Parçacık Sürü Optimizasyonu ile Depremin Dış Merkezinin belirlenmesi: Ayvacık Depremi Örneği

Yıl 2022, Cilt: 4 Sayı: 1, 1 - 25, 08.06.2022

Şenol Özyalın

https://doi.org/10.46464/tdad.1033302

Öz

Optimizasyon problemlerinin çözümü için kullanılan birçok optimizasyon tekniği doğadaki olaylardan esinlenilerek geliştirilmiştir. Parçacık Sürüsü Optimizasyonu (PSO), yiyecek veya ortak hedef arayışında sürü (kuş sürüleri, balık sürüleri, böcekler vb.) davranışını işbirlikçi bir şekilde benimseyen, doğadan ilham alan optimizasyon algoritmalarından biridir. Sürüdeki parçacıklar (ya da ajanlar), arama uzayında kendilerini geliştirmelerinin yanı sıra komşularından da bilgi öğrenirler. Bir parçacığın arama algoritması, süreç sırasında o parçacığın en iyi konumu (bireysel öğrenme terimi) ve belirli bir yinelemede çevresindeki en iyi parçacık (sosyal öğrenme terimi) tarafından belirlenir. PSO'daki temel arama stratejisi, sürüdeki parçacıkların bilişsel bilgilerinin ve sosyal davranışlarının sürekli güncellenmesi yoluyla algoritmayı en iyi çözüme doğru yönlendirmesidir. Bu çalışmada önce yöntemin performansını sentetik model ile test edildikten sonra Çanakkale-Ayvacık depreminin dış merkezinin belirlenmesinde bu algoritmanın uygulaması gösterilmiştir. Bu çalışma sonucunda, Afet ve Acil Durum Yönetimi Başkanlığı (AFAD) tarafından yayınlanan 06.02.2017 depreminin dış merkezi (26.1351, 39.5303) ile PSO çözümü (26.03,39.50) bulunmuştur. Boylam ve enlem için yüzde bağıl hatalar sırasıyla % 0.402 ve %0.077 bulunmuş ve ortalama yüzde bağıl hata %0.239 olarak hesaplanmıştır.

Anahtar Kelimeler

Parçacık Sürü Optimizasyonu, Sürü Zekâsı, Deprem dış merkezi, Çanakkale-Ayvacık, Metasezgisel

Determining the Epicenter of an Earthquake with Particle Swarm Optimization: Ayvacik Earthquake Example

Öz

Many optimization techniques used for the solution of optimization problems have been developed by being inspired by the events in nature. Particle Swarm Optimization (PSO) is one of the nature-inspired optimization algorithms that cooperatively adopts swarm behavior (e.g. flocks of birds, flocks of fish, insects, etc.) in search of food or common target. Particles (or agents) in the swarm learn information from their neighbors as well as evolve themselves in search space. A particle's search algorithm is determined by that particle's best position during the process (individual learning term) and the best particle around it at a given iteration (social learning term). The basic search strategy in PSO is to guide the algorithm towards the best solution through continuous updating of the cognitive information and social behavior of the particles in the swarm. In this study, the performance of the method was tested with a synthetic model, and then the application of these algorithms in determining the epicenter of the Canakkale-Ayvacik earthquake was demonstrated. As a result of this study, the epicenter of the 06.02.2017 earthquake published by the Disaster and Emergency Management Presidency (AFAD) and the PSO solution were found as (26.1351, 39.5303) and (26.03, 39.50), respectively. The percent relative errors for longitude and latitude are determined as 0.402%, and 0.077%, and the mean percent relative error is computed as 0.239%

Anahtar Kelimeler

Particle swarm optimization, Swarm Intelligence, Epicenter, Canakkale-Ayvacik, Metaheuristic

