Frequency Slices Research Articles

Characterization of leakage signatures in buried water pipes by ground penetrating radar(GPR) and instantaneous frequency analysis

The aging and complexity of underground water pipes pose significant challenges to modern society, necessitating long-term monitoring and maintenance to prevent the socioeconomic costs. So, effective nondestructive and geophysical methods are needed to localize the possible leakage areas before any rational opening up is conducted. In this study, we propose a workflow to generate instantaneous frequency slices (IFS) from raw ground penetrating radar (GPR) data. Characterizing the horizontal and vertical patterns of water leakage using IFS revealed two key mechanisms—reflections due to the dry and wet interface and absorption due to water distributed in soil—that significantly influence instantaneous frequency. A well-designed real site (Q-Leak), in collaboration with the Water Supplies Department (WSD) of the government, was conducted to replicate typical leakage scenarios, validating the effectiveness of our IFS-based approach. The results of proposed method demonstrate that IFS is a reliable approach for characterizing leakages in buried water pipes, effectively addressing several challenges associated with amplitude slice analysis. Our proposed methodology enriches the toolkit available for large-scale, future-oriented water leakage detection.

A novel time-frequency slice extraction method for target recognition and local enhancement of non-stationary signal features

Blind deconvolution can remove the effects of complex paths and extraneous disturbances, thus recovering simple features of the original fault source, and is used extensively in the field of fault diagnosis. However, it can only identify and extract the statistical mean of the fault impact features in a single domain and is unable to simultaneously highlight the local features of the signal in the time-frequency domain. Therefore, the extraction effect of weak fault signals is generally not ideal. In this paper, a new time-frequency slice extraction method is proposed. The method first computes a high temporal resolution spectrum of the signal by short-time Fourier transform to obtain multiple frequency slices with distinct temporal waveforms. Subsequently, the constructed harmonic spectral feature index is used to quantify and target the intensity of feature information in each frequency slice and enhance their fault characteristics using maximum correlation kurtosis deconvolution. Enhancing the local features of selected frequency slice clusters can reduce noise interference and obtain signal components with more obvious fault signatures. Finally, the validity of the method was confirmed by a simulated signal and fault diagnosis of the rolling bearing outer and inner rings was accomplished sequentially. Compared with other common deconvolution methods, the proposed method obtains more accurate and effective results in identifying fault messages.

Three dimensional seismic data reconstruction based on truncated nuclear norm

The rank reduction method is widely used to reconstruct three dimensional (3D) seismic data. The traditional multichannel singular spectrum analysis (MSSA) utilizes truncated singular value decomposition (TSVD) to approximately solve the rank function of the block Hankel structure matrix constructed by frequency slices of seismic data. However, the TSVD algorithm discards all nonzero singular values except a few largest singular values and ignores the useful seismic information in small nonzero singular values. To further utilize the seismic data information contained in these small singular values, this paper proposes a method to recover 3D seismic data with truncated nuclear norm (TNN). The proposed method imposes different constraints on large singular values and small singular values. Essentially, the TNN is closer to the rank function than the TSVD. Finally, the proposed model is addressed by the alternating direction method of multipliers, and closed-form solutions are used to update the optimization variables. The experimental results demonstrate that the proposed method achieves superior reconstruction results than the traditional MSSA method.

Three-Dimensional Seismic Data Reconstruction Based on Fully Connected Tensor Network Decomposition

Rank-reduction approaches assume that seismic data in the frequency-space domain is of low-rank after a specific pre-transformation. The presence of noise or missing traces will increase the rank; therefore, seismic data can be denoised and recovered via rank-reduction techniques. The iterative weighted project onto the convex set (POCS) framework can be used for noise attenuation and data reconstruction simultaneously. Multichannel singular spectrum analysis (MSSA) is a classic 3D seismic data reconstruction algorithm that rearranges the temporal frequency slices of the data with missing traces into a block Hankel matrix, and then uses randomized singular value decomposition (RSVD) to interpolate slices. To further improve the efficiency and precision of 3D seismic data reconstruction, we introduce the fully connected tensor network (FCTN) decomposition over the Hankel tensor of the frequency slices. We show that our novel rank-reduction method estimates fewer parameters than MSSA, yielding more accurate and robust results. Moreover, FCTN decomposes a fourth-order tensor into four factor contractions, which breaks the limitations that traditional tensor decomposition methods such as CANDECOMP/PARAFAC (CP) and Tucker decomposition cannot establish the connections between different factors, and are less effective at characterizing relationships. The newly proposed approach does not require singular value decomposition (SVD), leading to an overall reduction in computational complexity. Synthetic and field examples are used to compare the performance of our method with MSSA, and our numerical results reveal the better performance of the proposed FCTN decomposition method for seismic data with large gaps or a high missing ratio.

