The objective of this work is to develop a surface modeling system capable of characterizing large gridded bathymetric databases with potential applications to problems such as the extrapolation of survey data to higher resolution, the interpolation of bottom characteristics between surveyed regions, and the correlation of bottom features with other geophysical measurements. The system employs a two-dimensional stochastic seafloor model to represent blocks of gridded bathymetry by a small number of model parameters that describe the physical features of the ocean bottom. This study focuses on the inversion component which is designed to estimate the model parameters quickly without iteration or starting values. Quality high-speed inversions are obtained using machine learning techniques, overcoming many practical limitations imposed by conventional least squares techniques. This approach leads to the approximation of a direct mapping between the moments of the Fourier transformed data and the model parameter variables. Mapping is accomplished by the universal approximation capabilities of multilayer networks. Validation tests performed on synthetic examples spanning the entire model parameter space yield parameter estimation errors less than 5%. The modeling system is applied to a large northeast Pacific data set to examine correlations between bottom characteristics and sediment thickness. Errors in model parameters estimated from the bathymetric data by Monte Carlo simulation are generally less than 10% dependent on data quality and conformance to model assumptions. The major advantages offered by this approach are clearly demonstrated by its error tolerance and the speed of inversion processing. ¿ American Geophysical Union 1996