ALBERT

Description: 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 mining-related 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 ground-motion models.

Description: The Geysers geothermal field in Northern California, which has been actively exploited since the 1960s, is the world’s largest geothermal field. The continuous injection of fluids and the consequent stress perturbations induce seismicity that is clearly felt in the surrounding communities. In order to evaluate seismic hazard due to induced seismicity and the effects of seismicity rate level on the population and buildings in the area, reliable ground-motion prediction equations (GMPEs) must be developed. This paper introduces the first GMPEs specific for The Geysers area in terms of peak ground velocity (PGV), peak ground acceleration (PGA), and 5% damped spectral acceleration SA( T ) at T =0.2 s, 0.5 s, and 1.0 s. The adopted non-linear mixed-effect regression technique to derive the GMPE includes both fixed and random effects, and it permits to account for both inter-event and intra-event dependencies in the data. Site-specific effects are also estimated from the data and are corrected in the final ground-motion model. We used data from earthquakes recorded at 29 stations of the Berkeley-Geysers network during the period September 2007 through November 2010. The magnitude range is 1.3≤ M w ≤3.3, whereas the hypocentral distances range between 0.5 km and 20 km. The comparison of our new GMPE for The Geysers with a standard model derived in a different tectonic context shows that our model is more robust when predictions have to be made for induced earthquakes in this geothermal area.

Description: The growing installation of industrial facilities for subsurface exploration worldwide requires continuous refinements in understanding both the mechanisms by which seismicity is induced by field operations and the related seismic hazard. Particularly in proximity of densely populated areas, induced low-to-moderate magnitude seismicity characterized by high-frequency content can be clearly felt by the surrounding inhabitants and, in some cases, may produce damage. In this respect we propose a technique for time-dependent probabilistic seismic-hazard analysis to be used in geothermal fields as a monitoring tool for the effects of on-going field operations. The technique integrates the observed features of the seismicity induced by fluid injection and extraction with a local ground-motion prediction equation. The result of the analysis is the time-evolving probability of exceedance of peak ground acceleration (PGA), which can be compared with selected critical values to manage field operations. To evaluate the reliability of the proposed technique, we applied it to data collected in The Geysers geothermal field in northern California between 1 September 2007 and 15 November 2010. We show that the period considered the seismic hazard at The Geysers was variable in time and space, which is a consequence of the field operations and the variation of both seismicity rate and b -value. We conclude that, for the exposure period taken into account (i.e., two months), as a conservative limit, PGA values corresponding to the lowest probability of exceedance (e.g., 30%) must not be exceeded to ensure safe field operations. We suggest testing the proposed technique at other geothermal areas or in regions where seismicity is induced, for example, by hydrocarbon exploitation or carbon dioxide storage.

Description: We investigate the possibility of inferring the dominant horizontal-rupture direction for moderate earthquakes from the inversion of peak ground-motion parameters. To this aim, we adopt a technique that was devised and applied to large earthquakes for retrieving both the dominant rupture direction and the surface fault projection to be used with a proper distance metric to refine the ShakeMap computation. In the present paper, the procedure was applied to three moderate earthquakes that occurred in 2012 in Northern Italy three days apart: the M  4.2 Pre-Alpi Venete earthquake on 24 January, the M  4.9 Reggio Emilia earthquake on 25 January, and the M  5.4 Parma earthquake on 27 January. For two of the three analyzed events, the technique identifies a dominant horizontal-rupture direction, which is consistent with the strike directions inferred from the focal mechanisms. For the M  5.4 event, which is a deep (about 61 km) thrust-faulting mechanism earthquake, the inferred dominant rupture direction allows identification of the northeast-dipping plane as the fault plane in accordance with the aftershocks distribution.