Distributed Acoustic Sensing from mHz to kHz: Empirical Investigations of DAS Instrument Response

With the upside of high spatial and temporal sampling even in remote or urban areas using existing fiber-optic infrastructure, Distributed Acoustic Sensing (DAS) is in the process of revolutionising the way we look at seismological data acquisition.&#160;However, recent publications show variations of the quality of DAS measurements along a single cable. In addition to site- and orientation effects, data quality is strongly affected by the transfer function between the deforming medium and the fiber, which in turn depends on the fiber-ground coupling and the cable properties. Analyses of the DAS instrument response functions in a limited part of the seismological frequency band are typically based on comparisons with well-coupled conventional seismometers for which the instrument response is sufficiently well known to be removed from the signal.In this study, we extend the common narrow-band analyses to DAS response analyses covering a frequency range of five orders of magnitude ranging from ~4000 s period to frequencies up to ~100&#160;Hz. This is based on a series of experiments in Switzerland, including (1) active controlled-source experiments with co-located seismometers and geophones, (2)&#160;low-frequency strain induced by hydraulic injection in a borehole with co-located Fiber-Bragg-Grating (FBG) strain-meters, and (3) local to teleseismic ice- and earthquake recordings with &#160;co-located broadband stations.Initial results show a site-unspecific, approximately flat instrument response for all experiments.The initial results suggest that the amplitude and phase information of DAS recordings are sufficient for conventional geophysical methods such as event localisation, full-waveform inversion, ambient noise tomography and even event magnitude estimation. Despite the promising initial results, further engagement by the DAS community is required to evaluate the DAS performance and repeatability among different interrogation units and study sites.

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Empirical Investigations of the Instrument Response for Distributed Acoustic Sensing (DAS) across 17 Octaves

Bulletin of the Seismological Society of America ◽

10.1785/0120200185 ◽

2020 ◽

Cited By ~ 1

Author(s):

Patrick Paitz ◽

Pascal Edme ◽

Dominik Gräff ◽

Fabian Walter ◽

Joseph Doetsch ◽

...

Keyword(s):

Ground Motion ◽

Urban Areas ◽

Waveform Inversion ◽

Acoustic Sensing ◽

Full Waveform ◽

Environmental Properties ◽

Instrument Response ◽

Distributed Acoustic Sensing ◽

Reference Measurements ◽

Seismic Networks

ABSTRACT With the potential of high temporal and spatial sampling and the capability of utilizing existing fiber-optic infrastructure, distributed acoustic sensing (DAS) is in the process of revolutionizing geophysical ground-motion measurements, especially in remote and urban areas, where conventional seismic networks may be difficult to deploy. Yet, for DAS to become an established method, we must ensure that accurate amplitude and phase information can be obtained. Furthermore, as DAS is spreading into many different application domains, we need to understand the extent to which the instrument response depends on the local environmental properties. Based on recent DAS response research, we present a general workflow to empirically quantify the quality of DAS measurements based on the transfer function between true ground motion and observed DAS waveforms. With a variety of DAS data and reference measurements, we adapt existing instrument-response workflows typically in the frequency band from 0.01 to 10 Hz to different experiments, with signal frequencies ranging from 1/3000 to 60 Hz. These experiments include earthquake recordings in an underground rock laboratory, hydraulic injection experiments in granite, active seismics in agricultural soil, and icequake recordings in snow on a glacier. The results show that the average standard deviations of both amplitude and phase responses within the analyzed frequency ranges are in the order of 4 dB and 0.167π radians, respectively, among all experiments. Possible explanations for variations in the instrument responses include the violation of the assumption of constant phase velocities within the workflow due to dispersion and incorrect ground-motion observations from reference measurements. The results encourage further integration of DAS-based strain measurements into methods that exploit complete waveforms and not merely travel times, such as full-waveform inversion. Ultimately, our developments are intended to provide a quantitative assessment of site- and frequency-dependent DAS data that may help establish best practices for upcoming DAS surveys.

