Automatic Detection of Earthquake Surface Ruptures with Deep Learning: The Case of the February 6, 2023 Kahramanmaraş Earthquakes

Abstract

Earthquakes are a type of disaster that cause highest loss of life and property. Detecting and examining surface ruptures has great importance in understanding earthquakes and minimizing damage. This study aims to automatically detect surface ruptures that occurred during earthquakes with magnitudes of 7.7 and 7.6 that occurred in Kahramanmaraş province on February 6, 2023 and affected 11 provinces. MAXAR satellite images were used as the base image and YOLOv8n-seg was used as the segmentation model. An area where surface ruptures were visible was identified on the satellite images and the model was trained with the help of ruptures in this area. Of the images, 66% was used for training and 34% for testing. The mAP@0.5 value for the trained model was determined to be 0.88. The YOLOv8 model, as a method supported by deep learning techniques, offers high efficiency for these tasks. Using this method, surface ruptures that occur during earthquakes can be easily detected on satellite images. The method provides researchers with an important basis for field studies by allowing them to quickly detect surface fractures in large areas. In this way, time and economic losses will be minimized.

Keywords

• Kahramanmaraş earthquakes
• Deep learning
• Segmentation
• YOLOv8
• Surface rupture

Derin Öğrenme ile Deprem Yüzey Kırıklarının Otomatik Belirlenmesi: 6 Şubat 2023 Kahramanmaraş Depremleri Örneği

Abstract

Depremler dünyada en çok can ve mal kaybına neden olan afet türüdür. Bu yüzden deprem öncesi ve sonrası yapılacak çalışmalar önem kazanmaktadır. Depremler esnasında oluşan yüzey kırıklarının tespit edilmesi ve anlaşılması da depremlerin anlaşılmasında önem arz etmektedir. Bu çalışmada 02.06.2023 tarihinde Kahramanmaraş ilinde meydana gelen 7.7 ve 7.6 büyüklüğündeki depremlerde oluşmuş yüzey kırıklarının otomatik olarak tespit edilmesi amaçlanmıştır. Altlık olarak uydu görüntüleri, segmentasyon modeli olarak YOLOv8 kullanılmıştır. Uydu görüntüleri üzerinde yüzey kırıklarının gözle görülebildiği bir alan belirlenmiş ve bu alandaki kırıklar yardımı ile model eğitilmiştir. Verilerin %66 eğitim için %34 ise test için kullanılmıştır. Eğitilen modelin mAP@0.5 değeri 0.88 olarak tespit edilmiştir. YOLOv8 modeli, derin öğrenme teknikleriyle desteklenen bir yöntem olarak, bu tür görevlerde yüksek verimlilik vaat etmektedir. Bu yöntem kullanılarak deprem sonrası oluşacak yüzey kırıkları uydu görüntüleri üzerinde kolaylıkla tespit edilebilmektedir. Yöntem, araştırmacılara büyük alanlarda hızlı bir şekilde yüzey kırıklarının tespit edilmesine olanak sağlayarak arazi çalışmaları için önemli bir altlık sağlamaktadır. Bu sayede zaman ve ekonomik anlamdaki kayıplar en aza indirilmiş olacaktır.

Keywords

• Kahramanmaraş depremi
• Derin öğrenme
• Segmentasyon
• YOLOv8
• Yüzey kırığı

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