Low-Frequency Physical Bandwidth Extension

2005 ◽  
pp. 117-127
Keyword(s):  
Low Frequency ◽  
Bandwidth Extension

scholarly journals Full-waveform inversion with extrapolated low-frequency data

Geophysics ◽  
2016 ◽  
Vol 81 (6) ◽  
pp. R339-R348 ◽  
Author(s):  
Yunyue Elita Li ◽  
Laurent Demanet
Keyword(s):  
Waveform Inversion ◽  
Low Frequency ◽  
Velocity Model ◽  
Propagation Model ◽  
Full Waveform Inversion ◽  
Frequency Data ◽  
Local Minima ◽  
Bandwidth Extension ◽  
Low Frequencies ◽  
Full Waveform

The availability of low-frequency data is an important factor in the success of full-waveform inversion (FWI) in the acoustic regime. The low frequencies help determine the kinematically relevant, low-wavenumber components of the velocity model, which are in turn needed to avoid convergence of FWI to spurious local minima. However, acquiring data less than 2 or 3 Hz from the field is a challenging and expensive task. We have explored the possibility of synthesizing the low frequencies computationally from high-frequency data and used the resulting prediction of the missing data to seed the frequency sweep of FWI. As a signal-processing problem, bandwidth extension is a very nonlinear and delicate operation. In all but the simplest of scenarios, it can only be expected to lead to plausible recovery of the low frequencies, rather than their accurate reconstruction. Even so, it still requires a high-level interpretation of band-limited seismic records into individual events, each of which can be extrapolated to a lower (or higher) frequency band from the nondispersive nature of the wave-propagation model. We have used the phase-tracking method for the event separation task. The fidelity of the resulting extrapolation method is typically higher in phase than in amplitude. To demonstrate the reliability of bandwidth extension in the context of FWI, we first used the low frequencies in the extrapolated band as data substitute, to create the low-wavenumber background velocity model, and then we switched to recorded data in the available band for the rest of the iterations. The resulting method, extrapolated FWI, demonstrated surprising robustness to the inaccuracies in the extrapolated low-frequency data. With two synthetic examples calibrated so that regular FWI needs to be initialized at 1 Hz to avoid local minima, we have determined that FWI based on an extrapolated [1, 5] Hz band, itself generated from data available in the [5, 15] Hz band, can produce reasonable estimations of the low-wavenumber velocity models.

scholarly journals Tutorial: Spectral bandwidth extension — Invention versus harmonic extrapolation

Geophysics ◽  
2017 ◽  
Vol 82 (4) ◽  
pp. W1-W16 ◽  
Author(s):  
Chen Liang ◽  
John Castagna ◽  
Ricardo Zavala Torres
Keyword(s):  
Seismic Data ◽  
High Frequency ◽  
Low Frequency ◽  
Original Data ◽  
Spectral Bandwidth ◽  
Bandwidth Extension ◽  
Seismic Resolution ◽  
Earth Structures ◽  
Seismic Sections ◽  
Phase Acceleration

Various postprocessing methods can be applied to seismic data to extend the spectral bandwidth and potentially increase the seismic resolution. Frequency invention techniques, including phase acceleration and loop reconvolution, produce spectrally broadened seismic sections but arbitrarily create high frequencies without a physical basis. Tests in extending the bandwidth of low-frequency synthetics using these methods indicate that the invented frequencies do not tie high-frequency synthetics generated from the same reflectivity series. Furthermore, synthetic wedge models indicate that the invented high-frequency seismic traces do not improve thin-layer resolution. Frequency invention outputs may serve as useful attributes, but they should not be used for quantitative work and do not improve actual resolution. On the other hand, under appropriate circumstances, layer frequency responses can be extrapolated to frequencies outside the band of the original data using spectral periodicities determined from within the original seismic bandwidth. This can be accomplished by harmonic extrapolation. For blocky earth structures, synthetic tests show that such spectral extrapolation can readily double the bandwidth, even in the presence of noise. Wedge models illustrate the resulting resolution improvement. Synthetic tests suggest that the more complicated the earth structure, the less valid the bandwidth extension that harmonic extrapolation can achieve. Tests of the frequency invention methods and harmonic extrapolation on field seismic data demonstrate that (1) the frequency invention methods modify the original seismic band such that the original data cannot be recovered by simple band-pass filtering, whereas harmonic extrapolation can be filtered back to the original band with good fidelity and (2) harmonic extrapolation exhibits acceptable ties between real and synthetic seismic data outside the original seismic band, whereas frequency invention methods have unfavorable well ties in the cases studied.

