Abstract
Todoeschuck and Jensen [1], [2] recently reported that some reflectivity sequences μ(k) calculated from sonic logs are not white and have a power spectral density approximately proportional to frequency, called a Joseph spectrum. The well-known MLD algorithms [7]–[13] can simultaneously provide estimates of μ(k), source wavelet which need not be minimum-phase, and statistical parameters. Although these MLD algorithms work well, they are based on the white Bernoulli-Gaussian (B-G) model for μ(k). In this paper, assuming that spectrum measurements of μ(k) are available, we propose a ML algorithm for blind deconvolution as μ(k) is nonwhite with a general spectrum meanwhile the spectrum of the obtained maximum-likelihood estimate μML(k) is consistent with the measured spectrum. © 1991 IEEE