College of Geo- Exploration Science and Technology��Jilin University��Changchun 130026��China

The transformation of basic functions is one of the most commonly used techniques for seismic denois- ing��which employs sparse representation of seismic data in the transform domain�� The choice of transform base functions has an influence on denoising results�� We propose a learning- type overcomplete dictionary based on the K- singular value decomposition (K- SVD) algorithm�� To construct the dictionary and use it for random seis- mic noise attenuation��we replace fixed transform base functions with an overcomplete redundancy function library�� Owing to the adaptability to data characteristics��the learning- type dictionary describes essential data characteristics much better than conventional denoising methods�� The sparsest representation of signals is ob- tained by the learning and training of seismic data�� By comparing the same seismic data obtained using the learning- type overcomplete dictionary based on K- SVD and the data obtained using other denoising methods�� we find that the learning- type overcomplete dictionary based on the K- SVD algorithm represents the seismic data more sparsely��effectively suppressing the random noise and improving the signal- to- noise ratio��