Kaynakça

• AFAD, 2017. 12.02.2017 Ayvacık-Çanakkale Depremi Raporu Erişim adresi: http://tdvm.afad.gov.tr
• Ahmadi M.A., Zendehboudi S., Lohi A., 2013. Reservoir permeability prediction by neural networks combined with hybrid genetic algorithm and particle swarm optimization: reservoir permeability prediction by neural networks, Geophys Prospect 61, 582-598
• AlRashidi M.R., El-Hawary M.E., 2009. A survey of particle swarm optimization applications in electric power systems, IEEE Trans. Evol. Comput. 13, 913-918
• Ambraseys N. ,2001. The earthquake of 1509 in the Sea of Marmara, Turkey, revisited, Bulletin of the Seismological Society of America 91(6), 1397-1416
• Ambraseys N. 2009. Earthquakes in the Mediterranean and Middle East, a multidisciplinary study of seismicity up to 1900, Cambridge University Press, UK, 947p.
• Armaghani D.J., Mohamad E.T., Narayanasamy M.S., 2017. Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition, Tunn. Undergr. Space Technol. 63, 29-43
• 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 105, 235-247
• Caputo R., Chatzipetros A., Pavlides S., Sboras S., 2012. The Greek database of seismogenic sources (GreDaSS): state-of-the-art for northern Greece, Ann Geophys. 55(5),859-894
• Carlisle A., Dozier G., 2001. An off-the-shelf PSO: Proceedings of the Workshop on Particle Swarm Optimization, April, Indianapolis, p1-6. Erişim adresi: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.589.485
• Chen Z., Zhu B., He Y., 2017. A PSO based virtual sample generation method for small sample sets: Applications to regression datasets, Engineering Applications of Artificial Intelligence 59, 236-243
• Cheng Y.M., Li L., Chi S., Wei W.B., 2007. Particle swarm optimization algorithm for the location of the critical non-circular failure surface in two-dimensional slope stability analysis, Comput. Geotech. 34, 92-103
• 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, p 1-5. Erişim adresi: https://www.semanticscholar.org/paper/2D-Gravity-Inversion-Using-Particle-Swarm-Method-Darisma-Said/e32ae8e42b8895a679f898572889dd3713f8d30c
• DAUM, 2017. Ayvacik Depremi Değerlendirme Raporu, Deprem Araştırma ve Uygulama Merkezi, Dokuz Eylül Üniversitesi, İzmir, 22 p. Erişim adresi: http://daum.deu.edu.tr
• Donelli M., Franceschini G., Martini A., Mass A., 2006. An integrated multiscaling strategy based on a particle swarm algorithm for inverse scattering problems, IEEE Transactions on Geoscience and Remote Sensing 44, 298-312
• Emre O., Dogan A., 2010. 1:250.000 Ölçekli Türkiye Diri Fay Haritaları Serisi, Balıkesir Ayvalık (NJ 35-2) Paftası, Maden Tetkik ve Arama Genel Müdürlüğü, Ankara.
• Essa K.S., 2020. Self potential data interpretation utilizing the particle swarm method for the finite 2D inclined dike mineralized zones delineation, Acta Geod. Geophys. 55, 203-221
• Essa K.S., Elhussein M., 2018. PSO (particle swarm optimization) for interpretation of magnetic anomalies caused by simple geometrical structures, Pure Appl. Geophys. 175, 3539-3553
• Essa K.S., Elhussein M., 2020. Interpretation of magnetic data through particle swarm optimization mineral exploration cases studies, Nat. Resour. Res. 29, 521-537
• Essa K.S., Geraud Y., 2020. Parameters estimation from the gravity anomaly caused by the two-dimensional horizontal thin sheet applying the global particle swarm algorithm, J. Petrol Sci. Eng. 193, 2-14
• Essa K.S., Munschy M., 2019. Gravity data interpretation using the particle swarm optimisation method with application to mineral exploration, J. Earth Syst. Sci. 128, 123 Erişim adresi: http://doi.org/10.1007/s12040-019-1143-4
• 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, Phys. Earth Planet Inter. 311, 106631
• Fernandez-Alvarez J.P., Fernandez-Martinez J.L., Garcia-Gonzalo E., Menendez-Perez C.O., 2006. Application of a Particle Swarm Optimisation (PSO) algorithm to the solution and appraisal of the VES inverse problem, Liege, Belgium, 12-17.
