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如何通过匹配地震数据提取更有价值的信息越来越受地球物理学家的关注。匹配滤波广泛的用于资料拼接、新老资料匹配、不同震源资料匹配、四维地震监测等重要领域。传统地匹配滤波方法受多方面的限制,难以克服噪声的影响。基于传统的匹配滤波,提出小波域内L1范数最优匹配处理。文中将两种不同类型的地震数据分解到小波域,利用L1范数稀疏解收敛性好和抗噪性强的特点,在小波域中针对各个不同的细节部分提取有效地震信号,再进行L1范数最优匹配。模型试算证明,经本文方法能有效地压制随机噪声,匹配后的数据在波形、振幅和相位一致性等方面较常规方法效果更好。实际资料处理结果也证实:小波域内L1范数最优匹配后的地震数据同相轴连续性更好,达到了高精度地震数据匹配的目标。
How to extract more valuable information by matching seismic data has drawn more and more attention from geophysicists. Matching filter is widely used in material splicing, matching of new and old data, matching of different source data, and monitoring of 4-D seismic data. Conventional matching filtering method is subject to many restrictions, it is difficult to overcome the impact of noise. Based on the traditional matched filtering, the L1 norm optimal matching in wavelet domain is proposed. In this paper, two different types of seismic data are decomposed into wavelet domain. According to the characteristics of L1 norm sparse solvation convergence and strong anti-noise, an effective seismic signal is extracted for each different detail in wavelet domain, and L1 Number of the best match. The model test proves that the proposed method can effectively suppress random noise, and the matched data is more effective than conventional methods in waveform, amplitude and phase consistency. The actual data processing results also confirm that the continuity of seismic data after L1 norm optimal matching in wavelet domain is better, and the target of high-precision seismic data matching is achieved.