The rapid development of Internet technology has made social networks central platforms for information dissemination and acquisition. As key participants, users exhibit increasingly complex connection patterns, reflecting the dynamic nature of online interactions. Therefore, in order to better understand and manage social networks, analyzing these connection patterns, particularly identifying the potential connections between users with similar tendencies, has become a critical research focus in social network studies. Nevertheless, the existing methods exhibit limitations in comprehensively harnessing the heterogeneous nature of social networks and usually over-rely on local network structures while neglecting global patterns. To address these problems, we propose an innovative method based on weighted random walks within heterogeneous information networks (HINs). We first employ HINs to structurally represent and systematically organize complex social network data, leveraging meta-paths to model user connection patterns at semantic levels. Then, based on the meta-paths, we develop an adaptive weighted random walk strategy to integrate global structural features with local semantic information and connect users with similar tendencies. Experimental results on both Twitter and public HIN datasets demonstrate that our method outperforms other classical methods in accurately connecting users with similar tendencies.

Internet Autonomous System (AS) level topology includes AS topology structure and AS business relationships, describes the essence of Internet inter-domain routing, and is the basis for Internet operation and management research. Although the latest topology inference methods have made significant progress, those relying solely on local information struggle to eliminate inference errors caused by observation bias and data noise due to their lack of a global perspective. In contrast, we not only leverage local AS link features but also re-examine the hierarchical structure of Internet AS-level topology, proposing a novel inference method called topoKG. TopoKG introduces a knowledge graph to represent the relationships between different elements on a global scale and the business routing strategies of ASes at various tiers, which effectively reduces inference errors resulting from observation bias and data noise by incorporating a global perspective. First, we construct an Internet AS-level topology knowledge graph to represent relevant data, enabling us to better leverage the global perspective and uncover the complex relationships among multiple elements. Next, we employ knowledge graph meta paths to measure the similarity of AS business routing strategies and introduce this global perspective constraint to infer the AS business relationships and hierarchical structure iteratively. Additionally, we embed the entire knowledge graph upon completing the iteration and conduct knowledge inference to derive AS business relationships. This approach captures global features and more intricate relational patterns within the knowledge graph, further enhancing the accuracy of AS-level topology inference. Compared to the state-of-the-art methods, our approach achieves more accurate AS-level topology inference, reducing the average inference error across various AS link types by up to 1.2 to 4.4 times.

The separation of upgoing and downgoing waves in vertical seismic profiling (VSP) data is crucial for subsequent imaging, interpretation, and inversion. The interweaving of upgoing and downgoing waves and the presence of noise complicate the entire wave field, making it difficult to separate upgoing and downgoing waves. Various model-driven separation methods including low-rank approximation (LRA) and data-driven methods have achieved promising results. However, the performance of pure model-driven methods, such as F-K filtering and Radon transform, rely on domain transformation sparse representation for both upgoing and downgoing wavefield. Additionally, pure data-driven methods require a large number of precisely separated signals as training samples, which is a nontrivial task. To overcome these difficulties, this article proposes a model-data-driven VSP wavefield separation framework that iteratively completes the task of wavefield separation in an unsupervised manner. The key to this model is to use the powerful feature representation capability of the deep convolutional autoencoder (DCAE) to model the downgoing waves and use LRA to model the upgoing waves. By accurately modeling the upgoing and downgoing waves, we integrate the model-driven and data-driven methods together, while protecting both the upgoing and downgoing waves, and inheriting the advantages of the DCAE and LRA methods. Subsequently, we also proposed an alternating minimization optimization strategy to optimize the parameters of the model and iteratively obtain high-quality solutions. Comparative experiments on synthetic data and real data show that our method can achieve effective separation results while suppressing Gaussian noise.

Inferring the home locations of online social network (OSN) users from their corresponding account data is an important process for many applications, such as personal privacy protection and business advertising applications. The existing methods typically use a supervised learning method to infer a user's home location according to a single or partial aspect of their OSN information. However, the home location of a user may be represented in a biased way if only a single or partial aspect of the information is used, and the performances of the supervised learning-based methods are also very sensitive to the quality of the training set utilized. To address these problems, this article presents a novel unsupervised method for inferring the home locations of the OSN users. The method first builds a heterogeneous network model to comprehensively represent the complex location information in the OSN data and then recursively infers users’ home locations by fusing the direct and indirect location information of the users. Experiments that compared our method with five existing typical Twitter user home location inference methods on a Twitter dataset demonstrate that the proposed method can significantly improve the accuracy and reliability of user home location inference.

Seismic inversion accuracy significantly affects the quality of reservoir modeling. Given the limited samples of well logging, prior information such as geological data and stratigraphic characteristics is crucial for enhancing the accuracy and reliability of inversion results. The inability to fully exploit prior information in the data to compensate for data deficiencies may impair network performance. Moreover, the traditional artificial neural networks (ANNs) cannot fully exploit prior information in the data to compensate for data deficiencies, which may impair network performance and further leads to the network’s limited ability to effectively assimilate external prior knowledge. Therefore, a prestack seismic inversion driven by prior information neural network (PINN) and statistical characteristic is proposed to integrate various geological prior information. Initially, lithological analysis is conducted on the well logging data, leading to the classification of the strata into a series of sub-layers based on lithology. After obtaining the statistical characteristics of thickness and elastic parameters for each sub-layer, a geostatistical algorithm is employed to generate numerous pseudo-wells that conform to actual sedimentary laws. Subsequently, the PINN is pre-trained using substantial samples, encompassing both pseudo and real well logging data, to integrate geological structural prior information into its network parameters. Lastly, PINN served as a specially designed prior constraint for the inversion network, allowing its geological information to effectively guide the inversion process via backpropagation. This inversion method is applied to both synthetic and field examples, and in comparison to conventional inversion algorithms, it demonstrated superior accuracy in blind well verification.

The 3-D seismic multihorizon extraction is crucial for 3-D sequence stratigraphy analysis and reservoir modeling. Deep neural networks (DNNs) often cause dislocated horizons in regions with complex geological structures, such as faults and unconformities. This issue arises from two main factors. First, obtaining horizon labels from real seismic data is subjective and expensive, resulting in existing DNNs lacking field seismic data labels, which limits their ability to extract multiple horizons across complex geological structures. Second, directly extracting multiple horizons using only seismic data reduces precision in discontinuous areas with faults and unconformities. To address these issues, this article proposes a domain adaptation layer based on multikernel maximum mean discrepancy (MK-MMD) and designs a domain adaptive DNN (DA-DNN) for seismic multihorizon extraction. We map synthetic and field seismic data to the reproducing kernel Hilbert space (RKHS) and use MK-MMD to minimize feature differences between them. Unlike the traditional multiscale Gaussian kernel function used in MK-MMD, this article constructs a hybrid kernel function that integrates multiscale Gaussian and multiscale Laplacian kernels. The multiscale Gaussian kernel evaluates local-to-global feature differences in continuous areas, whereas the multiscale Laplacian kernel captures rapid feature variations in complex geological structures. Finally, a few seismic horizons and fault attributes guide the training process of DA-DNN, further improving the prediction accuracy of multihorizon extraction. Synthetic and field seismic examples show our model can extract seismic multiple horizons more accurately in field seismic data and performs better in discontinuous areas.

