On Pareto Joint Inversion of guided waves

TL;DR

This paper presents a method combining Pareto Joint Inversion and Particle Swarm Optimization to accurately infer subsurface elastic properties from surface wave dispersion curves, highlighting sensitivity to input errors.

Contribution

It introduces a novel application of Pareto Joint Inversion with PSO for surface wave data, analyzing error sensitivity and mode-specific parameter sensitivities.

Findings

Inversion accurately retrieves model parameters with error-free data.

Results degrade significantly with 5% input error, showing error sensitivity.

Fundamental mode mainly sensitive to layer parameters; higher modes sensitive to both layer and halfspace.

Abstract

We use the Pareto Joint Inversion, together with the Particle Swarm Optimization, to invert the Love and quasi-Rayleigh surface-wave speeds, obtained from dispersion curves, in order to infer the elasticity parameters, mass densities and layer thickness of the model for which these curves are generated. For both waves, we use the dispersion relations derived by Dalton et al. (2017). Numerical results are presented for three angular frequencies, 15 Hz, 60 Hz and 100 Hz, and for two, five and seven modes, respectively. Comparisons of the model parameters with the values inverted with error-free input indicate an accurate process. If, however, we introduce a 5% error to the input, the results become significantly less accurate, which indicates that the inverse operation, even though stable, is error-sensitive. Correlations between the inverted elasticity parameters indicate that the layer parameters are more sensitive to input errors than the halfspace parameters. In agreement with Dalton et al. (2017), the fundamental mode is mainly sensitive to the layer parameters whereas higher modes are sensitive to both the layer and halfspace properties; for the second mode, the results are more accurate for low frequencies.