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.
Highlights
Natural disasters occur on the globe every year with earthquakes and floods being the most devastating and horrible on the loss and damage benchmarks
The transmission from the structural health monitoring (SHM) nodes to the gateway was started, and the packets PN were sent by the smart geospatial bi-axial inclinometer nodes (SGBINs) to gateway(from Equation (6))
The systematically ordered process was initiated with a gradient map auto-calibration that eradicated the heterogeneous sensor placement plane errors and centered the inclinometer’s x-axis at y = 0, followed by data refinement by impairing noise of frequency higher than 24 Hz using a de-noising filter
Summary
Natural disasters occur on the globe every year with earthquakes and floods being the most devastating and horrible on the loss and damage benchmarks. The number of people reported affected by natural disasters (564.4 million) was the highest since 2006, as compared to the last 10 years [1], amounting to 1.5 times its annual average (224 million). The estimates of natural disaster economic damages (US$154 billion) place last year as the fifth costliest since 2006, 12% above the 2006–2015 annual average registered in the CRED database. Earthquakes or seismic events have proven to be the most obvious and recurring in all [2] the natural disasters, i.e., 14,568 in 2018. The event characterization and detection would have resulted in countermeasures against these disasters and considerable safety would have been observed. The necessity of an expeditious mechanism was observed in [1,2] that
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