|本期目录/Table of Contents|

[1]卢明德.基于L1范数的全变分地震信号反褶积优化算法[J].地震研究,2023,46(01):107-115.[doi:10.20015/j.cnki.ISSN1000-0666.2023.0020 ]
 LU Mingde.Deconvolution Optimization Algorithm of Seismic Signals Based on L1 Norm of Total Variation[J].Journal of Seismological Research,2023,46(01):107-115.[doi:10.20015/j.cnki.ISSN1000-0666.2023.0020 ]
点击复制

基于L1范数的全变分地震信号反褶积优化算法(PDF/HTML)

《地震研究》[ISSN:1000-0666/CN:53-1062/P]

卷:
46
期数:
2023年01期
页码:
107-115
栏目:
地震地下流体监测预报理论及技术应用专栏
出版日期:
2023-01-01

文章信息/Info

Title:
Deconvolution Optimization Algorithm of Seismic Signals Based on L1 Norm of Total Variation
作者:
卢明德
(辽河油田公司勘探开发研究院,辽宁 盘锦 124010)
Author(s):
LU Mingde
(Exploration and Development Research Institute of Liaohe Oilfield Company,Panjin 124010,Liaoning,China)
关键词:
地震信号 反褶积 去噪 L1范数 全变分理论 交替方向乘子法
Keywords:
seismic signals deconvolution denoising L1 norm the Total Variation theory the alternating direction method of multipliers *
分类号:
P631
DOI:
10.20015/j.cnki.ISSN1000-0666.2023.0020
摘要:
基于借范数最优化的思想,提出一种基于L1范数的全变分地震信号反褶积优化算法。该算法基于L1范数全变分理论构建地震信号重建模型,同时将其转化为符合迭代与交替最小化的求解形式,通过交替方向乘子法设计地震信号的反褶积优化算法。该算法无需考虑反褶积使用的限制条件,可以在含噪声的情况下有效恢复地震信号,同时提高地震信号的分辨率和信噪比。使用该算法对合成信号和野外采集地震数据进行实验,结果表明:该算法提高了子波的主频,拓宽了有效频带,即使在信号受到较重噪声污染时,也可以获得较好的处理结果。
Abstract:
Deconvolution is an effective method to improve the resolution of seismic signals.Due to the influence of noises,the stability of the sub-waves obtained by many deconvolution methods is poor,and the reflection coefficients are mixed with the sub-waves more seriously.In the light of the idea of norm optimization,a deconvolution optimization algorithm of seismic signals based on L1 norm of total variation is proposed in this paper.Based on the L1 norm of total variational theory,the seismic signal reconstruction model is constructed and transformed into a solution form conforming to iterative and alternating minimization.Then,the deconvolution optimization algorithm of seismic signals is designed by alternating direction multiplier method.The proposed algorithm does not need to consider the restrictions of deconvolution.It can effectively recover the seismic signals with noise,and improve the resolution and signal-to-noise ratio(SNR)of the seismic signals.Experiments on synthetic signals and field seismic data show that the algorithm improves the dominant frequency of sub-waves and widens the effective frequency band.It also performs well even if the signals are polluted by serious noises.

参考文献/References:

