Extraction of reflected waves from acoustic logging data using variation mode decomposition and curvelet transform

Remote reflection waves, essential for acquiring high-resolution images of geological structures beyond boreholes, often suffer contamination from strong direct mode waves propagating along the borehole.Consequently, the extraction of weak reflected waves becomes pivotal for optimizing migration image quality. This paper introduces a novel approach to extracting reflected waves by sequentially operating in the spatial frequency and curvelet domains. Using variation mode decomposition(VMD), single-channel spatial domain signals within the common offset gather are iteratively decomposed into high-wavenumber and low-wavenumber intrinsic mode functions(IMFs). The low-wavenumber IMF is then subtracted from the overall waveform to attenuate direct mode waves. Subsequently, the curvelet transform is employed to segregate upgoing and downgoing reflected waves within the filtered curvelet domain. As a result, direct mode waves are substantially suppressed, while the integrity of reflected waves is fully preserved. The efficacy of this approach is validated through processing synthetic and field data, underscoring its potential as a robust extraction technique.

A debris-flow forecasting method with infrasound-based variational mode decomposition and ARIMA

Infrasound,known for its strong penetration and low attenuation,is extensively used in monitoring and warning systems for debris flows.Here,a debris-flow forecasting method was proposed by combining infrasound-based variational mode decomposition and Autoregressive Integrated Moving Average(ARIMA)model.High-precision infrasound sensor was utilized in experiments to record signals under twelve varying conditions of debris flow volume and velocity.Variational mode decomposition was performed on the detected raw signals,and the optimal decomposition scale and penalty factor were obtained through the sparrow search algorithm.The Hilbert transform,rescaled range analysis,power spectrum analysis,and Pearson correlation coefficients judgment criteria were employed to separate and reconstruct the signals.Based on the reconstructed infrasound signals,an ARIMA model was constructed to forecast the trend of debris flow infrasound signal.Results reveal that the Hilbert transform effectively separated noise,and the predictive model’s results fell within a 95%confidence interval.The Mean Absolute Percentage Error(MAPE)across four experiments were 4.87%,5.23%,5.32%and 4.47%,respectively,showing a satisfactory accuracy and providing an alternative for predicting debris flow by infrasound signals.

Variational Mode Decomposition-Informed Empirical Wavelet Transform for Electric Vibrator Noise Analysis

Electric vibrators find wide applications in reliability testing, waveform generation, and vibration simulation, making their noise characteristics a topic of significant interest. While Variational Mode Decomposition (VMD) and Empirical Wavelet Transform (EWT) offer valuable support for studying signal components, they also present certain limitations. This article integrates the strengths of both methods and proposes an enhanced approach that integrates VMD into the frequency band division principle of EWT. Initially, the method decomposes the signal using VMD, determining the mode count based on residuals, and subsequently employs EWT decomposition based on this information. This addresses mode aliasing issues in the original method while capitalizing on VMD’s adaptability. Feasibility was confirmed through simulation signals and ultimately applied to noise signals from vibrators. Experimental results demonstrate that the improved method not only resolves EWT frequency band division challenges but also effectively decomposes signal components compared to the VMD method.

Research on Modulation Signal Denoising Method Based on Improved Variational Mode Decomposition

In order to further analyze the micro-motion modulation signals generated by rotating components and extract micro-motion features,a modulation signal denoising algorithm based on improved variational mode decomposition(VMD)is proposed.To improve the time-frequency performance,this method decomposes the data into narrowband signals and analyzes the internal energy and frequency variations within the signal.Genetic algorithms are used to adaptively optimize the mode number and bandwidth control parameters in the process of VMD.This approach aims to obtain the optimal parameter combination and perform mode decomposition on the micro-motion modulation signal.The optimal mode number and quadratic penalty factor for VMD are determined.Based on the optimal values of the mode number and quadratic penalty factor,the original signal is decomposed using VMD,resulting in optimal mode number intrinsic mode function(IMF)components.The effective modes are then reconstructed with the denoised modes,achieving signal denoising.Through experimental data verification,the proposed algorithm demonstrates effective denoising of modulation signals.In simulation data validation,the algorithm achieves the highest signal-to-noise ratio(SNR)and exhibits the best performance.

