بررسی وارون منحنی پاشش امواج سطحی با استفاده از بهینه¬سازی فراابتکاری به منظور برآورد ساختار سرعتی موج‌برشی زمین در دشت تبریز
محورهای موضوعی : آنالیز سازه - زلزله
شهرام انگردی
1
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رامین وفائی پور
2
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احمد زارعان
3
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روزبه دبیری
4
1 - دانشجوی دکتری، گروه مهندسی عمران، واحد تبریز، دانشگاه آزاد اسلامی، تبریز، ایران
2 - گروه عمران دانشگاه آزاد تبریز
3 - گروه مهندسی عمران، واحد شبستر، دانشگاه آزاد اسلامی، شبستر، ایران
4 - گروه مهندسی عمران، واحد تبریز، دانشگاه آزاد اسلامی، تبریز، ایران
کلید واژه: بهینه¬سازی فراابتکاری, الگوریتم همسایگی, امواج سطحی, منحنی پاشش.,
چکیده مقاله :
در این مطالعه، یک روش جدید برای وارون‌سازیِ منحنی پاشش امواج سطحی، با استفاده از الگوریتم بهینه‌سازی کلونی مورچه¬ها، در محیط متلب ارائه شده است. این روش، به دلیل طبیعت غیرخطی مسئله و وجود اکسترمم‌های متعدد، برتری قابل‌توجهی نسبت به روش‌های جستجوی محلی دارد. برای ارزیابی عمل¬کرد این الگوریتم، داده‌های مصنوعی مربوط به سه مدلِ زمین‌شناسی، شش‌لایه مورد بررسی قرار گرفتند. در این مدل‌ها، بازه¬ی مقادیر سرعت و ضخامت به‌صورت گسترده تعریف گردیده و روند تغییرات سرعت با عمق، بر اساس قانون λ/2 تعیین گردید. فرآیند وارون‌سازی، هم‌زمان شامل تعیین سرعت موج برشی، سرعت موج طولی و ضخامت لایه‌ها، با فرض نسبت پوآسون متغیر (1/0 تا 5/0) و چگالی ثابت انجام شد. نتایج نشان داد که میانگین خطای نسبی پارامترهای سرعت در روش کلونی مورچه 5/3 درصد است، در حالی که این مقدار برای الگوریتم همسایگی موجود در نرم‌افزار ژئوپسی ۱۵ درصد محاسبه شد. این یافته‌ها بیان¬گرِ برتری روش پیشنهادی، از نظر قابلیّت اعتماد و توان محاسباتی است. از دیگر مزایای این روش می‌توان به عدم نیاز به مدل اولیه، توانایی جستجوی کل فضای پارامترها، و حساسیت کم به اکسترمم‌های محلی اشاره کرد. این الگوریتم بر روی داده‌های تجربی ایستگاهی در مرکز دشت تبریز اعمال شد؛ که نتایج حاصل، همخوانی بالایی با اطلاعات گمانه‌های موجود داشت. بر اساس این نتایج، مدل دوبعدی سرعت موج برشی برای این منطقه استخراج شد.
This study presents a novel approach for inverting surface wave dispersion curves using the Ant Colony Optimization (ACO) algorithm in the MATLAB environment. Due to the problem's nonlinear nature and multiple extrema, this method demonstrates significant advantages over local search techniques. To evaluate the performance of the proposed algorithm, synthetic data from three six-layer geological models were analyzed. In these models, the velocity and thickness values were defined over a wide range, and the velocity variation with depth was determined based on the λ/2 rule. The simultaneous inversion process involved estimating shear wave velocity, compressional wave velocity, and layer thickness, assuming a variable Poisson’s ratio (0.1 to 0.5) and a constant density. The results indicate that the average relative error in velocity parameters using the ACO method is 3.5%, whereas this error reaches 15% for the neighborhood algorithm available in the Geopsy software. These findings highlight the superiority of the proposed approach in terms of computational efficiency and reliability. Additional advantages include its independence from an initial model, the ability to explore the entire parameter space, and reduced sensitivity to local extrema. Finally, the algorithm was applied to experimental field data from a station in the Tabriz Plain, yielding results that closely matched available borehole data. Based on these findings, a 2D shear wave velocity model for the region was developed.
