This paper addresses a problem commonly encountered in the interpretation of Ground Penetrating Radar (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.

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 Soil/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 classification system is highly efficient for coding large transects of GPR data in the field. The coded results can easily be displayed graphically in 2D or 3D formats which are readily comprehensible to persons without specialist GPR technical knowledge.

The coding system is also suitable for training artificial neural networks.