Subsurface classification of high-resolution seismic data with multivariate statistical techniques: case study from the Strait of Georgia

Abstract

The use of multivariate statistics on the features of echo sounder returns has empirically and repeatedly been shown to be successful in classifying bottom types. The features are derived from the shape and the spectral character of the first echo. They, and other aspects of the data processing, have been optimized for sounder frequencies for which surface scattering dominates over that from volume inhomogeneities. Many engineering applications, such as dredging, the laying of submarine pipelines and cables, the siting of drill and production platforms, and the building of bridges and dams require knowledge of the subsurface. With the development of high-resolution digital seismic systems such as chirp sonars and the IKB SEISTEC/sup TM/ system in the last decade, mapping of the subsurface in shallow water has become cost-effective. This paper describes results from applying these statistical techniques to normal-incidence high-resolution seismic reflection data in which the surface scattering is far less significant, with the hope that these methods will be an adjunct to, and perhaps eventual replacement for, manual expert classification decisions.