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Received: 10 March 2019 Accepted: 1 June 2019 For effective removal of noises in seismic signals, this paper proposes an adaptive threshold denoising algorithm that integrates wavelet transform with entropy and inter-scale correlation (EIS) model. Firstly, noisy signals were decomposed by discrete wavelet transform, the highfrequency sub-bands on each scale were divided into equal subintervals, and the wavelet entropies of the subintervals were computed one by one. Secondly, the correlation coefficients of sampling points on each scale were calculated, and then compared with the high-frequency coefficients at corresponding positions. The comparison results, coupled with the wavelet entropies, were determine the noise variance of high-frequency sub-bands on each scale. Finally, the signals were reconstructed from the above results according to the new threshold function and the self-adaptive threshold rule. The experimental results show that our method outperformed several popular denoising approaches in terms of signal-to-distortion ratio (SDR), signal-to-noise ratio (SNR) and mean squared error (MSE).

Denoising of Seismic Signals Through Wavelet Transform Based on Entropy and Inter-scale Correlation Model