Title :
Minimum-variance deconvolution and maximum-likelihood deconvolution for nonwhite Bernoulli-Gaussian processes with a Joseph spectrum
Author_Institution :
Dept. of Electr. Eng., Nat. Tsing Hua Univ., Hsinchu
fDate :
3/1/1992 12:00:00 AM
Abstract :
Todoeschuck and Jensen (1988) recently reported that the reflectivity sequences, denoted p(k), calculated from some sonic logs are not white and have a power spectral density approximately proportional to frequency, called a Joseph spectrum. It is shown here how to compute the minimum-variance estimate and maximum-likelihood estimate for a μ(k) modeled as a nonwhite Bernoulli-Gaussian (B-G) process with a Joseph spectrum. Also presented are the corresponding estimates for a statistically equivalent white B-G process μ*(k) which mimics μ(k). Some conclusions regarding the acceptability of these estimates are drawn
Keywords :
acoustic signal processing; random processes; spectral analysis; Joseph spectrum; maximum-likelihood deconvolution; minimum variance deconvolution; nonwhite Bernoulli-Gaussian processes; power spectral density; reflectivity sequences; sonic logs; white Bernoulli-Gaussian process; Biomedical acoustics; Biomedical measurements; Deconvolution; Frequency; Gaussian processes; Geology; Maximum likelihood estimation; Noise measurement; Seismic measurements; Signal processing algorithms;
Journal_Title :
Signal Processing, IEEE Transactions on