Evaluation of datasets and deep learning methods used in earthquake prediction in the context of the February 6, 2023 earthquake

Öz

On February 6, 2023, Türkiye experienced its most severe earthquakes in over 80 years, beginning with a 7.8 (Mw) earthquake, followed by two consecutive 7.5 (Mw) earthquakes nine hours later. The most distinctive feature of this earthquake compared to others is not only that it was more destructive than the others, but also that its impact covered a vast geographical area. There are many studies on earthquake prediction; these studies address topics such as emergency preparations and response planning, risk analysis, or damage estimation. Due to the success of deep learning (DL) algorithms in various fields, using DL methods in earthquake prediction has become a very popular research topic in recent years. Studies using DL methods for earthquake prediction were examined in terms of the DL algorithms and data sets used, with a focus on of whether the earthquakes that occurred on February 6, 2023 and after could be predicted before the earthquake occurred. According to the findings suggest that ionospheric reactions observed before and after the earthquake and the use of the earthquake time series that occurred before the earthquake can be used to predict future earthquakes. However, these results are still preliminary predictions, therefore, it is crucial to expand the early warning system network and to increase the accuracy of real-time prediction models using DL algorithms. Additionally, this study aims to guide future research through a multidisciplinary review of the existing literature. Ultimately, such work will help improve prediction models and contribute to better preparedness for earthquake risks.

Anahtar Kelimeler

• Deep learning
• Earthquake prediction
• Earthquake datasets
• February 6 2023

Deprem tahmininde kullanılan veri setleri ve derin öğrenme yöntemlerinin 6 Şubat 2023 depremi açısından değerlendirilmesi

Öz

Türkiye 6 Şubat 2023'te 7.8 (Mw) büyüklüğünde depremin ardından dokuz saat sonra büyüklüğü 7.5 (Mw) olan art arda iki deprem ile 80 yılı aşkın süredir Türkiye de yaşanan en şiddetli depremleri yaşamış oldu. Bu depremi diğer depremlerden ayıran en belirgin özellik sadece diğerlerinden daha yıkıcı olması değil aynı zamanda etki alanının oldukça geniş bir coğrafyaya yayılmış olmasıdır. Deprem tahmini ile ilgili oldukça fazla çalışma bulunmakta, bunlar acil durum hazırlıkları ve müdahale planlamaları, risk analizi veya hasar tahmini gibi konuları ele almaktadır. Derin öğrenme algoritmalarının birçok alanda elde ettiği başarılar sonucunda, deprem tahmininde derin öğrenme yöntemlerinin kullanımı son yıllarda oldukça popüler bir araştırma konusu haline gelmiştir. Deprem tahmini için derin öğrenme yöntemlerini kullanan çalışmalar incelenmiş ve bunun sonucunda kullanılan derin öğrenme algoritmaları ve veri setleri incelenerek 6 Şubat ve sonrasında gerçekleşen depremlerin, deprem gerçekleşmeden önce tahmin edilip edilemeyeceği sorusuna yanıt aranmıştır. Elde edilen bulgulara göre hem deprem öncesinde hem de sonrasında 'İyonosferik tepkilerin' varlığı ve depremden önce gerçekleşen deprem zaman serisinin kullanımı ile gelecekteki depremlerin tahminlerinin yapılabileceği sonucuna ulaşılmıştır. Ancak buralardan elde edilen sonuçlar tahmin mahiyetindedirler bu sebepten erken uyarı sistemleri ağının genişletilmesi ve gerçek zamanlı çalışan derin öğrenme algoritmaları ile yapılan tahmin sistemlerinin doğruluğunu artırmak oldukça önemlidir. Ayrıca literatürün farklı bakış açısını içeren multi disipliner yaklaşımla inceleyerek gelecekte yapılacak çalışmalara rehberlik etmek amaçlanmaktadır. Sonuç olarak bu tür bir araştırma daha iyi tahmin modelleri geliştirilmesine yardımcı olarak toplumların deprem riskine karşı daha hazırlıklı olmasına katkı sağlayacaktır.

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