Estimating the recurrence of earthquakes with statistical methods in the city of Bingöl, Eastern Turkey: a district-based approach

Abstract

This study discusses the temporal distribution of earthquake magnitudes in the city of Bingöl, near Karlıova Triple Junction. We determine the probability distributions and return periods of earthquakes for all districts of Bingöl. Bingöl has eight districts; namely Adaklı, Central, Genç, Karlıova, Kiğı, Solhan, Yayladere, and Yedisu. In six of them, active faults were mapped previously (Adaklı, Central, Genç, Karlıova, Solhan, and Yedisu). We consider 5 time-dependent probability distributions for analysis. Using the annual maximum earthquake magnitudes, the best fit arises from the Gumbel distribution for Central, Karlıova, and Adaklı Districts. For the Genç District, where the least maximum earthquake magnitude is reported, the Weibull distribution gives the best fit. The return period and maximum annual earthquake magnitude relations suggest the following results. For the Central and Karlıova Districts along which maximum earthquake magnitudes are reported, every 250 years a 6.7 M, and 7.2 M occurs respectively. These results are compatible with the results of paleo-seismological data reported along the NAFZ and the EAFZ. For a 10-year return period, earthquake magnitudes reach 3.9 and 5.1 in all districts. It is important to note that in the Yedisu District, the maximum earthquake magnitudes seem as 5.1 M for the 1000-year return period, incompatible with previously published findings probably because low quality seismic data in this region.

Evaluation of Return Period and Occurrence Probability of the Maximum Magnitude Earthquakes in Iraq and Surroundings

“…Although it is difficult to provide an exact date for a predicted earthquake, it is feasible to estimate its likelihood with a certain degree of inaccuracy. Probability distribution functions are important for estimating earthquake hazards [15]. A method for applying a straightforward point process approach to various characteristics of recorded seismicity is the statistical modeling of earthquakes [16], [17].…”

“…A number of studies have used Gutenberg-Richter to evaluate the probability of earthquake occurrence and the return period, for example, in Chile [1], India [21], Indonesia [22], [23], [24], Nebal [25], Turkey [26], Algeria [27]. Many researchers used one or more statistical distribution models (Gumbel, Gaussian, Lognormal, Gamma, and Weibull), to estimate the probability of earthquakes occurrence and the return period, e.g., Indonesia [28], [29], Japan [30], and Red Sea [31], [14], Saudi Arabia [32], and Turkey [33], [15], and China [8], [34].…”