Sisam/Samos Adası Depremine ait Konum Bilgilerinin Parçacık Sürü Optimizasyonu Yöntemi ile Belirlenmesi

Öz

Bu çalışmada, rastlantısal veri tabanına sahip meta-sezgisel algoritmalar içinde yer alan Parçacık Sürü Optimizasyonunun (PSO) bir depremin konumunun belirleme çalışmalarında kullanılması irdelenmiştir. Bu algoritmanın hem sentetik deprem modeli hem de gerçek bir depremin konum belirleme çözümlerinde uygulanabilirliği ve etkinliği gösterilmiştir. Gürültüsüz ve gürültü içeren sentetik deprem modelinin konum belirlemesi başarı ile değerlendirilmiştir. Hem sentetik deprem modeli hem de gerçek depremin konum belirleme sonuçlarının olasılık yoğunluk fonksiyonları hesaplanarak elde edilen kestirim parametre değerlerinin güven aralığı içinde kaldığını göstermiştir. Ayrıca enlem-boylam, enlem-derinlik ve boylam-derinlik için üretilen hata enerjisi haritaları hazırlanarak çözüm sonuçları irdelenmiştir. Yöntem, Ege Denizi içinde Samos fayı üzerinde meydana gelen depremin 39 adet sismik istasyonda kaydedilen P ve S dalgalarının varış zamanları kullanılarak konum belirleme çalışması yapılmıştır. Bu deprem çeşitli sismolojik merkezlerince çözümü yapılmış olup, sadece Afet ve Acil Durum Yönetimi Başkanlığı (AFAD) tarafından açıklanan sonuçları ile karşılaştırılarak, yüzde bağıl hata oranları ile birlikte konumlar arasındaki uzaklıkları belirlenmiştir. PSO çözümünden parametre kestirim sonuçları enlem, boylam ve odak derinliği sırasıyla 37,827o, 26,650o ve 16,544 km’dir. AFAD tarafından belirlenen merkez üssü ve odak derinliği değerleri ile PSO çözümüyle elde edilen değerler karşılaştırıldığında enleme göre birbirlerinden olan uzaklık binde bir hata ile 5,26 km, boylamda binde iki hata ile 6,0 km ve derinlikte ise yüzde 1 hata ile 1,64 km elde edilmiştir. Son olarak iki merkez üssü arasındaki uzaklık farkı 7,98 km elde edilmiştir.

Anahtar Kelimeler

• Parçacık Sürü Optimizasyonu
• merkez üssü
• odak derinliği

Determination of the Location Information of the Sisam/Samos Island Earthquake by Particle Swarm Optimization Method

Öz

In this study, the use of Particle Swarm Optimization (PSO), which is one of the meta-heuristic algorithms with a random database, in the determination of the location of an earthquake is examined. The applicability and effectiveness of this algorithm in both the synthetic earthquake model and the location determination solutions of a real earthquake have been demonstrated. The position determination of the noiseless and noise-containing synthetic earthquake model has been successfully evaluated. It has been shown that the estimation parameter values obtained by calculating the probability density functions of the location determination results of both the synthetic earthquake model and the real earthquake remain within the confidence interval. In addition, error energy maps produced for latitude-longitude, latitude-depth and longitude-depth were prepared and the solution results were examined. The method was used to determine the location of the earthquake that occurred on the Samos fault in the Aegean Sea by using the arrival times of the P and S waves recorded at 39 seismic stations. This earthquake was solved by various seismological centers, and the distances between the locations were determined, along with the percentage error rates, by comparing only the results announced by the Disaster and Emergency Management Presidency (AFAD). The parameter estimation results from the PSO solution are latitude, longitude, and focal depth 37,827o, 26,650o and 16,544 km, respectively. When the epicenter and focal depth values determined by AFAD are compared with the values obtained with the PSO solution, the distance from each other with respect to latitude is 5,26 km with an error of one thousand, 6,0 km with an error of two per thousand in longitude and 1,64 km with an error of 1 percent in depth. Finally, the distance difference between the two epicenters was 7,98 km.

