scholarly journals Nonparametric estimation of scalar diffusions based on low frequency data

2004 ◽  
Vol 32 (5) ◽  
pp. 2223-2253 ◽  
Author(s):  
Markus Rei� ◽  
Marc Hoffmann ◽  
Emmanuel Gobet
Keyword(s):  
Nonparametric Estimation ◽  
Low Frequency ◽  
Frequency Data

Predicting risk in energy markets: Low-frequency data still matter

2021 ◽  
Vol 282 ◽  
pp. 116146
Author(s):  
Štefan Lyócsa ◽  
Neda Todorova ◽  
Tomáš Výrost
Keyword(s):  
Low Frequency ◽  
Energy Markets ◽  
Frequency Data

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 FX market volatility modelling: Can we use low-frequency data?

2020 ◽  
pp. 101776
Author(s):  
Štefan Lyócsa ◽  
Tomáš Plíhal ◽  
Tomáš Výrost
Keyword(s):  
Low Frequency ◽  
Market Volatility ◽  
Frequency Data ◽  
Fx Market
Sign in / Sign up

Export Citation Format

Share Document