Figure - available from: Nature Communications
This content is subject to copyright. Terms and conditions apply.
Explosion at Etna New South-East Crater (NSEC), September 5, 2018, at 10:54:11 a Strain rate from distributed acoustic sensing (DAS) records at channels 484 (blue), 494 (red) and 505 (yellow), corresponding to positions of infrasound sensors in (c). Fibre channel position accuracy ±3 m (Method: DAS interrogator, fibre optic cable and conventional sensor network characteristics). b. Velocity seismograms from broadband seismometer CAZG (Supplementary Table 3), near DAS channel 494. c Pressure records from infrasound sensors CARB-IF1, 2, 3. d Strain rate (a) spectra. e Ground velocity (b) spectra. f Pressure (c) spectra. g Strain rate record at the 710 DAS channels along the 1.3 km fibre around the explosion time. B1 and B2 are the two geographically distinct branches in Fig. 1. FZ: fault zone (~50 m width), at channels 315–340 (deep cable) and channels >700 (shallow cable). h Strain rate-frequency distribution along the cable. Note higher strain rate amplitudes at low frequencies 1–10 Hz (seismic signal) for branch B1 and at high frequencies 18–21 Hz (infrasound induced signal) for both branches.
Source publication
Understanding physical processes prior to and during volcanic eruptions has improved significantly in recent years. However, uncertainties about subsurface structures distorting observed signals and undetected processes within the volcano prevent volcanologists to infer subtle triggering mechanisms of volcanic phenomena. Here, we demonstrate that d...
Similar publications
Seismic tremor observed near active volcanoes is an important tool for volcano monitoring as it often appears shortly before eruptions. Although tremor can be generated by a variety of physical processes it is usually interpreted as direct evidence for flowing magma in the sub-surface. These interpretations typically feed into risk assessments for...
Citations
... Distributed dynamic strain sensing (also called DAS) is a technique that transforms an optical fiber into a dense array of strain sensors (e.g., Zhan, 2019). In multiple studies, it has been shown that DAS resolves structure and dynamics of the sub-surface at unprecedented spatial resolution and at low cost compared to conventional seismometers (e.g., Diaz-Meza et al., 2023;Jousset et al., 2018Jousset et al., , 2022Lindsey et al., 2019;Williams et al., 2019;Zhan, 2019). In the context of ambient noise, DAS has been applied to obtain dispersion curves (e.g., Dou et al., 2017;Luo et al., 2020;Shao et al., 2022;Song et al., 2022;Zhou et al., 2022) and extract body waves through cross-correlation (Tonegawa et al., 2022). ...
... This suggests that we can stack adjacent traces in space without introducing bias to the recorded wavefield, in order to increase the SNR of the data. In the context of ambient noise interferometry, spatial stacking of large-N arrays and DAS has been successfully applied to improve the stability of retrieved Green's functions, for example, in order to reveal body waves in cross-correlations (e.g., Lin et al., 2013;Nakata et al., 2015;Tonegawa et al., 2022;Wang et al., 2014) and improve surface wave dispersion measurements (e.g., Cheng et al., 2023;Czarny & Zhu, 2022;Jousset et al., 2022;Li et al., 2022;Shragge et al., 2021). It is important to emphasize that we measure velocity changes in the coda of cross-correlations. ...
Plain Language Summary
In 2020, an intense unrest period took place on the Reykjanes peninsula, in southwest Iceland, preceding the Fagradalsfjall volcano eruption in 2021. The unrest was characterized by ground movements of several centimeters (measured by GNSS stations) and accompanied by an increased number of local earthquakes. We investigate whether the unrest affects velocities of seismic waves that propagate through the crust in Reykjanes. Instead of conventional seismometers, we use seismic data recorded by a fiber optic cable. This technology has the advantage that measurements can be made every few meters along the cable. We exploit this high spatial sampling to improve methods traditionally applied to seismometer records. These improvements enable us to infer seismic velocity changes as a function of time and space along the fiber optic cable. We detect velocity changes that strongly correlate with the observed ground deformation in Reykjanes and are, therefore, likely linked to the unrest and/or its associated processes.
