In this paper,we use the double difference location method based on waveform crosscorrelation algorithm for precise positioning of the Three Gorges Reservoir( TGR)earthquakes and analysis of seismic activity. First,we...In this paper,we use the double difference location method based on waveform crosscorrelation algorithm for precise positioning of the Three Gorges Reservoir( TGR)earthquakes and analysis of seismic activity. First,we use the bi-spectrum cross-correlation method to analyze the seismic waveform data of TGR encrypted networks from March,2009 to December,2010,and evaluate the quality of waveform cross-correlation analysis.Combined with the waveform cross-correlation of data obtained, we use the double difference method to relocate the earthquake position. The results show that location precision using bi-spectrum verified waveform cross-correlation data is higher than that by using other types of data,and the mean 2 sig-error in EW,NS and UD are 3.2 m,3.9 m and 6.2 m,respectively. For the relocation of the Three Gorges Reservoir earthquakes,the results show that the micro-earthquakes along the Shenlongxi river in the Badong reservoir area obviously show the characteristics of three linear zones with nearly east-west direction,which is in accordance with the small faults and carbonate strata line of the neotectonic period,revealing the reservoir water main along the underground rivers or caves permeated and induced seismic activity. The stronger earthquakes may have resulted from small earthquakes through the active layers.展开更多
Detecting coupling pattern between elements in a complex system is a basic task in data-driven analysis. The trajectory for each specific element is a cooperative result of its intrinsic dynamic, its couplings with ot...Detecting coupling pattern between elements in a complex system is a basic task in data-driven analysis. The trajectory for each specific element is a cooperative result of its intrinsic dynamic, its couplings with other elements, and the environment. It is subsequently composed of many components, only some of which take part in the couplings. In this paper we present a framework to detect the component correlation pattern. Firstly, the interested trajectories are decomposed into components by using decomposing methods such as the Fourier expansion and the Wavelet transformation. Secondly, the cross-correlations between the components are calculated, resulting into a component cross-correlation matrix(network).Finally, the dominant structure in the network is identified to characterize the coupling pattern in the system. Several deterministic dynamical models turn out to be characterized with rich structures such as the clustering of the components. The pattern of correlation between respiratory(RESP) and ECG signals is composed of five sub-clusters that are mainly formed by the components in ECG signal. Interestingly, only 7 components from RESP(scattered in four sub-clusters) take part in the realization of coupling between the two signals.展开更多
To address the issue of low measurement accuracy caused by noise interference in the acquisition of low fluid flow rate signals with ultrasonic Doppler flow meters,a novel signal processing algorithm that combines ens...To address the issue of low measurement accuracy caused by noise interference in the acquisition of low fluid flow rate signals with ultrasonic Doppler flow meters,a novel signal processing algorithm that combines ensemble empirical mode decomposition(EEMD)and cross-correlation algorithm was proposed.Firstly,a fast Fourier transform(FFT)spectrum analysis was utilized to ascertain the frequency range of the signal.Secondly,data acquisition was conducted at an appropriate sampling frequency,and the acquired Doppler flow rate signal was then decomposed into a series of intrinsic mode functions(IMFs)by EEMD.Subsequently,these decomposed IMFs were recombined based on their energy entropy,and then the noise of the recombined Doppler flow rate signal was removed by cross-correlation filtering.Finally,an ideal ultrasonic Doppler flow rate signal was extracted.Simulation and experimental verification show that the proposed Doppler flow signal processing method can effectively enhance the signal-to-noise ratio(SNR)and extend the lower limit of measurement of the ultrasonic Doppler flow meter.展开更多
"Repeating earthquakes",identified by cross-correlation of seismic waveforms,are found to be much more abundant in the nature than conventionally expected. In recent years, with the development of digital se..."Repeating earthquakes",identified by cross-correlation of seismic waveforms,are found to be much more abundant in the nature than conventionally expected. In recent years, with the development of digital seismic networks, waveform cross correlation and "repeating earthquakes"have caused much attention to the measuring the variation of crustal medium properties and estimation of location accuracy and fault slip rate at depth or earthquake recurrence intervals. Moreover, as a useful tool, the "repeating earthquake" approach has also been used in the assessment of the accuracy of seismic phase picking,hypocenter location,fault structure and physics of earthquake sources,as well as the study of earthquake prediction. In this paper, we summarized the latest research and applications of "repeating earthquakes".展开更多
The elastic thickness parameter was estimated using the mobile correlation technique between the observed isostatic disturbance and the gravity disturbance calculated through direct gravimetric modeling. We computed t...The elastic thickness parameter was estimated using the mobile correlation technique between the observed isostatic disturbance and the gravity disturbance calculated through direct gravimetric modeling. We computed the vertical flexure value of the crust for a specific elastic thickness using a given topographic dataset. The gravity disturbance due to the topography was determined after the calculation. A grid of values for the elastic thickness parameter was generated. Then, a moving correlation was performed between the observed gravity data(representing actual surface data) and the calculated data from the forward modeling. The optimum elastic thickness of the particular point corresponded to the highest correlation coefficient. The methodology was tested on synthetic data and showed that the synthetic depth closely matched the original depth, including the elastic thickness value. To validate the results, the described procedure was applied to a real dataset from the Barreirinhas Basin, situated in the northeastern region of Brazil. The results show that the obtained crustal depth is highly correlated with the depth from known models. Additionally, we noted that the elastic thickness behaves as expected, decreasing from the continent towards the ocean. Based on the results, this method has the potential to be employed as a direct estimate of crustal depth and elastic thickness for any region.展开更多
