基于迭代反演的层间多次波压制方法

层间多次波与有效波在走时、频率和叠加速度上差异较小,因此层间多次波压制常难以获得理想效果.本文提出一种基于迭代反演的层间多次波压制方法(MSI,Multiple Suppression Inversion),该方法以共聚焦点(CFP,Common Focus Point)层间多次波压制理论为基础,通过构建卷积因子,将层间多次波压制转变为迭代反演的问题,直接利用观测的地震数据进行迭代反演计算,进而完成多次波压制.MSI方法避免了共聚焦点方法中构建CFP道集的聚焦运算,大幅降低层间多次波预测的计算成本;同时该方法为全数据驱动方法,无需地下介质任何先验信息,算法容易实现.模型数据测试表明,本文提出的方法可有效压制层间多次波,而且对有效波也具有很好的保幅性.     

分层层间多次波预测与自适应压制方法研究

在地震勘探中,层间多次波在地震剖面上形成的虚假界面会严重影响对地质构造的解释.由于层间多次波在走时、频率和形态上与一次波的差异较小,因此较难对层间多次波进行识别和压制.本文使用分层层间多次波预测方法与基于2D卷积信号盲分离的多次波自适应相减方法相结合的策略对层间多次波进行压制处理.首先推导并阐述了分层层间多次波预测方法的理论基础,然后对地下介质中存在的多个多次波产生界面实现了层间多次波的分层预测,最后采用基于2D卷积信号盲分离的多次波自适应相减方法对层间多次波进行压制,以更好的保护有效信号.分层预测的方法通过分离层间多次波产生层位,对目标层位产生的所有层间多次波直接进行预测,该方法避免了共聚焦点方法构建共聚焦点道集的聚焦运算,降低了计算成本,同时该方法是基于数据驱动的,算法容易实现.模型数据和实际地震资料试算结果表明,本文方法能直接利用地表数据对层间多次波进行压制,并有效的保护一次波．     

波路径偏移压制层间多次波的理论与应用

消除层间多次波是地震勘探资料处理研究领域的难题,尤其对于实际资料的处理,到目前为止还很难找到一种完全有效的方法. 本文给出了仅对一次波成像既波路径偏移方法压制层间多次波方法,在共炮道集和共检波点道集分别计算炮点射线的入射角和检波点射线的出射角,由此计算的角度作为射线追踪的初始角度,计算地震波射线的传播路径. 结合由程函方程计算的走时表,判断偏移范围是反射波还是多次波. 在前期偏移过程压制多次波的理论研究基础上,本文主要研究波路径偏移消除多次波的应用部分. 为了进一步说明效果的有效性,计算了在单炮和共成像点道集压制层间多次波,给出了实际资料的压制多次波的偏移结果.     

自适应虚同相轴方法压制地震层间多次波

地震数据中发育的层间多次波是影响速度分析和偏移成像的精度和可靠性的关键.通常情况下,层间多次波的动校正量、叠加速度和频率与一次波并无明显差异,从而对识别、预测和压制多次波带来了极大挑战.传统虚同相轴方法基于物理图像和定性公式,其预测的层间多次波振幅和相位精度难以满足实际需求,造成了其对匹配算法的过度依赖.本文针对传统虚同相轴方法的理论缺陷和计算精度问题,通过理论推导得到了新的自适应虚同相轴方法.相比于传统方法,自适应虚同相轴方法能够显著提高压制多次波能力,同时减少对匹配算法的依赖.本文给出了自适应虚同相轴方法的推导过程,并运用一维和二维模型算例验证了方法相较于传统虚同相轴方法的多次波预测精度优势.通过在PLUTO模型和实际陆地地震数据上的应用实例,证明了本文新研究的自适应虚同相轴方法对去除层间多次波,恢复并突出目标储层同相轴,提高地震成像分辨率的显著作用.     