Two sets of rotationally invariant, horizontal and vertical fractures permeated in a homogeneous isotropic background rock can yield a long-wavelength effective orthorhombic medium. Amplitude variation with angle and azimuth (AVAZ) inversion is an effective tool to invert azimuthal seismic data for orthorhombic medium parameters. Given the inherently band-limited nature of real seismic data, orthorhombic AVAZ inversion without reasonable model constraints is a challenging task to obtain stable and reliable estimated results. In this paper, we have developed a novel joint Laplace- and Fourier-domain seismic inversion method to estimate P- and S-wave velocities, density, and horizontal and vertical fracture densities. The orthorhombic AVAZ seismic forward modeling in the Laplace domain is established by introducing the Fourier and damping operators. We present a two-step strategy for joint Laplace- and Fourier-domain seismic inversion in a Bayesian inference framework. Firstly, we perform the Laplace-domain seismic inversion to obtain long-wavelength models of elastic parameters and fracture parameters by exploiting the low-frequency components of the damped azimuthal seismic data. Secondly, with the Laplace-domain inversion results as initial models, the Fourier-domain seismic inversion is implemented to obtain complete inversion results, where all frequency components of original azimuthal seismic data are employed. We demonstrate the feasibility of our method through a synthetic data example and an application of a field data set acquired over a fractured shale reservoir. Meanwhile, we compare our method with the Fourier-domain seismic inversion, results show that our method can yield more reasonable estimated results of orthorhombic medium parameters than the Fourier-domain seismic inversion.

Natural fractures and rock brittleness play an important role in the fracturing and development of shale gas reservoirs. Amplitude variation with angle and azimuth (AVAZ) inversion provides an effective tool for estimating brittleness and fracture properties. However, AVAZ inversion generally employs a linearized reflection coefficient approximation, commonly not applicable for cases with strong contrasts in rock properties and long-offset data. Meanwhile, the large number of model parameters poses great challenges for multiparameter simultaneous inversion. To overcome these limitations, we propose a novel optimal AVAZ inversion approach to estimate the brittleness indicator and fracture density in horizontal transversely isotropic (HTI) media. We first utilize the anisotropic Zoeppritz equation and the convolution model to generate reference AVAZ gathers from well-log data. The singular value decomposition (SVD) method is then employed to obtain optimal basis functions and optimal coefficients that directly link band-limited elastic and fracture reflectivities to observed AVAZ data. This enables the direct estimation of band-limited elastic and fracture reflectivities from observed AVAZ data. Finally, Bayesian poststack seismic inversion, constrained by modified Cauchy prior and low-frequency models, is utilized to invert these band-limited reflectivities for elastic and fracture parameters individually. The feasibility of our method is demonstrated on a synthetic example and a field dataset from the southern Sichuan Basin. Results reveal the ability of our method to produce more satisfactory estimated results of the brittleness indicator and fracture density than the AVAZ simultaneous inversion method, which could aid in the seismic characterization of rock brittleness and natural fractures.

Due to the non-Gaussian distribution of erratic noise, conventional Gaussian denoising methods often encounter substantial challenges and pressures when suppressing this kind of noise. To overcome this challenge, several state-of-the-art (SOTA) schemes, for instance, robust low-rank approximation (LRA) and deep learning (DL) methods, have been designed and achieved promising results in the treatment of erratic noise. However, these SOTA denoising methods focus mainly on matrix-based modeling representations and fail to fully reflect the correlations associated with erratic noise and valid signals in the spatial dimension and thus may display suboptimal performance. As an alternative, a robust tensor DL (RTDL) denoising method for unsupervised 3-D seismic erratic noise suppression that involves the use of a reasonable combination of tensor sparse representation (SR) and a tensor neural network (tNN) is proposed in this study. The key to RTDL is to introduce a robust tensor sparse norm for erratic noise to exhaustively exploit the spatial tubular sparse distribution properties in 3-D space; notably, adding a tensor sparse norm to the tNN model yields a new data-driven model with 3-D erratic noise reduction capabilities. To find the optimized parameters of the new model, an efficiency tensor optimization method is established on the basis of alternating minimization (Alt), the aim of which is to alternately solve two subproblems involving tensor SR and a tNN. This paper presents well-designed experiments and satisfactory results compared with those of SOTA methods based on both synthetic and real field datasets.

Due to the poorly coupled geophones present in boreholes, vertical seismic profiling (VSP) data are known to suffer from intense coupling noise, which causes severe VSP image deterioration and significantly hinders subsequent processing. Thus, diverse denoising approaches are indispensable preprocessing steps for suppressing this kind of noise to achieve good results. Among them, robust principal component analysis (RPCA) is a common signal and noise separation model that is generally considered a highly promising method for removing intense coupling noise; however, handcrafted priors have limited denoising ability, especially for the low-rank assumption of useful signals. As an alternative, following the RPCA framework, this article proposes an unsupervised iterative robust deep convolutional autoencoder (IRDCAE) model to suppress intense VSP coupling noise without any assumptions regarding valuable signals. The key to the IRDCAE approach is the use of weighted column sparsity (WCS) to characterize the behavior of the intense coupling noise, where the weight prior is derived from the pure noise component before the first break. By adding a WCS regularization term to the conventional deep convolutional autoencoder (DCAE), our IRDCAE method transforms the model from an entirely data-driven model to a model+data driven approach. Thus, the IRDCAE approach has the advantages of both RPCA and DCAE, resulting in the ability to separate intense coupling noise from useful signals in an unsupervised manner by optimizing the IRDCAE model via an alternating minimization algorithm. The exceptional performance of the IRDCAE model is exhibited with synthetic and field VSP data.

