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Enhanced data reconstruction for true-azimuth 3D SRME

机译：真正方位3D SRME的增强数据重建

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摘要

In current production processing, the most widely used data reconstruction method is based on finding a best fitting trace from the acquired traces by minimizing midpoint, offset, and azimuth differences, and then applying a differential normal- moveout (NMO) correction for the residual difference in offset. Using two controlled, synthetic examples, it is demonstrated that a differential NMO correction alone, although very robust and efficient, can lead to phase and amplitude errors after 3D multiple prediction. We pro-pose to modify the current best practice by extending the corrections to be applied to the best fitting trace, by accounting for dip-dependent azimuth, midpoint, and offset variations dur-ing data reconstruction. The method is successfully demonstrated on both synthetic and field data. The method is shown to work very well for primaries, multiples, and diffracted events and gives significant uplift on the removal of complex 3D multiples on a deep water marine dataset.
机译：在当前的生产过程中，最广泛使用的数据重建方法是基于以下方法：从采集的轨迹中找到最合适的轨迹，方法是将中点，偏移和方位角差减至最小，然后对剩余差值应用差分法向偏移（NMO）校正偏移量。使用两个受控的合成示例，证明了单独的差分NMO校正尽管非常鲁棒和有效，但在3D多重预测后可能导致相位和幅度误差。我们建议通过考虑在数据重建过程中倾角相关的方位角，中点和偏移变化，通过扩展适用于最佳拟合轨迹的校正来修改当前的最佳实践。该方法已在合成数据和现场数据上得到了成功证明。该方法对于原始事件，多次波和绕射事件显示出很好的效果，并且在去除深水海洋数据集上复杂的3D多次波方面具有显着提升。

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