Noise correction and integration of HR-GNSS and seismological data for small earthquakes

2021 ◽  
Author(s):  
Iwona Kudłacik ◽  
Jan Kapłon
Keyword(s):  
Time Series ◽  
High Frequency ◽  
Dynamic Range ◽  
Full Range ◽  
Low Frequency ◽  
High Rate ◽  
Discrete Wavelet ◽  
Frequency Noise ◽  
Mining Tremors ◽  
Gnss Observations

<p>High-rate GNSS (HR-GNSS) observations are used for high-precision applications, where the point position changes in short intervals are required, such as earthquake analysis or structural health monitoring. We aim to apply the HR-GNSS observations into mining tremors monitoring, where the dynamic displacement amplitudes reach maximally dozens of millimetres. The study contains the analysis of several mining tremors of magnitudes 3-4 in Poland, recorded within the EPOS-PL project.</p><p>The HR-GNSS position is obtained with over 1 Hz frequency in kinematic mode with relative or absolute approaches. For short periods (up to several minutes), the positioning accuracy is very high, but the displacement time series suffer from low-frequency fluctuations. Therefore, it is not possible to apply them directly in the analysis of seismic phenomena, thus it is necessary to filter out low- and high-frequency noise.</p><p>In this study, we discussed some methods that are useful to reduce the noise in HR-GNSS displacement time series to obtain precise and physically correct results with reference to seismological observations, which for dynamic position changes are an order of magnitude more accurate. We presented the band-pass filtering application with automatic filtration limits based on occupied bandwidth detection and the discrete wavelet transform application with multiresolution analysis. The correction of noise increases the correlation coefficient by over 40%, reaching values over 0.8. Moreover, we tested the application of the basic Kalman filter to the integration of sensors: HR-GNSS and an accelerometer to visualize the most actual displacements of the station during a small earthquake - a mining tremor. The usefulness of this algorithm for the assumed purpose was confirmed. This algorithm allows to reduce the noise from HR-GNSS results, and on the other hand, to minimize the potential seismograph drift and its errors caused by the limited dynamic range of the seismograph. An unquestionable advantage is the possibility of obtaining a time series of displacements with a high frequency (equal to the frequency of seismograph observations, e.g. 250 Hz) showing the full range of station motion: dynamic and static displacements caused by an earthquake.</p>

Aerodynamic noise from an asymmetric airfoil with perforated extension plates at the trailing edge

2020 ◽  
pp. 1475472X2097838
Author(s):  
CK Sumesh ◽  
TJS Jothi
Keyword(s):  
High Frequency ◽  
Sound Pressure ◽  
Pore Diameter ◽  
Low Frequency ◽  
Aerodynamic Noise ◽  
Frequency Noise ◽  
Low Frequency Noise ◽  
Trailing Edge ◽  
High Frequency Noise ◽  
Displacement Thickness

This paper investigates the noise emissions from NACA 6412 asymmetric airfoil with different perforated extension plates at the trailing edge. The length of the extension plate is 10 mm, and the pore diameters ( D) considered for the study are in the range of 0.689 to 1.665 mm. The experiments are carried out in the flow velocity ( U∞) range of 20 to 45 m/s, and geometric angles of attack ( αg) values of −10° to +10°. Perforated extensions have an overwhelming response in reducing the low frequency noise (<1.5 kHz), and a reduction of up to 6 dB is observed with an increase in the pore diameter. Contrastingly, the higher frequency noise (>4 kHz) is observed to increase with an increase in the pore diameter. The dominant reduction in the low frequency noise for perforated model airfoils is within the Strouhal number (based on the displacement thickness) of 0.11. The overall sound pressure levels of perforated model airfoils are observed to reduce by a maximum of 2 dB compared to the base airfoil. Finally, by varying the geometric angle of attack from −10° to +10°, the lower frequency noise is seen to increase, while the high frequency noise is observed to decrease.

scholarly journals Multi-focus Image Fusion Using an Effective Discrete Wavelet Transform Based Algorithm

2014 ◽  
Vol 14 (2) ◽  
pp. 102-108 ◽  
Author(s):  
Yong Yang ◽  
Shuying Huang ◽  
Junfeng Gao ◽  
Zhongsheng Qian
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Low Frequency ◽  
Objective Evaluation ◽  
Discrete Wavelet ◽  
Evaluation Indexes ◽  
Fused Image ◽  
Focus Image

Abstract In this paper, by considering the main objective of multi-focus image fusion and the physical meaning of wavelet coefficients, a discrete wavelet transform (DWT) based fusion technique with a novel coefficients selection algorithm is presented. After the source images are decomposed by DWT, two different window-based fusion rules are separately employed to combine the low frequency and high frequency coefficients. In the method, the coefficients in the low frequency domain with maximum sharpness focus measure are selected as coefficients of the fused image, and a maximum neighboring energy based fusion scheme is proposed to select high frequency sub-bands coefficients. In order to guarantee the homogeneity of the resultant fused image, a consistency verification procedure is applied to the combined coefficients. The performance assessment of the proposed method was conducted in both synthetic and real multi-focus images. Experimental results demonstrate that the proposed method can achieve better visual quality and objective evaluation indexes than several existing fusion methods, thus being an effective multi-focus image fusion method.

Paper 14: Acoustic Detection of Cavitation

1966 ◽  
Vol 181 (1) ◽  
pp. 1-8 ◽  
Author(s):  
I. S. Pearsall
Keyword(s):  
High Frequency ◽  
Critical Frequency ◽  
Low Frequency ◽  
Francis Turbine ◽  
Frequency Noise ◽  
Performance Tests ◽  
Acoustic Detection ◽  
Cavitation Noise ◽  
Further Development ◽  
Onset Of Cavitation

The onset of cavitation in a hydraulic machine can be determined visually and its effect on performance by performance tests. It would be convenient to have an alternative method that required neither transparent sections nor expensive tests. Initial tests have been made measuring noise over a frequency range of 20 c/s-20 kc/s in one-third octave bands, on a number of pumps and turbines. An accelerometer attached to the casing was used. The tests indicated that, generally, the onset of cavitation was accompanied by a rise in the high-frequency noise, whilst the low-frequency noise increased as the cavitation developed. A maximum of cavitation noise was reached before the efficiency and load fell off. In some cases difficulty was experienced because blade cavitation was drowned by noise caused by other cavitation, such as the vortex in a Francis turbine. It also appears that the noise following the onset of cavitation is at the frequency which is used as a critical frequency in accelerated erosion tests. Further development of techniques is required, but the initial tests are encouraging.

scholarly journals Recognition of Emotional States using Multiscale Information Analysis of High Frequency EEG Oscillations

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 609 ◽  
Author(s):  
Gao ◽  
Cui ◽  
Wan ◽  
Gu
Keyword(s):  
Emotion Recognition ◽  
High Frequency ◽  
Low Frequency ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Emotional States ◽  
Circumplex Model ◽  
Information Analysis ◽  
Eeg Signals ◽  
High Frequency Oscillations

Exploring the manifestation of emotion in electroencephalogram (EEG) signals is helpful for improving the accuracy of emotion recognition. This paper introduced the novel features based on the multiscale information analysis (MIA) of EEG signals for distinguishing emotional states in four dimensions based on Russell's circumplex model. The algorithms were applied to extract features on the DEAP database, which included multiscale EEG complexity index in the time domain, and ensemble empirical mode decomposition enhanced energy and fuzzy entropy in the frequency domain. The support vector machine and cross validation method were applied to assess classification accuracy. The classification performance of MIA methods (accuracy = 62.01%, precision = 62.03%, recall/sensitivity = 60.51%, and specificity = 82.80%) was much higher than classical methods (accuracy = 43.98%, precision = 43.81%, recall/sensitivity = 41.86%, and specificity = 70.50%), which extracted features contain similar energy based on a discrete wavelet transform, fractal dimension, and sample entropy. In this study, we found that emotion recognition is more associated with high frequency oscillations (51–100Hz) of EEG signals rather than low frequency oscillations (0.3–49Hz), and the significance of the frontal and temporal regions are higher than other regions. Such information has predictive power and may provide more insights into analyzing the multiscale information of high frequency oscillations in EEG signals.

scholarly journals The Scaling LInear Macroweather model (SLIM): using scaling to forecast global scale macroweather from months to decades

2015 ◽  
Vol 6 (1) ◽  
pp. 489-545 ◽  
Author(s):  
S. Lovejoy ◽  
L. del Rio Amador ◽  
R. Hébert
Keyword(s):  
Stochastic Models ◽  
High Frequency ◽  
Low Frequency ◽  
Global Scale ◽  
Forecast Skill ◽  
Previous Attempt ◽  
Frequency Noise ◽  
Forecast Errors ◽  
System Memory ◽  
Two Parameter