The deconvolution method is crucial for enhancing seismic resolution. Traditional multichannel schemes incorporate lateral constraints to enhance data continuity and integrity. However, a predominant challenge is that many existing methods assume that the estimated model parameters have certain features, which are always inconsistent with the true situation. In this paper, we present a novel data-driven multi-channel seismic deconvolution method based on Convolutional Sparse Coding (CSC) and 2D K-SVD. We partition the seismic profile into low and high-frequency segments, addressing CSC’s limitation in representing low-frequency components effectively. We leverage 2D K-SVD for extracting lateral features in low-frequency components and apply CSC to the high-frequency segments. Distinctively, our method accounts for the lateral characteristics within both frequency bands during decomposition, a notable advancement over conventional filtering-based techniques. It ensures the preserved convolutional relationship in frequency separation. We enhance the conventional 1D K-SVD with 2D sample patches, evolving it into a more adept 2D K-SVD dictionary for low-frequency components. We apply this to multi-channel deconvolution regularization, enabling low-frequency seismic data deconvolution. To address the challenge of information fragmentation in high-frequency components caused by block-based sparse coding, the orthogonal constrains are added into the feature map instead of the image, making the convolutional dictionary learning more precise than the conventional CSC algorithm. This refinement is integral to optimizing the high-frequency deconvolution objective function. In the final step, an iterative processing of both frequency components yields the refined Multichannel Seismic Deconvolution based on orthogonal Convolutional Sparse Coding (MSD-oCSC). Our method’s efficacy is corroborated through rigorous model tests and real data applications.

Fault detection is one of the core tasks in fault interpretation, which is of great significance to the study of underground oil and gas transport and reservoir distribution. In recent years, with the development of artificial intelligence (AI), especially artificial neural network technology, many new intelligent fault detection methods have emerged. They often have fixed input sizes. When detecting faults in field seismic data, it is necessary to split the data into patches that fit the input size and then combine the detection results. Thus, the information used for fault detection is derived from a single data patch at most, without considering the relations between patches. We propose a Transformer-based network that considers relations between patches and incorporates a larger range of information for 3-D fault detection. Our network employs an encoder-decoder architecture. We construct the encoder using Transformers, which are more effective at extracting global features compared to convolutional neural networks. Moreover, we propose a feature fusion block based on Transformers, which is able to introduce the relations between adjacent data patches, allowing our network to utilize information beyond a single patch and extend to multiple patches. Compared to some existing studies, our network can cover a larger perspective. We apply our method on several datasets, including synthetic and field data, and our method performs well and shows improvements in noise resistance, fault continuity, and the ability to handle complex fault situations.

Fault surface extraction is a crucial step in fault interpretation, which aids in the analysis of subsurface oil and gas migration and reservoir distribution. One challenge of fault surface extraction is the ability to deal with complex fault situations. To enhance such ability, we transfer the fault attribute data into a multirelationship graph and employ the provided graph clustering method to obtain the distribution of each entire fault by considering the constraints of multiple interrelationships between faults. First, we develop a method for transforming fault attribute data into graph data, in which we can identify and modify problematic fault nodes, determine the edge relationships, and compute probability values that indicate the likelihood of fault nodes belonging to the same fault. Second, we propose a graph clustering method for multirelationship graphs, which can provide a systematic analysis of fault distribution by considering multiple relationships between fault nodes and obtaining the distribution of each entire fault. Finally, based on the spatial distribution of each fault, we extract all the fault surfaces in the data. The consideration of multiple relationships provides our method with the ability to figure out the distribution of faults in some complex situations, such as X-shaped faults and Y-shaped faults with similar fault orientations while avoiding producing erroneous results. We apply our method to several data and the results illustrate the effectiveness of our method. We also compare our method with an existing method, and our method outperforms the compared method by both qualitative and quantitative evaluations.

The application of artificial intelligence in seismic impedance inversion makes the prediction of stratigraphic information more efficient. Semi-supervised framework for impedance inversion is the latest breakthrough method in this field. However, the 1-D semi-supervised methods now in use are unable to extract the spatiotemporal properties of the data solely through the network itself. Moreover, the initial model, a critical input for this method, is typically derived through extrapolation and interpolation of well log data. This can lead to significant errors, especially when the well data are sparse and the subsurface structures are complex. Well data only provide information for a limited section of the reservoir, thereby making it challenging to capture the overall behavior accurately. As a result, the creation of an accurate initial model is often fraught with errors. A more desirable approach is to use seismic attribute-guided methods, such as neural networks, which incorporate both seismic and well log data, leading to a more accurate low-frequency model with lateral variations. In this article, we develop a semi-supervised domain adversarial and spatial fusion (DASF) inversion framework. This method uses a 1-D convolutional neural network (CNN)-based global spatiotemporal analysis module and a 2-D CNN-based local spatiotemporal analysis module to complete the inversion and forward task simultaneously. Multiple spatiotemporal characteristics from two submodules can be successfully fused using an adaptive fusion approach. In this network, the step of extracting the initial model is incorporated into the learning process. Moreover, we adopt adversarial learning in the impedance domain to guide the training process, thereby reducing the network’s dependence on labels. The experiments on the synthetic and field dataset show that the proposed method can efficiently improve the prediction accuracy of the inversion results compared with conventional methods. Meanwhile, the local spatiotemporal analysis module can be used to create a more trustworthy initial model that incorporates the characteristic of seismic and well-logging data.

In this work, we revisit a classical network tomography problem of inferring a tree topology using end-to-end measurements, with two key differences: (i) instead of relying on the correlation of source-to-destination end-to-end probe packets routing behaviors, we leverage round-trip network traffic behavior characteristics, which enables us to therefore utilize passive measurements and require only source node without available to destination nodes; (ii)rather than assuming that the network is stationary and uses only a single network performance parameter, we leverage network traffic behavior in addition to network performance parameters. Our key idea is to uncover multiple features that can reveal the network topology by exploring the traffic behavior and features observed during data transmission in the network. We then infer the network topology by combining these features through iterative optimization. Simulation and real network experiments demonstrate that our method accurately infers the network topology using network traffic behaviors, with only one source detection node.

A complex network is a fundamental tool to describe real-world complex systems, with most real-world systems containing multiple object types and relationships that can be described as heterogeneous information networks. However, with the increasing network complexity, understanding the complex patterns and finding the meta paths or meta-structures of the heterogeneous information networks has become challenging. This paper proposes a sparse representation for heterogeneous information networks and extracts the heterogeneous information atoms that describe the basic connection pattern of the original heterogeneous information network. The heterogeneous information atoms help extract the main meta-paths or meta-structures and understand the complex patterns of the original heterogeneous information network. Furthermore, the heterogeneous information networks can be decomposed, dimension-reduced, and reconstructed through the heterogeneous information atoms. Extensive experimental results demonstrate that heterogeneous information atoms and sparse coding represent the basic connection pattern of real-world heterogeneous information networks. Indeed, the developed method can reconstruct a network with a recovery exceeding 90%.

A key challenge of 3-D seismic horizon tracking lies in effectively utilizing the appropriate seismic attributes to enhance tracking precision. Numerous existing horizon tracking methods based on models or deep learning (DL), hinge on mathematical or data-driven mapping relationships between seismic data and the target horizons.This mapping utilizes only seismic data or a single attribute, resulting in inaccurate horizon tracking across discontinuities. As an alternative approach, we propose a multiple attribute regression network (MARN) for 3-D seismic horizon tracking, which leverages multiple seismic attributes to achieve precise and robust tracking results. In this study, we initiate by formulating the problem as a multiple attribute regression model, subsequently the system state equation is introduced to establish temporal relationship. To address this nonlinear regression model, the deep convolutional autoencoder (DCAE), which possesses the capability to automatically learning spatial correlation from attributes, thereby extracting deep features. In this case, these features from DCAE serve as input sequences, enabling long short-term memory (LSTM) to effectively model the spatial-temporal relationships between multiple seismic attributes and target horizons. The proposed MARN is thoroughly compared with the single attribute regression network (SARN) on two real field 3-D datasets.

