Seismic Random Noise Suppression Using Optimal ANFIS as an Adaptive Self-Tuning Filter and Wavelet Thresholding
Random noise attenuation plays a vital step in seismic signal processing. Numerous attenuation algorithms have been developed to separate and remove the random noise; nevertheless, they have failed to attain high precision. In this work, a hybrid framework based on an optimal adaptive neuro-fuzzy in...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10472038/ |
| _version_ | 1797243304522285056 |
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| author | K. Geetha Malaya Kumar Hota |
| author_facet | K. Geetha Malaya Kumar Hota |
| author_sort | K. Geetha |
| collection | DOAJ |
| description | Random noise attenuation plays a vital step in seismic signal processing. Numerous attenuation algorithms have been developed to separate and remove the random noise; nevertheless, they have failed to attain high precision. In this work, a hybrid framework based on an optimal adaptive neuro-fuzzy inference system (OANFIS) and a recent wavelet thresholding (WT), specifically OANFIS WT, is proposed to attenuate the random noise present in the seismic signals. In the suggested OANFIS WT method, the OANFIS extract the relevant seismic signal information from the contaminated signal using the premise and consequence parameters of ANFIS. These parameters are determined optimally using the Honey badger algorithm with mean square error value as an objective function. Here, OANFIS acts as an adaptive self-tuning filter that extracts the appropriate seismic signal information without any knowledge of the amount of noise in the contaminated signal. Therefore, some noise may be present in the output of OANFIS. Thus, the WT is applied to the extracted signal, with different values of the adjusting parameters in the thresholding function, to attenuate the noise effectually. Lastly, the experimental results on the synthetic and real seismic signals reveal that the proposed OANFIS WT method is more effective in reducing random noise and preserving relevant signal information than other contrastive methods. |
| first_indexed | 2024-04-24T18:52:59Z |
| format | Article |
| id | doaj.art-92c5c11bd99f4ac982dcaa8f7182e2a6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T18:52:59Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-92c5c11bd99f4ac982dcaa8f7182e2a62024-03-26T17:47:52ZengIEEEIEEE Access2169-35362024-01-0112395783958810.1109/ACCESS.2024.337714310472038Seismic Random Noise Suppression Using Optimal ANFIS as an Adaptive Self-Tuning Filter and Wavelet ThresholdingK. Geetha0https://orcid.org/0000-0002-3276-3657Malaya Kumar Hota1https://orcid.org/0000-0002-3669-1611Department of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaDepartment of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaRandom noise attenuation plays a vital step in seismic signal processing. Numerous attenuation algorithms have been developed to separate and remove the random noise; nevertheless, they have failed to attain high precision. In this work, a hybrid framework based on an optimal adaptive neuro-fuzzy inference system (OANFIS) and a recent wavelet thresholding (WT), specifically OANFIS WT, is proposed to attenuate the random noise present in the seismic signals. In the suggested OANFIS WT method, the OANFIS extract the relevant seismic signal information from the contaminated signal using the premise and consequence parameters of ANFIS. These parameters are determined optimally using the Honey badger algorithm with mean square error value as an objective function. Here, OANFIS acts as an adaptive self-tuning filter that extracts the appropriate seismic signal information without any knowledge of the amount of noise in the contaminated signal. Therefore, some noise may be present in the output of OANFIS. Thus, the WT is applied to the extracted signal, with different values of the adjusting parameters in the thresholding function, to attenuate the noise effectually. Lastly, the experimental results on the synthetic and real seismic signals reveal that the proposed OANFIS WT method is more effective in reducing random noise and preserving relevant signal information than other contrastive methods.https://ieeexplore.ieee.org/document/10472038/Adaptive noise cancellation (ANC)honey badger algorithm (HBA)optimal adaptive neuro-fuzzy inference system (OANFIS)random noiseseismic signalwavelet thresholding (WT) |
| spellingShingle | K. Geetha Malaya Kumar Hota Seismic Random Noise Suppression Using Optimal ANFIS as an Adaptive Self-Tuning Filter and Wavelet Thresholding IEEE Access Adaptive noise cancellation (ANC) honey badger algorithm (HBA) optimal adaptive neuro-fuzzy inference system (OANFIS) random noise seismic signal wavelet thresholding (WT) |
| title | Seismic Random Noise Suppression Using Optimal ANFIS as an Adaptive Self-Tuning Filter and Wavelet Thresholding |
| title_full | Seismic Random Noise Suppression Using Optimal ANFIS as an Adaptive Self-Tuning Filter and Wavelet Thresholding |
| title_fullStr | Seismic Random Noise Suppression Using Optimal ANFIS as an Adaptive Self-Tuning Filter and Wavelet Thresholding |
| title_full_unstemmed | Seismic Random Noise Suppression Using Optimal ANFIS as an Adaptive Self-Tuning Filter and Wavelet Thresholding |
| title_short | Seismic Random Noise Suppression Using Optimal ANFIS as an Adaptive Self-Tuning Filter and Wavelet Thresholding |
| title_sort | seismic random noise suppression using optimal anfis as an adaptive self tuning filter and wavelet thresholding |
| topic | Adaptive noise cancellation (ANC) honey badger algorithm (HBA) optimal adaptive neuro-fuzzy inference system (OANFIS) random noise seismic signal wavelet thresholding (WT) |
| url | https://ieeexplore.ieee.org/document/10472038/ |
| work_keys_str_mv | AT kgeetha seismicrandomnoisesuppressionusingoptimalanfisasanadaptiveselftuningfilterandwaveletthresholding AT malayakumarhota seismicrandomnoisesuppressionusingoptimalanfisasanadaptiveselftuningfilterandwaveletthresholding |