Velocity‐Based Earthquake Detection Using Downhole Distributed Acoustic Sensing—Examples from the San Andreas Fault Observatory at Depth

2019 ◽  
Vol 109 (6) ◽  
pp. 2491-2500 ◽  
Author(s):  
Ariel Lellouch ◽  
Siyuan Yuan ◽  
William L. Ellsworth ◽  
Biondo Biondi
Keyword(s):  
San Andreas Fault ◽  
Seismic Velocity ◽  
Incidence Angle ◽  
Signal To Noise Ratio ◽  
Plane Waves ◽  
Detection Algorithm ◽  
Angle Of Incidence ◽  
Acoustic Sensing ◽  
San Andreas ◽  
Distributed Acoustic Sensing

Abstract Conventional seismographic networks sparsely sample the wavefields excited by earthquakes. Thus, standard event detection is conducted by analyzing separate stations and merging their results. Emerging distributed acoustic sensing recording technologies allow for unbiased spatial sampling of the wavefield and, as a result, array‐based processing of the recorded signals. Using a cemented fiber in the San Andreas Fault Observatory at Depth main hole, 800 virtual receivers are sampled at a 1 m interval from the surface to 800 m depth. Recorded earthquakes are approximated as plane waves reaching the bottom of the array first. Following this assumption, the relative travel times of the recorded event depend on the local velocity at the array location and the angle of incidence at which the planar wavefront reaches it. Given the seismic velocity, a newly proposed detection algorithm amounts to a single‐parameter scan of the incidence angle and measurement of data coherency along the different possible travel‐time curves. Using the entire recording array, a much higher effective signal‐to‐noise ratio can be obtained when compared to individual channel processing. About 20 days of recorded seismic activity from the San Andreas Fault is analyzed. Using a downhole single array, the majority of cataloged events in the area are detected. In addition, a previously unknown event is unveiled. We estimate its magnitude at roughly −0.5.

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

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. KS149-KS160 ◽  
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.

Distributed acoustic sensing for seismic monitoring in challenging environments

2020 ◽  
Author(s):  
Zack Spica ◽  
Takeshi Akuhara ◽  
Gregory Beroza ◽  
Biondo Biondi ◽  
William Ellsworth ◽  
...  
Keyword(s):  
Seismic Velocity ◽  
Velocity Estimation ◽  
Motion Sensors ◽  
Recording Technology ◽  
Acoustic Sensing ◽  
Seismic Properties ◽  
Seismic Recording ◽  
Distributed Acoustic Sensing ◽  
Geotechnical Information ◽  
Observation Bias

<p>Our understanding of subsurface processes suffers from a profound observation bias: ground-motion sensors are rare, sparse, clustered on continents and not available where they are most needed. A new seismic recording technology called distributed acoustic sensing (DAS), can transform existing telecommunication fiber-optic cables into arrays of thousands of sensors, enabling meter-scale recording over tens of kilometers of linear fiber length. DAS works in high-pressure and high-temperature environments, enabling long-term recordings of seismic signals inside reservoirs, fault zones, near active volcanoes, in deep seas or in highly urbanized areas.</p><p>In this talk, we will introduce this laser-based technology and present three recent cases of study. The first experiment is in the city of Stanford, California, where DAS measurements are used to provide geotechnical information at a scale normally unattainable (i.e., for each building) with traditional geophone instrumentation. In the second study, we will show how downhole DAS passive recordings from the San Andreas Fault Observatory at Depth can be used for seismic velocity estimation. In the third research, we use DAS (in collaboration with Fujitec) to understand the ocean physics and infer seismic properties of the seafloor under a 100 km telecommunication cable.</p>

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>

scholarly journals Damage and seismic velocity structure of pulverized rocks near the San Andreas Fault

