Manta Vatozu Beslenme Optimizasyon Algoritması Kullanılarak Sismik Kırılma Verisinin Ters Çözümü

Sismik kırılma yöntemi, mühendislik jeofiziği, mühendislik jeolojisi ve jeoteknik mühendisliği araştırma alanlarında kullanılan, özellikle mühendislik yapılarının inşasından önce zeminin özelliklerinin ortaya konmasında önemli bir role sahip olup etkili bir jeofizik yöntemdir. Bu çalışma, P dalgasının ilk varış zamanlarından P dalga hızının (Vp) 1B dağılımını tahmin etmek için yeni bir ters çözüm algoritmasının uygulamasını amaçlamaktadır. Tanıtılan ters çözüm algoritması, Manta Vatozu Beslenme Optimizasyonu (MVBO) algoritması, mühendislik problemlerin çözümü için geliştirilmiş olan biyolojik tabanlı sezgisel üstü alternatif bir optimizasyon yaklaşımıdır. Farklı optimizasyon problemlerini çözmek için manta vatozların hayatta kalabilmesi amacıyla sergiledikleri farklı yiyecek arama stratejilerinden ( zincir beslenme, siklon beslenme ve takla atarak beslenme) yararlanır. Bu çalışma, MVBO algoritmasının sismik kırılma yönteminde gözlenen ve hesaplanan varış zamanları arasındaki farkı en aza indiren 1B hız modelini bulmaya yönelik ilk örnektir. Sunulan yöntemin etkinlik değerlendirmesi için önce farklı çok tabakalı yapay sismik modellere uygulanmış ve daha sonra bu veri setine gürültü eklenerek yöntemin etkinliği irdelenmiştir. Son olarak, MVBO ters çözüm algoritması gerçek arazi verisine uygulanmıştır. İran'ın Doğu Azerbaycan eyaleti Malekan ilçesinde bulunan Leylanchay baraj sahasında toplanmış olan gerçek sismik kırılma veri kümesi kullanılmıştır. Hem yapay hem de arazi verisine ait model parametrelerinin kestirimi ve güvenilirliğinin belirlenmesi için, rölatif frekans dağılımları ve olasılık yoğunluk fonksiyonları (OYF) yardımıyla kestirim parametreleri istatistiksel olarak da test edilmiştir. Bulgular, çalışma alanının üç tabakadan oluştuğunu, ilk iki tabakanın alüvyon ve son tabakanın ana kayayı temsil ettiğini göstermektedir. Sonuçlar, sismik kırılma verilerinin yorumlanmasında MVBO ters çözüm algoritmasının uygun ve güvenilir sonuçlar verdiğini ortaya koymaktadır.

Anahtar Kelimeler:

Global optimization, Manta ray foraging optimization, Metaheuristic, Seismic refraction, Küresel optimizasyon, Manta vatozu beslenme optimizasyon, Metasezgisel, Sismik kırılma

Seismic Refraction Data Inversion using a Manta Ray Foraging Optimization Algorithm

The seismic refraction method is an effective geophysical method used in engineering geophysics, engineering geology, and geotechnical engineering research fields, especially having an important role in revealing the properties of the soil before the construction of engineering structures. This study is the first example to find the 1D velocity model that minimizes the difference between the observed and calculated arrival times in the seismic refraction method of the MVBO algorithm. The introduced inversion algorithm, the Manta Rays Foraging Optimization (MRFO) algorithm, is a biological-based metaheuristic alternative optimization approach developed for the solution of engineering problems. It uses different foraging strategies (chain foraging, cyclone foraging, and somersault foraging) that manta rays exhibit to survive in order to solve different optimization problems. This study is the first example of using the MRFO algorithm to optimize the 1D distribution of seismic refraction data. In order to evaluate the effectiveness of the presented method, it was first applied to different multilayer synthetic seismic models and then the efficiency of the method was examined by adding noise to this data set. Finally, the MRFO inversion algorithm was applied to real-field data. A real seismic refraction dataset collected at the Leylanchay dam site in the Malekan district of the East Azerbaijan province of Iran was used. In order to determine the reliability of the model parameters of both synthetic and field data, the estimation parameters were also tested statistically through relative frequency distributions and probability density functions (PDF). The findings show that the study area consists of three layers, with the first two layers representing alluvium and the last layer being bedrock. The results reveal that the MRFO inversion algorithm gives appropriate and reliable results in the interpretation of seismic refraction data.

