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Advanced Technology and Data Analysis in Seismology

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 December 2025 | Viewed by 268

Special Issue Editor


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Guest Editor
Departamento de Geociencias, Universidad Nacional de Colombia, Bogota, Colombia
Interests: geophysics; seismotectonics; geodynamics; basin analysis; hydrocarbon exploration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Various of the planet’s physical properties interact in a way that generates seismic phenomena. Hence, seismic monitoring observatories have become monitoring networks for geophysical variables that involve elastic, magnetic, electric, gravimetric, and thermal fields. The study of these broad types of signals implies the development of diverse detection and analysis strategies to understand the seismic source, the propagation environment, and possible space–time windows in which to consolidate early warning systems.

This Special Issue seeks to document new experiences in monitoring seismic and volcanic sources based on recent technological trends. Additionally, we hope to further analyze approaches using traditional or disruptive techniques in order to understand the physics of the sources and the development of early warning systems for making decisions within the framework of public disaster management policies.

Prof. Dr. Carlos Alberto Vargas-Jiménez
Guest Editor

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Keywords

  • networks
  • seismic signals
  • instrumentation
  • sensors
  • early warning systems
  • geophysical variables
  • AI in seismology

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Published Papers (1 paper)

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Research

12 pages, 876 KiB  
Article
Self-Contained Earthquake Early Warning System Based on Characteristic Period Computed in the Frequency Domain
by Marinel Costel Temneanu, Codrin Donciu and Elena Serea
Appl. Sci. 2025, 15(16), 9026; https://doi.org/10.3390/app15169026 - 15 Aug 2025
Abstract
This study presents the design, implementation, and experimental validation of a self-contained earthquake early warning system (EEWS) based on real-time frequency-domain analysis of ground motion. The proposed system integrates a low-noise triaxial micro-electro-mechanical system (MEMS) accelerometer with a high-performance microcontroller, enabling autonomous seismic [...] Read more.
This study presents the design, implementation, and experimental validation of a self-contained earthquake early warning system (EEWS) based on real-time frequency-domain analysis of ground motion. The proposed system integrates a low-noise triaxial micro-electro-mechanical system (MEMS) accelerometer with a high-performance microcontroller, enabling autonomous seismic event detection without dependence on external communications or centralized infrastructure. The characteristic period of ground motion (τc) is estimated using a spectral moment method applied to the first three seconds of vertical acceleration following P-wave arrival. Event triggering is based on a short-term average/long-term average (STA/LTA) algorithm, with alarm logic incorporating both spectral and amplitude thresholds to reduce false positives from low-intensity or distant events. Experimental validation was conducted using a custom-built uniaxial shaking table, replaying 10 real earthquake records (Mw 4.1–7.7) in 20 repeated trials each. Results show high repeatability in τc estimation and strong correlation with event magnitude, demonstrating the system’s reliability. The findings confirm that modern embedded platforms can deliver rapid, robust, and cost-effective seismic warning capabilities. The proposed EEW solution is well-suited for deployment in critical infrastructure and resource-limited seismic regions, supporting scalable and decentralized early warning applications. Full article
(This article belongs to the Special Issue Advanced Technology and Data Analysis in Seismology)
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