Single nucleotide variations in the development of diabetic foot ulcer: A narrative review

Diabetes mellitus has become a global health problem,and the number of patients with diabetic foot ulcers(DFU)is rapidly increasing.Currently,DFU still poses great challenges to physicians,as the treatment is complex,with high risks of infection,recurrence,limb amputation,and even death.Therefore,a comprehensive understanding of DFU pathogenesis is of great importance.In this review,we summarized recent findings regarding the DFU development from the perspective of single-nucleotide variations(SNVs).Studies have shown that SNVs located in the genes encoding C-reactive protein,interleukin-6,tumor necrosis factor-alpha,stromal cell-derived factor-1,vascular endothelial growth factor,nuclear factor erythroid-2-related factor 2,sirtuin 1,intercellular adhesion molecule 1,monocyte chemoattractant protein-1,endothelial nitric oxide synthase,heat shock protein 70,hypoxia inducible factor 1 alpha,lysyl oxidase,intelectin 1,mitogen-activated protein kinase 14,toll-like receptors,osteoprotegerin,vitamin D receptor,and fibrinogen may be associated with the development of DFU.However,considering the limitations of the present investigations,future multi-center studies with larger sample sizes,as well as in-depth mechanistic research are warranted.

Studies of Variational Assimilation for the Inversion of the Coupled Air-sea Model

For the prediction of ENSO, the accuracy of the model including the parameters, initial value and others of the model is important, which can be retrieved by the variational data assimilation methods developed in recent years. However, when the nonlinearity of the model is quite strong, the effect of the improvement made by the 4-D variational data assimilation may be poor due to the bad approximation of the tangent linear model to the original model. So in the paper the ideas in the optimal control is introduced to improve the effect of 4-DVAR in the inversion of the parameters of a nonlinear dynamic ENSO model. The results indicate that when the terminal controlling term is added to the cost functional of 4DVAR, which originated from the optimal control, the effect of the inversion may be largely improved comparing to the traditional 4DVAR, as can be especially obvious from the phase orbit of the model variables. The results in the paper also suggest that the method of 4DVAR in combination with optimal control cannot only reduce the error resulting from the inaccuracy of the model parameters but also can correct the parameters itself. This gives a good method in modifying the model and improving the quality of prediction of ENSO.

A Stylistic Analysis of the Narrative Mode in A Rose for Emily

A Rose for Emily is a short story written by William Faulkner, the famous and prolific writer of novels and short stories in America. His unique style of writing fiction always draws much attention of scholars. The narrative mode in A Rose for Emily is studied in this paper thus to explore the impact of the narrating technique on fortifying the theme of the novel.

Variational Mode Decomposition for Rotating Machinery Condition Monitoring Using Vibration Signals

The failure of rotating machinery applications has major time and cost effects on the industry.Condition monitoring helps to ensure safe operation and also avoids losses.The signal processing method is essential for ensuring both the efficiency and accuracy of the monitoring process.Variational mode decomposition(VMD)is a signal processing method which decomposes a non-stationary signal into sets of variational mode functions(VMFs)adaptively and non-recursively.The VMD method offers improved performance for the condition monitoring of rotating machinery applications.However,determining an accurate number of modes for the VMD method is still considered an open research problem.Therefore,a selection method for determining the number of modes for VMD is proposed by taking advantage of the similarities in concept between the original signal and VMF.Simulated signal and online gearbox vibration signals have been used to validate the performance of the proposed method.The statistical parameters of the signals are extracted from the original signals,VMFs and intrinsic mode functions(IMFs)and have been fed into machine learning algorithms to validate the performance of the VMD method.The results show that the features extracted from VMD are both superior and accurate for the monitoring of rotating machinery.Hence the proposed method offers a new approach for the condition monitoring of rotating machinery applications.

