ABSTRACT Random noise attenuation utilizing predictive filtering achieves great performance in denoising seismic data. Conventional predictive filtering methods are based on fixed filter operators and neglect the complexity of structures. In this way, the denoised data cannot meet the requirement of balancing the signal preservation and noise removal. In this study, we proposed a structural complexity‐guided predictive filtering method that utilizes an adapted filter operator to adjust the changes of structural complexity. The proposed structural complexity‐guided predictive filtering mainly consists of two stages. A slope field information is acquired according to plane‐wave destruction to assess the structural complexity. In addition, an adaptive filter operator is obtained to denoise the seismic data according to the adaptive factor. Both synthetic data and real seismic profiles are employed to examine the denoising capacity and flexibility of the refined predictive filtering using adaptive lengths. The analysis of the predicted results shows that adaptive predictive filtering is powerful and has the ability to eliminate random noises with negligible distortions.

中文翻译：

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结构复杂度引导的预测过滤

摘要 利用预测滤波的随机噪声衰减在去噪地震数据方面取得了很好的性能。传统的预测滤波方法基于固定的滤波器算子，忽略了结构的复杂性。这样，去噪后的数据就不能满足平衡信号保持和去噪的要求。在这项研究中，我们提出了一种结构复杂度引导的预测滤波方法，该方法利用自适应滤波器算子来调整结构复杂度的变化。所提出的结构复杂度引导的预测滤波主要包括两个阶段。根据平面波破坏获取斜坡场信息以评估结构复杂性。此外，获得自适应滤波器算子以根据自适应因子对地震数据进行去噪。使用合成数据和真实地震剖面来检查使用自适应长度的精细预测滤波的去噪能力和灵活性。对预测结果的分析表明，自适应预测滤波功能强大，能够消除失真可忽略的随机噪声。