Arabian Journal of Geosciences

, Volume 7, Issue 7, pp 2893–2904 | Cite as

A spatial statistical analysis of the occurrence of earthquakes along the Red Sea floor spreading: clusters of seismicity

Original Paper

Abstract

The aim of this study is to apply spatial pattern analysis techniques to a seismic data catalog of earthquakes beneath the Red Sea to try and detect clusters and explore global and local spatial patterns in the occurrence of earthquakes over the years from 1900 to 2009 using a geographical information system (GIS). The spatial pattern analysis techniques chosen for this study were quadrant count analysis, average nearest neighbor, global Moran’s I, Getis–Ord general G, Anselin Local Moran’s I, Getis–Ord Gi*, kernel density estimation, and geographical distributions. Each of these techniques was implemented in the GIS so that computations could be carried out quickly and efficiently. Results showed that (1) these techniques were capable of detecting clusters in the spatial patterns of the occurrence of the earthquakes; (2) both global and local spatial statistics indicate that earthquakes were clustered in the study area beneath the Red Sea; (3) earthquakes with higher magnitudes on the Richter scale were notably concentrated in the central and southern parts of the Red Sea where seismic activities were most active; and (4) earthquakes with moderate magnitudes on the Richter scale were particularly concentrated in the northern part of the Red Sea where there is an area of late-stage continental rifting comprised of a broad trough without a recognizable spreading center, although there were several small, isolated deep troughs. We conclude that the pattern analysis techniques applied to the seismic data catalog of earthquakes beneath the Red Sea could detect clusters in the occurrence of earthquakes from 1900 to 2009.

Keywords

Earthquakes Clusters Spatial pattern analysis Spatial statistics GIS Red Sea 

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Copyright information

© Saudi Society for Geosciences 2013

Authors and Affiliations

  • Khalid Al-Ahmadi
    • 1
  • Abdullah Al-Amri
    • 2
  • Linda See
    • 3
    • 4
  1. 1.King Abdulaziz City for Science and TechnologyRiyadhSaudi Arabia
  2. 2.King Saud UniversityRiyadhSaudi Arabia
  3. 3.International Institute for Applied Systems AnalysisLaxenburgAustria
  4. 4.Centre for Advanced Spatial AnalysisUniversity College LondonLondonUK

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