The temperature change and rate of CO2 change are correlated with a time lag, as reported in a previous paper. The correlation was investigated by calculating a correlation coefficient r of these changes for selected ...The temperature change and rate of CO2 change are correlated with a time lag, as reported in a previous paper. The correlation was investigated by calculating a correlation coefficient r of these changes for selected ENSO events in this study. Annual periodical increases and decreases in the CO2 concentration were considered, with a regular pattern of minimum values in August and maximum values in May each year. An increased deviation in CO2 and temperature was found in response to the occurrence of El Niño, but the increase in CO2 lagged behind the change in temperature by 5 months. This pattern was not observed for La Niña events. An increase in global CO2 emissions and a subsequent increase in global temperature proposed by IPCC were not observed, but an increase in global temperature, an increase in soil respiration, and a subsequent increase in global CO2 emissions were noticed. This natural process can be clearly detected during periods of increasing temperature specifically during El Niño events. The results cast strong doubts that anthropogenic CO2 is the cause of global warming.展开更多
This document presents a framework for recognizing people by palm vein distribution analysis using cross-correlation based signatures to obtain descriptors. Haar wavelets are useful in reducing the number of features ...This document presents a framework for recognizing people by palm vein distribution analysis using cross-correlation based signatures to obtain descriptors. Haar wavelets are useful in reducing the number of features while maintaining high recognition rates. This experiment achieved 97.5% of individuals classified correctly with two levels of Haar wavelets. This study used twelve-version of RGB and NIR (near infrared) wavelength images per individual. One hundred people were studied;therefore 4,800 instances compose the complete database. A Multilayer Perceptron (MLP) was trained to improve the recognition rate in a k-fold cross-validation test with k = 10. Classification results using MLP neural network were obtained using Weka (open source machine learning software).展开更多
Waveform generation and digitization play essential roles in numerous physics experiments.In traditional distributed systems for large-scale experiments,each frontend node contains an FPGA for data preprocessing,which...Waveform generation and digitization play essential roles in numerous physics experiments.In traditional distributed systems for large-scale experiments,each frontend node contains an FPGA for data preprocessing,which interfaces with various data converters and exchanges data with a backend central processor.However,the streaming readout architecture has become a new paradigm for several experiments benefiting from advancements in data transmission and computing technologies.This paper proposes a scalable distributed waveform generation and digitization system that utilizes fiber optical connections for data transmission between frontend nodes and a central processor.By utilizing transparent transmission on top of the data link layer,the clock and data ports of the converters in the frontend nodes are directly mapped to the FPGA firmware at the backend.This streaming readout architecture reduces the complexity of frontend development and maintains the data conversion in proximity to the detector.Each frontend node uses a local clock for waveform digitization.To translate the timing information of events in each channel into the system clock domain within the backend central processing FPGA,a novel method is proposed and evaluated using a demonstrator system.展开更多
A Mixed Numerology OFDM(MN-OFDM)system is essential in 6G and beyond.However,it encounters challenges due to Inter-Numerology Interference(INI).The upcoming 6G technology aims to support innovative applications with h...A Mixed Numerology OFDM(MN-OFDM)system is essential in 6G and beyond.However,it encounters challenges due to Inter-Numerology Interference(INI).The upcoming 6G technology aims to support innovative applications with high data rates,low latency,and reliability.Therefore,effective handling of INI is crucial to meet the diverse requirements of these applications.To address INI in MN-OFDM systems,this paper proposes a User-Based Numerology and Waveform(UBNW)approach that uses various OFDM-based waveforms and their parameters to mitigate INI.By assigning a specific waveform and numerology to each user,UBNW mitigates INI,optimizes service characteristics,and addresses user demands efficiently.The required Guard Bands(GB),expressed as a ratio of user bandwidth,vary significantly across different waveforms at an SIR of 25 dB.For instance,OFDM-FOFDM needs only 2.5%,while OFDM-UFMC,OFDM-WOLA,and conventional OFDM require 7.5%,24%,and 40%,respectively.The time-frequency efficiency also varies between the waveforms.FOFDM achieves 85.6%,UFMC achieves 81.6%,WOLA achieves 70.7%,and conventional OFDM achieves 66.8%.The simulation results demonstrate that the UBNW approach not only effectively mitigates INI but also enhances system flexibility and time-frequency efficiency while simultaneously reducing the required GB.展开更多
Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy...Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy procedures.Adaptive waveform inversion(AWI),a variant of full waveform inversion(FWI),has shown potential in intracranial ultrasound imaging.However,the robustness of AWI is affected by the parameterization of the Gaussian penalty matrix and the challenges posed by transcranial scenarios.Conventional AWI struggles to produce accurate images in these cases,limiting its application in critical medical settings.To address these issues,we propose a stabilized adaptive waveform inversion(SAWI)method,which introduces a user-defined zero-lag position for theWiener filter.Numerical experiments demonstrate that SAWI can achieve accurate imaging under Gaussian penalty matrix parameter settings where AWI fails,perform successful transcranial imaging in configurations where AWI cannot,and maintain the same imaging accuracy as AWI.The advantage of this method is that it achieves these advancements without modifying the AWI framework or increasing computational costs,which helps to promote the application of AWI in medical fields,particularly in transcranial scenarios.展开更多
This paper establishes an amplitude modulation heating model, simulating the far-field radiation of ELF/VLF signals generated by modulation heating, as well as the specific location and longitudinal extent of the radi...This paper establishes an amplitude modulation heating model, simulating the far-field radiation of ELF/VLF signals generated by modulation heating, as well as the specific location and longitudinal extent of the radiation source. We consider various modulation waveforms and find that square-wave modulation has the highest excitation efficiency for ELF/VLF signals, and that square-wave modulation with a smaller duty cycle(<50%) exhibits higher excitation efficiency for ELF/VLF signals, while the sin^(2)t waveform modulation yields the lowest proportion of harmonic energy in the generated signals. The amplitude of the second harmonic generated by the sin^(2)t waveform is less than one-tenth that of the fundamental frequency, and the energy of higher-frequency harmonics can be negligibly small compared with those of the fundamental wave. It is a challenging task to achieve a balance between enhancing the excitation efficiency of ELF/VLF signals and also suppressing harmonics generated by the modulated heating process. This is because the harmonics are correspondingly enhanced as the excitation efficiency of the signals is increased. However, we find that under conditions of varying effective radiant power and modulation frequency, as long as the modulation waveform is unchanged, the energy ratio between the fundamental frequency signal generated by modulated heating and each harmonic is relatively fixed, with changes only in signal intensity and the location of the radiation source zone. This implies that one can first select modulation waveforms that make the signal less prone to distortion, then increase the effective radiated power to enhance the signal strength, without concern for harmonic interference of the fundamental signal.展开更多