The volumetric local slope, which indicates the orientation of seismic events, plays a prominent role in the subsequent geological interpretation typically including horizon tracking, seismic facies analysis, and fault interpretation. Although numerous existing estimation methods are available, they still suffer from the challenge of reaching a balance between resolution preservation and resisting the heavy random noise. As an alternative, this letter proposes a seismic volumetric local slope estimation method named the multiscale gradient structure tensor (MGST), combining GST with the 3-D multiscale Gaussian pyramid (GP). In this regard, to preserve the details of the original resolution and fully exploit the unique information at different scales, the GP is reconstructed in 3-D space by decomposing the data into multiple scales. After that, we attempt to employ the GST to derive the local slopes in two directions at each scale, along with a corresponding quality metric. Finally, within the Kalman filter framework, the local slope of each scale is sequentially integrated using the quality metric as the weighting mechanism, resulting in an accurate and robust estimation. Experiments on both synthetic and real field datasets indicate that the proposed MGST method outperforms the traditional GST and plane-wave destruction (PWD) methods.

Seismic facies analysis (SFA) plays a pivotal role in the interpretation of subsurface structures, with a pressing need to develop automated techniques for analyzing 4-D prestack seismic data. Various automated SFA methods, encompassing both supervised and unsupervised paradigms, have shown encouraging potential in fulfilling this demand. Nonetheless, supervised methods heavily hinge upon precious labeled seismic datasets of high caliber, and unsupervised methods handle all seismic samples indiscriminately during training, resulting in pronounced biases toward the majority classes of seismic data. As an alternative, this letter proposes class-imbalanced deep embedding clustering (CDEC), an unsupervised deep clustering methodology devised to analyze seismic data with class-imbalanced facies distributions. Within CDEC, a focal loss meticulously tailored to address class imbalance challenges is seamlessly integrated into the classic deep convolutional embedding clustering (DCEC). By balancing weights between the minority and majority seismic samples during network training, this approach adeptly attenuates biases toward the majority classes while concurrently bolstering the efficacy of SFA. Experimental evaluations conducted on synthetic and real field datasets compellingly underscore the effectiveness and utility of the proposed CDEC method.

In the last two years, the outbreak of COVID-19 has significantly affected human life, society, and the economy worldwide. To prevent people from contracting COVID-19 and mitigate its spread, it is crucial to timely distribute complete, accurate, and up-to-date information about the pandemic to the public. In this article, we propose a spatial–temporally bursty-aware method calledSTBAfor real-time detection of COVID-19 events from Twitter.STBAhas three consecutive stages. In the first stage,STBAidentifies a set of keywords that represent COVID-19 events according to the spatiotemporally bursty characteristics of words using Ripley’s$K$function.STBAwill also filter out tweets that do not contain the keywords to reduce the interference of noise tweets on event detection. In the second stage,STBAuses online density-based spatial clustering of applications with noise clustering to aggregate tweets that describe the same event as much as possible, which provides more information for event identification. In the third stage,STBAfurther utilizes the temporal bursty characteristic of event location information in the clusters to identify real-world COVID-19 events. Each stage ofSTBAcan be regarded as a noise filter. It gradually filters out COVID-19-related events from noisy tweet streams. To evaluate the performance ofSTBA, we collected over 116 million Twitter posts from 36 consecutive days (from March 22, 2020 to April 26, 2020) and labeled 501 real events in this dataset. We comparedSTBAwith three state-of-the-art methods, EvenTweet, event detection via microblog cliques (EDMC), and GeoBurst+ in the evaluation. The experimental results suggest thatSTBAoutperforms GeoBurst+ by 13.8%, 12.7%, and 13.3% in terms of precision, recall, and$F_{1}$score.STBAachieved even more improvements compared with EvenTweet and EDMC.

Random plus footprint noise provokes severe seismic image deterioration and makes it challenging for interpreters to recognize and analyze accurate subsurface responses. Thus, as an elementary and indispensable preprocessing step, diverse footprint removal approaches, including filtering in the frequency or time–frequency domain and dictionary learning (DL), have been documented to achieve promising results in tackling this challenge. However, the prevailing denoising methods tend to treat 3-D seismic data as images for processing, but such flattening or matricization operations inevitably obliterate the 3-D image structures concealed in noisy observational seismic data, which hinders the removal performance of these approaches. To resolve this issue, this article proposes a new tensor model for 3-D seismic random plus footprint noise suppression, and this model is based on unidirectional total variation regularized low-rank tensor approximation (UTV-LRTA). In this model, UTV regularization is imposed to obtain the innately structural and directional behavior of the acquisition footprint. In this way, the footprint is removed by effectively decomposing the footprint-contaminated seismic image into a footprint-free image and a footprint component by UTV. In contrast, random noise is mitigated by regularizing the low rankness of the third-order seismic tensors using the tensor nuclear norm. Moreover, a simple and powerful optimization algorithm based on the split Bregman iteration is introduced to resolve the proposed UTV-LRTA model. The suggested model is thoroughly assessed on synthetic and field datasets and significantly surpasses the state-of-the-art approaches quantitatively and qualitatively evaluated in the analyzed field examples.

Seismic acquisition footprints appear as stably faint and dim structures and emerge fully spatially coherent, causing inevitable damage to useful signals during the suppression process. Various footprint removal methods, including filtering and sparse representation (SR), have been reported to attain promising results for surmounting this challenge. However, these methods, e.g., SR, rely solely on the handcrafted image priors of useful signals, which is sometimes an unreasonable demand if complex geological structures are contained in the given seismic data. As an alternative, this article proposes a footprint removal network (dubbed FR-Net) for the unsupervised suppression of acquired footprints without any assumptions regarding valuable signals. The key to the FR-Net is to design a unidirectional total variation (UTV) model for footprint acquisition according to the intrinsically directional property of noise. By strongly regularizing a deep convolutional autoencoder (DCAE) using the UTV model, our FR-Net transforms the DCAE from an entirely data-driven model to a prior-augmented approach, inheriting the superiority of the DCAE and our footprint model. Subsequently, the complete separation of the footprint noise and useful signals is projected in an unsupervised manner, specifically by optimizing the FR-Net via the backpropagation (BP) algorithm. We provide qualitative and quantitative evaluations conducted on three synthetic and field datasets, demonstrating that our FR-Net surpasses the previous state-of-the-art (SOTA) methods.

