基于数据规则化和稀疏反演的三维表面多次波压制方法

表面多次波是海洋地震勘探中的主要问题.目前,二维数据驱动的表面多次波压制技术(SRME)已经比较成熟,并且已经成为工业界压制海洋表面多次波的主流方法.但是由于二维SRME算法没有考虑横测线方向上多次波的贡献,导致在处理实际三维海洋资料时存在比较大的误差.将二维SRME算法扩展到三维空间后可以得到三维SRME算法,但是由于目前实际采集的三维海洋资料的观测系统存在拖缆漂移,而且横测线方向采样过于稀疏,直接应用三维SRME算法无法准确预测表面多次波.本文提出的通过数据规则化配合稀疏反演的三维表面多次波压制方法能够解决这种实际资料和三维SRME算法之间的矛盾.本文通过研究数据规则化与反规则化技术,使得数据分布满足三维SRME的要求;通过研究稀疏反演技术,有效解决了横测线方向采样稀疏对于多次波预测的影响,三维实际海洋资料的应用结果验证了方法的有效性和可行性.     

多次波压制的研究现状与进展

目前地震资料处理技术主要利用一次波对地下地质体进行成像或反演,多次波的存在会严重影响地震成像、反演与解释结果.因此,多次波通常作为噪声在地震数据叠前处理阶段进行压制.为了合理选择不同的多次波压制方法,提高地震资料信噪比和保真度,本文系统综述了多次波压制的国内外研究现状,简要介绍了滤波法、预测相减法、稀疏反演法的基本原理,并围绕理论假设条件与实际数据要求,分析比较了各种方法的适用性与优缺点.基于波动方程的预测相减法克服了滤波法关于地震信号统计特征的假设,增强了对复杂地震数据的适应性,但最小二乘自适应相减会损害有效信号.稀疏反演法采用全波形反演的方法估计一次波与多次波,避免了自适应相减步骤,能够更加保真地恢复有效信号.本文通过实际算例重点阐述了稀疏反演法相对于反馈迭代法在多次波压制中的突破性进展,同时指明了稀疏反演法存在的问题并展望了未来的发展方向.     

多道自适应匹配滤波方法压制表面多次波

表面多次波压制是海洋地震数据处理的关键环节,基于波动方程的多次波压制方法可由多次波模型预测和自适应相减两步实现.因此,除了采用有效的算法实现高精度的多次波预测外,多次波的自适应相减也是改善多次波压制效果的重要手段.文中采用多道自适应滤波器完成表面多次波的自适应相减,利用数据驱动的基于波动方程预测多次波模型的高频重建道、Hilbert变换道、以及它们相应的上下延拓道改善预测多次波模型的振幅、相位、旅行时以及频带信息,使得修正后多次波模型与实际地震数据中的多次波更好地匹配.实际数据测试表明,文中所述方法可有效地完成表面多次波的自适应相减.通过讨论分析交叠时窗和滤波器的长度对自适应相减效果的影响,表明合理地利用时窗以及选择滤波器长度将有效地改善自适应相减的效果.     

基于波动方程表面多次波预测与 自适应相减方法研究

多次波预测与自适应相减是基于波动方程表面多次波压制的两个重要环节.文中利用具有并行计算优势的GPU加速表面多次波预测,使得预测效率大为提高.在自适应相减算法中,文中将预测的多次波道、预测多次波道的Hilbert变换道、预测多次波道的高频重建道、以及它们的平移道用作自适应相减中的多次波模型道.Hilbert变换道用以补偿预测多次波的相位信息,高频重建道用以改善预测多次波的高频信息,补偿频带能量差异.文中在预测和相减过程中均采用迭代算法,迭代预测,可较好地获得多次波的运动学特性,迭代相减,可较好地获得多次波的动力学特性,迭代预测与相减使预测的多次波与地震数据中实际的多次波更好地匹配.将该方法应用于理论模拟的SMAART模型和实际海洋数据中,测试结果表明,该方法预测多次波效率较高,在保持有效波振幅条件下可有效地压制地震数据中的表面多次波.     

