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25 results for “Distributed Acoustic Sensing”
DAS4Whale: Svalbard distributed acoustic sensing dataset for baleen whale monitoring
<p> </p> <p> </p> <p>This dataset aims to support the work presented in</p> <blockquote> <p>Bouffaut, L., Taweesintananon, K., Kriesell, H. J., Rørstadbotnen, R. A., Potter, J. R., Landrø, M., Johansen, S. E., Brenne, J. K., Haukanes, A., Schjelderup, O., & Storvik, F. (2022). Eavesdropping at the Speed of Light: Distributed Acoustic Sensing of Baleen Whales in the Arctic. Frontiers in Marine Science, 9, 901348. <a href="https://doi.org/10.3389/fmars.2022.901348">https://doi.org/10.3389/fmars.2022.901348</a>.</p> </blockquote> <p>It contains recordings from a dark fiber optic (FO) cable converted into a distributed acoustic sensing (DAS) array of 120km long spreading from Longyearbyen, Svalbard, Norway, out to the open ocean, through Isfjorden. <a href="https://www.frontiersin.org/files/Articles/901348/fmars-09-901348-HTML/image_m/fmars-09-901348-g002.jpg">This DAS array</a>, measuring nano strain, was spatially sampled every ~4m and had a sampling frequency of 645.16 Hz, generating data stored into spatio-temporal matrices. </p> <p>The exact position of the FO cable is proprietary information belonging to Uninett. The space component is therefore given as a vector in “channel number” (sensing node number along the FO cable) and distance from the shore station (m).</p> <p>The data necessary to produce each manuscript example is saved into multiple files corresponding to subsequent groups of channels along the FO cable, to facilitate storage and sharing. The file naming system satisfies the following: Date in the format <em>YYYYMMDD</em>, UTC time at the beginning of the file, channels, whale_raw, duration of the file L<em>xx</em>s, all separated by underscores “_”. Data is shared as *.mat file saved in HDF format and readable in different programming languages. For example </p> <ul> <li>in <a href="https://www.mathworks.com/help/matlab/ref/load.html">Matlab</a> <pre><code>load('20200627_052441_ch08751_to_ch10000_whale_raw_L160s.mat')</code></pre> <p> </p> </li> </ul> <ul> <li>in <a href="http://https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html#scipy.io.loadmat">Python</a> <pre><code>scipy.io.loadmat('20200627_052441_ch08751_to_ch10000_whale_raw_L160s.mat')</code></pre> <p> </p> </li> </ul> <p><strong>Each file contains the following variables</strong></p> <ul> <li><em>data: </em>The DAS-recorded nano strain data</li> <li><em>info_GL_m:</em> Used gauge length (m)</li> <li><em>info_nsamples</em>: Number of temporal samples in the file</li> <li><em>info_ntraces</em>: Number of spatial samples (channels) in the file</li> <li><em>info_sample_interval_s</em>: Sampling period (s)</li> <li><em>info_sampling_frequency_Hz</em>: Sampling frequency (Hz)</li> <li><em>info_SSI_m</em>: Spatial sampling interval (m)</li> <li><em>info_timestamp</em>: Date and time (UTC) of the first sample</li> <li>info_units: Global unit information</li> <li><em>x1_absolute_channel</em>: Vector containing the absolute channel number</li> <li><em>x1_distance_from_shore_m</em>: Vector containing the distance along the FO cable from shore (m)</li> <li><em>x1_position_m</em>: Vector containing the distance along the FO cable from the interrogator (m)</li> <li><em>x1_recwdepthz_m</em>: Vector containing the water column depth used as a proxy for the fiber optic cable depth at each sensing location (m)</li> <li><em>x1_relative_channel</em>: Vector containing the channel number</li> <li><em>x2_time_s</em>: Time vector (s)</li> </ul> <p> </p> <p><strong>List of the files and related manuscript examples</strong></p> <p>Example of at least 3 vocalizing baleen whales recorded simultaneously at three different locations along the Svalbard fiber optic DAS array - Figure 4 in Bouffaut et al. (2022) - between 35-95 km and on 2020-06-26 between 052440-052720 UTC</p> <ul> <li><em>20200627_052441_ch08751_to_ch10000_whale_raw_L160s.mat</em> </li> <li><em>20200627_052441_ch10001_to_ch15000_whale_raw_L160s.mat</em> </li> <li><em>20200627_052441_ch15001_to_ch20000_whale_raw_L160s.mat</em> </li> <li><em>20200627_052441_ch20001_to_ch25000_whale_raw_L160s.mat</em> </li> </ul> <p>Example of<strong> </strong>series of blue whale calls recorded with a move out on the Svalbard DAS array - Figure 5 & &B in Bouffaut et al. (2022) - between 85-90 km and on 2020-07-16 between 154300-155500 UTC</p> <ul> <li><em>20200716_154302_ch20001_to_ch21000_whale_raw_L720s.mat </em></li> <li><em>20200716_154302_ch21001_to_ch22000_whale_raw_L720s.mat </em></li> <li><em>20200716_154302_ch22001_to_ch23000_whale_raw_L720s.mat</em></li> <li><em>20200716_154302_ch23001_to_ch24000_whale_raw_L720s.mat</em></li> <li><em>20200716_154302_ch24001_to_ch25000_whale_raw_L720s.mat</em></li> </ul> <p>Example of a blue whale non-stereotyped call recorded inside Isfjorden and further used to provide correlated seismic profiles - Figure 6A n Bouffaut et al. (2022) - between 23-28 km on 2020-06-27 between 192255-192805 UTC</p> <ul> <li><em>20200627_192255_ch05001_to_ch07000_whale_raw_L310s.mat </em></li> <li><em>20200627_192255_ch07001_to_ch08500_whale_raw_L310s.mat </em></li> </ul> <p><strong>--------------</strong></p> <p><strong>Analysis tools </strong></p> <p>To reproduce the paper's result, we suggest using the following Python package available on <a href="https://github.com/leabouffaut/DAS4Whales">GitHub</a>:</p> <blockquote> <p>Léa Bouffaut (2023). DAS4Whales: A Python package to analyze Distributed Acoustic Sensing (DAS) data for marine bioacoustics (v0.1.0). Zenodo. <a href="https://doi.org/10.5281/zenodo.7760187">https://doi.org/10.5281/zenodo.7760187</a></p> </blockquote> <p>Here is an example of the use of the DAS4Whales package with this dataset's data format: <a href="https://gist.github.com/leabouffaut/b42ec74e2cee880877bfc4c94e81bdaa">https://gist.github.com/leabouffaut/b42ec74e2cee880877bfc4c94e81bdaa</a></p> <p><strong>--------------</strong></p> <p><strong>Please cite as </strong></p> <blockquote> <p>Léa Bouffaut and Kittinat Taweesintananon, “DAS4Whale: Svalbard distributed acoustic sensing dataset for baleen whale monitoring”. Zenodo, Jan. 10, 2022. doi: <a href="https://doi.org/10.5281/zenodo.7760187">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.5823343">10.5281/zenodo.5823343</a>.</p> </blockquote> <p><strong>--------------</strong></p> <p><strong>Contact</strong></p> <p><a href="mailto:lb736@cornell.edu">Contact</a> | <a href="https://www.birds.cornell.edu/ccb/lea-bouffaut/">Webpage</a> | <a href="https://twitter.com/LeaBouffaut">Twitter</a></p>
Test experiments with distributed acoustic sensing and hydrophone arrays for locating underwater sounds.
