Passive seismic source imaging can be utilized to recover geophysical information from subsurface ambient noise. Compared with conventional active seismic exploration, passive seismic source imaging is cost-effective and environmentally friendly. However, passive data acquisition cannot easily satisfy the theoretical condition, leading to noised virtual-shot gathers. Furthermore, coherent noise limits the application of passive source data. Although image quality improvement techniques for passive source data have recently attracted considerable interest, the denoising problem for virtual-shot gathers is seldom considered. In this study, we propose an iterative denoising approach for passive seismic data. The criterion used to extract useful signals is the difference between the wavefield similarity of useful events and the coherent noise in various gathers, i.e., the common shot gather and common receiver gather. We adopted local similarity to measure the similarity level and extract major useful events. However, the close local similarity between weak events and coherent noise may cause signal leakages and singular noise residuals. We incorporated an iterative two-dimensional model shrinkage algorithm into the denoising process to suppress the singular noise residual and highlight useful events. The proposed approach can overcome the limits of strong coherent noise in virtual-shot gathers, which can extend the choice range for data processing. Synthetic and field examples demonstrate a promising coherent noise attenuation performance, illustrating the effectiveness and feasibility of the proposed method. The denoised migrated section exhibits a smaller depth error and higher quality.

被动地震源成像可用于从地下环境噪声中恢复地球物理信息。与传统的主动地震勘探相比，被动地震源成像具有成本效益和环境友好性。但是，被动数据采集不能轻易满足理论条件，从而导致虚拟镜头集的噪声。此外，相干噪声限制了无源源数据的应用。尽管近来用于被动源数据的图像质量改进技术引起了人们的极大兴趣，但是很少考虑虚拟镜头集的去噪问题。在这项研究中，我们提出了一种用于被动地震数据的迭代去噪方法。用于提取有用信号的标准是有用事件的波场相似性与各种聚集（即，公共散布聚集和公共接收器聚集）中的相干噪声之间的差。我们采用局部相似性来衡量相似性水平并提取主要有用事件。但是，弱事件和相干噪声之间的紧密局部相似性可能会导致信号泄漏和奇异噪声残留。我们将迭代二维模型收缩算法纳入去噪过程中，以抑制奇异噪声残留并突出显示有用事件。所提出的方法可以克服虚拟镜头集合中强相干噪声的局限性，从而可以扩展数据处理的选择范围。合成和现场实例证明了有希望的相干噪声衰减性能，说明了该方法的有效性和可行性。去噪的迁移部分显示出较小的深度误差和较高的质量。