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Bayesian Rayleigh wave inversion with an unknown number of layers
Yuen, Ka-Veng; Yang, Xiao-Hui
2020-10-19
Source PublicationEarthquake Engineering and Engineering Vibration
ISSN1671-3664
Volume19Issue:4Pages:875-886
Abstract

Surface wave methods have received much attention due to their efficient, flexible and convenient characteristics. However, there are still critical issues regarding a key step in surface wave inversion. In most existing methods, the number of layers is assumed to be known prior to the process of inversion. However, improper assignment of this parameter leads to erroneous inversion results. A Bayesian nonparametric method for Rayleigh wave inversion is proposed herein to address this problem. In this method, each model class represents a particular number of layers with unknown S-wave velocity and thickness of each layer. As a result, determination of the number of layers is equivalent to selection of the most applicable model class. Regarding each model class, the optimization search of S-wave velocity and thickness of each layer is implemented by using a genetic algorithm. Then, each model class is assessed in view of its efficiency under the Bayesian framework and the most efficient class is selected. Simulated and actual examples verify that the proposed Bayesian nonparametric approach is reliable and efficient for Rayleigh wave inversion, especially for its capability to determine the number of layers.

KeywordBayesian Model Class Selection Generalized R/t Coefficients Algorithm Genetic Algorithm Inversion Of Rayleigh Wave Number Of Layers
DOI10.1007/s11803-020-0601-y
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering
WOS SubjectEngineering, Civil ; Engineering, Geological
WOS IDWOS:000581065100005
PublisherSPRINGER, ONE NEW YORK PLAZA, SUITE 4600 , NEW YORK, NY 10004, UNITED STATES
Scopus ID2-s2.0-85092769657
Fulltext Access
FWCI0.30446565
Citation statistics
Document TypeJournal article
CollectionUniversity of Macau
Faculty of Science and Technology
THE STATE KEY LABORATORY OF INTERNET OF THINGS FOR SMART CITY (UNIVERSITY OF MACAU)
DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING
AffiliationState Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering,University of Macau,999078,Macao
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Yuen, Ka-Veng,Yang, Xiao-Hui. Bayesian Rayleigh wave inversion with an unknown number of layers[J]. Earthquake Engineering and Engineering Vibration,2020,19(4):875-886.
APA Yuen, Ka-Veng,&Yang, Xiao-Hui.(2020).Bayesian Rayleigh wave inversion with an unknown number of layers.Earthquake Engineering and Engineering Vibration,19(4),875-886.
MLA Yuen, Ka-Veng,et al."Bayesian Rayleigh wave inversion with an unknown number of layers".Earthquake Engineering and Engineering Vibration 19.4(2020):875-886.
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