Apparatus for Mode Extraction Using Multiple Frequencies

Patent metadata

Field	Value
Publication number	US-2016209538-A1
Application number	US-201414908530-A
Country	US
Kind code	A1
Filing date	Aug 5, 2014
Priority date	Aug 5, 2013
Publication date	Jul 21, 2016
Grant date	—

Abstract

An apparatus is provided for extracting slowness dispersion characteristics of sonic wave forms in broadband acoustic waves received by multiple sensors including means to digitize the sonic wave forms to form discrete time wave forms and converting the discrete time wave forms into frequency domain wave forms and means to divide a processing band of the wave forms into frequency sub-bands. For each sub-band approximating a family of candidate dispersion curves for multiple modes, parameterizing each of the curves by phase and group slowness, and forming a frequency dependent over-complete dictionary of basis elements, each corresponding to a pair of phase and group slownesses. In addition, forming multiple measurement vectors from the frequency domain data and implementing a sparse Bayesian learning (SBL) algorithm on the vectors with a block sparse signal model and outputting the results. Also, a means for generating a fmal dispersion curve.

First claim

What is claimed is:

1 . An apparatus for extracting slowness dispersion characteristics of one or more sonic wave forms in broadband acoustic waves received by an array of two or more sensors comprising:
means for digitizing the sonic wave forms to form discrete time wave forms and converting the discrete time wave forms into frequency domain wave forms; means for dividing a processing band of the frequency domain wave forms into frequency sub-bands and for each sub-band;
approximating a family of candidate dispersion curves for multiple modes;
parameterizing each of the family of approximated candidate dispersion curves by phase and group slowness;
forming a frequency dependent over-complete dictionary of basis elements, each corresponding to a pair of phase and group slownesses, and spanning a range of values thereof;
forming multiple measurement vectors from the frequency domain data;
implementing a sparse Bayesian learning (SBL) algorithm on the multiple measurement vectors with a block sparse signal model by:
estimating a mode spectrum amplitude with a Bayesian approach;
estimating a mode spectrum variance, noise variance and other model parameters with a maximum likelihood estimation;
optionally utilizing spectrum correlation patterns of multiple modes;
pruning mode candidates by comparing the estimated mode parameters with a threshold;
iterating between the Bayesian approach and the maximum likelihood estimation until convergence according to a criterion;

outputting the phase and group slowness and the mode spectrum amplitude for the given sub-band; and
means for generating a final dispersion curve over the processing frequency band.

2 . The apparatus according to claim 1 , wherein;
the Bayesian minimum mean square estimation and/or the Bayesian maximum a posteriori estimation is implemented during the broadband SBL method.

3 . The apparatus according to claim 1 , wherein;
the maximum marginal likelihood estimation or the Type-II maximum likelihood estimation is implemented during the broadband SBL method.

4 . The apparatus according to claim 1 , wherein;
the broadband SBL method uses a fixed-point (FP) algorithm.

5 . The apparatus according to claim 1 , wherein;
the broadband SBL uses an Expectation-Maximization (EM) algorithm.

6 . The apparatus according to claim 1 , wherein;
the utilization of spectrum correlation in the SBL is achieved by a means of joint Gaussian distributions and/or conditional stochastic distributions.

7 . The apparatus according to claim 6 , wherein;
the conditional stochastic distributions include a conditional uniform distribution and a truncated Gaussian distribution.

8 . The apparatus according to claim 1 , wherein:
the broadband SBL uses a global workflow and a peak estimating function applied over each frequency band.

9 . The apparatus according to claim 1 , wherein:
the broadband SBL uses a local workflow and a peak estimating function applied over a whole frequency range.

10 . The apparatus according to claim 1 , wherein:
the broadband SBL uses a correlation matrix structure for the parameter estimation.

11 . The apparatus according to claim 10 , wherein:
the correlation matrix structure includes a Hermittian, Toeplitz, persymmetric, band-limited structures.

12 . The apparatus according to claim 1 , wherein:
the pruning process is implemented by thresholding out the modes with estimated variances and/or amplitudes of less than a certain value.

Assignees

• Schlumberger Technology Corp

Inventors

• Wang Pu
• Bose Sandip

Classifications

• G01V1/48 Primary

  Processing data · CPC title