Keywords:

Global optimization, Manta ray foraging optimization, Metaheuristic, Seismic refraction,

PDF

___

[1] Kearey, P., Brooks M., Hill I. 2002. An introduction to geophysical exploration. Wiley, Oxford
[2] Öztürk, K. 1993. Prospeksiyon Jeofiziği (Sismik), İstanbul Üniversitesi yayını,17, 165s.
[3] Poormirzaee, R., Fister, I.Jr. 2021. Model-based inversion of Rayleigh wave dispersion curves via linear and nonlinear methods. Pure Appl Geophys 178(2):341–358. https:// doi. org/ 10. 1007/ s00024- 021- 02665-7
[4] Cerveny, V., and Ravindra, R. 1971. Theory of Seismic Head Waves: Toronto Press, 296p.
[5] Barry, K.M. 1967. Delay-time and its application to refraction profile interpretation: in Musgrave, A.W. (ed.), Seismic Refraction Prospecting: Society of Exploration Geophysicists, 348–361.
[6] Redpath, B. 1973. Seismic refraction for engineering site investigation: Explosives Excavation Research Lab., TR E-73-4, 51p.
[7] Hawkins, L.V. 1961. The Reciprocal method of routine shallow seismic refraction investigations: Geophysics, 26, 806–819.
[8] Hagedoorn, J.G. 1959. The plus-minus method of interpreting seismic refraction sections: Geophysical Prospecting, 7, 158–182.
[9] Whiteley, R.J., 2004, Shallow seismic refraction interpretation with visual interactive raytracing (VIRT): Exploration Geophysics, 35, 116–123.
[10] Yas, T., Aşçı, M. 2017. Doğal kaynaklı potansiyel alanlarının birleşik ters çözümü. Uygulamalı Yerbilimleri Dergisi, Cilt: 16, No: 1,27-50.
[11] Backus, G.E., Gilbert, J.F.A. 1967. Numerical application of formalism for geophysical inverse problems, Geophysical Journal of the Royal Astronomical Society, Vol. 13, pp. 24-279.
[12] Backus, G.E., Gilbert, J.F.A. 1968. The resolving power of gross earth data, Journal of the Royal Astronomical Society, Vol. 16, pp. 169-205.
[13] Jackson, D.D. 1972. Interpretation of Anaccurate, Insufficient and Inconsistent Data, Geophys. J. R. Astr. Soc., Vol. 28, pp. 97-109.
[14] Wiggins, R.A. 1972. The General Linear Inverse Problem: Implication of Surface Waves and Free Oscillations for Earth Structure, Rev.Geophysics and Space Physics, Vol. 10, pp. 251, 285.
[15] Rao, D.A., Ram Babu, H.V., Raju, D.V, 1985. Inversion of Gravity and Magnetic Anomalies Over Some Bodies of Simple Geometric Shape, Pure and Applied Geophysics, Vol. 123, No. 2, pp. 239–249.
[16] Murthy, I.V.R., Krishnamacharyulu, S.K.G. 1990. A FORTRAN 77 Program to Invert Gravity Anomalies of Sheet-Like Bodies, Computers & Geosciences, Vol. 16, No. 7, pp. 991–1001.
[17] Raju, D.V.Ch. 2003. LIMAT: A Computer Program for Least-Squares Inversion of Magnetic Anomalies Over Long Tabular Bodies, Computers&Geosciences, Vol. 29, No. 1, pp. 91-98.
[18] Hussain, K., Mohd Salleh, M.N., Cheng, S., Shi, Y. 2019. Metaheuristic research: a comprehensive survey. Artif Intell Rev 52(4):2191–2233. https:// doi. org/ 10. 1007/ s10462- 017- 9605-z
[19] Gabis, A.B., Meraihi, Y., Mirjalili, S.A., Cherif, A.R.A. 2021. Comprehensive survey of sine cosine algorithm: variants and applications. Artif Intell Rev 54:5469–5540
[20] Chou JS, Truong DN (2021) A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean. Appl Math Comput 389. https:// doi. org/ 10. 1016/j. amc. 2020. 125535
[21] Slowik, A., Kwasnicka, H. 2020. Evolutionary algorithms and their applications to engineering problems. Neural Comput & Applic 32, 12363–12379. https://doi.org/10.1007/s00521-020-04832-8
[22] Can, U., Alataş, B. 2015. Physics-Based Metaheuristic Algorithms for Global Optimization. American Journal of Information Science and Computer Engineering. Vol. 1, No. 3, 2015, pp. 94-106