Microseismic signal denoising by combining variational mode decomposition with permutation entropy

Remarkable progress has been achieved on microseismic signal denoising in recent years,which is the basic component for rock-burst detection.However,its denoising effectiveness remains unsatisfactory.To extract the effective microseismic signal from polluted noisy signals,a novel microseismic signal denoising method that combines the variational mode decomposition(VMD)and permutation entropy(PE),which we denote as VMD–PE,is proposed in this paper.VMD is a recently introduced technique for adaptive signal decomposition,where K is an important decomposing parameter that determines the number of modes.VMD provides a predictable eff ect on the nature of detected modes.In this work,we present a method that addresses the problem of selecting an appropriate K value by constructing a simulation signal whose spectrum is similar to that of a mine microseismic signal and apply this value to the VMD–PE method.In addition,PE is developed to identify the relevant effective microseismic signal modes,which are reconstructed to realize signal filtering.The experimental results show that the VMD–PE method remarkably outperforms the empirical mode decomposition(EMD)–VMD filtering and detrended fl uctuation analysis(DFA)–VMD denoising methods of the simulated and real microseismic signals.We expect that this novel method can inspire and help evaluate new ideas in this field.

The Variations of Dominant Convection Modes over Asia, Indian Ocean, and Western Pacific Ocean during the Summers of 1997-2004

The NOAA daily outgoing longwave radiation （OLR） and the Global Precipitation Climatology Project （GPCP）daily precipitation data are used to study the variation of dominant convection modes and their relationships over Asia, the Indian Ocean, and the western Pacific Ocean during the summers from 1997 to 2004. Major findings are as follows： （1） Regression analysis with the OLR indicates the convective variations over Asian monsoon region are more closely associated with the convective activities over the western subtropical Pacific （WSP） than with those over the northern tropical Indian Ocean （NTIO）. （2） The EOF analysis of OLR indicates the first mode （EOF1） exhibits the out-of-phase variations between eastern China and India, and between eastern China and the WSP. The OLR EOF1 primarily exhibits seasonal and even longer-term variations. （3） The OLR EOF2 mostly displays in-phase convective variations over India, the Bay of Bengal, and southeastern China. A wavelet analysis reveals intraseasonal variation （ISV） features in 2000, 2001, 2002, and 2004. However, the effective ISV does not take place in every year and it seems to occur only when the centers of an east-west oriented dipole reach enough intensity over the tropical Indian and western Pacific Oceans. （4） The spatial patterns of OLR EOF3 are more complicated than those of EOF1 and EOF2, and an effective ISV is noted from 1999 to 2004. The OLR EOF3 implies there is added complexity of the OLR pattern when the effective ISV occurs. （5） The correlation analysis suggests the precipitation over India is more closely associated with the ISV, seasonal variations, and even longer-term variations than precipitation occurring over eastern China.

An extraction method for pressure beat vibration characteristics of hydraulic drive system based on variational mode decomposition

In the pump-controlled motor hydraulic transmission system,when the pressure pulsation frequencies seperately generated by the pump and the motor are close to each other,the hydraulic system will generate a strong pressure beat vibration phenomenon,which will seriously affect the smooth running of the hydraulic system.However,the modulated pressure signal also carries information related to the operating state of the hydraulic system,and a accurate extraction of pressure vibration characteristics is the key to obtain the operating state information of the hydraulic system.In order to extract the pressure beat vibration signal component effectively from the multi-component time-varying aliasing pressure signal and reconstruct the time domain characteristics,an extraction method of the pressure beat vibration characteristics of the hydraulic transmission system based on variational mode decomposition(VMD)is proposed.The experimental results show that the VMD method can accurately extract the pressure beat vibration characteristics from the high-pressure oil pressure signal of the hydraulic system,and the extraction effect is preferable to that of the traditional signal processing methods such as empirical mode decomposition(EMD).

