OptaSense: Fibre Optic Distributed Acoustic Sensing for Border Monitoring

2012 ◽  
Author(s):  
Arch Owen ◽  
Gregory Duckworth ◽  
Jerry Worsley
Keyword(s):  
Fibre Optic ◽  
Acoustic Sensing ◽  
Distributed Acoustic Sensing

Danish earthquake recorded by distributed acoustic sensing (DAS)

2020 ◽  
Author(s):  
Camilla Rasmussen ◽  
Peter H. Voss ◽  
Trine Dahl-Jensen
Keyword(s):  
Field Test ◽  
Signal To Noise Ratio ◽  
Field Tests ◽  
Small Subset ◽  
Fibre Optic ◽  
Data Set ◽  
Acoustic Sensing ◽  
Arrival Times ◽  
Fibre Optic Cable ◽  
Distributed Acoustic Sensing

<p>On September 16th 2018 a Danish earthquake of local magnitude 3.7 was recorded by distributed acoustic sensing (DAS) in a ~23 km long fibre-optic cable. The data are used to study how well DAS can be used as a supplement to conventional seismological data in earthquake localisation. One of the goals in this study is extracting a small subset of traces with clear P and S phases to use in an earthquake localisation, from the 11144 traces the DAS system provide. The timing in the DAS data might not be reliable, and therefore differences in arrival times of S and P are used instead of the exact arrival times. <br>The DAS data set is generally noisy and with a low signal-to-noise ratio (SNR). It is examined whether stacking can be used to improve SNR. The SNR varies a lot along the fibre-optic cable, and at some distances, it is so small that the traces are useless. Stacking methods for improving SNR are presented.</p><p>A field test at two location sites of the fibre-optic cable was conducted with the purpose of comparing DAS data with seismometer data. At the field sites, hammer shots were recorded by a small array of three STS-2 sensors located in a line parallel to the fibre-optic cable. The recordings generally show good consistency between the two data sets. <br>In addition, the field tests are used to get a better understanding of the noise sources in the DAS recording of the earthquake. There are many sources of noise in the data set. The most prominent are a line of windmills that cross the fibre-optic cable and people walking in the building where the detector is located. Also, the coupling between the fibre-optic cable and the ground varies along the cable length due to varying soil type and wrapping around the fibre-optic cable, which is also evident in field test data. Furthermore, the data from the field tests are used to calibrate the location of the fibre-optic cable, which is necessary for using the DAS data in an earthquake localisation. <br>Data processing is done in Matlab and SEISAN.</p>

A self-supervised Deep Learning approach for improving signal coherence in Distributed Acoustic Sensing

2021 ◽  
Author(s):  
Martijn van den Ende ◽  
Itzhak Lior ◽  
Jean Paul Ampuero ◽  
Anthony Sladen ◽  
Cédric Richard
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
Direction Of Arrival ◽  
Fibre Optic ◽  
Coherent Signals ◽  
Signal To Noise ◽  
Coherent Signal ◽  
Acoustic Sensing ◽  
Distributed Acoustic Sensing ◽  
Noise Ratio

<p>Fibre-optic Distributed Acoustic Sensing (DAS) is an emerging technology for vibration measurements with numerous applications in seismic signal analysis as well as in monitoring of urban and marine environments, including microseismicity detection, ambient noise tomography, traffic density monitoring, and maritime vessel tracking. A major advantage of DAS is its ability to turn fibre-optic cables into large and dense seismic arrays. As a cornerstone of seismic array analysis, beamforming relies on the relative arrival times of coherent signals along the optical fibre array to estimate the direction-of-arrival of the signals, and can hence be used to locate earthquakes as well as moving acoustic sources (e.g. maritime vessels). Naturally, this technique can only be applied to signals that are sufficiently coherent in space and time, and so beamforming benefits from signal processing methods that enhance the signal-to-noise ratio of the spatio-temporally coherent signal components. DAS measurements often suffer from waveform incoherence, and processing submarine DAS data is particularly challenging.</p><p>In this work, we adopt a self-supervised deep learning algorithm to extract locally-coherent signal components. Owing to the similarity of coherent signals along a DAS system, one can predict the coherent part of the signal at a given channel based on the signals recorded at other channels, referred to as "J-invariance". Following the recent approach proposed by Batson & Royer (2019), we leverage the J-invariant property of earthquake signals recorded by a submarine fibre-optic cable. A U-net auto-encoder is trained to reconstruct the earthquake waveforms recorded at one channel based on the waveforms recorded at neighbouring channels. Repeating this procedure for every measurement location along the cable yields a J-invariant reconstruction of the dataset that maximises the local coherence of the data. When we apply standard beamforming techniques to the output of the deep learning model, we indeed obtain higher-fidelity estimates of the direction-of-arrival of the seismic waves, and spurious solutions resulting from a lack of waveform coherence and local seismic scattering are suppressed.</p><p>While the present application focuses on earthquake signals, the deep learning method is completely general, self-supervised, and directly applicable to other DAS-recorded signals. This approach facilitates the analysis of signals with low signal-to-noise ratio that are spatio-temporally coherent, and can work in tandem with existing time-series analysis techniques.</p><p>References:<br>Batson J., Royer L. (2019), "Noise2Self: Blind Denoising by Self-Supervision", Proceedings of the 36th International Conference on Machine Learning (ICML), Long Beach, California</p>