宽频高分辨率处理与多次波衰减新方法在南海珠江口盆地X工区的应用

南海珠江口盆地是我国重要的油气资源产区,该产区的浅海区域内多次波发育严重。文中以信号预处理得到的高信噪比数据为基础,测试了一套宽频提高分辨率及多次波衰减的处理思路。在宽频高分辨率处理方面,通过应用自适应鬼波压制技术结合成像域保幅性BWXT拓频技术得到高分辨率数据体。针对浅海自由表面多次波,在数据域采用确定性水层多次波预测技术与广义表层多次波预测技术进行联合压制,结果显示该两种方法的组合使用能有效压制该区域内的水面相关多次波。通过对各技术方法处理前后的道集、频谱、叠前时间偏移剖面的质控分析可以发现,该处理流程在珠江口盆地X工区的地震资料处理中有很好的应用效果,多次波得到有效衰减。最终生成的宽频高分辨率地震数据为后期高精度储层预测及地质研究工作奠定了坚实的基础。且文中提出的处理思路对浅海海洋资料高分辨率处理具有很强的适用性,可推广应用于类似海洋环境的数据处理中。     

基于波动方程三维表面多次波预测方法研究

与传统的二维表面多次波预测算法相比,基于波动方程的全三维表面多次波预测方法无需对地下介质做简单近似,其更符合地震波在地下介质中传播的真实状况,是地震资料处理中解决多次波预测问题的强有力工具.本文从三维多次波预测的基本理论出发,给出了全三维多次波预测算法的预测矩阵表示、计算方法以及实现条件,采用GPU(图形处理器)加速全三维表面多次波预测,较传统的CPU串行计算,GPU并行预测表面多次波的计算效率约提高165倍.文中分别利用二维和三维表面多次波预测算法对理论模拟的含表面多次波的三维地震数据进行多次波预测计算,对比分析结果表明,相比于二维算法,文中所述的基于波动方程的全三维表面多次波预测效果明显改善,其计算精度更高,辅以合理有效的自适应相减算法,可获得高精度的地震勘探资料表面多次波压制数据.     

基于波动方程预测和双曲Radon变换联合压制表面多次波

基于波动方程预测的表面多次波压制方法可处理复杂地下介质的地震资料,但计算成本较高.基于滤波的多次波压制方法计算效率较高,但其成功应用仅局限于一次波和多次波有明显时差差别的地震数据,对来自速度逆转等复杂介质数据则较难获得满意的压制效果.本文将波动方程预测的反馈迭代法和滤波法有效结合,采用GPU(图形处理器)和CPU协同并行加速计算粗略预测表面多次波,随后在双曲Radon域比较分析原始数据和预测的多次波,设计合理有效的Butterworth型自适应滤波器,滤出原始数据Radon域中的多次波能量,进行Radon反变换后,在时空域将多次波从原始数据中减去,得多次波压制结果.文中对理论模拟的单炮数据、复杂的SMAART模型以及实际地震数据进行了计算,结果表明,结合基于波动方程预测和双曲Radon变换的方法有效突破了两种方法各自的局限性,可高效高精度地压制复杂地下介质的表面多次波.     

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.     

基于稀疏反演的OBS数据多次波压制方法

自由表面多次波压制是海底地震仪（Ocean Bottom Seismometer,OBS）数据处理和成像中的难点,OBS数据多次波能量强,周期长,严重影响深层一次反射波的处理和成像.不同于常规拖缆观测系统,OBS数据站点一般相隔较远,仅仅利用检波点稀疏的波场信息难以压制OBS数据中的自由表面多次波.本文采用拖缆数据与OBS数据联合,利用稀疏反演估计（Estimation of Primaries and Multiples by Sparse Inversion,EPSI）方法,研究了OBS数据自由表面多次波压制理论,分析了OBS多次波产生的机理,详细推导了拖缆数据与OBS数据联合预测OBS多次波的EPSI方法基本原理.通过利用拖缆数据的信息,实现了OBS检波点稀疏数据多次波的压制问题.EPSI方法通过稀疏反演直接估计一次反射波,避免了SRME（Surface Related Multiple Elimination）方法中自适应相减对有效信号的损害,保真了一次反射有效信号,理论模拟OBS数据验证了方法的有效性.     

