Elia, Luca

Since 2002 OGS (Istituto Nazionale di Oceanografia e di Geofisica Sperimentale) in Udine (Italy), the Agencija Republike Slovenije za Okolje (ARSO) in Ljubljana (Slovenia) and the Zentralanstalt für Meteorologie und Geodynamik (ZAMG) in Vienna (Austria), are collecting, analyzing, archiving and exchanging seismic data in real time. The data exchange has proved to be effective and very useful in case of seismic events at the borders between Italy, Austria and Slovenia, where the poor coverage of individual national seismic networks precluded a precise earthquake location, while the usage of common data from the integrated networks improves significantly the overall capability of real time event detection and rapid characterization in this area. In order to extend the seismic monitoring in North-eastern Italy, Slovenia and Southern Austria, towards earthquake early warning applications, at the end of 2013 OGS, ARSO and ZAMG teamed with the RISSCLab group (http://www.rissclab.unina.it) of the Department of Physics at the University of Naples Federico II in Italy. The collaboration focuses on massive testing on OGS, ARSO and ZAMG data of the EW platform PRESTo (Probabilistic and Evolutionary early warning SysTem) developed by RISSC-Lab (http://www.prestoews.org).

The Campi Flegrei caldera (southern Italy) is one of the most hazardous areas in the World as several hundred thousand people live there and where important socio-economic activities have developed. The caldera includes the western-most part of the city of Naples and extends into the Gulf of Pozzuoli (eastern Tyrrhenian basin; Fig. 1). The main feature of the present volcanic activity of the caldera is the episodic slow and high-amplitude soil movement (bradyseism) accompanied by intense and shallow seismic activity that only occurs during the uplift phase.

The Near Fault Observatories (NFOs) community is one of the European Plate Observing System (EPOS, http://www.epos-eu.org) Thematic Communities, today consisting of six research infrastructures that operate in regions characterised by high seismic hazard originating from different tectonic regimes. Earthquakes respond to complex natural systems whose mechanical properties evolve over time. Thus, in order to understand the multi-scale, physical/chemical processes responsible for the faulting that earthquakes occur on, it is required to consider phenomena that intersect different research fields, i.e., to put in place multidisciplinary monitoring. Hence, NFOs are grounded on modern and multidisciplinary infrastructures, collecting near fault high resolution raw data that allows generation of innovative scientific products. The NFOs usually complement regional backbone networks with a higher density distribution of seismic, geodetic, geochemical and other geophysical sensors, at surface and sometimes below grade. These dense and modern networks of multi-parametric sensors are sited at and around active faults, where moderate to large earthquakes have occurred in the past and are expected in the future. They continuously monitor the underlying Earth instability processes over a broad time interval. Data collected at each NFO results in an exceptionally high degree of knowledge of the geometry and parameters characterizing the local geological faults and their deformation pattern. The novel data produced by the NFO community is aggregated in EPOS and is made available to a diverse set of stakeholders through the NFO Federated Specific Data Gateway (FRIDGE). In the broader domain of the Solid Earth sciences, NFOs meet the growing expectations of the learning and communication sectors by hosting a large variety of scientific information about earthquakes as a natural phenomenon and a societal issue. It represents the EPOS concept and objective of aggregating and harmonising the European research infrastructures capabilities to facilitate broader scientific opportunity. The NFOs are at the cutting edge of network monitoring. They conduct multidisciplinary experiments for testing multi-sensor stations, as well as realise robust and ultra-low latency, transmission systems that can routinely accommodate temporary monitoring densification. The effort to continuously upgrade the technological efficiency of monitoring systems positions the NFO at the centre of marketing opportunities for the European enterprises devoted to new sensor technology. The NFOs constitute ideal test beds for generating expertise on data integration, creating tools for the next generation of multidisciplinary research, routine data analysis and data visualization. In particular focus is often on near-real time tools and triggering alarms at different levels are tested and implemented, strengthening the cooperation with the Agencies for risk management. NFOs have developed innovative operational actions such as the Testing Centre for Earthquake Early Warning and Source Characterisation (CREW) and detailed fast ground shaking and damage characterization. Complementing the recent growth of modern laboratory and computational models, the NFOs can provide interdisciplinary observations of comparable high resolution to describe the behaviour of fault slip over a vast range of spatial and temporal scales and aiding to provide more accurate earthquake hazard characterizations.

