Random Noise Attenuation by a Hybrid Approach Using f-x Empirical Mode Decomposition and Multichannel Singular Spectrum

2014 ◽  
Author(s):  
Y. Chen ◽  
T. Liu ◽  
Y. Zhang
Keyword(s):  
Empirical Mode Decomposition ◽  
Random Noise ◽  
Hybrid Approach ◽  
Noise Attenuation ◽  
Random Noise Attenuation ◽  
Singular Spectrum ◽  
Mode Decomposition

Random noise attenuation by a selective hybrid approach using f-x empirical mode decomposition

2014 ◽  
Author(s):  
Yangkang Chen ◽  
Shuwei Gan ◽  
Tingting Liu ◽  
Jiang Yuan ◽  
Yizhuo Zhang ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Random Noise ◽  
Hybrid Approach ◽  
Noise Attenuation ◽  
Random Noise Attenuation ◽  
Mode Decomposition

Improved random noise attenuation using f−x empirical mode decomposition and local similarity

2016 ◽  
Vol 13 (1) ◽  
pp. 127-134 ◽  
Author(s):  
Shu-Wei Gan ◽  
Shou-Dong Wang ◽  
Yang-Kang Chen ◽  
Jiang-Long Chen ◽  
Wei Zhong ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Random Noise ◽  
Local Similarity ◽  
Noise Attenuation ◽  
Random Noise Attenuation ◽  
Mode Decomposition

Random noise attenuation by a selective hybrid approach usingf-xempirical mode decomposition

2014 ◽  
Vol 12 (1) ◽  
pp. 12-25 ◽  
Author(s):  
Yangkang Chen ◽  
Shuwei Gan ◽  
Tingting Liu ◽  
Jiang Yuan ◽  
Yizhuo Zhang ◽  
...  
Keyword(s):  
Random Noise ◽  
Hybrid Approach ◽  
Noise Attenuation ◽  
Random Noise Attenuation ◽  
Mode Decomposition

Random noise attenuation by f-x spatial projection-based complex empirical mode decomposition predictive filtering

2015 ◽  
Vol 12 (1) ◽  
pp. 47-54
Author(s):  
Yan-Yan Ma ◽  
Guo-Fa Li ◽  
Yao-Jun Wang ◽  
Hui Zhou ◽  
Bao-Jiang Zhang
Keyword(s):  
Empirical Mode Decomposition ◽  
Random Noise ◽  
Noise Attenuation ◽  
Random Noise Attenuation ◽  
Mode Decomposition ◽  
Predictive Filtering

Random noise attenuation by f-x empirical-mode decomposition predictive filtering

Geophysics ◽  
2014 ◽  
Vol 79 (3) ◽  
pp. V81-V91 ◽  
Author(s):  
Yangkang Chen ◽  
Jitao Ma
Keyword(s):  
Empirical Mode Decomposition ◽  
Seismic Data ◽  
Random Noise ◽  
Prediction Errors ◽  
Noise Attenuation ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Random Noise Attenuation ◽  
Mode Decomposition ◽  
Predictive Filtering

Random noise attenuation always played an important role in seismic data processing. One of the most widely used methods for suppressing random noise was [Formula: see text] predictive filtering. When the subsurface structure becomes complex, this method suffered from higher prediction errors owing to the large number of different dip components that need to be predicted. We developed a novel denoising method termed [Formula: see text] empirical-mode decomposition (EMD) predictive filtering. This new scheme solved the problem that makes [Formula: see text] EMD ineffective with complex seismic data. Also, by making the prediction more precise, the new scheme removed the limitation of conventional [Formula: see text] predictive filtering when dealing with multidip seismic profiles. In this new method, we first applied EMD to each frequency slice in the [Formula: see text] domain and obtained several intrinsic mode functions (IMFs). Then, an autoregressive model was applied to the sum of the first few IMFs, which contained the high-dip-angle components, to predict the useful steeper events. Finally, the predicted events were added to the sum of the remaining IMFs. This process improved the prediction precision by using an EMD-based dip filter to reduce the dip components before [Formula: see text] predictive filtering. Synthetic and real data sets demonstrated the performance of our proposed method in preserving more useful energy.

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