计算机工程

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数字化地震信号压缩方法研究

李寅,王立夫,孙怡   

  1. (大连理工大学 信息通信工程学院,辽宁 大连 116031)
  • 收稿日期:2015-09-18 出版日期:2016-09-15 发布日期:2016-09-15
  • 作者简介:李寅(1986-),男,硕士研究生,主研方向为稀疏表示、数据压缩;王立夫,博士研究生;孙怡,教授、博士、博士生导师。
  • 基金项目:
    国家自然科学基金资助项目(41174044)。

Research on Digital Seismic Signal Compression Method

LI Yin,WANG Lifu,SUN Yi   

  1. (School of Information and Communication Engineering,Dalian University of Technology,Dalian,Liaoning 116031,China)
  • Received:2015-09-18 Online:2016-09-15 Published:2016-09-15

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

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

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