杜鑫,张广智,王若.2021.一种提高浅层地震资料分辨率的反褶积组合方法应用效果[C].中国石油学会2021年物探技术研讨会,304-307.
冯志强,孙国昕,蒙启安,等.2011.海拉尔盆地贝中次凹——残留型叠合小断陷盆地油气勘探的成功案例[J].石油学报,32(4):551-563.
马涛,王彦春,柳兴刚.2020.力信号反褶积方法在可控震源单炮资料提取中的应用[J].地球物理学进展,35(4):1438-1444.
牛和明,杜茜,张建勋.2011.一种自适应全变分信号去噪算法[J].模式识别与人工智能,24(6):798-803.
潘树林,闫柯,李凌云,等.2019.自适应步长FISTA算法稀疏脉冲反褶积[J].石油地球物理勘探,54(4):737-743.
石明珠,许廷发,张坤.2011.运动成像混合模糊的全变分信号复原[J].光学精密工程,19(8):1973-1981.
王宇,韩立国,周家雄,等.2009.L1-L2范数联合约束稀疏脉冲反演的应用[J].中国地质大学学报(地球科学),34(5):835-840.
张联海,王璐,郑志超,等.2021.基于深度卷积神经网络的稀疏反褶积方法[J].中国海洋大学学报,51(12):81-88.
朱振宇,刘洪.2005.稀疏反褶积方法及其应用[J].石油大学学报(自然科学版),29(6):20-22.
Beck A,Teboulle M.2009.A fast iterative shrinkage-thresholding algorithm for linear inverse problems[J].SIAM Journal on Imaging Sciences,2(1):183-202.
Chen Q,Montesinos P,Sun Q S, et al.2010.Adaptive total variation denoising based on difference curvature[J].Image and Vision Computing,28(3):298-306.
Danilo R V.2006.Parametric sparse-spike deconvolution and the recovery of the acoustic impedance[C]//The 76th Annual International Meeting of Society of Exploration Geophysicists.New Orleans,Tulsa:Society of Exploration Geophysicists,2141-2144.
Gabay D,Mercier B.1976.A dual algorithm for the solution of nonlinear variational problems via finite-element approximations[J].Computing Mathematics Application,2(1):17-40.
Hennenfent G,Herrmann F J,Neelamani R.2005.Sparseness-constrained seismic deconvolution with Curvelets[C]//Canadian Society of Exploration Geophysicists National Convention.Canada,Vancouver,85-92.
Lei L,Terence P S.2000.Parametric deconvolution of positive spike trains[J].Annals of Statistics,28(5):1279-1301.
Ng M K,Chan R H,Tang W C.1999.A fast algorithm for deblurring models with Neumann boundary conditions[J].SIAM Journal on Science Computing,21(3):851-866.
Pan S L,Yan K,Liu X G.2019.A bregman adaptive sparse-spike deconvolution method in the frequency domain[J].Applied Geophysics,16(4):463-472.
Rudin L I,Osher S,Fatemi E.1992.Nonlinear variation based image removal algorithms[J].Physica D,60:259-268.
Stewart R R,Schieck D G.1993.3-D f-k filtering[J].Journal of Seismic Exploration,2:41-54.
Taylor H L,Banks S C,Mccoy J F.1979.Deconvolution with L1-norm[J].Geophysics,44(1):39-52.
Treitel S.1974.The complex Wiener filter[J].Geophysics,39(2):169-173.
Vogel C R,Oman M E.1998.Fast,robust total variation based reconstruction of noisy,blurred images[J].IEEE Transactions on Image Processing,7(6):813-824.
Wang L,Zhao Q,Gao J, et al.2016.Seismic sparse-spike deconvolution via Toeplitz-sparse matrix factorization[J].Geophysics,81(3):169-182.
Wang Y L,Yang J F,Yin W T, et al.2008.A new alternating minimization algorithm for total variation image reconstruction[J].SIAM Journal on Imaging Sciences,1(3):248-272.
Yang J F,Yin W T,Zhang Y, et al.2009a.A fast algorithm for edge-preserving variational multichannel image restoration[J].SIAM Journal on Imaging Sciences,2(2):569-592.
Yang J F,Zhang Y,Yin W T.2009b.An efficient TVL1 algorithm for deblurring multichannel images corrupted by impulsive noise[J].SIAM Journal on Scientific Computing,31(4):2842-2865.

备注/Memo

备注/Memo:
收稿日期:2022-01-16.
基金项目:国家重大专项示范工程“鄂尔多斯盆地大型低渗透岩性地层油气藏开发示范工程(2016ZX05050)”和中国石油股份公司重大科技专项“辽河油田千万吨稳产关键技术研究与应用(2017E-1602)”联合资助.
第一作者简介:卢明德(1982-),高级工程师,主要从事地震资料处理技术、去噪和反褶积方法研究.E-mail:lumingde@petrochina.com.cn.
更新日期/Last Update: 2023-01-01