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.
Monitoring and understanding induced seismicity is critical in order to estimate and mitigate seismic risk related to numerous existing and emerging techniques for natural resource exploitation in the shallow-crust. State of the art approaches for guiding decision making, such as traffic light systems, rely heavily on data such as earthquake location and magnitude that are provided to them. In this context we document the monitoring of a deep geothermal energy project in St Gallen, Switzerland. We focus on the issues of earthquake magnitude, ground motion and macroseismic intensity which are important components of the seismic hazard associated to the project. We highlight the problems with attenuation corrections for magnitude estimation and site amplification that were observed when trying to apply practices used for monitoring regional seismicity to a small-scale monitoring network. Relying on the almost constant source-station distance for events in the geothermal 'seismic cloud' we developed a simple procedure, calibrated using several M L > 1.3 events, which allowed the unbiased calculation of M L using only stations of the local monitoring network. The approach determines station specific M L correction terms that account for both the bias of the attenuation correction in the near field and amplification at the site. Since the smallest events (M L < −1) were only observed on a single borehole instrument, a simple relation between the amplitude at the central borehole station of the monitoring network and M L was found. When compared against magnitudes computed over the whole network this single station approach was shown to provide robust estimates (±0.17 units) for the events down to M L = −1. The relation could then be used to estimate the magnitude of even smaller events (M L < −1) only recorded on the central borehole station. Using data from almost 2700 events in Switzerland, we then recalibrated the attenuation correction, extending its range of validity from a minimum source-station distance of 20 km down to 1 km. Based on this we could determine the component of the previously derived station specific M L corrections due to local amplification. We analysed ground-motion and detailed macroseismic reports resulting from the 2013 July 20 St Gallen M L = 3.5 ± 0.1 (M w = 3.3-3.5 ± 0.1) 'main shock' and compared it to a similar M L = 3.4 ± 0.1 event (M w = 3.2 ± 0.1) that occurred in 2006 at another deep geothermal project in Basel, Switzerland. Differences in ground motion amplitudes between the Basel and St Gallen events and to an extent, the associated macroseismic observations, were investigated in terms of the different source terms: M w for long-period motions and the source-corner frequency (related to the source rupture velocity and stress-drop) for short periods.
The potential for building damage and personal injury due to induced earthquakes in the Groningen gas field is being modeled in order to inform risk management decisions. To facilitate the quantitative estimation of the induced seismic hazard and risk, a ground motion prediction model has been developed for response spectral accelerations and duration due to these earthquakes that originate within the reservoir at 3 km depth. The model is consistent with the motions recorded from small-magnitude events and captures the epistemic uncertainty associated with extrapolation to larger magnitudes. In order to reflect the conditions in the field, the model first predicts accelerations at a rock horizon some 800 m below the surface and then convolves these motions with frequency-dependent nonlinear amplification factors assigned to zones across the study area. The variability of the ground motions is modeled in all of its constituent parts at the rock and surface levels.
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