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Application of Data Processing in Earthquake Science

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Physics General".

Deadline for manuscript submissions: 20 April 2026 | Viewed by 489

Special Issue Editor


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Guest Editor
Department of Physics and Astronomy, Faculty of Environment, Science and Economy, University of Exeter, Exeter EX4 4QL, UK
Interests: tectonics; time series analysis; geographic information system

Special Issue Information

Dear Colleagues,

Modern data processing techniques are reshaping various fields of Earthquake Science, including remote sensing, early warning and forecasting, and even data recovery and storage. Machine learning (ML) pipelines, hybrid models that combine crowdsourced smartphone triggers, satellite geodesy, acoustic sensing with well-established time-series analysis, and probabilistic decision-making have been reported to successfully address issues such as location and magnitude estimation, magnitude saturation, coseismic slip, and postseismic deformation.

We are pleased to invite researchers, experts, and scholars to submit their work to this Special Issue of Applied Sciences. We welcome original research, case studies and review articles that highlight advances in data processing models and techniques for addressing seismology and earthquake-related hazards. This Special Issue aims to bring together cutting-edge research that will advance both theory and practice in this field, fostering interdisciplinary collaboration and providing actionable insights for earthquake monitoring and mitigation. Contributions may address, but are not limited to, the following topics:  

  • Earthquake ground motion;
  • Next-generation earthquake monitoring;
  • Statistical methods in earthquake data analysis and interpretation;
  • Case studies from recent large seismic events;
  • Resilient and intelligent earthquake warning systems;
  • Management of big data in earthquake monitoring;
  • Real-time magnitude estimation and localization;
  • Machine learning in earthquake early warning;
  • Time domain analysis.

We look forward to receiving your contributions.

Dr. Konstantina Papadopoulou
Guest Editor

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Keywords

  • time domain analysis
  • earthquake ground motion
  • next-generation earthquake monitoring
  • statistical methods in earthquake data analysis and interpretation
  • case studies from recent large seismic events
  • resilient and intelligent earthquake warning systems
  • big data management in earthquake monitoring
  • real-time magnitude estimation and localization
  • machine learning in earthquake early warning

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Published Papers (1 paper)

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Research

18 pages, 2725 KB  
Article
Recent Advances on the VAN Method
by Nicholas V. Sarlis, Efthimios S. Skordas and Panayiotis A. Varotsos
Appl. Sci. 2025, 15(19), 10516; https://doi.org/10.3390/app151910516 - 28 Sep 2025
Viewed by 322
Abstract
In the 1980s, Varotsos, Alexopoulos and Nomicos (VAN) introduced a short -term earthquake (EQ) prediction method based on measurements of the electric field of the Earth at various locations on the Earth’s surface. The corresponding electric signals are called Seismic Electric Signals (SES). [...] Read more.
In the 1980s, Varotsos, Alexopoulos and Nomicos (VAN) introduced a short -term earthquake (EQ) prediction method based on measurements of the electric field of the Earth at various locations on the Earth’s surface. The corresponding electric signals are called Seismic Electric Signals (SES). Here, we present the advances of the VAN method during the period 2022–2025. For this purpose, we make use of the VAN telemetric network comprising of eight geoelectric field stations that have operated in Greece since the 1990s. The SES reported and documented well in advance (at arxiv.org) are compared with the subsequent seismicity in Greece during the same study period. The comparison reveals that all strong EQs of magnitude M5.8 within the area N34.541.5E20.027.5 have been preceded by SES activities, thus leading to a hit rate of 100%. The study of the present results points to the need of continuing VAN experimentation in Greece. Moreover, we employ the Receiver Operation Characteristics (ROC) method to evaluate the performance of the method. Study of the ROC reveals a false alarm rate of approximately 5% which is shown to be statistically significant, while the method can be characterized as outstanding. Full article
(This article belongs to the Special Issue Application of Data Processing in Earthquake Science)
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