Fuzzy Threshold Based Wavelet Processing of Random Time Series and Highly Noisy Accelerograms

Accelerograms are the random time series representation of the strong ground motions called earthquake. Processing of accelerograms is done before using them for any seismic and engineering applications. This article focuses the impact of threshold and noise in wavelet processing using experiments on random generated time series signals. Experiments conducted on the global thresholds currently in use lead us to propose a novel fuzzy based threshold to remove the random noise. Therefore a fuzzy based threshold determination in the domain of wavelet coefficients has been proposed and effectively used in denoising the real observed accelerograms. Also, the experimental results on synthetic signals has outperformed the existing universal and sure thresholds in terms of SNR of 20.3 and RMSE of 0.0019, respectively.