24 Ocak 2020 Sivrice (Elazığ) Depreminin Şartlı Olasılığının Değerlendirilmesi

Bu çalışmada dört farklı istatistiksel dağılım modeli (Weibull, Rayleigh, üstel ve log-normal dağılım modelleri) uygulanarak 24 Ocak 2020 Sivrice (Elazığ) depreminin şartlı olasılığı hesaplanmıştır. Bu hesaplamalar için, bölgede 1900 yılı ile 2019 yılları arasında meydana gelmiş M≥4,7 depremlerin tekrarlama periyotları kullanılmıştır. Modeller ile kullanılan veri arasındaki uyum ilişkisi üç farklı test kriteri (Olabilirlik değeri (lnL), Akaike ve Bayesian bilgi kriteri) ile değerlendirilirmiştir. Log-normal model, çalışma verisini en iyi temsil eden model olarak belirlenmiştir. Çalışma sonuçlarına göre Sivrice (Elazığ) depreminin şartlı olasılık değerleri (t=0 ve te=2 için); Log-normal modele göre %80, üstel modele göre %72, Weibull modele göre %70, Rayleigh modele göre %36 olarak hesaplanmıştır.

Evaluation of the Conditional Probability of January 24, 2020 Sivrice (Elazığ) Earthquake

In this study, the conditional probability of 24 January 2020 Sivrice (Elazığ) earthquake was calculated by applying four different statistical distribution models (Weibull, Rayleigh, exponential and log-normal distribution models). For these calculations, the recurrence periods of M≥4.7 earthquakes that occurred between 1900 and 2019 in the region were used. The fitting between the models and the data was evaluated with three different test criteria (Likelihood value (lnL), Akaike and Bayesian information criteria). Log-normal model was determined as the model that best represents the study data. According to the results of the study, the conditional probability values of the Elazig (Sivrice) earthquake (for t = 0 and te = 2) were calculated as 80% to the log-normal model, as 72% to the exponential model, as 70% to the Weibull model, and as 36% to the Rayleigh model.

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Çukurova Üniversitesi Mühendislik Fakültesi dergisi-Cover
  • ISSN: 2757-9255
  • Yayın Aralığı: Yılda 4 Sayı
  • Başlangıç: 2009
  • Yayıncı: ÇUKUROVA ÜNİVERSİTESİ MÜHENDİSLİK FAKÜLTESİ
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