Multicomponent Seismic Data Reconstruction via a Biquaternion-Based Vector POCS Method

Multicomponent seismic exploration as a powerful geophysical technology has attracted more and more attention in seeking and identifying subtle oil and gas reservoirs. However, like traditional single-component acquisition, multicomponent seismic data acquisition also encounters sparse and irregular sampling problems. The conventional reconstruction methods treat multicomponent data as independent components and recover the missing traces in a component-by-component manner. These componentwise approaches ignore the internal mutual relationships among different components and damage the vector characteristics of the seismic wavefield. Quaternion algebra provides an effective vector representation tool for multicomponent data. Upon that, we present a new vector Projection Onto Convex Sets (POCS) method with biquaternion Fourier transform to reconstruct the irregularly missing traces of 3-D and three-component (3-D-3C) seismic data. Compared to the current real-valued quaternion reconstruction methods implemented in the time domain, the proposed method performed in the frequency domain can fully maintain the conjugate symmetry property of the biquaternion data and only reconstruct the positive or negative frequency slices of the observed 3-D-3C data. Besides, the new method can simultaneously interpolate the 3C or 4C data with different missing patterns. We compare the proposed vector POCS method with the scalar POCS method through experiments with a synthetic 3-D-3C dataset and a field 3-D-3C volume. Both experiments demonstrate the effectiveness and superiority of the proposed method.

A GPR 2D Teager-Kaiser energy operator based on the multivariate variational mode decomposition

ABSTRACT To delineate the anomalies related to subsurface target bodies and the geophysical events, a novel method based on the effective combination of the multivariate variational mode decomposition (MVMD) and 2D Teager-Kaiser operator (2D-TKEO) is proposed for ground-penetrating radar (GPR) data processing in this letter. The proposed method takes advantage of the multi-channel decomposition scheme of MVMD and the multi-directional localization of 2D-TKEO for tracking energy attributes. The time-frequency spectrum can be generated based on the IMF-slices decomposed by the MVMD, on which the 2D-TKEO is introduced to focus the spectrum energy, then the common frequency slices are extracted to reveal the energy components of different frequency ranges. The forward model and GPR field data are applied here to demonstrate that the time-frequency spectrum and the subsequent common-frequency slices based on the proposed MVMD-2DTKEO approach can depict the subsurface target bodies (e.g., voids and pipes) and subsurface layers with higher resolution and better stratigraphic continuity, compared with that of the S-transform and the VMD-1DTKEO.

Interactive Multimedia Data Coscattering Point Imaging for Low Signal-to-Noise Ratio 3D Seismic Data Processing

In this paper, low signal-to-noise ratio 3D seismic data are processed by the method of coscattered point imaging, and the imaging method is analyzed in combination with interactive multimedia for 3D seismic data. The reconstruction is carried out using a convex set projection algorithm based on the curvilinear wave transform. The track set is extracted from the 3D data body and transformed into the common offset distance-center point tract set to achieve the reconstruction of seismic data in the common offset distance track set domain and through comparison. It is concluded that the reconstruction effect is better in the common. The reconstruction results are better in the common offset distance track set domain. To shorten the processing time and obtain better reconstruction results, this paper proposes the idea of direct reconstruction of frequency slices. Experiments on the actual seismic three-component wavefield based on velocity-type and acceleration-type three-component geophones are carried out to reveal the signal characteristics of the actual seismic wavefield under the mining space. Due to the limitation of the construction observation space and the particularity of the actual needs of mine detection, the application of the scattered wave imaging method in the mine must be based on the corresponding detection space and detection purpose. The implementation of this thesis improves the signal-to-noise ratio, resolution, and fidelity of the 3D seismic data of the Shawan Formation, which is more conducive to the search for lithological traps. Combined with the seismic geological data, several traps were finally found and implemented, indicating that the fidelity of the resultant information is good and can meet the needs of interpretation and comprehensive research. The multiwave scattering imaging method in this paper can complete multiwave field imaging of longitudinal, transverse, and slot waves, which has the advantages of data redundancy, high superposition number, and more accurate imaging than conventional reflection wave imaging and provides field application value for ensuring mine safety production.

