The High Agri Valley is a tectonically active area in southern Italy characterized by high seismic hazard related to fault systems capable of generating up to M=7 earthquakes (i.e. the 1857 M_w=7 Basilicata earthquake). In addition to the natural seismicity, two different clusters of induced microseismicity were recognized to be caused by industrial operations carried out in the area: (1) the water loading and unloading operations in the Pertusillo artificial reservoir and (2) the wastewater disposal at the Costa Molina 2 injection well. The twofold nature of the recorded seismicity in the High Agri Valley makes it an ideal study area to deepen the understanding of driving processes of both natural and anthropogenic earthquakes and to improve the current methodologies for the discrimination between natural and induced seismic events by collecting high-quality seismic data. Here we present the dataset gathered by the INSIEME seismic network that was installed in the High Agri Valley within the SIR-MIUR research project INSIEME (INduced Seismicity in Italy: Estimation, Monitoring, and sEismic risk mitigation). The seismic network was planned with the aim to study the two induced seismicity clusters and to collect a full range of open-access data to be shared with the whole scientific community. The seismic network is composed of eight stations deployed in an area of 17 km×11 km around the two clusters of induced microearthquakes, and it is equipped with triaxial weak-motion broadband sensors placed at different depths down to 50 m. It allows us to detect induced microearthquakes, local and regional earthquakes, and teleseismic events from continuous data streams transmitted in real time to the CNR-IMAA Data Centre. The network has been registered at the International Federation of Digital Seismograph Networks (FDSN) with code 3F. Data collected until the end of the INSIEME project (23 March 2019) are already released with open-access policy through the FDSN web services and are available from IRIS DMC (https://doi.org/10.7914/SN/3F_2016; Stabile and INSIEME Team, 2016). Data collected after the project will be available with the permanent network code VD (https://doi.org/10.7914/SN/VD, CNR IMAA Consiglio Nazionale delle Ricerche, 2019) as part of the High Agri Valley geophysical Observatory (HAVO), a multi-parametric network managed by the CNR-IMAA research institute.

The High Agri Valley (southern Italy) is one of the largest intermontane basin of the southern Apennines affected by intensive agricultural and industrial activities. The study of groundwater chemical features provides much important information useful in water resource management. In this study, hydrogeochemical investigations coupled with multivariate statistics, saturation indices, and stable isotope composition (δD and δ¹⁸O) were conducted in the High Agri Valley to determine the chemical composition of groundwater and to define the geogenic and anthropogenic influences on groundwater quality. Twenty-four sampling point ( including well and spring waters) have been examined. The isotopic data revealed that groundwater has a meteoric origin. Well waters, located on recent alluvial-lacustrine deposits in shallow porous aquifers at the valley floor, are influenced by seasonal rainfall events and show shallow circuits; conversely, spring waters from fissured and/or karstified aquifers are probably associated to deeper and longer hydrogeological circuits. The -mode factor analysis shows that three factors explain 94% of the total variance, and F1 represents the combined effect of dolomite and silicate dissolution to explain most water chemistry. In addition, very low contents of trace elements were detected, and their distribution was principally related to natural input. Only two well waters, used for irrigation use, show critical issue for NO₃^- concentrations, whose values are linked to agricultural activities. Groundwater quality strongly affects the management of water resources, as well as their suitability for domestic, agricultural, and industrial uses. Overall, our results were considered fulfilling the requirements for the inorganic component of the Water Framework Directive and Italian legislation for drinking purposes. The water quality for irrigation is from “good to permissible” to “excellent to good” although salinity and relatively high content of Mg²⁺ can occasionally be critical.

