多源地震混合采集波场高保真分离方法研究
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摘要
地震采集往往是在经济效益和采集质量之间寻求平衡，传统的地震采集在相邻震源之间设置足够的激发间隔，以避免接收记录相互混合，其代价是地震测量的参数（炮距、道距、方位角、偏移距分布等）都不一定能够达到最佳。与地震勘探作业链的其它环节相比，地震采集方法技术特别是采集思路的变化是缓慢的。多源同时激发采集（simultaneous acquisition）是地震数据记录方面的重大变革，多个不同空间位置的震源以一定编码方式同时激发，获得时间空间波场相互干涉的混合炮记录。从时间域的间断激发、逐炮接收，到同时激发、连续接收，这是地震采集思路的重要突破，极大加快了数据记录速度，进而提升采集效率和成像质量。
多源同时激发采集的关键在于炮分离，即将多震源混合激发的在时域中相互重叠的超炮道集分离成为相当于传统采集得到的单炮记录，才能进行常规的数据处理和解释。从数学上看，炮分离可以看做形如Ax=b的线性反问题，当采用可控震源时，不同位置处的震源需按照不同的相位多次扫描，当扫描次数高于或等于震源数量时，能够通过最小平方法求解该线性方程组得到分离炮记录；但当采用脉冲型震源时，其震源相位无法控制，而由于采集成本所限，无法在同一位置多次激发，此时该线性方程组转变为欠定方程组，简单的最小平方算法无法有效求解，只能得到伪分离记录，不能实现炮分离。我们针对二维数据矩阵观测系统，通过互易定理将同时激发采集记录的共炮点道集和共检波点道集联合，降阶该欠定方程组。在具体实现过程中，首先通过稀疏反褶积从混合记录中得到稀疏的反射系数，然后通过基追踪稀疏反演算法求解单炮记录的反射系数，最终褶积得到分离结果。模型试算以及实际资料处理都取得了较好效果。
多源混合激发采集（blended acquisition）是在同时激发采集的基础上进一步发展起来的。与同时激发采集技术不同的是，为不同的震源设置一定范围内的随机激发时间，而不是在同一时间同时激发。混合激发采集记录的伪分离结果与同时激发采集所得结果不同的是，混合噪声仅在共炮点道集连续分布，而在共炮点道集以外的其他时域均呈现为随机分布，因此去噪滤波成为了一种思路。在高混合度的混合数据中，混合噪声的能量往往数倍于有效信号，炮分离难度倍增。我们研究提出了一种多时域组合迭代去噪的炮分离技术：通过运用多级中值滤波与Curvelet阈值迭代去噪算法，在不同时域根据混合噪声特性采用相应的去噪手段，并设计迭代算法优化炮分离结果。实际资料处理结果证明：将本方法应用于高混合度的混合数据，无论是分离质量还是计算效率，都有明显提升。
多源同时激发采集技术带来了地震数据采集方法的一场变革，必将使得许多地震数据处理方法从中受益。我们将这种采集方法应用到虚拟震源法中，将常规采集记录构建的虚源道集与多震源同时激发采集后炮分离记录所构建的道集对比：由于虚源构建质量与震源间距密切相关，常规采集往往需要兼顾质量和效益，因此震源密度的不足严重影响所构建道集的质量，而多震源同时激发采集技术能够很好的改善这一点，从而得到高质量的虚源道集。
Seismic acquisition is a trade-off between economic and quality. Conventional acquisitionsurveys are designed such that the time intervals between successive shots are sufficientlylarge to avoid the interference between different source responses. However in thissituation both the image quality and measure parameters, such as azimuths and samplingdensity may not be achieve the best. Unfortunately the improvement of seismic acquisitionmethod is fall behind other technologies in seismic processing. Recently a great change ofmindset in seismic acquisition has occurred which is known as blended acquisition orsimultaneous acquisition, where different sources at different locations are shot in anoverlapping fashion. Both which significantly improved the acquisition efficiency andpotentially image quality can be significantly improved.
Deblending is the procedure of recovering data as if they were acquired in theconventional survey. From the mathematics point of view, deblending can be regarded assolving an underdetermined equation such as Ax=b, yet a simple least-squares procedurecannot remove the blending noise. Focus on the2D regularized acquisition system, wecombine the blended data in common source domain and common receiver domain underthe umbrella of reciprocity theorem, In this paper we focus on the blended acquisitionsystem instead of the inversion algorithm for the purpose of solving this ill posed inversionproblem. According to our derivation in the regularized2D acquisition system, the blendeddata in common receiver domain can be connected with that in common source domain,which making this inversion problem the above mentioned equation less moreunderdetermined. When applied to a numerically blended dataset, reflection coefficients ofsingle source gather is calculated using a simple spgL1norm basic pursuit sparse inversionalgorithm at first, and then separation results are acquired via convolution. Field data testshows that our method can get high quality separation results, which verified our theoryand derivation.
While dealing with blended seismic acquisition data, a simple least-squares procedure isonly able to get pseudodeblend results, where the blending noises cannot be removed. Inhigh blending factor data, the blending noises are usually several times higher than theuseful signals, which multiply the difficulty of source separation. Fortunately in pseudodeblend records, these noises are only coherent in the common source domain, butincoherent in other domain. For this character, multilevel median filter and Curveletthreshold iteration denoising are combined used in this paper, and a new source separationmethod based on the iterative denoising in different domain is introduced: Focus on thedifferent character of blendiong noises in different domain, corresponding denoisingmethod is utilized, and an iteration method is designed for the optimization. While dealingwith numerical blended real dataset, ideal results could be produced after only a fewiterations, which verify that our method can largely improve the separation quality andcalculation efficiency.
