Source parameter estimation from DAS microseismic data

Cole, Steve; Karrenbach, Martin; Kahn, Dan; Rich, Jamie; Silver, Ken; Langton, David

doi:10.1190/segam2018-2995716.1

Cited by 27 publications

(13 citation statements)

References 3 publications

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“…Generally, DAS studies have focused on seismic wave phase information, which is sufficient to model seismic wavefield velocities, for example, in vertical seismic profiling (Daley et al, ; Mateeva et al, ), ambient noise velocity inversions (Ajo‐Franklin et al, ; Dou et al, ; Zeng et al, ), and earthquake phase identification (Ajo‐Franklin et al, ; Jousset et al, ; Lindsey et al, ; Yu et al, ). However, true ground motion amplitudes are necessary for many other seismological processing tasks, including full‐waveform inversion, AVO analysis, moment tensor inversion, and attenuation analysis, which the DAS community will likely investigate in the near future (Cole et al, ; Paitz et al, ).…”

Section: Introductionmentioning

confidence: 99%

On the Broadband Instrument Response of Fiber‐Optic DAS Arrays

2020

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Distributed Acoustic Sensing (DAS) is a novel tool in array seismology that measures the phase of backscattered laser pulses traveling in a fiber‐optic cable, and relates this measurement to the axial strain induced on the cable by a propagating seismic wavefield. Combining DAS with telecommunications optical fiber networks has begun to address a range of earth science questions where cost and field logistics have historically hindered observations. Unlike classic inertial seismometers, DAS instrument response is presently unquantified. This topic includes a variable sensing element—the fiber, including packaging and installation—which changes between experiments. Ignoring this element, one DAS record should approximate a fixed‐length strain gauge, which exactly measures Earth's motion down to quasi‐static frequencies relevant to geodesy. In this paper, we test this hypothesis using seismological observations of teleseismic earthquakes and microseism noise spanning the 1 to 120 s period range. We use a commercial DAS interrogator unit connected to an optical fiber previously used for telecommunication and a colocated broadband seismometer to estimate the DAS transfer function. We find a 1:1 correspondence with actual ground motion from 10–120 s. At shorter periods (1–10 s), DAS amplitude response is enhanced by 3–11 dB. Phase response is flat over this range of periods. We interpret the recovered DAS response function in terms of hypothesized fiber coupling and photonic effects. We propose this calibration methodology for future DAS experiments where seismic amplitude information is desired.

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Section: Introductionmentioning

confidence: 99%

On the Broadband Instrument Response of Fiber‐Optic DAS Arrays

2020

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“…They simultaneously utilize spatiotemporal patterns across hundreds or thousands of channels and can thus overcome the lower SNR of individual channels. DAS can also be used for focal mechanism [99,111] and magnitude [14,105] estimation. For both, the directional measurement of DAS poses a significant challenge, as not all components of the seismic wavefield excited by the source can be recorded.…”

Section: Figurementioning

confidence: 99%

Seismic Applications of Downhole DAS

Lellouch

Biondi

2021

Sensors

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Distributed Acoustic Sensing (DAS) is gaining vast popularity in the industrial and academic sectors for a variety of studies. Its spatial and temporal resolution is ever helpful, but one of the primary benefits of DAS is the ability to install fibers in boreholes and record seismic signals in depth. With minimal operational disruption, a continuous sampling along the trajectory of the borehole is made possible. Such resolution is highly challenging to obtain with conventional downhole tools. This review article summarizes different seismic uses, passive and active, of downhole DAS. We emphasize current DAS limitations and potential ways to overcome them.

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“…DAS data provides only a single-component recording (the fiber is sensitive to changes in strain along the fiber, but insensitive to changes broadside to the cable) whereas standard geophones and seismometers can provide three-component recordings. Nevertheless, processing techniques are advancing to determine microseismic event locations (Webster et al, 2016;Verdon et al, 2020) and source mechanisms (Cole et al, 2018;Baird et al, 2020) from DAS data. The technology is also being explored for microseismic monitoring in other industrial settings, for example at geothermal sites (Mondanos and Coleman, 2019), geological CO 2 storage sites and for volcano monitoring.…”

Section: Das For Microseismic Monitoringmentioning

confidence: 99%

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

et al. 2020

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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 is being increasingly 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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Source parameter estimation from DAS microseismic data

Cited by 27 publications

References 3 publications

On the Broadband Instrument Response of Fiber‐Optic DAS Arrays

On the Broadband Instrument Response of Fiber‐Optic DAS Arrays

Seismic Applications of Downhole DAS

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

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