The rapid and accurate detection of network traffic anomaly is one of the preconditions to guarantee the effective work of the network. Aiming at the deficiency of present methods of network traffic anomaly detection, we propose a scale-adaptive method based on wavelet packet. By means of wavelet packet decomposition, our method can adjust the decomposition process adaptively, has the same detective ability to the anomaly of various frequency, especially the middle and high frequency ones which can not be checked out by the multi-resolution analysis. By means of adaptive reconstruction of the wavelet packet coefficient of different wavelet domains which anomaly, our method is able to confirm the characteristics of anomaly and enhance the reliability of detection. The simulation results prove that the method can detect the network traffic anomaly efficiently

Seismic data are always in low-resolution due to the limitations of seismic acquisition and processing technology, which bring challenges to subsequent seismic interpretation. Deep learning has been successfully applied to the seismic data super-resolution, all methods tend to directly learn the mapping relationship between low-resolution seismic images and super-resolution seismic images through some complex convolutional neural networks. But blindly increasing the depth of the network brings limited improvement to the super-resolution work. We propose a novel geophysical prior guided framework for seismic data super-resolution to solve this problem. Specifically, we select fault prior to guide the training of deep learning model: we use knowledge distillation technology to progressively propagate the fault prior from the teacher network (trained with the low-resolution synthetic seismic data/high-resolution fault prior and high-resolution synthetic seismic data pairs) to the student net-work (trained with the low-resolution synthetic seismic data and high-resolution synthetic seismic data pairs). To better propagate fault priors, we use feature space loss and soft ground truth loss in student network training. Finally, we use the trained student network to complete the super-resolution of synthetic validation seismic data and real seismic data. In addition, in our super-resolution framework, we directly process 3D seismic data instead of 2D seismic images, which further improves the effects of super-resolution and the subsequent seismic interpretation. Compared with the state-of-the-art seismic super-resolution method, the experimental results show that the super-resolution results of our method can depict the faults more clearly.

Extracting horizons from a seismic image has been playing an important role in seismic interpretation. However, how to fully use global-level information contained in the seismic images such as the order of horizon sequences is not well-studied in existing works. To address this issue, we have developed a novel method based on a directed and colored graph, which encodes effective context information for horizon extraction. Following the commonly used framework, which generates horizon patches and then groups them into horizons, we first build a directed and colored graph by representing horizon patches as vertices. In addition, edges in the graph encode the relative spatial positions of horizon patches. This graph explicitly captures the geologic context, which guides the grouping of the horizon patches. Then, we conduct premerging to group horizon patches through matching some predefined subgraph patterns that are designed to capture some special spatial distributions of horizon patches. Finally, we have developed an ordered clustering method to group the rest of the horizon patches into horizons based on the pairwise similarities of horizon patches while preserving geologic reasonability. Experiments on real seismic data indicate that our method can outperform the autotracking approach solely based on the similarity of local waveforms and can correctly pick the horizons even across the fault without any crossing, which demonstrates the effectiveness of exploring the spatial information, i.e., the order of horizon sequences and special spatial distribution of horizon patches.

Fault detection is a key step in seismic structure interpretation. Current research has achieved good results in fault detection by using synthetic training data to train deep-learning models. However, there is an inevitable difference in frequency bandwidth between synthetic training data and real seismic data, which makes it difficult for deep-learning models to obtain ideal fault detection results on real seismic data. To solve this problem, the feature pyramid network (FPN) is introduced to obtain multiscale deep-learning features, which can reduce the impact of seismic data frequency bandwidth differences on fault detection. Then, we apply the multiscale wavelet transform to extract multiscale frequency spectral features of the seismic data and combine them with the multiscale deep-learning features through concatenation operation. Furthermore, the seismic data is decomposed into signals with different frequency bands through the wavelet transform, and we use the energy of these signals as the network weights of multiscale mixed features to further improve the frequency adaptability of our method. Based on these works, we not only improve the fault detection effect in a specific work area but also improve the generalization ability of the deep-learning model in different work areas, thus further promoting the application of deep learning in actual production. Compared with the fault detection results by the traditional deep-learning model U-Net and the traditional FPN on multiple real seismic data and synthetic seismic data, experimental results demonstrate the effectiveness of our method.

Fault surface extraction plays a vital role in structural interpretation and structural modeling, which can enhance our understanding of geologic structures. Significant effort has been invested in fault surface extraction in the past few years. These contributions can be roughly divided into two types: one constructs fault sticks or fault patches and links them to form fault surfaces, and the other constructs a complete fault surface by roughly determining the location of a fault. The former is likely to extract incomplete fault surfaces in areas with low data quality or complex fault structures. The latter has problems dealing with faults in the case of complex fault structures, such as intersecting faults. To effectively address these problems, we have developed a novel fault extraction workflow from a global perspective, which can extract fault surfaces on the premise of obtaining the fault distribution of the entire data set. First, we introduce a multilayer complex network to characterize the overall distribution of faults, in which nodes represent faults, node attributes cover information, such as the location of the faults, and edge attributes give the relationship between faults. Then, for each fault represented by the node, we develop a fault surface extraction method based on computational topology, which can guarantee the completeness of the fault surfaces. We apply our method to synthetic data and field seismic data to evaluate the method quantitatively and qualitatively, respectively. The results indicate that the proposed approach extracts the fault effectively, ensures the completeness of the fault surface, and has high performance in areas with complex faults. Thus, the approach can work well in complex situations, such as intersecting faults.

Drag reduction technologies mainly include the mechanical method and the chemical method. Mechanical drag reduction technologies are widespread in the drilling field due to their environmental friendliness and ease of use. Vibration technologies are among some of the most-used mechanical drag reduction technologies. However, various types of vibration tools have the negative effect of obstructing the promotion and application of mechanical drag reduction technologies. This paper widely investigated the types and applications of vibration tools. A drilling agitator system and slider drilling technology were included. The structure and mechanism of the vibration tool were studied. A multiscale friction model was proposed based on the Dahl model according to the drilling environment. The model was verified using experimental data. The multiscale friction model was used to analyze the drag reduction mechanism and the effect of different kinds of vibration technologies. Simulations demonstrated that vibration technologies can effectively reduce the axial friction of the drill string. Longitudinal vibration can reduce the axial friction such that the dependence of the reduced coefficient of friction on the reduced velocity does not change significantly after the reduced oscillation amplitude exceeds the critical value of one. Axial friction decreased with the increasing amplitude of the radial vibration. However, the reduction effect has no relationship with the rate of penetration (ROP). Torsional vibration and rocking motion can reduce the axial friction force. When the oscillation amplitude increases, the axial friction decreases. The reduction effect of slider drilling technology is better than torsional vibration and two other technologies in terms of friction reduction.