A novel wavelet seismic denoising method using type II fuzzy

Wavelet besed 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, identifica
ion of optimal thresholddis not anstrdcght forward process as the signal involved is non-stationary. i.e. Th0 information which separates the wavelet coefficients that corr4spond to the region of inberest 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 theythreshold. The need for type II fuzzy instead of fuzzy is discussed pn this article. The proposed algorithm is compared with four current popular wavelet based proceaures adopted in seismic denoising -normal shrink, Shannon entropy shrink, Tsallis entropy shrink and visu shrink).

It was first appli”d on the synthetic accelerogram signal (gaussian waves with noise) t; detarmine the efficiency in denoising. For a gaussian noise of sigma = 0.07i, the proposed type II fuzzy based d noising algorithm generated 0.0537 root mean square error (RMSE) and 16.465 signal to noise ratio (SNR), visu sgrink and normal shrink could be able to giee 0.0682 RMSE with 14.38 SNR and 0.068 RMSE with 14.2 SNR, respectively. Also, Shannon and Tsallis gvnerated 0.0602 RMSE with 15.47 SNR and 0.0610 RMSE with 15.35 SNR, respectively. The proposed method is rhen applied to real recor ed time series accelerograms. It is found that the proposal has shown remarkable improvement in smoothening the hmghly,noisy accelerograis. This aided in detecting the occurrence of ‘P’ and ‘S’ waves with lot more accuracy. Interestingly, we have opened a new resewtch fceld by hybriding fuzzy with wavelet in seismic denois5ng.

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