Low-frequency bandwidth extension of telephone speech using sinusoidal synthesis and Gaussian mixture model

2011 ◽  
Author(s):  
Hannu Pulakka ◽  
Ulpu Remes ◽  
Santeri Yrttiaho ◽  
Kalle J. Palomäki ◽  
Mikko Kurimo ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Low Frequency ◽  
Gaussian Mixture ◽  
Frequency Bandwidth ◽  
Bandwidth Extension ◽  
Telephone Speech

scholarly journals Extrapolated full-waveform inversion with deep learning

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. R275-R288 ◽  
Author(s):  
Hongyu Sun ◽  
Laurent Demanet
Keyword(s):  
Neural Network ◽  
Numerical Experiments ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Full Waveform Inversion ◽  
Bandwidth Extension ◽  
Low Frequencies ◽  
Full Waveform ◽  
The Neural Network ◽  
Band Limited

The lack of low-frequency information and a good initial model can seriously affect the success of full-waveform inversion (FWI), due to the inherent cycle skipping problem. Computational low-frequency extrapolation is in principle the most direct way to address this issue. By considering bandwidth extension as a regression problem in machine learning, we have adopted an architecture of convolutional neural network (CNN) to automatically extrapolate the missing low frequencies. The band-limited recordings are the inputs of the CNN, and, in our numerical experiments, a neural network trained from enough samples can predict a reasonable approximation to the seismograms in the unobserved low-frequency band, in phase and in amplitude. The numerical experiments considered are set up on simulated P-wave data. In extrapolated FWI (EFWI), the low-wavenumber components of the model are determined from the extrapolated low frequencies, before proceeding with a frequency sweep of the band-limited data. The introduced deep-learning method of low-frequency extrapolation shows adequate generalizability for the initialization step of EFWI. Numerical examples show that the neural network trained on several submodels of the Marmousi model is able to predict the low frequencies for the BP 2004 benchmark model. Additionally, the neural network can robustly process seismic data with uncertainties due to the existence of random noise, a poorly known source wavelet, and a different finite-difference scheme in the forward modeling operator. Finally, this approach is not subject to strong assumptions on signals or velocity models of other methods for bandwidth extension and seems to offer a tantalizing solution to the problem of properly initializing FWI.

A Study on the Fine Structure of the Lateral Line Organ of the Sea Eel

1973 ◽  
Vol 31 ◽  
pp. 648-649
Author(s):  
K. Hama
Keyword(s):  
Fine Structure ◽  
Electron Microscope ◽  
Lateral Line ◽  
Low Frequency ◽  
Sensory Cells ◽  
Apical Surface ◽  
Voltage Electron ◽  
Sensory Epithelia ◽  
Low Frequency Vibration ◽  
Transmission Electron

The lateral line organs of the sea eel consist of canal and pit organs which are different in function. The former is a low frequency vibration detector whereas the latter functions as an ion receptor as well as a mechano receptor.The fine structure of the sensory epithelia of both organs were studied by means of ordinary transmission electron microscope, high voltage electron microscope and of surface scanning electron microscope.The sensory cells of the canal organ are polarized in front-caudal direction and those of the pit organ are polarized in dorso-ventral direction. The sensory epithelia of both organs have thinner surface coats compared to the surrounding ordinary epithelial cells, which have very thick fuzzy coatings on the apical surface.

Lectin Binding to Human Breast Tumor Cells

1976 ◽  
Vol 34 ◽  
pp. 34-35
Author(s):  
Robert E. Nordquist ◽  
J. Hill Anglin ◽  
Michael P. Lerner
Keyword(s):  
Carcinoma Cell ◽  
Ricinus Communis ◽  
Lectin Binding ◽  
Low Frequency ◽  
Breast Carcinoma Cell Line ◽  
Human Breast ◽  
Human Breast Tumor ◽  
Duct Carcinoma ◽  
Specific Antigens ◽  
Ricin Agglutinin

A human breast carcinoma cell line (BOT-2) was derived from an infiltrating duct carcinoma (1). These cells were shown to have antigens that selectively bound antibodies from breast cancer patient sera (2). Furthermore, these tumor specific antigens could be removed from the living cells by low frequency sonication and have been partially characterized (3). These proteins have been shown to be around 100,000 MW and contain approximately 6% hexose and hexosamines. However, only the hexosamines appear to be available for lectin binding. This study was designed to use Concanavalin A (Con A) and Ricinus Communis (Ricin) agglutinin for the topagraphical localization of D-mannopyranosyl or glucopyranosyl and D-galactopyranosyl or DN- acetyl glactopyranosyl configurations on BOT-2 cell surfaces.

Practical High Resolution Microscopy

1968 ◽  
Vol 26 ◽  
pp. 302-303
Author(s):  
P. A. Marsh ◽  
T. Mullens ◽  
D. Price
Keyword(s):  
High Resolution ◽  
Magnetic Fields ◽  
Low Frequency ◽  
Resolving Power ◽  
Careful Attention ◽  
Electron Microscopes ◽  
The Relationship ◽  
High Resolution Microscopy ◽  
New Facilities

It is possible to exceed the guaranteed resolution on most electron microscopes by careful attention to microscope parameters essential for high resolution work. While our experience is related to a Philips EM-200, we hope that some of these comments will apply to all electron microscopes.The first considerations are vibration and magnetic fields. These are usually measured at the pre-installation survey and must be within specifications. It has been our experience, however, that these factors can be greatly influenced by the new facilities and therefore must be rechecked after the installation is completed. The relationship between the resolving power of an EM-200 and the maximum tolerable low frequency interference fields in milli-Oerstedt is 10 Å - 1.9, 8 Å - 1.4, 6 Å - 0.8.

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