[23] Krause, J., Cordeiro, J., Parpinelli, R.S., Lopes,H.S. 2013. 7 - A Survey of Swarm Algorithms Applied to Discrete Optimization Problems, Swarm Intelligence and Bio-Inspired Computation, Theory and Applications. Pages 169-191. https://doi.org/10.1016/B978-0-12-405163-8.00007-7.
[24] Zhao, W., Wang, L., Zhang, Z. 2019. Atom search optimization and its application to solve a hydrogeologic parameter estimation problem, Knowledge-Based Systems, Volume 163,283-304. https://doi.org/10.1016/j.knosys. 2018.08.030.
[25] Askari, Q., Younas, I., Saeed, M. 2020. Political optimizer: a novel socio-inspired meta-heuristic for global optimization, Knowl. Based Syst. 195, 105709.
[26] Holland, J.H. 1992. Genetic algorithms, Sci. Am. 267 66–73.
[27] Kennedy, J., Eberhart, R. 1995. Particle Swarm Optimization, IEEE, Piscataway, NJ, United States, 1995, pp. 1942–1948.
[28] Yang, X.S. Firefly algorithms for multimodal optimization, in: O. Watanabe, T. Zeugmann (Eds.), Stochastic Algorithms: Foundations and Applications, Springer Berlin Heidelberg, Berlin, Heidelberg, 2009, pp. 169–178.
[29] Karaboğa, D., Basturk, B., 2007. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, J. Glob. Optim. 39, 459–471.
[30] Storn, R., Price, K.V., 1995. Differential evolution — a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012. International Computer Science Institute, Berkeley.
[31] Dorigo, M, Blum, C. 2005. Ant colony optimization theory: A survey, Theoretical Computer Science, 344, 2–3, 243-278, https://doi.org/10.1016/j.tcs.2005.05.020.
[32] Civicioglu, P. 2012. Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm; Comput. Geosci., https://doi.org/10.1016/j. cageo.2011.12.011.
[33] Yang, X.S., Deb, S., 2009. Cuckoo search via L´evy flights. In: IEEE World Congress on Nature and Biologically Inspired Computing (NaBIC); Coimbatore, India, pp. 210-214.
[34] Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P. 1983. Optimization by simulated annealing. Science 220, 671–680. https://doi.org/10.1126/science.220.4598.671.
[35] Yang, X.S. 2012. Flower pollination algorithm for global optimization. In: Durand-Lose J, Jonoska N (eds) Unconventional computation and natural computation. UCNC 2012. Lecture notes in computer science, vol 7445. Springer, Berlin. https:// doi. org/10. 1007/ 978-3- 642- 32894-7_ 27
[36] Mirjalili, S., Gandomi, A.H., Mirjalili, S.Z., Saremi, S., Faris, H., Mirjalili, S.M. 2017. Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191. https://doi. org/ 10. 1016/j. adven gsoft. 2017. 07. 002
[37] Geem, Z.W., Kim, J.H., Loganathan, G.V. 2001. A new heuristic optimization algorithm. Harmony Search Simul 76(2):60–68. https://doi.org/10.1177/ 00375 49701 07600 201
[38] Tan, Y., Zhu, Y. 2010. Fireworks algorithm for optimization. Lecture notes in computer science (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics). Springer, Berlin, pp 355–364
[39] Rao, R.V., Savsani, V.J., Vakharia, D.P. 2011. Teaching–learning based optimization: a novel method for constrained mechanical design optimization problems. Comput Des 43(3):303–315. https:// doi. org/ 10. 1016/j. cad. 2010. 12. 015
[40] Poormirzaee, R. Moghadam, R.H., Zarean, A. 2015. Inversion seismic refraction data using particle swarm optimization: a case study of Tabriz, Iran. Arab J Geosci. 8:5981–5989. DOI 10.1007/s12517-014-1662-x
[41] Poormirzaee, R., Sarmady, S., Sharghi, Y. 2019. A new inversion method using a modified bat algorithm for analysis of seismic refraction data in dam site investigation. J Environ Eng Geophys 24(2):201–214