Removal of Ocular Artifacts from Electroencephalo-Graph by Improving Variational Mode Decomposition

Ocular artifacts in Electroencephalography(EEG)recordings lead to inaccurate results in signal analysis and process.Variational Mode Decomposition(VMD)is an adaptive and completely nonrecursive signal processing method.There are two parameters in VMD that have a great influence on the result of signal decomposition.Thus,this paper studies a signal decomposition by improving VMD based on squirrel search algorithm(SSA).It’s improved with abilities of global optimal guidance and opposition based learning.The original seasonal monitoring condition in SSA is modified.The feedback of whether the optimal solution is successfully updated is used to establish new seasonal monitoring conditions.Opposition-based learning is introduced to reposition the position of the population in this stage.It is applied to optimize the important parameters of VMD.GOSSA-VMD model is established to remove ocular artifacts from EEG recording.We have verified the effectiveness of our proposal in a public dataset compared with other methods.The proposed method improves the SNR of the dataset from-2.03 to 2.30.

Dominant variation modes associated with Yangtze–Huai River Basin summer heavy rainfall events

The Yangtze–Huai River Basin(YHRB)always suffers from anomalously heavy rainfall during the warm season,and has been well explored as a whole area during the past several decades.In this study,the YHRB is divided into two core regions-the northern YHRB(nYHRB)and southern YHRB(sYHRB)-based on 29-year(1979–2007)June–July–August(JJA)temporally averaged daily rainfall rates and the standard deviation of rainfall.A spectral analysis of JJA daily rainfall data over these 29 years reveals that a 3–7-day synoptic-timescale high-frequency mode is absolutely dominant over the nYHRB,with 10–20-day and 15–40-day modes playing a secondary role.By contrast,3–7-day and 10–20-day modes are both significant over the sYHRB,with 7–14-day,15–40-day,and 20–60-day modes playing secondary roles.Based on a comparison between bandpass-filtered rainfall anomalies and original rainfall series,a total of 42,1,5,and 3 heavy rainfall events(daily rainfall amounts in the top 5%of rainy days)are detected over the nYHRB,corresponding to 3–7-day,7–14-day,10–20-day,and 15–40-day variation disturbances.Meanwhile,a total of 28,8,12,and 6 heavy rainfall events are detected over the sYHRB,corresponding to 3–7-day,7–14-day,10–20-day,and 20–60-day variation disturbances.The results have important implications for understanding the duration of summer heavy rainfall events over both regions.

A PRINCIPAL MODE OF CIRCULATION COVARIATION BETWEEN THE NORTHERN AND SOUTHERN HEMISPHERES AND ITS ASSOCIATION WITH ENSO DURING BOREAL WINTER

By employing the singular value decomposition(SVD) analysis, we have investigated in the present paper the covariations between circulation changes in the Northern(NH) and Southern Hemispheres(SH) and their associations with ENSO by using the NCEP/NCAR reanalysis, the reconstructed monthly NOAA SST, and CMAP precipitation along with NOAA Climate Prediction Center(CPC) ENSO indices. A bi-hemispheric covariation mode(hereafter BHCM) is explored, which is well represented by the first mode of the SVD analysis of sea surface pressure anomaly(SLPA-SVD1). This SVD mode can explain 57.36% of the total covariance of SLPA. BHCM varies in time with a long-term trend and periodicities of 3—5 years. The long term trend revealed by SVD1 shows that the SLP increases in the equatorial central and eastern Pacific but decreases in the western Pacific and tropical Indian Ocean, which facilitates easterlies in the lower troposphere to be intensified and El Ni觡o events to occur with lower frequency. The spatial pattern of the BHCM looks roughly symmetric about the equator in the tropics, whereas it is characterized by zonal disturbances in the mid-latitude of NH and is highly associated with AAO in the mid-latitude of SH. On inter-annual time scales, the BHCM is highly correlated with ENSO. The atmosphere in both the NH and SH responds to sea surface temperature anomalies in the equatorial region, while the contemporaneous circulation changes in the NH and SH in turn affect the occurrence of El Ni觡o/La Ni觡a. In boreal winter, significant temperature and precipitation anomalies associated with the BHCM are found worldwide. Specifically, in the positive phase of the BHCM,temperature and precipitation are anomalously low in eastern China and some other regions of East Asia. These results are helpful for us to better understand interactions between circulations in the NH and SH and the dynamical mechanisms behind these interactions.

