Denoising of Seismic Data Based on Block Dictionary Learning Theory

With the increasingly complex observation environment of oil and gas exploration, the seismic data collected are often mixed with various noise signals, resulting in the effective weak signal caused by the exploration target is covered, which seriously affects the high-precision seismic data interpretation, so it is more and more important to effectively suppress the seismic data noise. In this paper, the dictionary learning strategy is used to block the complex seismic data. The dictionary atoms are obtained through the dictionary learning of the block data, and the sparse representation of the seismic data is constructed by high-precision dictionary learning. The dictionary atoms are updated through two iterations for data denoising. The dictionary learning algorithm is applied to the processing of simulated and measured seismic data with random noise. The analysis results show that the algorithm can effectively removes the random noise while retains the effective signal phase axis, improves the signal-to-noise ratio which verifies the feasibility and effectiveness of the algorithm. The research results provide a new technical means for complex noisy seismic data denoising.