Thanks to the broadband nature of the Distributed Acoustic Sensing (DAS) measurement, a roadside section of the Stanford DAS-2 array can record seismic signals from various sources. For example, it measures the earth's quasi-static distortion caused by the weight of cars (<0.8 Hz), and Rayleigh waves induced by earthquakes (<3 Hz) and by dynamic car-road interactions (3-20 Hz). We directly utilize the excited surface waves for shallow shear-wave velocity inversion. Rayleigh waves induced by passing cars have a consistent fundamental mode and a noisier first mode. By stacking dispersion images of 33 passing cars, we obtain stable dispersion images. The frequency range of the fundamental mode can be extended by adding the low-frequency earthquake-induced Rayleigh waves. Thanks to the extended frequency range, we can achieve better depth coverage and resolution for shear-wave velocity inversion. In order to assure clear separation from Love waves and aligning apparent velocity with phase velocity, we choose an earthquake that is approximately in line with the array. The inverted models match those obtained by a conventional geophone survey performed by a geotechnical service company contracted by Stanford University using active sources from the surface until about 50 meters. In order to automate the Vs inversion process, we introduce a new objective function that avoids manual dispersion curve picking. We construct a 2-D Vs profile by performing independent 1-D inversions at multiple locations along the fiber. From the low-frequency quasi-static distortion recordings, we invert for a single Poisson's ratio at each location along the fiber. We observe spatial heterogeneity of both Vs and Poisson's ratio profiles. Our approach is dramatically cheaper than ambient field interferometry and reliable estimates can be obtained more frequently as no lengthy cross-correlations are required.
Long-term marine acoustic and seismic monitoring using distributed acoustic sensing and deep learning
University of California, Berkeley（加州大学伯克利分校）；Berkeley Seismology Laboratory, University of California, Berkeley（加州大学伯克利分校伯克利地震实验室）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）
Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）
Deep Learning approaches for real, large, and complex scientific data sets can be very challenging to design. In this work, we present a complete search for a finely-tuned and efficiently scaled deep learning classifier to identify usable energy from seismic data acquired using Distributed Acoustic Sensing (DAS). While using only a subset of labeled images during training, we were able to identify suitable models that can be accurately generalized to unknown signal patterns. We show that by using 16 times more GPUs, we can increase the training speed by more than two orders of magnitude on a 5…
Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Rice University（莱斯大学）
Moving loads such as cars and trains are very useful sources of seismic waves, which can be analyzed to retrieve information on the seismic velocity of subsurface materials using the techniques of ambient noise seismology. This information is valuable for a variety of applications such as geotechnical characterization of the near-surface, seismic hazard evaluation, and groundwater monitoring. However, for such processes to converge quickly, data segments with appropriate noise energy should be selected. Distributed Acoustic Sensing (DAS) is a novel sensing technique that enables acquisition of…
Near real-time channel selection for Distributed Acoustic Sensing technology
University of Pisa（比萨大学）；Ruhr University Bochum（波鸿鲁尔大学）；Institut de Ciències del Mar, CSIC（海洋科学研究所，西班牙国家科学研究委员会）；Istituto Nazionale di Geofisica e Vulcanologia（意大利国家地球物理与火山学研究所）
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 …
We show the capabilities of a downhole Distributed Acoustic Sensing (DAS) array in detecting, locating and characterizing low-magnitude earthquakes occurring in the vicinity of the Frontier Observatory for Research in Geothermal Energy (FORGE) site in Utah. 10.5 days of continuous data were acquired in a monitoring well at the FORGE geothermal site during the initial stimulation of an Enhanced Geothermal System in April-May 2019. Earthquake activity beneath Mineral Mountains, Utah also occurred within 10 km of the FORGE monitoring well. During the experiment, four events from those areas were …
Training a Distributed Acoustic Sensing Traffic Monitoring Network With Video Inputs
School of Physics and Astronomy, Tel-Aviv University（物理与天文学院，特拉维夫大学）；School of Electrical Engineering, Tel-Aviv University（电气工程学院，特拉维夫大学）；Porter School of the Environment and Earth Sciences, Tel-Aviv University（环境与地球科学波特学院，特拉维夫大学）
We rigorously assess the potential for extracting high-resolution, multi-mode surface wave dispersion data from distributed acoustic sensing (DAS) measurements using active-source multichannel analysis of surface waves (MASW). We have utilized both highly-controlled, broadband vibroseis shaker trucks and more-variable, narrow-band sledgehammer sources to excite the near surface, and compare the DAS-derived dispersion data obtained from both source types directly with concurrently acquired traditional geophone-derived dispersion data. We find that the differences between the two sensing approac…
A Distributed Acoustic Sensing Dataset for Vessel Detection and Localization in Submarine Cable Protection
Universidad de Alcalá（阿尔卡拉大学）；Institute of Technology, Universidad San Pablo-CEU, CEU Universities（CEU大学联盟圣巴勃罗-CEU大学理工学院）；Daza de Valdés Institute of Optics (IO-CSIC)（达萨·德·巴尔德斯光学研究所（西班牙国家研究委员会））
Source footprint elimination in full-waveform inversion by model extension: Application to elastic guided waves recorded by distributed acoustic sensing in unconventional reservoir
Distributed Acoustic Sensing (DAS) is an emerging technology for earthquake monitoring and subsurface imaging. The recorded seismic signals by DAS have several distinct characteristics, such as unknown coupling effects, strong anthropogenic noise, and ultra-dense spatial sampling. These aspects differ from conventional seismic data recorded by seismic networks, making it challenging to utilize DAS at present for seismic monitoring. New data analysis algorithms are needed to extract useful information from DAS data. Previous studies on conventional seismic data demonstrated that deep learning m…
An End-to-End Workflow for Fin Whale Song Detection, Note Characterization, and Localization with Distributed Acoustic Sensing
Institut de Ciències del Mar (ICM-CSIC)（海洋科学研究所（ICM-CSIC））；Institut de Física Corpuscular (IFIC), Centre Mixte Universitat de València (UV-CSIC)（粒子物理研究所（IFIC），瓦伦西亚大学-西班牙高等科研理事会混合中心（UV-CSIC））