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Reciprocity-gap misfit functional for Distributed Acoustic Sensing, combining data from passive and active sources

Geophysics ◽

10.1190/geo2020-0305.1 ◽

2020 ◽

pp. 1-59 ◽

Cited By ~ 1

Author(s):

Florian Faucher ◽

Maarten V. de Hoop ◽

Otmar Scherzer

Keyword(s):

Quantitative Imaging ◽

A Priori ◽

Waveform Inversion ◽

Low Frequency ◽

Inversion Method ◽

Acoustic Sensing ◽

Use Of Data ◽

Combining Data ◽

Distributed Acoustic Sensing ◽

Priori Information

Quantitative imaging of sub-surface Earth’s properties in elastic media is performed from Distributed Acoustic Sensing data. A new misfit functional based upon the reciprocity-gap is designed, taking cross-correlations of displacement and strain, and these products further associate an observation with a simulation. In comparison with other misfit functionals, this one has the advantage to only require little a-priori information on the exciting sources. In particular, the misfit criterion enables the use of data from regional earthquakes (teleseismic events can be included as well), followed by exploration data to perform a multi-resolution reconstruction. The data from regional earthquakes contain the low-frequency content which is missing in the exploration ones, allowing for the recovery of the long spatial wavelength, even with very few sources. These data are used to build prior models for the subsequent reconstruction from the higher-frequency exploration data. This gives the elastic Full Reciprocity-gap Waveform Inversion method, and we demonstrate its performance with a pilot experiment for elastic isotropic reconstruction.

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Combining Distributed Acoustic Sensing and Beamforming in a Volcanic Environment on Mount Meager, British Columbia.

10.5194/egusphere-egu21-7927 ◽

2021 ◽

Author(s):

Sara Klaasen ◽

Patrick Paitz ◽

Jan Dettmer ◽

Andreas Fichtner

Keyword(s):

British Columbia ◽

Power Distribution ◽

High Frequency ◽

Low Frequency ◽

Volcanic Tremor ◽

Extreme Environment ◽

Volcanic Complex ◽

Acoustic Sensing ◽

Distributed Acoustic Sensing ◽

Volcanic Environment

We present one of the first applications of Distributed Acoustic Sensing (DAS) in a volcanic environment. The goals are twofold: First, we want to examine the feasibility of DAS in such a remote and extreme environment, and second, we search for active volcanic signals of Mount Meager in British Columbia (Canada).&#160;The Mount Meager massif is an active volcanic complex that is estimated to have the largest geothermal potential in Canada and caused its largest recorded landslide in 2010. We installed a 3-km long fibre-optic cable at 2000 m elevation that crosses the ridge of Mount Meager and traverses the uppermost part of a glacier, yielding continuous measurements from 19 September to 17 October 2019.We identify ~30 low-frequency (0.01-1 Hz) and 3000 high-frequency (5-45 Hz) events. The low-frequency events are not correlated with microseismic ocean or atmospheric noise sources and volcanic tremor remains a plausible origin. The frequency-power distribution of the high-frequency events indicates a natural origin, and beamforming on these events reveals distinct event clusters, predominantly in the direction of the main peaks of the volcanic complex. Numerical examples show that we can apply conventional beamforming to the data, and that the results are improved by taking the signal-to-noise ratio of individual channels into account.The increased data quantity of DAS can outweigh the limitations due to the lower quality of individual channels in these hazardous and remote environments. We conclude that DAS is a promising tool in this setting that warrants further development.

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Distributed Acoustic Sensing Using Dark Fiber for Array Detection of Regional Earthquakes

Seismological Research Letters ◽

10.1785/0220200416 ◽

2021 ◽

Author(s):

Avinash Nayak ◽

Jonathan Ajo-Franklin ◽

Keyword(s):

Urban Areas ◽

Geothermal Field ◽

Seismic Events ◽

Broadband Seismometer ◽

Acoustic Sensing ◽

Array Detection ◽

Distributed Acoustic Sensing ◽

Back Azimuth ◽

Ubiquitous Presence

Abstract The intrinsic array nature of distributed acoustic sensing (DAS) makes it suitable for applying beamforming techniques commonly used in traditional seismometer arrays for enhancing weak and coherent seismic phases from distant seismic events. We test the capacity of a dark-fiber DAS array in the Sacramento basin, northern California, to detect small earthquakes at The Geysers geothermal field, at a distance of ∼100 km from the DAS array, using beamforming. We use a slowness range appropriate for ∼0.5–1.0 Hz surface waves that are well recorded by the DAS array. To take advantage of the large aperture, we divide the ∼20 km DAS cable into eight subarrays of aperture ∼1.5–2.0 km each, and apply beamforming independently to each subarray using phase-weighted stacking. The presence of subarrays of different orientations provides some sensitivity to back azimuth. We apply a short-term average/long-term average detector to the beam at each subarray. Simultaneous detections over multiple subarrays, evaluated using a voting scheme, are inferred to be caused by the same earthquake, whereas false detections caused by anthropogenic noise are expected to be localized to one or two subarrays. Analyzing 45 days of continuous DAS data, we were able to detect all earthquakes with M≥2.4, while missing most of the smaller magnitude earthquakes, with no false detections due to seismic noise. In comparison, a single broadband seismometer co-located with the DAS array was unable to detect any earthquake of M<2.4, many of which were detected successfully by the DAS array. The seismometer also experienced a large number of false detections caused by spatially localized noise. We demonstrate that DAS has significant potential for local and regional detection of small seismic events using beamforming. The ubiquitous presence of dark fiber provides opportunities to extend remote earthquake monitoring to sparsely instrumented and urban areas.

