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 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

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.

scholarly journals Seismic Random Noise Attenuation Method Based on Variational Mode Decomposition and Correlation Coefficients

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 280 ◽  
Author(s):  
Yaping Huang ◽  
Hanyong Bao ◽  
Xuemei Qi
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Correlation Coefficients ◽  
Ensemble Empirical Mode Decomposition ◽  
Noise Attenuation ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Random Noise Attenuation ◽  
Mode Decomposition

Seismic data is easily affected by random noise during field data acquisition. Therefore, random noise attenuation plays an important role in seismic data processing and interpretation. According to decomposition characteristics of seismic signals by using variational mode decomposition (VMD) and the constraint conditions of correlation coefficients, this paper puts forward a method for random noise attenuation in seismic data, which is called variational mode decomposition correlation coefficients VMDC. Firstly, the original signals were decomposed into intrinsic mode functions (IMFs) with different characteristics by VMD. Then, the correlation coefficients between each IMF and the original signal were calculated. Next, based on the differences among correlation coefficients of effective signals and random noise as well as the original signals, the corresponding treatment was carried out, and the effective signals were reconstructed. Finally, the random noise attenuation was realized. After adding random noise to simple sine signals and the synthetic seismic record, the improved complementary ensemble empirical mode decomposition (ICEEMD) and VMDC were used for testing. The testing results indicate that the proposed VMDC has better random noise attenuation effects. It was also used in real-world seismic data noise attenuation. The results also show that it could effectively improve the signal-to-noise ratio (SNR) of seismic data and could provide high-quality basic data for further interpretation of seismic data.

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
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