Nonlinear Rayleigh wave inversion based on the shuffled frog-leaping algorithm

At present, near-surface shear wave velocities are mainly calculated through Rayleigh wave dispersion-curve inversions in engineering surface investigations, but the required calculations pose a highly nonlinear global optimization problem. In order to alleviate the risk of falling into a local opti...

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Bibliographic Details
Published in:Applied geophysics 2017-12, Vol.14 (4), p.551-558
Main Authors: Sun, Cheng-Yu, Wang, Yan-Yan, Wu, Dun-Shi, Qin, Xiao-Jun
Format: Article
Language:English
Subjects:
Algorithms
Amphibians
Artificial intelligence
Computer applications
Computer simulation
Computing time
Dispersion
Dispersion curve analysis
Earth and Environmental Science
Earth Sciences
Frogs
Geophysics
Geophysics/Geodesy
Geotechnical Engineering & Applied Earth Sciences
Global optimization
Inversions
Mathematical analysis
Noise
Rayleigh waves
Reliability
S waves
Searching
Shear wave velocities
Wave dispersion
Wave velocity
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Summary:At present, near-surface shear wave velocities are mainly calculated through Rayleigh wave dispersion-curve inversions in engineering surface investigations, but the required calculations pose a highly nonlinear global optimization problem. In order to alleviate the risk of falling into a local optimal solution, this paper introduces a new global optimization method, the shuffle frog-leaping algorithm (SFLA), into the Rayleigh wave dispersion-curve inversion process. SFLA is a swarm-intelligence-based algorithm that simulates a group of frogs searching for food. It uses a few parameters, achieves rapid convergence, and is capability of effective global searching. In order to test the reliability and calculation performance of SFLA, noise-free and noisy synthetic datasets were inverted. We conducted a comparative analysis with other established algorithms using the noise-free dataset, and then tested the ability of SFLA to cope with data noise. Finally, we inverted a real-world example to examine the applicability of SFLA. Results from both synthetic and field data demonstrated the effectiveness of SFLA in the interpretation of Rayleigh wave dispersion curves. We found that SFLA is superior to the established methods in terms of both reliability and computational efficiency, so it offers great potential to improve our ability to solve geophysical inversion problems.
ISSN:1672-7975
1993-0658
DOI:10.1007/s11770-017-0641-x