Predicting ground motion from induced earthquakes in geothermal areas

John Douglas, Benjamin Edwards, Vincenzo Convertito, Nitin Sharma, Anna Tramelli, Dirk Kraaijpoel, Banu Mena Cabrera, Nils Maercklin, Claudia Troise

Research output: Contribution to journalArticlepeer-review

63 Citations (Scopus)


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 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 regard 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, although they have similar characteristics to recent GMPEs for natural and miningrelated seismicity, the standard deviations are higher. To account for epistemic uncertainties, stochastic models subsequently 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. Online Material: Sets of coefficients and standard deviations for various groundmotion models.

Original languageEnglish
Pages (from-to)1875-1897
Number of pages23
JournalBulletin of the Seismological Society of America
Issue number3
Publication statusPublished - 1 Jun 2013


  • ground‐motion dataset
  • geothermal power plant
  • geothermal power production
  • seismic hazards


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