Chaotic Dynamics and Analysis with Artificial Neural Networks of Aftershocks of 2019 Silivri Earthquake

Year 2024, Volume: 11 Issue: 4, 732 - 741, 30.12.2024

Fatma Aydogmus , Yeşim Öniz , Eljan Simuratli , Eren Tosyalı , İsmail Kaplanvural , Ahu Kömeç Mutlu , Deniz Çaka , Halil Türker , Zeynep Önem

https://doi.org/10.54287/gujsa.1569701

Abstract

Earthquakes, whose physical, economic, psychological, and social damages can last for many years, are of vital importance for Türkiye, which is located in the most active earthquake zone that causes many earthquakes in the world. The North Anatolian Fault (NAF) is one of Türkiye's most important tectonic elements as it is the world’s fastest-moving right-lateral and strike-slip active fault zone consisting of many segments. The recent 5.8 magnitude 2019 Silivri earthquake, which occurred in the part of the NAF zone crossing the Marmara Sea, is an indicator that earthquake activity continues in the region. Aftershocks play a crucial role in seismicity research and seismic hazard assessments in terms of providing data and usable information in the examination of seismic dynamics with the changes observed in their time-dependent behavior and regional distribution. In this study, the aftershocks of the Silivri earthquake were examined as a natural laboratory using nonlinear analysis methods. Within the scope of the study, aftershocks of the Silivri earthquake were analyzed with a hybrid artificial neural network as well as different neural network structures, and for this purpose, data from 361 aftershocks with a magnitude greater than 1.5 in the year following the earthquake were used.

Keywords

Aftershock, Chaos Theory, Time Series, Artificial Neural Network

Thanks

This study was derived from the master's thesis titled "An Investigation of North Anatolian Fault Zone Seismicity Dynamics Using Machine Learning Algorithms" in the Department of Physics, Institute of Science, Istanbul University.

References

• Abarbanel, H. D. I., Brown, R., Sidorowich, J. J., & Tsimring, L. Sh. (1993). The Analysis of Observed Chaotic Data in Physical Systems. Reviews of Modern Physics, 65(4), 1331-1392. https://doi.org/10.1103/RevModPhys.65.1331
• Alves, E. I. (2006). Earthquake forecasting using neural networks: results and future work. Nonlinear Dynamics, 44(1-4), 341-349, https://doi.org/10.1007/s11071-006-2018-1
• Asim, K. M., Martínez-Álvarez, F., Basit, A., & Iqbal, T. (2017). Earthquake magnitude prediction in Hindukush region using machine learning techniques. Natural Hazards, 85(1), 471-486. https://doi.org/10.1007/s11069-016-2579-3
• Bolt, B. A. (1993). Earthquakes and Geological Discovery. Scientific American Library.
• Cambel, A. B. (1993). Applied Chaos Theory a Paradigm for Complexity. Academic Press, Boston.
• Chang, Y., Zhang, Y., & Zhang, H. (2024). Tectonic geomorphology of Türkiye and its insights into the neotectonic deformation of the Anatolian Plate. Earthquake Research Advances, 4(1), 100267. https://doi.org/10.1016/j.eqrea.2023.100267
• Çalım, Z., Çam Taşkıran, Z. G., & Yıldırım, T. (2023). Risk zone analysis using chaos theory from earthquake data. Recent Advances in Science and Engineering, 3(2), 15-32. https://doi.org/10.14744/rase.2023.0003
• Davies, B. (1999). Exploring Chaos: Theory And Experiment. Westview Press. https://doi.org/10.1201/9780429502866
• Devaney, R. L. (2021). An Introduction to Chaotic Dynamical Systems (3rd Ed.). Boston University Press. https://doi.org/10.1201/9780429280801
• Gurel, E., Nazli, A., & Yoltay, S. (2024, February, 16-18). Deprem Okuryazarlığı: İletişimsel Bir Yaklaşım. In: D. Solisworo (Eds.), Anadolu 14th International Conference on Social Sciences, Gaziantep, Türkiye.
• Hilborn, R. C. (2003). Chaos and Nonlinear Dynamics. Oxford University Press, New York.
• Joelianto, E., Widiyantoro, S., & Ichsan, M. (2009). Time series estimation on earthquake events using ANFIS with mapping function. International Journal of Artificial Intelligence, 3(A09), 37-63.
• Kamgar, R., Dadkhah, M., & Naderpour, H. (2022). Earthquake-induced nonlinear dynamic response assessment of structures in terms of discrete wavelet transform. Structures, 39, 821-847. https://doi.org/10.1016/j.istruc.2022.03.060
• Konstantaras, A., Vallianatos, F., Varley, M. R., & Makris, J. P. (2008). Soft-computing modelling of seismicity in the southern Hellenic arc. IEEE Geoscience and Remote Sensing Letters, 5(3), 323-327. https://doi.org/10.1109/LGRS.2008.916069
• Lakshmi, S. S., & Tiwari, R. K. (2007). Are northeast and western Himalayas earthquake dynamics better "organized" than Central Himalayas: An artificial neural network approach. Geofísica Internacional, 46(1), 65-75. https://doi.org/10.22201/igeof.00167169p.2007.46.1.2152
• Mignan, A., & Broccarda, M. (2020). Neural Network Applications in Earthquake Prediction (1994–2019): Meta‐Analytic and Statistical Insights on Their Limitations. Seismological Research Letters, 91(4), 2330-2342. https://doi.org/10.1785/0220200021
• Moustra, M., Avraamides, M., & Christoduolou, C. (2011). Artificial neural networks for earthquake prediction using time series magnitude data or Seismic Electric Signals. Expert Systems with Applications, 38(12), 15032-15039. https://doi.org/10.1016/j.eswa.2011.05.043
• Panakkat, A., & Adeli, H. (2007). Neural network models for earthquake magnitude prediction using multiple seismicity indicators. International Journal of Neural Systems, 17(1), 13–33. https://doi.org/10.1142/S0129065707000890
• Rızaoğlu, E., & Sünel N. (2011). Klasik Mekanik. Seçkin Yayıncılık.
• Scott, A. (1999). Nonlinear Science. Oxford University Press.
• Simuratli, E. (2023). Kuzey Anadolu Fay Zonu Depremsellik Dinamiğinin Makine Öğrenmesi Algoritmaları Kullanılarak İncelenmesi. MSc Thesis, İstanbul University.
• Takanashi, K., Udagawa, K., Seki, M., Okada, T., & Tanaka, H. (1975). Non-linear earthquake response analysis of structures by a computer-actuator on-line system: Part 1 Detail of the System. Transactions of the Architectural Institute of Japan, 229, 77-83. https://doi.org/10.3130/aijsaxx.229.0_77
• Türker, T. (2021). Kuzey Anadolu Fay Zonu (KAFZ) ve Doğu Anadolu Fay Zonu (DAFZ) İçin Bayes Yöntemi İle Farklı Dağılım Yöntemleri Birleştirilerek Deprem Tahmin ve Tehlike Analizleri. PhD Thesis, Karadeniz Teknik University.
• Utkucu, M., Uzunca, F., Utkucu, Y., Durmuş, H., & Kırım, S. (2023). The September 26, 2019 Silivri Earthquake (Mw=5.6), NW Türkiye. Academic Platform Journal of Natural Hazards and Disaster Management, 4(2), 65-75. https://doi.org/10.52114/apjhad.1219257
• Zamani, A., Sorbi, M. R., & Safavi, A. A. (2013). Application of neural network and ANFIS model for earthquake occurrence in Iran. Earth Science Informatics, 6(2), 71-85. https://doi.org/10.1007/s12145-013-0112-8