APPLICATION OF A MAXIMUM-LIKELIHOOD PROCESSOR TO ACOUSTIC BACKSCATTERFOR THE ESTIMATION OF SEA-FLOOR ROUGHNESS PARAMETERS

Maximum likelihood (ML) estimation is used to extract seafloor roughne ss parameters from records of acoustic backscatter. The method relies on the Helmholtz-Kirchhoff approximation under the assumption of a pow er-law roughness spectrum and on the statistical modeling of bottom re verberation. The result is a globally optimum, highly automated techni que that is a useful tool in the context of seafloor classification vi a remote acoustic sensing. The general geometry of the Sea Beam bathym etric system is incorporated into the design of the ML processor in or der to make it applicable to real acoustic data collected by this syst em. The processor is initially tested on simulated backscatter data an d is shown to be very effective in estimating the seafloor parameters of interest. The simulated data are also used to study the effect of d ata averaging and normalization in the absence of system calibration i nformation. The same estimation procedure is applied to real data coll ected over two central North Pacific seamounts, Horizon Guyot and Mage llan Rise. The Horizon Guyot results are very close to estimates obtai ned through a curve-fitting procedure presented by de Moustier and Ale xandrou [J. Acoust. Soc. Am. 90, 522-531 (1991)]. In the case of Magel lan Rise, discrepancies. are observed between the results of ML estima tion and curve fitting.