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.

The sample labeling is the basis for wireline logs-based automatic lithology identification (WLALI). Due to the paucity of core data, the expert-annotation accounts for most labeling workload. In the traditional workflow of WLALI, the samples for labeling are passively selected. The labeled samples thus fail to reflect the overall distribution and feature of whole data set, leading to the difficulty in training the prediction model with robust generalization performance. To solve this problem, a novel workflow of hybrid active and semi-supervised learning-based WLALI has been proposed. First, we alter the conventional labeling process to active selection mode by employing the approach of active learning. In such way the labeled samples are representative of whole data features. Second, we propose a novel active learning algorithm based on density difference of Gaussian probability (DDGP) to obtain a better performance of sample query. Third, the semi-supervised learning method is introduced to combine the active learning algorithm, attempting to minimize the involvement of hand-crafted labeling under the target performance. In the evaluation, the proposed workflow not only has achieved favorable result in the uppercase image dataset, but also the practical application shows great promise. When compared with the workflow of random sampling + semi-supervised learning that is similar to the traditional mode, the average F1-score of proposed workflow of DDGP + semi-supervised learning can increase by approximately 3.47%.

It is crucial to retain as many signal components as possible, while noise is eliminated for wavelet packet noise reduction algorithms. Conventional thresholding functions take coefficients smaller than a given threshold as the noise component and get them removed in many ways. In this letter, considering the potential probability of being the signal-related component of coefficients whose value is smaller than the given threshold and the nonnegligible fact that microseismic (MS) event is sparse compared with the noise, we proposed a new wavelet packet-based denoising method via fuzzy partition. First, instead of a hard partition from a given threshold, wavelet packet coefficients get reduced by a fuzzy partition to retain more potential signal elements and suppress the noise. Second, we employ fuzzy c-means (FCM) clustering to identify the interval period of the MS event further to remove the residual noise and additional resonance in processed time series. We tested our method on synthetic datasets and real-field data from an MS monitoring experiment in a coal mine in Sichuan Basin, China. We utilize Pearson correlation coefficient and root-mean-square error between ideal signal and denoised data as performance indicators in synthetic tests, while sample entropy and kurtosis of denoised data are involved in the real field dataset. Test results from synthetic datasets and the real field dataset demonstrate that the proposed noise reduction method is superior to traditional hard-, soft-, and garrote-thresholding and is more applicable and effective in MS data processing.

Accurate first arrival picking plays a crucial role in microseismic data processing. However, it is challenging to guarantee satisfactory accuracy with conventional approaches when the signal-to-noise ratio (SNR) of data is low. This article proposes an automatic first arrival time picking method based on fuzzy$C$means clustering (FCM) and Akaike information criterion (AIC). The proposed method consists of three steps: clustering, rough picking, and adjusting. First, we employ FCM to divide each data point into the signal cluster and the noise cluster according to a fuzzy partition. Second, unlike conventional FCM-based picking approaches, we utilize Otsu’s method to determine a data-dependent threshold, instead of an artificially predefined one, to obtain a coarse result of the microseismic event interval from clustering partition. Finally, note that the microseismic event data points are concentrated in amplitude and also correlated in time. Therefore, we employ the AIC of the clustering partition to seek time-varying information to adjust the coarse result. Besides, we investigated several commonly used characteristic factors to introduce a supervised guideline for feature selection in first arrival picking with FCM. At last, we carried out simulations and real field data tests to verify the reliability of the proposed method. The experimental results demonstrate that the proposed method outperforms the short-and long-time average ratio (SLTA) method, the AIC method, and the conventional FCM-based picking method.

Seismic attributes comprise an effective method for oil and gas reservoir characterization and prediction. Hundreds of seismic attributes have been introduced in the last 30 years. Among the seismic attributes targeting different reservoir features, the autoencoder (AE) receives a significant amount of attention, as it extracts deep attributes of seismic data, providing more details of seismic lateral features than other seismic waveform data and seismic attributes. However, data-driven deep attributes bring new challenges to interpretation as they lack the support of intrinsic physical mechanisms. Hence, a shared AE (S-AE) method is proposed in this article, which can extract seismic deep attributes and fuse traditional seismic attributes simultaneously. An S-AE is a revised version of an AE, which consists of an encoder and decoder. An S-AE takes the seismic waveform as the input of the encoder and obtains the deep attribute, and the decoder then transforms the deep attribute to reconstruct the seismic waveforms and attributes. In an S-AE, the network in front of the decoder is shared, while the networks after the decoder consist of independent layers. Such a network structure ensures the effect of reconstruction and associates seismic attributes with the extracted deep attribute, so as to achieve the purpose of attribute fusion and deep attribute extraction. The proposed S-AE method is compared with conventional seismic data fusion methods, such as RGB and principal component analysis, and the superiority of the S-AE is demonstrated in both synthetic and field applications.

Erratic seismic noise, following a (known or unknown) non-Gaussian distribution, poses a formidable challenge to conventional methods of random noise attenuation. Many erratic noise cancellation methods, for instance, robust reduced-rank and sparsity-promoting filtering, have been proven to achieve promising results in overcoming this challenge. Among them, deep learning (DL) methods require no assumptions about the underlying clear seismic image and are also more robust against erratic and random noise. However, the success of existing DL-based denoising methods strongly depends on supervised learning from a large number of ground-truth seismic images affected by erratic noise and their clean counterparts, which are typically unavailable in a real-world setting. As an alternative, this article presents an unsupervised DL method for erratic-plus-Gaussian noise removal based on a robust deep convolutional autoencoder (RDCAE). In the RDCAE, the mean squared error (mse) loss in a classic DCAE is replaced by the smooth Welsch function to exploit the concept of robust image denoising. In this way, the erratic noise is downweighted by means of a curbed weight defined in terms of the Welsch function. In contrast, the random noise is diluted by combining the mean square in the Welsch function and the total variation (TV). Subsequently, the training procedures required for solving the RDCAE are derived on the basis of the backpropagation (BP) algorithm for a neural network. Experiments conducted on both synthetic and real field datasets are reported to illustrate the efficacy of the proposed method.

The core challenge of seismic data interpolation is how to capture latent spatial-temporal relationships between unknown and known traces in 3-D space. The prevailing tensor-based interpolation schemes seek a globally low-rank approximation to mine the high-dimensional relationships hidden in 3-D seismic data. However, when the low-rank assumption is violated for data involving complex geological structures, the existing interpolation schemes fail to precisely capture the trace relationships, which may influence the interpolation results. As an alternative, this article presents a basic deep tensor autoencoder (DTAE) and two variants to implicitly learn a data-driven, nonlinear, and high-dimensional mapping to explore the complicated relationship among traces without the need for any underlying assumption. Then, tensor backpropagation (TBP), which can be essentially viewed as a tensor version of traditional backpropagation (BP), is introduced to solve for the new model parameters. For ease of implementation, a mathematical relationship between tensor and matrix autoencoders is constructed by taking advantage of the properties of a tensor–tensor product. Based on the derived relationship, the DTAE weight parameters are inferred by applying a matrix autoencoder to each frontal slice in the discrete cosine transform (DCT) domain, and this process is further summarized into a general theoretical and practical framework. Finally, the performance benefits of the proposed DTAE-based method are demonstrated in experiments with both synthetic and real field seismic data.