SRME多次波衰减方法在海洋地震资料中的应用

多次波问题一直是海洋地震资料处理中的难题,基于波动方程的多次波压制方法SRME是近20年来发展起来的一种有效的多次波衰减方法.该方法的特点是基于波动理论,利用地震数据本身来预测多次波,而不需要地下介质的任何信息,即这种方法可以应对地下任何复杂的情况,因此应用范围较广.本文通过SRME方法在海洋地震资料LH油田的应用实例证明了该方法的有效性--SRME多次波衰减方法对海底有关的多次波的衰减非常有效,特别是对近道多次波模拟准确,未损伤有效波,一定程度上提高了地震数据的分辨率.     

Closed-loop SRME based on 3D L1-norm sparse inversion

In many situations, the quality of seismic imaging is largely determined by a proper multiple attenuation as preprocessing step. Despite the widespread application of surface-related multiple elimination (SRME) and estimation of primaries by sparse inversion (EPSI) for the removal of multiples, there still exist some limitations in the process of prediction and subtraction (SRME) or inversion (EPSI), which make the efficiency of multiple attenuation less satisfactory. To solve these problems, a new fully data-driven method called closed-loop SRME was proposed, which combines the robustness of SRME and the multi-dimensional inversion strategy of EPSI. Due to the selection of inversion approach and constraint, primary estimation by closed-loop SRME may fall into a local optimum during the solving process, which lowers the accuracy of deep information and weakens the continuity of seismic events. To avoid these shortcomings, we first modified the solving method for closed-loop SRME to an L1 norm-based bi-convex optimization method, which stabilizes the solution. Meanwhile, in the L1 norm constraint-based optimization process, the 3D sparsifying transform, being a 2D Curvelet-1D wavelet transform, is brought in as a 3D sparse constraint. In the 3D sparsifying domain, the data become sparser, thus making the result of optimization more accurate, the information of seismic events more continuous and the resolution higher. Examples on both synthetic and field data demonstrate that the method proposed in this paper, compared with the traditional SRME and closed-loop SRME, have an excellent effect on primary estimation and suppress multiples effectively.     

Eliminating 3D pre‐stack migration artefacts by 6D filtering

State‐of‐the‐art 3D seismic acquisition geometries have poor sampling along at least one dimension. This results in coherent migration noise that always contaminates pre‐stack migrated data, including high‐fold surveys, if prior‐to‐migration interpolation was not applied. We present a method for effective noise suppression in migrated gathers, competing with data interpolation before pre‐stack migration. The proposed technique is based on a dip decomposition of common‐offset volumes and a semblance‐type measure computation via offset for all constant‐dip gathers. Thus the processing engages six dimensions: offset, inline, crossline, depth, inline dip, and crossline dip. To reduce computational costs, we apply a two‐pass (4D in each pass) noise suppression: inline processing and then crossline processing (or vice versa). Synthetic and real‐data examples verify that the technique preserves signal amplitudes, including amplitude‐versus‐offset dependence, and that faults are not smeared.     

Seeing below the basalt – offshore Faroes

Exploration in the basalt covered areas of the Faroes offshore has always suffered from poor seismic imaging below the basalt. Long offset 2D and 3D seismic data were acquired and a significant improvement in the seismic image below top basalt has been achieved. Deep towing of the source and receiver cables helped by extending the seismic bandwidth towards lower frequencies. Bubble‐tuned rather than conventional peak‐tuned source arrays gave little, if any, incremental benefit. The improvement in the imaging comes primarily from the approach to processing the data. High frequencies (dominantly noise) are filtered out of the data early in the processing to concentrate on the low frequency data. Careful multiple removal is important with several passes of demultiple being applied to the data using both Surface‐Related Multiple Elimination (SRME) and Radon techniques. Velocity analysis is performed as an iterative process taking into account the geological model. Reprocessing legacy 2D surveys, acquired with wide‐ranging parameters, using these processing techniques improved these datasets significantly, indicating that sub‐basalt imaging seems to be more sensitive to processing than to the choice of acquisition parameters.     

