Full-Waveform Inversion of Time-Lapse Crosshole GPR Data Using Markov Chain Monte Carlo Method

Crosshole ground-penetrating radar (GPR) is an important tool for a wide range of geoscientific and engineering investigations, and the Markov chain Monte Carlo (MCMC) method is a heuristic global optimization method that can be used to solve the inversion problem. In this paper, we use time-lapse G...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Shengchao Wang, Liguo Han, Xiangbo Gong, Shaoyue Zhang, Xingguo Huang, Pan Zhang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	time-lapse inversion MCMC full-waveform double difference strategy crosshole ground-penetrating radar (GPR) Science Q
Acceso en línea:	https://doaj.org/article/c2b928f12e8b46ae85658562f8efaaf9
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:c2b928f12e8b46ae85658562f8efaaf9
record_format	dspace
spelling	oai:doaj.org-article:c2b928f12e8b46ae85658562f8efaaf92021-11-25T18:53:59ZFull-Waveform Inversion of Time-Lapse Crosshole GPR Data Using Markov Chain Monte Carlo Method10.3390/rs132245302072-4292https://doaj.org/article/c2b928f12e8b46ae85658562f8efaaf92021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4530https://doaj.org/toc/2072-4292Crosshole ground-penetrating radar (GPR) is an important tool for a wide range of geoscientific and engineering investigations, and the Markov chain Monte Carlo (MCMC) method is a heuristic global optimization method that can be used to solve the inversion problem. In this paper, we use time-lapse GPR full-waveform data to invert the dielectric permittivity. An inversion based on the MCMC method does not rely on an accurate initial model and can introduce any complex prior information. Time-lapse ground-penetrating radar has great potential to monitor the properties of a subsurface. For the time-lapse inversion, we used the double difference method to invert the time-lapse target area accurately and full-waveform data. We propose a local sampling strategy taking advantage of the a priori information in the Monte Carlo method, which can sample only the target area with a sequential Gibbs sampler. This method reduces the calculation and improves the inversion accuracy of the target area. We have provided inversion results of the synthetic time-lapse waveform data that show that the proposed method significantly improves accuracy in the target area.Shengchao WangLiguo HanXiangbo GongShaoyue ZhangXingguo HuangPan ZhangMDPI AGarticletime-lapse inversionMCMCfull-waveformdouble difference strategycrosshole ground-penetrating radar (GPR)ScienceQENRemote Sensing, Vol 13, Iss 4530, p 4530 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	time-lapse inversion MCMC full-waveform double difference strategy crosshole ground-penetrating radar (GPR) Science Q
spellingShingle	time-lapse inversion MCMC full-waveform double difference strategy crosshole ground-penetrating radar (GPR) Science Q Shengchao Wang Liguo Han Xiangbo Gong Shaoyue Zhang Xingguo Huang Pan Zhang Full-Waveform Inversion of Time-Lapse Crosshole GPR Data Using Markov Chain Monte Carlo Method
description	Crosshole ground-penetrating radar (GPR) is an important tool for a wide range of geoscientific and engineering investigations, and the Markov chain Monte Carlo (MCMC) method is a heuristic global optimization method that can be used to solve the inversion problem. In this paper, we use time-lapse GPR full-waveform data to invert the dielectric permittivity. An inversion based on the MCMC method does not rely on an accurate initial model and can introduce any complex prior information. Time-lapse ground-penetrating radar has great potential to monitor the properties of a subsurface. For the time-lapse inversion, we used the double difference method to invert the time-lapse target area accurately and full-waveform data. We propose a local sampling strategy taking advantage of the a priori information in the Monte Carlo method, which can sample only the target area with a sequential Gibbs sampler. This method reduces the calculation and improves the inversion accuracy of the target area. We have provided inversion results of the synthetic time-lapse waveform data that show that the proposed method significantly improves accuracy in the target area.
format	article
author	Shengchao Wang Liguo Han Xiangbo Gong Shaoyue Zhang Xingguo Huang Pan Zhang
author_facet	Shengchao Wang Liguo Han Xiangbo Gong Shaoyue Zhang Xingguo Huang Pan Zhang
author_sort	Shengchao Wang
title	Full-Waveform Inversion of Time-Lapse Crosshole GPR Data Using Markov Chain Monte Carlo Method
title_short	Full-Waveform Inversion of Time-Lapse Crosshole GPR Data Using Markov Chain Monte Carlo Method
title_full	Full-Waveform Inversion of Time-Lapse Crosshole GPR Data Using Markov Chain Monte Carlo Method
title_fullStr	Full-Waveform Inversion of Time-Lapse Crosshole GPR Data Using Markov Chain Monte Carlo Method
title_full_unstemmed	Full-Waveform Inversion of Time-Lapse Crosshole GPR Data Using Markov Chain Monte Carlo Method
title_sort	full-waveform inversion of time-lapse crosshole gpr data using markov chain monte carlo method
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c2b928f12e8b46ae85658562f8efaaf9
work_keys_str_mv	AT shengchaowang fullwaveforminversionoftimelapsecrossholegprdatausingmarkovchainmontecarlomethod AT liguohan fullwaveforminversionoftimelapsecrossholegprdatausingmarkovchainmontecarlomethod AT xiangbogong fullwaveforminversionoftimelapsecrossholegprdatausingmarkovchainmontecarlomethod AT shaoyuezhang fullwaveforminversionoftimelapsecrossholegprdatausingmarkovchainmontecarlomethod AT xingguohuang fullwaveforminversionoftimelapsecrossholegprdatausingmarkovchainmontecarlomethod AT panzhang fullwaveforminversionoftimelapsecrossholegprdatausingmarkovchainmontecarlomethod
_version_	1718410617762611200

Full-Waveform Inversion of Time-Lapse Crosshole GPR Data Using Markov Chain Monte Carlo Method

Ejemplares similares