The inherent challenge of 3-D seismic noise attenuation is determining how to uncover high-dimensional concise structures that only exist in true signals to eliminate random noise. The prevailing deep learning (DL) denoising methods have achieved promising performance in revealing the compact structures underlying contaminated seismic data. However, as clean ground-truth seismic data are generally unavailable in real-world settings, most existing matrix-based DL denoising schemes fail to automatically describe this type of high-dimensional structure in an unsupervised manner, potentially rendering them unable to effectively perform 3-D seismic data denoising tasks. To tackle this challenge, this article presents a tensor convolutional neural network (TCNN)-based data denoising scheme using Stein’s unbiased risk estimate (SURE) (called SURE-TCNN) to learn intrinsic high-dimensional structures without ground-truth seismic data. Considering that SURE provides an almost unbiased estimate of the mean squared error (MSE), SURE-TCNN has the potential to provide similar results to those of the supervised MSE-based TCNN with ground-truth data. For ease of implementation, the properties of a transform-based tensor-tensor product (t-product) are followed to establish a solid theoretical connection between the SURE-TCNN tensor and matrix. Derived from this connection, the SURE-TCNN weight parameters are determined by implementing matrix-based SURE-convolutional neural networks (CNNs) on each frontal slice in the time-frequency domain (e.g., the wavelet domain). Synthetic and field data examples demonstrate the superior performances of the proposed model against three state-of-the-art (SOTA) methods.

Multidimensional (M-D) seismic data denoising is cast as an underdetermined inverse problem whose solution hinges on effective image priors extracted from machine learning knowledge. However, modeling seismic image priors is challenging due to the M-D nature of seismic images. Among the most promising prevailing image prior techniques is learning prior knowledge of the underlying structure by various 2-D or 3-D deep learning (DL)-based methods. However, for higher-dimensional seismic data such as 4-D prestack data, these DL denoising schemes undoubtedly fail to capture the complete image structure in the absence of the flattening operation. To address this challenge, we present a framelet-based order-ptensor neural network (dubbed the FPTNN) model to implicitly learn the priors reflecting the typical behavior of clear M-D seismic images in a data-driven manner. First, motivated by the supremacy of the framelet transform over the Fourier transform, replacing the Fourier transform with the framelet gives a new definition with respect to the order-ptensor-tensor product (t-product). Then, through the redefined order-pt-product, the order-ptNN framework is a straightforward extension of the tNN with a standard t-product for M-D seismic denoising. By exploiting the fact that the order-pt-product can be computed through matrix multiplication in the framelet domain, we can readily reach the optimal weighted parameters in the FPTNN via DL on a set of transformed matrix frontal slices. The experiments on both synthetic and real field seismic datasets comprehensively demonstrate the advantages of our method against other state-of-the-art (SOTA) methods.

The transfer learning between the source and target domain has already achieved significant success in machine learning areas. However, the existing methods can not achieve satisfactory result when solving the two distant domains transfer learning problem. In the worst case, it could lead to the negative transfer. In this paper, we propose a novel framework called transitive transfer sparse coding (TTSC) to solve the two distant domains transfer learning problem. On the one hand, as an extension of the sparse coding, the TTSC framework constructs a robust and high-level dictionary across three different domains and simultaneously obtains three good feature sparse representations. On the other hand, TTSC utilizes the intermediate domain as a strong bridge to transfer valuable knowledge between the source domain and target domain. Empirical studies validated that the TTSC framework significantly could outperform state-of-the-art methods.

Existing seismic inversion methods are usually 1D, mainly focusing on improving the vertical resolution of inversion results. A few 2D or 3D inversion techniques are either too simple and lack the consideration of stratigraphic structures, or are too complicated which need to extract dip information and solve a complex constrained optimization problem. In this work, with the help of gradient structure tensor (GST) and dictionary learning and sparse representation (DLSR) technologies, we propose a 3D inversion approach (GST-DLSR) that considers both vertical and horizontal structural constraints. In the vertical direction, we investigate the vertical structural features of subsurface models from well-log data by DLSR. In the horizontal direction, we obtain the stratigraphic structural features from a 3D seismic image by GST. We then apply the acquired structural features to constraint the entire inversion procedure. The experiments show that GST-DLSR takes good advantages of both techniques, enabling to produce inversion results with high resolution, good lateral continuity, and enhanced structural features.

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.

Efficient urban traffic monitoring is a key enabler for intelligent planning and management of modern cities. Network tomography can monitor the urban traffic with a comparably small number of traffic detectors like cameras, and has become an appealing technique for urban traffic management. However, previous work on network tomography based traffic monitoring focuses primarily on developing estimators using the given end-to-end travel time measurements, while the design of data collection for efficiently distributed collecting and processing the raw monitoring videos to such measurements is often neglected. We fill this gap by exploring the vision of edge intelligence for optimal urban traffic monitoring, and tackle the following two problems in regard of limited telecommunications resources: 1) when the total number of monitoring videos that are successfully processed into the end-to-end travel time measurements is pre-bounded, we employ a Fisher Information Matrix (FIM) to help determine the best quota scheme for the monitoring videos that each traffic detector need to generate and 2) when the centralised processing of monitoring videos alone is insufficient, we make use of the computation capabilities from these edge devices, i.e., traffic detectors, and employ a multi-agent reinforcement learning approach to help them conduct intelligent computation offloading individually. Extensive simulations demonstrate that our proposed scheme effectively reduces the estimation error of network tomography compared to common approaches with either uniform or random strategy.

Delineating the seismic fault, which is an important type of geologic structures in seismic images, is a key step for seismic interpretation. Comparing with conventional methods that design a number of hand-crafted features based on the observed characteristics of the seismic fault, convolutional neural networks (CNNs) have proven to be more powerful for automatically learning effective representations. However, the CNN usually serves as a black box in the process of training and inference, which would lead to trust issues. The inability of humans to understand the CNN would be more problematic, especially in critical areas like seismic exploration, medicine and financial markets. To include domain knowledge to improve the interpretability of the CNN, we propose to jointly optimize the prediction accuracy and consistency between explanations of the neural network and domain knowledge. Taking the seismic fault segmentation as an example, we show that the proposed method not only gives reasonable explanations for its predictions, but also more accurately predicts faults than the baseline model.