[42] Poormirzaee, R. 2022. Seismic refraction data inversion via jellyfish search algorithm for bedrock characterization in dam sites. SN Appl. Sci. 4, 288. https://doi.org/10.1007/s42452-022-05171-0
[43] Balkaya, Ç., Ekinci Y.L., Göktürkler, G., Turan, Seçil. 2017. 3D non-linear inversion of magnetic anomalies caused by prismatic bodies using differential evolution algorithm, Journal of Applied Geophysics, 136,372–386. DOI: 10.1016/j.jappgeo.2016.10.040.
[44] Kaftan, I. 2017. Interpretation of magnetic anomalies using a genetic algorithm. Acta Geophys. 65 (4), 627–634. DOI: 10.1007/s11600-017-0060-7.
[45] Ekinci, Y.L., Özyalın, Ş., Sındırgı, P., Balkaya, Ç., Göktürkler, G. 2017. Amplitude inversion of 2D analytic signal of magnetic anomalies through differential evolution algorithm, Journal of Geophysics and Engineering, 14(6): 1492-1508. DOI: 10.1088/1742-2140/aa7ffc.
[46] Balkaya, C., Kaftan, İ. 2021. Inverse modelling via differential search algorithm for interpreting magnetic anomalies caused by 2D dyke-shaped bodies, Journal of Earth System Sciences, Cilt. 130, s. 135. DOI: 10.1007/s12040-021-01614-1
[47] Özyalın, Ş. 2022. Interpretation of volcanic magnetic anomalies using differential search algorithm: case study from the Kula volcanic park, western Türkiye. Acta Geophys. https://doi.org/10.1007/s11600-022-00975-5
[48] Ai, H., Essa, K.S., Ekinci, Y.L. et al. 2022. Magnetic anomaly inversion through the novel barnacles mating optimization algorithm. Sci Rep 12, 22578 https://doi.org/10.1038/s41598-022-26265-0
[49] 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, 129:133-147. https://doi.org/10.1016/j.jappgeo.2016.03.040.
[50] Ekinci, Y.L., Balkaya, Ç., Göktürkler, G. 2020. Global Optimization of Near-Surface Potential Field Anomalies Through Metaheuristics. In: Biswas, A., Sharma, S. (eds) Advances in Modeling and Interpretation in Near Surface Geophysics. Springer Geophysics. Springer, Cham. https://doi.org/10.1007/978-3-030-28909-6_7
[51] Ekinci, Y.L., Balkaya, Ç. & Göktürkler, G. 2021. Backtracking Search Optimization: A Novel Global Optimization Algorithm for the Inversion of Gravity Anomalies. Pure Appl. Geophys. 178, 4507–4527. https://doi.org/10.1007/s00024-021-02855-3
[52] 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(2), 923–944. https://doi.org/10.1093/gji/ggaa492
[53] Turan-Karaoğlan, S., Göktürkler, G. 2021. Cuckoo Search Algorithm for model parameter estimation from self-potential data, Journal of Applied Geophysics, 194, 104461. https://doi.org/10.1016/j.jappgeo.2021.104461.
[54] Sharma, S.P., Biswas, A., 2013. Interpretation of self-potential anomaly over a 2D inclined structure using very fast simulated-annealing global optimization – an insight about ambiguity. Geophysics 78 (3), WB3–WB15. https://doi.org/10.1190/ geo2012-0233.1.
[55] Shaw, R., Srivastava, S., 2007. Particle swarm optimization: a new tool to invert geophysical data. Geophysics 72 (2), F75–F83. https://doi.org/10.1190/1.2432481.
[56] Zhao, W., Zhang, Z., Wang, L. 2020. Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications, Engineering Applications of Artificial Intelligence, 87,103300, https://doi.org/10.1016/j.engappai.2019.103300.
[57] Stevens, G.M.W. 2016. Conservation and Population Ecology of Manta Rays in the Maldives. Ph.D. Thesis, University of York, York, UK, 2016.
[58] Turgut, O.E. 2021. A novel chaotic manta-ray foraging optimization algorithm for thermo-economic design optimization of an air-fin cooler. SN Appl. Sci. 3, 3 https://doi.org/10.1007/s42452-020-04013-1
[59] Rostami, S., Sharghi, Y. 2018. Determination of Alluvium thickness in LeylanChai dam site using refraction seismic method. In: 18th Iranian geophysics conference, pp 482–485