Maximum likelihood (ML) estimation is used to extract seafloor roughness parameters from records of acoustic backscatter. The method relies on the Helmholtz–Kirchhoff approximation under the assumption of a power‐law roughness spectrum and on the statistical modeling of bottom reverberation. The result is a globally optimum, highly automated technique that is a useful tool in the context of seafloor classification via remote acoustic sensing. The general geometry of the Sea Beam bathymetric system is incorporated into the design of the ML processor in order to make it applicable to real acoustic data collected by this system. The processor is initially tested on simulated backscatter data and is shown to be very effective in estimating the seafloor parameters of interest. The simulated data are also used to study the effect of data averaging and normalization in the absence of system calibration information. The same estimation procedure is applied to real data collected over two central North Pacific seamounts, Horizon Guyot and Magellan Rise. The Horizon Guyot results are very close to estimates obtained through a curve‐fitting procedure presented by de Moustier and Alexandrou [J. Acoust. Soc. Am. 90, 522–531 (1991)]. In the case of Magellan Rise, discrepancies are observed between the results of ML estimation and curve fitting.


Center for Coastal and Ocean Mapping

Publication Date


Journal or Conference Title

Journal of the Acoustical Society of America


95, Issue 5



Publisher Place

Melville, NY, USA


Acoustical Society of America

Digital Object Identifier (DOI)


Document Type

Journal Article


© 1994 Acoustical Society of America