Investigation of Earthquakes in Turkey with Cluster Analysis

Abstract

In this study, earthquakes with a magnitude of 3 and above occurring between 36-42 degrees North latitude and 26-45 degrees East longitude, within and around Turkey from 1980 to 2022, were considered. Turkey and its immediate surroundings were divided into 114-unit squares using latitude and longitude coordinates to investigate seismic similarities. The variables including 50130 earthquake depths and magnitudes in our data set were analyzed using the {K-Means Cluster Analysis} method in the SPSS program. Hierarchical clustering method was used to determine the number of clusters and the number of clusters was determined as 5 with the help of the dendrogram obtained. As a result of the cluster analysis, it was observed that 47% of the earthquakes in Turkey were at a depth of 5 to 10 km. It has been observed that 93% of the earthquake intensities occur between 3 and 4 magnitudes. A cluster analysis was conducted to assess the similarities and dissimilarities among earthquakes within these unit squares in terms of their magnitudes and depths. Due to the large sample size and the analysis involving continuous variables, the earthquake data were grouped into 5 clusters using non-hierarchical clustering methods, and the relationships within and between these clusters were observed.

Keywords

• Cluster Analysis
• Earthquake
• Depth
• Magnitude

Ethical Statement

There are no ethical issues after the publication of this manuscript.

References

1. [1]. Özel, G., Solmaz, A. (2012). Estimation of earthquake recurrence time in Turkey and investigation of seismicity according to neotectonic regions with Markov chain. Çankaya University Journal of Science and Engineering, 9(2), 125-138.
2. [2]. Doğaner, A., Çalık S. (2012). Investigation of the relationship between seismic activities in and around Elazig and earthquake parameters. Fırat University Journal of Science, 26(1), 73-77.
3. [3]. Kasap, R., Gürlen, Ü. (2003). Obtaining recurrence years for earthquake magnitudes: The case of Marmara Region. Journal of Doğuş University, 4(2), 157-166.
4. [4]. Orman, F. (2021), Investigation of Eurasian Countries in terms of Economic Freedom with Cluster Analysis. International Conference on Eurasian Economies: 24-25 August 2021, Istanbul: p.79-86.
5. [5]. Boehmke, B., & Greenwell, B. (2020). Hands-on machine learning with R. Boca Raton: CRC Press, Taylor and Francis Group.
6. [6]. Alpar, R. (2022). Applied Statistics and Validity-Reliability with Examples from Sports. Health and Educational Sciences 7th Edition. Detay Publishing, 2022.
7. [7]. Demircioğlu M., Eşiyok, S. (2019). Classification of countries by cluster analysis in the fight against the Covid-19 pandemic. Istanbul Commerce University Journal of Social Sciences, 37, 369-389.
8. [8]. Koldere Akın, Y. (2008). Clustering Algorithms and Clustering Analysis in Data Mining. Marmara University Doctoral Thesis, 8, 10, 16, 26-29.

9. [9]. Lean, L., Ricardo ve Powers L, Thomas. (1997). A Taxonomy of Countries Based on Inventive Activity. International Marketing Rewiev, Volume 14, No.6, 1997, ss.445-460.
10. [10]. Tatlıdil, Hüseyin. Applied Multivariate Statistical Analysis. Ankara: Ziraat Printing House, 2002, ss 330-341.
11. [11]. Özdamar, Kazım. Statistical Data Analysis with Package Programs. Eskisehir: Kaan Bookstore, 1999.
12. [12]. Ravinda, Khattree ve Naik N. Dayanand. Multivariate Data Reduction and Discrimination. First Edition. North Caroline: Cary, 2002.
13. [13]. Latin, M. James, Carroll J. Douglas ve Paul E. Green. Analyzing Multivariate Data. United States: Thomson Brooks-Cole, 2003.
14. [14]. NCSS. User Guide, Kaysville, National Council for the Social Studies Incorporated, 2001
15. [15]. Novanti P., Setyorini D., Rafflesia U., 2017. K-Means Cluster Analysis in Eartquake Epicenter Clustering. International Journal of Advances in Intelligent Informatics, 3(2). 2017.
16. [16]. Kamat R.K. ve Kamat R.S., 2017. Earthquake Cluster Analysis: K-Means Approach, 2017. Journal of Chemical and Pharmaceutical Scienses, 10(1). 2017.
17. [17]. Samadi et al. (2020), Robust Earthquake Cluster Analysis Based On K-Nearest Neighbor Search, 2020. Pure and Applied Geophysics, ISSN 0033-4553. 2020.
18. [18]. Scitovski, R., Scitovski, S. (2013). A fast partitioning algorithm and its application to earthquake investigation. Computers & Geosciences Volume 59, 124-131.
19. [19]. Morales-Esteban, A., Martínez-Alvarez, F., Scitovski, S., Scitovski, R., (2014). A fast partitioning algorithm using adaptive Mahalanobis clustering with application to seismic zoning. Computers & Geosciences Volume 73, 132-141.
20. [20]. Prozorov, A. G., Dziewonski, A. M. (1982). A Method of Studying Variations in the Clustering Property of Earthquakes: Application to the Analysis of Global Seismicity. Journal of Geophysical Research, Volume 87, No. B4, pp. 2829-2839.
21. [21]. Yan Y. Kagan, David D. Jackson (1991). Long-term earthquake clustering. Geophysical Journal International, Volume 104, 117-133.
22. [22]. Scitovski, S. (2018). A density-based clustering algorithm for earthquake zoning. Computers & Geosciences Vol.110, 90-95.
23. [23]. Kandilli observatory and earthquake research institute website active fault maps accessed on 17.11.2023. http://www.koeri.boun.edu.tr/sismo/zeqdb/
24. [24]. Hair, J.F., Anderson, R. E., Tatham, R. L., & Black, W. C., (1995). Multivariate Data Analysis with Readings. New Jersy: Prentice Hall (1995).
25. [25]. Yalçın, N., (2013). Clustering Analysis and Application. Fırat University Institute of Natural and Applied Sciences Master Thesis, 21,
26. [26]. Jafarzadegan, M., Safi-Esfahani, F.ve Beheshti, Z. (2019). Combining hierarchical clustering approaches using the PCA method, Expert Systems with Applications, 137(15), 1-10. https://doi.org/10.1016/j.eswa.2019.06.064.
27. [27]. General Directorate of Mining Technical Exploration website active fault maps accessed on 04.12.2023. https://www.mta.gov.tr/v3.0/sayfalar/hizmetler/diri-fay-haritalari/