Maximum entropy analysis of dispersed seismic signals

Geophysics ◽  
1986 ◽  
Vol 51 (12) ◽  
pp. 2225-2234 ◽  
Author(s):  
K. B. Cox ◽  
I. M. Mason
Keyword(s):  
Maximum Entropy ◽  
Fourier Transforms ◽  
Signal To Noise Ratio ◽  
Characteristic Parameter ◽  
Moving Window ◽  
Power Spectral ◽  
Maximum Entropy Spectrum ◽  
Waveguide System ◽  
Nyquist Rate ◽  
Spectral Estimator

Conventional moving‐window analyzers based on Fourier transforms sometimes lack the resolution required to separate each of the modes in a seismic waveguide. It is possible to enhance the resolution of a moving‐window analyzer by using a maximum entropy power spectral estimator to approximate the spectrum of each windowed segment of a trace. Barrodale and Erickson have developed a suitable maximum entropy algorithm which can also be applied to estimating the parameters required in the fast recompression of inseam seismic arrivals. The Barrodale‐Erickson maximum entropy algorithm appears to need a sample rate of approximately ten times the Nyquist rate in order to generate meaningful maximum entropy spectra. The required increase can be achieved in the laboratory by applying an accurate interpolator to field records. Noise captures the maximum entropy spectrum if the input signal‐to‐noise ratio falls much below 10 dB. Use of a maximum entropy spectral analyzer aids in both identifying modes in a waveguide system and estimating the group velocity‐frequency characteristic parameter.

Enhancement of Radar Imagery by Maximum Entropy Processing

1977 ◽  
Vol 21 (3) ◽  
pp. 241-243 ◽  
Author(s):  
Clanton E. Mancill
Keyword(s):  
Maximum Entropy ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Doppler Frequency ◽  
Frequency Measurement ◽  
Entropy Method ◽  
Frequency Resolution ◽  
Sampled Data ◽  
Maximum Entropy Spectrum ◽  
Using Data

The maximum entropy spectrum (MES), a sampled data power spectrum estimator, is applied to the enhancement of imagery obtained by synthetic array radar (SAR) imaging systems. MES offers better frequency resolution than conventional Fourier transform methods for certain signal classes. Since azimuth ground resolution in SAR systems is obtained by doppler frequency measurement of the radar return, the method is capable of enhancing the resolution of SAR maps. The principal signal requirement is adequate signal-to-noise ratio. The maximum entropy method has been tested using data obtained by the Hughes FLAMR radar system. The super-resolution capabilities of the method are demonstrated using FLAMR images of corner reflector arrays.

Properties of the Evolutionary Maximum Entropy Spectral Estimator

2005 ◽  
Author(s):  
S.I. Shah ◽  
L.F. Chaparro ◽  
A. El-Jaroudi
Keyword(s):  
Maximum Entropy ◽  
Spectral Estimator

Twenty-Four-Hour Power Spectral Analysis by Maximum Entropy Method of Blood Pressure in Primary Hyperaldosteronism

1993 ◽  
Vol 2 (3) ◽  
pp. 189-196 ◽  
Author(s):  
Franco Veglio ◽  
Giuliano Pinna ◽  
Remo Melchio ◽  
Franco Rabbia ◽  
Paola Molino ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Spectral Analysis ◽  
Maximum Entropy ◽  
Maximum Entropy Method ◽  
Power Spectral Analysis ◽  
Entropy Method ◽  
Primary Hyperaldosteronism ◽  
Power Spectral

Autocorrelation estimation using constrained iterative spectral deconvolution

Geophysics ◽  
1989 ◽  
Vol 54 (3) ◽  
pp. 381-391 ◽  
Author(s):  
Murali Ramaswamy ◽  
George E. Ioup
Keyword(s):  
Power Spectrum ◽  
Iterative Method ◽  
Maximum Entropy ◽  
Spectral Resolution ◽  
Spectral Estimate ◽  
Data Sequence ◽  
Spectral Deconvolution ◽  
Power Spectral ◽  
Nonnegativity Constraints ◽  
Entropy Technique

Computing an autocorrelation conventionally produces a biased estimate, especially for a short data sequence. Windowing the autocorrelation can remove the bias but at the expense of violating the nonnegativity of the corresponding power spectrum. Constrained iterative deconvolution provides a basis for improving an autocorrelation estimate by reducing the bias while guaranteeing nonnegative definiteness. The length of the autocorrelation is increased in order to satisfy the nonnegativity constraints on the power spectral estimate. The constraints can also have significant effects on small, poorly determined values of the autocorrelation. The technique is applied to synthetic and real examples to show the improvements in the autocorrelation and power spectrum which are possible. The method is reasonably stable in the presence of noise and it approximately preserves the area of the power spectrum. Comparison to the maximum entropy technique shows that the iterative method gives power spectral resolution which is sometimes better and sometimes not as good, but that there are cases for which it is the more desirable approach.

scholarly journals Ultrasonic Signal Reconstruction Using Compressed Sensing

2016 ◽  
Vol 855 ◽  
pp. 165-170
Author(s):  
Ren Jean Liou
Keyword(s):  
Compressed Sensing ◽  
Spline Interpolation ◽  
Signal To Noise Ratio ◽  
Signal Reconstruction ◽  
Sampling Theorem ◽  
Ultrasonic Signal ◽  
Ultrasonic Signals ◽  
Data Rates ◽  
Nyquist Rate ◽  
Simulation Results

Ultrasonic signal reconstruction for Structural Health Monitoring is a topic that has been discussed extensively. In this paper, we will apply the techniques of compressed sensing to reconstruct ultrasonic signals that are seriously damaged. To reconstruct the data, the application of conventional interpolation techniques is restricted under the criteria of Nyquist sampling theorem. The newly developed technique - compressed sensing breaks the limitations of Nyquist rate and provides effective results based upon sparse signal reconstruction. Sparse representation is constructed using Fourier transform basis. An l1-norm optimization is then applied for reconstruction. Signals with temperature characteristics were synthetically created. We seriously corrupted these signals and tested the efficacy of our approach under two different scenarios. Firstly, the signal is randomly sampled at very low rates. Secondly, selected intervals were completely blank out. Simulation results show that the signals are effectively reconstructed. It outperforms conventional Spline interpolation in signal-to-noise ratio (SNR) with low variation, especially under very low data rates. This research demonstrates very promising results of using compressed sensing for ultrasonic signal reconstruction.

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