Predicting Ground Motion from Induced Earthquakes in Geothermal Areas

Induced seismicity from anthropogenic sources can be a significant nuisance to a local population and in extreme cases lead to damage to vulnerable structures. One type of induced seismicity of particular recent concern, which, in some
cases, can limit development of a potentially important clean energy source, is that
associated with geothermal power production. A key requirement for the accurate
assessment of seismic hazard (and potential eventual risk) is a ground-motion prediction equation (GMPE) that predicts the level of earthquake shaking (in terms of, for
example, peak ground acceleration) of an earthquake of a certain magnitude at a particular distance. Few such models currently exist in regards to geothermal-related seismicity and consequently the evaluation of seismic hazard in the vicinity of geothermal
power plants is associated with high uncertainty.
Various ground-motion datasets of induced and natural seismicity (from Basel,
Geysers, Hengill, Roswinkel, Soultz, and Voerendaal) were compiled and processed,
and moment magnitudes for all events were recomputed homogeneously. These data
are used to show that ground motions from induced and natural earthquakes cannot be
statistically distinguished. Empirical GMPEs are derived from these data and it is
shown that although they have similar characteristics to other recent GMPEs for natural and mining-related seismicity, the standard deviations are higher. Subsequently
stochastic models to account for epistemic uncertainties are developed based on a
single corner frequency and with parameters constrained by the available data. Predicted ground motions from these models are fitted with functional forms to obtain
easy-to-use GMPEs. These are associated with standard deviations derived from the
empirical data to characterize aleatory variability. As an example, we demonstrate the
potential use of these models using data from Campi Flegrei.

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