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Development of smoothed seismicity models for seismic hazard assessment in the Red Sea region

Author

Listed:
  • Mohamed S. Abdalzaher

    (National Research Institute of Astronomy and Geophysics (NRIAG))

  • Sayed S. R. Moustafa

    (National Research Institute of Astronomy and Geophysics (NRIAG))

  • Mohamed Yassien

    (National Research Institute of Astronomy and Geophysics (NRIAG))

Abstract

The Red Sea region, situated between the Arabian and African Plates, experiences significant seismic activity due to its tectonic dynamics, with earthquakes ranging from minor to potentially destructive events. This study aims to develop smoothed seismicity models for the region by using an enhanced seismic catalog specific to the Red Sea. This facilitates a detailed spatial and temporal analysis of seismic events, focusing on seismic source characterization essential for probabilistic seismic hazard assessments. A rigorous declustering method excludes foreshocks and aftershocks, focusing on independent seismic events. The analysis uses a spatial grid (0.1 $$^{\circ }$$ ∘ cells in latitude and longitude) to determine seismic event rates, which are then refined using various smoothing techniques. Special attention is given to seismic activity within 0–35 kms of depth, leading to distinct rate models that inform urban development and seismic hazard mitigation strategies in the Red Sea area. These models are crucial for improving resilience, safety, and informed decision-making for urban planning and disaster preparedness, addressing the challenges posed by the region’s tectonic and seismic complexities.

Suggested Citation

  • Mohamed S. Abdalzaher & Sayed S. R. Moustafa & Mohamed Yassien, 2024. "Development of smoothed seismicity models for seismic hazard assessment in the Red Sea region," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(13), pages 12515-12544, October.
    • Handle: RePEc:spr:nathaz:v:120:y:2024:i:13:d:10.1007_s11069-024-06695-x
    DOI: 10.1007/s11069-024-06695-x
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    References listed on IDEAS

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    1. Omar Hamdy & Hanan Gaber & Mohamed S. Abdalzaher & Mahmoud Elhadidy, 2022. "Identifying Exposure of Urban Area to Certain Seismic Hazard Using Machine Learning and GIS: A Case Study of Greater Cairo," Sustainability, MDPI, vol. 14(17), pages 1-24, August.
    2. H. Hussein & K. Abou Elenean & I. Marzouk & A. Peresan & I. Korrat & E. Abu El-Nader & G. Panza & M. El-Gabry, 2008. "Integration and magnitude homogenization of the Egyptian earthquake catalogue," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 47(3), pages 525-546, December.
    3. Mohamed S. Abdalzaher & Hussein A. Elsayed & Mostafa M. Fouda & Mahmoud M. Salim, 2023. "Employing Machine Learning and IoT for Earthquake Early Warning System in Smart Cities," Energies, MDPI, vol. 16(1), pages 1-22, January.
    4. L. Hagos & R. Arvidsson & R. Roberts, 2006. "Application of the Spatially Smoothed Seismicity and Monte Carlo Methods to Estimate the Seismic Hazard of Eritrea and the Surrounding Region," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 39(3), pages 395-418, December.
    Full references (including those not matched with items on IDEAS)

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