Anahtar Kelimeler

• Particle Swarm Optimization
• epicenter
• depth of focus

Kaynakça

1. Qiu, N., Liu, Q., Zeng, Z. 2010. Particle swarm optimization and least-squares method for geophysical parameter inversion from magnetic anomalies data, IEEE International Conference on Intelligent Computing and Intelligent Systems, 29-31 Oct. 2010, Xiamen, 1-5. DOI: 10.1109/ICICISYS.2010.5658365
2. Essa, K.S., Elhussein, M. 2018. PSO (particle swarm optimization) for interpretation of magnetic anomalies caused by simple geometrical structures, Pure and Applied Geophysics, Cilt. 175, s. 3539-3553. DOI:10.1007/s00024-018-1867-0
3. Liu, S., Liang, M., Hu, X. 2018. Particle swarm optimization inversion of magnetic data: Field examples from iron ore deposits in China, Geophysics, Cilt. 83(4), s. 43-59. DOI: 10.1190/geo2017-0456.1
4. Ekinci, Y.L., Özyalın, Ş., Sındırgı, P., Balkaya, Ç., Göktürkler, G. 2017. Amplitude inversion of the 2D analytic signal of magnetic anomalies through the differential evolution algorithm, Journal of Geophysics and Engineering, Cilt. 14(6), 1492-1508. DOI: 10.1088/1742-2140/aa7ffc
5. Balkaya, Ç., Ekinci, Y.L., Göktürkler, G., Turan, S. 2017. 3D non-linear inversion of magnetic anomalies caused by prismatic bodies using differential evolution algorithm, journal of applied geophysics, Cilt. 136, s. 372-386. DOI: 10.1016/j.jappgeo.2016.10.040.
6. Ekinci, Y.L., Balkaya, Ç., Göktürkler, G. 2019. Parameter estimations from gravity and magnetic anomalies due to deep-seated faults: Differential evolution versus particle swarm optimization, Turkish Journal of Earth Sciences, Cilt. 28, s.860-881. DOI: 10.3906/yer-1905-3.
7. Kaftan, İ. 2017. Interpretation of magnetic anomalies using a genetic algorithm, Acta Geophysica, Cilt. 65, s. 627-634. DOI: 10.1007/s11600-017-0060-7
8. Darisma, D., Said, U., Srigutomo, W. 2017. 2D gravity inversion using particle swarm optimization method. In: 23rd European meeting of environmental and engineering geophysics, European Association of Geoscientists and Engineers, Malmö, Sweden, 1-5.

9. Pallero, J.L.G. Fernández-Martínez, J.L., Bonvalot, S., Fudym, O. 2017. 3D gravity inversion and uncertainty assessment of basement relief via Particle Swarm Optimization, Journal of Applied Geophysics, Cilt. 139, s. 338-350. DOI:10.1016/j.jappgeo.2017.02.004
10. Essa, K.S., Munschy, M. 2019. Gravity data interpretation using the particle swarm optimization method with application to mineral exploration, Journal of Earth System Science, Cilt. 128, s. 123. DOI: 10.1007/s12040-019-1143-4
11. Essa, K.S., Géraud, Y. 2020. Parameters estimation from the gravity anomaly caused by the two-dimensional horizontal thin sheet applying the global particle swarm algorithm, Journal of Petroleum Science and Engineering, Cilt. 193 s. 107421. DOI: 10.1016/j.petrol.2020.107421
12. Essa, K.S., Mehanee, S.A., Elhussein, M. 2021. Gravity data interpretation by a two-sided fault-like geologic structure using the global particle swarm technique, Physics of the Earth and Planetary Interiors, Cilt. 311, s. 106631. DOI: 10.1016/j.pepi.2020.106631
13. Ekinci, Y.L., Balkaya, Ç., Göktürkler, G., Turan, S. 2016. Model parameter estimations from residual gravity anomalies due to simple-shaped sources using differential evolution algorithm, Journal of Applied Geophysics, Cilt. 129, s. 133-147. DOI: 10.1016/j.jappgeo.2016.03.040.
14. Ekinci, Y.L., Balkaya, Ç., Göktürkler, G. 2020b. Global optimization of near-surface potential Beld anomalies through metaheuristics; In: Advances in Modelling and Interpretation in Near Surface Geophysics (eds) Biswas A and Sharma S P, Series of Springer Geophysics, Springer International Publishing, s. 155–188, ISBN: 978-3-030-28909-6.
15. Ekinci, Y.L., Balkaya, Ç., Göktürkler, G., Özyalın Ş. 2021. Gravity data inversion for the basement relief delineation through global optimization: A case study from the Aegean Graben System, western Anatolia, Turkey, Geophysical Journal International, Cilt. 224, s. 923-944. DOI: 10.1093/gji/ggaa492.
16. Göktürkler, G, Balkaya, Ç., Ekinci, Y.L., Turan, S. 2016. Metaheuristics in applied geophysics (in Turkish), Pamukkale University Journal of Engineering Sciences, Cilt. 22, s. 563-580. DOI: 10.5505/pajes.2015.81904
17. Ekinci, Y.L., Balkaya, Ç., Göktürkler, G., 2021. Backtracking Search Optimization: A novel global optimization algorithm for the inversion of gravity anomalies. Pure and Applied Geophysics, Cilt. 178, s. 4507–4527. DOI: 10.1007/s00024-021-02855-3
18. Santos, F.A.M. 2010. Inversion of self-potential of idealized bodies’ anomalies using particle swarm optimization, Computers & Geosciences, Cilt. 36, s. 1185-1190. DOI: 10.1016/j.cageo.2010.01.011.
19. Pekşen, E., Yas, T., Kayman, A.Y., Özkan, C. 2011. Application of particle swarm optimization on self-potential data, Journal of Applied Geophysics Cilt. 75, s. 305-318. DOI: 10.1016/j.jappgeo.2011.07.013.
20. Göktürkler, G., Balkaya, Ç. 2012 Inversion of self-potential anomalies caused by simple geometry bodies using global optimization algorithms, Journal of Geophysics and Engineering, Cilt.9, s. 498-507. DOI: 10.1088/1742-2132/9/5/498.
21. Biswas, A., Sharma, S.P. 2014. Optimization of self-potential interpretation of 2-D inclined sheet-type structures based on very fast simulated annealing and analysis of ambiguity, Journal of Applied Geophysics, Cilt. 105, s. 235-247. DOI: 10.1016/ j.jappgeo.2014.03.023.
22. Essa, K.S. 2020. Self-potential data interpretation utilizing the particle swarm method for the finite 2D inclined dike: mineralized zones delineation, Acta Geodaetica et Geophysica, Cilt. 55, s. 203-221. DOI 10.1007/s40328-020-00289-2
23. Essa, K.S., Elhussein, M. 2020. Interpretation of magnetic data through particle swarm optimization: mineral exploration cases studies, Natural Resources Research, Cilt. 29, s. 521-537. DOI: 10.1007/s11053-020-09617-3.