Waveform regulator in charge is a method that can realize multi-source detonation wave superposition through a single point detonation.The method does not need to weaken the strength of shell,and relies on the high st...Waveform regulator in charge is a method that can realize multi-source detonation wave superposition through a single point detonation.The method does not need to weaken the strength of shell,and relies on the high stress generated by superposition to cut shell into regular fragments.Additionally,it can be combined with different initiation methods to alter the fragmentation outcomes.In this study,aiming at the fracture strain of metal cylindrical shell driven by explosive charge with waveform regulator,theoretical analysis was first adopted to obtain the prediction model of the fracture strain of cylindrical shell with waveform regulator and the model of the axial distribution of the stress concentration factor.On this basis,both theoretical analysis and numerical models were utilized to investigate the effect of waveform regulator on the initial velocity of fragments.Finally,experiments were conducted to validate the fracture strain prediction model for cylindrical shell with waveform regulator.The research results show that the collision angles of the detonation waves at different axial positions are different,which leads to the stress concentration factor on the shell presenting a trend of gradually decreasing,then sharply increasing,and then rapidly decreasing along the axial direction.Additionally,the changes in the slot spacing and the thickness of outer charge will also affect the stress concentration factor,and the influence of outer charge thickness is relatively large.The smaller the ratio of charge volume to waveform regulator volume,the larger the axial sparse wave intensity and the more the fragment initial velocity decrease.From the initiation end to the non-initiation end,the failure modes of the shell sequentially change from pure shear,to mixed tensile-shear,and finally to pure tensile failure.The experimental results are in good agreement with the calculated results of the fracture strain model,and the maximum relative error is less than 10%,which indicates that the fracture strain prediction model of the cylindrical shell with waveform regulator established in this paper by considering the increase of elastic energy per unit volume caused by stress concentration on the shell is reliable.展开更多
Landslides significantly threaten lives and infrastructure, especially in seismically active regions. This study conducts a probabilistic analysis of seismic landslide runout behavior, leveraging a large-deformation f...Landslides significantly threaten lives and infrastructure, especially in seismically active regions. This study conducts a probabilistic analysis of seismic landslide runout behavior, leveraging a large-deformation finite-element (LDFE) model that accounts for the three-dimensional (3D) spatial variability and cross-correlation in soil strength — a reflection of natural soils' inherent properties. LDFE model results are validated by comparing them against previous studies, followed by an examination of the effects of univariable, uncorrelated bivariable, and cross-correlated bivariable random fields on landslide runout behavior. The study's findings reveal that integrating variability in both friction angle and cohesion within uncorrelated bivariable random fields markedly influences runout distances when compared with univariable random fields. Moreover, the cross-correlation of soil cohesion and friction angle dramatically affects runout behavior, with positive correlations enlarging and negative correlations reducing runout distances. Transitioning from two-dimensional (2D) to 3D analyses, a more realistic representation of sliding surface, landslide velocity, runout distance and final deposit morphology is achieved. The study highlights that 2D random analyses substantially underestimate the mean value and overestimate the variability of runout distance, underscoring the importance of 3D modeling in accurately predicting landslide behavior. Overall, this work emphasizes the essential role of understanding 3D cross-correlation in soil strength for landslide hazard assessment and mitigation strategies.展开更多
Full waveform inversion is a precise method for parameter inversion,harnessing the complete wavefield information of seismic waves.It holds the potential to intricately characterize the detailed features of the model ...Full waveform inversion is a precise method for parameter inversion,harnessing the complete wavefield information of seismic waves.It holds the potential to intricately characterize the detailed features of the model with high accuracy.However,due to inaccurate initial models,the absence of low-frequency data,and incomplete observational data,full waveform inversion(FWI)exhibits pronounced nonlinear characteristics.When the strata are buried deep,the inversion capability of this method is constrained.To enhance the accuracy and precision of FWI,this paper introduces a novel approach to address the aforementioned challenges—namely,a fractional-order anisotropic total p-variation regularization for full waveform inversion(FATpV-FWI).This method incorporates fractional-order total variation(TV)regularization to construct the inversion objective function,building upon TV regularization,and subsequently employs the alternating direction multiplier method for solving.This approach mitigates the step effect stemming from total variation in seismic inversion,thereby facilitating the reconstruction of sharp interfaces of geophysical parameters while smoothing background variations.Simultaneously,replacing integer-order differences with fractional-order differences bolsters the correlation among seismic data and diminishes the scattering effect caused by integer-order differences in seismic inversion.The outcomes of model tests validate the efficacy of this method,highlighting its ability to enhance the overall accuracy of the inversion process.展开更多
Full waveform inversion(FWI)is a complex data fitting process based on full wavefield modeling,aiming to quantitatively reconstruct unknown model parameters from partial waveform data with high-resolution.However,this...Full waveform inversion(FWI)is a complex data fitting process based on full wavefield modeling,aiming to quantitatively reconstruct unknown model parameters from partial waveform data with high-resolution.However,this process is highly nonlinear and ill-posed,therefore achieving high-resolution imaging of complex biological tissues within a limited number of iterations remains challenging.We propose a multiscale frequency–domain full waveform inversion(FDFWI)framework for ultrasound computed tomography(USCT)imaging of biological tissues,which innovatively incorporates Sobolev space norm regularization for enhancement of prior information.Specifically,we investigate the effect of different types of hyperparameter on the imaging quality,during which the regularization weight is dynamically adapted based on the ratio of the regularization term to the data fidelity term.This strategy reduces reliance on predefined hyperparameters,ensuring robust inversion performance.The inversion results from both numerical and experimental tests(i.e.,numerical breast,thigh,and ex vivo pork-belly tissue)demonstrate the effectiveness of our regularized FWI strategy.These findings will contribute to the application of the FWI technique in quantitative imaging based on USCT and make USCT possible to be another high-resolution imaging method after x-ray computed tomography and magnetic resonance imaging.展开更多
