横向二阶导数TV正则化三维反射系数反演

doi:10.13810/j.cnki.issn.1000-7210.2023.03.021

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (3915 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract 3D reflectivity volume is usually obtained by trace-by-trace (or line-by-line) inversion manner. Howe-ver, jitter effect is remarkable in the inversion results because there are no connections between traces (or between lines). In order to suppress the jitter effect and improve the lateral continuity of the inverted reflectivity, TV (Total Variation) constraint item, constructed by lateral second-order derivatives of synthetic 3D data, is added into our inversion objective function. Thereby, traditional L₁-norm regularized 1D inversion objective function is extended as 3D form. Unfortunately, 3D time-space domain objective function involves 3D convolution which is extremely difficult to be solved, directly. In order to solve this 3D inverse problem efficiently, we transform the time-space domain objective function into frequency-wavenumber domain according to the framework of split-Bregman optimization. The results of synthetic and field data examples show that our method can not only obtain 3D reflectivity volume efficiently, but also significantly improve the imaging quality of small structures and thin layers that are usually blurred by the jitter noise.

Key words： reflectivity 3D inversion jitter effect TV regularization lateral two-order derivative split-Bregman optimization

Received: 21 March 2022

PACS:

P631

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	HAO Yaju
	ZHANG Peng
	WEN Xiaotao
	ZHANG Hongjing
	ZHU Baoheng
	DAI Yichen

Cite this article:

HAO Yaju,ZHANG Peng,WEN Xiaotao等. 3D reflectivity inversion method based on TV regularization of lateral second-order derivatives[J]. OGP, 2023, 58(3): 680-689.

URL:

http://www.ogp-cn.com/EN/10.13810/j.cnki.issn.1000-7210.2023.03.021 OR http://www.ogp-cn.com/EN/Y2023/V58/I3/680

