Matching Pursuit Decomposition Research Articles

Dispersion aware matching pursuit decomposition of Lamb wave signals for structural health monitoring of thin plates structures

Dispersion aware matching pursuit decomposition of Lamb wave signals for structural health monitoring of thin plates structures

Seismic resolution enhancement with variational modal-based fast-matching pursuit decomposition

Enhancing vertical resolution and signal-to-noise ratio (S/N) is a key objective in seismic data processing. Considering the underground medium is inhomogeneous and incompletely elastic, seismic wave energy attenuation occurs during underground propagation, which has a significant impact on seismic data resolution and S/N. Traditional fast-matching pursuit (FMP) algorithms make it difficult to separate valid signals and noise effectively while reconstructing the noisy signals. Therefore, an improved FMP algorithm that combines the variational-mode decomposition (VMD) strategy is developed. The VMD algorithm is used to obtain intrinsic mode functions with varying amplitudes, frequencies, and center times. It can achieve a multiscale decomposition of nonstationary seismic data. Based on the intrinsic mode functions of different scales, the FMP algorithm can reconstruct prior information of the amplitude, frequency, and center time of valid signals and noise signals in the mode functions. Thus, the high-resolution sparse representation of intrinsic mode functions is achieved. The numerical results indicate that our method not only separates the effective signal and noise but also preserves the valid signal as much as possible. In addition, the feasibility of the method is further verified by field exploration data. The results indicate that this strategy can enhance the resolution of seismic data while restoring the attenuated energy using multiscale seismic data.

Neonatal EEG seizure detection using a new signal structural complexity measure based on matching pursuit decomposition with nonstationary dictionary

Background and objectiveIn newborns, it is often difficult to accurately differentiate between seizure and non-seizure based solely on clinical manifestations. This highlights the importance of electroencephalogram (EEG) in the recognition and management of neonatal seizures. This paper proposes an effective algorithm for the detection of neonatal seizure using multichannel EEG. MethodsNeonatal EEG changes morphology as it alternates between seizure and non-seizure states. A new signal complexity measure based on matching pursuit (MP) decomposition is proposed and used to detect transitions between these two states. The new measure, referred to as weighted structural complexity (WSC), was used for the detection of seizures in 30 newborn EEG records. Multiple IIR filters and an MP-based filter were designed and used to remove artifacts from the EEG data. Geometrical correlation between the EEG data channels was applied to reduce the number of false detections caused by remnant artifacts. The seizure detector's performance was assessed using several epoch-based (e.g., accuracy) and event-based (GDR = good detection rate and FD/h = false detections per hour) metrics. ResultsCompared to the neurologist marking, the proposed detector was able to detect EEG seizures with 94% accuracy, 90.9% GDR, and 0.14 FD/h (95% CI: [0.06, 0.34]). ConclusionsThe high performance of the MP-based detector may have significant implications for the accurate diagnosis of neonatal seizures and the appropriate use of anticonvulsants and ongoing clinical assessment and care of the newborn.

Lamb Wave-Based Damage Localization and Quantification in Composites Using Probabilistic Imaging Algorithm and Statistical Method.

Quantitatively and accurately monitoring the damage to composites is critical for estimating the remaining life of structures and determining whether maintenance is essential. This paper proposed an active sensing method for damage localization and quantification in composite plates. The probabilistic imaging algorithm and the statistical method were introduced to reduce the impact of composite anisotropy on the accuracy of damage detection. The matching pursuit decomposition (MPD) algorithm was utilized to extract the precise TOF for damage detection. The damage localization was realized by comprehensively evaluating the damage probability evaluation results of all sensing paths in the monitoring area. Meanwhile, the scattering source was recognized on the elliptical trajectory obtained through the TOF of each sensing path to estimate the damage size. Damage size was characterized by the Gaussian kernel probability density distribution of scattering sources. The algorithm was validated by through-thickness hole damages of various locations and sizes in composite plates. The experimental results demonstrated that the localization and quantification absolute error are within 11 mm and 2.2 mm, respectively, with a sensor spacing of 100 mm. The algorithm proposed in this paper can accurately locate and quantify damage in composite plate-like structures.