Low sidelobe waveform can reduce mutual masking between targets and increase the detection probability of weak targets.A low sidelobe waveform design method based on complementary amplitude coding(CAC)is proposed in t...Low sidelobe waveform can reduce mutual masking between targets and increase the detection probability of weak targets.A low sidelobe waveform design method based on complementary amplitude coding(CAC)is proposed in this paper,which can be used to reduce the sidelobe level of multiple waveforms.First,the CAC model is constructed.Then,the waveform design problem is transformed into a nonlinear optimization problem by constructing an objective function using the two indicators of peak-to-sidelobe ratio(PSLR)and integrated sidelobe ratio(ISLR).Finally,genetic algorithm(GA)is used to solve the optimization problem to get the best CAC waveforms.Simulations and experiments are conducted to verify the effectiveness of the proposed method.展开更多
Based on waveform fitting,full waveform inversion(FWI)is an important inversion method with the ability to reconstruct multi-parameter models in high precision.However,the strong nonlinear equation used in FWI present...Based on waveform fitting,full waveform inversion(FWI)is an important inversion method with the ability to reconstruct multi-parameter models in high precision.However,the strong nonlinear equation used in FWI presents the following challenges,such as low convergence efficiency,high dependence on the initial model,and the energy imbalance in deep region of the inverted model.To solve these inherent problems,we develop a timedomain elastic FWI method based on gradient preconditioning with the following details:(1)the limited memory Broyden Fletcher Goldfarb Shanno method with faster convergence is adopted to im-prove the inversion stability;(2)a multi-scaled inversion strategy is used to alleviate the nonlinear inversion instead of falling into the local minimum;(3)in addition,the pseudo-Hessian preconditioned illumination operator is involved for preconditioning the parameter gradients to improve the illumination equilibrium degree of deep structures.Based on the programming implementation of the new method,a deep depression model with five diffractors is used for testing.Compared with the conventional elastic FWI method,the technique proposed by this study has better effectiveness and accuracy on the inversion effect and con-vergence,respectively.展开更多
Temporal-spatial cross-correlation analysis of non-stationary wind speed time series plays a crucial role in wind field reconstruction as well as in wind pattern recognition.Firstly,the near-surface wind speed time se...Temporal-spatial cross-correlation analysis of non-stationary wind speed time series plays a crucial role in wind field reconstruction as well as in wind pattern recognition.Firstly,the near-surface wind speed time series recorded at different locations are studied using the detrended fluctuation analysis(DFA),and the corresponding scaling exponents are larger than 1.This indicates that all these wind speed time series have non-stationary characteristics.Secondly,concerning this special feature( i.e.,non-stationarity)of wind signals,a cross-correlation analysis method,namely detrended cross-correlation analysis(DCCA) coefficient,is employed to evaluate the temporal-spatial cross-correlations between non-stationary time series of different anemometer pairs.Finally,experiments on ten wind speed data synchronously collected by the ten anemometers with equidistant arrangement illustrate that the method of DCCA cross-correlation coefficient can accurately analyze full-scale temporal-spatial cross-correlation between non-stationary time series and also can easily identify the seasonal component,while three traditional cross-correlation techniques(i.e.,Pearson coefficient,cross-correlation function,and DCCA method) cannot give us these information directly.展开更多
In order to solve the problem of ambiguous acquisition of BOC signals caused by its property of multiple peaks,an unambiguous acquisition algorithm named reconstruction of sub cross-correlation cancellation technique(...In order to solve the problem of ambiguous acquisition of BOC signals caused by its property of multiple peaks,an unambiguous acquisition algorithm named reconstruction of sub cross-correlation cancellation technique(RSCCT)for BOC(kn,n)signals is proposed.In this paper,the principle of signal decomposition is combined with the traditional acquisition algorithm structure,and then based on the method of reconstructing the correlation function.The method firstly gets the sub-pseudorandom noise(PRN)code by decomposing the local PRN code,then uses BOC(kn,n)and the sub-PRN code cross-correlation to get the sub cross-correlation function.Finally,the correlation peak with a single peak is obtained by reconstructing the sub cross-correlation function so that the ambiguities of BOC acquisition are removed.The simulation shows that RSCCT can completely eliminate the side peaks of BOC(kn,n)group signals while maintaining the narrow correlation of BOC,and its computational complexity is equivalent to sub carrier phase cancellation(SCPC)and autocorrelation side-peak cancellation technique(ASPeCT),and it reduces the computational complexity relative to BPSK-like.For BOC(n,n),the acquisition sensitivity of RSCCT is 3.25 dB,0.81 dB and 0.25 dB higher than binary phase shift keying(BPSK)-like,SCPC and ASPeCT at the acquisition probability of 90%,respectively.The peak to average power ratio is 1.91,3.0 and 3.7 times higher than ASPeCT,SCPC and BPSK-like at SNR=–20 dB,respectively.For BOC(2n,n),the acquisition sensitivity of RSCCT is 5.5 dB,1.25 dB and 2.69 dB higher than BPSK-like,SCPC and ASPeCT at the acquisition probability of 90%,respectively.The peak to average power ratio is 1.02,1.68 and 2.12 times higher than ASPeCT,SCPC and BPSK-like at SNR=–20 dB,respectively.展开更多
This paper describes the estimation of cloud motion using lag cross-correlation. In order to compute the lag cross correlation, the Bayes Decision method is used first to identify cloud and surface of earth. Then clou...This paper describes the estimation of cloud motion using lag cross-correlation. In order to compute the lag cross correlation, the Bayes Decision method is used first to identify cloud and surface of earth. Then cloud motion vectors are retrieved at a subset of points through multiple applications of a cross-correlation analysis. An objective analysis is used to define displacement at every satellite pixel throughout the domain and smooth the local inconsistencies. Cloud motions are then produced with a backward trajectory technique by using these displacement vectors.展开更多
