On Pareto Joint Inversion of guided waves
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 genera...
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Main Authors: | , , , , |
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Format: | Journal Article |
Language: | English |
Published: |
28-12-2017
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Subjects: | |
Online Access: | Get full text |
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Summary: | 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. |
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DOI: | 10.48550/arxiv.1712.09850 |