The reliability and accuracy of the ground‐motion prediction equations (GMPEs) are of prime interest while evaluating seismic hazard for any region. The regular updates and minimization of the uncertainties associated with the coefficients of the GMPEs are important for improving ground‐motion predictions and consequent performance of seismic hazard maps.
Thus, in the present study, we propose an update of the GMPEs estimated by Sharma et al. (2013) in The Geysers geothermal area. The update is done using the huge dataset available and by extending the magnitude range as well as distance range. The previous dataset used by Sharma et al. (2013) was composed of 212 earthquakes recorded at 29 stations with the magnitude range between and distance range between . The new dataset encloses 10,974 induced earthquakes recorded at 29 stations with the magnitude range between and distance range between . We compute updated GMPEs for peak ground velocity (PGV), peak ground acceleration (PGA), and 5% damped spectral acceleration (SA) () at 0.05, 0.1, 0.2, 0.5, and 1.0 s.
The mean ground‐motion predictions of the updated model proposed in the present study and the associated uncertainties are compared with the previous model proposed by Sharma et al. (2013) and with other models specifically developed for small‐magnitude earthquakes. The GMPEs are derived using a nonlinear mixed‐effect regression technique that accounts for both interevent and intraevent dependencies in the data. We also demonstrate the dependency of aleatory (random) uncertainties and epistemic (informative) uncertainties on source, medium, and site properties. We also concluded that the medium is behaving homogeneously in terms of peak ground‐motion attenuation by analyzing uncertainties associated with different ground‐motion periods.