Deep learning (DL) has emerged as a focal point in addressing various challenges within the field of exploration seismology, prominently featuring applications in seismic data interpolation. Existing neural networks utilized in exploration seismology predominantly employ real-valued nonlinear transforms on time—space seismic data. Nevertheless, the seismic signal contains significant information in its phase, whereas the real-valued transforms meet with challenges to take into account the entire phase information of nonstationary seismic data. To surmount this challenge, we propose a novel framework termed frequency-space-dependent unsupervised DL interpolation (FUDLInter). The primary objective of FUDLInter is to interpolate high-dimensional seismic data within the frequency-space domain, thereby optimizing the exploitation of intricate information derived from the fast Fourier transform representation of seismic signals. In this framework, we meticulously explore and harness the capability of a complex-valued deep convolutional neural network employing the U-Net architecture, designated as CVU-Net. This network is designed to autonomously recover each frequency component of both 3-D and 5-D seismic data. We leverage the Bernoulli sampling technique and the nonmissing elements in the subsampled data to construct a data misfit model. The efficacy of the proposed method is evaluated using both high-dimensional synthetic data and field data examples. The interpolation results from the proposed FUDLInter method outperform those achieved by alternative methods, i.e., the projection onto convex sets with an adaptive threshold schedule (APOCS), damped rank-reduction (DRR), and DenseNet methods.

To tackle the pressing issue of climate change stemming from carbon emissions, carbon capture and storage (CCS) projects have emerged worldwide, which aim to store carbon dioxide (CO2) produced during industrial production in subsurface geological structures. To ensure the efficacy of these projects, 4D seismic surveys are conducted to monitor the stored CO2and identify potential leakage at an early stage. In recent years, deep learning has been widely employed for seismic data interpretation, which has shown promising results in terms of objectivity and efficiency when compared to manual interpretation. In this study, we address the CO2monitoring challenge using a 3D encoder-decoder network with convolutional vision transformer (CvT) called CvTNet, through the supervised learning scheme. By formulating the CO2monitoring task as an image segmentation problem, we use CvTNet to generate a 3D CO2probability image from a 4D seismic image. CvTNet leverages the CvT module, which provides superior dynamic attention and global context compared to convolutional neural networks. We evaluate the effectiveness of CvTNet on the Sleipner CCS project, using a 4D seismic image (comprising a 3D baseline image from 1994 and a 3D time-lapse image from 2010) and a CO2probability image from 2010 as the CvTNet training input and label, respectively. We apply the trained model to seismic images from other monitoring years to analyze CO2plume growth during the Sleipner CCS project. Tests indicate that CvTNet achieves higher CO2segmentation accuracy than U-net and can be generalized across other 4D seismic images.

Elastic parameters play pivotal roles in geophysics, with seismic amplitude variation with offset (AVO) inversion being a common method for obtaining the parameters. In contrast to isotropic media, vertical transversely isotropic (VTI) media, which introduce anisotropic parameters to describe geological characteristics, align more closely with field strata. Conducting AVO inversion based on VTI media enhances the accuracy of inverted parameters. Conventional AVO inversion methods typically rely on low-frequency parameters or training samples, which are often generated from well-log data. However, well-log data are usually insufficient, and obtaining accurate anisotropic parameters from well-log data is challenging. These hinder the generation of low-frequency anisotropic parameters or the creation of training samples with anisotropic parameters as labels, thus impacting the accuracy of inverted parameters for VTI media. Addressing these challenges, we construct a double discriminator conditional generative adversarial network (DDCGAN) models under the constraints of the convolution model theory. Building upon the foundation, we propose a seismic AVO inversion method tailored for VTI media. The DDCGANs combine the conditional generative adversarial networks (CGANs), which have superior feature extraction ability, with the well-established convolution model theory, making it suitable for addressing AVO inversion challenges in VTI media. Iterative optimization of the constructed DDCGANs is achieved by building combined loss functions, including errors of elastic parameters and seismic data. Trial calculations using model and field data demonstrate that the proposed method can improve the accuracy of inverted parameters compared to conventional AVO inversion methods, showcasing its feasibility, advancement, and practicality.

Seismic amplitude variation with offset (AVO) inversion provides elastic parameters for reservoir identification. When processing field seismic data, conventional elastic medium AVO inversion methods typically inadequately account for the absorption of seismic waves by subsurface media, and inverted elastic parameters have accuracy upper bounds. The absorption and attenuation characteristics of subsurface media are described by quality factors introduced by viscoelastic media. The accuracy of inverted parameters will increase by studying AVO inversion methods based on viscoelastic media. Typically, low-frequency elastic parameters or conventional training samples affect how accurate conventional AVO inverted elastic parameters are. Since quality factors are typically absent from well-log data, it is challenging to produce suitable low-frequency elastic parameters and training samples. To address this issue, we propose an AVO inversion method for the viscoelastic method based on invertible neural networks (INNs) with bijective structures. We first construct a tandem INN for fitting the bidirectional mapping between elastic parameters and seismic data. Then, training parameters, which are easier to obtain than conventional training samples, are randomly generated based on the characteristics of the target work area data. Next, the forward process of the tandem INN is trained to fit the forward process from elastic parameters to seismic data. Finally, elastic parameter inversion is achieved through the reverse process of the trained tandem INN. The proposed method does not require initial elastic parameters and training samples. Model and field data tests prove that the proposed method is feasible, practical, and progressive.

Incoherent noise suppression is an indispensable step in seismic data processing. Recently, deep learning (DL) methods have gained commendable success in seismic data denoising, one of which is the supervised DL denoising method using clean data as the training label, whereas the cost of obtaining clean data is high. We investigate a robust self-supervised DL denoising method without using clean data. Bernoulli-sampled training pairs of the raw noisy data produced by the dropout layer are served to train the NN, and a Monte Carlo (MC) self-integrated technique results in further improving the denoising quality of the trained NN during the testing. Compared with the f-x deconvolution (FXDECON), deep image prior (DIP), and sparse autoencoder (SAE) methods via synthetic and real data examples, the proposed method outperforms these methods for enhancing the signal-to-noise ratio (SNR) and reducing the signal loss.