形态分量分析在去除地震资料随机噪声中的应用  

Application of Morphological Component Analysis to Remove of Random Noise in Seismic Data

作　　者：李海山 吴国忱 印兴耀 

机构地区：[1]中国石油大学地球科学与技术学院,山东青岛266555

出　　处：《吉林大学学报（地球科学版）》2012年第2期554-561,共8页Journal of Jilin University：Earth Science Edition

基　　金：国家自然科学基金项目(40739908);国家油气重大专项(2008zx05014-001-010hz)

摘　　要：以数学形态学和稀疏信号理论为依据,采用形态分量分析(MCA)方法去除地震数据中的随机噪声。应用MCA方法的关键在于选取合适的字典,从地震数据的特点和计算复杂性出发,选取UWT字典和Curvelet字典,一个用来稀疏表示地震数据的局部奇异部分,一个用来稀疏表示地震数据的线状变化部分。采用BCR算法求解目标函数,通过将数据分解为形态特征不同的2个分量,舍弃在字典中不能有效稀疏表示的随机噪声来达到去噪目的。作为一种二维去噪方法,MCA去噪方法在时间和空间方向上都具有很强的随机噪声抑制能力;由于UWT字典和Curvelet字典能够比传统的小波变换有更强的稀疏表示能力,MCA去噪方法对有效信息的损害较小,是一种保真保幅的去噪方法。模型测试和实际资料处理验证了该方法的有效性。According to the morphology and sparse signal theory,morphological component analysis（MCA） method is used for random noise attenuation in seismic data.The key of MCA is to select the appropriate dictionaries.In view of the characteristics of seismic data and computational complexity,UWT dictionary and Curvelet dictionary are selected.One sparsely represents for local singular part of the seismic data,the other sparsely represents for smooth and linear part of seismic data.BCR algorithm is used to solve objective function,and the denoised results are obtained by decomposing the seismic data into two morphologically different components and discarding the random noise which can＇t be sparsely represented in dictionaries efficiently.As a 2D denoising method,MCA denoising method can efficiently suppress random noise both in time and spatial directions;Because the sparse representation abilities of UWT dictionary and Curvelet dictionary are stronger than traditional wavelet transform,MCA denoising is an amplitude and fidelity preserved denoising method,its damage to effective information is quite smaller.Theoretical and real data processing verified the efficiency of MCA method.

关 键 词：地震数据去噪 形态分量分析 稀疏表示 曲波变换 小波变换 

分 类 号：P631.4[天文地球—地质矿产勘探]