The elastic full-waveform inversion (EFWI) method efficiently utilizes the amplitude, phase, and travel time information present in multi-component seismic recordings to create detailed parameter models of subsurface structures. Within full-waveform inversion (FWI), accurate source wavelet estimation significantly impacts both the convergence and final result quality. The source wavelet, serving as the initial condition for the wave equation’s forward modeling algorithm, directly influences the matching degree between observed and synthetic data. This study introduces a novel method for estimating the source wavelet utilizing cross-correlation norm elastic waveform inversion (CNEWI) and outlines the EFWI algorithm flow based on this CNEWI source wavelet inversion. The CNEWI method estimates the source wavelet by employing normalized cross-correlation processing on near-offset direct waves, thereby reducing the susceptibility to strong amplitude interference such as bad traces and surface wave residuals. The proposed CNEWI method exhibits a superior computational efficiency compared to conventional L2-norm waveform inversion for source wavelet estimation. Numerical experiments, including in ideal scenarios, with seismic data with bad traces, and with multi-component data, validate the advantages of the proposed method in both source wavelet estimation and EFWI compared to the traditional inversion method.

Full waveform inversion (FWI) of deep seismic reflection data is an important tool to obtain the high-resolution velocity structures of the earth’s crust. Due to complex source excitation conditions and wavefield characteristics, FWI of land deep seismic reflection data faces more difficulties than offshore applications. Aiming at the difficulty of large-volume land explosive source estimation, we propose a double time-shift waveform inversion method, which can obtain accurate source functions in the presence of inaccurate near-surface velocities and strong interference. In order to improve the stability of FWI on land deep seismic reflection data, a dual multi-scale waveform inversion strategy based on frequency band and waveform is constructed, which can sequentially recover the large-scale and fine-scale velocity structure information of the subsurface medium. Numerical tests on the modified Marmousi model demonstrate that the method proposed in this paper can obtain relatively accurate source functions and velocity models under complex conditions such as inaccurate near-surface velocity, noise, and strong near-offset interference. The proposed method is applied to the first deep seismic reflection profile in the Kumkol Basin, and fine-scale velocity structures shallower than 6km are successfully obtained, revealing the uplift, fault distribution and stratigraphic sedimentation characteristics inside the basin, which all support the view that the Kumkol Basin may have good oil and gas prospects.

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.