Seismic Random Noise Attenuation Using Optimal Empirical Wavelet Transform With a New Wavelet Thresholding Technique

The most vital challenge in seismic signal processing is the attenuation of random noise in seismic data. Many attenuation methods are formulated to mitigate the random noise but fail to retain high accuracy. In this article, a hybrid methodology based on optimal empirical wavelet transform (OEWT),...

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Bibliographic Details
Published in:IEEE sensors journal 2024-01, Vol.24 (1), p.596-606
Main Authors: Geetha, K., Hota, Malaya Kumar
Format: Article
Language:English
Subjects:
Algorithms
Attenuation
Decomposition
Discrete Wavelet Transform
Discrete wavelet transforms
Energy kurtosis mean filtering (EKMF)
Kurtosis
Mathematical analysis
mountain gazelle optimizer (MGO)
new wavelet thresholding (NWT) technique
Noise measurement
Noise reduction
optimal empirical wavelet transform (OEWT)
Optimization
Random noise
Segmentation
seismic signal
Sensors
Signal processing
Time-frequency analysis
Wave attenuation
Waveforms
Wavelet transforms
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Summary:The most vital challenge in seismic signal processing is the attenuation of random noise in seismic data. Many attenuation methods are formulated to mitigate the random noise but fail to retain high accuracy. In this article, a hybrid methodology based on optimal empirical wavelet transform (OEWT), energy kurtosis mean filtering (EKMF), and new wavelet thresholding (NWT) technique, namely, OEWT-EKMF-NWT, is proposed to suppress the random noise in the seismic signals. In the proposed OEWT-EKMF-NWT method, the OEWT decomposes the seismic signal into several intrinsic mode functions (IMFs) using the segmentation method. The mountain gazelle optimizer (MGO) algorithm with correlation waveform index (CWI) as an objective function is used here to choose the segmentation method optimally. Then, the EKMF is applied to select relevant optimal IMFs, which partially suppress the random noise. Furthermore, an NWT is proposed based on the discrete wavelet transform (DWT) to the relevant optimal IMFs. Here, an adaptive threshold calculation is designed based on the wavelet coefficients at different decomposition levels, which suppresses the random noise effectively. Finally, experiments are conducted on synthetic and real seismic signals to assess the efficacy of the proposed OEWT-EKMF-NWT method. The results reveal that the proposed OEWT-EKMF-NWT method attains better performance than the existing denoising methods.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2023.3334819