Depremler Arası Sürelerin Dağılım Modelleriyle Değerlendirilmesinde Yeni Bir Yöntem, Türkiye Örneği

Depremler arası sürelerin (DAS) dağılımlarını modellemek amacıyla birçok dağılım modeli kullanılmıştır. Kullanılan dağılım modellerinin başarı ölçütü olarak da, verilerin modellerle olan uyumu göz önüne alınmış ancak bu dağılım modellerinin uygulamadaki başarısı denenmemiştir. Ayrıca, hangi dağılım modelinin daha başarılı olduğu konusunda ise kesinlik derecesinde sonuç elde edilememiştir. Bu bağlamda, çalışmanın amacı Türkiye depremlerinin zaman alanında simülasyonunu gerçekleştirerek hangi modelin daha uyumlu veri ürettiğini test etmektir. Çalışma kapsamında, geçmiş depremlerin zamansal dağılımları kullanılarak DAS verileri elde edilmiş, Monte-Karlo (MK) yöntemi ile de yapay depremler üretilmiştir. Üretilen depremlerin zamansal dağılımları gözlemlenmiş, yıllık deprem sayılarının ortalama ve standart sapma değerleri hesaplanmıştır. Sonuç olarak, hem verilerin modellenmesi açısından hem de üretilen verilerin benzerliği göz önüne alındığında, log-normal dağılım modelinin en başarılı model olduğu görülmüştür.

A New Method for the Evaluation of Interevent Time Distribution of Earthquakes, Case of Turkey

Several distribution models are used in order to model the distribution of the inter-event times of earthquakes. The performances of these models are mostly tested through goodness-of-fit tests but not through putting the models into application. Moreover, there is not a definitive conclusion about which model can be the best fitting one. Within this context, the objective of study is to test a number of distribution models in simulating the distribution of Turkish earthquakes in temporal domain. The earthquake time series are subjected to analysis for its IET distribution and future earthquakes are simulated by using Monte-Carlo techniques. The variation of number of earthquakes per unit time and the standard deviations are monitored for each IET model. Finally, log-normal distribution is identified as the most successful distribution model in modelling and simulating the earthquake time series

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