Abstract：The K-means singular value decomposition （K-SVD） algorithm is an effective seismic data denoising method. However, due to the uncertainty problem of its sparse decomposition, it is necessary to be improved by introducing regularization terms. Therefore, a regularization approximation K-SVD （RAK-SVD） denoising method for optimizing regularization parameters by using an adaptive dynamic particle swarm optimization algorithm based on a conventional particle swarm optimization algorithm was proposed. Firstly, by modifying the dictionary atoms and related parameters, the problem of decreased search efficiency in the later stage due to the fixed inertia parameters of the conventional particle swarm optimization algorithm was solved. Secondly, regularization coefficients were introduced into the approximate K-SVD method, which significantly improved the denoising effect. Finally, the adaptive dynamic particle swarm optimization algorithm was used to automatically optimize the regularization parameters in the AK-SVD method, which improved the determinacy of sparse decomposition and enhanced the protection of weak signals while denoising strong reflection signals. Model tests and practical applications have shown that this method is beneficial for extracting and identifying weak signals. It can not only significantly improve the denoising effect of weak seismic signals but also enhance computational efficiency. This method has certain practical application value.
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