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 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

scholarly journals Low Power AVLS-TSPC based 2/3 Pre-Scaler

2019 ◽  
Vol 9 (1) ◽  
pp. 6687-6693
Keyword(s):  
Low Power ◽  
High Efficiency ◽  
Supply Voltage ◽  
Low Frequency ◽  
Cmos Technology ◽  
Frequency Signal ◽  
High Frequency Signal ◽  
Power Requirement ◽  
Fast Pace ◽  
Integer Division

The technology has grown at an ultra-fast pace along with the world. Small devices with less power and high efficiency are in demand. As the circuit size gets smaller, the power requirement increases due to a greater number of transistors. A pre-scaler is a circuit which reduces the high frequency signal to a low frequency signal by integer division. A new approach to low power pre-scaler is proposed in this paper, which is an add-on to the conventional pre-scaler circuit. A true single-phase clock (TSPC) circuit reduces the skew problems in the clock and is used to realize latches and flip-flops. The objective of low power is fulfilled by incorporating the Adaptive Voltage Level Source (AVLS) to TSPC based circuit. The proposed AVLS-TSPC based pre-scaler was analyzed for a frequency of 10 MHz with a supply voltage of 1.8 V for both divide by 2 and 3 modes. The proposed pre-scaler consumes considerably lesser power when compared to that of the existing pre-scaler circuit. The circuits are implemented in 180 nm CMOS technology using Cadence Virtuoso and simulated using Cadence Spectre.

The origin of the anomalous low-frequency vibrational behaviour of amorphous solids

1992 ◽  
Vol 02 ◽  
pp. C2-279-C2-283
Author(s):  
S. R. ELLIOTT
Keyword(s):  
Low Frequency ◽  
Peak Frequency ◽  
Amorphous Solids ◽  
Boson Peak ◽  
Low Frequencies ◽  
Crystalline Materials ◽  
Medium Range ◽  
Phonon Localization ◽  
Strong Scattering ◽  
Vibrational Density

The anomalous vibrational behaviour exhibited by non-crystalline materials - a peak in the vibrational density of states, and in the Raman spectrum (the boson peak) at low frequencies, and a peak in the heat capacity and a plateau in the thermal conductivity at low temperatures - is ascribed ta phonon localization associated with the strong scattering of phonons by density-fluctuation domains in the structure. Within such domains, short-range and medium-range arder is maintained, and outside them the material is structurally homogeneous and isotropie. This model can also explain the correlation between the boson-peak frequency and the position of the first sharp diffraction peak in the structure factor observed in a number of inorganie and polymerie amorphous solids.

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