24. Sındırgı, P., Özyalın, Ş. 2021. A Comparison of the Model Parameter Estimations from Self-Potential Anomalies by Levenberg-Marquardt (LM), Differential Evolution (DE) and Particle Swarm Optimization (PSO) Algorithms: An Example from Tamış-Çanakkale, Turkey. In:Self-Potential Method: Theoretical Modeling and Applications in Geosciences, Biswas, Arkoprovo, (Editor), Springer, CHAM, s.133-153. DOI: 10.1007/978-3-030-79333-3_4
25. Di Maio, R., Rani, P., Piegari, E., Milano, L. 2016. Self- potential data inversion through a genetic-price algorithm, Computers & Geosciences, Cilt. 94, s.86-95. DOI: 10.1016/j.cageo.2016.06.005
26. Balkaya, Ç. 2013. An implementation of differential evolution algorithm for inversion of geoelectrical data, Journal of Applied Geophysics, Cilt. 98, s. 160-175. DOI: 10.1016/j.jappgeo.2013.08.019
27. Gallardo, L.A., Meju, M.A. 2003. Characterization of heterogeneous near-surface materials by joint 2D inversion of dc resistivity and seismic data: characterization of heterogeneous near-surface materials, Geophysical Research Letters, Cilt. 30 s. 1658-1664. DOI: 10.1029/2003GL017370
28. Fernández-Álvarez, J.P., Fernández-Martínez, J.L., García-Gonzalo, E., Menéndez-Pérez, C.O. 2006. Application of a Particle Swarm Optimisation (PSO) algorithm to the solution and appraisal of the VES inverse problem, Liège, Belgium, ss. 12-17.
29. Shaw, R., Srivastava, S. 2007. Particle swarm optimization: A new tool to invert geophysical data, Geophysics, Cilt. 72, s. 75–83. DOI: 10.1190/1.2432481
30. Fernandez Martínez, J.L., Mukerji, T., García Gonzalo E., Suman, A. 2012. Reservoir characterization and inversion uncertainty via a family of particle swarm optimizers, Geophysics, Cilt. 77, s.1-16. DOI: 10.1190/geo2011-0041.1
31. Pekşen, E., Yas, T., Kıyak, A. 2014. 1-D DC resistivity modeling and interpretation in anisotropic media using particle swarm optimization, Pure Applied Geophysics, Cilt. 171, s. 2371-2389. DOI:10.1007/s00024-014-0802-2
32. Balkaya, Ç., Göktürkler, G., Erhan, Z., Ekinci, Y.L. 2012. Exploration for a cave by magnetic and electrical resistivity surveys: Ayvacık Sinkhole example, Bozdağ, İzmir western (Turkey), Geophysics, Cilt. 77, s. 135-146.
33. Grandis, H., Maulana, Y. 2017. Particle swarm optimization (PSO) for magnetotelluric (MT) 1D inversion modeling, Earth and Environmental Science, Cilt. 62, ss 012033. DOI:10.1088/1755-1315/62/1/012033
34. Karcıoğlu, G., Gürer, A. 2019. Implementation and model uniqueness of Particle Swarm Optimization method with a 2D smooth modeling approach for Radio-Magnetotelluric data, Journal of Applied Geophysics, Cilt. 169, s. 37-48. DOI: 10.1016/j.jappgeo.2019.06.001
35. Pace, F., Santilano, A., Godio, A. 2019a. Particle swarm optimization of 2D magnetotelluric data, Geophysics, Cilt. 84, s. 125-141. DOI: 10.1190/geo2018-0166.1
36. Godio, A., Massarotto, A., Santilano, A. 2016. Particle swarm optimization of electromagnetic soundings. In: 78th Annual international conference and exhibition. European Association of Geoscientists and Engineers, Barcelona, Spain, s. 1-5 DOI: 10.3997/2214-4609.201602024
37. Alkan, H., Balkaya, Ç. 2018. Parameter estimation by Differential Search Algorithm from horizontal loop electromagnetic (HLEM) data, Journal of Applied Geophysics, Cilt. 149, s. 77-94. DOI:0.1016/j.jappgeo.2017.12.016
38. Özyalın, Ş., Kartal, R.F., Polat, O. 2017. Odak mekanizmasinin parçacik sürü optimizasyonu (pso) ile çözümü, 4. uluslararası deprem mühendisliği ve sismoloji konferansı, 11-13 Ekim 2017, Anadolu Üniversitesi – Eskişehir, 234.
39. Afet ve Acil Durum Yönetimi Başkanlığı (AFAD) 2020. Samos Depremi Değerlendirme Raporu https://deprem.afad.gov.tr/downloadDocument?id=2065 (Erişim tarihi: 25.12.2021)
40. McKenzie, D. 1972. Active tectonics of the Mediterranean region, Geophysical Journal International, Cilt. 30, s. 109-185. DOI: 10.1111/j.1365-246X.1972.tb02351.x
41. Alptekin, Ö. 1973. Focal mechanism of earthquakes in western Turkey and their tectonic implications. Ph.D. Thesis, New Mexico Institute of Mining and Technology, (unpublished). 190s, Socorro, New Mexico.
