S2S-WTV: Seismic Data Noise Attenuation Using Weighted Total Variation Regularized Self-Supervised Learning

Seismic data often undergoes severe noise due to environmental factors, which seriously affects subsequent applications. Traditional hand-crafted denoisers such as filters and regularizations utilize interpretable domain knowledge to design generalizable denoising techniques, while their representation capacities may be inferior to deep learning denoisers, which can learn complex and representative denoising mappings from abundant training pairs. However, due to the scarcity of high-quality training pairs, deep learning denoisers may sustain some generalization issues over various scenarios. In this work, we propose a self-supervised method that combines the capacities of deep denoiser and the generalization abilities of hand-crafted regularization for seismic data random noise attenuation. Specifically, we leverage the Self2Self (S2S) learning framework with a trace-wise masking strategy for seismic data denoising by solely using the observed noisy data. Parallelly, we suggest the weighted total variation (WTV) to further capture the horizontal local smooth structure of seismic data. Our method, dubbed as S2S-WTV, enjoys both high representation abilities brought from the self-supervised deep network and good generalization abilities of the hand-crafted WTV regularizer and the self-supervised nature. Therefore, our method can more effectively and stably remove the random noise and preserve the details and edges of the clean signal. To tackle the S2S-WTV optimization model, we introduce an alternating direction multiplier method (ADMM)-based algorithm. Extensive experiments on synthetic and field noisy seismic data demonstrate the effectiveness of our method as compared with state-of-the-art traditional and deep learning-based seismic data denoising methods

Attention and Hybrid Loss Guided 2-D Network for Seismic Impedance Inversion

Deep learning methods, especially convolutional neural networks, achieve state-of-the-art performance on seismic impedance inversion. Most of the methods are based on one-dimensional (1-D) convolution, tending to yield lateral discontinuities of impedance on field data applications. To alleviate this problem, we design a network equipped with 2-D convolutions and a coordinate attention (CA) block. The former can take the relationship between adjacent traces into consideration. The latter can capture the positional relationship of the geological structure, both horizontally and vertically. At the same time, we use a hybrid loss combined with an edge operator and mean square error to further improve the stability of the designed network. Comparison experiments on the synthetic SEAM model and field seismic data demonstrate the effectiveness of the adopted components, 2-D convolution, CA, and hybrid loss function in improving the lateral continuity of inverted impedance. For field seismic data, the impedance predicted by the proposed method shows improved lateral continuity and high resolution compared with the 1-D network and constrained sparse spike inversion method using commercial software (InverTrace Plus module in Jason)

Semi-Supervised Learning for Seismic Impedance Inversion Using Generative Adversarial Networks

Seismic impedance inversion is essential to characterize hydrocarbon reservoir and detect fluids in field of geophysics. However, it is nonlinear and ill-posed due to unknown seismic wavelet, observed data band limitation and noise, but it also requires a forward operator that characterizes physical relation between measured data and model parameters. Deep learning methods have been successfully applied to solve geophysical inversion problems recently. It can obtain results with higher resolution compared to traditional inversion methods, but its performance often not fully explored for the lack of adequate labeled data (i.e., well logs) in training process. To alleviate this problem, we propose a semi-supervised learning workflow based on generative adversarial network (GAN) for acoustic impedance inversion. The workflow contains three networks: a generator, a discriminator and a forward model. The training of the generator and discriminator are guided by well logs and constrained by unlabeled data via the forward model. The benchmark models Marmousi2, SEAM and a field data are used to demonstrate the performance of our method. Results show that impedance predicted by the presented method, due to making use of both labeled and unlabeled data, are better consistent with ground truth than that of conventional deep learning methods

Preliminary Investigation of Wavefield Depth Extrapolation by Two-Way Wave Equations

Most of the wavefield downward continuation migration approaches are relying on one-way wave equations, which move the seismic energy always in one direction along depth. The one-way downward continuation migrations only use the primaries for imaging and do not treat secondary reflections recorded on the surface correctly. In this paper, we investigate wavefield depth extrapolators based on the full acoustic wave equations, which can propagate wave components to opposite directions. Several two-way wavefield downward continuation propagators are numerically tested in this study. Recursively implementing of the depth extrapolator makes it necessary and important to eliminate the unstable wave modes, that is, evanescent waves. For the laterally varying velocity media, distinction between the propagating and evanescent wave mode is less clear. We demonstrate that the spatially localized two-way beamlet propagator is an effective way to remove the evanescent waves while maintain the propagating mode in laterally inhomogeneous media