A major aim in the study of crustal fluids is the development of automatic methodologies for monitoring deep‐source, non‐volcanic gas emissions’ spatio‐temporal evolution. Crustal fluids play a significant role in the generation of large earthquakes and the characterization of their emissions on the surface can be essential for better understanding crustal processes generating earthquakes. We investigate seismic tremors recorded over 4 days in 2019 at the Mefite d’Ansanto (southern Apennines, Italy) that is located at the northern end of the fault system that generated the Mw 6.9 1980 Irpinia Earthquake. The Mefite d’Ansanto is hypothesized to be the largest natural, non‐volcanic, CO₂‐rich gas emission on Earth. The seismic tremor is studied by employing a dense temporary seismic network and an automated detection algorithm based on non‐parametric statistics of the recorded signal amplitudes. We extracted signal characteristics (RMS amplitude and statistical moments of amplitudes both in time and frequency domains) for use in the subsequent supervised machine‐learning classification of the target tremor and accidently detected anthropogenic and background noise. The data set is used for the training and optimization of station‐based KNN (k‐Nearest‐Neighbors) binary classifiers obtaining good classification performances with a median overall accuracy across all stations of 92.8%. The classified tremor displayed common features at all stations: variable duration (16 s to 30–40 min), broad peak frequency (4–20 Hz) with varying amplitudes, and two types of signals: (a) long‐duration, high‐amplitude tremor and (b) pulsating tremor. Higher tremor amplitudes recorded at stations closer to local bubbling and pressurized vents suggest multiple local tremor sources.

The accurate characterization of microearthquake sequences allows seismologists to better understand the physical processes involved in earthquake nucleation and rupture propagation and to gain insights on fault geometry at depth. Standard procedures for seismic sequences analysis are based on manual detection and phase-picking, requiring a huge amount of work from expert seismologists, particularly in the case of microseismic events. Here we show how the investigation of a low-magnitude foreshock-mainshock-aftershock sequence, occurred in August 2020 close to Castelsaraceno village (southern Italy), greatly benefited from the application of a semi-automated template matching and machine-learning based workflow. The phase-picking was automatically performed through a deep-learning algorithm on 202 microearthquakes detected between July and October 2020, followed by an automatic multi-step absolute and relative earthquake location procedure. The 72 relocated events of the seismic sequence were clustered in time (7–12 August) and in a narrow range of depths (10–12 km). The Ml 2.1 foreshock doublet and the Ml 2.9 mainshock identified a persistent asperity. The joint analysis of aftershocks distribution, the mainshock focal mechanism, and the geology of the study area suggest the occurrence of the sequence along a NNE-SSW left-lateral, transtensional fault in the brittle portion of the crystalline basement.

SUMMARY: Characterizing the sources of small earthquakes allows us to explore the physical mechanisms involved in earthquake nucleation and provides insights into the deformation and stress propagation processes that trigger ruptures; understanding the properties of the sources is also crucial for assessing the seismic risk associated with seismicity induced by human activity. Within this context accurate estimation of source parameters for low-magnitude events is challenging, mainly due to the significant impact of high-frequency attenuation, which acts like a low-pass filter on seismic spectra of microearthquakes, leading to a possible underestimation of the true corner frequency. To limit these effects and to improve the accuracy of the source parameters for microseismicity, the k0 parameter is included in the spectra inversion. We focus on the microseismicity that occurred in the High Agri Valley (southern Apennines, Italy), an area affected by both natural and induced microseismicity. The high-frequency energy decay is modelled by estimating, at each station, the parameter k0 from the ambient seismic noise to avoid the trade-off between the high-frequency attenuation and the stress drop of the earthquake source. We estimated the k0 parameter using noise displacement spectra in the 15–40 Hz frequency range at eight broad-band stations belonging to the High Agri Valley Geophysical Observatory. Our results indicate that k0 varies between 0.02 and 0.04 s at the considered stations, with well constrained values in the selected frequency range. The estimated k0 values are integrated into the SourceSpec code to correct the high-frequency displacement amplitude spectra for the near-site attenuation when calculating the source parameters. Our procedure is tested on 72 microearthquakes (0.4 ≤ MW ≤ 2.7) of the Castelsaraceno sequence that occurred in August 2020 in the southwestern sector of the High Agri Valley. The results show the cut-off frequency of 10 Hz observed in uncorrected spectra of earthquakes with MW < 2.0, is no longer recognizable, and the threshold to estimate reliable corner frequency decreases by almost an order of magnitude, from MW = 2.0 to MW = 1.2. Moreover, events with MW < 1.5 also align with the 0.1 MPa stress-drop curve after k0 correction, and the fc–M0 scaling approaches conditions close to self-similarity. We therefore propose that smaller earthquakes with MW ≈ 1.5 may follow a different physical mechanism, as they exhibit stress drop values around 0.1 MPa - an order of magnitude lower than those of events with MW > 2.0.