时-空局域化地震波传播方法:Dreamlet叠前深度偏移

提出了一种在时间和空间上完全局域化的波场分解和传播算法─dreamlet偏移方法.Dreamlet是一种脉冲-小波束形式的波场分解原子,它利用多维局部分解变换,把时空域波场映射到局部时间-频率-空间-波数相空间,并用局部相空间的传播算子(dreamlet算子)沿深度延拓.本文利用多维局部余弦变换实现dreamlet算法,分解后的波场系数和传播算子不仅有很好的稀疏性,且均为实数,也即波的传播和成像过程完全在实数域实现.文中推导了局部余弦基dreamlet波场分解和传播算子理论公式并将其应用于叠前深度偏移.在dreamlet相空间波的传播过程为稀疏矩阵相乘,而且延拓后的地表数据波场的有效时间长度随深度的增加不断减小,从而可以减少需要传播的波场系数.二维SEG/EAGE盐丘和SIGSBEE模型算例验证了理论推导的正确性,成像结果显示该方法在横向速度变化剧烈情况下有很好的精度.     

双平方根单程波动方程叠前τ偏移方法

本文将常规双平方根（DSR）单程波动方程从深度域变换到双程垂直走时（τ）域，由此推导出可从数学上实现“沉降观测”的单程波DSR传播算子. 其递归波场延拓算法包含波数域针对常速背景的相移处理和空间域针对横向速度扰动的相位校正，可以应对上覆地层速度横向变化对构造成像的影响. 结合零炮检距、零时间成像条件，提出了在τ域进行波场延拓与成像的DSR方程叠前偏移新方法. 为了克服其全三维偏移算法在实际应用中可能面临的困难，本文采用稳相近似，在crossline常炮检距偏移理论基础上推导了实用的共方位角叠前τ偏移方法. 数值试验表明，DSR方程叠前τ偏移在强横向非均匀介质中的成像精度与分辨率优于传统的时间域成像技术.     

基于局部余弦基小波束的观测系统沉降法叠前深度偏移

将局部余弦基小波束波场分解、传播与观测系统沉降法叠前深度偏移相结合,推导了源-检波器观测系统沉降法传播算子.本算法中,先对频率域的共点源和共点检波器道集做局部余弦小波束分解,然后分别沿共小波束源和共小波束检波器在深度方向延拓得到下一层波场.每个深度的波场,都等效于把源和检波器放在该层后所能接收到的地震记录,每点的像值由炮点和检波点重合时的零时刻波场值给出.通过二维SEG/EAGE盐丘模型和Marmousi模型的偏移成像结果验证该方法理论推导的正确性.另外,结果显示该方法继承了小波束域波场延拓在速度扰动较大情况下波传播及成像精度高的优点.     

利用矩阵分解理论的双程波方程叠前深度偏移方法

叠前深度偏移理论及方法一直是地震数据成像中研究的热点问题.业界对单程波叠前深度偏移方法和逆时深度偏移开展了深入的研究,但对双程波方程波场深度延拓理论及成像方法的研究还鲜有报道.本文以地表记录的波场值为基础,利用单程波传播算子估计波场对深度的偏导数,为在深度域求解双程波方程提供充分的边界条件,并提出利用矩阵分解理论实现双程波方程的波场深度外推.通过对强速度变化介质中传播波场的计算,与传统的单程波偏移方法相比,本文提出的偏移方法计算的波场与常规有限差分技术计算的波场相一致,证明了本方法计算的准确性.通过对SEAM模型的成像,在相同的成像参数下,与传统的单程波偏移算法和逆时深度偏移算法方法相比,本文提出的偏移方法能够提供更少的虚假成像和更清晰的成像结果.本文所提偏移算法具有深度偏移和双程波偏移的双重特色,推动和发展了双程波叠前深度偏移的理论和实践.     

基于单程波偏移算子的地表相关多次波成像

在常规地震资料处理中,多次反射波被视为噪声并从地震数据中去除,以免在之后的地震资料解释中造成误解.而事实上,多次波也是地震信号,是照明波场的一部分,能够对地下构造成像的精度做出贡献.本文分析了多次波在传统单程波叠前深度偏移中产生构造假象的机制和表现,为实现基于单程波偏移算子的多次波成像,修改了单程波叠前深度偏移的边界条件,即将输入的震源波场用包含多次波的记录来替代,输入的记录波场用预测出的表层相关多次波来替代,实现了基于单程波偏移算子的地表相关多次波成像,并从理论上给出了其成像依据.通过基于二范式最小能量差原则求取的匹配因子,将多次波成像结果与一次波成像结果进行匹配叠加,应用多次波成像来弥补一次波成像的不足.简单模型验证了基于单程波偏移算子的多次波成像方法的有效性,最后对Sigsbee2B模型进行了一次波与多次波联合成像试算,盐边界高陡构造成像质量得到了明显改善.     