基金funded by the National Science and Technology Pillar Program(2008BAC38B04)the Special Research Fund for Seismology(16A44ZX282)
文摘In this paper,we use the double difference location method based on waveform crosscorrelation algorithm for precise positioning of the Three Gorges Reservoir( TGR)earthquakes and analysis of seismic activity. First,we use the bi-spectrum cross-correlation method to analyze the seismic waveform data of TGR encrypted networks from March,2009 to December,2010,and evaluate the quality of waveform cross-correlation analysis.Combined with the waveform cross-correlation of data obtained, we use the double difference method to relocate the earthquake position. The results show that location precision using bi-spectrum verified waveform cross-correlation data is higher than that by using other types of data,and the mean 2 sig-error in EW,NS and UD are 3.2 m,3.9 m and 6.2 m,respectively. For the relocation of the Three Gorges Reservoir earthquakes,the results show that the micro-earthquakes along the Shenlongxi river in the Badong reservoir area obviously show the characteristics of three linear zones with nearly east-west direction,which is in accordance with the small faults and carbonate strata line of the neotectonic period,revealing the reservoir water main along the underground rivers or caves permeated and induced seismic activity. The stronger earthquakes may have resulted from small earthquakes through the active layers.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 11875042 and 11505114)the Shanghai Project for Construction of Top Disciplines (Grant No. USST-SYS-01)。
文摘Detecting coupling pattern between elements in a complex system is a basic task in data-driven analysis. The trajectory for each specific element is a cooperative result of its intrinsic dynamic, its couplings with other elements, and the environment. It is subsequently composed of many components, only some of which take part in the couplings. In this paper we present a framework to detect the component correlation pattern. Firstly, the interested trajectories are decomposed into components by using decomposing methods such as the Fourier expansion and the Wavelet transformation. Secondly, the cross-correlations between the components are calculated, resulting into a component cross-correlation matrix(network).Finally, the dominant structure in the network is identified to characterize the coupling pattern in the system. Several deterministic dynamical models turn out to be characterized with rich structures such as the clustering of the components. The pattern of correlation between respiratory(RESP) and ECG signals is composed of five sub-clusters that are mainly formed by the components in ECG signal. Interestingly, only 7 components from RESP(scattered in four sub-clusters) take part in the realization of coupling between the two signals.
基金supported by National Natural Science Foundation of China(No.61973234)Tianjin Science and Technology Plan Project(No.22YDTPJC00090)。
文摘To address the issue of low measurement accuracy caused by noise interference in the acquisition of low fluid flow rate signals with ultrasonic Doppler flow meters,a novel signal processing algorithm that combines ensemble empirical mode decomposition(EEMD)and cross-correlation algorithm was proposed.Firstly,a fast Fourier transform(FFT)spectrum analysis was utilized to ascertain the frequency range of the signal.Secondly,data acquisition was conducted at an appropriate sampling frequency,and the acquired Doppler flow rate signal was then decomposed into a series of intrinsic mode functions(IMFs)by EEMD.Subsequently,these decomposed IMFs were recombined based on their energy entropy,and then the noise of the recombined Doppler flow rate signal was removed by cross-correlation filtering.Finally,an ideal ultrasonic Doppler flow rate signal was extracted.Simulation and experimental verification show that the proposed Doppler flow signal processing method can effectively enhance the signal-to-noise ratio(SNR)and extend the lower limit of measurement of the ultrasonic Doppler flow meter.
基金funded by the Spark Program of China Earthquake Administration (XH1006)
文摘"Repeating earthquakes",identified by cross-correlation of seismic waveforms,are found to be much more abundant in the nature than conventionally expected. In recent years, with the development of digital seismic networks, waveform cross correlation and "repeating earthquakes"have caused much attention to the measuring the variation of crustal medium properties and estimation of location accuracy and fault slip rate at depth or earthquake recurrence intervals. Moreover, as a useful tool, the "repeating earthquake" approach has also been used in the assessment of the accuracy of seismic phase picking,hypocenter location,fault structure and physics of earthquake sources,as well as the study of earthquake prediction. In this paper, we summarized the latest research and applications of "repeating earthquakes".
文摘The elastic thickness parameter was estimated using the mobile correlation technique between the observed isostatic disturbance and the gravity disturbance calculated through direct gravimetric modeling. We computed the vertical flexure value of the crust for a specific elastic thickness using a given topographic dataset. The gravity disturbance due to the topography was determined after the calculation. A grid of values for the elastic thickness parameter was generated. Then, a moving correlation was performed between the observed gravity data(representing actual surface data) and the calculated data from the forward modeling. The optimum elastic thickness of the particular point corresponded to the highest correlation coefficient. The methodology was tested on synthetic data and showed that the synthetic depth closely matched the original depth, including the elastic thickness value. To validate the results, the described procedure was applied to a real dataset from the Barreirinhas Basin, situated in the northeastern region of Brazil. The results show that the obtained crustal depth is highly correlated with the depth from known models. Additionally, we noted that the elastic thickness behaves as expected, decreasing from the continent towards the ocean. Based on the results, this method has the potential to be employed as a direct estimate of crustal depth and elastic thickness for any region.
文摘The temperature change and rate of CO2 change are correlated with a time lag, as reported in a previous paper. The correlation was investigated by calculating a correlation coefficient r of these changes for selected ENSO events in this study. Annual periodical increases and decreases in the CO2 concentration were considered, with a regular pattern of minimum values in August and maximum values in May each year. An increased deviation in CO2 and temperature was found in response to the occurrence of El Niño, but the increase in CO2 lagged behind the change in temperature by 5 months. This pattern was not observed for La Niña events. An increase in global CO2 emissions and a subsequent increase in global temperature proposed by IPCC were not observed, but an increase in global temperature, an increase in soil respiration, and a subsequent increase in global CO2 emissions were noticed. This natural process can be clearly detected during periods of increasing temperature specifically during El Niño events. The results cast strong doubts that anthropogenic CO2 is the cause of global warming.
