###### 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. Keywords:applied methods; earthquake; seismic waves; real-time detection; early warning; inclinometers; Internet of Things (IoT) +
Footnote †: journal: Applied  
could gradually create awareness using amplitude thresholding and frequency filtering in a swift manner in the form of early-stage warnings for human life and asset safety. Recent advancements [3] in the earth, planetary, and space studies have created new horizons for real-time early warning studies of disasters. The subsurface [4] camera (SAMERA) capitalizing the three-layered distributed iterative computing paradigm for three prototypes, (1) travel-time seismic tomography (TomoTT); (2) migration-based microseismic imaging (MMI); (3) ambient noise seismic imaging (ANSI), was very comprehensive in 3D velocity maps and subsurface realization with a need for early warning and orientation compensation. The development of an integrated earthquake simulation system for Istanbul and utilization of ambient vibration measurements for risk assessment [5] at an urban scale for Beirut had created a need for a geospatially oriented seismic early warning awareness. The four degrees of freedom [6] ground motion simulation platform (GMSP) was also a comprehensive contribution to a gap of real-time ground motion detection capabilities. The design challenges with the Marmara Sea case study [7] for such a system that was resourceful in major seismic events like volcanic, lithospheric, and tsunamic activity could only be provisioned in ubiquitous scenarios, i.e., geo-spatially present in urban infrastructure. Recent achievements [8] in geo-seismic or ground motion instrumentation and multi-variable urban-scale telemetry created the potential for damage post-analysis by immense computation at the national level with high costs. The GPS assisted geo-magnetic storm technique [9] and broadband seismic network health monitoring method needed improvement focused on the real-time systems-level approach. A few seconds to less than a minute warning before the arrival of damaging ground motion needed minimum calculations and discrete mechanism defined in [10] as multi-objective SHM prediction algorithm (MOSPA) needed further enrichment for geo-seismic cases that have not been observed in the literature before.