Abstract
The refraction microtremor method has been increasingly used as an appealing tool for investigating near surface S-wave structure. However, inversion, as a main stage in processing refraction microtremor data, is challenging for most local search methods due to its high nonlinearity. With the development of data optimization approaches, fast and easier techniques can be employed for processing geophysical data. Recently, particle swarm optimization algorithm has been used in many fields of studies. Use of particle swarm optimization in geophysical inverse problems is a relatively recent development which offers many advantages in dealing with the nonlinearity inherent in such applications. In this study, the reliability and efficiency of particle swarm optimization algorithm in the inversion of refraction microtremor data were investigated. A new framework was also proposed for the inversion of refraction microtremor Rayleigh wave dispersion curves. First, particle swarm optimization code in MATLAB was developed; then, in order to evaluate the efficiency and stability of proposed algorithm, two noise-free and two noise-corrupted synthetic datasets were inverted. Finally, particle swarm optimization inversion algorithm in refraction microtremor data was applied for geotechnical assessment in a case study in the area in city of Tabriz in northwest of Iran. The S-wave structure in the study area successfully delineated. Then, for evaluation, the estimated Vs profile was compared with downhole data available around of the considered area. It could be concluded that particle swarm optimization inversion algorithm is a suitable technique for inverting microtremor waves.
Similar content being viewed by others
References
Blum C, Li X (2008) Swarm intelligence in optimization. Natural Computing Series, Springer Berlin Heidelberg, pp. 43–85
Carlisle A, Dozier G (2001) An off-the-shelf PSO. Proceedings of the Workshop On Particle Swarm Optimization, Indianapolis, USA, pp 1–6
Cha YH, Kang JS, Jo CH (2006) Application of linear-array microtremor surveys for rock mass classification in urban tunnel design. Explor Geophys 37:108–113
Clerc M (1999) The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. In: Angeline PJ, Michalewicz Z, Schoenauer M, Yao X, Zalzala A (eds) Proceedings of the Congress of Evolutionary Computation, vol 3. IEEE Press, Washington DC, pp. 1951–1957
Clerc MA, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6:58–73
Coccia S, Del GV, Venisti N, Wasowski N (2010) Application of refraction microtremor (ReMi) technique for determination of 1-D shear wave velocity in a landslide area. J of Applied Geophysics 71:71–89
Evers GI (2009) An automatic regrouping mechanism to deal with stagnation in particle swarm optimization. Thesis for the degree of Master of Science University of Texas—Pan American. Texas, USA
Fernández Martínez JL, García Gonzalo E (2008) The generalized PSO: a new door to PSO evolution. J Artif Evol Appl. doi:10.1155/2008/861275
Fernández Martínez JL, García Gonzalo E, Fernández Álvarez JP, Kuzma HA, Menéndez Pérez CO (2010a) PSO: a powerful algorithm to solve geophysical inverse problems: application to a 1D–DC resistivity case. J Appl Geophys 71:13–25
Fernández Martínez JL, García Gonzalo E, Fernández Muñiz Z, Mariethoz G, Mukerji T (2010b) Posterior sampling using particle swarm optimizers and model reduction techniques. Int J Appl Evol Comput 1(3):27–48
Gardner GF, Gardner LW, Gregory AR (1974) Formation velocity and density the diagnostic basic for stratigraphic trap. Geophysics 39:770–780
Herrmann RB (1987) Computer programs in seismology. St Louis University
Kennedy J, Eberhart RC (1995) Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks (Perth, Australia), Piscataway, pp 1942–1948
Louie JN (2001) Faster, better: shear wave velocity to 100 meters depth from refraction microtremor arrays Bull. Seism Soc Am 91:347–364
Dal Moro G (2008) VS and VP vertical profiling via joint inversion of Rayleigh waves and refraction travel times by means of bi-objective evolutionary algorithm. J Appl Geophys 66:15–24
Naudet V, JL Fernández Martínez, E García Gonzalo, JP Fernández Alvarez (2008) Estimation of water table from self-potential data using particle swarm optimization (PSO). Annual Meeting, SEG, Expanded Abstracts, pp 1203–1207
Panzera F, Lombardo G (2013) Seismic property characterization of lithotypes cropping out in the Siracusa urban area, Italy. Eng Geol 153:12–24
Pekşen E, Yas TA, Kayman Y, Özkan C (2011) Application of particle swarm optimization on self-potential data. J Appl Geophys 75:305–318
Pérez-Santisteban I, García-Mayordomo J, Muñoz Martín A, Carbó A (2011) Comparison among SASW, ReMi and PS-logging techniques: application to a railway embankment. J Appl Geophys 73:59–64
Poormirzaee R, Moghadam HR, Zarean A (2014) Introducing particle swarm optimization (PSO) to invert seismic refraction data. In: 20th European Meeting of Environmental and Engineering Geophysics (EAGE). Greece, Athens
Rucker ML (2003) Applying the refraction microtremor (ReMi) shear wave technique to geotechnical characterization. Proc. In. Conf. of the third international conference on the application of geophysical methodologies and NDT to transportation and infrastructure (Florida) p. 8–12
Sanyi Y, Shangxu W, Nan T (2009) Swarm intelligence optimization and its application in geophysical data inversion. Appl Geophys 6(2):166–174
Schutte JF, Groenwold AA (2005) A study of global optimization using particle swarms. J Glob Optim 31:93–108
Scott JB, Clark M, Rennie T, Pancha A, Park H, Louie JN (2004) A shallow shear-wave velocity transect across the Reno, Nevada, area. Basin Bull Seismol Soc Am 94:2222–2228
Sen MK, Stoffa PL (1995) Global optimization methods in geophysical inversion. Science, Elsevier
Shaw R, Srivastava S (2007) Particle swarm optimization: a new tool to invert geophysical data. Geophysics 72(2):75–83
Shi Y, Eberhart RC (1998) A modified particle swarm optimizer. Proceedings of IEEE International Conference on Evolutionary Computation, Anchorage, USA, pp. 69–73
Soupios P, Akca I, Mpogiatzis P, Basokur AT, Papazachos C (2011) Applications of hybrid genetic algorithms in seismic tomography. J Appl Geophys 75:479–489
Stephenson WJ, Louie JN, Pullammanappallil S, Williams RA, Odum JK (2005) Blind shear-wave velocity comparison of ReMi and MASW results with boreholes to 200 m in Santa Clara Valley: implications for earthquake ground–motion assessment. Bull Seismol Soc Am 95(6):2506–2516
Trelea IC (2003) The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf Process Lett 85:317–325
Wathelet M, Jongmans D, Ohrnberger M (2004) Surface wave inversion using a direct search algorithm and its application to ambient vibration measurements. Near Surf Geophys 2:211–221
Xia J, Miller RD, Park CB (1999) Estimation of near-surface shear-wave velocity by inversion of Rayleigh waves. Geophysics 64:691–700
Yang XS (2010) Engineering optimization: an introduction with metaheuristic applications. Wiley, Cambridge, pp. 19–22
Acknowledgments
The author would like to thank the anonymous reviewers for their useful and constructive comments which helped improve the content of this paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Poormirzaee, R. S-wave velocity profiling from refraction microtremor Rayleigh wave dispersion curves via PSO inversion algorithm. Arab J Geosci 9, 673 (2016). https://doi.org/10.1007/s12517-016-2701-6
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12517-016-2701-6