Abstract. At scales of ≈ 10 days (the lifetime of planetary scale structures), there is a drastic transition from high frequency weather to low frequency macroweather. This scale is close to the predictability limits of deterministic atmospheric models; so that in GCM macroweather forecasts, the weather is a high frequency noise. But neither the GCM noise nor the GCM climate is fully realistic. In this paper we show how simple stochastic models can be developped that use empirical data to force the statistics and climate to be realistic so that even a two parameter model can outperform GCM's for annual global temperature forecasts. The key is to exploit the scaling of the dynamics and the enormous stochastic memories that it implies. Since macroweather intermittency is low, we propose using the simplest model based on fractional Gaussian noise (fGn): the Scaling LInear Macroweather model (SLIM). SLIM is based on a stochastic ordinary differential equations, differing from usual linear stochastic models (such as the Linear Inverse Modelling, LIM) in that it is of fractional rather than integer order. Whereas LIM implicitly assumes there is no low frequency memory, SLIM has a huge memory that can be exploited. Although the basic mathematical forecast problem for fGn has been solved, we approach the problem in an original manner notably using the method of innovations to obtain simpler results on forecast skill and on the size of the effective system memory. A key to successful forecasts of natural macroweather variability is to first remove the low frequency anthropogenic component. A previous attempt to use fGn for forecasts had poor results because this was not done. We validate our theory using hindcasts of global and Northern Hemisphere temperatures at monthly and annual resolutions. Several nondimensional measures of forecast skill – with no adjustable parameters – show excellent agreement with hindcasts and these show some skill even at decadal scales. We also compare our forecast errors with those of several GCM experiments (with and without initialization), and with other stochastic forecasts showing that even this simplest two parameter SLIM model is somewhat superior. In future, using a space–time (regionalized) generalization of SLIM we expect to be able to exploiting the system memory more extensively and obtain even more realistic forecasts.

Multisource Image Fusion Based on DWT and Simplified Pulse Coupled Neural Network

2013 ◽  
Vol 457-458 ◽  
pp. 736-740 ◽  
Author(s):  
Nian Yi Wang ◽  
Wei Lan Wang ◽  
Xiao Ran Guo
Keyword(s):  
Neural Network ◽  
Image Fusion ◽  
High Frequency ◽  
Low Frequency ◽  
Discrete Wavelet ◽  
Cortical Model ◽  
Fusion Algorithm ◽  
Global Coupling ◽  
Maximum Selection ◽  
Pulse Synchronization

In this paper, a new image fusion algorithm based on discrete wavelet transform (DWT) and spiking cortical model (SCM) is proposed. The multiscale decomposition and multi-resolution representation characteristics of DWT are associated with global coupling and pulse synchronization features of SCM. Two different fusion rules are used to fuse the low and high frequency sub-bands respectively. Maximum selection rule (MSR) is used to fuse low frequency coefficients. As to high frequency subband coefficients, spatial frequency (SF) is calculated and then imputed into SCM to motivate neural network. Experimental results demonstrate the effectiveness of the proposed fusion method.

Effects of Noise Frequency on Performance and Annoyance for Women and Men

1981 ◽  
Vol 52 (2) ◽  
pp. 435-441 ◽  
Author(s):  
Kelli F. Key ◽  
M. Carr Payne
Keyword(s):  
Sex Differences ◽  
Magnitude Estimation ◽  
High Frequency ◽  
Ambient Noise ◽  
Direct Relationship ◽  
Low Frequency ◽  
Frequency Noise ◽  
Complex Task ◽  
High Frequency Noise ◽  
Noise Frequency

Effects of noise frequencies on both performance on a complex psychomotor task and annoyance were investigated for men ( n = 30) and women ( n = 30). Each subject performed a complex psychomotor task for 50 min. in the presence of low frequency noise, high frequency noise, or ambient noise. Women and men learned the task at different rates. Little effect of noise was shown. Annoyance ratings were subsequently obtained from each subject for noises of various frequencies by the method of magnitude estimation. High frequency noises were more annoying than low frequency noises regardless of sex and immediate prior exposure to noise. Sex differences in annoyance did not occur. No direct relationship between learning to perform a complex task while exposed to noise and annoyance by that noise was demonstrated.

Application with Wavelet Detection to Signal Process of SINS

2012 ◽  
Vol 571 ◽  
pp. 671-675
Author(s):  
Xiang Yuan Huang ◽  
Xia Qing Tang ◽  
Li Bi Guo ◽  
Xu Wei Cheng
Keyword(s):  
High Frequency ◽  
Simulation Experiment ◽  
Detection Method ◽  
Low Frequency ◽  
Frequency Noise ◽  
Wavelet Filter ◽  
Initial Alignment ◽  
Signal Process ◽  
Signal Characteristic ◽  
Alignment Process

Aimed at disturbance caused from motor running and personnel ambulation during initial alignment process of SINS, a new signal detection method of disturbance based on wavelet analysis is brought out. Through analyzing original signal characteristic of FOG and the data with wavelet filter on disturbance base, finds out wavelet filter just have effectiveness to high frequency noise. Then T&L signal detecting law is introduced, and builds T&L signal with high frequency part of wavelet decomposing to estimates interfere time and then resample. Offline simulation experiment results indicate the method can eliminate low frequency disturbance effectively and has certain apply value.

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