<p>Whales and dolphins rely on sound for navigation and communication, making them an intriguing subject for studying language evolution. Traditional hydrophone arrays have been used to record their acoustic behavior, but optical fibers have emerged as a promising alternative. This study explores the use of distributed acoustic sensing (DAS), a technique that detects local stress in optical fibers, for underwater sound recording. An experiment was conducted in Lake Zurich, where a fiber-optic cable and a self-made hydrophone array were deployed. A test signal was broadcasted at various locations, and the resulting data was synchronized and consolidated into files. Analysis revealed distinct frequency responses in the DAS channels and provided insights into sound propagation in the lake. Challenges related to cable sensitivity, sample rate, and broadcast fidelity were identified. This dataset serves as a valuable resource for advancing acoustic sensing techniques in underwater environments, especially for studying marine mammal vocal behavior.</p>
Multigrid spatially constrained dispersion curve inversion package: towards distributed acoustic sensing surface wave imaging
<p>Surface wave methods, commonly applied in diverse fields, encounter challenges in complex subsurface environments due to limitations inherent in traditional inversion techniques. Conventional one-dimensional inversion (1DI), with its reliance on fixed grids and deterministic linear approaches, often introduces biases, diminishing lateral resolution. Laterally constrained inversion (LCI) improves robustness by addressing lateral coherency but falls short in delineating arbitrary interfaces due to its dependency on fixed grid models. The advent of Distributed Acoustic Sensing (DAS) technology offers extensive seismic data, yet its potential for high-resolution imaging remains underutilized. We introduce a Multigrid Spatially Constrained Dispersion Curve Inversion (MCI) method to overcome these challenges, aiming to harness high-resolution DAS surface wave imaging capabilities. </p> <p>The package includes essential scripts and models required to replicate key figures from the study by Guan et al. (2023, currently under review). These codes are designed to help readers evaluate the effectiveness of the MCI approach using synthetic demonstrations. Additionally, the package includes a refined 2D Vs (shear wave velocity) model derived from a DAS (Distributed Acoustic Sensing) field study conducted in Imperial Valley, California. This model offers new insights into the regional fault system, underscoring the importance of enhanced spatial resolution in large-scale geophysical investigations.</p> <p>It is organized into three directories and contains a total of 14 files. The directory structure is as follows:<br>├── DAS field data<br>│ ├── Pltmodels.m<br>│ ├── README.txt<br>│ ├── field_models.pdf<br>│ ├── model_1DI.mat<br>│ ├── model_LCI.mat<br>│ └── model_MCI.mat<br>├── MCI_Main<br>│ ├── DisForward.p<br>│ ├── InvForward.p<br>│ ├── InvJacobian.p<br>│ ├── MCI.p<br>│ ├── readme.txt<br>│ └── whitejet3.m<br>└── Synthetic demos<br> ├── MCI_Main.m<br> └── syndata.mat</p>
Seismic noise interferometry and Distributed Acoustic Sensing (DAS): Inverting for the firn layer S-velocity structure on Rutford Ice Stream, Antarctica
<p>This dataset contains files including continuous DAS and geophone data and a refracted P wave travel time data collected on Rutford Ice Stream, Antarctica. The seismic data is used to perform seismic noise interferometry. The travel time data is used to perform refraction inversion to get the P wave velocity profile.</p> <p><br> 1. 7 hours of continuous DAS data (100 Hz sampling): 2020-01-14T00:00:19.598000Zoffset_****.mseed, with offset referring to the distance from the DAS channel to the interrogator.</p> <p>2. Corresponding 7 hours of vertical component continuous geophone (A000, located at DAS channel offset 570 m) data.</p> <p>3. Refraction P wave travel time from a geophone array refraction survey.</p>