Observing avalanche dynamics with Distributed Acoustic Sensing

2021 ◽  
Author(s):  
Andreas Fichtner ◽  
Pascal Edme ◽  
Patrick Paitz ◽  
Nadja Lindner ◽  
Michael Hohl ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Flow Regimes ◽  
Dynamic Strain ◽  
Seismic Signals ◽  
Fibre Optic ◽  
Snow Avalanches ◽  
Acoustic Sensing ◽  
Fibre Optic Cable ◽  
Distributed Acoustic Sensing ◽  
Seismic Instrumentation

<p><span>Avalanche research requires comprehensive measurements of sudden and rapid snow mass movement that is hard to predict. Automatic cameras, radar and infrasound sensors provide valuable observations of avalanche structure and dynamic parameters, such as velocity. Recently, seismic sensors have also gained popularity, because they can monitor avalanche activity over larger spatial scales. Moreover, seismic signals elucidate rheological properties, which can be used to distinguish different types of avalanches and flow regimes. To date, however, seismic instrumentation in avalanche terrain is sparse. This limits the spatial resolution of avalanche details, needed to characterise flow regimes and maximise detection accuracy for avalanche warning.</span></p><p><span>As an alternative to conventional seismic instrumentation, we propose Distributed Acoustic Sensing (DAS) to measure avalanche-induced ground motion. DAS is based on fibre-optic technology, which has previously been used already for environmental monitoring, e.g., of snow avalanches. Thanks to recent technological advances, modern DAS interrogators allow us to measure dynamic strain along a fibre-optic cable with unprecedented temporal and spatial resolution. It therefore becomes possible to record seismic signals along many kilometres of fibre-optic cables, with a spatial resolution of a few metres, thereby creating large arrays of seismic receivers. We test this approach at an avalanche test site in the Valleé de la Sionne, in the Swiss Alps, using an existing 700 m long fibre-optic cable that is permanently installed underground for the purpose of data transfer of other, independent avalanche measurements.</span></p><p><span>During winter 2020/2021, we recorded numerous snow avalanches, including several which reached the valley bottom, travelling directly over the cable during runout. The DAS recordings show clear seismic signatures revealing individual flow surges and various phases/modes that may be associated with roll waves and avalanche arrest. We compare our observations to state-of-the-art radar and seismic measurements which ideally complement the DAS data.</span></p><p><span>Our initial analysis highlights the suitability of DAS-based monitoring and research for avalanches and other hazardous granular flows. Moreover, the clear detectability of avalanche signals using existing fibre-optic infrastructure of telecommunication networks opens the opportunity for unrivalled warning capabilities in Alpine environments.</span></p>

Integration of Machine Learning on Distributed Acoustic Sensing Surveys

2020 ◽  
Author(s):  
Camille Jestin ◽  
Clément Hibert ◽  
Gaëtan Calbris ◽  
Vincent Lanticq
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Fibre Optic ◽  
Rock Falls ◽  
Near Surface ◽  
Acoustic Sensing ◽  
Seismic Waveforms ◽  
Fibre Optic Cable ◽  
Distributed Acoustic Sensing