Mobile Edge Computing (MEC) enables mobile users to offload their computation loads to nearby edge servers, and is seen to be integrated in the 5G architecture to support a variety of low-latency applications and services. However, an edge server might soon be overloaded when its computation resources are heavily requested, and would then fail to process all of its received computation loads in time. Unlike most of existing schemes that ingeniously instruct the overloaded edge server to transfer computation loads to the remote cloud, we make use of the spare computation resources from other local edge servers by specially taking the risk of network link failures into account. We measure such link failure risks with the financial risk management metric of Conditional Value-at-Risk (CVaR), and well constrain it to the offloading decisions using a Minimum Cost Flow (MCF) problem formulation. Numerical results validate the enhancement of the MEC service's availability by our risk-aware offloading scheme.

In this paper, we propose a novel method for generating high-granularity three-dimensional (3D) seismic data from low-granularity data based on tensor sparse coding, which jointly trains a high-granularity dictionary and a low-granularity dictionary. First, considering the high-dimensional properties of seismic data, we introduce tensor sparse coding to seismic data interpolation. Second, we propose that the dictionary pairs trained by low-granularity seismic data and high-granularity seismic data have the same sparse representation, which are used to recover high-granularity data with the high-granularity dictionary. Finally, experiments on the seismic data of an actual field show that the proposed method effectively perform seismic trace interpolation and can improve the resolution of seismic data imaging.

Network congestion will result in significant performance degradation or even failures of many bandwidth-hungry Internet of Things (IoT) applications. Accurate and efficient congested link identification has become a foundational issue to IoT applications like self-driving cars, digital health, smart city, and so on. However, directly monitoring the massive number of interior links often introduces high operation cost or even is infeasible in practice, giving rise to indirect monitoring techniques like network Boolean tomography. Nevertheless, in many networks, the number of their interior links is larger than their end-to-end paths, making it very challenging for network Boolean tomography to find a determined solution. To resolve this issue, most of current methods try to utilize some prerequisites, such as the link congestion probabilities. While these probabilities might be hard or even unable to be obtained accurately in dynamical networks, limiting the practical deployment. In this paper, we are motivated to design a framework of congested link identification without any prerequisite or assumption. We first novelly model the congested link identification procedures as a Markov decision processes (MDPs), and then employ a reinforcement learning technology, i.e., Q-learning, to solve this MDP. The simulation results show that our proposed scheme can autonomously and efficiently explore the unknown network environment, and is able to achieve better adaptivity and correctness, without any prior knowledge comparing to existing methods.

How to efficiently detect communities for dynamic graphs has attracted significant attention due to its widespread applications, such as “Recommended System” in social networking or “Anti-Money Laundry” for banks. However, with the growing size and the increasing of changing frequency for dynamic graphs, it is better to detect communities incrementally than applying community detection algorithm for each of the graph. In this paper, we propose a generalized bottom-up community detection framework to help standard community detection algorithms to detect communities in highly dynamic graphs incrementally. The evaluations on real data sets show that our generalized framework does have the ability to accelerate standard community detection methods for highly dynamic graphs (up to 96%). In addition, for super hubs in large scale graph, we also propose an approximate process to meet the requirement of real-time processing. By giving “mathematical proof”, “efficiency” and “accuracy” evaluations from real data sets, we demonstrate this approximate process can not only accelerate the community detection process but also can preserve the accuracy, as the normalized mutual information for regular communities and approximate communities is larger than 92%.

Multipath routing, which is increasingly common in today's Internet, can lead two end-hosts to own multiple routing paths. This will impose adverse effects on most of the current network measurement methods, as they generally need to assume a single active path between any pair of end-hosts at any given time. In this paper, we attempt to identify whether multipath routing exists between two end-hosts using end-to-end packet order. We show theoretically that the probability of observing no out-of-order delivery among a strip of packets is always lower when the two end-hosts get multiple instead of a single path to forward them. Based on such investigation, we design a probe and use it to propose an end-to-end measurement scheme that can simultaneously identify multipath routing and its type (e.g., flow-based or packet-based). Both experiment and simulation results validate the effectiveness of our proposed scheme.

Congestion links can not only introduce great packet losses, but also cause significant delay flutters to paths that traverse them. However, most of current approaches just try to identify congestion links that meet end-to-end loss observations. What's worse, most of them also take no consideration of multipath routing, while which will introduce more than a single routing path between two end-hosts and can make a single-source network own a non-tree topology instead of the tree one. In this paper, we employ both end-to-end loss and delay observations to identify congestion links in a single-source network where multipath routing is enabled. We first prove that under certain topology conditions, the link delay variances in such non-tree topology can be inferred solely from end-to-end delay measurements. Then, we propose an algorithm to identify as congested a set of links, which can not only account for end-to-end path losses but also demonstrate great delay variances at the meantime. Simulation results validate the desirable performance of our proposed scheme.

Geographic routing in wireless sensor networks suffers from the local minimum problem when data packets encounter the concave boundary of a hole (void area), i.e., no neighbor node are closer to the destination than the current node. In this paper, we propose a hole plastic scheme which adaptively fill the concave area of a hole with potential stuck nodes according to 1-hop information of neighbors, to relieve the local minimum problem faced by geographic routing. The basic idea is To mark the nodes located in the concave area of the hole as potential stuck nodes which do not participate in data delivery unless a source/destination is located on the concave area of the hole. Once the hole plastic process is achieved, subsequently arriving data flows will be prevented from entering the concave area of the hole by potential stuck nodes. The proposed hole plastic scheme is achieved in a local self-organized manner, i.e., potential stuck node marking is based on the information of 1-hop neighbors, and traditional multi-hop cooperation methods such as hole detection, hole boundary tracing, hole modeling are not required.

ABSTRACT Detecting distributed abnormal events has become an increasingly significant task for efficient network management and operation. However, it is still challenging to uncover these distributed behaviors in backbone networks because of the voluminous amount of noisy, high‐dimensional traffic data. In this paper, we present a novel system for detecting distributed abnormal events in backbone networks. The proposed system emphasizes on detecting distributed correlated abnormal events, which are caused by the same reason. In contrast, existing methods are not able to distinguish correlated abnormal events from the independent abnormal events. In our proposed system, a set of data mining techniques is used for modeling and detecting distributed correlated abnormal events by analyzing the traffic features. Specifically, traffic behavior representation is constructed to define and select traffic features for describing the traffic behaviors of interest, feature clustering is performed to group together similar transformations in each feature, behavioral data mining is employed to discover the most significant patterns in network interactions with respect to typical behavior, and behavior classification is used to expose the behaviors of interest. Experiment results using real traffic data present the effectiveness of our proposed methods for detecting distributed correlated abnormal events in the backbone network. Copyright © 2013 John Wiley & Sons, Ltd.