... In that scenario identification of seismic activities around the volcanic active area enables the forecasting of volcanic activity. For this, both DAS [127] and FBG [128]have been used. It has been found that the magma masses migration-related events can be detected in the frequency range of 0.001-0.01 ...
... Early warning systems work best for earthquakes with moderate to large magnitudes and when the distances from the epicentre to populated areas are more than 10 miles from the epicentre [132]. How ever laying DAS in earthquake and volcano active areas is challenging as installation of fibres need the support of a structure [127]. ...
Geohazards pose significant risks to human life, infrastructure, and the environment, bringing out the necessity of advanced monitoring techniques for early detection and mitigation. This review paper explores the application of optical fibre sensors in geohazard monitoring. Optical fibre sensors have emerged as promising tools due to their inherent advantages. Various types of optical fibre sensors used in geohazard monitoring, categorized as distributed optical fibre sensors and fibre Bragg grating sensors and explains their mechanisms and applications are outlined in the context. Extracting from an ample amount of research and case studies, the successful design and deployment of optical fibre sensors in detecting disaster-causing changes in the environment and providing early warnings for diverse geohazards: landslides, earthquakes, and volcanic activity are brought up in detail. Furthermore, it addresses challenges and limitations in implementing optical fibre sensors for geohazard monitoring while highlighting opportunities for future advancements.
... If only a major wave train is of interest, the single dominant slope can be gained b y locall y maximizing eq. ( 2 ). If ho wever , interference phenomena are important and need to be preserved, the semblance can be e v aluated on a predefined horizontal slowness grid (Schwarz 2019 ;Jousset et al. 2022 ). As a result of the procedure, every data point ( x 0 , t 0 ) can be attributed with one or several semblance values, depending on whether the most dominant or a range of data slopes where investigated. ...
Ambient noise tomography on the basis of distributed acoustic sensing (DAS) deployed on existing telecommunication networks provides an opportunity to image the urban subsurface at regional scales and high-resolution. This capability has important implications in the assessment of the urban subsurface’s potential for sustainable and safe utilization, such as geothermal development. However, extracting coherent seismic signals from the DAS ambient wavefield in urban environments at low cost remains a challenge. One obstacle is the presence of complex sources of noise in urban environments, which may not be homogeneously distributed. Consequently, long recordings are required for the calculation of high-quality virtual shot gathers, which necessitates significant time and computational cost. In this paper, we present the analysis of 15 d of DAS data recorded on a pre-existing fibre optic cable (dark fibres), running along an 11-km-long major road in urban Berlin (Germany), hosting heavy traffic including vehicles and trains. To retrieve virtual shot gathers, we apply interferometric analysis based on the cross-correlation approach where we exclude low-quality virtual shot gathers to increase the signal-to-noise ratio of the stacked gathers. 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 to retrieve 1-D velocity models for two exemplary fibre subsegments, and compare the results of the conventional and modified workflows. The resulting 1-D velocity models correspond well with available lithology information. The modified workflow yields improved dispersion spectra, particularly in the low-frequency band (<1 Hz) of the signal. This leads to an increased investigation depth along with lower uncertainties in the inversion result. Additionally, these improved results were achieved using significantly less data than required using conventional approaches, thus opening the opportunity for shortening required acquisition times and accordingly lowering costs.
... The successful applications of the DAS technique have led to an improved understanding in fields such as underwater seismicity detection, tsunami warnings, and marine resource development [16]. Aside from the marine environment, Jousset et al. [17] installed fiber optics near volcanoes and analyzed the recorded signals to study the subsurface structure of volcanoes. This study offers new constraints on temporal variation in volcanic activity and provides important insights for eruption prediction and risk assessment. ...
