Automatic noise attenuation based on clustering and empirical wavelet transform
Automatic noise attenuation based on clustering and empirical wavelet transform
复制标题
基于聚类和经验小波变换的自动噪声衰减
DOI：
10.1016/j.jappgeo.2018.09.025
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发表时间：
2018-12-01
影响因子：
2
通讯作者：
Song, Hui
中科院分区：
文献类型：
--
作者：
Chen, Wei;Song, Hui
地震资料中的强噪声严重影响了地震资料处理和成像的许多环节。传统方法依赖于人工调整输入参数，而本文提出的方法是一种自动噪声衰减算法，可以对大规模叠前地震数据进行快速预处理。该算法首先根据非平稳地震数据的频率成分，利用经验小波变换（EWT）自适应地将非平稳地震数据分解为多个经验分量。然后，选择第一分量来代表有用信号。为了处理前一步粗估计信号中的残余噪声，我们提出了一种基于聚类的阈值化方法。最主要的信号通过一个简单的聚类步骤检测和其他组件阻尼与自适应百分位阈值。这两个步骤是一个新的自动算法，以消除地震资料高保真。我们通过合成和现场数据的例子证明了所提出的方法的性能。(C)2018 Elsevier B.V.版权所有。
Strong noise in seismic data seriously affects many steps in seismic data processing and imaging. While most traditional methods depend on carefully tuned input parameters by human, the method proposed in this paper is an automatic noise attenuation algorithm to facilitate a fast preprocessing of massive prestack seismic data. In the proposed algorithm, the non-stationary seismic data is first adaptively decomposed into several empirical components adaptively via empirical wavelet transform (EWT) according the frequency contents in the data. Then, the first component is selected to stand for the useful signals. To deal with the residual noise in the roughly estimated signal from the previous step, we propose a clustering based thresholding method. The most dominant signals are detected via a simple clustering step and other components are damped with an adaptive percentile threshold. The two steps refer to a new automatic algorithm to denoise the seismic data with high fidelity. We demonstrate the performance of the proposed method via both synthetic and field data examples. (C) 2018 Elsevier B.V. All rights reserved.