基于Gabor-Daubechies小波束叠前深度偏移的角度域共成像道集

传统炮检距域共像集（CIG）在复杂介质中因波传播的多路径而存在反射体位置不确定的问题. 角度域CIG由于克服了这一缺陷而逐步成为速度分析、AVA以及振幅保真偏移成像等研究的主要手段. 以波动理论为基础的地震偏移成像方法的发展为获得高质量的角度域CIG提供了可靠的实现途径. 其中,基于波场局域化分解和传播的小波束域波场延拓和偏移成像方法,因其波场分解基本函数和传播算子在空间和方向上的双重局域特性,而成为角度相关分析研究的有效工具. 本文在采用Gabor Daubechies框架分解的小波束叠前角度域偏移成像基础上,利用不同的叠加方法由局部角度域像矩阵得到了反射角域CIG（CRAIG）和倾角域CIG（CDAIG）. 以SEG EAGE二维盐体模型为例,通过对CRAIG和CDAIG的对比,探讨了这两种角度域CIG的特点及其在地震偏移成像中的潜在应用.     

双平方根单程波动方程叠前τ偏移方法

本文将常规双平方根（DSR）单程波动方程从深度域变换到双程垂直走时（τ）域，由此推导出可从数学上实现“沉降观测”的单程波DSR传播算子. 其递归波场延拓算法包含波数域针对常速背景的相移处理和空间域针对横向速度扰动的相位校正，可以应对上覆地层速度横向变化对构造成像的影响. 结合零炮检距、零时间成像条件，提出了在τ域进行波场延拓与成像的DSR方程叠前偏移新方法. 为了克服其全三维偏移算法在实际应用中可能面临的困难，本文采用稳相近似，在crossline常炮检距偏移理论基础上推导了实用的共方位角叠前τ偏移方法. 数值试验表明，DSR方程叠前τ偏移在强横向非均匀介质中的成像精度与分辨率优于传统的时间域成像技术.     

退化的Fourier偏移算子及其在复杂断块成像中的应用

波动方程宽角抛物逼近得到的通常是非常系数的单程波传播算子，其系数是速度横向变化的函数，因此需要利用有限差分(FD)进行数值实施. 通过对Lippmann Schwinger单程波动积分方程的退化核逼近,本文研究了一类宽角退化算子的偏移成像. 这种退化偏移算子只用快速Fourier变换进行波场延拓，将常规的Fourier分裂步地震偏移方法(SSF)推广适应强速度横向变化介质和大角度传播波场. 退化的Fourier偏移算子通过在两个分裂步项之间作波数域线性插值来实现波场延拓，每延拓一层需要比常规的SSF地震偏移方法多一次快速Fourier变换(FFT). 通过SEG/EAGE盐丘模型和实际地震资料的应用表明，退化Fourier偏移算子能很好地对盐下的陡倾角断层和实际地震剖面上的复杂小断块和大断裂地质构造成像.     

共炮检距道集波动方程保幅叠前深度偏移方法

本文提出了一种基于双平方根算子的共炮检距道集波动方程保幅叠前深度偏移方法,将振幅误差补偿作为偏移的一部分与“运动学偏移”一起在偏移过程中实现.其基本内容包括：(1)从保幅的单平方根算子方程出发,推导出由双平方根算子定义的保幅单程波方程;(2)根据地震波摄动理论把速度场分裂为层内常速背景和变速扰动,分别在频率－波数域和频率－空间域求得波场深度延拓的偏移时移量及振幅校正系数,从而得到最终的DSR保幅波场延拓算子;(3)在高频假设条件下,把DSR保幅波场延拓公式中的积分运算进行稳相近似,得到保幅波场延拓的相移公式.理论分析和模型数值试验表明,该方法不但可以使散射能量聚焦、归位,提高成像精度;而且可以输出正确反映地下反射系数的振幅信息,为后续的地震属性分析（如AVO/AVA）提供更真实的地震信息.     

特征高斯波包叠前深度偏移方法

高斯波包（Gaussian packet）传播算子可在局部时空域高效地计算局部波包的传播.高斯波包叠前深度偏移的基础是在Gabor变换域描述观测数据,再利用高斯波包传播算子计算炮点波场和检波点波场,两者相关即可得到偏移结果.利用炮道集的局部τ-p特征在Gabor变换域表达观测数据,可以仅关注部分高斯波包框架函数上的数据投影,这样既实现了波场的压缩存储,同时可利用高斯波包传播算子反传框架函数以实现整个炮道集的快速反传.这些综合了观测数据局部τ-p特征的高斯波包函数称为特征高斯波包（characteristic Gaussian packet,CGP）,相应的波场反传称为特征高斯波包反传.理论及数值分析证明了上述特征高斯波包反传方法是有效且快速的.炮点正传波场也利用高斯波包传播算子模拟.利用互相关成像条件可实现特征高斯波包叠前深度偏移（characteristic Gaussian packet pre-stack depth migration,CGPM）.由于高斯波包传播算子描述了局部方向及局部空间的波场,所以CGPM可以自然地提取角度域成像道集（ADCIG）,并易于实现面向目标叠前深度偏移,从而作为偏移引擎为偏移速度分析（MVA）服务.数值实验证明了CGPM和面向目标CGPM的有效性和实用性.     

