Random noise attenuation using the novel Estimated Noise Pattern Denoising Algorithm

Abstract

This paper introduces the estimated pattern denoising (EPD) wavelet transform for random noise attenuation in geophysical data. The proposed approach combines the capability of the Gaussian filter and dual-tree rational dilation wavelet transform (DT-RADWT) in random noise detection and suppression; we called this method Estimated Pattern Denoising (EPD). The EPD is an innovative approach in terms of estimation of the location and amplitude of the noise pattern, directly from the data. The employed approach produces a higher quality factor (Q-factor) than the conventional dyadic discrete wavelet transform (DWT) and separates the noise from the signal with higher accuracy. The EPD provides a data-driven scheme that resolves the complexity of the random noise model in noise suppression, using an auxiliary Gaussian filter. This approach does not require prior information about the noise source, statistical distribution, or frequency range. We show successful suppression of random noise using the proposed approach on synthetic and real field data.