逆散射级数法预测层间多次波的算法改进及其策略

波动方程预测层间多次波有两类算法.一是由Berkhout和Verschuur（1998）提出的基于CFP（共聚焦点）延拓的算法,另一是由Weglein（1997）等人提出的基于逆散射级数法的算法.ISS（逆散射级数）算法具有不依赖速度模型的优点,但是其计算成本很高.本文改进了Weglein提出的1-D预测公式,提高了计算效率.从理论方面,改进算法的计算速度提高了大约12倍.实际结果表明其计算速度更快,提高近80倍左右,主要原因在于后者的空间复杂度小.此外,由于时间-空间域比伪深度-波数域的层间多次波预测有更多的优点,本文推导了1.5-D时间-空间域ISS层间多次波预测算法,该算法有计算速度快、预测噪音小、适应观测系统能力强和不依赖地震子波等优点.在实际应用中,计算成本高是ISS预测层间多次波算法的主要障碍.因此,本文还讨论了预测层间多次波的策略.结合本文提出的改进算法和应用策略,能够在实际地震资料处理中较好地应用ISS预测层间多次波.     

基于构建虚同相轴压制地震层间多次波

基于构建虚同相轴来估计层间多次波的方法是克希霍夫积分表示定理的一个延伸发展.本文通过构建虚地震同相轴巧妙地将散射点从地下移到了表面,利用表面的散射点来预测层间多次波.由于预测与实际地震记录中的层间多次波振幅存在偏差,采用了多道的L₁范数匹配算法来实现真振幅的多次波压制.对简单模型和南黄海地质模型的数据处理实例验证了本文研究方法的正确性和有效性.     

基于Marchenko理论压制自由表面多次波方法研究

常规虚源点Marchenko自聚焦多次波预测方法只适用于预测不含自由表面的多次波模型,局限于压制层间多次波,该方法在构建上下行格林函数场前,必须从反射响应中去除所有与表面相关的多次波.本文对构建上下行Marchenko格林函数方程进行改进,得到了包含一次波、层间多次波和自由表面多次波的格林函数,利用改进的Marchenko自聚焦预测方法预测自由表面多次波.本文利用水平层状模型数据及SMARRT模型数据证明,改进后的Marchenko法预测海底相关的自由表面多次波效果较为理想,该方法避免了常规SRME自由表面多次波预测方法需要近道重构的缺陷,能够有效提高地震资料的信噪比和分辨率.     

基于相关迭代的非因果匹配滤波器多次波压制方法

针对常规多次波压制(SRME)方法存在的缺陷进行了改进.首先在多次波模型道预测阶段,采用数据相关和迭代更新的策略预测多次波模型道,该方法降低了SRME方法采用数据空间褶积对输入数据要求严格的限制,提高了对近偏移距缺失和空间假频数据的适应性.在匹配相减阶段,本文设计了一种非因果非平稳的匹配滤波器,该滤波器可以对整道多次波模型进行处理,而且当预测多次波模型道滞后于实际多次波时,也能够对多次波模型道进行很好的匹配.模型数据和实际数据试算证明该方法在多次波模型道预测和匹配相减阶段的优越性.     

基于自适应变步长波场延拓的可控层分阶层间多次波模拟

地下低速夹层的存在导致地震数据中包含较强能量的层间多次波,有效识别和预测深部储层上覆地层产生的层间多次波是提高深部储层解释精度的重要环节,而准确模拟层间多次波是辅助识别地震数据中层间多次波的一种非常有效的方法.本文提出了一种基于自适应变步长波场延拓的可控地层分阶层间多次波模拟方法,该方法基于自适应变步长波场延拓,以递归循环的方式实现分阶层间多次波的模拟.通过对模型添加双重层位约束,可以模拟指定地层产生的各阶层间多次波.利用二维反周期延拓方法压制波场延拓的边界反射优于传统方法,例如吸收边界法.提出自适应变步长波场延拓技术,大大提升了波场模拟的效率.理论和数值例子表明,本文方法模拟的一次波和各阶层间多次波与常用的有限差分方法模拟结果具有很好的一致性,且克服了有限差分方法无法分阶模拟波场的不足,显著提升了层间多次波识别的效率.     

