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Earthquake magnitude prediction in Turkey: a comparative study of deep learning methods, ARIMA and singular spectrum analysis

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Abstract

The Aegean region is geologically situated at the western end of the Gediz Graben system, influenced by the Western Anatolian Regime. In addition, the region is characterized by various active fault lines that can generate earthquake activity. Numerous earthquakes have been recorded in the region, causing significant material and moral damage from the past to the present. In this study, earthquake data from three different catalogs are examined. The non-clustered catalog is compiled for the years 1970 to 2020, including earthquakes with a moment magnitude (Mw) greater than 3.0. The monthly average magnitudes of earthquakes in the region are obtained and analyzed using ARIMA, singular spectrum analysis (SSA), and deep learning methods including convolutional neural network (CNN) and long short-term memory (LSTM), as these methods have not been compared for the region previously. Each method has a different benefit. ARIMA analyzes time series trends and seasonal patterns, while SSA focuses on decomposition and feature extraction. LSTM attempts to capture complex relationships using memory mechanisms, while CNN is powerful at pattern recognition and extracting important features. Thanks to this diversity, our study allows for more comprehensive and reliable forecasts of average earthquake magnitudes for the next 36 periods. The estimation capabilities and error rates of each method were analyzed based on earthquake magnitude data, and it was determined that the LSTM method provided the most effective and accurate predictions.

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Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

We thank the support of The Scientific and Technological Research Council of Turkey (TUBITAK) ARDEB 1001 [Project number: 121F208] program. The authors also thank Assistant Prof. Dr. Tuba Eroğlu Azak for preparing dataset, Prof. Dr. Tolga Çan for his valuable advice and reviewers for their constructive comments.

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  1. Department of Statistics, Hacettepe University, 06800, Ankara, Turkey

    Hatice Öncel Çekim, Hatice Nur Karakavak & Gamze Özel

  2. Department of Mining and Mineral Extraction, School of Technical Science, Adıyaman University, 02040, Adıyaman, Turkey

    Senem Tekin

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  1. Hatice Öncel Çekim
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Contributions

H. O. Cekim and H. N. Karakavak composed methods and results, G. Ozel wrote the main manuscript text, and S. Tekin prepared the data and Figures 1 and 2. All authors reviewed the manuscript.

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Correspondence to Hatice Öncel Çekim.

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Öncel Çekim, H., Karakavak, H.N., Özel, G. et al. Earthquake magnitude prediction in Turkey: a comparative study of deep learning methods, ARIMA and singular spectrum analysis. Environ Earth Sci 82, 387 (2023). https://doi.org/10.1007/s12665-023-11072-1

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