<p>Distributed Acoustic Sensing (DAS) is an innovative technique which has been recently employed for near-surface geophysics purposes. It involves the use of fibre-optic cable as a sensor. The fibre is analysed by sending a laser pulse from an interrogator unit. The phase of the backscattered signal contains the information on the strain on the cable, enabling the detection of a passing acoustic wave with enough energy for the cable excitation. Allowing the interrogation of long profiles and the generation of a dense spatial sampling, uneasy to obtain with classic geophysical techniques, DAS instrumentation then proved its relevance for seismic applications but also for infrastructure monitoring.</p><p>During DAS acquisition, and more precisely when closely looking at infrastructures integrity, it is necessary to clearly identify the source of the acoustic vibrations at the structure neighbourhood. Indeed, in the context of pipeline monitoring for example, it appears important to be able to classify events which generate seismic signals recorded by DAS systems and which can be related to a potential threat for the structure. In order to launch an alarm if necessary, the source identification must be fast, accurate and robust. Moreover, because DAS acquisition can generate traces every few meters along fibres of tens of kilometres, the used machine-learning algorithm must demonstrate its ability to handle a big amount of data.</p><p>In this study, we analyse the efficiency of the Random Forests (RF) machine-learning algorithm applied to data acquired with DAS system for the discrimination of event sources. RF algorithm has been selected because of its ability to handle large numbers of attributes related to signal characteristics and to enable a good reliability for the discrimination of sources. This algorithm has already proved its efficiency for automated classification of seismic waveforms (e.g. earthquakes, volcanic tremors, rock falls, avalanches, etc.).</p><p>We focus our study on tests lead along a gas pipeline instrumented with fibre-optic cable. Different third-party works have been conducted: excavation, saw sections, drill, jackhammer, etc. We work on the discrimination of six classes of seismic source. After running a detection phase based on a threshold on signal energy, we obtain several hundred of exploitable seismic traces to inject to the RF algorithm. We demonstrate the efficiency of the application of machine learning on DAS data to discriminate seismic waveforms from the correct class, with an overall precision on our test set of 99%.</p>

Monitoring volcanic and seismic activity with multiple fibre-optic Distributed Acoustic Sensing units at Etna volcano

2020 ◽  
Author(s):  
Charlotte Krawczyk ◽  
Philippe Jousset ◽  
Gilda Currenti ◽  
Michael Weber ◽  
Rosalba Napoli ◽  
...  
Keyword(s):  
Strain Rate ◽  
Scattered Light ◽  
Active Faults ◽  
Dynamic Strain ◽  
Fibre Optic ◽  
Etna Volcano ◽  
Acoustic Sensing ◽  
Sea Floor ◽  
Eastern Flank ◽  
Distributed Acoustic Sensing

<p>Volcanic and seismic activities produce a variety of phenomena that put population at risk. Etna volcano provides an example where volcanic and tectonic processes are strongly coupled. Distributed Acoustic Sensing (DAS) technology has been for the first time tested both in 2018 and 2019 as a new tool for monitoring the complex tectonic and volcanic interactions at Etna volcano from summit to the sea floor. We connected up to 3 iDAS interrogators, sometimes simultaneously, to optical cables close to the summit, in urban areas and offshore. Each iDAS measured the dynamic strain rate along the whole length of the optical fibre, from the interferometric analysis of the back-scattered light.</p><p>In the summit area, we connect an iDAS interrogator inside the Volcanological Observatory of Pizzi Deneri (2800 m elevation close to Etna summit) to record dynamic strain signals along a 1.5 km-long fibre optic cable that we deployed in the scoria of Piano delle Concazze. We recorded signals associated with various volcanic events, local and distant earthquakes, thunderstorm, as well as many other anthropogenic signals (e.g., tourists). To validate the DAS signal we collocated along the fibre cable multi-parametric arrays (comprising geophones, broadband seismometers, infrasonic arrays). During the survey periods, Etna activity was mainly characterized by moderate but frequent explosive and/or effusive activity from summit craters. Our observations suggests that DAS technology can record volcano-related signals (in the order of tens nanostrain) with unprecedented spatial and temporal resolutions, opening new opportunities for the understanding of volcano processes.</p><p>In urban environments, taking advantage of the existence of fibre optic telecommunication infrastructures, we connected iDAS interrogator to fibre optic cables, known to cross active faults linked to the volcano eastern flank dynamics. We recorded dynamic strain rate along a 4 km cable for about 20 days in Zafferana village and along a 12 km-long cable running from Linera to Fleri. We also tested DAS recording along a 40 km-long fiber optic telecommunication cable on the western side of the volcano, at the border between the sedimentary layer and the volcano edifice.</p><p>On the sea floor, we connected an iDAS interrogator to a 30-km long fibre within a cable transmitting data from sub-marine instrumentation to INFN-LNS facility at the Catania harbour. We record dynamic strain signals from local and regional earthquakes and detect some previously unknown faults offsetting the sea floor below the eastern flank of the volcano.</p><p>Our results demonstrate that DAS technology is able to contribute to the monitoring system of earthquake and volcanic phenomena at Etna volcano, and thereby could improve the volcanic and seismic hazard assessment in the future.</p>

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