文摘This document presents a framework for recognizing people by palm vein distribution analysis using cross-correlation based signatures to obtain descriptors. Haar wavelets are useful in reducing the number of features while maintaining high recognition rates. This experiment achieved 97.5% of individuals classified correctly with two levels of Haar wavelets. This study used twelve-version of RGB and NIR (near infrared) wavelength images per individual. One hundred people were studied;therefore 4,800 instances compose the complete database. A Multilayer Perceptron (MLP) was trained to improve the recognition rate in a k-fold cross-validation test with k = 10. Classification results using MLP neural network were obtained using Weka (open source machine learning software).
基金supported by the National Key Research and Development Program of China(No.2022YFA1604703)the National Natural Science Foundation of China(No.12375189)the National Key Research and Development Program of China(No.2021YFA1601300)。
文摘Waveform generation and digitization play essential roles in numerous physics experiments.In traditional distributed systems for large-scale experiments,each frontend node contains an FPGA for data preprocessing,which interfaces with various data converters and exchanges data with a backend central processor.However,the streaming readout architecture has become a new paradigm for several experiments benefiting from advancements in data transmission and computing technologies.This paper proposes a scalable distributed waveform generation and digitization system that utilizes fiber optical connections for data transmission between frontend nodes and a central processor.By utilizing transparent transmission on top of the data link layer,the clock and data ports of the converters in the frontend nodes are directly mapped to the FPGA firmware at the backend.This streaming readout architecture reduces the complexity of frontend development and maintains the data conversion in proximity to the detector.Each frontend node uses a local clock for waveform digitization.To translate the timing information of events in each channel into the system clock domain within the backend central processing FPGA,a novel method is proposed and evaluated using a demonstrator system.
文摘A Mixed Numerology OFDM(MN-OFDM)system is essential in 6G and beyond.However,it encounters challenges due to Inter-Numerology Interference(INI).The upcoming 6G technology aims to support innovative applications with high data rates,low latency,and reliability.Therefore,effective handling of INI is crucial to meet the diverse requirements of these applications.To address INI in MN-OFDM systems,this paper proposes a User-Based Numerology and Waveform(UBNW)approach that uses various OFDM-based waveforms and their parameters to mitigate INI.By assigning a specific waveform and numerology to each user,UBNW mitigates INI,optimizes service characteristics,and addresses user demands efficiently.The required Guard Bands(GB),expressed as a ratio of user bandwidth,vary significantly across different waveforms at an SIR of 25 dB.For instance,OFDM-FOFDM needs only 2.5%,while OFDM-UFMC,OFDM-WOLA,and conventional OFDM require 7.5%,24%,and 40%,respectively.The time-frequency efficiency also varies between the waveforms.FOFDM achieves 85.6%,UFMC achieves 81.6%,WOLA achieves 70.7%,and conventional OFDM achieves 66.8%.The simulation results demonstrate that the UBNW approach not only effectively mitigates INI but also enhances system flexibility and time-frequency efficiency while simultaneously reducing the required GB.
基金supported by the National Natural Science Foundation of China(Grant No.82151302)the National High Level Hospital Clinical Research Funding(Grant No.2022-PUMCH-B-113)+1 种基金the National High Level Hospital Clinical Research Funding(Grant No.2022-PUMCH-A-019)the CAMS Innovation Fund for Medical Sciences(Grant No.2021-12M-1-014).
文摘Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy procedures.Adaptive waveform inversion(AWI),a variant of full waveform inversion(FWI),has shown potential in intracranial ultrasound imaging.However,the robustness of AWI is affected by the parameterization of the Gaussian penalty matrix and the challenges posed by transcranial scenarios.Conventional AWI struggles to produce accurate images in these cases,limiting its application in critical medical settings.To address these issues,we propose a stabilized adaptive waveform inversion(SAWI)method,which introduces a user-defined zero-lag position for theWiener filter.Numerical experiments demonstrate that SAWI can achieve accurate imaging under Gaussian penalty matrix parameter settings where AWI fails,perform successful transcranial imaging in configurations where AWI cannot,and maintain the same imaging accuracy as AWI.The advantage of this method is that it achieves these advancements without modifying the AWI framework or increasing computational costs,which helps to promote the application of AWI in medical fields,particularly in transcranial scenarios.
基金supported by the National Key R&D Program of China (No. 2022YFE0204100)the National Natural Science Foundation of China (12205067 and 12375199)the Fundamental Research Funds for the Central Universities (Grant No. HIT.OCEF. 2022036)。
文摘This paper establishes an amplitude modulation heating model, simulating the far-field radiation of ELF/VLF signals generated by modulation heating, as well as the specific location and longitudinal extent of the radiation source. We consider various modulation waveforms and find that square-wave modulation has the highest excitation efficiency for ELF/VLF signals, and that square-wave modulation with a smaller duty cycle(<50%) exhibits higher excitation efficiency for ELF/VLF signals, while the sin^(2)t waveform modulation yields the lowest proportion of harmonic energy in the generated signals. The amplitude of the second harmonic generated by the sin^(2)t waveform is less than one-tenth that of the fundamental frequency, and the energy of higher-frequency harmonics can be negligibly small compared with those of the fundamental wave. It is a challenging task to achieve a balance between enhancing the excitation efficiency of ELF/VLF signals and also suppressing harmonics generated by the modulated heating process. This is because the harmonics are correspondingly enhanced as the excitation efficiency of the signals is increased. However, we find that under conditions of varying effective radiant power and modulation frequency, as long as the modulation waveform is unchanged, the energy ratio between the fundamental frequency signal generated by modulated heating and each harmonic is relatively fixed, with changes only in signal intensity and the location of the radiation source zone. This implies that one can first select modulation waveforms that make the signal less prone to distortion, then increase the effective radiated power to enhance the signal strength, without concern for harmonic interference of the fundamental signal.
