3D structural complexity-guided predictive filtering: a comparison between different non-stationary strategies
3D structural complexity-guided predictive filtering: a comparison between different non-stationary strategies
复制标题
3D 结构复杂性引导的预测过滤：不同非平稳策略之间的比较
DOI：
10.1109/tgrs.2022.3172940
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发表时间：
2022
影响因子：
8.2
通讯作者：
Yangkang Chen
中科院分区：
文献类型：
--
作者：
Chao Fu;Zhifei Gong;Lei Chen;Senlin Yang;Lingli Zhang;Yangkang Chen
预测滤波方法由于其稳定性和有效性而被广泛应用于工业中去除随机噪声。传统的预测滤波方法在空间方向上使用固定的自回归阶数（滤波因子）。然而，去噪空间变化的地震数据迄今为止是无效的。在这项研究中，非平稳地震数据的变化的影响是克服使用局部窗口或非平稳去噪方法。我们提出了一种非平稳预测滤波方法，在该方法中，自回归模型的构建使用空间变化的滤波因子。首先，我们评估的结构复杂性的全球数据的基础上，使用平面波破坏的局部窗口选择。然后，根据地震数据的结构复杂性，提出了自适应滤波因子。最后，求解含有自适应滤波因子的自回归模型。我们使用三个质量指标来评估去噪性能：信噪比（S/N），峰值信噪比（PSNR）和局部相似性（LS）。所提出的方法作用于合成和现场数据，包括叠前和叠后地震数据。实验结果表明，与现有的非平稳滤波方法相比，结构复杂度引导的预测滤波能够有效地抑制随机噪声，减少信号泄漏。
Predictive filtering methods are widely used in industry to remove random noise, owing to their stability and efficiency. Traditional predictive filtering methods use a fixed autoregressive order (filtering factor) in the spatial direction. However, denoising spatially varying seismic data has thus far been ineffective. In this study, the effect of non-stationary seismic data variations is overcome using local windowing or non-stationary denoising methods. We propose a non-stationary predictive filtering method in which an autoregressive model is constructed using spatially varying filtering factors. First, we evaluate the structural complexity of the global data based on a local window selection using plane-wave destruction. Then, adaptive filtering factors are proposed depending on the structural complexity of the seismic data. Finally, the autoregressive model containing the adaptive filtering factors is solved. We use three quality measures to evaluate the denoising performance: the signal-to-noise ratio (S/N), the peak signal-to-noise ratio (PSNR), and local similarity (LS). The proposed method acts on both synthetic and field data, including both pre- and post-stack seismic data. Compared with prior non-stationary filtering methods, experimental results demonstrate that structural complexity-guided predictive filtering enables efficient random noise attenuation and reduces signal leakage.