Autors: Zlatev Z., Vitanova M.K., Hinov, N. L., Marae A.A.O.
Title: A Geospatial Survey of Earthquake Distribution Along Global Fault Lines
Keywords: filtering data, investigating, natural hazard, processing data, seismic excitation

Abstract: Earthquakes are dynamic and often destructive phenomena that occur globally, with some regions experiencing seismic activity on a daily basis. This paper presents a geospatial analysis of earthquake distribution using seismic data recorded by the United States Geological Survey (USGS) between 2023 and 2024. The objective is to investigate the correlation between earthquake depth and magnitude and to identify spatial patterns associated with tectonic processes. The study analyzes two distinct datasets comprising deep-focus and high-magnitude events. Geospatial visualization techniques and statistical tools, including scatter plots and frequency distributions, are employed to highlight depth-magnitude trends across different regions. Findings indicate that shallow earthquakes are more frequent and severe, especially in tectonically active zones such as the Pacific Ring of Fire and the Himalayan belt, while deep-focus events tend to be less frequent and moderate in magnitude. The results emphasize the importance of shallow seismic activity monitoring for disaster preparedness and risk assessment. This study contributes to the broader understanding of global seismic behavior and provides a framework for integrating spatial data with seismological analysis to support hazard mitigation efforts.

References

  1. Scholz, C. H. The Mechanics of Earthquakes and Faulting, 2nd ed., Cambridge University Press, 2002.
  2. Stein, S., & Wysession, M. An Introduction to Seismology, Earthquakes, and Earth Structure, Blackwell Publishing, 2003.
  3. Global Seismic Hazard Assessment Program (GSHAP), International Seismological Centre.[
  4. USGS Earthquake Catalog. Available online: https://earthquake.usgs.gov/earthquakes/search/
  5. Mousavi, S. M., Ellsworth, W. L., Zhu, W., Chuang, L. Y., & Beroza, G. C. "Earthquake transformer—An attentive deep-learning model for simultaneous earthquake detection and phase picking." Nature Communications, 2020, 11(3952).
  6. Kong, Q., Trugman, D. T., Ross, Z. E., Bianco, M. J., & Gerstoft, P. "Machine learning in seismology: Turning data into insights." Seismological Research Letters, 2021, 92(1), pp. 3–14.
  7. Shakibay Senobari, N., Jafari, M. K., & Khodaei, A. "GIS-based spatial analysis and seismic risk mapping in earthquake-prone regions." Natural Hazards, 2022, 110, pp. 1231–1249.
  8. Allen, R. M., et al. (2009). "Earthquake Early Warning Systems: Current Status and Perspectives." Annual Review of Earth and Planetary Sciences.
  9. Petersen, M. D., et al. (2015). "The 2014 United States National Seismic Hazard Model." Earthquake Spectra.

Issue

2025 39th International Conference on Information Technologies, InfoTech 2025 - Proceedings, 2025, Bulgaria, https://doi.org/10.1109/InfoTech67177.2025.11175965

Copyright IEEE

Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus