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稀疏化方法由于能够实现地震信号的高精度分解,已经成为重要的地震信号处理技术.目前地震信号稀疏分解常采用的方法是匹配追踪算法(Matching Pursuit,MP),但所得结果不够稀疏.针对此局限,提出了一种基于重复加权提升搜索算法(Repeated Weighted Boostmg Search,RWBS)的快速分解方法.首先,根据地震信号的频谱图缩小频率搜索范围;然后,将搜索算法RWBS与正交匹配追踪(Orthogonal Matching Pursuit,OMP)方法相结合,就得到一种快速的稀疏分解方法,将本文的方法应用到人工合成和实际的地震数据处理中,并与MP和OMP追踪算法作比较,说明采用本文方法进行地震信号分解在稀疏度和分解速度方面都有提高,仿真实验结果表明,与MP和OMP分解算法相比,在满足相同的分解精度条件下,RWBS算法不仅大大提高了分解的稀疏度,而且提高分解速度.与OMP算法相比较.基于RWBS的新方法分解所需的时间减少了约87%;与MP算法相比较,新方法分解所需的时间减少约50%.
Sparse method has become an important seismic signal processing technology because it can decompose seismic signals with high accuracy.At present, the commonly used method of sparse decomposition of seismic signals is Matching Pursuit (MP), but the result is not sparse, This paper proposes a fast decomposition method based on Repeated Weighted Boostmg Search (RWBS) .Firstly, the frequency search range is reduced according to the spectrum of the seismic signal. Then, the search algorithm RWBS is compared with the orthogonal matching pursuit Orthogonal Matching Pursuit (OMP) method, a fast sparse decomposition method is obtained, which is applied to synthetic data processing and actual seismic data processing. Compared with the MP and OMP tracking algorithms, it is proved that the proposed method The results of simulation experiments show that compared with MP and OMP decomposition algorithms, RWBS algorithm not only greatly improves the sparseness of decomposition, but also satisfies the same decomposition accuracy. Moreover, Improve the speed of decomposition compared with the OMP algorithm. About 87%; compared with MP algorithm, a new method for the time required for decomposition is reduced by about 50%.