We use a multidisciplinary approach to gather preliminary evidence for a Quaternary east‐dipping extensional detachment in Central Italy. This structure crops out in the Sabini‐Eastern Simbruini (SES) and would be hidden at mid‐crustal depths beneath the L'Aquila 2009 (M_w6.3) epicentral area. TheSESgeometry is reconstructed through geological mapping, structural analysis and seismic line interpretation. The geometry of the mid‐crustal segment, referred to as the Ocre Segment (OS), is interpreted through seismological analyses of the largest aftershock (M_w5.4) of the L'Aquila 2009 sequence. The kinematic compatibility between theSESand theOSunder a commonSW–NEtensional field is tested through stress inversion of both geological and seismological data. The reliability ofOSactivation is tested through slip tendency analysis. Like other Italian cases, theSESand theOSare preliminarily interpreted as expressions at different depths of the same unknown east‐dipping extensional detachment, characterized by a ramp–flat–ramp geometry.

Seismic sequences are a powerful tool to locally infer geometrical and mechanical properties of faults and fault systems. In this study, we provided detailed location and characterization of events of the 3–7 July 2020 Irpinia sequence (southern Italy) that occurred at the northern tip of the main segment that ruptured during the 1980 Irpinia earthquake. Using an autocorrelation technique, we detected more than 340 events within the sequence, with local magnitude ranging between −0.5 and 3.0. We thus provided double difference locations, source parameter estimation, and focal mechanisms determination for the largest quality events. We found that the sequence ruptured an asperity with a size of about 800 m, along a fault structure having a strike compatible with the one of the main segments of the 1980 Irpinia earthquake, and a dip of 50–55° at depth of 10.5–12 km and 60–65° at shallower depths (7.5–9 km). Low stress drop release (average of 0.64 MPa) indicates a fluid-driven initiation mechanism of the sequence. We also evaluated the performance of the earthquake early warning systems running in real-time during the sequence, retrieving a minimum size for the blind zone in the area of about 15 km.

SUMMARY: Microseismic monitoring is a primary tool for understanding and tracking the progress of mechanical processes occurring in active rock fracture systems. In geothermal or hydrocarbon fields or along seismogenic fault systems, the detection and location of microseismicity facilitates resolution of the fracture system geometry and the investigation of the interaction between fluids and rocks, in response of stress field perturbations. Seismic monitoring aims to detect locate and characterize seismic sources. The detection of weak signals is often achieved at the cost of increasing the number of false detections, related to transient signals generated by a range of noise sources, or related to instrumental problems, ambient conditions or human activity that often affect seismic records. A variety of fast and automated methods has been recently proposed to detect and locate microseismicity based on the coherent detection of signal anomalies, such as increase in amplitude or coherent polarization, at dense seismic networks. While these methods proved to be very powerful to detect weak events and to reduce the magnitude of completeness, a major problem remains to discriminate among weak seismic signals produced by microseismicity and false detections. In this work, the microseimic data recorded along the Irpinia fault zone (Southern Apennines, Italy) are analysed to detect weak, natural earthquakes using one of such automated, migration-based, method. We propose a new method for the automatic discrimination of real vs false detections, which is based on empirical data and information about the detectability (i.e. detection capability) of the seismic network. Our approach allows obtaining high performances in detecting earthquakes without requiring a visual inspection of the seismic signals and minimizing analyst intervention. The proposed methodology is automated, self-updating and can be tuned at different success rates.