Application of RAKSVD Method Based on Adaptive Dynamic Particle Swarm Optimization in Seismic Data Denoising

Among many seismic data denoising methods, the KSVD denoising method is an effective method. However, due to its ill-conditioned problem, it is necessary to introduce regularization terms to improve the KSVD method, and the automatic optimization of regularization parameters is extremely important. Particle swarm optimization algorithms are widely used in parameter optimization, but the inertia parameters of conventional particle swarm optimization algorithms are generally fixed, which leads to the decline of the search efficiency in the later stage. In this paper, the adaptive dynamic particle swarm optimization algorithm is used to improve the setting of regularization approximation parameters, and the advantages of the AKSVD method for weak signal identification are used to propose the RAKSVD denoising method with the optimized regularization parameters of the adaptive dynamic particle swarm algorithm. Model testing and practical applications show that this method can not only achieve the expected denoising effect, but also pay more attention to the protection of weak seismic signals. After seismic denoising, the weak seismic signals are basically not distorted, which is conducive to the extraction and recognition of weak signal. At the same time, the computational efficiency of this method has also been improved.