Seismic interferometry using ambient noise provides an effective approach for subsurface imaging through reconstructing passive virtual source (PVS) responses. Traditional crosscorrelation (CC) seismic interferometry relies on a uniform dense distribution of passive sources in the subsurface, which is often challenging in practice. The multidimensional deconvolution method (MDD) alleviates reliance on passive-source distribution, but requires wavefield decomposition of the original data. This is difficult to accurately achieve for uncorrelated noise sources, leading to the existence of non-physical artifacts in the reconstructed PVS data. To address this issue, this study proposes a method to improve the accuracy of PVS data reconstruction using an enhanced U-Net. This data-driven approach circumvents the challenge of noise wavefield decomposition encountered in the traditional MDD. By integrating a feature fusion module into U-Net, multi-scale sampling information is leveraged to improve the network’s ability to capture detailed PVS data features. The combination of active-source data constraints and the modified MDD further optimizes PVS data retrieval during training. Numerical tests show that the proposed method effectively recovers waveform information in PVS retrieval records with non-ideally distributed sources, suppressing coherent noise and false events. The reconstructed recordings have a clear advantage in the reverse time migration (RTM) imaging results, with strong generalization performance across various velocity models.

Full waveform inversion (FWI) comprehensively utilizes phase and amplitude information of seismic waves to obtain high-resolution subsurface medium parameter models, applicable to both active-source and passive-source seismic data. Passive-source seismic exploration, using natural earthquakes or ambient noise, reduces costs and environmental impact, with growing marine applications in recent years. Its rich low-frequency content makes passive-source FWI (PSFWI) a key research focus. However, PSFWI inversion quality relies heavily on accurate virtual source reconstruction. While multi-dimensional deconvolution (MDD) can handle uneven source distributions, it struggles with irregular receiver sampling. We propose a robust MDD method based on multi-domain stepwise interpolation to improve reconstruction under non-ideal source and sampling conditions. This approach, validated via an adaptive PSFWI strategy, exploits MDD’s insensitivity to source distribution and incorporates normalized correlation objective functions to reduce amplitude errors. Numerical tests on marine and complex scattering models demonstrate stable and accurate velocity inversion, even in challenging acquisition environments.