Random Noise Redunction of Seismic Data in NSCT Domain

The suppression of random noise in seismic data is an essential step in the processing of seismic signals. However, as the exploration environment is becoming more and more complicated, the energy of valid signals is weaker and the signal to noise (SNR) of seismic data is much lower which bring great difficulty to seismic data processing and interpretation. In this paper, we propose a new seismic random noise suppression approach based on the non-subsampled contourlet transform (NSCT). Firstly, the noisy seismic data is decomposed into different sub-bands of frequency and orientation responses using NSCT. Then, we use an appropriate thresholding operator to denoise the sub-bands of NSCT coefficients. Finally, we perform the inverse NSCT transform on the denoised NSCT components to reconstruct the denoised seismic data. We use synthetic data examples to demonstrate the superior performance of the proposed approach over the wavelet transform based threshold denoising method.