Spectral Decomposition Made Simple

Spectral decomposition enables the resolution of seismic data to be improved significantly yielding a new possibility to map thin layers such channel sands and any other stratigraphic features. It has also been used in reservoir characterization. There are three methods for implementing spectral decomposition i.e., The Short Time Fourier Transform, the Continuous Wavelet Transform and the Matching Pursuit Decomposition. Among three of them, the Matching Pursuit Decomposition seems to be the most sophisticated one. It gives the best resolution among them. A simple and logical approach for explaining the spectral decomposition methods together with real data examples are presented in this paper by avoiding complex mathematical formulation.

Cross-Term Suppression Method for Time-Frequency Spectrum of Engineering Blasting Signals

Abstract Engineering blasting vibration signals are affected by the test environment and contain noise and trend components, which leads to significant cross-term in the time-frequency (TF) distribution. To address these problems, a combined algorithm of matching pursuit (MP) and smooth pseudo-Wigner-Ville distribution (SPWVD) is proposed to suppress the TF spectrum cross-term and extract the signal features. An overcomplete atom library is constructed for signal feature matching according to sparse signal representation theory. Atomic reconstruction of the subsignal is achieved by MP decomposition, and the SPWVD distribution of the reconstructed subsignal is calculated and superimposed, yielding a more accurate TF expression of the blasting signal. The results show that the combined MP-SPWVD method can accurately suppress cross-term and improve the TF identification and feature analysis ability, which verifies the accuracy of its application in signal TF analysis. The combined algorithm is thus suitable for TF feature extraction of engineering blasting signals.

Improved W-Transform Incorporating Fast Matching Pursuit Decomposition

Time-frequency analysis method is an effective tool to describe the relationship between the time and frequency of seismic signal. Accurate time-frequency results help to better delineate subsurface geological structures. S-transform (ST), due to its high resolution, is known as a kind of pragmatic time-frequency analysis tool in seismic signal processing. However, the center of the energy group estimated by the ST is shifted to the higher frequency. A recently proposed W-transform (WT), which brings in the dominant frequency, solves this problem to some extent. However, the dominant frequency calculated by the temporal average of the instantaneous frequency is unstable, which shows negative value sometimes. Therefore, an improved WT incorporating fast matching pursuit (WT-FMP) decomposition method is proposed to estimate the well-concentrated time-frequency distribution. It decomposes the seismic signal into a series of matched wavelets selected from an over-complete dictionary initially. Furthermore, the whole time-frequency distribution of the signal is generated by superposition of each wavelet’s WT coefficients. The proposed method helps to avoid the dependence on the instantaneous frequency and it owns higher resolution because the time-frequency map for signal is well localized using fast matching pursuit (FMP). Synthetic and field examples are utilized to validate the effectiveness of the proposed WT-FMP method. The results demonstrate its priority in providing more concentrated spectrum and revealing abundant stratigraphic and geological information.

Ultrasound Defect Localization in Shell Structures with Lamb Waves Using Spare Sensor Array and Orthogonal Matching Pursuit Decomposition.

Lamb waves have multimodal and dispersion effects, which reduces their performance in damage localization with respect to resolution. To detect damage with fewest sensors and high resolution, a method, using only two piezoelectric transducers and based on orthogonal matching pursuit (OMP) decomposition, was proposed. First, an OMP-based decomposition and dispersion removal algorithm is introduced, which is capable of separating wave packets of different propagation paths and removing the dispersion part successively. Then, two simulation signals, with nonoverlapped and overlapped wave packets, are employed to verify the proposed method. Thereafter, with the proposed algorithm, the wave packets reflected from the defect and edge are all separated. Finally, a sparse sensor array with only two transducers succeeds in localizing the defect. The experimental results show that the OMP-based algorithm is beneficial for resolution improvement and transducer usage reduction.

A novel method based on matching pursuit decomposition of gait signals for Parkinson’s disease, Amyotrophic lateral sclerosis and Huntington’s disease detection

Background and ObjectiveAn accurate detection of neurodegenerative diseases (NDDs) definitely improves the life of patients and has attracted growing attention. MethodsIn this paper, a general automatic method for detection of Parkinson’s disease (PD), Amyotrophic lateral sclerosis (ALS) and Huntington’s disease (HD) has been proposed based on the localized time–frequency information of gait signals. The new main part of the detection method is to obtain a small set of sparse coefficients for the local representation of gait signals with appropriate time and frequency resolution. For this purpose, a hybrid feature set based on sparse matching pursuit decomposition and two sets of nonlinear and linear features has been developed. Then, principal components of the proposed feature have been analyzed using a sparse coding classifier.ResultsThe proposed approach has achieved high average accuracy rates of 93%, 94%, and 97% for PD, ALS, and HD detection, respectively. ConclusionsThe obtained results have indicated that combination of time and frequency information of the gait signals through adaptive localized window length in MP makes it more efficient in comparison with the existing time, frequency or other time–frequency gait parameters. The great potential of nonlinear sparse features for PD and HD detection and linear ones for ALS detection has also been shown.

