scale, realistic DAS materials, cable-ground coupling, and the presence of unconsolidated trench materials around it dramatically affect wave propagation, each change affecting the synthetic DAS record, with differences exceeding at times the magnitude of the recorded signal. By expanding the scheme to 3D, we can accurately include the effects of realistic, complex–and at times sub-wavelength—cable geometries and how they influence DAS records. Our observations show that cable coupling and local site effects have to be considered both when designing a DAS deployment and analysing its data when either true or along-cable relative amplitudes are considered.

How to cite: Celli, N. L., Bean, C. J., and O'Brien, G. S.: Full-waveform modelling of coupling and site effects for DAS cables, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6263, https://doi.org/10.5194/egusphere-egu24-6263, 2024.

Microseismic source processes can be closely monitored during hydraulic stimulations with optical fiber deployed behind borehole casing, using Distributed Acoustic Sensing (DAS). The Bedretto Underground Laboratory for the Geosciences and Geoenergies (BULGG) provides a test site at the scale of hundreds of meters (meso-scale), where multiple boreholes are instrumented with fibers around a stimulation well. This enables the characterization of source properties of induced seismicity thanks to the dense sampling of the wavefield close to the stimulated region.

In 2023 various stimulation activities in the BedrettoLab produced M<-1 events that were recorded on three fibers surrounding the stimulated region. The interrogated fibers are running through the stimulated seismicity zone, and surround the majority of the events. Some of these events are recorded with high coherency and signal-to-noise ratio, making them suitable for further source characterization, such as location and moment tensor inversion. These events were at the same time recorded with other co-located point sensors such as geophones and acoustic emission sensors, which enables comparison to other instruments.

In this work, we select and process a subset of events with clear DAS recordings, and invert for their location, source time and moment tensor components using an adjoint inversion method. This includes computing the full forward and adjoint wavefield and gradient using a spectral-element solver. Using the full waveforms to invert for these events greatly improves the resolution of the source estimates, allows for incorporation of the full velocity model, and only two simulations per iteration are required: a forward and adjoint simulation, and gradient computation. The receiver coverage of the focal spheres by the surrounding optical fibers is an excellent test bed for the method, and the simulated domain remains on the order of hundreds of meters, which means that the simulation can be pushed to high frequencies (>100 Hz). This study provides a step forward to monitoring microseismicity in hydraulic stimulations with fiber-optic measurements.

How to cite: Tuinstra, K., Lanza, F., Noe, S., Fichtner, A., Rinaldi, A. P., Edme, P., Rosskopf, M., Obermann, A., Hertrich, M., Maurer, H., Giardini, D., and Wiemer, S. and the Bedretto Team: Adjoint-Source Inversion of Microseismic Sources with DAS in Boreholes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11389, https://doi.org/10.5194/egusphere-egu24-11389, 2024.

Distributed Acoustic Sensing (DAS) is a technology that enables continuous, realtime measurements along the entire length of a fiber optic cable. The low-frequency band of DAS can be used to analyze hydraulic fracture geometry and growth. In this study, the low-frequency strain waterfall plots with their corresponding pumping curves were analyzed to obtain information on fracture azimuth, propagation speed, number of fractures created in each stage, and re-stimulation of pre-existing fractures. We also use a simple geomechanical model, described in full detail in Ortega Perez and Van der Baan (Geophysics, 2024), to predict fracture growth rates while accounting for changes in treatment parameters. As expected, the hydraulic fractures principally propagate perpendicular to the treated well, that is, parallel to the direction of maximum horizontal stress. During many stages, multiple frac hits are visible indicating that multiple parallel fractures are created and/or re-opened. Secondary fractures deviate towards the heel of the well, likely due to the cumulative stress shadow caused by previous and current stages. The presence of heart-shaped tips reveals that some stress and/or material barrier is overcome by the hydraulic fracture. The lobes of the heart are best explained by the shear stresses at 45-degree angles from the fracture tip instead of the tensile stresses directly ahead of the tip. Antennas ahead of the fracture hits indicate the re-opening of pre-existing fractures. Tails in the waterfall plots provide information on the continued opening, closing, and interaction of the hydraulic fractures within the fracture domain and stage domain corridors. Analysis of the low-frequency DAS plots thus provides in-depth insights into the rock deformation and rock-fluid interaction processes occurring close to the observation well.

How to cite: van der Baan, M. and Ortega Perez, A.: Interpretation of low-frequency DAS data acquired during hydraulic fracturing treatments based on geomechanical models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13428, https://doi.org/10.5194/egusphere-egu24-13428, 2024.

In recent years, there has been an increasing interest in Distributed Acoustic Sensing (DAS) technology for microseismic monitoring, especially in operations involving borehole installations. Despite the widespread adoption of DAS systems in such contexts, many questions regarding the data quality of the recordings are still open. Is the DAS self-noise higher than traditional systems? How does the ambient noise recorded by a DAS system attenuate with the depth as observed with traditional geophones? It is known that various noise types, including optical, thermal, and mechanical noise coupled with the fiber, affect DAS data. Additionally, the noise frequency band often overlaps with the signal frequency band, making frequency filtering alone inadequate for denoising. Therefore, specialized noise reduction workflows, such as FK Filtering and SVD, are necessary. Mitigating the impact of noise on DAS data remains a primary challenge for the seismological and geophysical community. This study aims to examine and characterize the noise influencing DAS data collected in borehole installations, with a specific focus on the data recorded at the Frontier Observatory for Research in Geothermal Energy site in Utah, USA. We use Power Spectral Density analysis to assess depth-dependent noise reduction and its temporal variations. Furthermore, the depth dependence of the signal-to-noise ratio for various microseismic events is evaluated. Finally, a comparison is drawn with geophones data colocated with the fiber, offering a comprehensive exploration of the advantages and disadvantages of the two data acquisition technologies.