• Fernandez Martinez J.L., Mukerji T., Garcia Gonzalo E., Suman A., 2012. Reservoir characterization and inversion uncertainty via a family of particle swarm optimizers, Geophysics 77, 1-16.
• Garcia-Gonzalo, E., Fernandez-Martinez, J. L., 2014. Convergence and stochastic stability analysis of particle swarm optimization variants with generic parameter distributions, Applied Mathematics and Computation 249, 286-302.
• 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 30(13),1658-1658
• Godio A., Massarotto A., Santilano A., 2016. Particle swarm optimisation of electromagnetic soundings, 78th Annual international conference and exhibition, European Association of Geoscientists and Engineers, Barcelona, Spain, 1-5. Erişim adresi: https://iopscience.iop.org/article/10.1088/1755-1315/62/1/012033/pdf
• Godio A., Pace F., Vergnano A., 2020. SEIR modeling of the Italian epidemic of SARS-CoV-2 using computational swarm intelligence. Int. J. Environ. Res. Public Health 17, 3535, 1-19
• Gokalp H., 2021. Grid araştırma yöntemi ile yerel ve bölgesel depremlerin konumlarının belirlenmesi, Pamukkale Univ. Muh. Bilim. Dergisi 27(3), 393-409 Erişim adresi: https://doi.org/10.5505/pajes.2020.69922
• Grandis H., Maulana Y., 2017. Particle swarm optimization (PSO) for magnetotelluric (MT) 1D inversion modeling, IOP Conf. Ser. Earth. Environ. Sci. 62, 012033 Erişim adresi: https://iopscience.iop.org/article/10.1088/1755-1315/62/1/012033
• Jin X., Liu S., Baret F., 2017. Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery, Remote Sens. Environ. 198, 105-114
• Juang, C.F., 2004. A hybrid genetic algorithm and particle swarm optimization for recurrent network design, IEEE Transactions on Systems, Man, and Cybernetics 34, 997-1006
• Karacik Z., Yilmaz Y., 1995. Geology of the Ignimbrite Eruptions of Ezine-Ayvacik region, NW Anatolia, Int. Earth Sci. Colloquium on the Aegean Region (IESCA), 415-427 Erişim adresi: https://www.researchgate.net/publication/292608313_Geology_of_the_ignimbrite_eruptions_of_Ezine_-_Ayvacik_region_NW_Anatolia
• Karacik Z. Yilmaz Y., 1998. Geology of the ignimbirites and the associated volcanoplutonic complex of the Ezine area, Northwestern Anatolia, J. Volcanol. Geoth. Res. 85,1-4
• Karcioglu G., Gurer A., 2019. Implementation and model uniqueness of Particle Swarm Optimization method with a 2D smooth modeling approach for Radio-Magnetotelluric data, J. Appl. Geophys. 169, 37-48
• Kennedy J., Eberhart R.C.,1995. Particle swarm optimization, IEEE International Conf. on Neural Networks (Perth Australia), IEEE Service Center, Piscataway, NJ, 1942-1948
• Khare A., Rangnekar S., 2013. A review of particle swarm optimization and its applications in Solar Photovoltaic system, Appl. Soft. Comput. 13, 2997-3006
• Koukouvelas I.K., Aydin A., 2002. Fault structure and related basins of the North Aegean Sea and its surroundings, Tectonics 21(5), 1046
• Kurcer A., Yalcin H., Utkucu M., Gulen, L., 2016. Seismotectonics of the Southern Marmara Region, NW Turkey, Bulletin of the Geological Society of Greece 50(1), 173-181 Erişim adresi: https://doi.org/10.12681/bgsg.11717
• Kurcer A., Elmaci H., 2017. 06-14 Şubat 2017 Ayvacık (Çanakkale) deprem fırtınası saha gözlemleri ve değerlendirme raporu, MTA, Jeoloji Etütleri Dairesi, Ankara, 26 s.
• Liu S., Liang M., Hu X., 2018. Particle swarm optimization inversion of magnetic data: Field examples from iron ore deposits in China, Geophysics 83(4), 43-59
• Nalbant S.S., Hubert A., King, G.C.P., 1998. Stress coupling between earthquakes in northwest Turkey and the north Aegean Sea, J. Geophys. Res. 103(24), 469-486
• Nyst M., Thatcher W., 2004. New constraints on the active tectonic deformation of the Aegean, J. Geophys. Res. 109, B11406
• Özer C., Polat O., 2017a. İzmir ve Çevresinin 1-B (Bir-Boyutlu) Sismik Hız Yapısının Belirlenmesi. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 19(55), 147-168 Erişim adresi: https://doi.org/10.21205/deufmd.2017195512
