Bayesian seismic multi-scale inversion in complex Laplace mixed domains

Abstract Seismic inversion performed in the time or frequency domain cannot always recover the long-wavelength background of subsurface parameters due to the lack of low-frequency seismic records. Since the low-frequency response becomes much richer in the Laplace mixed domains, one novel Bayesian i...

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Autores principales:	Kun Li, Xing-Yao Yin, Zhao-Yun Zong
Formato:	article
Lenguaje:	EN
Publicado:	KeAi Communications Co., Ltd. 2017
Materias:	Low-frequency Complex mixed-domain Laplace inversion Bayesian estimation Multi-scale inversion Science Q Petrology QE420-499
Acceso en línea:	https://doaj.org/article/5c9076b49f15402b922ce2fbc8c6dff4
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spelling	oai:doaj.org-article:5c9076b49f15402b922ce2fbc8c6dff42021-12-02T07:35:27ZBayesian seismic multi-scale inversion in complex Laplace mixed domains10.1007/s12182-017-0191-01672-51071995-8226https://doaj.org/article/5c9076b49f15402b922ce2fbc8c6dff42017-10-01T00:00:00Zhttp://link.springer.com/article/10.1007/s12182-017-0191-0https://doaj.org/toc/1672-5107https://doaj.org/toc/1995-8226Abstract Seismic inversion performed in the time or frequency domain cannot always recover the long-wavelength background of subsurface parameters due to the lack of low-frequency seismic records. Since the low-frequency response becomes much richer in the Laplace mixed domains, one novel Bayesian impedance inversion approach in the complex Laplace mixed domains is established in this study to solve the model dependency problem. The derivation of a Laplace mixed-domain formula of the Robinson convolution is the first step in our work. With this formula, the Laplace seismic spectrum, the wavelet spectrum and time-domain reflectivity are joined together. Next, to improve inversion stability, the object inversion function accompanied by the initial constraint of the linear increment model is launched under a Bayesian framework. The likelihood function and prior probability distribution can be combined together by Bayesian formula to calculate the posterior probability distribution of subsurface parameters. By achieving the optimal solution corresponding to maximum posterior probability distribution, the low-frequency background of subsurface parameters can be obtained successfully. Then, with the regularization constraint of estimated low frequency in the Laplace mixed domains, multi-scale Bayesian inversion in the pure frequency domain is exploited to obtain the absolute model parameters. The effectiveness, anti-noise capability and lateral continuity of Laplace mixed-domain inversion are illustrated by synthetic tests. Furthermore, one field case in the east of China is discussed carefully with different input frequency components and different inversion algorithms. This provides adequate proof to illustrate the reliability improvement in low-frequency estimation and resolution enhancement of subsurface parameters, in comparison with conventional Bayesian inversion in the frequency domain.Kun LiXing-Yao YinZhao-Yun ZongKeAi Communications Co., Ltd.articleLow-frequencyComplex mixed-domainLaplace inversionBayesian estimationMulti-scale inversionScienceQPetrologyQE420-499ENPetroleum Science, Vol 14, Iss 4, Pp 694-710 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Low-frequency Complex mixed-domain Laplace inversion Bayesian estimation Multi-scale inversion Science Q Petrology QE420-499
spellingShingle	Low-frequency Complex mixed-domain Laplace inversion Bayesian estimation Multi-scale inversion Science Q Petrology QE420-499 Kun Li Xing-Yao Yin Zhao-Yun Zong Bayesian seismic multi-scale inversion in complex Laplace mixed domains
description	Abstract Seismic inversion performed in the time or frequency domain cannot always recover the long-wavelength background of subsurface parameters due to the lack of low-frequency seismic records. Since the low-frequency response becomes much richer in the Laplace mixed domains, one novel Bayesian impedance inversion approach in the complex Laplace mixed domains is established in this study to solve the model dependency problem. The derivation of a Laplace mixed-domain formula of the Robinson convolution is the first step in our work. With this formula, the Laplace seismic spectrum, the wavelet spectrum and time-domain reflectivity are joined together. Next, to improve inversion stability, the object inversion function accompanied by the initial constraint of the linear increment model is launched under a Bayesian framework. The likelihood function and prior probability distribution can be combined together by Bayesian formula to calculate the posterior probability distribution of subsurface parameters. By achieving the optimal solution corresponding to maximum posterior probability distribution, the low-frequency background of subsurface parameters can be obtained successfully. Then, with the regularization constraint of estimated low frequency in the Laplace mixed domains, multi-scale Bayesian inversion in the pure frequency domain is exploited to obtain the absolute model parameters. The effectiveness, anti-noise capability and lateral continuity of Laplace mixed-domain inversion are illustrated by synthetic tests. Furthermore, one field case in the east of China is discussed carefully with different input frequency components and different inversion algorithms. This provides adequate proof to illustrate the reliability improvement in low-frequency estimation and resolution enhancement of subsurface parameters, in comparison with conventional Bayesian inversion in the frequency domain.
format	article
author	Kun Li Xing-Yao Yin Zhao-Yun Zong
author_facet	Kun Li Xing-Yao Yin Zhao-Yun Zong
author_sort	Kun Li
title	Bayesian seismic multi-scale inversion in complex Laplace mixed domains
title_short	Bayesian seismic multi-scale inversion in complex Laplace mixed domains
title_full	Bayesian seismic multi-scale inversion in complex Laplace mixed domains
title_fullStr	Bayesian seismic multi-scale inversion in complex Laplace mixed domains
title_full_unstemmed	Bayesian seismic multi-scale inversion in complex Laplace mixed domains
title_sort	bayesian seismic multi-scale inversion in complex laplace mixed domains
publisher	KeAi Communications Co., Ltd.
publishDate	2017
url	https://doaj.org/article/5c9076b49f15402b922ce2fbc8c6dff4
work_keys_str_mv	AT kunli bayesianseismicmultiscaleinversionincomplexlaplacemixeddomains AT xingyaoyin bayesianseismicmultiscaleinversionincomplexlaplacemixeddomains AT zhaoyunzong bayesianseismicmultiscaleinversionincomplexlaplacemixeddomains
_version_	1718399337476653056

Bayesian seismic multi-scale inversion in complex Laplace mixed domains

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