MOPSO: a new computing algorithm for joint inversion of Rayleigh wave dispersion curve and refraction traveltimes
Rashed PoormirzaeeDepartment of Mining and Material Engineering, Urmia University of Technology, Urmia 57166-17165, Iran. Email: Rashed.poormirzaee@gmail.com
Exploration Geophysics 49(2) 163-175 https://doi.org/10.1071/EG16044
Submitted: 14 April 2016 Accepted: 27 October 2016 Published: 29 November 2016
Abstract
Adequate estimation of shear-wave (VS) and P-wave (VP) velocity profiles is one of the significant objectives in the course of seismic surveys; however, the main problem in obtaining VS and VP is the non-uniqueness of surface waves and refracted seismic inversion results. Moreover, the hidden-layer problem often exists in the case of the refraction seismic method. The main purpose of this study is to cope with the above problems and reconstruct subsurface structures by joint inversion of Rayleigh wave dispersion curve and refraction traveltimes. The proposed joint inversion is based on a multi-objective particle swarm optimisation (MOPSO) strategy as a new tool for joint inversion of seismic datasets. The Pareto front concept was applied in the proposed joint inversion scheme. Using the Pareto front, the presented inversion algorithm provided a useful tool to evaluate the results (i.e. the number of layers, thicknesses or Poisson ratio values for the estimated models). The proposed algorithm was tested on two synthetic datasets and also on an experimental dataset. Furthermore, the joint inversion results were compared with the results of individual datasets inverted using PSO inversion algorithm. To verify the applicability of the proposed method, it was applied at a sample site located in Tabriz city, north-western Iran. For a real dataset, the refraction microtremor (ReMi) was used to obtain Rayleigh wave dispersion curves. Moreover, sourced from a vertical-incident seismic source (sledgehammer), seismic refraction data were recorded via vertical component geophones. The results showed that the proposed joint inversion technique can considerably reduce uncertainties of the inverted models.
Key words: joint inversion, multi-objective optimisation, PSO, surface wave dispersion curve, traveltimes.
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