Cable reverberations during wireline distributed acoustic sensing measurements: their nature and methods for elimination

ABSTRACTThe application of distributed acoustic sensing in borehole measurements allows for the use of fibre optic cables to measure strain. This is more efficient in terms of time and costs compared with the deploying of conventional borehole seismometers. Nevertheless, one known drawback for temporary deployment is represented by the freely hanging wireline cable slapping and ringing inside the casing, which introduces additional coherent coupling noise to the data. The present study proposes an explanation for the mechanism of noise generation and draws an analogy with similar wave propagation processes and phenomena, such as ghost waves in marine seismics. This observation allows to derive a ringing noise filter function, to study its behaviour and to consider known effects of the gauge length filter. After examining existing methods aimed at eliminating ringing noise and results of their application, we propose a two‐step approach: (1) developing a denoising method based on a matching pursuit decomposition with Gabor atoms and (2) subtracting the noise model for imaging improvement. The matching pursuit method focuses on decomposing the original input signal into a weighted sum of Gabor functions. Analysing Gabor atoms properties for frequency, amplitude and position in time provides the opportunity to distinguish parts of the original signal denoting noise caused by the vibrating cable. The matching pursuit decomposition applied to the distributed acoustic sensing‐vertical seismic profiling data at the geothermal test site Groß Schönebeck provides a versatile processing instrument for noise suppression.

A Robust and Efficient Compressed Sensing Algorithm for Wideband Acoustic Imaging

Wideband acoustic imaging, which combines compressed sensing (CS) and microphone arrays, is widely used for locating acoustic sources. However, the location results of this method are unstable, and the computational efficiency is low. In this work, in order to improve the robustness and reduce the computational cost, a DCS-SOMP-SVD compressed sensing method, which combines the distributed compressed sensing using simultaneously orthogonal matching pursuit (DCS-SOMP) and singular value decomposition (SVD) is proposed. The performance of the DCS-SOMP-SVD is studied through both simulation and experiment. In the simulation, the locating results of the DCS-SOMP-SVD method are compared with the wideband BP method and the DCS-SOMP method. In terms of computational efficiency, the proposed method is as efficient as the DCS-SOMP method and more efficient than the wideband BP method. In terms of locating accuracy, the proposed method can still locate all sources when the signal to noise ratio (SNR) is − 20 dB, while the wideband BP method and the DCS-SOMP method can only locate all sources when the SNR is higher than 0 dB. The performance of the proposed method can be improved by expanding the frequency range. Moreover, there is no extra source in the maps of the proposed method, even though the target sparsity is overestimated. Finally, a gas leak experiment is conducted to verify the feasibility of the DCS-SOMP-SVD method in the practical engineering environment. The experimental results show that the proposed method can locate both two leak sources in different frequency ranges. This research proposes a DCS-SOMP-SVD method which has sufficient robustness and low computational cost for wideband acoustic imaging.

Micro‐motion feature extraction of narrow‐band radar target based on ROMP

In this study, a new method based on regularised orthogonal matching pursuit (ROMP) decomposition is proposed to extract the feature of the ballistic target's rotating micro-motion. Based on the establishment of the ballistic target rotation model, the general model of target's narrowband radar echo is obtained. An over-complete dictionary of atoms is established by combining the intrinsic characteristics of the signal model, and the selection of the matched atoms is carried out according to the regularisation principle. The extraction of the target's rotating micro-motion feature and the reconstruction of the original signal are realised. Simulation results show that the method can extract the micro-motion feature efficiently and accurately.

Parametric Description of EEG Profiles for Assessment of Sleep Architecture in Disorders of Consciousness.

