scholarly journals Automatic traveltime picking using instantaneous traveltime

Geophysics ◽  
2013 ◽  
Vol 78 (2) ◽  
pp. T53-T58 ◽  
Author(s):  
Christos Saragiotis ◽  
Tariq Alkhalifah ◽  
Sergey Fomel
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Field Data ◽  
Seismic Event ◽  
Seismic Signals ◽  
Recording Time ◽  
Time Frequency ◽  
Frequency Representation ◽  
Automatic Picking ◽  
The Time Domain

Event picking is used in many steps of seismic processing. We present an automatic event picking method that is based on a new attribute of seismic signals, instantaneous traveltime. The calculation of the instantaneous traveltime consists of two separate but interrelated stages. First, a trace is mapped onto the time-frequency domain. Then the time-frequency representation is mapped back onto the time domain by an appropriate operation. The computed instantaneous traveltime equals the recording time at those instances at which there is a seismic event, a feature that is used to pick the events. We analyzed the concept of the instantaneous traveltime and demonstrated the application of our automatic picking method on dynamite and Vibroseis field data.

scholarly journals A research on fiber-optic vibration pattern recognition based on time-frequency characteristics

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881346 ◽  
Author(s):  
Tabi Fouda Bernard Marie ◽  
Dezhi Han ◽  
Bowen An ◽  
Jingyun Li
Keyword(s):  
Pattern Recognition ◽  
Frequency Domain ◽  
Time Domain ◽  
Fiber Optic ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Vibration Pattern ◽  
The Time Domain

To detect and recognize any type of events over the perimeter security system, this article proposes a fiber-optic vibration pattern recognition method based on the combination of time-domain features and time-frequency domain features. The performance parameters (event recognition, event location, and event classification) are very important and describe the validity of this article. The pattern recognition method is precisely based on the empirical mode decomposition of time-frequency entropy and center-of-gravity frequency. It implements the function of identifying and classifying the event (intrusions or non-intrusion) over the perimeter to secure. To achieve this method, the first-level prejudgment is performed according to the time-domain features of the vibration signal, and the second-level prediction is carried out through time-frequency analysis. The time-frequency distribution of the signal is obtained by empirical mode decomposition and Hilbert transform and then the time-frequency entropy and center-of-gravity frequency are used to form the time-frequency domain features, that is, combined with the time-domain features to form feature vectors. Multiple types of probabilistic neural networks are identified to determine whether there are intrusions and the intrusion types. The experimental results demonstrate that the proposed method is effective and reliable in identifying and classifying the type of event.

scholarly journals Application of the Acoustic Emission Method for Diagnosis of On-Load Tap Changer

2017 ◽  
Vol 42 (1) ◽  
pp. 29-35 ◽  
Author(s):  
Henryk Majchrzak ◽  
Andrzej Cichoń ◽  
Sebastian Borucki
Keyword(s):  
Acoustic Emission ◽  
Frequency Domain ◽  
Time Domain ◽  
Operating Conditions ◽  
Time Frequency ◽  
Measurement Results ◽  
Tap Changer ◽  
The Time Domain ◽  
Load Tap Changer ◽  
Short Time

Abstract This paper provides an example of the application of the acoustic emission (AE) method for the diagnosis of technical conditions of a three-phase on-load tap-changer (OLTC) GIII type. The measurements were performed for an amount of 10 items of OLTCs, installed in power transformers with a capacity of 250 MVA. The study was conducted in two different OLTC operating conditions during the tapping process: under load and free running conditions. The analysis of the measurement results was made in both time domain and time-frequency domain. The description of the AE signals generated by the OLTC in the time domain was performed using the analysis of waveforms and determined characteristic times. Within the time-frequency domain the measured signals were described by short-time Fourier transform spectrograms.

scholarly journals Spillover effects from news to travel and leisure stocks during the COVID-19 pandemic: Evidence from the time and frequency domains

2021 ◽  
pp. 135481662110584
Author(s):  
Ying Wang ◽  
Hongwei Zhang ◽  
Wang Gao ◽  
Cai Yang
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Spillover Effects ◽  
Short Term ◽  
Time Frequency ◽  
Frequency Domains ◽  
Media Hype ◽  
The Time Domain ◽  
The Impact

The impact of the COVID-19 pandemic on tourism has received general attention in the literature, while the role of news during the pandemic has been ignored. Using a time-frequency connectedness approach, this paper focuses on the spillover effects of COVID-19-related news on the return and volatility of four regional travel and leisure (T&L) stocks. The results in the time domain reveal significant spillovers from news to T&L stocks. Specifically, in the return system, T&L stocks are mainly affected by media hype, while in the volatility system, they are mainly affected by panic sentiment. This paper also finds two risk contagion paths. The contagion index and Global T&L stock are the sources of these paths. The results in the frequency domain indicate that the shocks in the T&L industry are mainly driven by short-term fluctuations. The spillovers from news to T&L stocks and among these T&L stocks are stronger within 1 month.