42. Dewey, J. F., Şengör, A.M.C. 1979. Aegean and surrounding regions: Complex multiplate and continuum tectonics in a convergent zone, Geological Society of American Bulletin, Cilt. 90, s. 84-92.
43. Mckenzie, D. P. 1978. Active tectonics of the Alpine Himalaya Belt: The Aegean Sea and surrounding regions, Geophysical Journal of Royal Astronomical Society, Cilt. 55, s. 217-254. DOI: 10.1111/j.1365-246X.1978.tb04759.x
44. Mcclusky, S., Balassanian, S., Barka, A., Demir, C., Ergintav, S., Georgiev, I. 2000. Global positioning system constraints on plate kinematics and dynamics in the Eastern Mediterranean and Caucasus, Journal of Geophysical Research, Cilt. 105, s. 5695-5720.
45. Okay, A.I., Kaslılar, Ö.A., İmren, C., Boztepe, G.A., Demirbağ, E., Kuşçu, I. 2000. Active faults and evolving strike-slip basins in the Marmara Sea, northwest Turkey: a multichannel seismic reflection study, Tectonophysics, Cilt. 321, s. 189-218. DOI: 10.1016/S0040-1951(00)00046-9
46. https://blogs.openquake.org/ hazard/global-active-fault-viewer/ (Erişim Tarihi: 15.12.2021)
47. http://www.koeri.boun.edu.tr/sismo/2/wp_content/uploads/2020/10/20201030_izmir_V1.pdf (Erişim Tarihi: 11.04.2021)
48. Emre, Ö., Özalp, S., Doğan, A., Özaksoy, V., Yıldırım, C. ve Göktaş, F. 2005. İzmir yakın çevresinin diri fayları deprem potansiyelleri, MTA Genel Müdürlüğü, Rp: No:10754, s.1-80, Ankara.
49. Kuşçu, İ., Öcal, F., Kurtuluş, O. 2010. İzmir ve Sığacık Körfezlerinde Kıyı Ötesi Aktif Faylar, MTA Genel Müdürlüğü Jeoloji Etütleri Dairesi, Rapor No: 11273, 73s., Ankara.
50. 30 Ekim 2020 Sisam adası (İzmir Seferihisar açıkları) MW 6.6 depremi raporu Deprem Dairesi Başkanlığı Aralık 2020, Ankara.
51. KOERI 2020. Kandilli observatory and earthquake research institute, Istanbul-Turkey. http://koeri.boun.edu.tr. (Erişim Tarihi: 15.01.2021)
52. Akıncı, A., Cheloni, D., Dindar, A.A. 2021. The 30 October 2020, M7.0 Samos Island (Eastern Aegean Sea) Earthquake: effects of source rupture, path and local‑site conditions on the observed and simulated ground motions, Bulletin of Earthquake Engineering, Cilt. 19, s. 4745–4771. DOI: 10.1007/s10518-021-01146-5
53. Havskov, J., Ottemöller, L. 2010. Routine Data Processing in Earthquake Seismology. Springer Science, London, United Kingdom.
54. Kennedy, J., Eberhart R.C. 1995. Particle swarm optimization. IEEE International Conf. on Neural Networks (Perth Australia), 27 Nov.-1 Dec. 1995, Piscataway, NJ: IEEE Service Center, 1942-1948.
55. Shi, Y., Eberhart, R., May, A. 1998. Modified Particle Swarm Optimizer. In Evolutionary Computation Proceedings, 1998, IEEE World Congress on Computational Intelligence, 4 May 1998, 69-73. DOI:10.1109/ICEC.1998.699146
56. Salmon, S. 2011. Particle Swarm Optimization in Scilab. http://forge.scilab.org/index.php/p/pso-toolbox/downloads/ 2011.
57. Karaboğa, D. 2014. Yapay Zekâ Optimizasyon Algoritmaları. 3. baskı. Ankara, Türkiye, Nobel Yayın Dağıtım.
58. Gökalp H. 2021. Grid araştırma yöntemi ile yerel ve bölgesel depremlerin konumlarının belirlenmesi. Pamukkale Univ Muh Bilim Derg, Cilt. 27, s. 393-409. DOI: 10.5505/pajes.2020.69922
59. Balkaya, Ç., Kaftan, I. 2021. Inverse modeling via differential search algorithm for interpreting magnetic anomalies caused by 2D dyke-shaped bodies. Journal of Earth System Science, s. 130-135. DOI: 10.1007/s12040-021-01614-1
60. Deprem Araştırma ve Uygulama Merkezi (DAUM) 2020. Samos Depremi Değerlendirme Raporu https://daum.deu.edu.tr/wp-content/uploads/2020/11/Samos-Deprem-Raporu.pdf. (Erişim tarihi: 11.11.2020)
61. Özer Ç., Polat O. 2017. İzmir ve Çevresinin 1-B (Bir-Boyutlu) Sismik Hız Yapısının Belirlenmesi. Dokuz Eylül Üniversitesi-Mühendislik Fakültesi Fen ve Mühendislik Dergisi, Cilt:19, s. 147-168. DOI: 10.21205/deufmd.2017195512.
62. Özer Ç., Polat O. 2017b. 2017. İzmir ve Çevresinin 3-B Kabuk Hız Yapısı. Journal of the Faculty of Engineering and Architecture of Gazi University Cilt. 32, s.733-747. DOI: 10.17341/gazimmfd.337620