We propose a fully parametric approach to the assessment of sleep architecture, based upon the classical electroencephalographic criteria, applicable also to the recordings of patients with disorders of consciousness (DOC). Sleep spindles and slow waves are automatically detected from the matching pursuit decomposition of overnight EEG recordings. Their evolution can be presented in the form of EEG profiles, yielding a continuous description of sleep architecture, compatible with the classical criteria used in sleep staging. We propose assessment of these EEG profiles by five parameters, which can be combined by a linear classifier, assessing the quality of sleep architecture. Proposed methodology is evaluated on 59 overnight EEG recordings from 19 patients from a hospital for children with severe brain damage, in relation to their behavioral diagnosis according to the Coma Recovery Scale-Revised. Presented results indicate robustness of the proposed approach, which may serve as a valuable aid in diagnosis of DOC patients. Complete software environment for computing and presentation of EEG profiles is freely available from http://svarog.pl .

Spectral Decomposition and a Waveform Cluster to Characterize Strongly Heterogeneous Paleokarst Reservoirs in the Tarim Basin, China

The main components of the Ordovician carbonate reservoirs in the Tahe Oilfield are paleokarst fracture-cavity paleo-channel systems formed by karstification. Detailed characterization of these paleokarst reservoirs is challenging because of heterogeneities in characteristics and strong vertical and lateral non-uniformities. Traditional seismic analysis methods are not able to solve the identification problem of such strongly heterogeneous reservoirs. Recent developments in seismic interpretation have heightened the need to describe the fracture-cavity structure of a paleo-channel with more accuracy. We propose a new prediction model for fracture-cavity carbonate reservoirs based on spectral decomposition and a waveform cluster. By the Matching Pursuit decomposition algorithm, the single-frequency data volumes are obtained. The specific frequency data volume that is the most sensitive to the reservoir is chosen based on seismic synthesis traces of well-logging data and geological interpretability. The waveform cluster is then applied to delineate the complex paleokarst systems, particularly the fracture-caves in the runoff zone. This method was applied to the area around Well T615 in the Tahe oilfield, and a paleokarst fracture-cavity system with strong heterogeneity in the runoff zone was delineated and characterized. The findings of this research provide insights for predicting other similar karst systems, such as karstic groundwater and karst hydrogeological systems.

Time-Frequency Energy Distribution of Ground Motion and Its Effect on the Dynamic Response of Nonlinear Structures

The ground motion characteristics are essential for understanding the structural seismic response. In this paper, the time-frequency analytical method is used to analyze the time-frequency energy distribution of ground motion, and its effect on the dynamic response of nonlinear structure is studied and discussed. The time-frequency energy distribution of ground motion is obtained by the matching pursuit decomposition algorithm, which not only effectively reflects the energy distribution of different frequency components in time, but also reflects the main frequency components existing near the peak ground acceleration occurrence time. A series of artificial ground motions with the same peak ground acceleration, Fourier amplitude spectrum, and duration are generated and chosen as the earthquake input of the structural response. By analyzing the response of the elasto-perfectly-plastic structure excited by artificial seismic waves, it can be found that the time-frequency energy distribution has a great influence on the structural ductility. Especially if there are even multiple frequency components in the same ground motion phrase, the plastic deformation of the elasto-perfectly-plastic structure will continuously accumulate in a certain direction, resulting in a large nonlinear displacement. This paper reveals that the time-frequency energy distribution of a strong ground motion has a vital influence on the structural response.

Reference-free damage localization in time-space domain for structural health monitoring of X-COR sandwich composites

This article presents a guided wave based damage localization framework using a time-space analysis for structural health monitoring of X-COR sandwich composites with a reference-free perspective to overcome the difficulty in detecting reflected guided waves in a highly attenuated media. Transducers, including macro-fiber composites and piezoelectric wafers, are used to design the sensing paths. The time-space domain is constructed using de-noised signals that are processed by signal processing techniques including matching pursuit decomposition and Hilbert transform. The localization framework is then validated across a wide range of excitation frequencies in X-COR sandwich composites with seeded facesheet delamination. The results indicate that time-space analysis offers a high accuracy for detection and localization of internal damages and serves as a promising framework for structural health monitoring of complex sandwich composites with reinforcements. This work also provides a comprehensive study of the changes in group velocities, attenuation tendencies, and time-space resolution of actuated and converted modes under different excitation frequencies across a range of ultrasonic transducer sizes, thereby helping to improve reliability and accuracy of damage localization in time-space domain.

