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Sensor Technologies for Seismic Monitoring
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Sensor Technologies for Seismic Monitoring

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 951

Special Issue Editors

Prof. Dr. Yibo Wang

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Guest Editor
Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
Interests: seismic monitoring instruments and methods
Prof. Dr. Wentao Zhang

E-Mail Website
Guest Editor
Institute of Seimiconductors, Chinese Academy of Sciences, Beijing 100083, China
Interests: fiber optic sensor technology; optoelectronic sensor and application
Dr. Qingwen Liu

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Guest Editor
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: fiber-optic sensors and applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Danping Cao

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Guest Editor
School of Geosciences, China University of Petroleum (Huadong), Qingdao 266580, China
Interests: geophysics of oil and gas reservoirs; seismic wave propagation in viscoelastic media

Special Issue Information

Dear Colleagues,

Recently, we have seen a growing interest in seismic monitoring, which safeguards human lives through the comprehension of such phenomena. Advances in technology have allowed more and more sophisticated seismic networks to be designed; modern seismic networks based on new sensor technologies can detect a massive number of earthquakes, generating an extremely large dataset for analysis. The application of technology such as ultra-sensitive MEMS-based and interferometric accelerometers, distributed optical fiber sensing (DOFS), and distributed acoustic sensing (DAS) based on fiber optics technology has dramatically improved the seismic monitoring capability. Moreover, there are many geophysical phenomena that we are able to record with seismic networks.

This Special Issue, therefore, aims to highlight advances in sensor technologies for seismic monitoring and data analysis methods. Original research and review articles on advanced techniques of seismic monitoring are welcome.

Prof. Dr. Yibo Wang
Prof. Dr. Wentao Zhang
Dr. Qingwen Liu
Prof. Dr. Danping Cao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • seismic sensors
  • seismic monitoring instruments
  • seismic monitoring data analysis methods
  • seismic monitoring applications
  • AI for seismic sensing and monitoring
  • fiber-optic sensors for seismic applications

Published Papers (1 paper)

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Research

27 pages, 20869 KiB  
Article
Seismic Monitoring of a Deep Geothermal Field in Munich (Germany) Using Borehole Distributed Acoustic Sensing
by Jérôme Azzola and Emmanuel Gaucher
Sensors 2024, 24(10), 3061; https://doi.org/10.3390/s24103061 - 11 May 2024
Viewed by 400
Abstract
Geothermal energy exploitation in urban areas necessitates robust real-time seismic monitoring for risk mitigation. While surface-based seismic networks are valuable, they are sensitive to anthropogenic noise. This study investigates the capabilities of borehole Distributed Acoustic Sensing (DAS) for local seismic monitoring of a [...] Read more.
Geothermal energy exploitation in urban areas necessitates robust real-time seismic monitoring for risk mitigation. While surface-based seismic networks are valuable, they are sensitive to anthropogenic noise. This study investigates the capabilities of borehole Distributed Acoustic Sensing (DAS) for local seismic monitoring of a geothermal field located in Munich, Germany. We leverage the operator’s cloud infrastructure for DAS data management and processing. We introduce a comprehensive workflow for the automated processing of DAS data, including seismic event detection, onset time picking, and event characterization. The latter includes the determination of the event hypocenter, origin time, seismic moment, and stress drop. Waveform-based parameters are obtained after the automatic conversion of the DAS strain-rate to acceleration. We present the results of a 6-month monitoring period that demonstrates the capabilities of the proposed monitoring set-up, from the management of DAS data volumes to the establishment of an event catalog. The comparison of the results with seismometer data shows that the phase and amplitude of DAS data can be reliably used for seismic processing. This emphasizes the potential of improving seismic monitoring capabilities with hybrid networks, combining surface and downhole seismometers with borehole DAS. The inherent high-density array configuration of borehole DAS proves particularly advantageous in urban and operational environments. This study stresses that realistic prior knowledge of the seismic velocity model remains essential to prevent a large number of DAS sensing points from biasing results and interpretation. This study suggests the potential for a gradual extension of the network as geothermal exploitation progresses and new wells are equipped, owing to the scalability of the described monitoring system. Full article
(This article belongs to the Special Issue Sensor Technologies for Seismic Monitoring)
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