How to cite: Pecci, D., Cesca, S., Rapagnani, G., Gaviano, S., Bocchini, G. M., Carelli, G., Stucchi, E., Iannelli, R., and Grigoli, F.: Noise analysis of Distributed Acoustic Sensing (DAS) systems in borehole installations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5900, https://doi.org/10.5194/egusphere-egu24-5900, 2024.

FORGE (Frontier Observatory for Research in Geothermal Energy) is an underground field laboratory located in Utah, western United States, and sponsored by the US Department of Energy. The FORGE site is situated above a young, hot granitoid formation. The site has been used as a testbed for institutions interested in enhanced geothermal systems. As part of recent research work conducted at the site, a new deviated well was drilled adjacent to a previously stimulated well. Both wells were drilled into the granite volume. Circulation tests were performed over a period of approximately two days following completion of the second well. The circulation tests were monitored with two distributed optical sensing systems. One of the systems was recording in the new well and included measurements of distributed acoustics (DAS), temperature (DTS) and strain (DSS). The other system recorded from a preexisting well located about 500 m away and consisted of DAS measurements in a vertical cable.

Over the duration of the circulation tests, the two DAS systems detected on the order of 250 microseismic events in the close vicinity of the new well. A preliminary analysis of this group of events in terms of their hierarchical clustering allowed the selection of a small subset of them, which was used for the joint inversion of their absolute location and a layered velocity model for the site. The two-well DAS data of some of the events with absolute location was then used as a reference to estimate relative locations for the rest of the event’s catalogue. The estimated relative locations display a SW-NE alignment approximately perpendicular to the orientation of the new deviated well within the depth range of about 2300m to 2500m measured from the ground level. The location uncertainties more often show longer horizontal elongations suggesting a better depth constraint compared to the epicentral locations.

DTS data was analyzed in conjunction with slow strain data derived from the DAS data. Following the circulation tests, a ramp-like feature was observed beginning at approximate 1500m migrating upward throughout time in the slow strain data potentially indicating preferential fluid flow at this depth. Slow strain data is inextricably tied to temperature due to the thermal elongation of fiber, and as such, a thermal anomaly with similar characteristics is also observed at this depth. However, similar warming trends exist at various intervals throughout the well without the accompanied slow strain response indicating a truly anomalous interval that features unique DTS and slow strain response.

How to cite: Stanek, F., Vera Rodriguez, I., Wolpert, J., Podrasky, D., Stork, A., Chamarczuk, M., Becker, M., and Ajo-Franklin, J.: Analysis of DAS and slow strain measurements recorded during circulation tests at the FORGE geothermal underground laboratory, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17595, https://doi.org/10.5194/egusphere-egu24-17595, 2024.

The technology of distributed fibre-optic sensors (DFOS) has developed over more than four decades, initially confined to temperature sensing, which remains a valuable tool. In the last 15 years, however, the addition of distributed vibration/acoustic sensing has vastly increased the use of DFOS in geophysical applications.

The combination of acoustic, temperature and static strain measurement has provided a deeper insight in the subterranean and subsea realms, ranging from hydrocarbon and geothermal energy production, earthquake monitoring to oceanography and glaciology. Spare or disused capacity on long-distance fibre-optic communications links has opened up many opportunities for sensing the environment, especially in oceanography. Techniques developed for oil and gas exploration and production have led to reliable methods for conveying optical fibres in the very hostile found in boreholes and this has extended the applications of DFOS to understanding tectonic movements and also to decarbonising the energy supply industry, e.g. in carbon sequestration and geothermal production.

The talk will provide a brief overview of the instrumentation used for DFOS and it will discuss some of the key applications in geophysics. It will also examine some of the untapped opportunities and how technological improvements might enable these to be realised.  

How to cite: Hartog, A.: Overview of distributed fibre-optic sensing in geophysical applications., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2827, https://doi.org/10.5194/egusphere-egu24-2827, 2024.

Optical fiber networks, commonly known for data communications, could be extended beyond their conventional use. In our research, we propose a groundbreaking method by leveraging these existing terrestrial optical networks as wide distributed array sensors for earthquakes early detection. This approach is centered around the use of light polarization changes within the fiber cable, analyzed through a robust machine learning model that provide early warning alerts upon Primary earthquake waves arrival that induce strain, and precede earthquake’s destructive Surface waves. Unlike previous methods such as Distributed Acoustic Sensing and Frequency Interferometric Techniques, our approach avoids the use of expensive and specialized hardware. We introduce a centralized smart grid system that exploits the network’s existing terrestrial infrastructure, yet ensure cost effective and high efficient network modeling for initiating emergency plans and earthquake countermeasures. Our initial studies started by conducting experimental tests on a deployed fiber ring in Turin, Italy, using commercial Intensity Modulated – Direct Detection transceivers and polarimeters as polarization sensing devices, yield in promising results. Additionally, we identified the Primary waves arrival for a real 4.9 magnitude earthquake struck in the Marradi region, central Italy, with a 98% accuracy rate. This achievement was the result of a python-based waveplate model empowered by machine learning algorithm.  

