###### Abstract
The application-specific [33] seismic sensor nodes and body area network (BAN) makes SHM very intelligent and robust towards seismic event detection and identification and makes the choice of algorithms easier at the cost of flexibility in the SHM system. Recent works [39] realized the utility of (a) adaptive iterative hard thresholding (AIHT), (b) support vector machines (SVM), and (c) the consensus and innovations [40] sequential probability ratio test (CISPRT) [37] and proposed four different algorithms, namely: (1) the least-favorable-density CISPRT; (2) the median CISPRT; (3) the M-CISPRT; and (4) the myriad CISPRT. The approaches [36; 37; 38] were very plausible for uniformly distributed repetitive series of sensors data but required real-time testing on hardware through the instrumentation of micro-displacements. The [PERSON]'s functions [38] played a vital role in surface wave tomography of the Western United States from ambient seismic noise for Rayleigh and Love wave phase velocity maps. The assistance of [PERSON]'s functions in ADC parameter optimization [41] could lead to better results in Industry 4.0-based seismic detection solutions for desktop-scale machines. A promptly swift warning and geo-seismic waves detection solution was needed to address the following challenges:  
1. A geo-seismic constraint sensing node with ADC comprised of 2+ times higher sampling frequency for seismic signals as per the Nyquist criterion and a resolution of \(\pm\)0.0000X. 2. Normalization of a single geo-seismic sensing node (GSSN) or ISCS magnitude offsets induced by placement errors. 3. Normalization of clustered GSSNs or ISCS magnitudes complex orientation errors. 4. Heterogeneous surface orientation errors were reflected in entire measurements and geo-seismic data processing. 5. Gaussian or pseudo-random noise issues in the form of high frequencies and nano- or micro-seismic angular displacement anomalies. 6. Sequential detection of incremental displacement amplitudes to reduce post-computation costs and predict the upcoming threats [11] as primary, secondary, and tertiary alarms. 7. Adaptive amplitude real-time thresholds mapping to reduce incremental filtering costs. 8. Real-time estimation of pre-triggering and post-triggering parameters for recording or event capturing algorithms based on amplitudes, number of samples, and obliged frequencies. 9. Runtime or real-time similarity in amplitudes and frequencies with existing time series of earthquakes that happened in the past in parallel and compare triggering time intervals as discrete occurrences instead. 10. The geometrical exploration of seismic wave kinematics (frequency and amplitudes of two-dimensional angular displacements) for maximum details required for the geo-seismic realization using algorithmic calibration. 11. Methodological, adaptive, and event-driven sensor calibration and measurement optimization capability nodes are enablement towards Industry 4.0 applications. 12. Real-time remote calibration of ADC parameters to optimize low-cost geo-seismic detection using error-compensated, de facto, de jure industry-standard communication buses. This capability constitutes a steppingstone towards Industry 4.0-based condition monitoring benchmarks. 13. Sustainable distant and chronological optimization of real-time seismic event detection. 14. Adaptive real-time scaling of sensors to zoom+/zoom\(-\) in case of micro/macro magnitudes for iterative optimization. 15. The real-time sensor calibration and recording triggering feature for unit sensor nodes as well as sensor clusters is the Industry 4.0 application benchmark for SWEDA. The solution proposed in this work, featuring the above (b)-(o) enhancements, constitutes a sequential synergic combination of methods to address the above real-time challenges called real-time seismic wave event detection algorithm (SWEDA). The implementation of SWEDA was realized by developing a geo-seismic novel node with novelties (a) and (j) to(n).