石油地球物理勘探2024，Vol.59Issue(3)：494-503,10.DOI:10.13810/j.cnki.issn.1000-7210.2024.03.012
基于自适应动态粒子群优化的RAK-SVD方法
RAK-SVD method based on adaptive dynamic particle swarm optimization
乐友喜 1姚晓辰 1付俊楠 1葛传友1
作者信息
- 1. 中国石油大学(华东)地球科学与技术学院,山东青岛 266580
- 折叠
摘要
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 algo-rithm 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,regu-larization 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 automati-cally 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 sig-nals.It can not only significantly improve the denoising effect of weak seismic signals but also enhance computa-tional efficiency.This method has certain practical application value.关键词
自适应动态粒子群算法/K-SVD字典/正则化/去噪Key words
adaptive dynamic particle swarm algorithm/K-SVD dictionary/regularization/denoising分类
天文与地球科学引用本文复制引用
乐友喜,姚晓辰,付俊楠,葛传友..基于自适应动态粒子群优化的RAK-SVD方法[J].石油地球物理勘探,2024,59(3):494-503,10.