Distributed acoustic sensing (DAS) is a new technology for recording vibration signals using optical fibers, and is advantageous over traditional seismic geophones given high spatial sampling density, real-time monitoring capabilities and relatively low cost for large-scale data acquisition. In recent years, progress in applications of the DAS technique has been achieved in near-surface imaging, earthquake detection, and urban traffic monitoring. In this study, we propose to apply a machine learning (ML) method to recognize and extract vehicle signals from DAS data acquired in a typical urban environment in Hangzhou, China. To design an efficientML framework, we apply a series of processing steps to eliminate noise and strengthen the vehicle signal, which is crucial for preparing high-quality labels. Initially, a total of 190 features (62 1-D features and 128 2-D features) are extracted from raw data, which are filtered down to 31 through univariate feature selection, random forest, and similarity analyses. These selected features are classified into (traffic) signal or (non-traffic) noise using the classic ML method of support vector machine (SVM). The resulting model enables robustly extracting vehicle signals with only a small (e.g., 10) training dataset and achieve an overall accuracy of about 80% on the test data. We further demonstrate the application of city traffic monitoring by considering the slope and coherence of the extracted vehicle signals. The preservation of car signals leads to a more accurate estimate of vehicle speed and volume. This study highlights the potential of real-time monitoring of speed and volume of traffic flow using existing city infrastructure and sheds light on the promising applications of the DAS technique in developing smart cities.
... It is capable of real-time data analysis for a virtually unlimited deployment duration as long as the interrogator unit is connected to a power source. While DAS has been extensively used in geosciences (e.g., 27,28 ) and industry (e.g., 29,30 ), its underwater and volcanology monitoring applications are still in their infancy (e.g., [31][32][33]. ...
Continuous monitoring of volcanic gas emissions is crucial for understanding volcanic activity and potential eruptions. However, emissions of volcanic gases underwater are infrequently studied or quantified. This study explores the potential of Distributed Acoustic Sensing (DAS) technology to monitor underwater volcanic degassing. DAS converts fiber-optic cables into high-resolution vibration recording arrays, providing measurements at unprecedented spatio-temporal resolution. We conducted an experiment at Laacher See volcano in Germany, immersing a fiber-optic cable in the lake and interrogating it with a DAS system. We detected and analyzed numerous acoustic signals that we associated with bubble emissions in different lake areas. Three types of text-book bubbles exhibiting characteristic waveforms are all found from our detections, indicating different nucleation processes and bubble sizes. Using clustering algorithms, we classified bubble events into four distinct clusters based on their temporal and spectral characteristics. The temporal distribution of the events provided insights into the evolution of gas seepage patterns. This technology has the potential to revolutionize underwater degassing monitoring and provide valuable information for studying volcanic processes and estimating gas emissions. Furthermore, DAS can be applied to other applications, such as monitoring underwater carbon capture and storage operations or methane leaks associated with climate change.
... On terrestrial fiber routes, DAS has become the gold standard for pipeline surveillance [24], perimeter protection [25], traffic monitoring [26], environmental sensing [27, 28, 29, 30] and, most recently, network health monitoring [14]. In underwater environments, DAS has also been established as a reliable method for carrying out coastal geotechnical surveys, and for monitoring environmental processes, such as earthquakes, ocean currents, and volcanic events [31,32,33,34,14,35,28,36,37]. ...
... We quantify this empirical relationship between SOP and DAS by performing cross correlations between the three SOP Stokes components and an average representation of DAS strain rate along the entirety of the fiber. To generate the averaged signal, we apply the short-term-average-long-term-average (STA/LTA) algorithm [28] to each individual DAS channel and take the mean across all channels (see Section 4.2). For consistency, we apply the same STA/LTA algorithm to each Stokes component (as described in Section 4.2), and Figure 4: Cross correlation (f?g) between the rotated Stokes parameters (f )a n dD A Ss i g n a l( g) on September 26th, 2022. ...