Basically, when an earthquake occurs, networks nodes communicates with a centralized optical network controller that detects alterations in the light’s state of polarization by leveraging a pre-trained machine learning model. Upon the model confirmation, the controller activates early warning system in accordance with a predetermined emergency response mechanism. Building up on these findings, our current objective is to explore the impact of earthquake depth on seismic wave characteristics and their influence on light’s polarization to further investigate the potential of this advanced smart grid methodology. We aim to analyze real ground motion waves generated by two distinct earthquakes with same magnitudes but different depths. This knowledge is crucial in refining our machine learning model, which in turn will refine model prediction capabilities. Our approach promises more efficient optical network, by transforming the network into long range seismic sensors.

How to cite: Awad, H., Usmani, F., Virgillito, E., Bratovich, R., Straullu, S., Proietti, R., Pastorelli, R., and Curri, V.: Smart Grid Optical Fiber Network for Earthquake Early Warnings, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4418, https://doi.org/10.5194/egusphere-egu24-4418, 2024.

Optical fiber sensing represents a promising technology for seismological monitoring, leveraging the widespread deployment of optical networks, and representing an important opportunity for the development of early warning systems. While so far Distributed Acoustic Sensing (DAS) has been widely employed in geosciences, this technology shows some limitations, like a restricted working range, requirement of dedicated fibers and criticalities in the management of big datasets. We focus on an alternative technique, coherent interferometry relying on ultrastable lasers, which is characterized by high sensitivity, long range, and full compatibility with the existing telecommunication infrastructure. The method allows detecting perturbations induced by seismic events through the measurement of the phase accumulated by an optical signal along the fiber path. The best performances are obtained employing narrow-linewidth lasers inherited from metrological applications due to their high coherence. While the technique was initially demonstrated on subsea cables, its application to on-land fibers poses new challenges. Indeed, the phase measurement integrates all the perturbations occurring along the fiber: this means that anthropic activities, such as vehicle traffic, represent important noise sources that must be taken into account. 

We present the details of an in-field implementation over a commercial fiber deployed in a highly seismic region in central Italy and connecting two populated towns. The experimental setup employs self-heterodyne interferometry detection, utilizing a continuous wave laser stabilized to an optical cavity through the Pound-Drever-Hall technique. The laser operates within a single channel of the Dense Wavelength Division Multiplexing (DWDM) grid, sharing the fiber with standard internet services. We show the results of continuous observations performed over a period of two years. We demonstrate the detection of about one hundred earthquakes, distinguishing them from typical noise sources such as acoustic interference and infrastructure oscillations. The results include the detection of both local and distant earthquakes, demonstrating the robustness of the technique. This allowed us to characterize for the first time the sensitivity curve of the technique, described by the probability of the event detection as a function of its magnitude and epicenter distance. We also show the correlation between the source magnitude and signal spectral analysis.

In conclusion, we present an operational fiber-based earthquake observatory, highlighting the compatibility of coherent interferometry with the existing telecommunication infrastructures and its effectiveness in seismic monitoring. The results are promising for the development of scalable sensing networks utilizing the extensive optical fiber infrastructure already in place, which can conveniently integrate in real-time the data acquired with the existing networks of classical seismological sensors.

How to cite: Donadello, S., Clivati, C., Govoni, A., Margheriti, L., Vassallo, M., Brenda, D., Hovsepyan, M., Bertacco, E., Concas, R., Levi, F., Mura, A., Herrero, A., Carpentieri, F., and Calonico, D.: An Earthquake Observatory based on Coherent Interferometry over the Optical Fiber Network in Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10120, https://doi.org/10.5194/egusphere-egu24-10120, 2024.

Since almost two decades, there is a fast and steady progress in understanding the rotational part of the seismic wavefield and exploring possible applications. These achievements are based on studies using simulated data, array-derived measurements, and direct measurements of large (M > 5), teleseismic earthquakes by ringlasers. Since only a couple of years, direct measurements of smaller (M < 3) earthquakes in the local distance range are also feasible. This was made possible due to new instrumentation developments such as portable rotation sensors.

From experience with translational measurements, seismology has developed a relatively simple description of the seismic wavefield, as long as the observation is recorded in the source far-field, and site-effects at the point of observation can be neglected by choosing an appropriate frequency range for the analysis.

Within the NonDC-BoVo project two portable rotational sensors have been installed in the Vogtland/West-Bohemia earthquake swarm region with the goal to incorporate the rotational waveform data into the inversion for seismic moment tensors. Both sensors are located in an epicentral distance of ~10 km to the center of the swarm activity. So far, we have recorded 197 events with ML ≥ 1 and 6 events with ML ≥ 2.5.

Although we are positively surprised how well we can record rotational ground motion of earthquakes with even very small magnitudes, we encountered challenges in the details of the waveform recordings. At the sensor location in Landwüst (D) we recorded events down to ML ~ 0.5 with good signal-to-noise ratio in a frequency range of 5 to 25 Hz. At the second location in Skalna (CZ) the signal-to-noise ratio is worse and we recorded earthquakes only with ML ≥ 1.5. Relocating the sensor to Wernitzgrün (D), about 25 km to the North of Skalna, did not improve the quality of the waveform recordings. Technical issues with the sensor can be ruled out for both locations.