1. Yang J, Lin N, Zhang K, Jia L, Zhang D, Li G, Zhang J. A Parametric Study of MPSO-ANN Techniques in Gas-Bearing Distribution Prediction Using Multicomponent Seismic Data. Remote Sensing. 2023;15(16):3987.
https://doi.org/10.3390/rs15163987
2. Poormirzaee R. S-wave velocity profiling from refraction microtremor Rayleigh wave dispersion curves via PSO inversion algorithm. Arab J Geoscience. 2016; 9:673.
https://doi.org/10.1007/s12517-016-2701-6
3. Maghami S, Sohrabi-Bidar A, Bignardi S, Zarean A, Kamalian M. Extracting the shear wave velocity structure of deep alluviums of “Qom” Basin (Iran) employing HVSR inversion of microtremor recordings. Journal of Applied Geophysics. 2021;185:104246.
https://doi.org/10.1016/j.jappgeo.2020.104246
4. Asare EN, Affam M, Ziggah YY. A stacked generalization methodology for estimating the uniaxial compressive strength of rocks. Smart Constr Sustain Cities. 2023;1(1):8.
https://doi.org/10.1007/s44268-023-00010-6
5. Renalier F, Jongmans D, Savvaidis A, Wathelet M, Endrun B, Cornou C. Influence of parameterization on inversion of surface wave dispersion curves and definition of an inversion strategy for sites with a strong Vs contrast. Geophysics. 2010;75(6): B197-209.
https://doi.org/10.1190/1.37306556
6. Trung GK, Vinh ND. An overview of seismic ground response methods over the world and their applications in Vietnam. Geofizicheskiy Zhurnal. 2021;43(2):131-51.
https://doi.org/10.24028/gzh.v43i2.230193
7. Somerville PG, Graves RW. Characterization of earthquake strong ground motion. Landslide Tsunamis: Recent Findings and Research Directions. 2003:1811-28.
https://doi.org/10.1007/s00024-003-2407-z
8. Hunter JA, Benjumea B, Harris JB, Miller RD, Pullan SE, Burns RA, Good RL. Surface and downhole shear wave seismic methods for thick soil site investigations. Soil Dynamics and Earthquake Engineering. 2002; 1;22(9-12):931-41.
https://doi.org/10.1016/S0267-7261(02)00117-3
9. Olutoki JO, Zhao J, Siddiqui NA, Elsaadany M, Haque AE, Akinyemi OD, Said AH, Zhao Z. Shear wave velocity prediction: A review of recent progress and future opportunities. Energy Geoscience. 2024; 10:100338.
https://doi.org/10.1016/j.engeos.2024.100338
10. Dasios, McCann, Astin, McCann, Fenning. Seismic imaging of the shallow subsurface: shear‐wave case histories. Geophysical Prospecting. 1999;47(4):565-91.
https://doi.org/10.1046/j.1365-2478.1999.00138.x
11. Jongmans D. L'application de Méthodes de prospection sismique pour la caractérisation des sols en génie parasismique. Bulletin of the International Association of Engineering Geology-Bulletin de l'Association Internationale de Géologie de l'Ingénieur. 1992; 46:63-9.
https://doi.org/10.1007/BF02595035
12. Kovach RL. Seismic surface waves and crustal and upper mantle structure. Reviews of Geophysics. 1978;16(1):1-3.
https://doi.org/10.1029/RG016i001p00001
13. Gadallah MR, Fisher R, Gadallah MR, Fisher R. Seismic data processing. Exploration Geophysics. 2009:85-148.
https://doi.org/10.1007/978-3-540-85160-8_5
14. Castellani A, Zembaty Z. Stochastic modeling of seismic surface rotations. Natural hazards. 1994; 10: 181-91.
https://doi.org/10.1007/BF00643451
15. Roberts JC, Asten MW. Resolving a velocity inversion at the geotechnical scale using the microtremor (passive seismic) survey method. Exploration geophysics. 2004;35(1):14-8.
https://doi.org/10.1071/EG04014
16. McMechan, GA, Yedlin, MJ. Analysis of dispersive waves by wave field transformation. Geophysics. 1981; (46): 869-874.
https://doi.org/10.1190/1.1441225
17. Luo, Y, Xia, J, Miller, RD, Xu, Y, Liu, J, Liu, Q. Rayleigh-wave mode separation by high-resolution linear Radon transform. Geophysical Journal International. 2009; 179(1):254-264.
https://doi.org/10.1111/j.1365-246X.2009.04277.x
18. Socco LV, Foti S, Boiero D. Surface-wave analysis for building near-surface velocity models—Established approaches and new perspectives. Geophysics. 2010;75(5):75A83-102.
https://doi.org/10.1190/1.3479491
19. Foti S, Comina C, Boiero D, Socco LV. Non-uniqueness in surface-wave inversion and consequences on seismic site response analyses. Soil Dynamics and Earthquake Engineering. 2009; 1;29(6):982-93.
