中文版 | English
Title

Two-stage broad learning inversion framework for shear-wave velocity estimation

Author
Corresponding AuthorPeng,Han
Publication Years
2023-02-01
DOI
Source Title
ISSN
0016-8033
EISSN
1942-2156
Volume88Issue:1
Abstract
Shear-wave (S-wave) velocity is considered an essential parameter for the study of the earth, and Rayleigh wave inversion has been widely accepted and used to determine it. Given high -quality measured dispersion curves, the inversion performance depends on the applied optimization algorithm inside the inver-sion process. We propose a novel inversion framework to pro-mote efficient and accurate inversion, i.e., a two-stage broad learning inversion framework (TS-BL). The proposed TS-BL not only inherits the powerful mapping capability and simple con-figured structure of broad learning (BL) network but also makes two significant improvements to better acclimatize itself to Ray-leigh wave inversion. First, TS-BL adopts a two-stage inversion strategy to perform optimizing two times. It does not yield the same search space in the two inversion stages. In the first stage, because the inversion aims to find an approximation rather than the accurate value of model parameters, the difficulty in con-structing the mapping model is reduced by sacrificing accuracy. Then, an effective BL network can be established using smaller sample sizes. In the second stage, the search space becomes much narrower, commencing with the approximation results obtained in the prior stage. This helps the final BL network to easily and quickly model the actual relationship between measured dispersion curves and unknown model parameters. After that, the forward modeling of measurements rather than the validation data set is exploited for tuning the network's hyperparameters. The physical model is superior to the validation data set for se-lecting a suitable network complexity to adapt to the measured dispersion curves because the latter only describes an overall re-lationship. As a result, accurate S-wave velocities can be effi-ciently acquired by using the proposed TS-BL with a low cost of training samples. The efficiency and reliability of TS-BL have been demonstrated in numerical and field data examples.
URL[Source Record]
Indexed By
Language
English
SUSTech Authorship
Corresponding
Funding Project
Key Special Project for Intro- duced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)[GML2019ZD0203] ; Shenzhen Key Laboratory of Deep Offshore Oil and Gas Exploration Technol- ogy[ZDSYS20190902093007855] ; Science and Tech- nology Program of Shenzhen["JCYJ20210324104602006","JCYJ20210324104801004"]
WOS Research Area
Geochemistry & Geophysics
WOS Subject
Geochemistry & Geophysics
WOS Accession No
WOS:000944291200003
Publisher
ESI Research Field
GEOSCIENCES
Data Source
Web of Science
Citation statistics
Cited Times [WOS]:0
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/411965
DepartmentDepartment of Earth and Space Sciences
Affiliation
1.Shenzhen Key Laboratory of Deep Offshore Oil and Gas Exploration Technology, Southern University of Science and Technology, Shenzhen, 518055, China
2.Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, 511458, China
3.Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen, 518055, China
First Author AffilicationDepartment of Earth and Space Sciences
Corresponding Author AffilicationDepartment of Earth and Space Sciences
Recommended Citation
GB/T 7714
Xiao-Hui,Yang,Peng,Han,Zhentao,Yang,et al. Two-stage broad learning inversion framework for shear-wave velocity estimation[J]. GEOPHYSICS,2023,88(1).
APA
Xiao-Hui,Yang,Peng,Han,Zhentao,Yang,&Xiaofei,Chen.(2023).Two-stage broad learning inversion framework for shear-wave velocity estimation.GEOPHYSICS,88(1).
MLA
Xiao-Hui,Yang,et al."Two-stage broad learning inversion framework for shear-wave velocity estimation".GEOPHYSICS 88.1(2023).
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File Name/Size DocType Version Access License
Yang et al. - 2022 -(7730KB) Restricted Access--
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