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.

We analyzed the P and S wave displacement spectra of 717 microearthquakes in the moment range 4 × 10 9 À 2 × 10 14 N m recorded at the dense networks operating in southern Apennines (Italy) and deployed along the 1980 M s 6.9 Irpinia earthquake fault zone. Source, attenuation, and site parameters are estimated by using a parametric modeling approach, which is combined with a multistep, nonlinear inversion strategy. We found that in the analyzed frequency band, an attenuation model with constant Q has to be preferred to frequency-dependent Q models. Consistent estimates of the median P and S quality factors e Q P ¼ 167 (90; 296) and e Q S ¼ 226 (114; 417) are obtained from two different techniques and relatively high values of Q S /Q P (median value 1.3, (0.8; 2.1)) are found in the same depth range where high V P /V S and a peak in seismicity distribution are observed. This is the evidence for a highly fractured, partially, or completely fluid-saturated medium embedding the Irpinia fault zone, down to crustal depths of 15-20 km. A nearly constant stress drop ( f Δσ ¼ 1:4 MPa, (0.4; 5.0)) and apparent stress (e τ a ¼ 0:1 MPa, (0.03, 0.4)) scaling of P and S corner frequencies and seismic energies is observed above a seismic moment value of about 10 11 N m. The measured radiation efficiency is low (g η SW ¼ 0:06; 0:03; 0:13 ð Þ ), e.g., the radiated energy is only a small fraction of the whole energy spent by friction and fracture development. A large positive dynamic overshoot (high dynamic shear strength) can be the dominant mechanism controlling the microearthquake fractures along the Irpinia fault zone.

[1] We investigate the effect of extended faulting processes and heterogeneous wave propagation on the early warning system capability to predict the peak ground velocity (PGV) from moderate to large earthquakes occurring in the southern Apennines (Italy). Simulated time histories at the early warning network have been used to retrieve early estimates of source parameters and to predict the PGV, following an evolutionary, probabilistic approach. The system performance is measured through the Effective Lead-Time (ELT), i.e., the time interval between the arrival of the first S-wave and the time at which the probability to observe the true PGV value within one standard deviation becomes stationary, and the Probability of Prediction Error (PPE), which provides a measure of PGV prediction error. The regional maps of ELT and PPE show a significant variability around the fault up to large distances, thus indicating that the system's capability to accurately predict the observed peak ground motion strongly depends on distance and azimuth from the fault.

Triggered seismicity in karst regions has been explained assuming the existence of a hydraulically connected fracture system and downward diffusion of surface pore pressures. Karst systems are, in fact, able to swiftly channel large amount of rainfall through networks of conduits increasing the hydraulic head loading upon the fluid‐saturated, poroelastic crust. Here we use Global Positioning System and hydrological and seismicity data to show that poroelastic strain in the shallow crust (0–3.5 km) controls seasonal and multiannual modulation of seismicity along the Irpinia Fault Zone (Southern Italy) without requiring a hydraulically connected fracture system from the surface to hypocentral depths. We suggest that groundwater recharge of karst aquifers along the Irpinia Fault Zone produces stress perturbations large enough to modulate strain accumulation and seismicity and temporarily modify the probability of nucleation of seismic events such as the 1980 Irpinia, MS 6.9, earthquake.

The causative source of the first damaging earthquake instrumentally recorded in the Island of Ischia, occurred on 21 August 2017, has been studied through a multiparametric geophysical approach. In order to investigate the source geometry and kinematics we exploit seismological, Global Positioning System, and Sentinel‐1 and COSMO‐SkyMed differential interferometric synthetic aperture radar coseismic measurements. Our results indicate that the retrieved solutions from the geodetic data modeling and the seismological data are plausible; in particular, the best fit solution consists of an E‐W striking, south dipping normal fault, with its center located at a depth of 800 m. Moreover, the retrieved causative fault is consistent with the rheological stratification of the crust in this zone. This study allows us to improve the knowledge of the volcano‐tectonic processes occurring on the Island, which is crucial for a better assessment of the seismic risk in the area.