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中文摘要：
全局奇异值分解（SVD）算法存在难以处理非水平同相轴问题的先天局限,局部SVD算法在一定程度上能克服这一局限,但时间域局部SVD技术的去噪效果受制于时窗参数的准确性,频率域局部SVD技术则会损坏部分频带内的有效信号。对于宽频带随机噪声,单独使用上述任何一种方法都难以取得满意的压制效果。综合两种方法特点,研究时间、频率域联合局部SVD去噪方法：依据资料特点对输入数据进行合理的时窗划分;在时间域对时窗内数据进行同相轴拉平处理并进行时间域局部SVD去噪;将经上述处理的各时窗数据变换到频率域并构建Hankle矩阵,采用SVD技术对该矩阵进行去噪处理并变换回时间域。算法克服了单域处理的局限性,在有效压制随机噪声的同时,保护高低频有效信号。模型与实测资料的应用效果证明了联合去噪方法的有效性。
英文摘要：
Although local singular value decomposition（SVD）could overcome global SVD＇s deficiency in coping with non-level events to some extent,the denoising effect of local SVD in time domain was strongly influenced by filtering window parameters and local SVD in frequency domain damaged the energy of partial effective frequency band.For wide frequency random noise,satisfactory suppression effect could not be obtained by just taking any one of the two above methods.This paper focused on the study on local SVD denoising methods in time-frequency domain by combining the two methods.According to its characteristics,the input data was divided into several windows.The data in each window in time domain was processed by aligning non-level events and local SVD denoising in time domain was conducted.Then the processed data was transformed to frequency domain and Hankle matrix was constructed.After the matrix went through denoising processing with SVD,the data was transformed back to time domain.The algorithm,which overcame the limitations of traditional SVD,could effectively suppress random noise and protect high and low frequency signals.The results of modeling and field data processing reveal the validity of joint denoising.
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