A Deep Learning Approach for Series DC Arc Fault Diagnosing and Real-Time Circuit Behavior Predicting

Electromagnetic interference (EMI) produced by series arc fault has long been a concern in dc distribution systems. As EMI fault may lead to serious safety risks, it is important to detect and predict arc disturbance for EMI management purposes. This article proposes a deep-learning-based approach for series dc arc diagnosis and circuit behavior prediction. Using time–frequency slices generated from power supply-side signals as a reference input, the proposed method is capable of extracting supply-side circuit features with a time–frequency feature extractor. The two-step feature extractor adopts convolutional neural network to extract static features on each time–frequency slice, and combines a long short-term memory network to capture dynamic time-varying signatures of time–frequency slice sequence. Fully connected network layers are lined up behind to implement mapping relation between extracted features and expected output. To further evaluate the performance of the proposed method, an experimental platform is established for series dc arc simulation and circuit data collection. Experimental data under normal switching operation and various arcing states are captured and employed to train the deep learning model. In this article, trained models show an overall accuracy of 98.43% in arc fault diagnosis, and give a time-domain prediction result resembling the actual signal.

3-D Seismic Data Recovery via Neural Network-Based Matrix Completion

Due to the limitation of the field acquisition environment and economic cost, observed seismic data often contain lots of missing traces. Traditional low-rank matrix completion can recover missing traces by constructing linear models, but it is often insufficient for high-accuracy reconstruction. To better recover seismic data, we propose a novel 3-D seismic data reconstruction approach by constructing a nonlinear model in the frequency domain. Based on the low-rank characteristics of seismic data, we employ the low-dimensional neural network (NN) to construct a nonlinear matrix completion model, which can be optimized by the improved resilient backpropagation algorithm. After optimization, the low-dimensional variables and hidden features learned by the network can be used to update the complete frequency slice. Since the two adjacent frequency slices share highly correlative information during reconstructing data in the frequency domain, we adopt transfer learning to improve the learning efficiency of the network while sequentially reconstructing frequency slices. Compared with the traditional matrix completion, the proposed method demonstrates superior performances in the reconstruction of synthetic and field 3-D seismic data volume.

A prime-partition strategy for spectrum assignment problem in elastic optical networks

Elastic optical networking is an attractive technology for the next generation of backbone networks, because it can flexibly provide spectrum resources to connection requests. In elastic optical networks, spectrum resources are generally split into frequency slices with finer granularity and each connection request can use several frequency slices according to its bandwidth requirement. In the process of spectrum assignment, numbers of frequency slices used by different connection requests are diverse, which may lead to misalignment of frequency slices in different links. The misalignment would affect the effective use of spectrum resources and result in the increase of blocking probability of connection requests. For the purpose of reducing the misalignment of frequency slices and thereby decreasing the blocking probability, we propose a prime-partition strategy for the spectrum assignment problem in this paper. The strategy partitions frequency slices in each link into prime blocks with sizes equaling prime numbers 2, 3, 5, 7, etc., and the prime blocks with the same size are aligned in different links. After the partition operation, each connection request can be served by one or more adjacent prime blocks with the same size, no matter how many frequency slices it needs. Based on the prime-partition strategy, we design a novel spectrum assignment algorithm in which the enhanced-most-used policy is used to break ties. The enhanced-most-used policy can make idle prime blocks more aligned in different links. Simulation results show that the prime-partition-based spectrum assignment algorithm outperforms the existing well-performed algorithms in terms of blocking probability.

