Abstract
This article evaluates the occurrence of 0 [Formula: see text]M[Formula: see text] 8 earthquake data sets for the period of 50 years (that is, January 1, 1966 to December 31, 2015) in African and Western Asia region. It is bounded by latitude 40° S to 40° N and longitude 30° W to 60° E with the focal depth of 0-700 km. Seventy seven thousand, six hundred and ninety-six data points were presented for the analysis. The data used were extracted from earthquake catalog of Advanced National Seismic system via http://quake.geo.berkeley.edu/cnss/, an official website of the Northern California Earthquake Data Centre, USA. Each datum comprised the earthquake occurrence date, time of the earthquake occurrence, epicenter's coordinates, focal depth and magnitude. The Gutenberg-Richter's relationship being the longest observed empirical relationship in seismology, analysis of variance and time series were used to analyze the seismicity of the study area. Annual distributions of earthquake occurrence based on magnitude variations with the limit 0 [Formula: see text]M[Formula: see text] 8 were presented. The two constants a and b in the Gutenberg-Richter's equation, magnitude of completeness (MC) adjusted R-Square and F-value for the period of 1966-1975, 1976-1985, 1986-1995, 1996-2005, 2006-2015, and the entire period of investigation ranging from 1966 to 2015 were determined so as to investigate the variations of these parameters on earthquake occurrence over time. The histograms of earthquake occurrence against magnitude of earthquakes for the selected years (1966-1975, 1976-1985, 1986-1995, 1996-2005, 2006-2015, and 1966-2015), and the decadal frequency distributions of earthquake occurrence were also plotted. The focal depth occurrence for each magnitude bins (0-0.9, 1-1.9, 2-2.9, 3-3.9, 4-4.9, 5-5.9, 6-6.9, 7-7.9, 8-8.9) were grouped into shallow, intermediate, and deep depths ranging from 0 to 70, 71 to 300, and 301 to 700 km as being used in seismology. The neural network analysis was also applied to the magnitude of the earthquake. The network uses a time series magnitude data as input with the output being the magnitude of the following day. If the nature of the earthquakes time series is stochastic, modeling and prediction is possible. The earthquake data sets presented in this article can further be adopted in the study of seismicity pattern, b-value using series of models, earthquake prediction and variations of earthquake parameters on African and/or Arabian plates. When this approach is integrated with other technique(s), it can provide insights to stability of African lithospehric plates especially the coastal region of Africa.