Dispersive modes in the time domain: analysis and time-frequency representation

1994 ◽  
Vol 4 (1) ◽  
pp. 23-25 ◽  
Author(s):  
L. Carin ◽  
L.B. Felsen ◽  
D. Kralj ◽  
S.U. Pillai ◽  
W.C. Lee
Keyword(s):  
Time Domain ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Time Frequency ◽  
Frequency Representation ◽  
The Time Domain

Channel Estimating Based on Space-Time-Frequency Pilot for MIMO-OFDM

2012 ◽  
Vol 429 ◽  
pp. 179-185
Author(s):  
Hui Liu ◽  
Jing Shan Jiao ◽  
Fu Chun Zhang ◽  
Ling Zhou
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Space Time ◽  
Three Dimensions ◽  
Space Domain ◽  
Time Frequency ◽  
Time Space ◽  
Mimo Ofdm ◽  
The Time Domain ◽  
Inverse Operation

The pilots that are transmitted by different transmitting antennas must be orthogonal after being shifted. So the time domain channel estimating solution is deduced through LS based on the MIMO-OFDM channel estimating model. The time domain solution need the inverse operation of matrix, and its operating quantity is large. So the three dimensions pilot based on space domain, time domain and frequency domain is designed. The method need not the inverse operation of matrix for the time domain channel estimating solution and can reduce the complexity of channel estimating and make the channel estimating error minimum. It is shown from the simulation that the channel estimating method of this paper based on space domain, time space and frequency domain pilot has better MSE and BER performances compared with the traditional LS algorithm and the document algorithm.

scholarly journals Transformation Algorithm of Dielectric Response in Time-Frequency Domain

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Ji Liu ◽  
Daning Zhang ◽  
Xinlao Wei ◽  
Hamid Reza Karimi
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Dielectric Response ◽  
Low Frequency ◽  
Dissipation Factor ◽  
Depolarization Current ◽  
Ultralow Frequency ◽  
Time Frequency ◽  
Transformation Algorithm ◽  
The Time Domain

A transformation algorithm of dielectric response from time domain to frequency domain is presented. In order to shorten measuring time of low or ultralow frequency dielectric response characteristics, the transformation algorithm is used in this paper to transform the time domain relaxation current to frequency domain current for calculating the low frequency dielectric dissipation factor. In addition, it is shown from comparing the calculation results with actual test data that there is a coincidence for both results over a wide range of low frequencies. Meanwhile, the time domain test data of depolarization currents in dry and moist pressboards are converted into frequency domain results on the basis of the transformation. The frequency domain curves of complex capacitance and dielectric dissipation factor at the low frequency range are obtained. Test results of polarization and depolarization current (PDC) in pressboards are also given at the different voltage and polarization time. It is demonstrated from the experimental results that polarization and depolarization current are affected significantly by moisture contents of the test pressboards, and the transformation algorithm is effective in ultralow frequency of 10−3 Hz. Data analysis and interpretation of the test results conclude that analysis of time-frequency domain dielectric response can be used for assessing insulation system in power transformer.

scholarly journals Novel Burst Suppression Segmentation in the Joint Time-Frequency Domain for EEG in Treatment of Status Epilepticus

2016 ◽  
Vol 2016 ◽  
pp. 1-12
Author(s):  
Jaeyun Lee ◽  
Woo-Jin Song ◽  
Hyang Woon Lee ◽  
Hyun-Chool Shin
Keyword(s):  
Status Epilepticus ◽  
Frequency Domain ◽  
Time Domain ◽  
Suppression Ratio ◽  
Likelihood Estimation ◽  
Burst Suppression ◽  
Time Frequency ◽  
Frequency Domains ◽  
Conventional Methods ◽  
The Time Domain

We developed a method to distinguish bursts and suppressions for EEG burst suppression from the treatments of status epilepticus, employing the joint time-frequency domain. We obtained the feature used in the proposed method from the joint use of the time and frequency domains, and we estimated the decision as to whether the measured EEG was a burst segment or suppression segment by the maximum likelihood estimation. We evaluated the performance of the proposed method in terms of its accordance with the visual scores and estimation of the burst suppression ratio. The accuracy was higher than the sole use of the time or frequency domains, as well as conventional methods conducted in the time domain. In addition, probabilistic modeling provided a more simplified optimization than conventional methods. Burst suppression quantification necessitated precise burst suppression segmentation with an easy optimization; therefore, the excellent discrimination and the easy optimization of burst suppression by the proposed method appear to be beneficial.

scholarly journals Simulation and time-frequency analysis of the longitudinal train dynamics coupled with a nonlinear friction draft gear

2020 ◽  
Vol 9 (1) ◽  
pp. 124-144
Author(s):  
Caglar Uyulan ◽  
Ersen Arslan
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Controller Design ◽  
Response Characteristic ◽  
Domain Model ◽  
Step Response ◽  
Time Frequency ◽  
Draft Gear ◽  
Train Dynamics ◽  
The Time Domain

