Optimum seismic velocity estimators

Geophysics ◽  
1984 ◽  
Vol 49 (11) ◽  
pp. 1861-1868 ◽  
Author(s):  
R. L. Kirlin ◽  
Lois A. Dewey ◽  
Jonathan N. Bradley
Keyword(s):  
Seismic Velocity ◽  
A Priori ◽  
Mean Square ◽  
Least Mean Square ◽  
A Priori Information ◽  
Likelihood Estimator ◽  
Velocity Variance ◽  
Velocity Information ◽  
Priori Information ◽  
Test Use

Six “optimum” estimators for the root‐mean‐square (rms) seismic velocity are given and analyzed by simulation for rms error. Two of the estimators are used to test use of a priori velocity information in a Kalman‐type improvement on the time measurements. Parameters varied include center‐point depth (time), a priori velocity variance, and interdelay‐estimate correlation. The maximum likelihood estimator is shown to be best when a priori information is relatively good, but a least‐mean‐square estimator is equally good otherwise.

scholarly journals MS Lesion Segmentation based on Hidden Markov Chains

2008 ◽  
Author(s):  
Stephanie Bricq ◽  
Christophe Collet ◽  
Jean-Paul Armspach
Keyword(s):  
Hidden Markov ◽  
A Priori ◽  
Hidden Markov Chain ◽  
A Priori Information ◽  
Lesion Segmentation ◽  
Mr Images ◽  
Likelihood Estimator ◽  
Probabilistic Atlas ◽  
Brain Mr Images ◽  
Priori Information

In this paper, we present a new automatic robust algorithm to segment multimodal brain MR images with Multiple Sclerosis (MS) lesions. The method performs tissue classification using a Hidden Markov Chain (HMC) model and detects MS lesions as outliers to the model. For this aim, we use the Trimmed Likelihood Estimator (TLE) to extract outliers. Furthermore, neighborhood information is included using the HMC model and we propose to incorporate a priori information brought by a probabilistic atlas.

Resolution Enhancement of Composite Spectra with Fractal Noise in Derivative Spectrometry

2000 ◽  
Vol 54 (5) ◽  
pp. 721-730 ◽  
Author(s):  
S. S. Kharintsev ◽  
D. I. Kamalova ◽  
M. Kh. Salakhov
Keyword(s):  
A Priori ◽  
Resolution Enhancement ◽  
Numerical Differentiation ◽  
A Priori Information ◽  
Experimental Ir ◽  
Composite Spectra ◽  
Self Similar ◽  
Priori Information ◽  
Fractal Noise ◽  
Derivative Spectrometry

The problem of improving the resolution of composite spectra with statistically self-similar (fractal) noise is considered within the framework of derivative spectrometry. An algorithm of the numerical differentiation of an arbitrary (including fractional) order of spectra is produced by the statistical regularization method taking into account a priori information on statistical properties of the fractal noise. Fractal noise is analyzed in terms of the statistical Hurst method. The efficiency and expedience of this algorithm are exemplified by treating simulated and experimental IR spectra.

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