• Ozer C., Polat O., 2017b. 3-D crustal velocity structure of izmir and surroundings, Journal of the Faculty of Engineering and Architecture of Gazi University 32(3), 733-747 Erişim adresi: https://doi.org/10.17341/gazimmfd.337620
• Özyalın S., Kartal R.F., Polat O., 2017a. Odak mekanizmasının parçacık suru optimizasyonu (pso) ile cozumu, 4.Uluslararası Deprem Mühendisliği ve Sismoloji Konferansı, 11-13 Ekim 2017, Anadolu Üniversitesi, Eskişehir Erişim adresi: http://www.tdmd.org.tr/TR/pdf/BildiriOzetleriKitabi(abstracts).pdf
• Özyalın S., Özer Ç., Polat O., 2017b. Yapay Ari Kolonisi yardımıyla episantr tayini: ilksel sonuçları. 4.Uluslararası Deprem Mühendisliği ve Sismoloji Konferansı, 11-13 Ekim 2017, Anadolu Üniversitesi, Eskişehir Erişim adresi: http:/www.tdmd.org.tr/TR/pdf/BildiriOzetleriKitabi(abstracts).pdf
• Pace F., Santilano A., Godio A., 2019. Particle swarm optimization of 2D magnetotelluric data, Geophysics 84,125-141
• Pallero J.L.G. Fernández-Martinez J.L., Bonvalot S., Fudym O., 2017. 3D gravity inversion and uncertainty assessment of basement relief via Particle Swarm Optimization, J. Appl. Geophys. 139, 338-350
• Peksen E., Yas T., Kayman A.Y., Ozkan C., 2011. Application of particle swarm optimization on self-potential data, J. Appl. Geophys. 75, 305-318
• Peksen E., Yas T., Kiyak A., 2014. 1-D DC resistivity modeling and interpretation in anisotropic media using particle swarm optimization, Pure Appl. Geophys. 171, 2371-2389
• Perez R.E., Behdinan K., 2007. Particle swarm approach for structural design optimization, Comput. Struct. 85, 1579-1588
• Poli R., 2008. Analysis of the Publications on the Applications of Particle Swarm Optimisation, Journal of Artificial Evolution and Applications 2008(685175) Erişim adresi: https://doi.org/10.1155/2008/685175
• Reilinger R., McClusky S., Vernant P., Lawrence S., Ergintav S., Cakmak R., et al., 2006. GPS constraints on continental deformation in the Africa-Arabia-Eurasia continental collision zone and implications for the dynamics of plate interactions, Journal of Geophysical Research 111, B05411
• Robinson J., Rahmat-Samii Y., 2004. Particle swarm optimization in electromagnetics, IEEE Trans. Antennas Propag. 52, 397-407
• Sindirgi P., Ozyalin S., 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 Tamis-Canakkale, Turkey. (In: Self-Potential Method: Theoretical Modeling and Applications in Geosciences, Editor: Arkoprovo Biswas, Springer CHAM, Berlin/Heidelberg-Germany, 314 p.), 133-153 p.
• Song X., Tang L., Lv X., 2012. Application of particle swarm optimization to interpret Rayleigh wave dispersion curves, J. Appl. Geophys. 84,1-13
• Sözbilir H., Sümer O., Uzel B., Softa M., Tepe C., Eski S., Özkaymak C., Baba A., 2017. 14 Ocak-16 Şubat Çanakkale Ayvacık Depremleri Değerlendirme Raporu, Dokuz Eylül Üniversitesi Deprem Araştırma ve Uygulama Merkezi Diri Fay Araştırma Grubu, İzmir
• Sengor A.M.C., Tuysuz O., Imren C., Sakinc M., Eyidogan H., Gorur N., Le Pichon X., Rangin C., 2005. The North Anatolian Fault: A new look. Annual Review of Earth and Planetary Sciences 33, 37-112.
• Taymaz T., Jackson J., McKenzie D., 1991. Active tectonics of the north and central Aegean Sea, Geophys. J. Int. 106, 433-490.
• USGS, 2021. Erişim adresi: https://www.usgs.gov/media/images/triangulation-locate-earthquake
• Yin Z.Y., Jin Y.F., Shen J.S., Hicher P.Y., 2018. Optimization techniques for identifying soil parameters in geotechnical engineering: comparative study and enhancement, Int. J. Numer. Anal. Methods Geomech. 42,70-94.
• Wachowiak M.P., Smolikova R., Zheng Y., 2004. An approach to multimodal biomedical image registration utilizing particle swarm optimization, IEEE Trans. Evol. Computat. 8, 289-301
• Weimerskirch H., Martin J., Clerquin Y., Alexandre P., Jiraskova S., 2001, Energy savings in flight formation, Nature (London) 413, 697-698