Prediction of Ground Motion Intensity Measures Using an Artificial Neural Network

The present study aims at developing a prediction model for ground motion intensity measures using the artificial neural network (ANN) technique for active shallow crustal earthquakes in India. The database for the study consists of 659 ground motion records collected from 138 earthquakes recorded by various seismic networks in the study region. Owing to the lack of near-field data, we have added 116 records from seven earthquakes over a distance 6 from the NGA database. The developed model predicts 21 ground motion parameters (GMPs) in both horizontal and vertical directions, with input predictor variables of magnitude ( M ), hypocentral distance ( R ), site condition ( S ), and flag for the region ( f ). A multi-layer perceptron (MLP), with a total of 276 unknowns, constitutes the architecture of the model. The residuals associated with the GMPs are analyzed in detail to aid in hazard calculations. In addition, a comparison of the developed model with global relations is performed. Further, the model is demonstrated by performing seismic hazard analysis for GMPs for 2% and 10% probability of exceedance in 50 years. The ANN model is a first version and has to be improved as more strong motion data becomes available for the region. The developed ground motion model must be combined along with other global models in seismic hazard analysis.