scholarly journals Low frequency signal spectrum analysis for strong earthquakes

2012 ◽  
Vol 55 (1) ◽  
Author(s):  
Alexander Rozhnoi ◽  
Maria Solovieva ◽  
Pier Francesco Biagi ◽  
Konrad Schwingenschuh ◽  
Masashi Hayakawa
Keyword(s):  
Spectrum Analysis ◽  
Low Frequency ◽  
Signal Spectrum ◽  
Frequency Signal ◽  
Strong Earthquakes

Parameter Estimation of Low-Frequency Signal with Negative Frequency Contribution

2013 ◽  
Vol 380-384 ◽  
pp. 3457-3460 ◽  
Author(s):  
Si Wei Tan ◽  
Zhi Liang Ren ◽  
Jiong Sun
Keyword(s):  
Parameter Estimation ◽  
Phase Difference ◽  
Spectrum Analysis ◽  
Low Frequency ◽  
High Accuracy ◽  
Signal Parameter ◽  
Frequency Signal ◽  
Spectrum Method ◽  
Analysis Theory ◽  
Simulation Results

According to the problem that there is a decline in accuracy of low-frequency signal parameter estimation by using the algorithm of all-phase FFT, an improved phase difference correcting spectrum method based on all-phase FFT is proposed. The contribution of negative frequency to FFT calculation was considered while using phase difference correcting spectrum method. The all-phase FFT spectrum analysis theory was presented as well as a traditional phase difference correcting method based on it. The equations of parameter estimation such as frequency, amplitude and phase for low-frequency signals were derived with the negative frequency contribution to spectrum analysis. The simulation results show that the method proposed in this paper can be used to estimate the parameters of low-frequency signals in a high accuracy, and also achieves an improvement in anti-noise ability.

Medium range low‐frequency signal fluctuations

1978 ◽  
Vol 63 (S1) ◽  
pp. S61-S61
Author(s):  
A. W. Novick ◽  
R. A. Shade
Keyword(s):  
Low Frequency ◽  
Frequency Signal ◽  
Medium Range

Denoising for full-waveform inversion with expanded prediction-error filters

Geophysics ◽  
2021 ◽  
pp. 1-54
Author(s):  
Milad Bader ◽  
Robert G. Clapp ◽  
Biondo Biondi
Keyword(s):  
High Frequency ◽  
Prediction Error ◽  
Signal To Noise Ratio ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Full Waveform Inversion ◽  
Frequency Data ◽  
Signal To Noise ◽  
Frequency Signal ◽  
Full Waveform

Low-frequency data below 5 Hz are essential to the convergence of full-waveform inversion towards a useful solution. They help build the velocity model low wavenumbers and reduce the risk of cycle-skipping. In marine environments, low-frequency data are characterized by a low signal-to-noise ratio and can lead to erroneous models when inverted, especially if the noise contains coherent components. Often field data are high-pass filtered before any processing step, sacrificing weak but essential signal for full-waveform inversion. We propose to denoise the low-frequency data using prediction-error filters that we estimate from a high-frequency component with a high signal-to-noise ratio. The constructed filter captures the multi-dimensional spectrum of the high-frequency signal. We expand the filter's axes in the time-space domain to compress its spectrum towards the low frequencies and wavenumbers. The expanded filter becomes a predictor of the target low-frequency signal, and we incorporate it in a minimization scheme to attenuate noise. To account for data non-stationarity while retaining the simplicity of stationary filters, we divide the data into non-overlapping patches and linearly interpolate stationary filters at each data sample. We apply our method to synthetic stationary and non-stationary data, and we show it improves the full-waveform inversion results initialized at 2.5 Hz using the Marmousi model. We also demonstrate that the denoising attenuates non-stationary shear energy recorded by the vertical component of ocean-bottom nodes.

scholarly journals An Investigation into Error Source Identification of Machine Tools Based on Time-Frequency Feature Extraction

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Dongju Chen ◽  
Shuai Zhou ◽  
Lihua Dong ◽  
Jinwei Fan
Keyword(s):  
Machine Tool ◽  
Frequency Domain ◽  
Surface Topography ◽  
High Frequency ◽  
Low Frequency ◽  
Frequency Signal ◽  
High Frequency Signal ◽  
Time Frequency ◽  
Cross Correlation Analysis ◽  
Correlation Analysis Method

This paper presents a new identification method to identify the main errors of the machine tool in time-frequency domain. The low- and high-frequency signals of the workpiece surface are decomposed based on the Daubechies wavelet transform. With power spectral density analysis, the main features of the high-frequency signal corresponding to the imbalance of the spindle system are extracted from the surface topography of the workpiece in the frequency domain. With the cross-correlation analysis method, the relationship between the guideway error of the machine tool and the low-frequency signal of the surface topography is calculated in the time domain.

scholarly journals Strong earthquakes can be predicted: a multidisciplinary method for strong earthquake prediction

2003 ◽  
Vol 3 (6) ◽  
pp. 703-712 ◽  
Author(s):  
J. Z. Li ◽  
Z. Q. Bai ◽  
W. S. Chen ◽  
Y. Q. Xia ◽  
Y. R. Liu ◽  
...  
Keyword(s):  
Earthquake Prediction ◽  
Strong Earthquake ◽  
Low Frequency ◽  
Abnormal Behavior ◽  
Frequency Range ◽  
Strong Earthquakes ◽  
Physical Mechanisms ◽  
Innovative Methods ◽  
The World ◽  
Infrasonic Wave

Abstract. The imminent prediction on a group of strong earthquakes that occurred in Xinjiang, China in April 1997 is introduced in detail. The prediction was made on the basis of comprehensive analyses on the results obtained by multiple innovative methods including measurements of crustal stress, observation of infrasonic wave in an ultra low frequency range, and recording of abnormal behavior of certain animals. Other successful examples of prediction are also enumerated. The statistics shows that above 40% of 20 total predictions jointly presented by J. Z. Li, Z. Q. Ren and others since 1995 can be regarded as effective. With the above methods, precursors of almost every strong earthquake around the world that occurred in recent years were recorded in our laboratory. However, the physical mechanisms of the observed precursors are yet impossible to explain at this stage.

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