scholarly journals Using Dark Fiber and Distributed Acoustic Sensing for Near-Surface Characterization and Broadband Seismic Event Detection

2018 ◽  
Author(s):  
Jonathan Ajo-Franklin ◽  
Shan Dou ◽  
Nathaniel Lindsey ◽  
Inder Monga ◽  
Chris Tracy ◽  
...  
Keyword(s):  
Event Detection ◽  
Surface Characterization ◽  
Seismic Event ◽  
Near Surface ◽  
Acoustic Sensing ◽  
Seismic Event Detection ◽  
Distributed Acoustic Sensing

scholarly journals Distributed Acoustic Sensing Using Dark Fiber for Near-Surface Characterization and Broadband Seismic Event Detection

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Jonathan B. Ajo-Franklin ◽  
Shan Dou ◽  
Nathaniel J. Lindsey ◽  
Inder Monga ◽  
Chris Tracy ◽  
...  
Keyword(s):  
Event Detection ◽  
Surface Characterization ◽  
Seismic Event ◽  
Near Surface ◽  
Acoustic Sensing ◽  
Seismic Event Detection ◽  
Distributed Acoustic Sensing

scholarly journals Urban Near-surface Seismic Monitoring using Distributed Acoustic Sensing

2019 ◽  
Author(s):  
Gang Fang ◽  
Yunyue Li ◽  
Yumin Zhao ◽  
Eileen Martin
Keyword(s):  
Seismic Monitoring ◽  
Near Surface ◽  
Acoustic Sensing ◽  
Distributed Acoustic Sensing

scholarly journals Distributed acoustic sensing as a tool for exploration and monitoring: a proof-of-concept

2021 ◽  
Author(s):  
Nicola Piana Agostinetti ◽  
Alberto Villa ◽  
Gilberto Saccorotti
Keyword(s):  
High Resolution ◽  
Plane Wave ◽  
Geothermal Field ◽  
P Wave ◽  
Apparent Velocity ◽  
Near Surface ◽  
Acoustic Sensing ◽  
Geothermal Activity ◽  
Distributed Acoustic Sensing ◽  
Microseismic Event

Abstract. We use PoroTOMO experimental data to compare the performance of Distributed Acoustic Sensing (DAS) and geophone data in executing standard exploration and monitoring activities. The PoroTOMO experiment consists of two "seismic systems": (a) a 8.6 km long optical fibre cable deployed across the Brady geothermal field and covering an area of 1.5 x 0.5 km with 100 m long segments, and (b) an array of 238 co-located geophones with an average spacing of 60 m. The PoroTOMO experiment recorded continuous seismic data between March 10th and March 25th 2016. During such period, a ML 4.3 regional event occurred in the southwest, about 150 km away from the geothermal field, together with several microseismic local events related to the geothermal activity. The seismic waves generated from such seismic events have been used as input data in this study. For the exploration tasks, we compare the propagation of the ML 4.3 event across the geothermal field in both seismic systems in term of relative time-delay, for a number of configurations and segments. Defined the propagation, we analyse and compare the amplitude and the signal-to-noise ratio (SNR) of the P-wave in the two systems at high resolution. For testing the potential in monitoring local seismicity, we first perform an analysis of the geophone data for locating a microseismic event, based on expert opinion. Then, we a adopt different workflow for the automatic location of the same microseismic event using DAS data. For testing the potential in monitoring distant event, data from the regional earthquake are used for retrieving both the propagation direction and apparent velocity of the wavefield, using a standard plane-wave-fitting approach. Our results indicate that: (1) at a local scale, the seismic P-waves propagation and their characteristics (i.e. SNR and amplitude) along a single cable segment are robustly consistent with recordings from co-located geophones (delay-times δt ∼ 0.3 over 400 m for both seismic systems) ; (2) the interpretation of seismic wave propagation across multiple separated segments is less clear, due to the heavy contamination of scattering sources and local velocity heterogeneities; nonetheless, results from the plane-wave fitting still indicate the possibility for a consistent detection and location of the event; (3) at high-resolution (10 m), large amplitude variations along the fibre cable seem to robustly correlate with near surface geology; (4) automatic monitoring of microseismicity can be performed with DAS recordings with results comparable to manual analysis of geophone recordings (i.e. maximum horizontal error on event location around 70 m for both geophones and DAS data) ; and (5) DAS data pre-conditioning (e.g., temporal sub-sampling and channel-stacking) and dedicated processing techniques are strictly necessary for making any real-time monitoring procedure feasible and trustable.

An accelerometer digital front end for efficient seismic event detection support in a wireless sensor node

2014 ◽  
Author(s):  
Fabio Federici ◽  
Roberto Alesii ◽  
Andrea Colarieti ◽  
Fabio Graziosi ◽  
Marco Faccio
Keyword(s):  
Event Detection ◽  
Sensor Node ◽  
Seismic Event ◽  
Wireless Sensor ◽  
Wireless Sensor Node ◽  
Front End ◽  
Seismic Event Detection

Introduction to this special section: Distributed acoustic sensing

2020 ◽  
Vol 39 (11) ◽  
pp. 775-775
Author(s):  
Tieyuan Zhu ◽  
Ariel Lellouch ◽  
Kyle T. Spikes
Keyword(s):  
Event Detection ◽  
Special Section ◽  
Unique Problem ◽  
Fracture Characterization ◽  
Acoustic Sensing ◽  
Distributed Acoustic Sensing ◽  
Work Done ◽  
Microseismic Event

Distributed acoustic sensing (DAS) has seen many advances in recent years with many different applications. This special section contains six papers featuring applications that vary from microseismic event detection to subsea applications to surface deployments, fracture characterization, and well irregularity identification. Each paper introduces a unique problem and then poses the use of DAS in an appropriate way to solve the problem. Special sections on DAS are relatively frequent in the literature currently, so these papers are a snapshot of the work done with the technology. We hope you enjoy these publications.

Automatic seismic event detection using migration and stacking: a performance and parameter study in Hengill, southwest Iceland

2017 ◽  
Vol 209 (3) ◽  
pp. 1866-1877 ◽  
Author(s):  
F. Wagner ◽  
A. Tryggvason ◽  
R. Roberts ◽  
B. Lund ◽  
Ó. Gudmundsson
Keyword(s):  
Event Detection ◽  
Seismic Event ◽  
Parameter Study ◽  
Seismic Event Detection ◽  
A Performance

scholarly journals Event Detection for Distributed Acoustic Sensing: Combining Knowledge-Based, Classical Machine Learning, and Deep Learning Approaches

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7527
Author(s):  
Mugdim Bublin
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Event Detection ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
Efficient System ◽  
Acoustic Sensing ◽  
Advantages And Disadvantages ◽  
Knowledge Based ◽  
Distributed Acoustic Sensing

Distributed Acoustic Sensing (DAS) is a promising new technology for pipeline monitoring and protection. However, a big challenge is distinguishing between relevant events, like intrusion by an excavator near the pipeline, and interference, like land machines. This paper investigates whether it is possible to achieve adequate detection accuracy with classic machine learning algorithms using simulations and real system implementation. Then, we compare classical machine learning with a deep learning approach and analyze the advantages and disadvantages of both approaches. Although acceptable performance can be achieved with both approaches, preliminary results show that deep learning is the more promising approach, eliminating the need for laborious feature extraction and offering a six times lower event detection delay and twelve times lower execution time. However, we achieved the best results by combining deep learning with the knowledge-based and classical machine learning approaches. At the end of this manuscript, we propose general guidelines for efficient system design combining knowledge-based, classical machine learning, and deep learning approaches.

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