Combined Maximal Overlap DWT and Adaptive Filtering for Denoising Seismic Signals

In this paper, denoising of seismic signals based on Maximal Overlap Discrete Wavelet Transform and Adaptive Filtering (MODWT-AF) is discussed. In this method, first the seismic signals are converted into a set of coefficients called approximation and detailed coefficients by applying MODWT-based Multi Resolution Analysis (MODWT-MRA) technique. Then, adaptive filtering is applied to these coefficients, which removes noise present in the frequency sub bands of the signals. Finally, the denoised signal is reconstructed by performing the inverse MODWT to the modified set of coefficients. As a statistical analysis, the performance measures such as input signal-to-noise (SNRin), output signal-to-noise ratio (SNRout), SNR improvement (ΔSNR), Normalized Correction Coefficient (NCC), Mean Square Error (MSE), and RMS error are evaluated for different real earthquake records and synthetic seismic signals. The performance results of the proposed methodology are compared with other conventional thresholding methods such as MODWT-based thresholding (MODWT-TH) and Discrete Wavelet Transform based thresholding techniques namely Heursure (DWT-HE), Sqtwolog (DWT-SG), Minimaxi (DWT-MI), and Rigrsure (DWT-RE). The experimental results effectively demonstrated that the proposed methodology produced better outcomes compared to the conventional methods.