A real-time early warning seismic event detection algorithm using smart geo-spatial bi-axial inclinometer nodes for Industry 4.0 applications

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dc.contributor.authorTariq, Hasan
dc.contributor.authorTouati, Farid
dc.contributor.authorAl-Hitmi, Mohammed Abdulla E.
dc.contributor.authorCrescini, Damiano
dc.contributor.authorMnaouer, Adel Ben
dc.date.accessioned2020-01-23T11:52:39Z
dc.date.available2020-01-23T11:52:39Z
dc.date.copyright2019en_US
dc.date.issued2019
dc.descriptionThis article is not available at CUD collection. The version of scholarly record of this article is published in Applied Sciences (2019), available online at: https://doi.org/10.3390/app9183650.en_US
dc.description.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.en_US
dc.description.sponsorship"Qatar National Research Fund, Qatar Foundation"en_US
dc.identifier.citationTariq, H., Touati, F., Al-Hitmi, M. A. E., Crescini, D., & Mnaouer, A. B. (2019). A real-time early warning seismic event detection algorithm using smart geo-spatial bi-axial inclinometer nodes for industry 4.0 applications. Applied Sciences (Switzerland), 9(18). https://doi.org/10.3390/app9183650en_US
dc.identifier.issn20763417
dc.identifier.urihttp://dx.doi.org/10.3390/app9183650
dc.identifier.urihttp://hdl.handle.net/20.500.12519/13
dc.language.isoenen_US
dc.publisherMDPI AGen_US
dc.relationAuthors Affiliations: Tariq, H., Department of Electrical Engineering, College of Engineering, Qatar University, Doha, 2713, Qatar; Touati, F., Department of Electrical Engineering, College of Engineering, Qatar University, Doha, 2713, Qatar; Al-Hitmi, M.A.E., Department of Electrical Engineering, College of Engineering, Qatar University, Doha, 2713, Qatar; Crescini, D., Dipartimento di Ingegneria delI'Informazione, Brescia University, Brescia, 25121, Italy; Mnaouer, A.B., Department of Computer Engineering and Computational Sciences, Faculty of Engineering, Applied Sciences and Technology, Canadian University Dubai, Dubai, 117781, United Arab Emirates
dc.relation.ispartofseriesApplied Sciences (Switzerland);Vol. 9, no. 18
dc.rightsPermission to reuse abstract has been secured from MDPI AG.
dc.rights.holderCopyright : 2019 by the authors.
dc.subjectApplied methodsen_US
dc.subjectEarly warningen_US
dc.subjectEarthquakeen_US
dc.subjectInclinometersen_US
dc.subjectInternet of Things (IoT)en_US
dc.subjectReal-time detectionen_US
dc.subjectSeismic wavesen_US
dc.titleA real-time early warning seismic event detection algorithm using smart geo-spatial bi-axial inclinometer nodes for Industry 4.0 applicationsen_US
dc.typeArticleen_US

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