基金supported by the National Natural Science Foundation of China(Grant No.12302437)Natural Science Foundation of Jiangsu Province(Grant No.SBK2023045424)。
文摘Waveform regulator in charge is a method that can realize multi-source detonation wave superposition through a single point detonation.The method does not need to weaken the strength of shell,and relies on the high stress generated by superposition to cut shell into regular fragments.Additionally,it can be combined with different initiation methods to alter the fragmentation outcomes.In this study,aiming at the fracture strain of metal cylindrical shell driven by explosive charge with waveform regulator,theoretical analysis was first adopted to obtain the prediction model of the fracture strain of cylindrical shell with waveform regulator and the model of the axial distribution of the stress concentration factor.On this basis,both theoretical analysis and numerical models were utilized to investigate the effect of waveform regulator on the initial velocity of fragments.Finally,experiments were conducted to validate the fracture strain prediction model for cylindrical shell with waveform regulator.The research results show that the collision angles of the detonation waves at different axial positions are different,which leads to the stress concentration factor on the shell presenting a trend of gradually decreasing,then sharply increasing,and then rapidly decreasing along the axial direction.Additionally,the changes in the slot spacing and the thickness of outer charge will also affect the stress concentration factor,and the influence of outer charge thickness is relatively large.The smaller the ratio of charge volume to waveform regulator volume,the larger the axial sparse wave intensity and the more the fragment initial velocity decrease.From the initiation end to the non-initiation end,the failure modes of the shell sequentially change from pure shear,to mixed tensile-shear,and finally to pure tensile failure.The experimental results are in good agreement with the calculated results of the fracture strain model,and the maximum relative error is less than 10%,which indicates that the fracture strain prediction model of the cylindrical shell with waveform regulator established in this paper by considering the increase of elastic energy per unit volume caused by stress concentration on the shell is reliable.
基金supported by the National Natural Science Foundation of China(Grant No.U22A20596)the Shenzhen Science and Technology Program(Grant No.GJHZ20220913142605010)the Jinan Lead Researcher Project(Grant No.202333051).
文摘Landslides significantly threaten lives and infrastructure, especially in seismically active regions. This study conducts a probabilistic analysis of seismic landslide runout behavior, leveraging a large-deformation finite-element (LDFE) model that accounts for the three-dimensional (3D) spatial variability and cross-correlation in soil strength — a reflection of natural soils' inherent properties. LDFE model results are validated by comparing them against previous studies, followed by an examination of the effects of univariable, uncorrelated bivariable, and cross-correlated bivariable random fields on landslide runout behavior. The study's findings reveal that integrating variability in both friction angle and cohesion within uncorrelated bivariable random fields markedly influences runout distances when compared with univariable random fields. Moreover, the cross-correlation of soil cohesion and friction angle dramatically affects runout behavior, with positive correlations enlarging and negative correlations reducing runout distances. Transitioning from two-dimensional (2D) to 3D analyses, a more realistic representation of sliding surface, landslide velocity, runout distance and final deposit morphology is achieved. The study highlights that 2D random analyses substantially underestimate the mean value and overestimate the variability of runout distance, underscoring the importance of 3D modeling in accurately predicting landslide behavior. Overall, this work emphasizes the essential role of understanding 3D cross-correlation in soil strength for landslide hazard assessment and mitigation strategies.
基金supported by the China Postdoctoral Science Foundation(Grant No.2024MF750281)the Postdoctoral Fellowship Program of CPSF(Grant No.GZC20230326)+1 种基金the Natural Science Foundation Project of Sichuan Province(Grant No.2025ZNSFSC1170)Sichuan Science and Technology Program(Grant No.2023ZYD0158).
文摘Full waveform inversion is a precise method for parameter inversion,harnessing the complete wavefield information of seismic waves.It holds the potential to intricately characterize the detailed features of the model with high accuracy.However,due to inaccurate initial models,the absence of low-frequency data,and incomplete observational data,full waveform inversion(FWI)exhibits pronounced nonlinear characteristics.When the strata are buried deep,the inversion capability of this method is constrained.To enhance the accuracy and precision of FWI,this paper introduces a novel approach to address the aforementioned challenges—namely,a fractional-order anisotropic total p-variation regularization for full waveform inversion(FATpV-FWI).This method incorporates fractional-order total variation(TV)regularization to construct the inversion objective function,building upon TV regularization,and subsequently employs the alternating direction multiplier method for solving.This approach mitigates the step effect stemming from total variation in seismic inversion,thereby facilitating the reconstruction of sharp interfaces of geophysical parameters while smoothing background variations.Simultaneously,replacing integer-order differences with fractional-order differences bolsters the correlation among seismic data and diminishes the scattering effect caused by integer-order differences in seismic inversion.The outcomes of model tests validate the efficacy of this method,highlighting its ability to enhance the overall accuracy of the inversion process.
基金supported by the National Natural Science Foundation of China(Grant No.12474461)the Basic and Frontier Exploration Project Independently Deployed by Institute of Acoustics,Chinese Academy of Sciences(Grant No.JCQY202402)the Goal-Oriented Project Independently Deployed by Institute of Acoustics,Chinese Academy of Sciences(Grant No.MBDX202113).
文摘Full waveform inversion(FWI)is a complex data fitting process based on full wavefield modeling,aiming to quantitatively reconstruct unknown model parameters from partial waveform data with high-resolution.However,this process is highly nonlinear and ill-posed,therefore achieving high-resolution imaging of complex biological tissues within a limited number of iterations remains challenging.We propose a multiscale frequency–domain full waveform inversion(FDFWI)framework for ultrasound computed tomography(USCT)imaging of biological tissues,which innovatively incorporates Sobolev space norm regularization for enhancement of prior information.Specifically,we investigate the effect of different types of hyperparameter on the imaging quality,during which the regularization weight is dynamically adapted based on the ratio of the regularization term to the data fidelity term.This strategy reduces reliance on predefined hyperparameters,ensuring robust inversion performance.The inversion results from both numerical and experimental tests(i.e.,numerical breast,thigh,and ex vivo pork-belly tissue)demonstrate the effectiveness of our regularized FWI strategy.These findings will contribute to the application of the FWI technique in quantitative imaging based on USCT and make USCT possible to be another high-resolution imaging method after x-ray computed tomography and magnetic resonance imaging.
基金supported by the National Natural Science Foundation of China(62001481,61890542)the Natural Science Foundation of Hunan Province(2021JJ40686).
