A simple method inspired by empirical mode decomposition for denoising seismic data
A simple method inspired by empirical mode decomposition for denoising seismic data
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DOI：
10.1190/geo2015-0566.1
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发表时间：
2016-11-01
期刊：
影响因子：
3.3
通讯作者：
Velis, Danilo R.
中科院分区：
文献类型：
--
作者：
Gomez, Julian L.;Velis, Danilo R.
受数据驱动的经验模态分解（EMD）算法的启发，我们开发了一种新的简单的地震数据去噪方法。该方法既可以作为逐道处理，也可以应用于 f-x 域，取代了三次插值方案的使用，三次插值方案需要通过窗口平均来计算信号和残差的平均包络。由此产生的策略本身并不被视为 EMD，而是基于 EMD 的算法的用户友好版本，它允许我们在很短的时间内获得与标准 EMD 实现相同水平的噪声消除。此外，所提出的方法需要更少的用户干预，并且可以在几分钟内轻松处理数百万条轨迹，而不是标准 PC 上传统的基于 EMD 的技术所需的数小时。我们在计算成本和信号保存方面将新方法与标准 EMD 方法的性能进行了比较，并将其应用于包含随机、不稳定和相干噪声的合成数据和现场（微震和叠后）数据的降噪。分析了横向连续性增强的相应 f-x EMD 实现，并将其与经典的 f-x 反卷积进行比较，以测试该方法。
We developed a new and simple method for denoising seismic data, which was inspired by data-driven empirical mode decomposition (EMD) algorithms. The method, which can be applied either as a trace-by-trace process or in the f-x domain, replaces the use of the cubic interpolation scheme, which is required to calculate the mean envelopes of the signal and the residues, by window averaging. The resulting strategy is not viewed as an EMD per se, but a user-friendly version of EMD-based algorithms that permits us to attain, in a fraction of the time, the same level of noise cancellation as standard EMD implementations. Furthermore, the proposed method requires less user intervention and easily processes millions of traces in minutes rather than in hours as required by conventional EMD-based techniques on a standard PC. We compared the performance of the new method against standard EMD methods in terms of computational cost and signal preservation and applied them to denoise synthetic and field (microseismic and poststack) data containing random, erratic, and coherent noise. The corresponding f-x EMDs implementations for lateral continuity enhancement were analyzed and compared against the classical f-x deconvolution to test the method.