AbstractTrain safety and operational efficiency can be improved by investigating the dynamics of the train under varying conditions. Longitudinal train dynamics (LTD) simulations performed for such purposes, usually by utilising a nonlinear time-domain model. This paper covers two modes of LTD results corresponding to the time domain and frequency domain analysis. Time-domain solutions are essential to evaluate the full response used for parameter optimisation and controller design studies while frequency domain solutions can provide significant but straightforward clues regarding system dynamics. An advanced draft gear model, which works under a four-stage process is constructed considering all structural components, geometric relationships, friction modelling and dynamic characteristics such as hysteresis, stiffening, state transition, locked unloading, softening. Then, this model is parametrically reduced and implemented into an LTD simulation. The simulation in the time domain is conducted assuming a locomotive connected with a nine wagon via “ode3” fixed-step solver. The transfer function among the first wagon acceleration (output) and the locomotive force (input) estimated through system identification methodology. Then, the identification results interpreted by investigating step-response characteristic and best response giving parameter set is selected. Next, the modal and spectral analysis performed to reveal the behaviour of the in-train forces and the effects of vibration. This paper discusses a reliable methodology for the longitudinal dynamic analysis of the multi-bodied train in time and frequency domain and clarifies in-train vibration behaviour under the existence of sophisticated draft gear.

JOINT INTERPRETATION OF AIRBORNE ELECTROMAGNETIC DATA IN THE TIME DOMAIN AND FREQUENCY DOMAIN

2018 ◽  
Vol 12 (7-8) ◽  
pp. 76-83
Author(s):  
E. V. KARSHAKOV ◽  
J. MOILANEN
Keyword(s):  
Fourier Transform ◽  
Frequency Domain ◽  
Time Domain ◽  
Frequency Interval ◽  
Single Spectrum ◽  
Simultaneous Inversion ◽  
Homogeneous Half Space ◽  
Time Domain Data ◽  
The Time Domain

Тhe advantage of combine processing of frequency domain and time domain data provided by the EQUATOR system is discussed. The heliborne complex has a towed transmitter, and, raised above it on the same cable a towed receiver. The excitation signal contains both pulsed and harmonic components. In fact, there are two independent transmitters operate in the system: one of them is a normal pulsed domain transmitter, with a half-sinusoidal pulse and a small "cut" on the falling edge, and the other one is a classical frequency domain transmitter at several specially selected frequencies. The received signal is first processed to a direct Fourier transform with high Q-factor detection at all significant frequencies. After that, in the spectral region, operations of converting the spectra of two sounding signals to a single spectrum of an ideal transmitter are performed. Than we do an inverse Fourier transform and return to the time domain. The detection of spectral components is done at a frequency band of several Hz, the receiver has the ability to perfectly suppress all sorts of extra-band noise. The detection bandwidth is several dozen times less the frequency interval between the harmonics, it turns out thatto achieve the same measurement quality of ground response without using out-of-band suppression you need several dozen times higher moment of airborne transmitting system. The data obtained from the model of a homogeneous half-space, a two-layered model, and a model of a horizontally layered medium is considered. A time-domain data makes it easier to detect a conductor in a relative insulator at greater depths. The data in the frequency domain gives more detailed information about subsurface. These conclusions are illustrated by the example of processing the survey data of the Republic of Rwanda in 2017. The simultaneous inversion of data in frequency domain and time domain can significantly improve the quality of interpretation.

Separating Multiple Moving Sources by Microphone Array Signals for Wayside Acoustic Fault Diagnosis

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Wei Xiong ◽  
Qingbo He ◽  
Zhike Peng
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Feature Matching ◽  
Matching Pursuit ◽  
Microphone Array ◽  
Time Frequency ◽  
Moving Sources ◽  
The Time Domain ◽  
Envelope Spectrum ◽  
Threshold Setting

Wayside acoustic defective bearing detector (ADBD) system is a potential technique in ensuring the safety of traveling vehicles. However, Doppler distortion and multiple moving sources aliasing in the acquired acoustic signals decrease the accuracy of defective bearing fault diagnosis. Currently, the method of constructing time-frequency (TF) masks for source separation was limited by an empirical threshold setting. To overcome this limitation, this study proposed a dynamic Doppler multisource separation model and constructed a time domain-separating matrix (TDSM) to realize multiple moving sources separation in the time domain. The TDSM was designed with two steps of (1) constructing separating curves and time domain remapping matrix (TDRM) and (2) remapping each element of separating curves to its corresponding time according to the TDRM. Both TDSM and TDRM were driven by geometrical and motion parameters, which would be estimated by Doppler feature matching pursuit (DFMP) algorithm. After gaining the source components from the observed signals, correlation operation was carried out to estimate source signals. Moreover, fault diagnosis could be carried out by envelope spectrum analysis. Compared with the method of constructing TF masks, the proposed strategy could avoid setting thresholds empirically. Finally, the effectiveness of the proposed technique was validated by simulation and experimental cases. Results indicated the potential of this method for improving the performance of the ADBD system.

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