Earthquake early warning systems (EEWSs) are nowadays contributing to seismic risk mitigation actions, both in terms of losses and societal resilience, by issuing an alert promptly after the earthquake origin and before the ground-shaking impacts the target to be protected. In this work, we analyze the performance of network-based and stand-alone (on-site) early warning systems during the 2016–2017 central Italy sequence, characterized by events with magnitude as large as 6.5. For the largest magnitude event, both systems predict well the ground shaking nearby the event source, with a rate of success in the 85%–90% range, within the potential earthquake damage zone. However, the lead time, that is, the time available for security actions, is significantly larger for the network-based system. For the regional system, it increases to more than 10 s at 40 km from the event epicenter. The stand-alone system performs better in the near-source region, still showing a positive albeit small lead time (<2 s). Far away from the source (>60 km), the performances slightly degrade, mostly owing to the large uncertainty in the attenuation relationships. This study opens up the possibility for making an operational EEWS in Italy, based on the available acceleration networks, provided that the delay due to data telemetry has to be reduced.

Modern seismic networks have grown to become increasingly complex infrastructures, composed of hundreds of devices and data streams scattered over wide geographic regions. Among the components of such networks are heterogeneous seismic and environmental sensors, digitizers, data loggers, data collection servers, wired and wireless communication hardware, and other devices and software subsystems charged with different data handling tasks, such as continuous data storage or analysis. In order to be effectively managed, a seismic network therefore needs a tiered software application. This application encompasses tasks that range from the low-level (hardware monitoring for failure detection) to the mid-level (data quality control) to the high-level (managing the final output of the network: recorded events, waveforms, and parametric data). At the same time such an application should provide a centralized and easy-to-use graphical user interface (GUI). Over the past two decades, several institutions and commercial companies have devoted great efforts to the development of software tools to manage and centralize the data acquisition and analysis for regional to global seismic networks. Among the most valuable products worth mentioning are: Earthworm, an open-source real-time seismic management system developed by the U.S. Geological Survey (Johnson et al. 1995); Antelope, a commercial real-time system for environmental data collection, developed by Boulder Real Time Technologies (BRTT 2008); and the more recent SeisComP (Hanka et al. 2000), an open-source tool for real-time data acquisition and analysis developed by the German Research Centre for Geosciences (GFZ-Potsdam). Although well-suited for real-time data collection and analysis, these systems do not currently provide advanced features for managing the infrastructure of a seismic network, such as state-of-health monitoring of the instrumentation or tracking all the network appliances.Trying to fill this gap, Instrumental Software Technologies (ISTI 2008) has recently developed SeisNetWatch (SeisNetWatch 2008), a tool for monitoring and controlling the data quality and the status of several types of data loggers and real-time seismic management systems. This desktop- and Web-accessible tool features a core system and a user interface written in Java, plus several “agents” each interacting with a particular piece of hardware or system. During the development of the Irpinia Seismic Network (ISNet) in southern Italy (Weber et al. 2007), we decided to address our needs of hardware monitoring and data management by developing our own solution, a Web-based application called SeismNet Manager. The application is designed as a graphical front-end to ISNet for internal and external users of the network, as well as its administrators, with an interface that is simple to use. SeismNet Manager leverages an instrument database and a seismic database to keep track of the hardware components that comprise the network (such as stations, servers, devices) and the data they produce (such as recorded waveforms and events). The application, universally accessible through a Web browser, fulfills the following needs: • to keep a detailed inventory of the multiple components that constitute a seismic network, including stations, sensors, data loggers, network hardware, generic hardware, data servers, and communication links; • to maintain a historical record of the installations and of the configuration details, as well as of the mutual connections of said components; • to perform real-time monitoring of some of the devices (hardware state and “health” problems, quality of the output) for alerting network operators of problems and complementing the seismic data; • to manage the seismic data produced by the network, obtained either through automatic data retrieval procedures or manual insertion by administrators (detected events, seismic recordings, parametric information) and to perform some routine tasks on returned data, including inspection, filtering, picking, and flagging. • to offer a Web-based interface that lets data consumers or network operators insert, edit, search, download and visualize all the available information (as tables, graphs, maps, waveform plots, and 3D renderings). To accomplish these goals, which are not specific to ISNet but are shared by most seismic networks, we made use of opensource technological solutions such as Linux (Debian 2008), PostgreSQL (PostgreSQL 2008), and Tomcat (Tomcat 2008). Flexibility and configurability was a priority, so that we could tailor SeismNet Manager to the specific needs and actual hardware of different networks and could manage multiple networks. At the same time, SeismNet Manager is not designed as a “be-all do-all” system performing every task needed in a seismic network, some of which are better left to specialized and standard software packages. For instance, in ISNet the continuous data acquisition and storage from the stations and the real-time seismic data processing for seismic early warning are implemented elsewhere, as discussed below. SeismNet Manager is thus built on top of the various elements and subsystems already operating in a network.