Here, we want to present details of the challenges with the rotational ground motion data from these small and local earthquake recordings. First analyses hint to a much stronger effect of local site conditions onto rotational than translational ground motion data. In addition, with the above-mentioned setting, we have to consider the complexities of the near-field part of the wavefield as well. With our contribution we aim to add another aspect to the understanding of the rotational wavefield.

How to cite: Donner, S., Lehr, J., Krüger, F., Hoffmann, M., Hobiger, M., and Heimann, S.: Challenges of Rotational Ground Motion Measurements in the Local Distance Range, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14922, https://doi.org/10.5194/egusphere-egu24-14922, 2024.

Ambient noise tomography Derived from Distributed Acoustic Sensing (DAS) deployed on existing telecommunication networks provides an opportunity to image the urban subsurface at local to regional scales and high resolution effectively with a small footprint. This capability can contribute to the assessment of the urban subsurface's potential for sustainable and safe utilization in countless applications, such as geothermal development of an area. However, extracting coherent seismic signals from the DAS ambient wavefield in urban environments remains a challenge. One obstacle is the presence of complex noise sources in urban environments, which may not be homogeneously distributed. Consequently, long-duration recordings are required to calculate high-quality virtual shot gathers, which entails significant time and computational cost.

In this study, we present the analysis of 15 days of passive DAS data recorded on a pre-existing fiber optic cable (dark fibers) running along an 11~km long major road in urban Berlin (Germany). We identify anthropogenic activities, mainly traffic noise from vehicles and trains, as the dominant seismic source and use it for ambient noise interferometry. To retrieve Virtual Shot Gathers (VSGs), we apply interferometric analysis based on the cross-correlation approach. Before stacking, we designed a selection scheme to carefully identify high-quality VSGs, which optimizes the resultant stacked VSG . Moreover, we modify the conventional ambient noise interferometry workflow by incorporating a coherence-based enhancement approach designed for wavefield data recorded with large-N arrays. We then conduct Multichannel Analysis of Surface Waves (MASW) to retrieve 1D shear-wave velocity models of the subsurface along consecutive portions of the array and validate them against local lithologic models. Finally, a 2D velocity model of the subsurface is obtained by concatenation of individual 1D velocity models from overlapping array subsections. The expansion into 2D requires an automatic identification of high-quality VSGs based on unsupervised learning, such as clustering, to exclude transient incoherent noise in the process of selective stacking.

The clustering results reveal distinct groups of VSGs that exhibit similar patterns. These distinct groups provide valuable insights into the temporal variations in human activities and allow a better understanding and interpretation of the recorded DAS ambient noise data. We find that recordings obtained predominantly during rush hour are viable for further processing and improve the accuracy of dispersion measurements, in particular for traffic-induced noise data. Moreover, the resulting 1D velocity models correspond well with available lithographic information. The modified workflow yields improved dispersion spectra, particularly in the low-frequency band (< 1 Hz) of the signal. This improvement leads to an increased investigation depth along with lower uncertainties in the inversion result. Additionally, these enhanced results were achieved using significantly less data than required using conventional processing schemes, thus opening the opportunity for reduced acquisition times and efforts.

How to cite: Ehsaninezhad, L., Wollin, C., Rodríguez Tribaldos, V., Schwarz, B., and Krawczyk, C.: Enhancing 1D and 2D passive seismic imaging of urban ambient noise DAS recordings, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7791, https://doi.org/10.5194/egusphere-egu24-7791, 2024.

Distributed Acoustic Sensing (DAS) has effectively transformed traditional telecommunication fiber-optic cables into highly efficient, dense seismic arrays. In this paper, we perform cross-correlation and stacking on the signals recorded by DAS fiber arrays in a Zig-Zag pattern, which increases the spatial coverage and improves the ability to detect and analyze various seismic waves modes. We observed that the resulting signals include both Rayleigh and Love waves. Additionally, the propagation characteristics of the wave field exhibit a Zig-Zag distribution pattern, consistent with the spatial distribution of the fibers. The challenge arises from the need to distinguish and accurately interpret the overlapping signals of Rayleigh and Love waves, which have different propagation characteristics. To address these challenges, we propose an inversion method specifically tailored for the near surface imaging with DAS array data when the fiber-optic cables are not laid in straight lines. We also conducted an on-site experiment using Zig-Zag shaped DAS arrays with known subsurface velocity model. This experiment was designed to record ambient noise signals over a week and analyze the propagation characteristics of both Rayleigh and Love waves within the surface waves captured by the DAS system. The inversion results obtained from the analysis of the recorded data showed a high degree of consistency with the ground truth subsurface structure of the test area. This demonstrates the effectiveness of the proposed method in overcoming the limitations of traditional dispersion curve-based inversion techniques, particularly in the context of non-linear fiber layouts. This research provides strong support for the practical application of dark fibers in near surface imaging, demonstrating the potential of dark fibers.

How to cite: Wu, P., Gu, C., Zhong, Y., Yuan, Z., Chen, Z., and Kang, B.: Near Surface Imaging with Zig-Zag shaped DAS Arrays Based on Cross Correlation of Ambient Noise, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16151, https://doi.org/10.5194/egusphere-egu24-16151, 2024.