Comparison of switching policies in terms of switching cost and network performance in static SDM-EONs

In this paper, we concentrate on the comparison of different switching policies, as well as their corresponding spatially-spectrally flexible super-channel transmission technologies, in static SDM-EONs. These switching policies are different due to their different spatial switching granularities and/or whether the space lane change (SLC) is supported. According to our simulation results, the switching policy with a finer switching granularity can achieve better network performance in terms of required frequency slices in the networks but meanwhile results in higher device cost. Moreover, we find that the application of SLC can only provide negligible improvements (0.1% ~ 3.1%) on network performance. SLC is encouraged only when the spectrum resource is highly valued by network operators compared to the additional device cost it brings. Moreover, in some previous works, the application of SLC can achieve a 7% ~ 14% improvement on the network throughput in dynamic network scenarios, especially when a finer spatial switching granularity is applied. Consequently, the results in this paper suggest that such an improvement is likely to be achieved by the means of network planning, such as the design of an efficient algorithm considering spectrum defragmentation, because if the re-routing/re-assignment of spectrum is allowed, the dynamic scenario can be treated as a sequence of static scenarios that change with time.

Competition of Duopoly MVNOs for IoT Applications through Wireless Network Virtualization

Network performance is of great importance for processing Internet of Things (IoT) applications in the fifth-generation (5G) communication system. With the increasing number of the devices, how network services should be provided with better performances is becoming a pressing issue. The static resource allocation of wireless networks is becoming a bottleneck for the emerging IoT applications. As a potential solution, network virtualization is considered a promising approach to enhancing the network performance and solving the bottleneck issue. In this paper, the problem of wireless network virtualization is investigated where one wireless infrastructure provider (WIP), mobile virtual network operators (MVNOs), and IoT devices coexist. In the system model under consideration, with the help of a software-defined network (SDN) controller, the WIP can divide and reconfigure its radio frequency bands to radio frequency slices. Then, two MVNOs, MVNO1 and MVNO2, can lease these frequency slices from the WIP and then provide IoT network services to IoT users under competition. We apply a two-stage Stackelberg game to investigate and analyze the relationship between the two MVNOs and IoT users, where MVNO1 and MVNO2 firstly try to maximize their profits by setting the optimal network service prices. Then, IoT users make decisions on which network service they should select according to the performances and prices of network services. Two competition cases between MVNO1 and MVNO2 are considered, namely, Stackelberg game (SG) where MVNO1 is the leader whose price of network service is set firstly and MVNO2 is the follower whose network service price is set later and noncooperative strategic game (NSG) under which the service prices of MVNO1 and MVNO2 are simultaneously set. Each IoT user decides whether and which MVNO to select on the basis of the network service prices and qualities. The numerical results are provided to show the effectiveness of our game model and the proposed solution method.

Seismic spectrum decomposition based on sparse time-frequency analysis

Time-frequency analysis is widely used in many engineering fields. However, the traditional time-frequency analysis methods suffer from issues such as low resolution or the interference of cross terms. To solve the above issues, this paper proposes a sparse time-frequency analysis by using an L1-norm constraint, fitting the sparse prior of a signal's spectrum. This process begins with the relationship between the sparse spectrum and the short-time measurement in order to propose the short-time sparse spectrum inversion model. Then, the first-order primal-dual method is employed to solve the proposed model. In this way, the reconstructed spectrum is constrained to be sparse. On the one hand, the concentration of the proposed algorithm is high due to the adoption of the L1-norm constraint. On the other hand, cross terms are avoided because the proposed method is based on the short-time Fourier transform and convex optimization technology. To show the performance of the proposed method, experiments based on both the theoretical signal and the real seismic signal are then conducted and compared with state-of-the-art time-frequency methods. The results show that the proposed method can obtain more accurate time-frequency distributions than other algorithms. Finally, we apply the proposed method in the decomposition of a seismic signal spectrum. The experiments show that the proposed method is capable of obtaining high-resolution frequency slices and more precisely exploring the spatial distribution of a reservoir than traditional time-frequency methods.

Machine Learning Assisted Optimization of Dynamic Crosstalk-Aware Spectrally-Spatially Flexible Optical Networks