SPATIAL VARIATION OF WINTER SEA SURFACE TEMPERATURE IN NORTH PACIFIC AND ITS RELATION TO ATMOSPHERIC OSCILLATION MODES

The spatial variation of sea surface temperature anomalies(SSTA) in the North Pacific Ocean during winter is investigated using the EOF decomposition method.The first two main modes of SSTA are associated with Pacific Decadal Oscillation(PDO) mode and North Pacific Gyre Oscillation(NPGO) mode,respectively.Moreover,the first mode(PDO) is switched to the second mode(NPGO),a dominant mode after mid-1980.The mechanism of the modes' transition is analyzed.As the two oceanic modes are forced by the Aleutian Low(AL) and North Pacific Oscillation(NPO) modes,the AR-1 model is further used to examine the possible effect and mechanism of AL and NPO in generating the PDO and NPGO.The results show that compared to the NPO,the AL plays a more important role in generating the NPGO mode since the 1970s.Likewise,both the AL and NPO affect the PDO mode since the 1980s.

Leaf Vein Variations and Gene Control Mode of Cyclamen Hederifolium

In order to explore the heredity of leaf veins of Cyclamen Hederifolium and to breed excellent varieties, selling measurement for six types of different leaf veins were carried out and the genetie constitutions of leaf veins were studied according to the separation conditions of their progenies. The results showed that the inbred progenies of B or M types were B or M types with a percentage of 100% while the progenies of F, H, X and L types had character segregations. The separa- tion law illustrated that leaf veins of Cyclamen Hederifolium were eontrolled by minor multiple genes and each locus was consisted of a pair of alleles, RL of Rn. RL was responsible for the green phenotype of leaves and RB was responsible for the silvery white phenotype of leaves, leaves were deep green when the genotype of the locus was RLRL ; leaves were green when the genotype was RLRB ; leaves were silvery white when the genotype was RBRB. The aggregafive pattern of each locus formed different leaf vein types. The gene control modes of leaf vein variations were the basis for the breeding of excellent varieties of Cyclamen Hederifolium.

Water Gauge Image Denoising Model Based on Improved Adaptive Total Variation

As an important part of water level warning in water conservancy projects,often due to the influence of environmental factors such as light and stains,the acquired water gauge images have sticky,broken and bright spot conditions,which affect the identification of water gauges.To solve this problem,a water gauge image denoising model based on improved adaptive total variation is proposed.Firstly,the regular term exponent in the adaptive total variational equation is changed to an inverse cosine function;secondly,the differential curvature is used to distinguish the image noise points and increase the smoothing strength at the noise points;finally,according to the characteristics of the gradient mode and adaptive gradient threshold after Gaussian filtering,the New model can adaptively denoise in the smooth area and protect the edge area,so as to have the characteristics of both edge-preserving denoising.The experimental results show that the new model has a great improvement in image vision,higher iteration efficiency and an average increase of 1.6 dB in peak signal-to-noise ratio,and an average increase of 9%in structural similarity,which is more beneficial to practical applications.

