<p>Induced seismicity from anthropogenic sources can be a significant nuisance to a local<br /> population and, in extreme cases, can lead to damage to vulnerable structures. One<br /> type of induced seismicity that is of particular recent concern, and which in some cases<br /> is a limit on development of a potentially-important clean energy source, is that<br /> associated with geothermal power production. A key requirement for the accurate<br /> assessment of seismic hazard (and eventually risk) is a ground-motion prediction<br /> equation (GMPE), predicting the level of earthquake shaking (in terms of, for example,<br /> peak ground acceleration) given an earthquake of a certain magnitude at a particular<br /> distance. For geothermal-related seismicity, practically no such models currently exist<br /> and consequently the evaluation of seismic hazard in the vicinity of geothermal power<br /> plants is associated with high uncertainty.<br /> Various ground-motion datasets (from Basel, Geysers, Hengill, Roswinkel, Soultz and<br /> Voerendaal) were compiled and processed and the moment magnitudes for all events<br /> recomputed homogeneously. These data are used to statistically test the similarity<br /> between ground motions from induced and natural earthquakes. In addition, the ability<br /> to extrapolate recent GMPEs derived for natural seismicity to make them applicable for<br /> ground-motion prediction close to the source of (generally small) induced shocks is<br /> examined. Subsequently stochastic models are developed based on a single corner<br /> frequency and with parameters constrained by the available data. Predicted ground<br /> motions from these models are fitted with functional forms to obtain easy-to-use<br /> GMPEs that account for epistemic uncertainties as well as being associated with<br /> standard deviations characterizing the aleatory variability. As an example, we<br /> demonstrate the potential use of these models using data from Campi Flegrei. In<br /> addition, we study correlations between instrumental ground-motion parameters and<br /> macroseismic intensities and recommend that intensities are predicted directly rather<br /> than through such correlations, which underpredict observed intensities of induced<br /> events.</p>