We focus on optimization of dynamic spectrally-spatially flexible optical networks (SS-FONs), in which distance-adaptive, spectral super-channel (SCh) transmission is realized over weakly-coupled multi-core fibers (MCFs). In such networks, the inter-core crosstalk (XT) effect in MCFs impairs the quality of transmission (QoT) of optical signals, which has a negative impact on overall network performance. In this context, a key issue is the selection of a modulation format (MF) for a particular lightpath, since each MF offers a different tradeoff between spectral efficiency (number of occupied frequency slices) and transmission reach, and both these elements are in a close relation with the experienced XT. In fact, it is difficult to quantify the impact of the MFs used in a given network on the deterioration in QoT due to the XT effect and on overall network performance, as the levels of XT experienced by particular lightpaths depend directly on the ongoing transmissions in neighbor MCF cores, and those (i.e., established lightpaths) are subject to stochastic changes in dynamic SS-FONs. Therefore, in this article, we propose an approach that selects a MF for a lightpath based only on the transmission distance, i.e., length of the applied routing path. In particular, for each considered MF, we define a Modulation format Distance Limit (MDL) parameter that is used to select the MF for a lightpath by a simple comparison of the considered routing path length against the MDL parameter. To facilitate the very complex process of determining the best values of the MDL parameter, we propose a machine learning (ML) approach, namely, supervised learning regression method, with the overall goal to optimize network performance in terms of bandwidth blocking probability (BBP). Extensive simulation experiments performed in different network scenarios show that the proposed method ML allows to achieve performance gains when compared to various reference approaches, including a more computationally expensive method that is based on dynamic (flexible) selection of MFs.

A Resource-Periodic-Arrangement Strategy for RMSA Problem in Elastic Optical Networks

Elastic optical networks (EONs), which can flexibly assign spectrum resources to connection requests, have become the most promising technology for the next generation of optical core networks. Routing, modulation and spectrum assignment (RMSA) problem is one of the key issues in elastic optical networks. In the RMSA problem, spectrum resources in links are generally divided into frequency slices and connection requests with different capacities can use different numbers of frequency slices to transfer their data; however, this may lead to misalignment of idle frequency slices in links and then result in the increase of blocking probability of connection requests. In this article, we propose a resource-periodic-arrangement (RPA) strategy. The strategy partitions frequency slices into periodically arranged standard frequency blocks and each connection request exactly uses one or more standard frequency blocks. The RPA strategy can effectively reduce the misalignment of frequency slices. Furthermore, an RPA-based RMSA algorithm is designed, in which an enhanced-most-used policy is adopted to choose standard frequency blocks to connection requests. Simulation results show that the RPA-based RMSA algorithm can distinctly reduce blocking probability of connection requests, comparing with existing algorithms.

Application of time-frequency decomposition and seismic attributes for stratigraphic interpretation of thin reservoirs in “Jude Field”, Offshore Niger Delta

Precise reservoir characterization has been challenging and demanding due to the in-homogeneity involving sand and shale distribution. The complexity of the subtle structures within thin reservoirs in the deep-water depositional system is another factor. The reservoirs of interest are trapped within an important area noted for exploration activities. Regardless, they can be bypassed considering the complexity that results in poor resolution of the seismic data. Fewer research activities have been carried out within the thinly bedded reservoirs. Spectral Decomposition (Time-Frequency Decomposition) was used for the characterization of the complicated reservoirs to delineate structural changes and stratigraphic information along interpreted horizons. Also, it gains insight into the stratigraphic setting and the complexity of the geology. The transformed results include tuning volumes and a variety of frequency slices. When the fast Fourier algorithm was applied to seismic reflection data, the seismic signal was broken down into its frequency components and allowed visualization of the data at specific frequencies. Stratigraphic and structural features that otherwise would have been overlooked in full bandwidth display were identified. The obtained result revealed concealed and complex geologic features such as multi-complex and meandering channels, subtle faults and lobes. Time-frequency decomposition of seismic data for thinly bedded reservoirs interpreted using high frequency has led to the precise delineation of geologic features of interest. This study will also help in the recovery of more hydrocarbon as bypassed zones and subtle structures are revealed.