Matching Pursuit Decomposition on Electrocardiograms for Joint Compression and QRS Detection

The electrocardiogram (ECG) is relevant for several medical purposes. In this work, a novel analysis-by-synthesis method to process ECG signals is presented. It is based on the matching pursuit algorithm, which is employed here to decompose the ECG in the time domain. The main features of the ECG are extracted through a dictionary of triangular functions, due to their good correlation with the typical electrocardiographic waveforms, especially the R wave. The individual elements of this signal representation can be further employed for different processing tasks, such as ECG compression and QRS detection. Compression is required to store and transmit signals in situations related to massive acquisitions, frequent monitoring, high-resolution data, real-time needs or narrow bandwidths. QRS detection is not only essential to study the heart rate variability, but also the basis of automatic systems for ECG applications such as heartbeat classification or anomaly identification. In this study, it is shown how to employ the proposed processing approach to perform ECG compression and beat detection jointly. The resulting algorithm is tested over the whole MIT-BIH Arrhythmia Database, with a wide variety of ECG records, yielding both high compression and efficient QRS detection.

Bearing fault diagnosis based on spectrum image sparse representation of vibration signal

Bearings are crucial for industrial production and susceptible to malfunction in rotating machines. Image analysis can give a comprehensive description of vibration signal, thus, it has achieved much more attention recently in fault diagnosis field. However, it brings lots of redundant information from a single spectrum image matrix behind rich fault information, and massive spectrum image samples lead to exacerbation of this situation, which readily results in the accuracy-dropping problem of multiple local defective bearings diagnosis. To solve this issue, a novel feature extraction method based on image sparse representation is proposed. Original spectrum images are acquired through fast Fourier transformation. Sparse coefficient that reveals the underlying structure of spectrum image based on raw signals is extracted as the feature by implementing the orthogonal matching pursuit and K-singular value decomposition algorithm strategically, and then two-dimensional principal component analysis is applied for further processing of these features. Finally, fault types are identified based on a minimum distance strategy. The experimental results are given to demonstrate the effectiveness of the proposed method.

Multivariate Matching Pursuit Decomposition and Normalized Gabor Entropy for Quantification of Preictal Trends in Epilepsy.

Quantification of the complexity of signals recorded concurrently from multivariate systems, such as the brain, plays an important role in the study and characterization of their state and state transitions. Multivariate analysis of the electroencephalographic signals (EEG) over time is conceptually most promising in unveiling the global dynamics of dynamical brain disorders such as epilepsy. We employed a novel methodology to study the global complexity of the epileptic brain en route to seizures. The developed measures of complexity were based on Multivariate Matching Pursuit (MMP) decomposition of signals in terms of time–frequency Gabor functions (atoms) and Shannon entropy. The measures were first validated on simulation data (Lorenz system) and then applied to EEGs from preictal (before seizure onsets) periods, recorded by intracranial electrodes from eight patients with temporal lobe epilepsy and a total of 42 seizures, in search of global trends of complexity before seizures onset. Out of five Gabor measures of complexity we tested, we found that our newly defined measure, the normalized Gabor entropy (NGE), was able to detect statistically significant (p < 0.05) nonlinear trends of the mean global complexity across all patients over 1 h periods prior to seizures’ onset. These trends pointed to a slow decrease of the epileptic brain’s global complexity over time accompanied by an increase of the variance of complexity closer to seizure onsets. These results show that the global complexity of the epileptic brain decreases at least 1 h prior to seizures and imply that the employed methodology and measures could be useful in identifying different brain states, monitoring of seizure susceptibility over time, and potentially in seizure prediction.

Ultrasonic guided wave propagation in composites including damage using high-fidelity local interaction simulation

Composite materials are used in many advanced engineering applications because of high specific strength and stiffness. Their complex damage mechanisms and failure modes, however, are still not well-understood, thus challenging the application safety. Ultrasonic guided waves are promising structural health monitoring tools used to determine the operational safety of composite materials. In this article, a fully coupled numerical simulation model is used to study wave propagation and dispersion in composites under varying sensor locations, propagating orientations, excitation frequencies, and damage locations. The model is based on the local interaction simulation approaches/sharp interface model wherein output sensor signals are processed using the matching pursuit decomposition algorithm to study the signal features in the time–frequency domain. The changes in signals due to varying damage locations with respect to the through-thickness direction are studied under anti-symmetrical and symmetrical excitation scenarios. The results show that the signal from symmetric excitation is more sensitive to the damage location, while the signal from anti-symmetric excitation is less dispersive. It indicates that comprising effective feature extraction technique with the accurate physics-based numerical simulation model can be implemented to develop robust structural health monitoring framework for composites.