地下复杂介质地震处理中的CFP技术

要简要介绍CFP(Common Focus Point)方法技术的基本原理和主要应用。CFP是复杂介质地震处理中的一项新技术，它把叠前偏移分成两个独立的步骤：首先对检波点(炮点)进行聚焦处理，产生共聚焦点道集(CFP道集)，然后再对炮点(检波点)进行聚焦，产生叠前偏移的输出。两个步骤中间的CFP道集则可以进行其它处理。如果是为了寻找构造信息，那么可以应用共焦点CFP偏移；如果是为了寻找岩石、孔隙或流体的信息，则要应用双焦点CFP偏移。目前，该技术主要应用于：(1)CFP两步聚焦法偏移；(2)叠前深度偏移速度模型的建立；(3)试图通过算于而不是速度来解决复杂地表的静校正；(4)消除全程或层间多次波；(5)CFP方法基准面的延拓和盐下成像；(6)多分量地震资料的偏移成像。     

Surface-based Internal Multiple Elimination in the CMP Domain — Theory and Application Strategies on Land Seismic Data

The data-driven internal multiple elimination (IME) method based on feedback model, which includes CFP-based, surface-based and inversion- based methods, are successfully applied to marine datasets. However, these methods are computationally expensive and not always straightforward on land datasets. In this paper, we first proved that the surface-based IME method, which is the most computationally efficient method among the three methods, can be derived from the CFP theory. Then we extend it to CMP domain under the assumption of locally lateral invariance of the earth, which makes it more computationally efficient. In addition, we proposed applying a time-variant taper based on the first Fresnel zone to predict the multiples more percisely. Besides, the improved S/N ratio and dense offset distribution can be obtained by using the CMP supergather, which makes the CMP-oriented method more suitable for land data. Some practical processing strategies are proposed via case study. The effectiveness of the proposed method is demonstrated with the application to synthetic and field data.     

Infilling of sparse 3D data for 3D focusing operator estimation

Seismic migration can be formulated in terms of two consecutive downward extrapolation steps: refocusing the receivers and refocusing the sources. Applying only the first focusing step with an estimate of the focusing operators results in a common focal point (CFP) gather for each depth point at a reflecting boundary. The CFP gathers, in combination with the estimates of the focusing operators, can be used in an iterative procedure to obtain the correct operators. However, current 3D seismic data acquisition geometries do not contain the dense spatial sampling required for calculation of full 3D CFP gathers. We report on the construction of full 3D CFP gathers using a non‐full 3D acquisition geometry. The proposed method uses a reflector‐orientated data infill procedure based on the azimuthal redundancy of the reflection data. The results on 3D numerical data in this paper show that full 3D CFP gathers, which are kinematically and dynamically correct for the target event, can be obtained. These gathers can be used for iterative updating of the 3D focusing operators.     

逆散射级数法去除自由表面多次波

压制多次波是地震资料处理过程中的重要环节之一,多次波去除的效果直接影响到最后成像的质量．本文研究的逆散射级数法以波动方程和Born级数为基础,构造一个与自由表面多次波相关的子序列来预测并消除多次波,该方法不需要知道地下的速度结构信息,适用于各种复杂的地下情况．在逆散射级数法的实现过程中,地震子波和近道波场是必不可少的信息,本文采用能量最低法则和带限抛物Radon变换法来弥补原始地震资料中所缺失的这些信息．合成资料和实际地震资料的处理结果表明,逆散射级数法能够在去除自由表面多次波的同时保留有效波的信息,对于地下复杂结构的情况同样有效．     

基于共聚焦点道集的叠前深度偏移

共聚焦点(CFP)偏移技术是一种基于等时原理,将Kirchhoff积分法的一步偏移分两步聚焦（即激发聚焦和检波聚焦）来完成的叠前地震成像方法.该方法借助于逆时聚集算子和共聚焦点道集来实现叠前偏移成像.基于共聚焦点道集的叠前深度偏移是把基于共炮集的深度偏移的算法引入到CFP技术上来,基于波场延拓的理论来实现偏移成像,该方法首先生成共聚焦点道集,然后基于面炮合成的理论合成聚焦震源,最终通过相关成像来实现叠前偏移成像.该方法选取较少的聚焦点就可以实现对于地下构造的偏移成像,和炮域波动方程偏移相比,其计算效率得到了提高.通过模型试算和实际资料的试处理,验证了该方法在实现叠前深度偏移成像上的正确性和有效性.     

