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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 seismicity 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.
Ground motion database from the region of Żelazny Most tailings pond, the largest in Europe ore-flotation waste repository, is used to identify ground motion prediction equations (GMPE-s) for peak horizontal and peak vertical acceleration. A GMPE model including both geometrical spreading and anelastic damping terms cannot be correctly identified and the model with only spreading term is accepted. The analysis of variance of this model’s residuals with station location as grouping variable indicates that station locations contribute significantly to the observed ground motion variability. Therefore, a site specific GMPE model with relative site amplifications is assessed. Despite short distances among stations, the amplification considerably vary from point to point, up to 1.8 times for the horizontal and 3.5 times for the vertical peak amplitude. The model including site effects enhances GMPE-s fit to observations, explains more than 60% dependent variables variability and correctly accounts for site effects.
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