Dreamlet source‐receiver survey sinking prestack depth migration

Survey sinking migration downward continues the entire surface observed multi‐shot data to the subsurface step by step recursively. Reflected energy from reflectors at current depth appear at zero time and zero offset in the extrapolated wavefield. The data (seismic records) of t > 0 at this depth are equivalent to the data acquired by a survey system deployed at this depth. This is the reason to name the process ‘survey sinking’. The records of negative time need not to be further propagated since they carry no information to image structures beneath the new survey system. In this paper, we combine survey sinking with dreamlet migration. The dreamlet migration method decomposes the seismic wavefield and one‐way wave propagator by complete time‐space localized bases. The localization on time gives flexibility on time‐varying operations during depth extrapolation. In dreamlet survey sinking migration, it only keeps the data for imaging the structures beneath the sunk survey system and gets rid of the data already used to image structures above it. The deeper the depth is, the shorter is the valid time records of the remaining data and less computation is needed for one depth step continuation. For data decomposition, in addition to time axis, dreamlet survey sinking also decomposes the data for source and receiver gathers, which is a fully localized decomposition of prestack seismic data. A three‐scatter model is first used to demonstrate the computational feature and principle of this method. Tests on the two‐dimensional SEG/EAGE salt model show that with reduced data sets the proposed method can still obtain good imaging quality on complex geology structures and a strong velocity contrast environment.     

基于波场垂向和水平方向外推的高陡构造偏移

常规的单程波波动方程偏移成像方法对大角度的高陡构造偏移成像存在内在的限制.根据波动方程在各个空间方向的数学特性和高陡构造反射地震波的传播特征,通过把地震波分解为垂向的上下行波、水平方向的前后行波和左右行波,提出基于波场垂向外推和水平方向外推相结合的单程波波动方程高陡构造偏移成像方法,即用波场垂向外推的单程波波动方程偏移成像方法解决中低角度平缓构造的偏移成像,用波场水平方向外推的单程波波动方程偏移成像方法解决中高角度陡倾构造的偏移成像.这种基于波场垂向和水平方向外推相结合的高陡构造偏移成像方法是常规单程波波动方程叠前深度偏移成像方法的补充和改进,它相对基于全波方程的逆时偏移具有计算效率上的优势.     

基于波场传播方向的波动方程偏移有效去噪方法(英文)

本文研究分析了双程波波动方程偏移成像中广泛存在的三种主要噪声,特别是针对过去研究中没能很好解决的存在于高速盐丘悬垂边界附近的射线状噪声,提出了基于优化成像条件的有效去噪方法。射线状噪声主要来自于震源一侧波场的下行透射波分量和接收阵列一侧波场的上行散射波互相关成像。这一部分能量具有较强的互相关性,但并不携带真实的反射面信息。它广泛存在叠前偏移成像中,与信号在强度上同量级。多数情况下偏移成像中的相关噪声由方向性传播的波场能量产生。利用波场梯度得到的波场传播角度,可以分离出噪声对应的波场能量,并在成像条件中减去。采用这一方法可以有效地去除多种噪声,包括直达波噪声、散射波噪声和射线状噪声。该去噪方法不依赖波场外推算子,在需要时可以方便地运用到几乎所有的波动方程偏移中去。并且该去噪方法针对噪声的物理根源,对信号的损害很小。对去噪后的偏移成像结果额外地进行波数域滤波处理,可以进一步提高叠加图像的质量。这一去噪方法在超广角单程波偏移成像中取得良好效果,我们同时期待其在其他双程波波动方程偏移特别是逆时偏移(RTM)中的成功运用。     

