Pattern recognition of geophysical data

A new rock classification method for ground penetrating radar (GPR) data is presented for cases where no additional geological information is available from boreholes. There are non-linear relationships between petrophysical properties of rocks and electromagnetic waves which can be handled using two methods derived from statistical learning theory on pattern recognition. An investigation was carried out looking at proving the feasibility of the method in principle for use on synthetic models as well as measurement data. The different learning methods were also compared. The method is based on multivariate statistical learning algorithms for the discrimination of layer boundaries between different rocks. The discrimination developed works with artificial neural networks (ANN) and support vector machines (SVM). The processing procedure starts with geological models with varying petrophysical rock parameters, which are to be sought in the measurement data. The models are used to generate synthetic radargrams from which rock properties can be derived using wave attributes. The calculated values of the wave attributes are stored in a multivariate data pool. This data pool is used to train the ANN and the SVM. The same wave attributes are derived from the GPR data and also saved in a data pool. This generates two data sets for pattern recognition with which to directly classify rock layers. Wave attributes can therefore be used to derive the non-linear correlative relationships between rock properties and GPR data by the weighted matrices of ANN and SVM. The presented method can be used to match reflections in the GRR data directly with the layer boundaries of rock formations. The classification of a boundary horizon between rock salt and anhydrite is demonstrated on synthetic GPR traces and measurement data from a rock salt mine. The advantage of this method is that rock classification is not a priori dependent on borehole data.