... To directly compare SOP and DAS signals, we employ the recursive short-term-average-long-term-average (STA/LTA) algorithm commonly used in seismic and acoustic emission analyses to detect anomalous events in continuous time series [28]. This algorithm calculates the average values of the absolute amplitude of a given signal in two consecutive moving time windows of different length and takes their ratio. ...
Wide-scale sensing of natural and human-made events is critical for protecting against environmental disasters and reducing the monetary losses associated with telecommunication service downtime. However, achieving dense sensing coverage is difficult given the high deployment overhead of modern sensor networks. In this work, we offer an in-depth exploration of state of polarization (SOP) sensing over fiber-optic networks using unmodified optical transceivers, establishing a strong correlation with ground truth distributed acoustic sensing (DAS). To validate our sensing methodology, we collect 85 days of SOP and DAS measurements along two colocated, 50km fiber-optic cables in Southern California. We then examine how SOP sensing can improve network reliability by accurately modeling overall network health and preemptively detecting loss of traffic. Finally, we explore the feasibility of wide-scale seismic monitoring with SOP sensing, showcasing the SOP perturbations following low-intensity earthquakes, and the potential to more than double seismic monitoring coverage in Southern California alone.
... Distributed Acoustic Sensing (DAS) is a fiber optic-based data acquisition technology that is gaining popularity in a broad range of seismological applications. From seismic monitoring in volcanic (Jousset et al. 2022), volcano-glacial environments (Klaasen et al. 2021), offshore areas (Shinohara et al. 2022) and aftershock scenarios (Li et al. 2021), (Zeng et al. 2022); to downhole , surface applications (Lindsey & Martin 2021), traffic and railway monitoring . Different industrial applications also benefit from the use of DAS, such as induced seismicity monitoring (Webster et al. 2013;Karrenbach et al. 2019), CO 2 sequestration monitoring (Daley et al. 2013), hydraulic stimulation (Karrenbach et al. 2017) and geothermal sites (Lellouch et al. 2020;). ...
Distributed Acoustic Sensing (DAS) is becoming increasingly popular in microseismic monitoring operations. This data acquisition technology converts fiber-optic cables into dense arrays of seismic sensors that can sample the seismic wavefield produced by active or passive sources with a high spatial density, over distances ranging from a few hundred meters to tens of kilometers. However, standard microseismic data analysis procedures have several limitations when dealing with the high spatial (inter-sensor spacing up to sub-meter scale) sampling rates of DAS systems. Here we propose a semblance-based seismic event detection method that fully exploits the high spatial sampling of the DAS data. The detector identifies seismic events by computing waveform coherence of the seismic wavefield along geometrical hyperbolic trajectories for different curvatures and positions of the vertex, which are completely independent from external information (i.e. velocity models). The method detects a seismic event when the coherence values overcome a given threshold and satisfies our clustering criteria. We first validate our method on synthetic data and then apply it to real data from the FORGE geothermal experiment in Utah, USA. Our method detects about two times the number of events obtained with a standard method when applied to 24h of data.
... Parker et al. 2014 ;Ajo-Franklin et al. 2019 ;Lindsey et al. 2019 ;Shinohara et al. 2022 ). As such, they are well-suited to seismic monitoring in harsh or remote environments, such as volcanoes (Jousset et al. 2022 ), regions with extreme climate (e.g. glacial settings; Walter et al. 2020 ;Hudson et al. 2021b ;Zhou et al. 2022 ) and beneath oceans (Lindsey et al. 2019 ;Shinohara et al. 2022 ). ...