The notable benefits of Distributed Acoustic Sensing (DAS) technology—high coverage, high resolution, low cost—have led to its widespread application in the geophysical domain for high-quality data recording. Among possible applications, the ability to interrogate telecommunication cables has enabled the detection of a variety of seismic-volcanic events in poorly instrumented environments, such as densely populated urban areas.

Nevertheless, the sensing of commercial fiber optic cables has to deal with the presence of anthropogenic noise that frequently corrupts the seismic signal. Indeed, vibrations induced directly or indirectly by anthropogenic activities significantly reduce the signal-to-noise ratio by masking target events.

Taking advantage of the high spatiotemporal resolution of the DAS data, a deep learning approach has been adopted for noise removal. The architecture of the neural network together with the training strategy have enabled the extraction and preservation of salient information while neglecting anthropogenic noise.

The validation on real low-frequency seismic events recorded during the 2021 Vulcano Island unrest  has provided encouraging results, demonstrating the potential of the proposed approach as a pre-processing step to facilitate subsequent DAS signal analysis.

How to cite: Allegra, M., Cannavo, F., Corsaro, M., Currenti, G., Jousset, P., Palazzo, S., Prestifilippo, M., and Spampinato, C.: Denoising DAS data in urban volcanic areas through a Deep Learning Approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10925, https://doi.org/10.5194/egusphere-egu24-10925, 2024.

For a decade, Distributed Acoustic Sensing (DAS) has become an exciting new tool for research in geophysics. However, the vast amount of data produced by hundreds of virtual sensors presents a challenge, and the lack of labeled data hampers the training of new automated classifiers based on machine learning methods from scratch. Automatic classifiers are essential tools for labeling observations across large, complex, and continuous datasets. They are commonly used in seismology to classify all the geophysical events recorded by seismic stations with a high degree of accuracy. Transfer learning is a machine learning technique where a model trained on one task is repurposed on a second related task. This method is particularly beneficial as it allows for the reuse of pre-existing, labeled datasets, significantly reducing the need for new data collection and annotation. This approach is a promising way to create efficient DAS classifiers without requiring a lot of resources. We built three different classifiers: the first classifies events based on their location, distinguishing local earthquakes from distant events; the second differentiates anthropogenic events from natural ones, specifically quarry blasts from earthquakes; and the third is capable of identifying all three types of events - local earthquakes, distant events, and anthropogenic quarry blasts - simultaneously. We used random forest models to train our classifiers using a labeled dataset of 14345 seismometer signals from the NOA network made for seismological research and nuclear monitoring. We studied the transferability of those classifiers to DAS data from a new NORSAR facility called NORFOX. This infrastructure consists of 8 km of cables located in Norway with a sampling frequency of 100Hz. Its multidirectional cable configuration makes it ideal for seismological surveys. We built a small test dataset of 543 signals using 20 channels equally distributed along the array. Our approach succeeded in reaching F1 score thresholds above 76%. We also showed, by fine-tuning feature selection according to their correlations between the seismic and DAS domains, that these results can be improved. In this way, we were able to increase the scores of our models from 5.89% to 11.36%. Several transfer learning approaches were also explored and discussed. Those encouraging results highlight the potential of a transfer learning approach to build new DAS classifiers. By leveraging historical seismometer catalogs, this approach facilitates the creation of DAS classifiers, thereby saving substantial time and avoiding the need for creating specialized datasets exclusively for DAS. Several strategies for improving score thresholds for future classifiers based on these transfer learning methods can be derived.

How to cite: Donnadille, M., Turquet, A., Hibert, C., and Richard, C.: Distributed Acoustic Sensing automated classifiers design via transfer learning for seismology, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15284, https://doi.org/10.5194/egusphere-egu24-15284, 2024.

Distributed Acoustic Sensing (DAS) exploits Rayleigh light backscattering to extract images of seismic wave propagation along a fiber optic in time and distance. The spatial distribution of virtual point sensors represents an opportunity to develop innovative methods for seismic event sources detection and identification. We develop in this study a method based on Machine Learning solutions for events classification.

This method relies on the development of features which translate the characteristics of the signals we observe into quantities that can be processed by machine learning algorithms to achieve the source classification. Three families of features investigating temporal and spatial characteristics and similarity of the signal are proposed, such as spatial and temporal analysis of the standard deviation, kurtosis or skewness of the signal or cross-correlation and dynamic time warping characterization and enables to quantify their individual contribution. Then we use a supervised machine learning model named XGBoost to perform classification based on these developed features. We tested this approach with a dataset recorded along a 91 km-long fiber optic deployed in the Pyrenees in France. The data acquisition has been achieved using a FEBUS A1-R DAS interrogator and with the support of TotalEnergies, from August 30 to September 20, 2022. During this period, 11 earthquakes and 6 quarry blasts have been recorded.

The trained model is validated using cross-validation techniques. Our Machine Learning processing chain successfully detect and classify 13 regional events from continuous background noise made by natural and anthropogenic activities. In particular, spatial features help to reduce the contribution of moving vehicles, whose presence is unavoidable along existing long-distance telecommunication fiber sections installed alongside roads.  In the continuity of this study, we investigate the potential of transfer learning from geophones deployed along the studied cable to DAS data or to another fiber optic cable installed in the same area.

