Campi Flegrei is an active, resurgent caldera that is located a few kilometres west of the city of Naples, a densely populated urban settlement in southern Italy. Identifying, locating at depth and better defining the geometry of the magma feeding system of the caldera is highly relevant for assessing and monitoring its volcanic hazard. Based on a high resolution seismic reflection data set, we investigated the deep structure of the volcano. Here we show that seismic wave amplitude variations with distance from the radiating source provide clear evidence for large amplitude seismic reflections from the top of an extended supercritical fluid‐bearing rock formation at about 3,000 m and of an about 7,500 m deep, 1,000 m thick, low velocity layer, which is associated with a mid‐crust, partial melting zone beneath the caldera. The modeling of magma properties based on measured seismic velocities indicates a relatively high melt percentage (in the range 80–90%). These new data suggest that a large magmatic sill is present well within the basement formations, which is possibly linked to the surface through a system of deep fractures bordering the caldera. The lateral extension and similar depth of the melt zone observed beneath the nearby Mt.Vesuvius support the hypothesis of a single continuous magma reservoir feeding both of these volcanoes.

Most earthquake location algorithms are based on ray-theory travel-times and thus require as input data the onset times of body-wave phases such as P and S at a number of stations. It is often easy to detect and pick the first P onset times manually since the human eye can identify a change in amplitude or frequency in a signal even in the presence of high noise levels. The picking of S-phase onsets is more difficult because these onsets are often emergent and buried in the P coda, though at very short ranges the S onset can be impulsive...

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.

Modern seismic networks, either permanent or temporary, can nowadays easily produce such large volumes of data that manual analysis is not possible. Effective and consistent automatic procedures for the detection and processing of seismic events are required to homogeneously process large datasets and to provide rapid responses in near real time.