摘要
基于动态时间规整的叠前道集剩余时差校正方法存在动态时间规整算法对噪声敏感,准确计算规整路径困难;算法采用逐点搬家法,直接对地震道作剩余时差校正容易引起地震波形畸变的问题。提出一种联合稀疏贝叶斯学习(Sparse Bayesian Learning,SBL)和动态时间规整(Dynamic Time Warping,DTW)的叠前道集剩余时差校正方法,采用SBL对地震道集进行稀疏表示,再利用DTW对稀疏表示结果进行剩余时差校正,处理后重构地震记录。结果表明,SBL具有良好的噪声鲁棒性,较少的局部最小值,以及全局最优解同时也是最稀疏解,稀疏分解后得到地下地层单位冲击响应,消除了子波影响,再进行时差校正就能避免波形畸变,同时实现了高保真剩余时差校正和随机噪声压制。数值模拟和实际资料处理结果表明该方法具有良好的应用效果。
The residual moveout correction method based on dynamic time warping is faced with two problems:the dynamic time warping algorithm is sensitive to noise,and it is difficult to calculate the warping path accurately;the algorithm adopts a point-by-point moving method,which corrects residual moveout of seismic trace directly,and may cause seismic waveform distortion.Aiming at these problems,a residual moveout correction method of prestack gather was proposed based on sparse Bayesian learning(SBL)and dynamic time warping(DTW).The implementation steps are as follows:sparse representation of seismic gathers was realized via sparse Bayesian learning,and then the residual moveout correction of the sparse representation results was conducted by dynamic time warping,and the seismic records were reconstructed after processing.This method utilizes sparse Bayesian learning with good noise robustness and few local minimums.The global optimal solution is also the sparsest one.After sparse decomposition,the unit impulse response of subsurface was obtained,and the wavelet effect was eliminated,and then the distortion of waveform caused by using dynamic time directly can be avoided.The highlight of this method is that high fidelity residual moveout correction and random noise suppression are simultaneously achieved.Numerical simulation and actual data processing results show that the proposed method has a good application effect.
作者
石战战
夏艳晴
周怀来
王元君
SHI Zhanzhan;XIAYanqing;ZHOU Huailai;WANG Yuanjun(The Engineering & Technical College of Chengdu University of Technology,Leshan 614000,Sichuan,China;College of Geophysics,Chengdu University of Technology,Chengdu 610059,China)
出处
《岩性油气藏》
CSCD
北大核心
2019年第3期86-94,共9页
Lithologic Reservoirs
基金
国家重大科技专项课题子课题"双极子匹配追踪反演技术研究"(编号:2016ZX05026-001-005)
四川省教育厅项目"基于时频域波形分类的礁滩储层预测方法研究"(编号:16ZB0410)联合资助
关键词
叠前道集
剩余时差
稀疏表示
稀疏贝叶斯学习
动态时间规整
prestack gather
residual moveout
sparse representation
sparse Bayesian learning
dynamic time warping
