Sisam/Samos Adası Depremine ait Konum Bilgilerinin Parçacık Sürü Optimizasyonu Yöntemi ile Belirlenmesi

Year 2022, Volume: 24 Issue: 71, 613 - 630, 16.05.2022

Şenol Özyalın

https://doi.org/10.21205/deufmd.2022247125

Cited By: 1

Abstract

Bu çalışmada, rastlantısal veri tabanına sahip meta-sezgisel algoritmalar içinde yer alan Parçacık Sürü Optimizasyonunun (PSO) bir depremin konumunun belirleme çalışmalarında kullanılması irdelenmiştir. Bu algoritmanın hem sentetik deprem modeli hem de gerçek bir depremin konum belirleme çözümlerinde uygulanabilirliği ve etkinliği gösterilmiştir. Gürültüsüz ve gürültü içeren sentetik deprem modelinin konum belirlemesi başarı ile değerlendirilmiştir. Hem sentetik deprem modeli hem de gerçek depremin konum belirleme sonuçlarının olasılık yoğunluk fonksiyonları hesaplanarak elde edilen kestirim parametre değerlerinin güven aralığı içinde kaldığını göstermiştir. Ayrıca enlem-boylam, enlem-derinlik ve boylam-derinlik için üretilen hata enerjisi haritaları hazırlanarak çözüm sonuçları irdelenmiştir. Yöntem, Ege Denizi içinde Samos fayı üzerinde meydana gelen depremin 39 adet sismik istasyonda kaydedilen P ve S dalgalarının varış zamanları kullanılarak konum belirleme çalışması yapılmıştır. Bu deprem çeşitli sismolojik merkezlerince çözümü yapılmış olup, sadece Afet ve Acil Durum Yönetimi Başkanlığı (AFAD) tarafından açıklanan sonuçları ile karşılaştırılarak, yüzde bağıl hata oranları ile birlikte konumlar arasındaki uzaklıkları belirlenmiştir. PSO çözümünden parametre kestirim sonuçları enlem, boylam ve odak derinliği sırasıyla 37,827o, 26,650o ve 16,544 km’dir. AFAD tarafından belirlenen merkez üssü ve odak derinliği değerleri ile PSO çözümüyle elde edilen değerler karşılaştırıldığında enleme göre birbirlerinden olan uzaklık binde bir hata ile 5,26 km, boylamda binde iki hata ile 6,0 km ve derinlikte ise yüzde 1 hata ile 1,64 km elde edilmiştir. Son olarak iki merkez üssü arasındaki uzaklık farkı 7,98 km elde edilmiştir.

Keywords

Parçacık Sürü Optimizasyonu, merkez üssü, odak derinliği

Determination of the Location Information of the Sisam/Samos Island Earthquake by Particle Swarm Optimization Method

Abstract

In this study, the use of Particle Swarm Optimization (PSO), which is one of the meta-heuristic algorithms with a random database, in the determination of the location of an earthquake is examined. The applicability and effectiveness of this algorithm in both the synthetic earthquake model and the location determination solutions of a real earthquake have been demonstrated. The position determination of the noiseless and noise-containing synthetic earthquake model has been successfully evaluated. It has been shown that the estimation parameter values obtained by calculating the probability density functions of the location determination results of both the synthetic earthquake model and the real earthquake remain within the confidence interval. In addition, error energy maps produced for latitude-longitude, latitude-depth and longitude-depth were prepared and the solution results were examined. The method was used to determine the location of the earthquake that occurred on the Samos fault in the Aegean Sea by using the arrival times of the P and S waves recorded at 39 seismic stations. This earthquake was solved by various seismological centers, and the distances between the locations were determined, along with the percentage error rates, by comparing only the results announced by the Disaster and Emergency Management Presidency (AFAD). The parameter estimation results from the PSO solution are latitude, longitude, and focal depth 37,827o, 26,650o and 16,544 km, respectively. When the epicenter and focal depth values determined by AFAD are compared with the values obtained with the PSO solution, the distance from each other with respect to latitude is 5,26 km with an error of one thousand, 6,0 km with an error of two per thousand in longitude and 1,64 km with an error of 1 percent in depth. Finally, the distance difference between the two epicenters was 7,98 km.

Keywords

Particle Swarm Optimization, epicenter, depth of focus

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