Broadband Dispersion Extraction of Borehole Acoustic Modes via Sparse Bayesian Learning

This paper considers broadband extraction of multiple strong/weak borehole acoustic modes in acoustic array waveforms by processing the data from multiple frequency points. We first formulate it as basis selection in a multiple measurement vector (MMV) model with varying overcomplete dictionaries and then, propose a generalized sparse Bayesian learning (SBL) method for the application-specified MMV model. The SBL method results in an iterative, hyperparameterfree algorithm to estimate the mode spectrum and update prior parameters. Specifically, the iteration can be implemented in either the fixedpoint or expectation-maximization mechanism. Numerical validation with synthetic and field datasets confirms the effectiveness of the proposed method and its advantages over the narrowband (modified matrix pencil) approach.