稀疏反演多次波去除策略与效果分析

多次波去除是海上勘探中的一项关键技术,在一定程度上决定着最终成像结果的质量.表面相关多次波去除方法(Surface-Related Multiple Elimination,SRME)是一种基于反馈迭代模型的数据驱动类方法,该方法利用地震数据本身预测多次波,进一步对其进行匹配相减获得一次波结果,这往往导致与多次波重叠的一次波被错误地去除.稀疏反演一次波估计方法(Estimation of Primaries by Sparsity Inversion,EPSI)在原理上同样基于反馈迭代模型,通过求解一次波和表面相关多次波总残差最小化问题直接获得一次波.该方法无多次波的匹配相减过程,可减少与多次波发生重叠的一次波产生损失.另外,EPSI可以很好地解决SRME无法解决的近道缺失问题.理论数据和实际资料处理对比表明SRME对数据完整性以及一次波和多次波的分布情况有较强的依赖性,而EPSI几乎不受这两个因素的影响.EPSI相比SRME方法计算成本高,尚未在工业界广泛应用,但随着高性能计算的普及,该方法的实用性有望得到突破.     

Investigating a time‐shift extended imaging condition in a Kirchhoff pre‐stack depth migration algorithm

Extracting true amplitude versus angle common image gathers is one of the key objectives in seismic processing and imaging. This is achievable to different degrees using different migration techniques (e.g., Kirchhoff, wavefield extrapolation, and reverse time migration techniques) and is a common tool in exploration, but the costs can vary depending on the selected migration algorithm and the desired accuracy. Here, we investigate the possibility of combining the local‐shift imaging condition, specifically the time‐shift extended imaging condition, for angle gathers with a Kirchhoff migration. The aims are not to replace the more accurate full‐wavefield migration but to offer a cheaper alternative where ray‐based methods are applicable and to use Kirchhoff time‐lag common image gathers to help bridge the gap between the traditional offset common image gathers and reverse time migration angle gathers; finally, given the higher level of summation inside the extended imaging migration, we wish to understand the impact on the amplitude versus angle response. The implementation of the time‐shift imaging condition along with the computational cost is discussed, and results of four different datasets are presented. The four example datasets, two synthetic, one land acquisition, and a marine dataset, have been migrated using a Kirchhoff offset method, a Kirchhoff time‐shift method, and, for comparison, a reverse time migration algorithm. The results show that the time‐shift imaging condition at zero time lag is equivalent to the full offset stack as expected. The output gathers are cleaner and more consistent in the time‐lag‐derived angle gathers, but the conversion from time lag to angle can be considered a post‐processing step. The main difference arises in the amplitude versus offset/angle distribution where the responses are different and dramatically so for the land data. The results from the synthetics and real data show that a Kirchhoff migration with an extended imaging condition is capable of generating subsurface angle gathers. The same disadvantages with a ray‐based approach will apply using the extended imaging condition relative to a wave equation angle gather solution. Nevertheless, using this approach allows one to explore the relationship between the velocity model and focusing of the reflected energy, to use the Radon transformation to remove noise and multiples, and to generate consistent products from a ray‐based migration and a full‐wave equation migration, which can then be interchanged depending on the process under study.     

一阶多次波聚焦变换成像

将多次波转换成反射波并按传统反射波偏移算法成像,是多次波成像的一种方法.聚焦变换能准确的将多次波转换为纵向分辨率更高的新波场记录,其中一阶多次波转换为反射波.本文对聚焦变换提出了两点改进:1)提出局部聚焦变换,以减小存储量和计算量,增强该方法对检波点随炮点移动的采集数据的适应性;2)引入加权矩阵,理论上证明原始记录的炮点比检波点稀疏时,共检波点道集域的局部聚焦变换可以将多次波准确转换成炮点与检波点有相同采样频率的新波场记录.本文在第一个数值实验中对比了对包含反射波与多次波的原始记录做局部聚焦变换和直接对预测的多次波做局部聚焦变换两种方案,验证了第二种方案转换得到的波场记录信噪比更高且避免了第一个方案中切聚焦点这项比较繁杂的工作.第二个数值实验表明:在炮点采样较为稀疏时,该方法能有效的将一阶多次波转换成反射波;转换的反射波能提供更丰富的波场信息,成像结果更均衡、在局部有更高的信噪比,以及较高的纵向分辨率.     

