Seismic random noise attenuation and signal-preserving by multiple directional time-frequency peak filtering
Seismic random noise attenuation and signal-preserving by multiple directional time-frequency peak filtering
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DOI：
10.1016/j.crte.2014.10.003
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发表时间：
2015
影响因子：
1.4
通讯作者：
Chao Zhang;Yue Li;Hongbo Lin;Baojun Yang
中科院分区：
文献类型：
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作者：
Chao Zhang;Yue Li;Hongbo Lin;Baojun Yang
时频峰值滤波（TFPF）是地震随机噪声衰减的有效方法。信号的线性度对TFPF方法的精度有显着影响。待滤波信号的线性度越高，去噪效果越好。考虑到这一点，并考虑到反射事件的横向相干性，我们沿着反射事件进行TFPF，以提高线性程度并增强这些事件的连续性。实现这个想法的关键因素是找到反映事件的踪迹。然而，在复杂的现场地震数据中，事件的踪迹很难获得。在本文中，我们提出了一种多向 TFPF（MD-TFPF），其中对地震数据的某些方向分量进行滤波。这些分量是通过定向滤波器组获得的。在每个方向分量中，我们沿着这些分解的反射事件（事件的局部方向）而不是通道方向进行 TFPF。最终结果是将地震数据所有分解方向的滤波结果相加得到。这样就实现了沿着反射事件进行过滤，而无需准确找到方向。该方法的有效性在合成地震数据和现场地震数据上进行了测试。实验结果表明，与传统的TFPF、曲波去噪方法和F-X反卷积方法相比，MD-TFPF能够更有效地消除随机噪声，增强反射事件的连续性，并且保存效果更好。
Time-frequency peak filtering (TFPF) is an effective method for seismic random noise attenuation. The linearity of the signal has a significant influence on the accuracy of the TFPF method. The higher the linearity of the signal to be filtered is, the better the denoising result is. With this in mind, and taking the lateral coherence of reflected events into account, we do TFPF along the reflected events to improve the degree of linearity and enhance the continuity of these events. The key factor to realize this idea is to find the traces of the reflected events. However, the traces of the events are too hard to obtain in the complicated field seismic data. In this paper, we propose a Multiple Directional TFPF (MD–TFPF), in which the filtering is performed in certain direction components of the seismic data. These components are obtained by a directional filter bank. In each direction component, we do TFPF along these decomposed reflected events (the local direction of the events) instead of the channel direction. The final result is achieved by adding up the filtering results of all decomposition directions of seismic data. In this way, filtering along the reflected events is implemented without accurately finding the directions. The effectiveness of the proposed method is tested on synthetic and field seismic data. The experimental results demonstrate that MD–TFPF can more effectively eliminate random noise and enhance the continuity of the reflected events with better preservation than the conventional TFPF, curvelet denoising method and F–X deconvolution method.