Deblending of simultaneous-source seismic data is becoming more popular in seismic exploration since it can greatly improve the efficiency of seismic acquisition and reduce acquisition cost. At present, the deblending methods of simultaneous-source seismic data are mainly divided into two types: filtering method and sparse inversion method. Compared with the filtering method, the sparse inversion method has higher precision, but the selection of its parameter value mainly depends on experience, which is not suitable for large-scale seismic data processing. In this paper, an adaptive iterative deblending method based on sparse inversion is proposed. By improving the original iterative solution method of regularization inversion model, the effective signal and blending noise are weakened simultaneously in the iterative process, so that the energy intensity of blending noise is consistent with that of the effective signal in each iterative, so as to ensure the consistency of the regular parameter calculation method of each iteration. By analyzing the distribution of coefficients in the curvelet domain of pseudo-deblending data and blending noise, it is concluded that the value of regular parameters is the maximum amplitude of residual pseudo-deblending data in the curvelet domain multiplied by a coefficient between 0 and 1. In the process of iterative deblending, the regularized parameters are obtained adaptively from the data itself. It not only ensures the accuracy of the calculation results, but also improves the calculation efficiency, which is suitable for large-scale seismic data processing.

同时源地震数据的去混合在地震勘探中变得越来越普遍，因为它可以大大提高地震采集的效率并降低采集成本。目前，同时震源数据的去混方法主要分为两种：滤波法和稀疏反演法。与滤波方法相比，稀疏反演方法精度较高，但其参数值的选择主要取决于经验，不适合大规模地震数据处理。提出了一种基于稀疏反演的自适应迭代混合方法。通过改进正则化反演模型的原始迭代求解方法，在迭代过程中同时减弱了有效信号和混合噪声，这样在每次迭代中混合噪声的能量强度与有效信号的能量强度是一致的，从而保证了每次迭代的规则参数计算方法的一致性。通过分析伪混合数据在curvelet域中的系数分布和混合噪声，可以得出结论，正则参数的值是曲线域中残留的伪混合数据的最大幅度乘以0到1之间的系数在迭代混合过程中，从数据本身自适应获得正则化参数。它不仅保证了计算结果的准确性，而且提高了计算效率，适用于大规模地震数据处理。