https://doi.org/10.1016/j.soildyn.2008.11.004
20. Cercato M. Addressing non‐uniqueness in linearized multichannel surface wave inversion. Geophysical Prospecting. 2009; 5;57(1):27-47.
https://doi.org/10.1111/j.1365-2478.2007.00719.x
21. Yamanaka H, Ishida H. Application of genetic algorithms to an inversion of surface-wave dispersion data. Bulletin of the Seismological Society of America. 1996; 1;86(2):436-44.
https://doi.org/10.1785/BSSA0860020436
22. Pezeshk S, Zarrabi M. A new inversion procedure for spectral analysis of surface waves using a genetic algorithm. Bulletin of the Seismological Society of America. 2005; 1;95(5):1801-8.
https://doi.org/10.1785/0120040144
23. Dal Moro G, Pipan M, Gabrielli P. Rayleigh wave dispersion curve inversion via genetic algorithms and marginal posterior probability density estimation. Journal of Applied Geophysics. 2007; 1;61(1):39-55.
https://doi.org/10.1016/j.jappgeo.2006.04.002
24. Beaty KS, Schmitt DR, Sacchi M. Simulated annealing inversion of multimode Rayleigh wave dispersion curves for geological structure. Geophysical Journal International. 2002;1;151(2):622-31.
https://doi.org/10.1046/j.1365-246X.2002.01809.x
25. Wathelet M, Jongmans D, Ohrnberger M. Surface‐wave inversion using a direct search algorithm and its application to ambient vibration measurements. Near-surface geophysics. 2004;18;2(4):211-21.
https://doi.org/10.3997/1873-0604.2004018
26. Socco, LV, Boiero, D. Improved Monte Carlo inversion of surface wave data. Geophysical Prospecting. 2008; (56): 357-371.
https://doi.org/10.1111/j.1365-2478.2007.00678.x
27. Khanmohammadi, M, Razavi, S. Proposing New Artificial Intelligence Models to Estimate Shear Wave Velocity of Fine-grained Soils: A Case Study. International Journal of Engineering. 2024; (37): 1164-1174.
https://doi.org/10.5829/ije.2024.37.06c.13
28. Anushka, J, Balasubramanian, RC, Krishna, M, Linga, R. A new machine learning approach for estimating shear wave velocity profile using borelog data. Soil Dynamics and Earthquake Engineering. 2024; (177):108424
https://doi.org/10.1016/j.soildyn.2023.108424
29. Bekdaş G, Aydın Y, Işıkdağ U, Nigdeli SM, Hajebi D, Kim TH, Geem ZW. Shear Wave Velocity Prediction with Hyperparameter Optimization. Information. 2025 Jan 16;16(1):60.
https://doi.org/10.3390/info16010060
30. Dorigo, M, Maniezzo, V, Colorni, A. The Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics. 1996; (26): 29-41.
https://doi.org/10.1109/3477.484436
31. Angardi, S, Poursorkhabi, RV, Shirvanehdeh, AZ, Dabiri, R. Vs Profiling by the Inversion of Rayleigh Wave Ellipticity Curve Using a Hybrid Artificial Intelligence Method. Pure and Applied Geophysics, 2024; 1-14.
https://doi.org/10.1007/s00024-024-03514-z
32. Khalili-Maleki M, Poursorkhabi RV, Nadiri AA, Dabiri R. Prediction of hydraulic conductivity based on the soil grain size using supervised committee machine artificial intelligence. Earth Science Informatics. 2022;15(4):2571-83.
https://doi.org/10.1007/s12145-022-00848-x
33. Poursorkhabi RV, Ghasempour R. Possibilities to use the meta model and classical approaches to evaluate the impact of hydraulic conditions in the prediction of the critical submergence depth ratio. Water Supply. 2019; 19(4):1055-65.
https://doi.org/10.2166/ws.2018.153
34. Poursorkhabi, RV, Gharehaghaji, AH, Naseri, A. An Investigation of the Performance of the ANN Method for Predicting the Base Shear and Overturning Moment Time-Series Datasets of an Offshore Jacket Structure. International Journal of Sustainable Construction Engineering and Technology, 2023; 14(4), 79-93.
https://doi.org/10.30880/ijscet.2023.14.04.007
35. Nemati, Sh, Basiri, ME. Text-independent speaker verification using ant colony optimization-based selected features. Expert Systems with Applications. 2011; (38): 620-630.
https://doi.org/10.1016/j.eswa.2010.07.011
36. Haskell, NA. The dispersion of surface waves on a multi-layered medium. Bulletin of the Seismological Society of America. 1953;(43):17-34.
https://doi.org/10.1785/BSSA0430010017
