Predicting Ground Motion from Induced Earthquakes in Geothermal Areas

Abstract

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.

Development of a Response Spectral Ground‐Motion Prediction Equation (GMPE) for Seismic‐Hazard Analysis from Empirical Fourier Spectral and Duration Models

“…Moreover, in keeping with traditional response spectral GMPEs, the empirical FAS model of Bora et al (2014) also used only M w , R JB , and V S30 as the predictor variables. Importantly, the aleatory variability in the response spectral amplitudes obtained through our initial approach was also significantly larger than those of other GMPEs developed under the same project, with the exception of the model of Bindi et al (2014), as depicted in figure 9 of Douglas et al (2014).…”

“…Only the GMPEs of Akkar, Sandık-kaya, and Bommer (2014), Bindi et al (2014), andBora et al (2014) are considered for comparison, because they involve a parametric functional form in their regression analysis. Comparison among the median GMPEs was performed for the same scenarios of magnitude, distance, and V S30 , which have been used in Douglas et al (2014). It is worth noting that Douglas et al (2014) uses the comparison paper of Abrahamson et al (2008) as a template; hence a comparison between the figures presented here can also be made to those shown in Abrahamson et al (2008) and the recently published Next Generation Attenuation-West 2 (NGA-West 2) models of Gregor et al (2014).…”

“…Comparison among the median GMPEs was performed for the same scenarios of magnitude, distance, and V S30 , which have been used in Douglas et al (2014). It is worth noting that Douglas et al (2014) uses the comparison paper of Abrahamson et al (2008) as a template; hence a comparison between the figures presented here can also be made to those shown in Abrahamson et al (2008) and the recently published Next Generation Attenuation-West 2 (NGA-West 2) models of Gregor et al (2014). Moreover, the GMPE of is also used, which is expected to facilitate a comparison between our approach and NGA-West 2 models .…”

A Hybrid Empirical Green’s Function Technique for Predicting Ground Motion from Induced Seismicity: Application to the Basel Enhanced Geothermal System

“…It is noted that the within-event standard deviation of the GMPE was much larger than that the data exhibited. This was noted by the authors as a result of combining data from various datasets, with lower uncertainty evident if analysing data from specific regions [55]. The target for the simulations was to reproduce the ground-motion field of the M = 3.2 event (Figure 2).…”

“…Here we focussed on PGV, which is useful as it is a measure of ground motion on which Swiss norms for vibration disturbances are based. The GMPE of Douglas et al [55] is also shown in Figure 2. The GMPE performance is good at distances of interest for induced seismicity, (e.g., R < 30 km), with PGV values falling within the model's (large) standard deviation.…”