Abstract: Traditional seismic signal compression methods do not process the seismic signal according to its characteristics,so the compression effect is poor.Aiming at this problem,by using the self-similarity of seismic signal,a new method combining clustering with dictionary learning is proposed.The Fuzzy C-Means(FCM) clustering algorithm is used to cluster the samples,and a dictionary learning model is built.Through the transformation of the objective function,the model can be transformed into an ordinary dictionary learning model,and finally the K-Singular Value Decomposition(K-SVD) algorithm is used to solve the model.Experimental results show that when the range of compression ratio is between 8.5~18.8,the signal to Noise Ratio(SNR) of the method is 1 dB~4.5 dB higher than discrete cosine transform method,1 dB~4 dB higher than gabor,and 0.5 dB~1 dB higher than K-SVD algorithm.

Key words: sparse decomposition, K-Singular Value Decomposition(K-SVD) algorithm, seismic signal, self-similarity, coding

摘要： 传统的地震信号压缩方法没有根据地震信号本身的特点对其进行处理,而将其作为普通信号,压缩效果较差。为此,利用地震信号的自相似性,提出一种聚类和字典学习算法相结合的方法。采用模糊C均值聚类算法对样本进行聚类,构建字典学习模型,通过对目标函数的变换,使得模型变换为普通的字典学习模型,并使用K-奇异值分解算法(K-SVD)对字典学习模型进行求解。实验结果表明,当该方法压缩比范围在8.5~18.8之间时,信噪比高于离散余弦变换方法1 dB~4.5 dB,高于gabor方法1 dB~4 dB,比单纯使用K-SVD算法高0.5 dB~1 dB。

关键词: 稀疏分解, K-奇异值分解算法, 地震信号, 自相似性, 编码

CLC Number: 

• TP391