A Weighted Average of Multiple Inversions of Rayleigh Wave Dispersion Curve Using Particle Swarm Optimization for Geotechnical Site Characterization

Jamhir Safani, Rezki Wirawan, Al Rubaiyn Rubaiyn, Mohd Nawawi, Toshifumi Matsuoka

Abstract

Shear wave velocity is an important parameter in geotechnical engineering for studying liquefaction, finding bedrock for the basement of a building, and figuring out the presence of subsurface cavities. This study aims to develop and evaluate the accuracy of the multiple inversions by the Particle Swarm Optimization (MI-PSO) algorithm with a weighted average solution. This algorithm is applied to Rayleigh wave dispersion data for geotechnical site characterization. Two synthetic models, the HVL model and the complex model (i.e., a combination of models with LVL and HVL characteristics), are used to conduct algorithm tests. These synthetic models replicate subsurface characteristics that are frequently encountered in geotechnical cases. Synthetic data tests show that the MI-PSO algorithm with a weighted average solution works excellently. The MI-PSO technique with a weighted average solution resolves the model better than the conventional average solution. When applied to two field data sets, the MI-PSO algorithm with a weighted average solution can delineate target models that are consistent with the qualitative interpretation based on the observed dispersion curve characteristics.

Keywords

MI-PSO; weighted average; dispersion curve; Rayleigh wave; geotechnical site

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References

Karray, M. 2010. Shear wave velocity in geotechnical engineering. In Proceedings of the International Conference on Geotechnical Engineering, Hammamet, 20-23.

Aki, K. 1957. Space and Time Spectra of Stationary Stochastic Waves, with Special Reference to Microtremors. Earthq. Res. Inst., 415–456.

Everett, M. E. 2013. Near-surface applied geophysics. Cambridge University Press.

Park, C. B., Miller, R. D. & Xia, J. 1999. Multichannel analysis of surface waves. Geophysics 64, 800–808.

Park, C. B., Miller, R. D. & Xia, J. 2007. Multichannel analysis of surface waves (MASW)—active and passive methods. Lead. Edge, 26, 60–64.

Xia, J., Miller, R. D. & Park, C. B. 1999. Estimation of near‐surface shear‐wave velocity by inversion of Rayleigh waves. Geophysics, 64, 691–700.

Song, X., Gu, H., Zhang, X. & Liu, J. 2008. Pattern search algorithms for nonlinear inversion of high-frequency Rayleigh-wave dispersion curves. Comput. Geosci., 34, 611–624.

Song, X., Tang, L., Lv, X., Fang, H. & Gu, H. 2012. Application of particle swarm optimization to interpret Rayleigh wave dispersion curves. J. Appl. Geophys., 84, 1–13.

Dal Moro, G., Pipan, M. & Gabrielli, P. 2007. Rayleigh wave dispersion curve inversion via genetic algorithms and Marginal Posterior Probability Density estimation. J. Appl. Geophys., 61, 39–55.

Sahraeian, M. S., Ghalandarzadeh, A. & Kavand, A. 2008. Estimation of Shear Wave Velocity By Means of Array Measurement of Microtremors Using Genetic Algorithm Method. Thr 14th World Conf. Earthq. Eng., 1–9.

Rubaiyn, A., Priyono, A. & Yudistira, T. 2019. Near-surface S-wave Estimation base on Inversion of Rayleigh Wave Dispersion Curve Using Genetic Algorithm. IOP Conf. Ser. Earth Environ. Sci., 318, 1–6.

Safani, J. & Matsuoka, T. 2013. Soft-geotechnical Zone Determination using Surface-wave for Geotechnical Hazard Mitigation. Procedia Environ. Sci., 17, 354–360.

Beaty, K. S., Schmitt, D. R. & Sacchi, M. 2002. Simulated annealing inversion of multimode Rayleigh wave dispersion curves for geological structure. Geophys. J. Int., 151, 622–631.

Lu, Y., Peng, S., Du, W., Zhang, X., Ma, Z., & Lin, P. 2016. Rayleigh wave inversion using heat-bath simulated annealing algorithm. Journal of Applied Geophysics, 134, 267-280.

Wang, Y., Wang, H., Wu, X., Chen, K., Liu, S., & Deng, X. 2021. Near-surface velocity inversion from Rayleigh wave dispersion curves based on a differential evolution simulated annealing algorithm. Artificial Intelligence in Geosciences, 2, 35-46. h

Marini, F. & Walczak, B. 2015 Chemometrics and Intelligent Laboratory Systems Particle swarm optimization ( PSO ). A tutorial. Chemom. Intell. Lab. Syst. 149, 153–165.

Pace, F., Santilano, A. & Godio, A. 2021. A Review of Geophysical Modeling Based on Particle Swarm Optimization. Surveys in Geophysics, 42.

Pallero, J. L. G., Fernández-Martínez, J. L., Bonvalot, S. & Fudym, O. 2015. Gravity inversion and uncertainty assessment of basement relief via Particle Swarm Optimization. J. Appl. Geophys. 116, 180–191.

Pekşen, E., Yas, T. & Kıyak, A. 2014. 1-D DC Resistivity Modeling and Interpretation in Anisotropic Media Using Particle Swarm Optimization. Pure Appl. Geophys. 171, 2371–2389.

Oyeyemi, K. D. et al. 2023. Nonlinear inversion of electrical resistivity sounding data for multi-layered 1-D earth model using global particle swarm optimization (GPSO). Heliyon 9, e16528.

Liu, S., Liang, M. & Hu, X. 2018. Particle swarm optimization inversion of magnetic data: Field examples from iron ore deposits in China. Geophysics 83, J43–J59.

Schutte, J. F. & Groenwold, A. A. 2005. A study of global optimization using particle swarms. J. Glob. Optim. 31, 93–108.

Raha, R. 2019. Particle-Swarm-Optimization-using-Matlab. GitHub repository https://github.com/ritwikraha/Particle-Swarm-Optimization-using-Matlab.

Kennedy, J. & Eberhart, R. 1995. Particle swarm optimization. in Proceedings of ICNN'95 - International Conference on Neural Networks, IEEE.

Shi, Y. & Eberhart, R. 1998. A Modified particle swarm optimizer. Proc. IEEE Conf. Evol. Comput. ICEC 69–73.

Fourie, P. C. & Groenwold, A. A. 2002. The particle swarm optimization algorithm in size and shape optimization. Struct. Multidiscip. Optim. 23, 259–267.

Kausel, E. & Roësset, J. M. Stiffness matrices for layered soils. Bull. Seismol. Soc. Am. 71, 1743–1761 (1981).

Olafsdottir, E. A. 2019. Multichannel Analysis of Surface Waves for Soil Site Characterization. University of Iceland.

Safani, J. 2007. Surface Wave Dispersion Modelling by Full-Wavefield Reflectivity and Inversion for Shallow Subsurface Imaging. Doctoral Thesis. Kyoto University.

Safani, J., O'Neill, A. & Matsuoka T. 2006. Full SH-wavefield modelling and multiple-mode Love wave inversion. Exploration Geophysics, 37(4), 307-321.

DOI: https://doi.org/10.13057/ijap.v13i2.77921

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