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Application of machine learning to microseismic event detection in distributed acoustic sensing data

Geophysics ◽

10.1190/geo2019-0774.1 ◽

2020 ◽

Vol 85 (5) ◽

pp. KS149-KS160 ◽

Cited By ~ 1

Author(s):

Anna L. Stork ◽

Alan F. Baird ◽

Steve A. Horne ◽

Garth Naldrett ◽

Sacha Lapins ◽

...

Keyword(s):

Detection Algorithm ◽

Data Sets ◽

Data Set ◽

Acoustic Sensing ◽

Detection Techniques ◽

Real Noise ◽

Temporal Sampling ◽

Manual Inspection ◽

Microseismic Events ◽

Distributed Acoustic Sensing

This study presents the first demonstration of the transferability of a convolutional neural network (CNN) trained to detect microseismic events in one fiber-optic distributed acoustic sensing (DAS) data set to other data sets. DAS increasingly is being used for microseismic monitoring in industrial settings, and the dense spatial and temporal sampling provided by these systems produces large data volumes (approximately 650 GB/day for a 2 km long cable sampling at 2000 Hz with a spatial sampling of 1 m), requiring new processing techniques for near-real-time microseismic analysis. We have trained the CNN known as YOLOv3, an object detection algorithm, to detect microseismic events using synthetically generated waveforms with real noise superimposed. The performance of the CNN network is compared to the number of events detected using filtering and amplitude threshold (short-term average/long-term average) detection techniques. In the data set from which the real noise is taken, the network is able to detect >80% of the events identified by manual inspection and 14% more than detected by standard frequency-wavenumber filtering techniques. The false detection rate is approximately 2% or one event every 20 s. In other data sets, with monitoring geometries and conditions previously unseen by the network, >50% of events identified by manual inspection are detected by the CNN.

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Low-Frequency Distributed Acoustic Sensing for Early Gas Detection in a Wellbore

IEEE Sensors Journal ◽

10.1109/jsen.2020.3038738 ◽

2020 ◽

pp. 1-1

Author(s):

Jyotsna Sharma ◽

Theo Cuny ◽

Oloruntoba Ogunsanwo ◽

Otto Santos

Keyword(s):

Low Frequency ◽

Gas Detection ◽

Acoustic Sensing ◽

Distributed Acoustic Sensing

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Backward piping erosion detection in levees by fiber optic acoustic sensing

10.5194/egusphere-egu2020-7465 ◽

2020 ◽

Author(s):

Juan Pablo Aguilar-López ◽

Andres Garcia-Ruiz ◽

Thom Bogaard ◽

Miguel Gonzalez-Herraez

Keyword(s):

Ambient Noise ◽

Fiber Optic ◽

Geophysical Methods ◽

Innovative Technology ◽

Fiber Optic Cable ◽

Acoustic Sensing ◽

Characteristic Frequencies ◽

Distributed Acoustic Sensing ◽

Piping Erosion ◽

Set Up

Backward piping erosion (BEP) is considered the most dangerous failure mode for levees due to its unpredictable nature. This erosive process happens most of the time underneath the impermeable layers on which levees are commonly founded. This makes it very difficult to detect as conventional geophysical methods are either too expensive or too imprecise for real time monitoring of longitudinal soil made structures such as Dams or levees. Fiber optic based distributed acoustic sensing (DAS) is an innovative technology which allows to retrieve information from an acoustic propagating medium in a spatially dense manner by using a fiber optic cable. The present study aimed to explore the potential of DAS for early detection of BEP&#160; under levees based on the frictional emissions of the sand grains during the erosive process. The tests were performed in the lab under controlled ambient noise conditions. The technology was tested by embedding fiber optic based microphones underneath and outside a laboratory scaled aquifer set up capable of recreating BEP. The results show that indeed the process emits certain characteristic frequencies which may be located between 1200 to 1600 Hz and and that they can easily be captured by the fiber optic cables.

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