Boolean tomography is based on exploiting performance level correlations of end-to-end measurements to identify the congested links. Most work to date attempts to find the congested links according to the observed pattern of congested paths and the prior link congestion probabilities. In their work, the prior link congestion probabilities are either assumed to be unrealistically equal or estimated by a computationally complex algorithm. Furthermore, all congested paths are mapped down to the same “bad” state regardless of their congestion degrees, then separate causes of congestion may be identified as a common cause. In this paper, we propose a fast Bottom-Up Approach named BUA to estimate the prior probabilities based on a small number of measurement snapshots. BUA is computationally simpler than the existing approaches since it computes the congestion probability of each individual link through an explicit function of the measurements. We then extract the subsets of congested paths that might traverse the same congested links in current measurement snapshot according to their congestion degrees. The links that cause the congestion of each subset of paths are identified with the aid of the learnt probabilities. Simulations in different network scenarios demonstrate that our approach is able to improve the accuracy of the identification procedure.

Understanding the network link loss is particularly important for optimising delay-sensitive applications. This study addresses the issue of estimating temporal dependence characteristic of link loss by using network tomography. Different from existing works of network loss tomography, the authors use a kth order Markov Chain (k-MC for short, k>1) to model the packet loss process, and propose a constrained optimisation-based method to estimate the state transmission probabilities of the k-MC link loss model. The authors also propose a top–down algorithm in order to ensure that our method can be applied to large networks. Compared with existing loss tomography methods, our method is capable of obtaining more accurate packet loss probability estimates. The ns-2 simulation results show the good performance of our method.

In backbone networks, due to the frequent exchange of data between adjacent routers, the characteristics of network behaviors caused by network distributed abnormal events are closely related with routers' spatial location, their connection relationships and other associated information. Based on this observation, this paper presents a novel graph-based abnormal event detection framework that uses a set of data mining techniques for facilitating the detection of distributed abnormal events in backbone networks. The experiment results using real traffic data collected from an ISP backbone network show the effective and scalable of the proposed framework.

Understanding the topology of a network is very important for network control and management. There have been several methods designed for estimating network topology from end-to-end measurements. Among these methods, the maximum-likelihood-based topology inference method is superior to suboptimal and pair-merging approaches, because it is capable of finding the global optimal topology. However, the existing method which searches the maximum likelihood tree directly is time-consuming, and may not be able to obtain the accurate topology of a larger-scale network. To overcome these issues, this study presents a maximum-likelihood-based leaf nodes inserting topology inference method. The method first builds a binary tree with two leaf nodes, and then inserts the remaining nodes into the tree one by one according to the maximum-likelihood criterion. When compared with the previous methods, the proposed method has the advantages of less computational cost and higher estimate precision. The analytical and simulation results show good performances by the proposed method.

Energy conservation and interference reduction are the two ultimate goals in the design of network protocols for wireless sensor networks (WSNs). Energy-aware geographic routing has been considered as an attractive routing scheme for energy conservation in WSNs owing to its desirable scalability and simplicity. However, most energy-aware geographic routing protocols seldom consider interference reduction. The authors present an energy-aware interference-sensitive geographic routing (EIGR) protocol, which focuses on minimising the total network energy consumption and reducing interference. EIGR adaptively uses an anchor list to guide data delivery, and selects the minimum-interference link from energy-optimal relay region for data delivery. To further reduce the energy consumption and interference, EIGR adjusts the transmission power of each forwarding node so as just to reach the selected next forwarding node. Simulation results demonstrate that the proposed approach exhibits noticeably higher energy efficiency, shorter end-to-end delay and higher packet delivery ratio compared with other geographic routing protocols.

Most of methods for network link performance parameters inference are under the assumption that the link states are stationary during measurement period, as a result, the time-varying characteristics of link state can not be obtained. In this paper, we present a novel nonstationary internal loss tomography method to infer time-varying link loss characteristic. The method is based on improved three-packet stripe, which can provide more accurate unicast end-to-end measurements. The ns-2 simulation shows good performance of improved probe, and effectiveness of time-varying internal loss inference method in tracking variation of link loss.

Some network anomalous events caused by same reason (e.g., DDoS, link failure) tend to present similar unusual change on multiple traffic observations, and this part of traffic usually exhibits anomalous features either on time or frequency domain. Motivated by this fact, this paper introduces a multidimensional traffic anomaly detection method based on independent component analysis (ICA). Considering traffic observation as a mixture of normal and anomaly that respectively generated by different reasons, we generalize ICA technology of blind sources separation problem to separate the potentially anomalous part from characteristics of individual traffic signal on time and frequent domain. We show that how principle component analysis is combined with sliding window analysis, to measure the degree of similarity among multiple abnormal parts with fine granularity. The evaluation using Abilene trace shows that our method is useful to detect anomalous traffic with small volume, and performs better than previous method.

This paper proposes a novel method of large-scale IP traffic matrix estimation, based on the recurrent multiplayer perceptron (RMLP) network that is a kind of recurrent neural networks. Firstly, we model the large-scale IP traffic matrix estimation using the RMLP network that can well denote the dynamic behavior of IP network. Based on the conventional RMLP network, we present a new multi-input and multi-output RMLP network model. Then by the model, we present a novel approach to the large-scale IP traffic matrix estimation. Finally, we use the real data from the Abilene Network to validate our method. The results show that our method and model can perform well the accurate estimation of traffic matrix and track its dynamics.

Distributed detection mechanism of DDoS (Distributed Denial of Service) attack is often achieved by the corporation between many detection nodes, its final detection result largely depends on the judgements of local nodes. While DDoS attack flows are distributed enough in many links, it’s hard to derive exact judgement for every node only by the information collecting from local, consequently impact the performance of whole detection system. Despite DDoS attack could be unaware in local, the inherent dependency among attack flows transiting in many links do exists. This paper proposes an abnormal correlation analysis method from a global perspective for DDoS attack detection deploying in the backbone network, via extracting anomalous space from network-wide traffic, analyzing the correlation across them, revealing attacks through the change of correlation. Analyzing the network-wide traffic simultaneously helps to discover attacks indistinctive in single node; moreover, utilizing the correlation between attacks, rather than the volume of attack purely, makes our method can overcome the difficulties in detecting relatively small attacks comparing to the tremendous traffic in backbone network. Simulations demonstrate that our method has benefit of detecting DDoS attacks while they are small in single link and is superior to other methods proposed in present literatures.

Multicast routing protocols today still scale poorly to a large number of concurrent multicast sessions in terms of forwarding states. Unlike previous approaches which concentrated on reducing forwarding states after constructing multicast trees, our approach is to make the underlying routing algorithms aware of the scalability requirement. This scalability-aware approach can be applied to many existing multicast state reduction methods, such as aggregated multicast (AM) and dynamic tunnel multicast (DTM). We have formulated both AM-aware and DTM-aware routing problems as multicriteria optimization problems, and proposed algorithms to solve them.