基于模型分块交叉移动的学习型模拟退火算法地震反演
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摘要
针对地震非线性反演问题,提出一种基于模型分块交叉移动的学习型模拟退火的全局优化地震反演方法。其步骤为:首先,在模拟退火算法及粒子群算法基础上,在算法模型扰动项里面加入1个向目标优化的方向移动的学习项;其次,针对地震反演模型数量多及地震记录为褶积形式的特点,采用模型分块交叉移动的方法来实施模拟退火反演,给出模型分块交叉移动的学习型模拟退火算法流程。研究结果表明:该方法具有收敛速度快、精度高、实现简单、高效的特点,可以用于其他多维多极值的目标函数反演。
To solve the seismic nonlinear inverse problem,a seismic inversion method was presented based on simulated annealing of learning and cross-moving with divided block model.The algorithm was proposed in two aspects.Firstly,based on the algorithm of simulated annealing and particle swarm optimization,simulated annealing formula of perturbed model was added with a learning item which moved to the direction of objective optimization.In addition,because the seismic inversion model's number are large and form of seismic records is convolution,simulated annealing algorithm was implemented by the way of divided block model cross-moving.The results show that the proposed method has fast convergence and high accuracy.The new simulated annealing is simple and efficient,and it can be used to other multidimensional object function inversion.
To solve the seismic nonlinear inverse problem,a seismic inversion method was presented based on simulated annealing of learning and cross-moving with divided block model.The algorithm was proposed in two aspects.Firstly,based on the algorithm of simulated annealing and particle swarm optimization,simulated annealing formula of perturbed model was added with a learning item which moved to the direction of objective optimization.In addition,because the seismic inversion model's number are large and form of seismic records is convolution,simulated annealing algorithm was implemented by the way of divided block model cross-moving.The results show that the proposed method has fast convergence and high accuracy.The new simulated annealing is simple and efficient,and it can be used to other multidimensional object function inversion.
引文
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[16]聂茹,岳建华,邓帅奇.地震波阻抗反演的免疫粒子群算法[J].中国矿业大学学报,2010,39(5):733-739.NIE Ru,YUE Jian-hua,DENG Shuai-qi.Immune particle swarm optimization for seismic wave impedance inversion[J].Journal of China University of Mining&Technology,2010,39(5):733-739.
[17]纪震,周家锐,廖惠连.智能单粒子优化算法[J].计算机学报,2010,33(3)3:556-561.JI zhen,ZHOU Jia-rui,LIAO Hui-lian.A novel intelligent single particle optimizer[J].Chinese Journal of Computers,2010,33(3):556-561.
[18]张赛民.基于边界保持块约束叠后波阻抗及叠前AVA三参数同步反演研究[D].长沙:中南大学地球科学与信息物理学院,2011:70-73.ZHANG Sai-min.Research of post-stack impedance inversion and AVA three-term simultaneous inversion based on edge preserving blocky constrain[D].Changsha:Central South University.School of Geosciences and Info-Physics,2011:70-73.
[2]Press F.Earth models obtained by Monte-Carlo inversion[J].Journal of Geophysical Research,1968,73(1):5223-5234.
[3]Wiggins R A.Minimum entropy deconvolution[J].Geoexploration,1978,16(1):21-35.
[4]Jin S,Madiriaga R.Nonlinear velocity inversion by a two-step Monte Carlo method[J].Geophysics,1994,59(4):577-590.
[5]Kirkpatrick S,Gelatt C P,Vecchi M P.Optimization by simulated annealing[J].Science,1983,220(4598):671-680.
[6]Harold S,Ralph H.Fast simulated annealing[J].Physics Letters,1987,122(3/4):157-162.
[7]Ingber L.Very fast simulated re-annealing[J].Mathl Comput Modelling,1989,12(8):967-973.
[8]MA Xin-quan.Global joint inversion for the estimation of acoustic and shear impedances from AVO derived P-and S-wave reflectivity data[J].First Break,2001,19(10):557-566.
[9]MA Xin-quan.Simultaneous inversion of prestack seismic data for rock properties using simulated annealing[J].Geophysics,2002,67(6):1877-1885.
[10]Misra S,Sacchi M D.Nonlinear one dimensional prestackseismic inversion with edge preserving smoothing filter[C]//Extended Abstract,EAGE69th Conference and Exhibition.London,United Kingdom,2007:26-30.
[11]Misra S,Sacchi M D.Global optimization with model space preconditioning:Application to AVO inversion[J].Geophysics,2008,73(5):71-82.
[12]Varela O J,Tomes-Verdin C,Sen M K.Enforcing smoothness and assessing uncertainty in non-linear one dimensional prestack seismic inversion[J].Geophysical Prospecting,2006,54(3):239-259.
[13]杨午阳.模拟退火叠前AVA同步反演方法[J].地球物理学进展,2010,25(1):219-224.YANG Wu-yang.Pre-stack simultaneous AVA inversion algorithm with simulated annealing[J].Progress in Geophysics,2010,25(1):219-224.
[14]刘全新,方光建.利用模拟退火算法实现地震叠前反演[J].岩性油气藏,2010,22(1):87-92.LIU Quan-xin,FANG Guang-jian.Using simulated annealing algorithm to pre-stack seismic inversion[J].Lithologic Reservoirs,2010,22(1):87-92.
[15]易远元.地震波阻抗反演的粒子群算法实现[J].石油天然气学报,2007,29(3):79-81.YI Yuan-yuan.Implementation of particle swarm algorithm in seismic wave impedance inversion[J].Journal of Oil and Gas Technology,2007,29(3):79-81.
[16]聂茹,岳建华,邓帅奇.地震波阻抗反演的免疫粒子群算法[J].中国矿业大学学报,2010,39(5):733-739.NIE Ru,YUE Jian-hua,DENG Shuai-qi.Immune particle swarm optimization for seismic wave impedance inversion[J].Journal of China University of Mining&Technology,2010,39(5):733-739.
[17]纪震,周家锐,廖惠连.智能单粒子优化算法[J].计算机学报,2010,33(3)3:556-561.JI zhen,ZHOU Jia-rui,LIAO Hui-lian.A novel intelligent single particle optimizer[J].Chinese Journal of Computers,2010,33(3):556-561.
[18]张赛民.基于边界保持块约束叠后波阻抗及叠前AVA三参数同步反演研究[D].长沙:中南大学地球科学与信息物理学院,2011:70-73.ZHANG Sai-min.Research of post-stack impedance inversion and AVA three-term simultaneous inversion based on edge preserving blocky constrain[D].Changsha:Central South University.School of Geosciences and Info-Physics,2011:70-73.
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