Elastic reverse time migration (ERTM) is developed for better characterization of complex structures by imaging multicomponent seismic data. However, conventional ERTM is subject to limitations such as finite recording aperture, limited bandwidth, and imperfect illumination. Elastic least-squares reverse time migration (ELSRTM) can improve imaging accuracy gradually with iterations by minimizing the residuals between the observed and calculated multicomponent data. Conventional ELSRTM suffers from crosstalk artifacts caused by coupled elastic wavefields with different wave modes. Decomposing the coupled elastic wavefields into pure P- and S-waves is an effective method to suppress these crosstalk artifacts. Considering the trade-off between calculation accuracy and efficiency, we have developed a new ELSRTM scheme for isotropic media based on decoupled wave equations to suppress these wave mode-related crosstalk artifacts in the images of conventional ELSRTM. Pure wavefields are obtained by solving the decoupled wave equations using the finite-difference method in our new ELSRTM method. We also derive new decoupled adjoint-state wave equations that are suitable for the elastic velocity-stress equations in isotropic media. Furthermore, we use the gradient equations based on pure wavefields to update the reflectivity images. Synthetic examples demonstrate that our new ELSRTM method can generate images that better represent the subsurface when compared with conventional ERTM and conventional ELSRTM.

Dual-sensor seismic acquisition systems that record the pressure and particle velocity allow the recording of the full-vector-acoustic (VA) wavefields. Most previous studies have focused on data-domain processing methods based on VA seismic data; whereas, few studies focused on using full-VA seismic data in full-waveform inversion (FWI). Conventional acoustic FWI only takes advantage of the pressure recordings to estimate the medium’s velocity model. Some artifact events will appear in the adjoint-state wavefields based on the conventional acoustic FWI method. These artifact events further reduce the accuracy of acoustic FWI. To simultaneously use pressure and vertical particle velocity recordings, we introduced a new time-domain VA FWI method. The VA FWI method can take advantage of directivity information contained in the VA seismic data. Thus, the adjoint-state wavefields based on VA FWI are more accurate than those from the conventional acoustic FWI method. In addition, we applied a convolution-based objective function to eliminate the effects of the source wavelet and implement a time-domain multiscale strategy in VA FWI. Synthetic examples are presented to demonstrate that VA FWI can improve the accuracy of acoustic FWI in the presence and absence of a free surface in the acoustic case. In addition, VA FWI does not significantly increase the computation and memory costs, but it has better convergence when compared with conventional acoustic FWI.

Migration is an important step in seismic data processing for oil and gas exploration. The accuracy of migration directly affects the accuracy of subsequent oil and gas reservoir characterization. Reverse-time migration is one of the most accurate migration methods at present. Multi-wave and multicomponent seismic data contain more P- and S-wave information. Making full use of multi-wave and multicomponent seismic data can offer more information about underground structure and lithology, as well as improve the accuracy of seismic exploration. Elastic reverse-time migration (ERTM) has no dip restriction and can be applied to image multi-wave and multicomponent seismic data in complex structural areas and some special lithology structures. However, the surface topography of complex regions has an influence on wavefield and seriously degrades the quality of ERTM’s migration results. We developed a new ERTM method to migrate multi-wave and multicomponent seismic data in the region with complex surface topography. We first fill the layers between the highest and lowest undulating surface with near-surface elastic parameters in a complex topography model to obtain a new model with a horizontal surface. This allows the finite difference (FD) method based on the regular rectangular grid to be used to numerically solve elastic wave equations in the model with complex topography. The decoupled wave equations are used to generate source P- and S-waves and receiver P- and S-waves to reduce crosstalk artefacts in ERTM. A topography-related filter is further used to remove the influence of surface topography on migration results. The scalar imaging condition is also applied to generate PP and PS migration images. Some numerical examples with different complex topographies demonstrate that our proposed ERTM method can remove the influence of complex topography on ERTM’s images and effectively generate high-quality ERTM images.

Machine learning (ML) is emerging as a new approach for developing algorithms in geophysical research. In this study, a six-layer neural network is designed to estimate seafloor properties using seismic prestack data. In conjunction with the designed neural network architecture, a straightforward network training scheme is developed, which is efficient with two matrix inversions. The trained network can be stored for further use and be applied to multiple data sets, which offers a reduction in the corresponding computational cost. The trained neural network performs the inversion by directly mapping the seismic prestack data to seafloor elastic parameters, and therefore, it does not suffer from the typical problems, such as initial model, convergence, and local minima problems encountered in conventional iteration optimization-based inversion methods. The inversion accuracy of the network is comparable to that of the conventional inversion method, which means that the network architecture and corresponding training scheme proposed in this research are reliable for applications involving seafloor seismic prestack inversion. Numerical analysis verifies the efficacy of the ML-based method.