基于保幅拉东变换的多次波衰减

为在去除多次波时有效保护地震一次反射波数据的AVO现象,给后续反演、解释提供准确的地震数据,本文提出了一种基于保幅拉东变换的多次波衰减方法,该方法是对常规抛物拉东变换的修改,把常规的稀疏拉东变换在拉东域分成两部分：一部分用于模拟零偏移距处的反射波能量,增加的另一部分用于模拟反射波振幅的AVO特性.该方法不仅考虑了反射波同相轴的形状,还考虑了反射波同相轴振幅幅度的变化,从而可把反射波信息进行有效转换,进而有利于多次波的消除,更好地恢复有效波的能量.在把地震数据由时间域转换到拉东域时,本文采用了IRLS算法实现保幅拉东算子的反演.模型数据和实际地震道集的试算分析表明,与常规拉东变换相比,保幅拉东变换在去除多次波的同时可有效保护一次反射波的AVO现象.     

Common image gather conditioning using cycle generative adversarial networks

Seismically derived amplitude-versus-angle attributes along with well constraints are the base inputs into inverting seismic into subsurface properties. Conditioning the common image gathers is a common workflow in quantitative inversion and leads to a more accurate inversion product due to the removal of post-migration artefacts. Here, we apply a neural network to condition the post-migration gathers. The network is a cycle generative adversarial network, CycleGAN, which was designed for image-to-image translation. This can be considered the same problem as translating an artefact rich seismic gather to an artefact free seismic gather. To assess the feasibility of applying the network to pre-stack conditioning, synthetic data sets were generated to train different networks for different tasks. The networks were trained to remove white noise, residual de-multiples, gather flattening and a combination of the above for conditioning. The results show that a trained network was able remove white noise providing a more robust amplitude-versus-offset calculation. Another network trained using synthetic gathers with and without multiples assisted in multiple removal. However, instability around primary preservation has been observed so the network works better as a residual de-multiple method. For gather conditioning, a network was trained with the unpaired artefact-rich and artefact-free training data where the artefacts included complex moveout, noise and multiples. When applied to the test data sets, the networks cleaned the artefact-rich test data and translated complex moveout into flat gathers whilst preserving the amplitude response. Finally, two networks are applied to real data where a gather based on the well logs is used to quantify the match between the conditioned gathers and the raw gathers. The first network used synthetic data to train the network and, when applied to real data, provided a better tie with the well. The second network was trained with synthetic gathers whose properties were constrained by real seismic gathers from near the well. As anticipated, the network trained on the representative training data outperforms the network trained using the unconstrained data. However, the ability of the first network to condition the gather indicates that a sweep of networks can be trained without the need for real data and applied in a manner analogous to the way parameters are adjusted in traditional geophysical methods. The results show that the different neural networks can offer an alternative or augmentation to the existing geophysical workflow for conditioning pre-stack seismic gathers.     

3D targeted multiple attenuation

Short-period multiple reflections pose a particular problem in the North Sea where predictive deconvolution is often only partially successful. The targeted multiple attenuation (TMA) algorithm comprises computation of the covariance matrix of preflattened prestack or post-stack seismic data, the determination of the dominating eigenvectors of the covariance matrix, and subtraction of the related eigenimages followed by reverse flattening. The main assumption made is that the flattened multiple reflections may be represented by the first eigenimage(s) which implies that the spatial amplitude variations of primaries and associated multiples are similar. This assumption usually limits the method to short-period multiple reflections. TMA is applicable post-stack or prestack to common-offset gathers. It is computationally fast, robust towards random noise, irregular geometry and spatial aliasing, and it preserves the amplitudes of primaries provided they are not parallel to the targeted multiples. Application of TMA to 3D wavefields is preferable because this allows a better discrimination between primaries and multiples. Real data examples show that the danger of partially removing primary energy can be reduced by improving the raw multiple model that is based on eigenimages, for example by prediction filtering.