This paper presents a weakly supervised machine learning method, which we call DAS-N2N, for suppressing strong random noise in distributed acoustic sensing (DAS) recordings. DAS-N2N requires no manually produced labels (i.e. pre-determined examples of clean event signals or sections of noise) for training and aims to map random noise processes to a chosen summary statistic, such as the distribution mean, median or mode, whilst retaining the true underlying signal. This is achieved by splicing (joining together) two fibres hosted within a single optical cable, recording two noisy copies of the same underlying signal corrupted by different independent realizations of random observational noise. A deep learning model can then be trained using only these two noisy copies of the data to produce a near fully denoised copy. Once the model is trained, only noisy data from a single fibre is required. Using a data set from a DAS array deployed on the surface of the Rutford Ice Stream in Antarctica, we demonstrate that DAS-N2N greatly suppresses incoherent noise and enhances the signal-to-noise ratios (SNR) of natural microseismic icequake events. We further show that this approach is inherently more efficient and effective than standard stop/pass band and white noise (e.g. Wiener) filtering routines, as well as a comparable self-supervised learning method based on masking individual DAS channels. Our preferred model for this task is lightweight, processing 30 s of data recorded at a sampling frequency of 1000 Hz over 985 channels (approximately 1 km of fibre) in <1 s. Due to the high noise levels in DAS recordings, efficient data-driven denoising methods, such as DAS-N2N, will prove essential to time-critical DAS earthquake detection, particularly in the case of microseismic monitoring.
... DAS can provide spatial resolution of less than a metre, and has a sensitivity bandwidth ranging from quasi-static deformation to MHz (Hartog 2017 ). This, together with the relative ease of deployment and the large networks of telecommunication fibres already potentially available for interrogation (Marra et al. 2018 ), gives DAS the potential to fill the gap in scenarios where traditional ground motion sensors are either lacking sensitivity (very low, very high frequencies), or are difficult to deploy-for example volcanoes or ocean floor (Jousset et al. 2022 ;Marra et al. 2022 ). ...
... In addition, DAS is still an emerging technique and deployments are relati vel y fe w, so that we lack data for a comprehensive empirical approach. It is also important to note that some of the most beneficial applications of DAS are for scenarios where reference seismometers cannot be readily deployed, for example Ocean floors (Marra et al. 2022 ) or active volcanoes (Jousset et al. 2022 ). ...
Over the past several years, the use of optical fibre cables as ground motion sensors has become a central topic for seismologists, with successful applications of distributed acoustic sensing (DAS) in various key fields such as seismic monitoring, structural imaging and source characterization. DAS response is a combination of both instrument response and cable-ground coupling, with the latter having a strong, spatially variable, but yet largely unquantified effect. This limits the application of a large number of staple seismological techniques (e.g. earthquake magnitude estimation, waveform tomography) that can require accurate knowledge of a signal's amplitude and frequency content. Here we present a method for accurately simulating a DAS cable and its ground coupling. The scheme is based on molecular dynamic-like particle-based numerical modelling, allowing the investigation of the effect of varying DAS-ground coupling scenarios. We start by computing the full strain field directly, for each pair of neighbouring particles in the model. We then define a virtual DAS cable, embedded within the model and formed by a single string of interconnected particles. This allows us to control all aspects of the cable-ground coupling and their properties at an effective granular level through changing the bond stiffness and bond types (e.g. non-linearity) for both the cable and the surrounding medium. Arbitrary cable geometries and heterogeneous materials can be accommodated at the desired scale of investigation. We observe that at the meter scale, the cable-ground coupling and local site effects can substantially alter the recorded signal. We find that the stiffness of the thin layer of material to which the cable is coupled has the strongest effects, selectively amplifying portions of the wave train and contributing to substantial phase delays. These differences show that cable coupling and local site effects should be considered both when designing a DAS deployment and analysing its data when either true or along-cable relative amplitudes and/or frequencies are considered. The codes developed herein for calculating full waveform DAS responses and coupling are made publicly available.
... DAS has found success in various geoscience applications, such as exploration geophysics (Daley et al., 2016), near-surface geophysics Yang et al., 2022), seismology (Jousset et al., 2018;Lindsey et al., 2019), glaciology (Walter et al., 2020), and volcanology (Jousset et al., 2022). Despite this, few studies have focused on hazardous mass movements like rockfalls (Walter et al., 2020;Zhang et al., 2021), which generate high-frequency seismic signals during catastrophic failures. ...