Evolution of deghosting process for single‐sensor streamer data from 2D to 3D

In marine acquisition, reflections of sound energy from the water–air interface result in ghosts in the seismic data, both in the source side and the receiver side. Ghosts limit the bandwidth of the useful signal and blur the final image. The process to separate the ghost and primary signals, called the deghosting process, can fill the ghost notch, broaden the frequency band, and help achieve high‐resolution images. Low‐signal‐to‐noise ratio near the notch frequencies and 3D effects are two challenges that the deghosting process has to face. In this paper, starting from an introduction to the deghosting process, we present and compare two strategies to solve the latter. The first is an adaptive mechanism that adjusts the deghosting operator to compensate for 3D effects or errors in source/receiver depth measurement. This method does not include explicitly the crossline slowness component and is not affected by the sparse sampling in the same direction. The second method is an inversion‐type approach that does include the crossline slowness component in the algorithm and handles the 3D effects explicitly. Both synthetic and field data examples in wide azimuth acquisition settings are shown to compare the two strategies. Both methods provide satisfactory results.     

混合域高分辨率抛物Radon变换及在衰减多次波中的应用

高分辨率Radon变换存在计算效率和分辨率不能兼得的困境.时间域算法可以获得很高的分辨率,但计算效率非常低;频率域算法具有良好计算效率,但分辨率不理想.为此发展了混合域高分辨率抛物Radon变换,即对频率域抛物Radon变换引入时变的稀疏权.本文给出了一种新的混合域高分辨率抛物Radon变换实现方法,并将该算法应用于叠前数据衰减多次波.文中给出了Radon变换和衰减多次波的流程.理论和实际数据算例表明本文方法既有较高的分辨率又有很高的计算效率.     

高分辨率Radon变换方法及其在地震信号处理中的应用

Radon变换方法在地震资料处理中广泛采用,在地震同相轴识别和估计方面具有良好效果．无论是倾斜叠加,还是广义Radon变换方法,一般采用最小二乘反演方法实现．目前,在提高反演算法的效率和分辨率方面仍值得研究．本文从倾斜叠加的定义出发,阐明Radon变换分辨率问题的来源和解决办法．采用最小二乘反演方法研究高分辨率抛物线Radon变换和双曲Radon变换时,给出稀疏约束预条件共轭梯度法求解的高分辨率Radon变换的实现方法,同阻尼最小二乘方法相比,分辨率和精度明显提高,文中给出了模型算例．根据有效波和多次波NMO后剩余时差不同,采用高分辨率抛物线和双曲Radon变换可以压制多次波,分别给出了方法原理,最后给出应用实例．研究表明,稀疏约束预条件共轭梯度法可以有效实现高分辨率Radon变换;数值算例表明,算法计算效率和精度较高,可以更好地实现多次波压制．     

Estimating primaries by sparse inversion of the 3D curvelet transform and the L1-norm constraint

In this paper, we built upon the estimating primaries by sparse inversion (EPSI) method. We use the 3D curvelet transform and modify the EPSI method to the sparse inversion of the biconvex optimization and L1-norm regularization, and use alternating optimization to directly estimate the primary reflection coefficients and source wavelet. The 3D curvelet transform is used as a sparseness constraint when inverting the primary reflection coefficients, which results in avoiding the prediction subtraction process in the surface-related multiples elimination (SRME) method. The proposed method not only reduces the damage to the effective waves but also improves the elimination of multiples. It is also a wave equationbased method for elimination of surface multiple reflections, which effectively removes surface multiples under complex submarine conditions.