Applying the General Regression Neural Network to Ground Motion Prediction Equations of Induced Events in the Legnica-Głogów Copper District in Poland

This paper presents a study of the nonlinear estimation of the ground motion prediction equation (GMPE) using neural networks. The general regression neural network (GRNN) was chosen for its high learning rate. A separate GRNN was tested as well as a GRNN in cascade connection with linear regression (LR). Measurements of induced seis-micity in the Legnica-Głogów Copper District were used in this study. Various sets of input variables were tested. The basic variables used in every case were seismic energy and epicentral distance, while the additional variables were the location of the epicenter, the location of the seismic station, and the direction towards the epicenter. The GRNN improves the GMPE. The best results were obtained when the epicenter location was used as an additional input. The GRNN model was analysed for how it can improve the GMPE with respect to LR. The bootstrap resampling method was used for this purpose. It proved the statistical significance of the improvement of the GMPE. Additionally, this method allows the determination of smoothness parameters for the GRNN. Parameters derived through this method have better generalisation capabilities than the smoothness parameters estimated using the holdout method.