A data-driven artificial neural network model for the prediction of ground motion from induced seismicity: The case of The Geysers geothermal field - Prezioso, Edoardo; Sharma, Nitin; Piccialli, Francesco; Convertito, Vincenzo (Frontiers in Earth Science, 2022)
Ground-motion models have gained foremost attention during recent years for being capable of predicting ground-motion intensity levels for future seismic scenarios. They are a key element for estimating seismic hazard and always demand timely refinement in-order to improve the reliability of seismic hazard maps. In the present study we propose a ground motion prediction model for induced earthquakes recorded in The Geysers geothermal area. We use a fully connected data-driven Artificial Neural Network (ANN) model to fit ground motion parameters. Specifically, we used data from 212 earthquakes recorded at 29 stations of the Berkeley-Geysers network between September 2009 and November 2010. The magnitude range is 1.3 and 3.3 moment magnitude (Mw), whereas the hypocentral distances range is between 0.5 km and 20 km. The ground motions are predicted in terms of peak ground accelerations (PGA), peak ground velocities (PGV), and 5% damped spectral accelerations (SA) at T=0.2s, 0.5s and 1s. The predicted values from our deep learning model are compared with observed data and the predictions made by empirical ground motion prediction equations developed by Sharma et al. (2013) for the same data set by using the non-linear mixed-effect (NLME) regression technique. For the validation of the approach, we compared the models on a separate data made of 25 earthquakes in the same region with magnitudes ranging between 1.0 and 3.1 and hypocentral distances ranging between 1.2 and 15.5 km, with the ANN model providing a 3% improvement compared to the baseline GMM model. The results obtained in the present study show a moderate improvement in ground motion predictions and unravel modeling features that were not taken into account by the empirical model. The comparison is measured in terms of both the R2 statistic and the total standard deviation, together with inter-events and intra-event components.