文摘Low sidelobe waveform can reduce mutual masking between targets and increase the detection probability of weak targets.A low sidelobe waveform design method based on complementary amplitude coding(CAC)is proposed in this paper,which can be used to reduce the sidelobe level of multiple waveforms.First,the CAC model is constructed.Then,the waveform design problem is transformed into a nonlinear optimization problem by constructing an objective function using the two indicators of peak-to-sidelobe ratio(PSLR)and integrated sidelobe ratio(ISLR).Finally,genetic algorithm(GA)is used to solve the optimization problem to get the best CAC waveforms.Simulations and experiments are conducted to verify the effectiveness of the proposed method.
基金supported by the Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(Qingdao)(Grant No.2021QNLM020001)the National Key R&D Program of China(Grant No.2019YFC0605503C)+2 种基金the Major Scientific and Technological Projects of China National Petroleum Corporation(CNPC)(Grant No.ZD2019-183-003)the National Outstanding Youth Science Foundation(Grant No.41922028)the National Innovation Group Project(Grant No.41821002).
文摘Based on waveform fitting,full waveform inversion(FWI)is an important inversion method with the ability to reconstruct multi-parameter models in high precision.However,the strong nonlinear equation used in FWI presents the following challenges,such as low convergence efficiency,high dependence on the initial model,and the energy imbalance in deep region of the inverted model.To solve these inherent problems,we develop a timedomain elastic FWI method based on gradient preconditioning with the following details:(1)the limited memory Broyden Fletcher Goldfarb Shanno method with faster convergence is adopted to im-prove the inversion stability;(2)a multi-scaled inversion strategy is used to alleviate the nonlinear inversion instead of falling into the local minimum;(3)in addition,the pseudo-Hessian preconditioned illumination operator is involved for preconditioning the parameter gradients to improve the illumination equilibrium degree of deep structures.Based on the programming implementation of the new method,a deep depression model with five diffractors is used for testing.Compared with the conventional elastic FWI method,the technique proposed by this study has better effectiveness and accuracy on the inversion effect and con-vergence,respectively.
基金Projects(61271321,61573253,61401303)supported by the National Natural Science Foundation of ChinaProject(14ZCZDSF00025)supported by Tianjin Key Technology Research and Development Program,China+1 种基金Project(13JCYBJC17500)supported by Tianjin Natural Science Foundation,ChinaProject(20120032110068)supported by Doctoral Fund of Ministry of Education of China
文摘Temporal-spatial cross-correlation analysis of non-stationary wind speed time series plays a crucial role in wind field reconstruction as well as in wind pattern recognition.Firstly,the near-surface wind speed time series recorded at different locations are studied using the detrended fluctuation analysis(DFA),and the corresponding scaling exponents are larger than 1.This indicates that all these wind speed time series have non-stationary characteristics.Secondly,concerning this special feature( i.e.,non-stationarity)of wind signals,a cross-correlation analysis method,namely detrended cross-correlation analysis(DCCA) coefficient,is employed to evaluate the temporal-spatial cross-correlations between non-stationary time series of different anemometer pairs.Finally,experiments on ten wind speed data synchronously collected by the ten anemometers with equidistant arrangement illustrate that the method of DCCA cross-correlation coefficient can accurately analyze full-scale temporal-spatial cross-correlation between non-stationary time series and also can easily identify the seasonal component,while three traditional cross-correlation techniques(i.e.,Pearson coefficient,cross-correlation function,and DCCA method) cannot give us these information directly.
基金supported by the National Science Foundation of China(61561016 61861008+4 种基金 11603041)the Guangxi Natural Science Foundation Project(2018JJA170090)the Innovation Project of Guet Graduate Education(2018YJCX19 2018YJCX31)Guangxi Key Laboratory of Precision Navigation Technology and Application,Guilin University of Electronic Technology(DH201707)
文摘In order to solve the problem of ambiguous acquisition of BOC signals caused by its property of multiple peaks,an unambiguous acquisition algorithm named reconstruction of sub cross-correlation cancellation technique(RSCCT)for BOC(kn,n)signals is proposed.In this paper,the principle of signal decomposition is combined with the traditional acquisition algorithm structure,and then based on the method of reconstructing the correlation function.The method firstly gets the sub-pseudorandom noise(PRN)code by decomposing the local PRN code,then uses BOC(kn,n)and the sub-PRN code cross-correlation to get the sub cross-correlation function.Finally,the correlation peak with a single peak is obtained by reconstructing the sub cross-correlation function so that the ambiguities of BOC acquisition are removed.The simulation shows that RSCCT can completely eliminate the side peaks of BOC(kn,n)group signals while maintaining the narrow correlation of BOC,and its computational complexity is equivalent to sub carrier phase cancellation(SCPC)and autocorrelation side-peak cancellation technique(ASPeCT),and it reduces the computational complexity relative to BPSK-like.For BOC(n,n),the acquisition sensitivity of RSCCT is 3.25 dB,0.81 dB and 0.25 dB higher than binary phase shift keying(BPSK)-like,SCPC and ASPeCT at the acquisition probability of 90%,respectively.The peak to average power ratio is 1.91,3.0 and 3.7 times higher than ASPeCT,SCPC and BPSK-like at SNR=–20 dB,respectively.For BOC(2n,n),the acquisition sensitivity of RSCCT is 5.5 dB,1.25 dB and 2.69 dB higher than BPSK-like,SCPC and ASPeCT at the acquisition probability of 90%,respectively.The peak to average power ratio is 1.02,1.68 and 2.12 times higher than ASPeCT,SCPC and BPSK-like at SNR=–20 dB,respectively.
文摘This paper describes the estimation of cloud motion using lag cross-correlation. In order to compute the lag cross correlation, the Bayes Decision method is used first to identify cloud and surface of earth. Then cloud motion vectors are retrieved at a subset of points through multiple applications of a cross-correlation analysis. An objective analysis is used to define displacement at every satellite pixel throughout the domain and smooth the local inconsistencies. Cloud motions are then produced with a backward trajectory technique by using these displacement vectors.