2013 ◽  
Vol 118 (6) ◽  
pp. 2813-2831 ◽  
Author(s):  
Marieke Rempe ◽  
Thomas Mitchell ◽  
Jörg Renner ◽  
Stuart Nippress ◽  
Yehuda Ben-Zion ◽  
...  
Keyword(s):  
Velocity Structure ◽  
San Andreas Fault ◽  
Seismic Velocity ◽  
Seismic Velocity Structure ◽  
San Andreas ◽  
Pulverized Rocks

scholarly journals 3D vertical seismic profile acquired with distributed acoustic sensing on tubing installation: A case study from the CO2CRC Otway Project

2019 ◽  
Vol 7 (1) ◽  
pp. SA11-SA19 ◽  
Author(s):  
Julia Correa ◽  
Roman Pevzner ◽  
Andrej Bona ◽  
Konstantin Tertyshnikov ◽  
Barry Freifeld ◽  
...  
Keyword(s):  
Fiber Length ◽  
Fiber Optic ◽  
Signal To Noise Ratio ◽  
Seismic Profile ◽  
Lessons Learned ◽  
Vertical Seismic Profile ◽  
Acoustic Sensing ◽  
Vsp Data ◽  
Distributed Acoustic Sensing

Distributed acoustic sensing (DAS) can revolutionize the seismic industry by using fiber-optic cables installed permanently to acquire on-demand vertical seismic profile (VSP) data at fine spatial sampling. With this, DAS can solve some of the issues associated with conventional seismic sensors. Studies have successfully demonstrated the use of DAS on cemented fibers for monitoring applications; however, such applications on tubing-deployed fibers are relatively uncommon. Application of tubing-deployed fibers is especially useful for preexisting wells, where there is no opportunity to install a fiber behind the casing. In the CO2CRC Otway Project, we acquired a 3D DAS VSP using a standard fiber-optic cable installed on the production tubing of the injector well. We aim to analyze the quality of the 3D DAS VSP on tubing, as well as discuss lessons learned from the current DAS deployment. We find the limitations associated with the DAS on tubing, as well as ways to improve the quality of the data sets for future surveys at Otway. Due to the reduced coupling and the long fiber length (approximately 20 km), the raw DAS records indicate a high level of noise relative to the signal. Despite the limitations, the migrated 3D DAS VSP data recorded by cable installed on tubing are able to image interfaces beyond the injection depth. Furthermore, we determine that the signal-to-noise ratio might be improved by reducing the fiber length.

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>

scholarly journals Localization and Discrimination of the Perturbation Signals in Fiber Distributed Acoustic Sensing Systems Using Spatial Average Kurtosis

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2839 ◽  
Author(s):  
Fei Jiang ◽  
Honglang Li ◽  
Zhenhai Zhang ◽  
Yixin Zhang ◽  
Xuping Zhang
Keyword(s):  
False Alarm ◽  
Signal To Noise Ratio ◽  
Moving Average ◽  
Spatial Dimension ◽  
Time Domain Reflectometry ◽  
Spatial Average ◽  
Acoustic Sensing ◽  
Sensing Systems ◽  
Distributed Acoustic Sensing ◽  
Optical Time

Location error and false alarm are noticeable problems in fiber distributed acoustic sensing systems based on phase-sensitive optical time-domain reflectometry (Φ-OTDR). A novel method based on signal kurtosis is proposed to locate and discriminate perturbations in Φ-OTDR systems. The spatial kurtosis (SK) along the fiber is firstly obtained by calculating the kurtosis of acoustic signals at each position of the fiber in a short time period. After the moving average on the spatial dimension, the spatial average kurtosis (SAK) is then obtained, whose peak can accurately locate the center of the vibration segment. By comparing the SAK value with a certain threshold, we may to some degree discriminate the instantaneous destructive perturbations from the system noise and certain ambient environmental interferences. The experimental results show that, comparing with the average of the previous localization methods, the SAK method improves the pencil-break and digging locating signal-to-noise ratio (SNR) by 16.6 dB and 17.3 dB, respectively; and decreases the location standard deviation by 7.3 m and 9.1 m, respectively. For the instantaneous destructive perturbation (pencil-break and digging) detection, the false alarm rate can be as low as 1.02%, while the detection probability is maintained as high as 95.57%. In addition, the time consumption of the SAK method is adequate for a real-time Φ-OTDR system.

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