A novel wavelet seismic denoising method using type II fuzzy |
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| Affiliation: | 1. Department of Civil Engineering, Noorul Islam University, Tamil Nadu, India;2. Department of Electrical Engineering, College of Engineering, Pathanapuram, Kerala, India;3. Department of Civil Engineering, MEPCO SCHLENK, Sivakasi, Tamil Nadu, India;4. Department of Computer Engineering and Mathematics, University Rovira i Virgili, Spain;1. Department of Computer Sc. & Information Technology, Institute of Technical Education and Research, Siksha ‘O‘ Anusandhan, University, Khandagiri Square, Bhubaneswar, 751030 Odisha, India;2. Department of Computer Sc. & Engineering, Silicon Institute of Technology, Bhubaneswar, 751024 Odisha, India;1. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain;2. Leeds School of Business, University of Colorado, Boulder, USA;3. Department of Statistics and Operations Research, University of Valencia, Valencia, Spain;4. Department of Computer Science, University of Extremadura, Mérida, Spain;5. Department of Computing and Numerical Analysis, University of Córdoba, Córdoba, Spain;1. Oil Equipment Intelligent Control Engineering Laboratory of Henan Province, Physics and Electronic Engineering College, Nanyang Normal University, Nanyang 473061, China;2. Department of Electronic Engineering, Information School, Yunnan University, Kunming 650091, Yunnan, China;1. Mechanical Engineering Department, Jadavpur University, 700 032, India;2. Electrical Engineering Department, Jadavpur University, 700 032, India |
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| Abstract: | Wavelet based denoising of the observed non stationary time series earthquake loading has become an important process in seismic analysis. The process of denoising ensures a noise free seismic data, which is essential to extract features accurately (max acceleration, max velocity, max displacement, etc.). However, the efficiency of wavelet denoising is decided by the identification of a crucial factor called threshold. But, identification of optimal threshold is not a straight forward process as the signal involved is non-stationary. i.e. The information which separates the wavelet coefficients that correspond to the region of interest from the noisy wavelet coefficients is vague and fuzzy. Existing works discount this fact. In this article, we have presented an effective denoising procedure that uses fuzzy tool. The proposal uses type II fuzzy concept in setting the threshold. The need for type II fuzzy instead of fuzzy is discussed in this article. The proposed algorithm is compared with four current popular wavelet based procedures adopted in seismic denoising (normal shrink, Shannon entropy shrink, Tsallis entropy shrink and visu shrink).It was first applied on the synthetic accelerogram signal (gaussian waves with noise) to determine the efficiency in denoising. For a gaussian noise of sigma = 0.075, the proposed type II fuzzy based denoising algorithm generated 0.0537 root mean square error (RMSE) and 16.465 signal to noise ratio (SNR), visu shrink and normal shrink could be able to give 0.0682 RMSE with 14.38 SNR and 0.068 RMSE with 14.2 SNR, respectively. Also, Shannon and Tsallis generated 0.0602 RMSE with 15.47 SNR and 0.0610 RMSE with 15.35 SNR, respectively. The proposed method is then applied to real recorded time series accelerograms. It is found that the proposal has shown remarkable improvement in smoothening the highly noisy accelerograms. This aided in detecting the occurrence of ‘P’ and ‘S’ waves with lot more accuracy. Interestingly, we have opened a new research field by hybriding fuzzy with wavelet in seismic denoising. |
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| Keywords: | Wavelet Seismic signal Visu shrink Shannon entropy Tsallis entropy Normal shrink & type II fuzzy |
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