Dataset in "Near real-time in-situ monitoring of nearshore ocean currents using Distributed Acoustic Sensing on submarine fiber-optic cable"
<p>Dataset in "Near real-time in-situ monitoring of nearshore ocean currents using Distributed Acoustic Sensing on submarine fiber-optic cable" </p> <p><a href="../api/records/13133835/draft/files/tmdcm.txt/content" target="_blank" rel="noopener noreferrer">tmdcm.txt</a>: current meter data </p> <p><a href="../api/records/13133835/draft/files/tide.txt/content" target="_blank" rel="noopener noreferrer">tide.txt</a>: tidal gauge data </p> <p><a href="../api/records/13133835/draft/files/windspeed.txt/content" target="_blank" rel="noopener noreferrer">windspeed.txt</a>: windspeed data </p> <p>Figure 2: Figure2.npy</p> <p>Figure 3: Figure 3 abc .npy</p> <p>Figure16: <a href="../api/records/13133835/draft/files/spatial_Vc.npy/content" target="_blank" rel="noopener noreferrer">spatial_Vc.npy</a> & <a href="13133835" target="_blank" rel="noopener noreferrer">spatial_h.npy</a> </p> <p>Figure 17: <a href="../api/records/13133835/draft/files/streching_ncf.npy/content" target="_blank" rel="noopener noreferrer">streching_ncf.npy</a></p>
Dataset for the manuscript "Self-Supervised Coherence-Based Denoising on Cryoseismological Distributed Acoustic Sensing Data"
<p>The dataset contains cryoseismological data recorded in July 2020 on the Rhonegletscher, Switzerland, collected using both Distributed Acoustic Sensing and seismometers.<br>This dataset provides the necessary data to reproduce the results presented in the paper “Self-Supervised Coherence-Based Denoising on Cryoseismological Distributed Acoustic Sensing Data.” The corresponding code is available on GitHub, and the paper can be accessed via Authorea.</p> <p> </p> <p>Abstract: </p> <p>One major challenge in cryoseismology is that signals of interest are often buried within the high noise level emitted by a multitude of environmental processes. Events of interest potentially stay unnoticed and remain unanalyzed, particularly because conventional sensors cannot monitor an entire glacier. However, with Distributed Acoustic Sensing (DAS), we can observe seismicity over multiple kilometers. DAS systems turn common fiber-optic cables into seismic arrays that measure strain rate data, enabling researchers to acquire seismic data in hard-to-access areas with high spatial and temporal resolution. We deployed a DAS system on Rhonegletscher, Switzerland, using a 9 km long fiberoptic cable that covered the entire glacier, from its accumulation to its ablation zone, recording seismicity for one month. The highly active and dynamic cryospheric environ ment, in combination with poor coupling, resulted in DAS data characterized by a low Signal-to-Noise Ratio (SNR) compared to classical point sensors. Our objective is to ef fectively denoise this dataset.<br>We use a self-supervised J -invariant U-net autoencoder capable of separating incoherent environmental noise from temporally and spatially coherent signals of interest (e.g., stick-slip or crevasse signals). The method shows enhanced inter-channel coherence, increased SNR, and significantly improved visibility of the icequakes. Further, we compare different training data types varying in recording position, wavefield component, and waveform diversity. Our approach has the potential to enhance the detection capabilities of events of interest in cryoseismological DAS data, hence to improve the understanding of processes within Alpine glaciers.</p>
Dataset used in "Ocean floor imaging with Distributed Acoustic Sensing and water phases reverberations" by Spica et al. in Geophysical Research Letters
