【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。更多还原

【Abstract】 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.更多还原