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由无气泡气枪震源形成的简单波形可以大大简化子波相位函数的确定和控制,可以提高地震数据的地层学分层的可靠性。本文中,我们提出一种创新方法,即通过利用频率-波数域稀疏反演法用于消除气泡和增强频谱。与著名的气枪震源不同,我们使用目标源的概念。目标源是单一波瓣且无气泡的气枪震源。为了反演使用类似目标源获得的地震数据,我们计算一个估计问题。该方法的基本思想是利用卷积和反卷积,由于稳定性因素在时空域存在随机噪声。我们提出当通过频率-波数(f-k)域值约束做反卷积时,利用迭代消除随机噪声。与传统的维纳滤波法相比,此方法可以获取更为接近完美的结果,并消除其他噪声和人工干扰。我们利用一个线性事件合成数据和一个更为真实的Marmousi模型实例来演示此方法的性能。结果表明,此方法能有效地消除气泡影响并填补频谱凹槽。
The simple waveform formed by bubble-free airgun source can greatly simplify the determination and control of wavelet phase function, and can improve the reliability of stratigraphic stratification of seismic data. In this paper, we propose an innovative approach to eliminate bubbles and enhance the spectrum by exploiting frequency-wavenumber domain inversion. Unlike famous airgun sources, we use the concept of a target source. The target source is a single lobed and bubble-free airgun source. To invert the seismic data obtained using a similar target source, we calculate an estimation problem. The basic idea of this method is the use of convolution and deconvolution, due to the presence of random noise in the spatio-temporal domain due to stability factors. We propose to eliminate random noise by iteration when doing deconvolution by the frequency-wavenumber (f-k) domain constraint. Compared with the traditional Wiener filtering method, this method can get more nearly perfect results and eliminate other noise and interference. We use a linear event synthesis data and a more realistic Marmousi model example to demonstrate the performance of this method. The results show that this method can effectively eliminate the influence of bubbles and fill the groove of the spectrum.