<p>earthquake #1<br> earthquake #2</p>
A river on fiber: high resolution fluvial monitoring with distributed acoustic sensing – Data, Matlab Scripts and App
<p>Matlab software and data associated with Roth et al. (submitted to Seismica, 2025).</p>
Cross-correlated ambient data recorded on a distributed acoustic sensing array
<p>Distributed acoustic sensing (DAS) is a relatively new technology used in many geophysical applications. The versatility and high temporal-spatial resolution make DAS ideal for rapid deployment surveys such as earthquake-aftershock monitoring and hazard assessment. However, these applications often rely on trenched cable installations that are time consuming and cost prohibitive, or on existing telecom fibers that are limited in spatial coverage. We deploy a DAS array composed of six parallel linear subsections directly on ground surfaces with different conditions. We apply ambient interferometry and adopt a simplified spectral-analysis-of-surface waves (SASW) method to determine the average shear-wave velocity of the top 30 m (VS30). Our methodology results in robust VS30 estimates for each surface deployment subsection that are consistent with collocated 1 m-depth trenched cables. The implications of these findings support DAS as a viable method for non-invasive rapid deployment surface surveys for earthquake hazard assessment.</p>
Cross-spectra used in "Detailed S-wave velocity structure of sediment and crust off Sanriku, Japan by a new analysis method for distributed acoustic sensing data using a seafloor cable and seismic interferometry"
<p>Cross-spectra used in "Detailed S-wave velocity structure of sediment and crust off Sanriku, Japan, derived from distributed acoustic sensing data collected using a seafloor cable with seismic interferometry", by Shun Fukushima, Masanao Shinohara, Kiwamu Nishida, Akiko Takeo, Tomoaki Yamada, and Kiyoshi Yomogida </p> <p>For more information, please contact Shun Fukushima (s-fuku@eri.u-tokyo.ac.jp)</p>
Extended Supplement for De-noising distributed acoustic sensing data using an adaptive frequency-wavenumber filter
<p>This electronical extended supplement presents an adaptive frequency-wavenumber filter suppresses the incoherent seismic noise while amplifying the coherent wave field in seismic distributed acoustic sensing (DAS) data. We analyse the response of the filter in time and spectral domain, and we demonstrate its performance on a noisy data set that was recorded in a vertical borehole observatory showing active and passive seismic phase arrivals. In these data we can suppress the noise up to 20 dB. This data publication serves as a repository for data an plotting scripts.</p>
DAS Data for the figure in the paper entitled "A Hybrid Earthquake Detection Method for Distributed Acoustic Sensing Array Data and Its Application to the 2022 Menyuan Earthquake Sequence"
<p>The DAS data can be loaded using numpy. The sampling rate is 100 Hz, and each row is a time series for that channel.</p>
Cross-spectra used in "Retrieval and precise phase-velocity estimation of Rayleigh waves by the spatial autocorrelation method between distributed acoustic sensing and seismometer data"
<p>Cross-spectra used in "Retrieval and precise phase-velocity estimation of Rayleigh waves by the spatial autocorrelation method between distributed acoustic sensing and seismometer data</p> <p>", by Shun Fukushima, Masanao Shinohara, Kiwamu Nishida, Akiko Takeo, Tomoaki Yamada, and Kiyoshi Yomogida </p> <p>For more information, please contact Shun Fukushima (s-fuku@eri.u-tokyo.ac.jp)</p>
Cross-correlated ambient data recorded on a distributed acoustic sensing array
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Classification of a synthetic strain rate dataset acquired with Distributed Acoustic Sensing (DAS).
