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: 〈span〉〈div〉Abstract〈/div〉The reliability and accuracy of the ground‐motion prediction equations (GMPEs) are of prime interest while evaluating seismic hazard for any region. The regular updates and minimization of the uncertainties associated with the coefficients of the GMPEs are important for improving ground‐motion predictions and consequent performance of seismic hazard maps.Thus, in the present study, we propose an update of the GMPEs estimated by 〈a href="https://pubs.geoscienceworld.org/bssa#rf32"〉Sharma 〈span〉et al.〈/span〉 (2013)〈/a〉 in The Geysers geothermal area. The update is done using the huge dataset available and by extending the magnitude range as well as distance range. The previous dataset used by 〈a href="https://pubs.geoscienceworld.org/bssa#rf32"〉Sharma 〈span〉et al.〈/span〉 (2013)〈/a〉 was composed of 212 earthquakes recorded at 29 stations with the magnitude range between 1.3≤Mw≤3.3 and distance range between 0.6≤Rhypo≤20  km. The new dataset encloses 10,974 induced earthquakes recorded at 29 stations with the magnitude range between 0.7≤Mw≤3.3 and distance range between 0.1≤Rhypo≤73  km. We compute updated GMPEs for peak ground velocity (PGV), peak ground acceleration (PGA), and 5% damped spectral acceleration (SA) (T) at T 0.05, 0.1, 0.2, 0.5, and 1.0 s.The mean ground‐motion predictions of the updated model proposed in the present study and the associated uncertainties are compared with the previous model proposed by 〈a href="https://pubs.geoscienceworld.org/bssa#rf32"〉Sharma 〈span〉et al.〈/span〉 (2013)〈/a〉 and with other models specifically developed for small‐magnitude earthquakes. The GMPEs are derived using a nonlinear mixed‐effect regression technique that accounts for both interevent and intraevent dependencies in the data. We also demonstrate the dependency of aleatory (random) uncertainties and epistemic (informative) uncertainties on source, medium, and site properties. We also concluded that the medium is behaving homogeneously in terms of peak ground‐motion attenuation by analyzing uncertainties associated with different ground‐motion periods.〈/span〉

Description: The analysis of earthquake focal mechanisms provides information about the stress regime, fault geometry, and deformation processes acting in a given region. Generally, the techniques aimed at determining focal mechanism are designed to work in a specific magnitude range operating both in the time and frequency domain and using different data (e.g., P polarities, S -wave polarization, S / P -amplitude ratios, etc.). In this article, we present a new method, Bayesian inversion of spectral-level ratios and P -wave polarities (BISTROP), that can be applied to both small and moderate-to-large magnitude events. BISTROP uses a Bayesian approach to jointly invert the long-period spectral-level P / S ratios and the P polarities to infer the fault-plane solutions. We apply this method to analyze synthetic data as well as those generated by real earthquakes. We find that the obtained solutions for moderate earthquakes are comparable with those obtained using moment tensor inversion, and they are more constrained with respect to the solutions obtained using only P -polarity data for small earthquakes.

Description: One of the main challenges of modern engineering seismology is the mitigation of the adverse consequences of earthquakes. Although modern engineering techniques allow for the designing of earthquake-resistant structures, the ability to predict more reliable ground-motion estimates and associated uncertainties is needed. With this aim, this study investigated the possibility of using data recorded during an earthquake to improve the empirical ground-motion prediction equations (GMPEs). In particular, we propose a procedure that updates the coefficients of an initial GMPE to account for the specific features of a seismic source and propagation medium. We applied the technique in the immediate postevent time period of three well-recorded earthquakes that occurred recently in Italy and caused casualties and significant damage: the 6 April 2009 M w  6.3 L’Aquila earthquake, the 20 May 2012 M w  5.9 Emilia earthquake, and the 25 October 2012 M w  5.2 Pollino earthquake. For possible future development, using the same earthquakes and the networks with which they were recorded, we also explored the potential of the technique as a possible real-time application, as in the case of earthquake early warning systems.