Multi-source simultaneous acquisition has brought a revolution of seismic data acquisitionmethods, from which many seismic data processing methods will benefit. This acquisitionmethod is applied to the virtual source method. Compare the virtual source gathersconstructed by the conventional acquisition method and the simultaneous method we canfind that: the virtual source records builded from the separation results of blended data aremuch more closed to the synthetic ones.
多源同时激发采集的关键在于炮分离，即将多震源混合激发的在时域中相互重叠的超炮道集分离成为相当于传统采集得到的单炮记录，才能进行常规的数据处理和解释。从数学上看，炮分离可以看做形如Ax=b的线性反问题，当采用可控震源时，不同位置处的震源需按照不同的相位多次扫描，当扫描次数高于或等于震源数量时，能够通过最小平方法求解该线性方程组得到分离炮记录；但当采用脉冲型震源时，其震源相位无法控制，而由于采集成本所限，无法在同一位置多次激发，此时该线性方程组转变为欠定方程组，简单的最小平方算法无法有效求解，只能得到伪分离记录，不能实现炮分离。我们针对二维数据矩阵观测系统，通过互易定理将同时激发采集记录的共炮点道集和共检波点道集联合，降阶该欠定方程组。在具体实现过程中，首先通过稀疏反褶积从混合记录中得到稀疏的反射系数，然后通过基追踪稀疏反演算法求解单炮记录的反射系数，最终褶积得到分离结果。模型试算以及实际资料处理都取得了较好效果。
多源混合激发采集（blended acquisition）是在同时激发采集的基础上进一步发展起来的。与同时激发采集技术不同的是，为不同的震源设置一定范围内的随机激发时间，而不是在同一时间同时激发。混合激发采集记录的伪分离结果与同时激发采集所得结果不同的是，混合噪声仅在共炮点道集连续分布，而在共炮点道集以外的其他时域均呈现为随机分布，因此去噪滤波成为了一种思路。在高混合度的混合数据中，混合噪声的能量往往数倍于有效信号，炮分离难度倍增。我们研究提出了一种多时域组合迭代去噪的炮分离技术：通过运用多级中值滤波与Curvelet阈值迭代去噪算法，在不同时域根据混合噪声特性采用相应的去噪手段，并设计迭代算法优化炮分离结果。实际资料处理结果证明：将本方法应用于高混合度的混合数据，无论是分离质量还是计算效率，都有明显提升。
多源同时激发采集技术带来了地震数据采集方法的一场变革，必将使得许多地震数据处理方法从中受益。我们将这种采集方法应用到虚拟震源法中，将常规采集记录构建的虚源道集与多震源同时激发采集后炮分离记录所构建的道集对比：由于虚源构建质量与震源间距密切相关，常规采集往往需要兼顾质量和效益，因此震源密度的不足严重影响所构建道集的质量，而多震源同时激发采集技术能够很好的改善这一点，从而得到高质量的虚源道集。
Seismic acquisition is a trade-off between economic and quality. Conventional acquisitionsurveys are designed such that the time intervals between successive shots are sufficientlylarge to avoid the interference between different source responses. However in thissituation both the image quality and measure parameters, such as azimuths and samplingdensity may not be achieve the best. Unfortunately the improvement of seismic acquisitionmethod is fall behind other technologies in seismic processing. Recently a great change ofmindset in seismic acquisition has occurred which is known as blended acquisition orsimultaneous acquisition, where different sources at different locations are shot in anoverlapping fashion. Both which significantly improved the acquisition efficiency andpotentially image quality can be significantly improved.
Deblending is the procedure of recovering data as if they were acquired in theconventional survey. From the mathematics point of view, deblending can be regarded assolving an underdetermined equation such as Ax=b, yet a simple least-squares procedurecannot remove the blending noise. Focus on the2D regularized acquisition system, wecombine the blended data in common source domain and common receiver domain underthe umbrella of reciprocity theorem, In this paper we focus on the blended acquisitionsystem instead of the inversion algorithm for the purpose of solving this ill posed inversionproblem. According to our derivation in the regularized2D acquisition system, the blendeddata in common receiver domain can be connected with that in common source domain,which making this inversion problem the above mentioned equation less moreunderdetermined. When applied to a numerically blended dataset, reflection coefficients ofsingle source gather is calculated using a simple spgL1norm basic pursuit sparse inversionalgorithm at first, and then separation results are acquired via convolution. Field data testshows that our method can get high quality separation results, which verified our theoryand derivation.
While dealing with blended seismic acquisition data, a simple least-squares procedure isonly able to get pseudodeblend results, where the blending noises cannot be removed. Inhigh blending factor data, the blending noises are usually several times higher than theuseful signals, which multiply the difficulty of source separation. Fortunately in pseudodeblend records, these noises are only coherent in the common source domain, butincoherent in other domain. For this character, multilevel median filter and Curveletthreshold iteration denoising are combined used in this paper, and a new source separationmethod based on the iterative denoising in different domain is introduced: Focus on thedifferent character of blendiong noises in different domain, corresponding denoisingmethod is utilized, and an iteration method is designed for the optimization. While dealingwith numerical blended real dataset, ideal results could be produced after only a fewiterations, which verify that our method can largely improve the separation quality andcalculation efficiency.
Multi-source simultaneous acquisition has brought a revolution of seismic data acquisitionmethods, from which many seismic data processing methods will benefit. This acquisitionmethod is applied to the virtual source method. Compare the virtual source gathersconstructed by the conventional acquisition method and the simultaneous method we canfind that: the virtual source records builded from the separation results of blended data aremuch more closed to the synthetic ones.
引文
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