Application of seismic attribute analysis in fluvial seismic geomorphology

Seismic attributes can be important predictors, either qualitative or quantitative, of reservoir geometries when they are correctly used in reservoir characterization studies. This paper discusses seismic attribute analyses and their usefulness in seismic geomorphology study of Moragot field of Pattani Basin, Gulf of Thailand. Early to Middle Miocene fluvial channel and overbank sands are the reservoirs in Pattani Basin. Due to their limited horizontal and vertical distribution, it is not always possible to predict the geometry and distribution of these sands based on the conventional seismic interpretation. This study utilized various seismic attributes, e.g., RMS amplitude analysis, spectral decomposition, semblance and dip-steered similarity, RGB blending to image the geometry and the spatial distribution of sand bodies in horizon and stratal slices at different stratigraphic intervals. Attribute analyses reveal, at shallow stratigraphic levels, RMS and semblance can successfully identify channel-shaped sand bodies and mud-filled channels associated with channel belts. On the other hand in deeper stratigraphic intervals, sand distribution can be imaged more effectively by using spectral decomposition and dip-steered similarity volumes. High-frequency spectral decomposition slices can image thin sands, and low-frequency slices can image thick sands quite effectively in deeper intervals. RGB blending of different frequency slices is particularly useful in delineating channel systems of various dimensions at deeper intervals. These images show the distribution of sands and mud-filled channels at various stratigraphic levels. The width of channel belts varies from 200 m to 3 km. These channel belts are N–S or NW–SE oriented. From the channel pattern and their dimensions, depositional environments can be predicted. Mud-filled channels identified in the horizon slices will act as a connectivity barrier between sand bodies at either side of the channel. They can also act as lateral and up-dip seal to form stratigraphic traps. The seismic attribute analyses clearly show the geometry and spatial distribution of sand bodies. Hence, this method for predicting sand body geometry might help in field development planning as well as in reducing exploration risk.

Seismic Signal Denoising Using $f-x$ Variational Mode Decomposition

Ground roll is a common coherent noise in land seismic acquisition, which is usually characterized by high amplitudes, low frequencies, and low propagation velocities. Removing ground roll is a key procedure in seismic data processing. In this letter, we develop a novel ground roll suppression approach termed f-x variational mode decomposition (VMD), which has the characteristic of a frequency-dependent, high-wavenumber filter. This new algorithm first applies VMD on constant frequency slices in the frequency-offset domain within the frequency range of ground roll in order to extract the ground roll, and then the denoised data can be obtained by subtracting them adaptively from the original data. A synthetic example is given to demonstrate that the proposed method is able to suppress ground roll well and preserve seismic reflections effectively compared with other conventional techniques such as frequency filtering and f-k filtering. Application of the method on field data further demonstrates the potential of the f-x VMD algorithm in industrial applications.

A pre-split multi-flow RMSA algorithm in elastic optical networks

Abstract Elastic optical networking is a promising technology for the next generation optical networks, due to its flexibility of spectrum provisioning. Routing, modulation and spectrum assignment (RMSA) problem is one of the key issues in Elastic optical networks. In the problem, constraints of spectrum continuity and spectrum contiguity must be met, which limits the use of spectrum resources. Multi-flow measure, which splits a connection request into multiple flows if the connection request cannot be served as a whole, can relax the spectrum contiguity constraint. The existing multi-flow RMSA algorithms are post-split. In this paper, we propose a pre-split multi-flow RMSA algorithm, in which three innovative measures are adopted, namely partitioning frequency slices into standard blocks, pre-splitting connection requests into flows exactly using the standard blocks, and searching for available standard blocks for the flows with a proposed modified-most-used rule. The measures can make both the occupied and idle frequency slices aligned, so they are expected to lower the blocking probability of connection requests. Simulation results show that the proposed algorithm can get a better performance in term of blocking probability, comparing with three benchmarks.

Research on CLIB Routing and Spectrum Allocation Algorithm in Elastic Optical Networks

Abstract With the rapid development of mobile Internet, high-definition video and cloud computing, users’ bandwidth demands are not only larger and larger but also more and more diverse. To solve this problem, there searchers put forward the concept of elastic optical network (EON). EON adopts the transmission mode of elastic grid, which can allocate spectrum resources flexibly and meet high bandwidth and diversity requirements at the same time. Routing and spectrum allocation (RSA) is an important issue in EON. In this paper, we present a heuristic algorithm named constrained-lower-indexed-block (CLIB) allocation algorithm for the RSA problem. The algorithm is based on the K candidate paths. When there are available spectrum blocks on multiple candidate paths, if the increase of the path length does not exceed a given threshold, the lower index spectrum would be selected for the connection request on a longer path. The aim of the algorithm is to concentrate the occupied frequency slices on one side of the spectrum and leave another side of the spectrum to the later arrived connection requests as much as possible, to reduce the blocking probability of connection requests. Simulation results show that comparing with the first-last-fit and hybrid grouping algorithms, the CLIB algorithm can reduce the blocking probability of connection requests.