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: Ground-motion prediction equations (GMPEs) play a crucial role for estimating the seismic hazard in any region using either a deterministic or a probabilistic approach. Indeed, they represent a reliable and fast tool to predict strong ground motion, given source and propagation parameters. In this article, we estimated GMPEs for the South Korea peninsula. GMPEs were computed for peak ground displacement, peak ground velocity, peak ground acceleration, and spectral accelerations (damping at 5%) at 13 different periods from 0.055 to 5 s. We analyzed data from 222 earthquakes recorded at 132 three-component stations of the South Korea Seismic Network, from 2007 to 2012, with local magnitude ranging between 2.0 and 4.9 and epicentral distances varying from 1.4 to ~600 km. A nonlinear mixed effects technique is used to infer the GMPE coefficients. This technique includes both fixed and random effects and accounts for both inter- and intraevent dependencies in the data. Station-specific corrective coefficients were estimated by a statistical approach and were included in the final ground-motion prediction model. Finally, predictions for peak ground acceleration and spectral acceleration are compared with observations recorded for an M L  5.1 earthquake that occurred in 2014, the data for which were not included in the modeling. Online Material: Figures showing final ground-motion prediction equation models versus observations, and intra- and interevent residuals.

Description: Macroseismic intensities are the only available data for most historical earthquakes and often represent the unique source of information for crucial events in the definition of seismic hazard. In this paper, we attempt at getting insight into source characteristics by reproducing the observed intensity field. As a test case, we study the source of 1908 Messina Straits earthquake ( M W  = 7.1), by testing three distinct fault models deduced from the analysis of geodetic data. Starting from the static slip distribution, we develop kinematic source models for the investigated fault and compute full waveform synthetic seismograms in a 1-D structural model, also accounting for anelastic attenuation. Then, we convert both computed peak-ground acceleration (PGA) and peak-ground velocity (PGV) to macroseismic intensity at 100 selected sites, by means of specific empirical relations for the Italian region. By comparing the original data separately with PGA- and PGV-based intensity fields, we discriminate among the tested faults and determine the best values for the investigated kinematic parameters of the source. We also perform a misfit analysis for the best source model, in order to investigate the dependence of the results on the selected parametrization. The results of the analysis indicate that among the tested models, the one characterized by an east-dipping fault, with strike-oriented NS slightly rotated clockwise, better explains the observed macroseismic field of the 1908 Messina Straits earthquake. Besides, the fracture nucleated at the southern end of the fault and ruptured northward, producing considerable directivity effects. This is in agreement with the published results obtained from the investigation of the historical seismograms. We also determine realistic values for the rupture velocity and the rise-time. Our study confirms the great potential of the macroseismic data, demonstrating that they contain enough information to constrain important characteristics of the fault, which can be retrieved by using complex source models and computing complete wavefield. Moreover, we also show that the simultaneous comparison of both PGA- and PGV-based synthetic macroseismic fields with the original intensities provides tighter constraints for discriminating among different source models, with respect to what attainable from each of them.

Description: 〈span〉〈div〉Abstract〈/div〉The reliability and accuracy of the ground‐motion prediction equations (GMPEs) are of prime interest while evaluating seismic hazard for any region. The regular updates and minimization of the uncertainties associated with the coefficients of the GMPEs are important for improving ground‐motion predictions and consequent performance of seismic hazard maps.Thus, in the present study, we propose an update of the GMPEs estimated by 〈a href="https://pubs.geoscienceworld.org/bssa#rf32"〉Sharma 〈span〉et al.〈/span〉 (2013)〈/a〉 in The Geysers geothermal area. The update is done using the huge dataset available and by extending the magnitude range as well as distance range. The previous dataset used by 〈a href="https://pubs.geoscienceworld.org/bssa#rf32"〉Sharma 〈span〉et al.〈/span〉 (2013)〈/a〉 was composed of 212 earthquakes recorded at 29 stations with the magnitude range between 1.3≤Mw≤3.3 and distance range between 0.6≤Rhypo≤20  km. The new dataset encloses 10,974 induced earthquakes recorded at 29 stations with the magnitude range between 0.7≤Mw≤3.3 and distance range between 0.1≤Rhypo≤73  km. We compute updated GMPEs for peak ground velocity (PGV), peak ground acceleration (PGA), and 5% damped spectral acceleration (SA) (T) at T 0.05, 0.1, 0.2, 0.5, and 1.0 s.The mean ground‐motion predictions of the updated model proposed in the present study and the associated uncertainties are compared with the previous model proposed by 〈a href="https://pubs.geoscienceworld.org/bssa#rf32"〉Sharma 〈span〉et al.〈/span〉 (2013)〈/a〉 and with other models specifically developed for small‐magnitude earthquakes. The GMPEs are derived using a nonlinear mixed‐effect regression technique that accounts for both interevent and intraevent dependencies in the data. We also demonstrate the dependency of aleatory (random) uncertainties and epistemic (informative) uncertainties on source, medium, and site properties. We also concluded that the medium is behaving homogeneously in terms of peak ground‐motion attenuation by analyzing uncertainties associated with different ground‐motion periods.〈/span〉

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.