Gabor‐frame‐based Gaussian packet migration

We present a Gaussian packet migration method based on Gabor frame decomposition and asymptotic propagation of Gaussian packets. A Gaussian packet has both Gaussian‐shaped time–frequency localization and space–direction localization. Its evolution can be obtained by ray tracing and dynamic ray tracing. In this paper, we first briefly review the concept of Gaussian packets. After discussing how initial parameters affect the shape of a Gaussian packet, we then propose two Gabor‐frame‐based Gaussian packet decomposition methods that can sparsely and accurately represent seismic data. One method is the dreamlet–Gaussian packet method. Dreamlets are physical wavelets defined on an observation plane and can represent seismic data efficiently in the local time–frequency space–wavenumber domain. After decomposition, dreamlet coefficients can be easily converted to the corresponding Gaussian packet coefficients. The other method is the Gabor‐frame Gaussian beam method. In this method, a local slant stack, which is widely used in Gaussian beam migration, is combined with the Gabor frame decomposition to obtain uniform sampled horizontal slowness for each local frequency. Based on these decomposition methods, we derive a poststack depth migration method through the summation of the backpropagated Gaussian packets and the application of the imaging condition. To demonstrate the Gaussian packet evolution and migration/imaging in complex models, we show several numerical examples. We first use the evolution of a single Gaussian packet in media with different complexities to show the accuracy of Gaussian packet propagation. Then we test the point source responses in smoothed varying velocity models to show the accuracy of Gaussian packet summation. Finally, using poststack synthetic data sets of a four‐layer model and the two‐dimensional SEG/EAGE model, we demonstrate the validity and accuracy of the migration method. Compared with the more accurate but more time‐consuming one‐way wave‐equation‐based migration, such as beamlet migration, the Gaussian packet method proposed in this paper can correctly image the major structures of the complex model, especially in subsalt areas, with much higher efficiency. This shows the application potential of Gaussian packet migration in complicated areas.     

非零偏VSP弹性波叠前逆时深度偏移技术探讨

非零偏VSP地震资料是一种多分量资料,处理非零偏VSP资料,弹性波叠前逆时深度偏移技术无疑是最适合的处理技术.本文从二维各向同性介质的弹性波波动方程出发,研究了对非零偏VSP资料进行叠前逆时深度偏移的偏移算法,讨论了逆时传播过程中的边值问题和数值频散问题及其相应的解决方案;采用求解程函方程计算得到地下各点的地震波初至时间作为成像时间,实现了非零偏VSP资料的叠前逆时深度偏移.最后进行了模型试算和非零偏VSP地震资料的试处理,结果表明该方法不受地层倾角限制,较适用于高陡构造地区或介质横向速度变化较大地区的非零偏VSP地震资料处理.     

Directional‐oriented wavefield imaging: a new wave‐based subsurface illumination imaging condition for reverse time migration

The key objective of an imaging algorithm is to produce accurate and high‐resolution images of the subsurface geology. However, significant wavefield distortions occur due to wave propagation through complex structures and irregular acquisition geometries causing uneven wavefield illumination at the target. Therefore, conventional imaging conditions are unable to correctly compensate for variable illumination effects. We propose a generalised wave‐based imaging condition, which incorporates a weighting function based on energy illumination at each subsurface reflection and azimuth angles. Our proposed imaging kernel, named as the directional‐oriented wavefield imaging, compensates for illumination effects produced by possible surface obstructions during acquisition, sparse geometries employed in the field, and complex velocity models. An integral part of the directional‐oriented wavefield imaging condition is a methodology for applying down‐going/up‐going wavefield decomposition to both source and receiver extrapolated wavefields. This type of wavefield decomposition eliminates low‐frequency artefacts and scattering noise caused by the two‐way wave equation and can facilitate the robust estimation for energy fluxes of wavefields required for the seismic illumination analysis. Then, based on the estimation of the respective wavefield propagation vectors and associated directions, we evaluate the illumination energy for each subsurface location as a function of image depth point and subsurface azimuth and reflection angles. Thus, the final directional‐oriented wavefield imaging kernel is a cross‐correlation of the decomposed source and receiver wavefields weighted by the illuminated energy estimated at each depth location. The application of the directional‐oriented wavefield imaging condition can be employed during the generation of both depth‐stacked images and azimuth–reflection angle‐domain common image gathers. Numerical examples using synthetic and real data demonstrate that the new imaging condition can properly image complex wave paths and produce high‐fidelity depth sections.     

Prestack shot-gather depth migration by a rigid flow of Gaussian wave packets

We introduce a new method for prestack depth migration of seismic common-shot gathers. The computational procedure follows standard steps of the reverse-time migration, i.e., downward continuation of the source and the receiver wavefields, followed by application of an imaging condition (e.g. zero-lag cross-correlation of these fields). In our method we first find a sparse data representation with a small number of Gaussian wave packets. We then approximate the downward wavefield propagation (for the source and the receiver fields) by a rigid flow of these wave packets along seismic rays. In this case, the wave packets are simply translated and rotated according to the ray geometry. One advantage of using Gaussian wave packets is that analytic formulas can be used for translation, rotation, and the application of the cross-correlation imaging condition. Moreover, they allow more sparse representations than competing methods. Finally we formulate a computationally and memory efficient migration procedure, as only few rays have to be traced, and since it is cheap to compute the cross-correlation for the intersecting rays.