3D surface-related multiple elimination based on sparse inversion
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摘要: 三维表面多次波压制是海洋地震资料预处理中的重要研究课题,基于波动理论的三维表面多次波压制方法(3D SRME)是数据驱动的方法,理论上来说,可有效压制复杂构造地震数据表面多次波.但该方法因对原始地震数据采集要求高而很难在实际资料处理中广泛应用.本文基于贡献道集的概念,将稀疏反演方法引入到表面多次波压制中,应用稀疏反演代替横测线积分求和,无需对横测线进行大规模重建,进而完成三维表面多次波预测,这样可有效解决实际三维地震数据横测线方向稀疏的问题.基于纵测线多次波积分道集为抛物线的假设,为保证预测后三维表面多次波和全三维数据预测的多次波在运动学和动力学特征上基本一致,文中对预测数据实施基于稳相原理的相位校正.理论模型和实际数据的测试结果表明,本文基于稀疏反演三维表面多次波压制方法可在横测线稀疏的情况下,有效压制三维复杂介质地震资料中的表面多次波,从而更好地提高海洋地震资料的信噪比,为高分辨率地震成像提供可靠的预处理数据保障.Abstract: Three dimensional surface-related multiple elimination (SRME) is one of the important topics in the processing of seismic data from marine exploration,theoretically, the data-driven SRME based on wave-equation, can suppress all surface-related multiples from complex structure, both in 2D and 3D sense. But actually, because of the high requirement for seismic data acquisition, it is usually difficult to apply 3D SRME for field data demultiple processing.3D multiple suppression approach based on sparse inversion is analyzed.We classify multiple suppression method into two categories, filter and SRME method respectively. For the seismic data from complex geological structure, filter approach doesn't work well. However, the data-driven SRME approach based on wave equation, can suppress multiple better, which has no requirement for velocity information.For SRME, the full wavefield data requirement is an important disadvantage, which cannot be meet for almost all marine field data. Therefore the data reconstruction is necessary for traditional multiple suppression using SRME.For current marine acquisition geometries, the data is densely sampled in the inline direction, but very sparsely in the crossline direction, we introduce contribution gather concept, and calculate sparsely sampled crossline multiple contribution by means of sparse inversion algorithm.Therefore, the data reconstruction is unnecessary before demultiple,comparing with traditional 3D SRME algorithm, which decrease storage cost greatly. Based on the assumption that the crossline time-distance curves are hyperbolic or parabolic, i.e., after integrated along inline direction after finished the first step processing of contribution gathers, we applied the phase correction algorithm which based on the principle of stationary phase approximation, predicted multiples by the proposed method and result by the full 3D SRME method are basically the same on kinematics and dynamics characteristics. Multiple prediction and adaptive subtraction are two crucial steps for 3D multiple suppression using SRME. We simulate complex model data to test the proposed 3D multiple prediction algorithm, and 10 thousands shot records are modelled, the shot interval and trace interval are both 25 m. The result comparison show the 3D multiple prediction approach can predict the multiple's amplitude and phase correctly, and also, the subtraction result is superior than 2D algorithm. The horizontal four-layered media is also designed to test the sparse inversion 3D multiple suppression algorithm, there are 3136 shot records in total, the trace and shot intervals are both 25 m, the line interval is 75 m. The single trace and common-offset result show that the proposed approach can predict the multiple's amplitude and traveltime correctly, and they are very close to the full data circumstance. The test on field data from some area in China show that the proposed sparse inversion method is applicable and effective, where the trace interval is 12.5 m, shot interval is 50 m, and the line interval is 100 m. After summation along inline direction, the partial integration data is transformed to Radon domain using apex-shifted Radon approach based on the assumption of hyperbolic or parabolic events, stacked the Radon imaging, and also applied the phase correction to the sparse inversion solution, the predicted multiple is acquired.After theoretical investigation and data tests, we have the following conclusions, (1) the proposed method is suitable for simple and complex 3D model data, (2) for real seismic data, the inline direction reconstruction is needed, (3) because only forward Radon transform, not inverse Radon transform is used,phase correction is demanded, and (4) the algorithm do not rely on the assumption of full data using sparse inversion approach, and also, widen the application extent of 3D multiple suppression.The result show that the proposed 3D multiple suppression algorithm can improve the S/N ratio during the course of preprocessing, and provide the high quality data for the subsequent high resolution imaging.
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Key words:
- Sparse inversion /
- Surface-related multiple /
- Contribution gather /
- Multiple prediction
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