[1]	AKI K，RICHARDS P G.Quantitative Seismology (2nd Ed)[M].University Science Books，Sausalito，USA，2002.
[2]	DONDURUR D.An approximation to sparse-spike reflectivity using the gold deconvolution method[J].Pure and Applied Geophysics，2010，167(10)：1233-1245.
[3]	杜昕，范廷恩，范洪军，等.少井背景下基于稀疏层反射系数反演的薄层预测[J].石油地球物理勘探，2021，56(2)：356-363.DU Xin，FAN Tingen，FAN Hongjun，et al.Prediction of thin reservoirs with less well data based on sparse-layer reflectivity inversion[J].Oil Geophysical Prospecting，2021，56(2)：356-363.
[4]	夏红敏，刘兰锋，张显辉，等.地震数据谱反演压缩感知算法实现及应用[J].石油地球物理勘探，2021，56(2)：295-301.XIA Hongmin，LIU Lanfeng，ZHANG Xianhui，et al.Implementation and application of compressed sensing algorithm for seismic spectrum inversion[J].Oil Geophysical Prospecting，2021，56(2)：295-301.
[5]	张军华，王静，王延光，等.基于压缩感知的反射系数域沿层L2范数约束去强屏蔽方法[J].石油地球物理勘探，2022，57(2)：405-413.ZHANG Junhua，WANG Jing，WANG Yanguang，et al.A strong shielding removal method of reflection coefficient domain based on compressed sensing with L2 norm constraint along layer[J].Oil Geophysical Prospecting，2022，57(2)：405-413.
[6]	姚振岸，孙成禹，李红星，等.基于基追踪的时变子波提取与地震反射率反演[J].石油地球物理勘探，2019，54(1)：137-144.YAO Zhen’an，SUN Chengyu，LI Hongxing，et al.Time-variant wavelet extraction and seismic reflectivity inversion based on basis pursuit[J].Oil Geophysical Prospecting，2019，54(1)：137-144.
[7]	霍晗勇，邸志欣，舒梦珵，等.基于L0范数谱反演技术在顺北小尺度断裂体识别中的应用[J].石油物探，2021，60(增刊1)：126-134.HUO Hanyong，DI Zhixin，SHU Mengcheng，et al.Application of an L0-norm spectral inversion technique for the identification of small-scale faults in Shunbei area[J].Geophysical Prospecting for Petroleum，2021，60(S1)：126-134.
[8]	郝亚炬，文晓涛，李忠，等.基于基追踪分解算法的薄层波阻抗反演[J].科学技术与工程，2015，15(33)：10-17.HAO Yaju，WEN Xiaotao，LI Zhong，et al.Impedance inversion of thin-bed based on basis pursuit[J].Science Technology and Engineering，2015，15(33)：10-17.
[9]	TAYLOR H L，BANKS S C，McCoy J F.Deconvolution with the L1 norm[J].Geophysics，1979，44(1)：39-52.
[10]	VELIS D R.Stochastic sparse-spike deconvolution[J].Geophysics，2008，73(1)：R1-R9.
[11]	ZHANG R，CASTAGNA J.Seismic sparse-layer reflectivity inversion using basis pursuit decomposition[J].Geophysics，2011，76(6)：R147-R158.
[12]	YUAN S，WANG S.Spectral sparse Bayesian learning reflectivity inversion[J].Geophysical Prospecting，2013，61(4)：735-746.
[13]	HEIMER A，COHEN I.Multichannel seismic deconvolution using Markov-Bernoulli random-field mode-ling[J].IEEE Transactions on Geoscience and Remote Sensing，2009，47(7)：2047-2058.
[14]	KAZEMI N，SACCHI M D.Sparse multichannel blind deconvolution[J].Geophysics，2014，79(5)：V143-V152.
[15]	YUAN S，WANG S，TIAN N, et al.Stable inversion-based multitrace deabsorption method for spatial continuity preservation and weak signal compensation[J].Geophysics，2016，81(3)：V199-V212.
[16]	WANG R，WANG Y.Multichannel algorithms for seismic reflectivity inversion[J].Journal of Geophy-sics and Engineering，2017，14(1)：41-50.
[17]	MA M，WANG S，YUAN S，et al.Multichannel spatially correlated reflectivity inversion using block sparse Bayesian learning[J].Geophysics，2017，82(4)：V191-V199.
[18]	DU X，LI G，ZHANG M，et al.Multichannel band-controlled deconvolution based on a data-driven structural regularization[J].Geophysics，2018，83(5)：R401-R411.
[19]	纪永祯，张渝悦，朱立华，等.多道随机稀疏反射系数反演[J].石油物探，2020，59(6)：912-917.JI Yongzhen，ZHANG Yuyue，ZHU Lihua，et al.Multi-trace stochastic sparse-spike inversion for reflectivity[J].Geophysical Prospecting for Petroleum，2020，59(6)：912-917.
[20]	印兴耀，杨亚明，李坤，等.地震数据互相关驱动的多道反演方法[J].地球物理学报，2020，63(10)：3827-3837.YIN Xingyao，YANG Yaming，LI Kun，et al.Multitrace inversion driven by cross-correlation of seismic data[J].Chinese Journal of Geophysics，2020，63(10)：3827-3837.
[21]	LEFKIMMIATIS S，BOURQUARD A，UNSER M.Hessian-based norm regularization for image restoration with biomedical applications[J].IEEE Transactions on Image Processing，2012，21(3)：983-995.
[22]	CHAN T，MARQUINA A，MULET P.High-order total variation-based image restoration[J].SIAM Journal on Scientific Computing，2000，22(2)：503-516.
[23]	LYSAKER M，TAI X.Iterative image restoration combining total variation minimization and a second-order functional[J].International Journal of Computer Vision，2006，66(1)：5-18.
[24]	GOLDSTEIN T，OSHER S.The split Bregman me-thod for L1-regularized problems[J].SIAM Journal on Imaging Sciences，2009，2(2)：323-343.
[25]	郝亚炬.基于稀疏正则化的黏弹性地震衰减补偿及反演方法研究[D].北京：中国石油大学(北京)，2019.HAO Yaju.Viscoelastic Seismic Attenuation Compensation and Inversion Method Based on Sparse Re-gularization[D].China University of Petroleum(Beijing)，Beijing，2019.