Underwater acoustic signal denoising model based on secondary variational mode decomposition

Due to the complexity of marine environment,underwater acoustic signal will be affected by complex background noise during transmission.Underwater acoustic signal denoising is always a difficult problem in underwater acoustic signal processing.To obtain a better denoising effect,a new denoising method of underwater acoustic signal based on optimized variational mode decomposition by black widow optimization algorithm(BVMD),fluctuation-based dispersion entropy threshold improved by Otsu method(OFDE),cosine similarity stationary threshold(CSST),BVMD,fluctuation-based dispersion entropy(FDE),named BVMD-OFDE-CSST-BVMD-FDE,is proposed.In the first place,decompose the original signal into a series of intrinsic mode functions(IMFs)by BVMD.Afterwards,distinguish pure IMFs,mixed IMFs and noise IMFs by OFDE and CSST,and reconstruct pure IMFs and mixed IMFs to obtain primary denoised signal.In the end,decompose primary denoising signal into IMFs by BVMD again,use the FDE value to distinguish noise IMFs and pure IMFs,and reconstruct pure IMFs to obtain the final denoised signal.The proposed mothod has three advantages:(i)BVMD can adaptively select the decomposition layer and penalty factor of VMD.(ii)FDE and CS are used as double criteria to distinguish noise IMFs from useful IMFs,and Otsu algorithm and CSST algorithm can effectively avoid the error caused by manually selecting thresholds.(iii)Secondary decomposition can make up for the deficiency of primary decomposition and further remove a small amount of noise.The chaotic signal and real ship signal are denoised.The experiment result shows that the proposed method can effectively denoise.It improves the denoising effect after primary decomposition,and has good practical value.

Study of Vehicle Exhaust Variation with Test Modes

Nowadays harmful gas in vehicle exhaust has pollute d air heavily. To prevent the environment from polluting, the request of emissions control legislation becomes more stringent. New legislation prescribes not only the emissions limitation of vehicles, but also testing instruments and methods. Test car must be operated on the chassis dynamometer and data must be collect ed and analyzed with prescriptive exhaust analysis system as well. The mass of harmful exhaust gas, containing the concentration and volume of emis sion, which is independent from the model of automobile and engine, can be used as a criterion to evaluate the pollution of an automobile. Constant Volume Sampl e System (CVS) is used to measure vehicle emissions, but it is too expensive to apply extensively. The Vehicle Mass Analysis System(Vmas), a new vehicle exhaust mass analysis system produced in USA late 1990s,is used to test and analyze veh icle exhaust. As a test instrument, it has the virtue of cheapness and easy mana geability. In this paper, Vmas is used to measure the emissions of a light truck CA1020F. A ccording to 15 running modes of Vehicle Exhaust Legislation, the test car is ope rated on the chassis dynamometer and data are collected and analyzed with Vmas. The test results show that it is viable to measure and evaluate automobile emiss ion with Vmas. Most of HC exhaust is produced when the car is decelerating. The major factor that influences the mass of HC emission is the sudden decrease of e ngine load causing incomplete combustion in decelerating mode. Test results indi cate CO and NOx are mainly produced in the process of increasing load. The forme r reason is incomplete combustion and the latter is high burning temperature cau sed by the increasing load. The methods of reducing automobile emission are also discussed in this paper.

Spatio-Temporal Wind Speed Prediction Based on Variational Mode Decomposition

Improving short-term wind speed prediction accuracy and stability remains a challenge for wind forecasting researchers.This paper proposes a new variational mode decomposition(VMD)-attention-based spatio-temporal network(VASTN)method that takes advantage of both temporal and spatial correlations of wind speed.First,VASTN is a hybrid wind speed prediction model that combines VMD,squeeze-and-excitation network(SENet),and attention mechanism(AM)-based bidirectional long short-term memory(BiLSTM).VASTN initially employs VMD to decompose the wind speed matrix into a series of intrinsic mode functions(IMF).Then,to extract the spatial features at the bottom of the model,each IMF employs an improved convolutional neural network algorithm based on channel AM,also known as SENet.Second,it combines BiLSTM and AM at the top layer to extract aggregated spatial features and capture temporal dependencies.Finally,VASTN accumulates the predictions of each IMF to obtain the predicted wind speed.This method employs VMD to reduce the randomness and instability of the original data before employing AM to improve prediction accuracy through mapping weight and parameter learning.Experimental results on real-world data demonstrate VASTN’s superiority over previous related algorithms.