An imaging condition for reverse time migration based on wavefield decomposition

Reverse Time Migration(RTM) is a high precision imaging method of seismic wavefield at present,but low-frequency noises severely affect its imaging results.Thus one of most important aspect of RTM is to select the proper noise suppression method.The wavefield characteristics of the Poynting vector are analyzed and the upgoing,downgoing,leftgoing and rightgoing waves are decomposed using the Poynting vector of the acoustic wave equation.The normalized wavefield decomposition cross-correlation imaging condition is used to suppress low-frequency noises in RTM and improve the imaging precision.Numerical experiments using the Mamousi velocity model are performed and the results demonstrate that the upgoing,downgoing,leftgoing and rightgoing waves are well decomposed using the Poynting vector.Compared with the normalized cross-correlation imaging and Laplacian filtering method,the results indicate that the low-frequency noises are well suppressed by using the normalized wavefield decomposition cross-correlation imaging condition.     

Reverse time migration based on normalized wavefield decomposition imaging condition

With the increasing complexity of prospecting objectives,reverse time migration (RTM)has attracted more and more attention due to its outstanding imaging quality.RTMis based on two-way wave equation,so it can avoid the limits of angle in traditional one-way wave equation migration,image reverse branch,prism waves and multi-reflected wave precisely and obtain accurate dynamic information.However,the huge demands for storage and computation as well as low frequency noises restrict its wide application.The normalized cross-correlation ima-ging conditions based on wave field decomposition are derived from traditional cross-correlation imaging condition, and it can eliminate the low-frequency noises effectively and improve the imaging resolution.The practical proce-dure includes separating source and receiver wave field into one-way components respectively,and conducting cross-correlation imaging condition to the post-separated wave field.In this way,the resolution and precision of the imaging result will be promoted greatly.     

基于GPU并行加速的叠前逆时偏移方法

为了提高复杂地下介质的成像精度和偏移算法的计算效率,提出可高效对地下复杂构造进行准确成像的GPU加速叠前逆时偏移方法.该方法采用双程声波方程进行波场延拓,突破倾角限制,借助于高阶有限差分方法实现叠前逆时偏移成像;利用GPU(Graphic Processing Unit)并行加速技术对波场延拓和成像进行计算,相比于传统算法,其计算效率有较大提高,可以解决叠前逆时偏移算法计算量过大问题;在获取波场信息过程中,也采用随机边界条件,实施以计算换存储策略,解决逆时偏移计算中的海量存储问题.模型测试结果表明,该方法能够高效和高精度地对地下复杂地质体成像.     

Stationary phase point extraction based on high-resolution radon transform and its application to multiple attenuation by wavefield extrapolation

Multiple prediction and subtraction techniques based on wavefield extrapolation are effective for suppressing multiple related to water layers.In the conventional wavefield extrapolation method, the multiples of the seismic data are predicted from the known total wave field by the Green function convoluted with each point of the bottom.However, only the energy near the stationary phase point has an effect on the summation result when the convolutional gathers are added.The research proposed a stationary phase point extraction method based on high-resolution radon transform.In the radon domain, the energy near the stationary phase point is directly added along the convolutional gathers curve, which is a valid solution to the problem of the unstable phase of the events of multiple.The Curvelet matching subtraction technique is used to remove the multiple, which improved the accuracy of the multiple predicted by the wavefield extrapolation and the artifacts appearing around the events of multiple are well eliminated.The validity and feasibility of the proposed method are verified by the theoretical and practical data example.     

Least-squares reverse time migration in visco-acoustic media based on symplectic stereo-modeling method

The authors proposed a symplectic stereo-modeling method(SSM) in the Birkhoffian dynamics and apply it to the visco-acoustic least-squares reverse time migration(LSRTM). The SSM adopts stereo-modeling operator in space and symplectic Runge-Kutta scheme in time, resulting in great ability in suppressing numerical dispersion and long-time computing. These advantages are further confirmed by numerical dispersion analysis, long-time computation test and computational efficiency comparison. After the...     

Research on multiple elimination methods in inverse data space based on randomized singular value decomposition

Based on surfaced-related multiple elimination (SRME),this research has derived the methods on multiples elimination in the inverse data space.Inverse data processing means moving seismic data from forward data space (FDS) to inverse data space (IDS).The surface-related multiples and primaries can then be separated in the IDS,since surface-related multiples will form a focus region in the IDS.Muting the multiples energy can achieve the purpose of multiples elimination and avoid the damage to primaries energy during the process of adaptive subtraction.Randomized singular value decomposition (RSVD) is used to enhance calculation speed and improve the accuracy in the conversion of FDS to IDS.The synthetic shot record of the salt dome model shows that the relationship between primaries and multiples is simple and clear,and RSVD can easily eliminate multiples and save primaries energy.Compared with conventional multiples elimination methods and ordinary methods of multiples elimination in the inverse data space,this technique has an advantage of high calculation speed and reliable outcomes.     

基于自适应峰值分解的广义局部频率时频分析方法

往复压缩机振动信号具有复杂的多源冲击特性,表现较强的非平稳性,传统的时频分析方法难以提取有效的故障特征.以傅氏变换为基础的传统频率概念和以希尔伯特变换为基础的瞬时频率概念存在固有缺陷,提出一种广义局部频率的概念,并结合自适应峰值分解方法,实现信号时频分布的构造途径;与HHT时频分析方法进行仿真对比,并应用到往复压缩机振动信号故障特征提取.结果表明,基于自适应峰值分解的广义局部频率方法有效揭示往复压缩机不同故障的多源冲击振动信号时频特征,为往复压缩机故障诊断提供一种新的手段.     

Random seismic noise attenuation by learning-type overcomplete dictionary based on K-singular value decomposition algorithm

The transformation of basic functions is one of the most commonly used techniques for seismic denoising,which employs sparse representation of seismic data in the transform domain. The choice of transform base functions has an influence on denoising results. We propose a learning-type overcomplete dictionary based on the K-singular value decomposition( K-SVD) algorithm. To construct the dictionary and use it for random seismic noise attenuation,we replace fixed transform base functions with an overcomplete redundancy function library. Owing to the adaptability to data characteristics,the learning-type dictionary describes essential data characteristics much better than conventional denoising methods. The sparsest representation of signals is obtained by the learning and training of seismic data. By comparing the same seismic data obtained using the learning-type overcomplete dictionary based on K-SVD and the data obtained using other denoising methods,we find that the learning-type overcomplete dictionary based on the K-SVD algorithm represents the seismic data more sparsely,effectively suppressing the random noise and improving the signal-to-noise ratio.