How to cite: Huynh, C., Hibert, C., Jestin, C., Malet, J.-P., and Lanticq, V.: A complete feature set for classification of seismic sources with Distributed Acoustic Sensing (DAS) in the context of long-range monitoring, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17284, https://doi.org/10.5194/egusphere-egu24-17284, 2024.

The integration of state-of-the-art fibre optic sensing technologies with telecommunication systems has not yet been achieved. There are many challenges which need to be overcome to allow for a pan-continental research instrument, all of which requires international collaboration. Such international collaborations would allow for the creation of novel applications and research into Earth science, such as cetology and abiotic and biotic marine interactions, oceanography, seismology, volcanology, and soundscape monitoring, to name but a few.

Over the past 5 years, research teams from National seismic and oceanographic infrastructures, together with National Research and Education Networks (NRENs), and partners from universities, research institutes and industry have pioneered sensing techniques to use submarine optical telecommunication fibres to monitor the Earth and its systems.

Fibre sensing technology and collaborations created by developing these techniques have now reached a level where a new paradigm shift can occur. This presentation will discuss the SUBMERSE project (SUBMarinE cables for ReSearch and Exploration), which is creating and delivering a pilot research instrument which could serve as a blueprint for continuous monitoring upon many more existing submarine fibre optic cables in the future.

The SUBMERSE project, which started in May 2023, is a Horizon Europe-funded, 36-month long initiative which is investigating the combined acquisition of SOP (State-of-Polarisation) and DAS (Distributed Acoustic Sensing) data from live telecom cables, with the aim to then make that data available to researchers globally and F.A.I.R. The project team, consisting of 24 organisations, uses existing fibre-optic infrastructure deployed across multiple national research infrastructures to create a pan-European research instrument.

Our presentation will discuss the latest field deployments of DAS and SOP technologies across 5 geographic locations on 3 cable systems, which are spread across the European continent and Atlantic Ocean. It will offer a first glimpse of the effects of running a DAS in the L-and C- Bands on a live DWDM telecoms system, in combination with SOP, in a submarine and terrestrial environment.

The aim of the SUBMERSE project is to disseminate the data following FAIR principles through established community data centres. The main challenges we have faced relate to ensuring compliance to security restrictions and handling huge data quantities generated by DAS.  The approaches to down sampling, frequency filtering, and potentially time-and-space-gating will also be presented.  We will discuss the approaches taken for acquiring, streaming from remote sites, data staging, pre-processing and raw file retention. We will also highlight the tools and approaches that we have adopted to develop best practices for running such a multi-national, distributed, sensing instrument which must take these elements into account.

Our work has shown that a pragmatic approach, with collaboration at heart, is needed to address these challenges. Without a strong commitment and collaboration between research communities and research infrastructure providers, the potential to lose valuable research data is high. This risk can be mitigated by focusing on datasets which are valuable to communities and ensuring the long-term availability of those data sets in a F.A.I.R manner.

How to cite: Atherton, C. and the SUBMERSE Project: SUBMERSE: Exploring the planet with live submarine telecommunications cables., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10764, https://doi.org/10.5194/egusphere-egu24-10764, 2024.

Distributed acoustic sensing (DAS) on submarine fiber optic cables will contribute to resilient societies by significantly enhancing environment and hazard monitoring. Recent studies have emphasized applications to earthquake and tsunami early warning, volcanic eruptive events observation, and characterizing climate change effects. Widespread deployment of DAS instrumentation on the existing cable networks traversing the coastal zones and oceans can vastly expand data coverage with real-time capabilities at low cost. 

The use of DAS in existing long-haul telecommunication systems has so far been limited since most DAS interrogators rely on optical wavelengths that interfere with the existing telecom traffic. Recent developments in DAS technology enable co-existence of telecom traffic with the sensing application. Lab and field investigations have demonstrated that there is no detrimental effect from the interrogation on the transmission line performance when the DAS instrument is configured to operate at sufficiently separated wavelength channels. Thus, it is possible to utilize all existing cable networks for sensing applications in unison with continued telecom transmission without sacrificing any capacity in the link and maintaining high-quality DAS measurements.

This study describes a DAS deployment on a submarine cable system with live telecom traffic. The interrogation scheme facilitates consistent high sensing sensitivity exceeding 120 km range. The data quality and interrogator performance are quantified, and the localization and characterization for a representative set of environmental and anthropic signal sources is shown. We will describe real-time processing and detection implementations that transform such sensing enabled submarine telecom networks into measurement arrays suitable for seismic monitoring. Moreover, the technical solutions for DAS installation with existing terminal equipment and the practical aspects of data sharing will be described.

How to cite: Morten, J. P., Wienecke, S., Waagaard, O. H., Brenne, J. K., and Rønnekleiv, E.: Sensing enabled submarine telecom networks for seismology, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16375, https://doi.org/10.5194/egusphere-egu24-16375, 2024.

Distributed Acoustic Sensing (DAS) has become increasingly popular due to its capacity of extremely high spatial and temporal strain sensing over long distances. Due to the long reach of the current fiber sensing technology, and that all the electronic components are placed on land, it holds great potential in marine applications. This presentation shows how DAS can be used to observe ocean surface waves inside Kongsfjord and in the open ocean West of Svalbard.

