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

Date
2019
Authors
Tariq, Hasan
Touati, Farid
Al-Hitmi, Mohammed Abdulla E.
Crescini, Damiano
Mnaouer, Adel Ben
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI AG
Abstract
Earthquakes 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.
Description
This 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.
Keywords
Applied methods, Early warning, Earthquake, Inclinometers, Internet of Things (IoT), Real-time detection, Seismic waves
Citation
Tariq, 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/app9183650