The concept of neural network (NN)-based seismic resolution enhancement has gained a lot of traction recently. Yet, the majority of works on the topic rely on training NNs on synthetic data via a supervised learning strategy, often encountering generalization issues on real data. To address this problem, we develop a self-supervised learning (SSL) method for seismic resolution enhancement. Specifically, we reinterpret seismic resolution enhancement as a frequency extension task, particularly focusing on the reconstruction of high-frequency components. Initially, we warm up the NN using the original/available band-limited data as pseudolabels, with input data derived from filtering out high-frequency elements from the data. Subsequently, the network undergoes iterative data refinement (IDR), where pseudolabels are predicted from the NN trained in the previous epoch, and input data are obtained by filtering out high-frequency components from these predictions. Based on this strategy, we also present a hybrid framework for simultaneous seismic denoising and resolution enhancement. During the whole training, we used multiloss constraints to enhance the network performance. The efficacy of our method is demonstrated through tests on both synthetic and field data.

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.

Seismic resolution enhancement is a key step for subsurface structure characterization. Although many have proposed the use of deep learning (DL) for resolution enhancement, these are typically hindered by the limitations in the application of synthetically trained networks onto real datasets. Domain adaptation (DA) offers an approach to reduce this disparity between training and inference data, aiming through the application of data transformations to bring the distributions of both data closer to each other. We propose a simple DA procedure, termed MLReal-Lite (the light version of the earlier introduced MLReal), that mainly relies on linear operations, namely convolution and correlation; these transformations introduce aspects of the field data into the synthetic data prior to training, and vice-versa with regard to the inference stage. Taking 1-D and 2-D resolution enhancement tasks as examples, we show how the inclusion of MLReal-Lite improves the performance of neural networks. Not only do the results demonstrate notable improvements in seismic resolution, they also exhibit a higher signal-to-noise ratio (SNR) and better continuity of events, in comparison to the tests without MLReal-Lite. Finally, while illustrated on a resolution enhancement task, our proposed methodology is applicable for any seismic data of dimensions N-D, offering a DA applicable from well ties through to 3-D seismic volumes, and beyond.

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.

Seismic inversion is an indispensable part of the earth exploration to precisely obtain the properties of subsurface media based on seismic data. However, the lack or inaccuracy of low-frequency (LF) information in seismic data constrains the correctness of the inversion. Traditional techniques encounter challenges in compensating the LF component of seismic data. Accessing the ability of deep learning to nonlinearly map inputs to expected outputs, we develop a neural network that can map poststack data to broader band data and then to impedance. We first propose an effective preprocessing scheme incorporating both well-logging and seismic data. Then, we extrapolate the LF information in the seismic data and invert the P-wave impedance with supervised and semisupervised frameworks, respectively. In the synthetic data example, the coefficient of determination ($R^{\mathrm{ 2}}$) reaches 0.99 for LF extrapolation and 0.98 for impedance inversion. In the field data example,$R^{\mathrm{ 2}}$is 0.826 between the inverted impedance and the real impedance of the validation well. Our experiments also reveal that the LF extrapolation improves the results of the impedance inversion.

Seismic facies analysis is to study the sedimentary environment of stratigraphic sequence and provides an important basis for reservoir prediction. Most of the existing analysis methods have low efficiency and heavily rely on manual experience, and therefore, it is difficult to interpret increasingly complex seismic data. Deep learning techniques can help to solve these problems and achieve automatic seismic facies classification. We regard seismic facies classification as a target segmentation problem and propose new method and training strategies. Our workflow primarily involves four sections. First, we process the manually annotated labels and seismic data with mirroring and cropping operations to ensure that network can accept input with arbitrary size and the model training is not limited to GPU memory. Second, data augmentation is applied to automatically generate massive training samples from the processed data. Third, we build two independent networks based on encoder-decoder architecture: one identifies all seismic facies simultaneously, and the other identifies single seismic facies in each model. However, both the results of the two networks have some drawbacks. Fourth, to overcome these drawbacks, we propose an ensemble learning method to get optimized model and test it on 3-D seismic data. The testing results manifest that the proposed method can improve the predictive ability of model, accurately describe the seismic facies, and can be applicable to entire seismic data volume.

Incoherent noise is one of the most common noise widely distributed in seismic data. To improve the interpretation accuracy of the underground structure, incoherent noise needs to be adequately suppressed before the final imaging. We propose a novel method for suppressing seismic incoherent noise based on the robust low-rank approximation. After the Hankelization, seismic data will show strong low-rank features. Our goal is to obtain the stable and accurate low-rank approximation of the Hankel matrix and then reconstruct the denoised data. We construct a mixed model of the nuclear norm and the$l_{1}$norm to express the low-rank approximation of the Hankel matrix constructed in the frequency domain. Essentially, the adopted model is an optimization for the subspace similar to the online subspace tracking method, thus avoiding the time-consuming singular value decomposition (SVD). We introduce the orthonormal subspace learning to convert the nuclear norm to the$l_{1}$norm to optimize the orthonormal subspace and the corresponding coefficient. Finally, two optimization strategies—the alternating direction method and the block coordinate descent method—are applied to obtain the optimized orthonormal subspace and the corresponding coefficient for representing the low-rank approximation of the Hankel matrix. We perform incoherent noise attenuation tests on synthetic and real seismic data. Compared with other denoising methods, the proposed method produces small signal errors while effectively suppressing the seismic incoherent noise and has a high computational efficiency.