1. Institute of Geophysics,Research Institute of Petroleum Exploration & Development,Urumqi 830013,China; 2. School of Electronic & Information Engineering,Xi'an Jiaotong University,Xi'an 710049,China;
The double sparse dictionary is adopted for the seismic random noise attenuation.The seismic data are not represented well by the fixed dictionaries,which do not contain the effective information about the seismic data;the learning dictionaries are fully adaptable but are costly to deploy in the big data processing.The double sparse dictionary reduces the number of training sample and is more suitable for the construction of high-dimension dictionary and the analysis of the high-dimension signal. With the over completed discrete cosine transform as the base dictionary,the sparse dictionary is trained by the sparse K-SVD driven by the noisy seismic data samples.Thus the seismic random noise attenuation model based on the double sparse dictionary is established.A comparison of the results of the synthesized and real data in high dimensional case shows that the seismic random noise can be suppressed effectively by the method based on double sparse dictionary and the fault structure can be preserved in 3D case.
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