Predicting ground motion from induced earthquakes in geothermal areas
| Autor: | Vincenzo Convertito, Banu Mena Cabrera, Benjamin Edwards, Dirk Kraaijpoel, Nitin Sharma, N. Maercklin, Claudia Troise, Anna Tramelli, John Douglas |
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| Přispěvatelé: | Bureau de Recherches Géologiques et Minières (BRGM) (BRGM), Institute of Geophysics [ETH Zürich], Department of Earth Sciences [Swiss Federal Institute of Technology - ETH Zürich] (D-ERDW), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich)- Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Osservatorio Vesuviano, Istituto Nazionale di Geofisica e Vulcanologia - Sezione di Napoli (INGV), Istituto Nazionale di Geofisica e Vulcanologia-Istituto Nazionale di Geofisica e Vulcanologia, Dipartimentio di Scienze Fisiche, Università degli studi di Napoli Federico II, Seismology Division, Koninklijk Nederlands Meteorologisch Instituut, Department of Physics, Università degli studi di Napoli Federico II-RISSC-Lab, GEISER, European Project: 241321,EC:FP7:ENERGY,FP7-ENERGY-2009-1,GEISER(2010), University of Naples Federico II = Università degli studi di Napoli Federico II, RISSC-Lab-University of Naples Federico II = Università degli studi di Napoli Federico II |
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
Engineering
Peak ground acceleration Geothermal power 010504 meteorology & atmospheric sciences Stochastic modelling [SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph] [SDE.MCG]Environmental Sciences/Global Changes Magnitude (mathematics) [PHYS.PHYS.PHYS-GEO-PH]Physics [physics]/Physics [physics]/Geophysics [physics.geo-ph] Induced seismicity 010502 geochemistry & geophysics seismic hazard analysis 01 natural sciences Standard deviation Physics::Geophysics geothermal power production Geochemistry and Petrology Geothermal gradient induced seismicity 0105 earth and related environmental sciences enhanced geothermal systems business.industry earthquake ground motions Geophysics Seismic hazard ground-motion prediction equations 13. Climate action ground-motion models business Seismology |
| Zdroj: | Bulletin of the Seismological Society of America Bulletin of the Seismological Society of America, Seismological Society of America, 2013, 103 (3), pp.1875-1897. ⟨10.1785/0120120197⟩ Bulletin of the Seismological Society of America, 2013, 103 (3), pp.1875-1897. ⟨10.1785/0120120197⟩ |
| ISSN: | 0037-1106 |
| DOI: | 10.1785/0120120197⟩ |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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