Fault Detection via 2.5D Transformer U-Net with Seismic Data Pre-Processing

Seismic fault structures are important for the detection and exploitation of hydrocarbon resources. Due to their development and popularity in the geophysical community, deep-learning-based fault detection methods have been proposed and achieved SOTA results. Due to the efficiency and benefits of full spatial information extraction, 3D convolutional neural networks (CNNs) are used widely to directly detect faults on seismic data volumes. However, using 3D data for training requires expensive computational resources and can be limited by hardware facilities. Although 2D CNN methods are less computationally intensive, they lead to the loss of correlation between seismic slices. To mitigate the aforementioned problems, we propose to predict a 2D fault section using multiple neighboring seismic profiles, that is, 2.5D fault detection. In CNNs, convolution layers mainly extract local information and pooling layers may disrupt the edge features in seismic data, which tend to cause fault discontinuities. To this end, we incorporate the Transformer module in U-net for feature extraction to enhance prediction continuity. To reduce the data discrepancies between synthetic and different real seismic datasets, we apply a seismic data standardization workflow to improve the prediction stability on real datasets. Netherlands F3 real data tests show that, when training on synthetic data labels, the proposed 2.5D Transformer U-net-based method predicts more subtle faults and faults with higher spatial continuity than the baseline full 3D U-net model

Multi-Task Deep Learning Seismic Impedance Inversion Optimization Based on Homoscedastic Uncertainty

Seismic inversion is a process to obtain the spatial structure and physical properties of underground rock formations using surface acquired seismic data, constrained by known geological laws and drilling and logging data. The principle of seismic inversion based on deep learning is to learn the mapping between seismic data and rock properties by training a neural network using logging data as labels. However, due to high cost, the number of logging curves is often limited, leading to a trained model with poor generalization. Multi-task learning (MTL) provides an effective way to mitigate this problem. Learning multiple related tasks at the same time can improve the generalization ability of the model, thereby improving the performance of the main task on the same amount of labeled data. However, the performance of multi-task learning is highly dependent on the relative weights for the loss of each task, and manual tuning of the weights is often time-consuming and laborious. In this paper, a Fully Convolutional Residual Network (FCRN) is proposed to achieve seismic impedance inversion and seismic data reconstruction simultaneously, and a method based on the homoscedastic uncertainty of the Bayesian model is used to balance the weights of the loss function for the two tasks. The test results on the synthetic datasets of Marmousi2, Overthrust, and Volve field data show that the proposed method can automatically determine the optimal weight of the two tasks, and predicts impedance with higher accuracy than single-task FCRN model

Expressway Exit Station Short-Term Traffic Flow Prediction With Split Traffic Flows According Originating Entry Stations

As an essential component of Intelligent Transportation Systems (ITS), short-term traffic flow prediction is a key step to anticipate traffic congestion. Due to the availability of massive traffic data, data-driven methods with a variety of features have been applied widely to improve the traffic flow prediction. China has the longest total length of expressways in the world and there is significant information recorded when vehicles enter and exit the expressway. In this paper, we collect data at an expressway exit station in Shanghai, split the data according to its originating entry stations and predict the corresponding exit station traffic flow using the multi split traffic flows. First, the original records are collected, preprocessed, split, aggregated and normalized. Second, the Long Short-Term Memory (LSTM) model is applied to learn from the features of the overall flow and split traffic flows to predict the overall exit flow. The baselines are models which only overall flow information is considered. Compared with the baselines, in other models, the split flows according entry stations are also considered for prediction. Finally, the LSTM model is made comparison with the Convolutional LSTM(ConvLSTM), the K-Nearest Neighbor (KNN) model and the Support Vector Regression (SVR) model. When the information of overall flow and 6 split traffic flows is used and step is set to 11 (with 5 minute aggregation), the model prediction performs best. Compared with the best result of LSTM baseline model, the improvement of prediction accuracy is up to 5.48 percent by Mean Absolute Error (MAE)

Non-orthogonal beam coordinate system wave propagation and reverse time migration

Grid size has a significant influence on the computation efficiency and accuracy of finite-difference seismic modeling and can change the workload of reverse time migration (RTM) remarkably. This paper proposes a non-orthogonal analytical coordinate system, beam coordinate system (BCS), for the solution of seismic wave propagation and RTM. Starting with an optical Gaussian beam width equation, we expand the representation on vertically variable velocity media, which is the most common scenario in seismic exploration. The BCS based on this representation can be used to implement an irregular-grid increment finite-difference that improves the efficiency of RTM. Based on the Laplacian expression in Riemannian space, we derive the wave equation in the BCS. The new coordinate system can generate an irregular grid with increment increasing vertically along depth. Through paraxial ray tracing, it can be extended to non-analytical beam coordinate system (NBCS). Experiments for the RTM on the Marmousi model with the BCS demonstrate that the proposed method improves the efficiency about 52% while maintaining good image quality

Secure Construction of Virtual Organizations in Grid Computing Systems

Virtual organization (VO) is an important abstraction for designing large-scale distributed applications involving extensive resource-sharing. Existing works on VO mostly assumes that the VO already exists or is created by mechanisms outside of their system model. The VO construction is challenging and critical due to its dynamic and distributed nature. This paper presents a VO Construction Model and an implementation algorithm which is based on a threshold approach and is secure and robust in that events such as member admission, member revocation, VO splitting and merging etc. can be handled without centralized administration. Also authentication and communications among VO members are efficient and without tedious key exchanges and management usually needed in VO built upon the Grid Security Infrastructure (GSI).10 page(s