<p>The videos show the real-time acquisition of a strain rate dataset acquired with a Distributed Acoustic Sensing (DAS). The observations are collected per bloc of 4s and are segmented in six sources: noise, pedestrian, impact, backhoe, compactor, leaks. The first video shows the source identification and segmentation using a Random Forest classifier; the second video combines a Random Markov Field to the Random Forest classifier.</p>
Denoised data for the manuscript "Self-Supervised Coherence-Based Denoising on Cryoseismological Distributed Acoustic Sensing Data"
<p>This dataset contains denoised sections of Distributed Acoustic Sensing (DAS) data, generated using a J-invariant autoencoder. It facillitates the reproduction of the results presented in the paper "Self-Supervised Coherence-Based Denoising on Cryoseismological Distributed Acoustic Sensing Data."</p> <p><br>Abstract:<br><br>One major challenge in cryoseismology is that signals of interest are often buried within the high noise level emitted by a multitude of environmental processes. Events of interest potentially stay unnoticed and remain unanalyzed, particularly because conventional sensors cannot monitor an entire glacier. However, with Distributed Acoustic Sensing (DAS), we can observe seismicity over multiple kilometers. DAS systems turn common fiber-optic cables into seismic arrays that measure strain rate data, enabling researchers to acquire seismic data in hard-to-access areas with high spatial and temporal resolution. We deployed a DAS system on Rhonegletscher, Switzerland, using a 9 km long fiberoptic cable that covered the entire glacier, from its accumulation to its ablation zone, recording seismicity for one month. The highly active and dynamic cryospheric environ ment, in combination with poor coupling, resulted in DAS data characterized by a low Signal-to-Noise Ratio (SNR) compared to classical point sensors. Our objective is to ef fectively denoise this dataset.<br>We use a self-supervised J -invariant U-net autoencoder capable of separating incoherent environmental noise from temporally and spatially coherent signals of interest (e.g., stick-slip or crevasse signals). The method shows enhanced inter-channel coherence, increased SNR, and significantly improved visibility of the icequakes. Further, we compare different training data types varying in recording position, wavefield component, and waveform diversity. Our approach has the potential to enhance the detection capabilities of events of interest in cryoseismological DAS data, hence to improve the understanding of processes within Alpine glaciers.</p>
Deformation-rate distributed acoustic sensing: filtering and coupling considerations
<p class="MsoBodyText">Deformation-rate distributed acoustic sensing (DAS), made available by the unique designs of some interrogator units, can acquire seismic data that are equivalent to the along-fiber particle velocity motion recorded by geophones for scenarios involving (near-)elastic ground-fiber coupling. While near-elastic coupling is straightforward to achieve in cemented downhole installations, it is less clear how to do so in lower-cost horizontal fiber deployments. This investigation illustrates that near-elastic coupling can be achieved by installing and freezing in fiber in shallow backfilled trenches. Any existing residual unwanted signals can then be removed through 1D or our preferred 2D dip-velocity filtering approaches. The processed deformation-rate field records acquired at the surface and two different trenching depths show clear surface- and refracted-wave arrivals; however, the surface-deployed fiber records exhibit significant time-lapse amplitude variations corresponding variable ground-fiber coupling due to air-temperature fluctuations about 0ºC. These results indicate that ground freezing provides improved but more complex fiber coupling.</p>
Data and scripts for: An analysis of the dynamic range of Distributed Acoustic Sensing for Earthquake Early Warning
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Monitoring Spatiotemporal Seismic Velocity Changes Using Seismic Interferometry and Distributed Acoustic Sensing in Mexico City
<h2>Cross-Correlation Functions (CCFs) Data Files</h2> <p>The dataset consists of four zipped files:</p> <ul> <li> <p><strong>cc_25hz_das1_Freq_0.40-1.20hz.zip</strong></p> <ul> <li> <p>Contains CCFs using DAS fiber-1, sampled at 25 Hz, for the frequency range of 0.40–1.20 Hz.</p> </li> </ul> </li> <li> <p><strong>cc_25hz_das1_Freq_1.20-3.60hz.zip</strong></p> <ul> <li>Contains CCFs using DAS fiber-1, sampled at 25 Hz, for the frequency range of 1.20–3.60 Hz.</li> </ul> </li> <li> <p><strong>cc_25hz_das2_Freq_0.40-1.20hz.zip</strong></p> <ul> <li> <p>Contains CCFs using DAS fiber-2, sampled at 25 Hz, for the frequency range of 0.40–1.20 Hz.</p> </li> </ul> </li> <li> <p><strong>cc_25hz_das2_Freq_1.20-3.60hz.zip</strong></p> <ul> <li>Contains CCFs using DAS fiber-2, sampled at 25 Hz, for the frequency range of 1.20–3.60 Hz.</li> </ul> </li> </ul> <h4> </h4>
Deformation-rate distributed acoustic sensing: filtering and coupling considerations
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