褶积神经网络高分辨率地震反演
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摘要
随着地震勘探精细化要求的提高,薄层及横向变化大的复杂储层反演越来越重要。而当前反演方法大多基于褶积模型,分辨率较低。本文提出了基于褶积神经网络的反演方法,该方法完全由数据驱动,不受褶积模型的限制。褶积神经网络具有层状结构,其输入输出之间的映射关系用褶积算子来描述,而非内积算子。基于褶积神经网络结构,本文给出了映射算子的优化算法,并将其应用到地震反演中。应用结果表明,通过褶积神经网络地震反演,可以获得比常规稀疏脉冲反演分辨率更高的地层波阻抗剖面。
With the requirements of high-accuracy seismic exploration,the inversion technique for thin beds and complex reservoirs with large lateral variation is becoming more and more important.However,the current inversion methods are mainly based on the convolutional model,bearing with poor resolution.In order to improve the resolution of inversion results,this paper presents an inversion method based on the convolutional neural network,which is totally driven by data and not re-stricted by convolutional model.The convolutional neural network has a layered structure,whose mapping relationship between its input and output is described by convolutional operators instead of inner product operators.Based on the convolutional neural network structure,the paper further provides the optimization algorithm for mapping operators and applies it to seismic inversion process.Application results show that the convolutional neural network inversion can get higher resolution impedance profile than the conventional sparse pulse inversion method.
With the requirements of high-accuracy seismic exploration,the inversion technique for thin beds and complex reservoirs with large lateral variation is becoming more and more important.However,the current inversion methods are mainly based on the convolutional model,bearing with poor resolution.In order to improve the resolution of inversion results,this paper presents an inversion method based on the convolutional neural network,which is totally driven by data and not re-stricted by convolutional model.The convolutional neural network has a layered structure,whose mapping relationship between its input and output is described by convolutional operators instead of inner product operators.Based on the convolutional neural network structure,the paper further provides the optimization algorithm for mapping operators and applies it to seismic inversion process.Application results show that the convolutional neural network inversion can get higher resolution impedance profile than the conventional sparse pulse inversion method.
引文
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[12]Lecun Y et al.Backpropagation applied to handwritten zip code recognition.Neural Computation,1989,1(4):541-551.
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[15]陈善广,鲍勇.BP神经网络学习算法研究.应用基础与工程科学学报,1995,3(4):437-442.Chen Shanguang,Bao Yong.Studies on learning algorithms for BP net.Journal of Basic Science and Engineering,1995,3(4):437-442.
[16]张繁昌,印兴耀,印海燕.基于共轭方向置换的叠前三参数反演方法及其实现.石油地球物理勘探,2009,44(4):430-435.Zhang Fanchang,Yin Xingyao and Yin Haiyan.Prestack three-term inversion based on conjugate direction substitution and realization.OGP,2009,44(4):430-435.
[17]Robinson E A.Predictive decomposition of time series with application to seismic exploration.Geophysics,1967,32(3):418-484.
[18]Robinson E A.Predictive decomposition of seismic traces.Geophysics,1957,22(4):767-778.
[2]林忠民.塔河油田奥陶系碳酸盐岩储层特征及成藏条件.石油学报,2002,23(3):23-26.Lin Zhongmin.Carbonate rock reservoir features and oil gas accumulating conditions in the Ordovician of Tahe Oilfield in northern Tarim Basin.Acta Petrolei Sinica,2002,23(3):23-26.
[3]郭朝斌,杨小波,陈红岳等.约束稀疏脉冲反演在储层预测中的应用.石油物探,2006,45(4):397-400.Guo Chaobin,Yang Xiaobo,Chen Hongyue et al.Constrained sparse pulse inversion research in north of Haitongji depression.GPP,2006,45(4):397-400.
[4]李爱山,印兴耀,张繁昌等.叠前AVA多参数同步反演技术在含气储层预测中的应用.石油物探,2007,46(1):64-68.Li Aishan,Yin Xingyao,Zhang Fanchang et al.Application of prestack simultaneous AVA multi-parameter inversion in gas-bearing reservoir prediction.GPP,2007,46(1):64-68.
[5]印兴耀,张世鑫,张繁昌等.利用基于Russell近似的弹性波阻抗反演进行储层描述和流体识别.石油地球物理勘探,2010,45(3):373-380.Yin Xingyao,Zhang Shixin,Zhang Fanchang et al.Utilizing Russell approximation-based elastic wave impedance inversion to conduct reservoir description and fluid identification.OGP,2010,45(3):373-380.
[6]徐雷,迟惠生.静态前馈型网络的监督学习方法研究进展.电子学报,1992,20(10):106-113.Xu Lei,Chi Huisheng.Research advances on supervised learning of static feedforward networks.Acta Electronica Sinica,1992,20(10):106-113.
[7]王钰,郭其一,李维刚.基于改进BP神经网络的预测模型及其应用.计算机测量与控制,2005,13(1):39-42.Wang Yu,Guo Qiyi and Li Weigang.Predictive model based on improved BP neural networks and Its application.Computer Measurement&Control,2005,13(1):39-42.
[8]郑永爱.内积空间的特征.数学杂志,1995,15(4):477-484.Zheng Yongai.Characterizations of inner product spaces.Journal of Mathematics,1995,15(4):477-484.
[9]Fukushima K,Miyake S and Ito T.Neocognitron:A neural network model for a mechanism of visual pattern recognition.IEEE Transactions on Systems,Man and Cybernetics,1983,13(5):826-834.
[10]Lawrence S,Giles C L,Tsoi A C et al.Face recognition:A convolutional neural-network approach.IEEE Transactions on Neural Networks,1997,8(1):98-113.
[11]Fu L Y.Caianiello neural network method for geophysical inverse problems.Handbook of Geophysical Exploration:Seismic Exploration,2001,30:187-215.
[12]Lecun Y et al.Backpropagation applied to handwritten zip code recognition.Neural Computation,1989,1(4):541-551.
[13]Olshausen B A.Emergence of simple-cell receptive field properties by learning a sparse code for natural images.Nature,1996,381(6583):607-609.
[14]张繁昌,印兴耀.用模拟退火神经网络技术进行波阻抗反演.石油大学学报(自然科学版),1997,21(6):16-18.Zhang Fanchang,Yin Xingyao.Impedance inversion by using annealing neural network.Journal of the University of Petroleum,1997,21(6):16-18.
[15]陈善广,鲍勇.BP神经网络学习算法研究.应用基础与工程科学学报,1995,3(4):437-442.Chen Shanguang,Bao Yong.Studies on learning algorithms for BP net.Journal of Basic Science and Engineering,1995,3(4):437-442.
[16]张繁昌,印兴耀,印海燕.基于共轭方向置换的叠前三参数反演方法及其实现.石油地球物理勘探,2009,44(4):430-435.Zhang Fanchang,Yin Xingyao and Yin Haiyan.Prestack three-term inversion based on conjugate direction substitution and realization.OGP,2009,44(4):430-435.
[17]Robinson E A.Predictive decomposition of time series with application to seismic exploration.Geophysics,1967,32(3):418-484.
[18]Robinson E A.Predictive decomposition of seismic traces.Geophysics,1957,22(4):767-778.
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