Vertical seismic profiling (VSP) is widely applied in the field of seismic exploration to deliver high-quality subsurface images and enable quantitative characterization around the wellbore region. The separation of VSP upgoing and downgoing wavefields is a practical step in the wavefield processing, which sets the foundation for the final imaging quality. With recent advances in deep learning (DL), a number of new attempts in seismic signal processing have proven effective. Inspired by that, we propose an unsupervised VSP wavefield separation framework via dual convolutional autoencoders (dualCAEs). Our method is based on two characteristics of up- and downgoing wavefields: one is directional continuity, and the other is the zero-mean feature. Two regularizers are proposed to constrain these two features. Thanks to this, our method does not require any training data other than the input data itself. Ablation and comparison studies on synthetic data validate the effectiveness of our method. Generalization tests on SEAM open data and field VSP records in the Dong area show the superiority of robustness and fidelity over traditional$F$–$K$and median filtering methods.

Elastic full-waveform inversion (EFWI) has received significant attention in the industry for many years. Numerous studies have shown that parameter optimization methods based on physics-informed neural networks (PINNs) are more promising in full-waveform inversion (FWI) compared to traditional methods. Compared with surface seismic techniques, vertical seismic profiling (VSP) offers advantages such as lower acquisition costs, higher signal-to-noise ratios, and higher resolution. To address the problem of FWI for VSP, this study proposed a physics-informed recurrent neural network (RNN) method with prior knowledge constraint for elastic FWI. Our main idea is threefold. First, we mined and characterized the prior knowledge for VSP FWI, which primarily includes geological knowledge, the 1-D velocity profile around the wellbore, and the empirical relationships between compressional and shear wave velocities. Second, we implemented a PIRNN with prior knowledge constraint, using the RNN framework to optimize trainable parameters. Finally, using the PIRNN framework with prior knowledge constraint, we achieved elastic FWI for VSP. Numerical examples demonstrate that the inversion results for the salt and Marmousi models indicate that incorporating prior knowledge increases the accuracy of FWI. Compared to data-driven machine learning methods, this architecture eliminates the impact of sample quality on parameter optimization.

The separation of P- and S-waves is pivotal in the processing of multicomponent seismic data. The complexity of geological structures often leads to intricate P- and S-wavefields, which poses challenges for identifying and separating waves using conventional signal features, such as the F-K domain or$\tau $-p domain distributions, while data-driven machine learning methods overly rely on the quality of samples. This article proposes a novel approach for P- and S-wave separation in complex geological structures based on a knowledge-guided autoencoder network. First, the existing full waveform inversion (FWI) can obtain relatively accurate knowledge representations of P- and S-waves. A recurrent neural network (RNN) was employed for elastic wave FWI to acquire a knowledge representation of intricate P- and S-waves in complex structures. Subsequently, a dual-branch autoencoder network was constructed based on the obtained knowledge representation of the complex P- and S-waves. One branch was guided by the knowledge representation of P-waves for P-wave separation, whereas the other branch was guided by the knowledge representation of S-waves for S-wave separation. Finally, a comprehensive autoencoder network architecture was devised that incorporates waveform reconstruction loss, P-wave knowledge guidance loss, and S-wave knowledge guidance loss for effective P- and S-wave separation. Theoretical analyses and numerical simulations were performed, and they demonstrated the effectiveness of the proposed method for achieving P- and S-wave separation in complex geological structures.

The pre-/sub-salt fractured networks provide key clues to understanding the tectonic history and the subsurface reservoir evolution. Yet, they are among the most complicated targets for geophysical investigations. We develop a structure-oriented mapping technique to delineate the high-resolution dipping layers across the borehole region using the zero-offset vertical-seismic-profiling (ZVSP) survey, which is conventionally used to provide one-dimensional (1-D) wave propagation information. The key information for the single-shot VSP mapping, which is the structural dip across the borehole, is unreliable from the poor sub-salt surface seismic image. Alternatively, we obtain the structural dips via reflection layer tomography. Taking advantage of the accurate interval velocities and the high-fidelity identifications from the reflection events, we manage to invert for the geometry of the main reflectors identified across the wellbore. Based on such information, we propose an effective processing strategy and achieve a high-resolution image of the dipping structures around the borehole region from the ZVSP. The current result compares reasonably well with the corresponding surface seismic profile but supplies higher-resolution details of the dipping structures and fault networks below the thick evaporite caprock where the surface seismic image degrades sharply. The enhanced subsurface image encourages better structural evaluation, geologic interpretation, and future 2-D/3-D VSP survey design.

The 3-D seismic data reconstruction can be understood as an underdetermined inverse problem, and thus, some additional constraints need to be provided to achieve reasonable results. A prevalent scheme in 3-D seismic data reconstruction is to compute the best low-rank approximation of a formulated Hankel matrix by rank-reduction methods with a rank constraint. However, the predefined Hankel structure is easily damaged by the low-rank approximation, which leads to harming its recovery performance. In this article, we present a structured tensor completion (STC) framework to simultaneously exploit both the Hankel structure and the low-tubal-rank constraint to further enhance the performance. Unfortunately, under the assumption of elementwise sampling used by existing methods, STC is intractable to be solved since Hankel constraints cannot be expressed as linear tensor equations. Instead, tubal sampling is proposed to describe the missing trace behavior more accurately and further build a bridge between tensor and matrix completion (MC) to overcome the solving issue in two aspects: through the bridge from tensor to MC, STC can be solved efficiently using MC from random samplings of each frontal slice in the Fourier domain. Through the bridge from matrix to tensor completion, various tensor models within the framework can be developed from noise-specific MC to meet the need for data reconstruction in changeable noise environments. Moreover, alternating-minimization and alternating-direction methods of multipliers are developed to solve the proposed STC. The superior performance of STC is demonstrated in both synthetic and field seismic data.