Full waveform inversion (FWI) seeks a subsurface parameter model that optimally matches the true state by minimizing the differences between synthetic and observed data. However, when starting from a rough initial model, FWI is often limited by the weak low-frequency energy of the observed data and the difficulty of matching surface-related multiples (SRMs), especially when the source wavelet is not readily available. Source wavelet errors also affect the general inversion result. We propose a multiscale virtual wavefield waveform inversion (VWWI) based on multidimensional interferometric retrieval (MDIR) to mitigate these challenges. We use MDIR to retrieve the virtual response from the up- and down-going wavefields separated from the original data and infer the velocity using the virtual response instead of the original data. MDIR integrates the source functions using multidimensional cross correlation (MDCC) and then suppresses the source imprints from the original data through multidimensional deconvolution (MDD). The retrieved virtual responses have a broader bandwidth and are dominated by primary reflection events. It addresses simultaneously three major challenges that FWI faces through a one-time data retrieval. Assigning self-setting source functions with different dominant frequencies to the virtual response allows the extraction of virtual observed data to different frequency bands for multiscale velocity inversion. Considering the possible amplitude distortion and the computational cost, we propose the hybrid source cross-correlation objective function adapted to VWWI. Numerical examples of well-known models representing weak and strong scattering media show that the proposed VWWI method can stably achieve wide-scale velocity modeling from macroscopic background to delicate structures.

Full-waveform inversion (FWI) of deep seismic reflection (DSR) 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 DSR 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 DSR data, a dual multiscale 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 article 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 DSR profile in the Kumkol Basin, and fine-scale velocity structures shallower than 6 km 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.

In passive source seismic surveys, signal continuity and signal-to-noise ratios have always tended to be low. On the one hand, since passive-source seismic surveys are often used for large-scale illumination of subsurface formations, the distances between receivers and sampling point intervals tend to be large. On the other hand, interference from coherent noise and spurious in-phase axes is unavoidable in passive source reconstruction recordings because of the signal originating from noise in the subsurface. All these problems lead to the continuity and signal-to-noise ratio of the virtual shot reconstructed from passive source seismic surveys are not guaranteed, which affects further processing and seriously limits the application of passive source seismic surveys. The traditional interpolation reconstruction methods cannot take noise suppression into account, or require additional operations to achieve both interpolation reconstruction and denoising. Based on this, this paper utilizes the powerful data processing ability of convolutional neural networks to design a global multi-scale fusion residual shrinkage network (GMF-RS) to solve the above passive source seismic exploration problem. It is tested that the trained network not only eliminates coherent noise and false events, but also improves the continuity in horizontal and vertical directions, enhances and extracts the effective signals, and provides better virtual shot records for subsequent seismic data processing. In addition, we designed a dual-input network and introduced active source seismic records as a complement to the passive source virtual seismic records, so that the processed waveforms can show better details.

The source wavelet is the initial condition of wavefield forward modelling, so its accuracy has a direct impact on the quality of full waveform inversion and reverse time migration. At present, the source wavelet estimation method based on wave equation, such as the reverse-time propagation algorithm and the L2 norm waveform inversion method, are all affected by the data quality and shallow velocity accuracy. In this paper, a robust source wavelet inversion method based on correlation norm waveform inversion is proposed. The near-offset direct waves are used to construct the cross-correlated objective function. The gradient expression is deduced by taking the source wavelet as the unknown. The proposed method only needs to invert short-time near-offset direct waves, so it has high computational efficiency. The cross-correlation objective function is mainly used to invert the phase information of the source wavelet, which is not sensitive to the data amplitude error, so it is more robust than the existing methods. Numerical examples show that the proposed method can still provide reliable source wavelets even when the velocity model is inaccurate, the data contains noise and there are bad traces. We also obtain high-quality source function inversion results by applying the proposed method to land deep reflection seismic data.

Traditional full waveform inversion (FWI) highly depends on sufficient low-frequency data or a good initial model. Passive seismic data contain rich low-frequency components, and passive seismic FWI using virtual source data by seismic interferometry (SI) is a promising method. However, the distribution of passive sources in the subsurface is always inhomogeneous, which will lead to artifacts in the reconstruction results by SI using cross-correlation (CC). SI by multidimensional deconvolution (MDD) can counteract the source inhomogeneity but requires the separation of the reference wavefields, which is difficult to achieve for noise source data. To mitigate this problem, we propose an improved SI method by linear Radon transform based multidimensional deconvolution (LRTMDD). The interferometric point-spread function can be extracted more accurately and efficiently in the linear Radon domain, thus improving the reconstruction results. The passive virtual source full waveform inversion (PVSFWI) based on LRTMDD is further proposed, which can effectively use the low-frequency information in the virtual source data to invert the macroscopic velocity structures even in the case of inhomogeneous source distributions, and without the need to estimate the virtual source wavelets. A joint multi-source FWI strategy is proposed to solve the problem of missing low-frequency data suffered by active source FWI. Numerical experiments on the Marmousi model and the SEG/EAGE overthrust model show that the proposed methods can fully combine the respective advantages of multisource seismic data to stably achieve high-accuracy velocity models in the case of inhomogeneous passive source distributions and the lack of low-frequency data in active seismic data.

Time-frequency analysis can reveal local variations and allow for separation of phase and amplitude information of nonstationary seismic waveforms. Seismic signals are used since long as a robust tool for inversion of underground structures, as has been the practice in geophysical exploration. However, the mixing of phase and amplitude in seismic data increases the nonlinearity of seismic inversion. The authors first use Gabor transform to separate the phase and amplitude information of envelope data, and then introduce an adaptive factor into the misfit function to redistribute the weight of phase and amplitude information for direct envelope inversion (DEI) in the time-frequency domain. By adopting this procedure, greater flexibility can be achieved in operating the local phase of envelope and waveform spectra to enhance stability of multiscale phase inversion. For DEI, the direct envelope Fréchet derivative is used, and thus, no weak scattering assumption is imposed on the joint multiscale DEI of phase and amplitude (PADEI). Compared with the DEI method, the PADEI can better recover the deeper parts of salt-bottom and subsalt structures by boosting the signal energy and weakening the nonlinearity of the waveform inversion.