论文标题
自适应过滤器的新角色在基于Marchenko方程的方法中用于衰减内部倍数
A new role for adaptive filters in Marchenko equation-based methods for the attenuation of internal multiples
论文作者
论文摘要
在过去几年中，我们已经看到基于Marchenko方程的内部多重衰减方法的许多发展。从需要平滑速度模型的基于波浪方程的方法开始，现在有基于Marchenko方程的方法不需要任何模型信息或用户输入。原则上，这些方法准确地预测了内部倍数。因此，自适应滤波器的作用已改变了这些方法。与其需要积极的自适应过滤器来补偿内部多重预测不准确，不需要保守的自适应滤波器来补偿由于不完善的获取和预处理输入数据而导致的内部多重预测中的较小振幅和/或相误差。我们认为，在将Marchenko多重消除方法（MME）方法应用于2D线数据线数据时，可以使用保守的自适应过滤器来改善内部倍数的衰减。此外，我们建议可以将自适应过滤器用作反馈机制，以改善输入数据的预处理。
We have seen many developments in Marchenko equation-based methods for internal multiple attenuation in the past years. Starting from a wave-equation based method that required a smooth velocity model, there are now Marchenko equation-based methods that do not require any model information or user-input. In principle, these methods accurately predict internal multiples. Therefore, the role of the adaptive filter has changed for these methods. Rather than needing an aggressive adaptive filter to compensate for inaccurate internal multiple predictions, only a conservative adaptive filter is needed to compensate for minor amplitude and/or phase errors in the internal multiple predictions caused by imperfect acquisition and preprocessing of the input data. We demonstate that a conservative adaptive filter can be used to improve the attenuation of internal multiples when applying a Marchenko multiple elimination (MME) method to a 2D line of streamer data. In addition, we suggest that an adaptive filter can be used as a feedback mechanism to improve the preprocessing of the input data.