A well-known problem when monitoring ocean phenomena is the sensor undersampling in the world’s oceans. This problem limits observations of fundamental scientific questions, like the dynamics of the oceans (Sladen et al., 2019). Fiber optic sensing has already been used to observe numerous ocean phenomena, such as tides, OSGW (Ocean-Surface-Gravity-Wave), and internal waves (Lindsey et al., 2019, Ide et al, 2021, Williams et al., 2023).

A comprehensive DAS data set is being collected at the Centre for Geophysical Forecasting’s (CGF) research lab in Ny-Ålesund, Svalbard. Data has been collected continuously since June 2022 providing the possibility of investigating oceanographic signals over a long time period in different marine environments. In this presentation, we focus on how ocean surface wave signals are influenced by local wind conditions and how the signal changes characteristics as a function of wind speed and direction.

The results from analyzing this data will be presented, and it will be demonstrated how the local wind-induced waves interact with the background swell signal which hit West Svalbard from a South-West direction. The difference between the portion of the fiber located inside Kongsfjorden will be compared to the portions in open ocean. Additionally, the obtained results will be compared to previous studies from the area (e.g., Wojtysiak et al., 2018). Finally, we use the well-known dispersion relation for OSGW to compute the associated phase velocity along the whole stretch of the fiber cable.

How to cite: Rørstadbotnen, R. A. and Landrø, M.: On the variation of ocean surface waves with wind speed and direction: A case study from offshore Svalbard, Norway, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15616, https://doi.org/10.5194/egusphere-egu24-15616, 2024.

Reliable well completion technologies are mandatory for the safe and sustainable use of subsurface reservoirs. Achieving subsurface well integrity requires displacing the entire drilling mud with homogenous cement. For this purpose, surface pump parameters (rate, density, pressure, and volume) are generally measured, yielding average values along the borehole. Here, distributed fiber-optic sensing offers new continuous monitoring options with dense spatial sampling within a borehole.

In the GFK-monitor project (https://gfk-monitor.de/en/), we investigated the primary cementation of an 874 m surface casing at a geothermal well in Munich, Germany. A 699 m long fiber optic cable, implemented by the Geothermie Allianz Bayern (GAB), was attached to the outside of the casing in the annulus between the casing and formation and cemented. This allowed for monitoring distributed dynamic strain rate (DDSS or DAS) throughout cement placement with a sampling rate of 1000 Hz and a spatial resolution of 1 m.

We analyzed the average vibration energy in the borehole by DDSS data using a root mean square approach in rolling windows for different frequency bands. Linear features with two different characteristics appear prominently in the frequency band between 0.2-0.3 Hz. One feature, which we named the “slow feature”, shows a varying slope resulting in velocities ranging from 2.5 to 6 m/s. In contrast, the accordingly called “fast feature” indicates a relatively constant velocity of around 6 m/s.

To better understand these features, a theoretical model was developed that simulates the rising velocity of fluids along the annulus. This model uses a cumulative approach and considers the cement pumping rate, borehole geometry, and timings from the daily reports. We assume, that the predicted minimum velocity is required to fill the whole breakout volume. This means that the faster the velocity the smaller the flow paths cross section.

A comparison of data and models reveals that the varying velocities of the "slow feature" correlate with velocities predicted by using the borehole geometry from the caliper log. The rather constant “fast feature” correlates instead with the predicted velocity of the model based on a uniform borehole geometry with the smallest possible radius, the drill diameter, which thus neglects all breakouts.

In summary, due to the nearly constant pumping rate during the monitoring campaign, we hypothesize that different rising velocities result from variations in the cross-sectional area of the flow path.  Based on our observations, the majority of the water spacer does not flush the borehole breakouts. With a good match to the minimum required velocity, these breakouts are filled by the first arriving cement. The following cement, with the same density, rises again on the fastest track. Thus, measuring the cement rising velocities with fiber optics and comparing them to the minimum required velocity from modeling might be a new tool to assess displacement efficiency in real-time.

How to cite: Hart, J., Polat, B., Schölderle, F., Ledig, T., Lipus, M. P., Wollin, C., Reinsch, T., and Krawzcyk, C.: A Fiber-Optic Approach for Cement Placement Monitoring of Deep Boreholes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19804, https://doi.org/10.5194/egusphere-egu24-19804, 2024.

Since 2005 the Campi Flegrei caldera (Southern Italy) has been experiencing a long-term unrest characterized by a recent increase in seismicity and ground uplift rate. In August and September 2023, the unrest showed a sudden intensification in the number and maximum magnitude of earthquakes, culminated with the occurrence of a Md 4.2 event. In an effort to strengthen the monitoring activity, two Distributed Acoustic Sensing (DAS) devices were connected from October 2023 to telecommunication fiber optic cables in the densely populated Campi Flegrei area. The DAS interrogators are set up inside two TELECOM central offices in the city center of Naples and in Bagnoli. Dynamic strain rate data are continuously acquired with a gauge length of 10 m at an average spatial sampling of 4 and 5 m. In this framework, an automated real-time workflow has been implemented comprising both DAS data downsampling and transferring to INGV data processing center. 

