Classification system for GPR parameters

This paper addresses a problem commonly encountered in the processing and interpretation of GPR data for civil engineering and geotechnical applications. These projects typically involve large data sets composed of multivariate parameters, which need to be interpreted and coded in such a manner as to provide practical solutions to the site engineer. In this paper we propose a system for approaching this problem that has been field tested on many projects. This system uses two numerical descriptors for classifying complex geotechnical parameters derived from GPR field measurements. The first parameter, called GPR Class, is used to code the relative difficulty of excavation, or danger from instability. The second parameter, the GPR Rock Type Descriptor, is used to code the local soil or rock type and to describe the attributes of the material, such as its weathering condition or degree of cementation. The system is a highly efficient for coding large transects of GPR data. The coding system is also suitable for training artificial neural networks. Neural networks are being used with increasing frequency for high dimensional problems of regression or classification. These networks, combined with second-order statistical image descriptors, can then be combined to visualise the data in 3 dimensions in a format readily comprehensible to persons without specialist GPR technical knowledge.