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AuthorTariq H.
AuthorTouati F.
AuthorAl-Hitmi M.A.E.
AuthorCrescini D.
AuthorMnaouer A.B.
Available date2020-04-23T14:21:34Z
Publication Date2019
Publication NameApplied Sciences (Switzerland)
ISSN20763417
URIhttp://dx.doi.org/10.3390/app9183650
URIhttp://hdl.handle.net/10576/14360
AbstractEarthquakes are one of the major natural calamities as well as a prime subject of interest for seismologists, state agencies, and ground motion instrumentation scientists. The real-time data analysis of multi-sensor instrumentation is a valuable knowledge repository for real-time early warning and trustworthy seismic events detection. In this work, an early warning in the first 1 micro-second and seismic wave detection in the first 1.7 milliseconds after event initialization is proposed using a seismic wave event detection algorithm (SWEDA). The SWEDA with nine low-computation-cost operations is being proposed for smart geospatial bi-axial inclinometer nodes (SGBINs) also utilized in structural health monitoring systems. SWEDA detects four types of seismic waves, i.e., primary (P) or compression, secondary (S) or shear, Love (L), and Rayleigh (R) waves using time and frequency domain parameters mapped on a 2D mapping interpretation scheme. The SWEDA proved automated heterogeneous surface adaptability, multi-clustered sensing, ubiquitous monitoring with dynamic Savitzky-Golay filtering and detection using nine optimized sequential and structured event characterization techniques. Furthermore, situation-conscious (context-aware) and automated computation of short-time average over long-time average (STA/LTA) triggering parameters by peak-detection and run-time scaling arrays with manual computation support were achieved. - 2019 by the authors.
SponsorFunding: This publication was made possible by the NPRP grant # 8-1781-2-725 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Languageen
PublisherMDPI AG
SubjectApplied methods
SubjectEarly warning
SubjectEarthquake
SubjectInclinometers
SubjectInternet of Things (IoT)
SubjectReal-time detection
SubjectSeismic waves
TitleA real-time early warning seismic event detection algorithm using smart geo-spatial bi-axial inclinometer nodes for Industry 4.0 applications
TypeArticle
Issue Number18
Volume Number9


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