From the beginning of the DAS acquisition (19 October 2023), more than 300 seismic events have been recorded by the INGV local seismic network, including a Md 3.0 earthquake occurred on 23rd November 2023. Unknown cable installation conditions, poor coupling of the fiber optic cable with the ground, intense traffic and anthropogenic activities make the DAS data highly noisy and, hence, pose challenges for the application of traditional seismic data processing algorithms. In this study, we propose and apply AI based algorithms to improve and fasten earthquake detection and seismic phase picking on the large data volume associated with the high number of DAS channels.  In particular, the compared algorithms cover recently published state-of-the-art deep learning networks. We show preliminary results that demonstrate the ability of the synergy between DAS and AI to contribute to the rapid response to volcanic crises.

How to cite: Corsaro, M., Allegra, M., Cannavò, F., Currenti, G., Festa, G., Jousset, P., Pellegrino, D., Palazzo, S., Prestifilippo, M., Privitera, E., Pulvirenti, M., Ricciolino, P., Spampinato, C., Scotto di Uccio, F., and Tramelli, A.: Distributed Fiber Optic Sensing and Artificial Intelligence: preliminary results on the Campi Flegrei caldera unrest, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16414, https://doi.org/10.5194/egusphere-egu24-16414, 2024.

Mt. Etna, the largest volcano in Europe, is known for its almost persistent activity and complex seismic wavefield, making it an attractive location for examining volcanic explosions and testing new instrumentation in seismology (e.g., rotational sensors, strain-meters, fiber optic sensing). In 2018, a study was conducted at the Pizzi Deneri (PDN) observatory, situated near Mt. Etna’s summit to understand new instrumentation responses to the local seismo-acoustic wavefield. During volcanic explosions the released energy is mainly partitioned into seismic waves traveling through the ground, and sound waves traveling through the atmosphere. To capture this phenomenon, a temporary multi-parameter network comprised of infrasound sensors, broad-band seismometers (BB) and a fiber optic cable buried within the local loosed granular medium (scoria layer). The fiber optic cable was connected to a Distributed Dynamic Strain Sensing (DDSS) interrogator.

At Etna, volcanic explosions were observed at co-located BB, infrasound and DDSS virtual sensors. A notable example(visible in both BB and DDSS data) is the successive occurrence of a 1-2 Hz seismic signal with a duration of ~4 seconds, followed by a ~2 Hz acoustic signal originating from the explosion, recorded at infrasound sensors. Unusually, simultaneous to the arrival of the acoustic signal observed at the infrasound sensors, DDSS and BB sensors record a signal with a frequency content of 15-20 Hz with a duration of ~2 seconds. We hypothesize that the 15-20 Hz signal is resulting from a non-linear ground response due to the air-to-ground coupling of the air pressure wave.

In order to better characterize this phenomenon, a second experiment was conducted in 2019 at PDN with a similar instrumentation as in 2018, but with a different spatial arragenment. During three months the infrasound sensors recorded each about 65000 volcanic explosions. In this work we analyze the respective ground responses of volcanic explosions observed in the DDSS records of the 2019 campaign. We observe similar phenomenon as in 2018 (non-linear ground response), nevertheless, not all explosion can trigger this response. We first characterize the explosion events from both infrasound and DDSS records, and then classify them using their waveform similarity. The preliminary results provide a broad characterization of the non-linear ground response phenomenon and an insight into the physical properties and processes that are necessary for a pressure wave to trigger a non-linear ground response. The outcomes of this work provide a better understanding of acoustic-to-ground energy coupling in volcanic environments and their potential to trigger other hazards.

How to cite: Diaz-Meza, S., Jousset, P., Currenti, G., Costes, L., and Krawczyk, C.: Understanding non-linear ground response with Distributed Dynamic Strain Sensing (DDSS) at Mt. Etna volcano, Sicily., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17644, https://doi.org/10.5194/egusphere-egu24-17644, 2024.

Pyroclastic flows are highly hazardous phenomena demanding precise detection, localization, and comprehensive characterization for effective volcanic risk management. During October and December 2022, the volcanic activity of Stromboli produced more than 60 pyroclastic flows. The flows propagated from the craters (~700 m a.s.l.) to the sea, resulting in tsunami waves ranging from centimetres to meters in height. These events coincided with an experiment involving distributed acoustic sensing (DAS) data acquisition with a dedicated 4-kilometer-long fibre-optic cable. A 3-component array consisting of 27 geophones and the multi-parameter monitoring network managed by the Laboratory of Experimental Geophysics (LGS) at the University of Florence were active simultaneously. We study two distinct pyroclastic flows of varying intensities. Using array processing techniques applied to DAS, seismic, and infrasonic measurements, we estimate back-azimuths that consistently track flows moving at velocities between 40 and 50 m/s from the craters to the shoreline. Validation of these measurements was accomplished through georeferenced images obtained by a visible camera, affirming their accuracy. These results demonstrate the effectiveness of the three datasets in monitoring pyroclastic flows and the need for multi-parametric observations for a better interpretation of volcanic phenomena.

How to cite: Métaxian, J.-P., Biagioli, F., Trabattoni, A., Stutzmann, E., Lacanna, G., Risica, G., Bernard, P., Capdeville, Y., Mangeney, A., Monteiller, V., Diana, G., Innocenti, L., and Ripepe, M.: Using Distributed Acoustic Sensing, Seismic and Infrasonic Observation to Track Pyroclastic Flows at Stromboli Volcano (Italy) , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17953, https://doi.org/10.5194/egusphere-egu24-17953, 2024.