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.

Many regions in the world are affected by natural hazards such as earthquakes, tsunamis, volcanoes, floods, storms, landslides, etc., each of which can have devastating socio- economic impacts. Among these natural events, earthquakes, have been among the most recurrent and damaging hazards during last few decades, resulting in large numbers of casualties, and massive economic losses [30]. The problem of earthquake risk mitigation is faced using different approaches, depending upon the time scale being considered. Whilst over time scales of decades it is of utmost importance that land use regulations and building/ infrastructure codes are continuously updated and improved, for time scales of a few years, the main risk mitigation actions are at the level of information and education in order to increase individual and social community awareness about potentially damaging hazards. Over shorter time scales (months to hours), it would naturally be of great benefit to society as a whole if the capability to accurately predict the time, location and size of a potentially catastrophic natural event were available. However, due to the great complexity of the natural processes of concern, such predictions are currently not possible. On the other hand, on very short time scales (seconds to minutes), new strategies for earthquake risk mitigation are being conceived and are under development worldwide, based on real-time information about natural events that is provided by advanced monitoring infrastructures, denoted as “early warning systems”.

PRESTo (PRobabilistic and Evolutionary early warning SysTem) is a software platform for regional earthquake early warning that integrates recently developed algorithms for real-time earthquake location and magnitude estimation into a highly configurable and easily portable package. The system is under active experimentation in Southern Italy on the Irpinia Seismic Network (ISNet), which is deployed in a seismogenic area that is expected to produce a large earthquake within the next 20 years. In this paper we describe the architecture of the system and test its performances using both small earthquakes (M<3) recorded at the ISNet, and a large event recorded in Japan, through a simulation mode. The results show that, when a dense seismic network is deployed in the fault area, PRESTo can produce reliable estimates of earthquake location and size within 5-6 seconds from the event origin. Each estimate is provided as a probability density function, with an uncertainty that typically decreases with time: a stable solution is generally reached within 10 seconds from the origin. Thanks to its fully probabilistic approach, PRESTo can be a powerful tool for end-users in addressing the trade-off problem of whether and when to init safety measures. The software makes use of widespread standards for real-time data input and output, and can be finely tuned to easily adapt it to different networks and seismogenic regions.

A seafloor multisensor module with real-time data transmission, known as CUMAS, has been successfully deployed in the Gulf of Pozzuoli, in the Campi Flegrei caldera, which is one of the most hazardous volcanic areas in the World. CUMAS records seismological signals and provides measurements related to the water-current system. A test for the detection of sea level changes, potentially related to the seafloor uplift or subsidence, is ongoing with the use of the pressure gauge data. A surface buoy is equipped with additional sensors for meteorological measurements and receives the continuous scientific and status data streams from the CUMAS station via cable. These data are then transmitted by a wireless system to the INGV monitoring center in Naples. CUMAS is fully integrated into the geophysical land-based monitoring system that is managed by INGV, and it is the first off-shore station of the local network. An Earthworm-based system provides userfriendly data visualization and retrieval, which was adopted to straightforwardly integrate all of the data acquired by CUMAS with the land data, which is managed by a similar system. Following the results of a previous investigation performed using two ocean-bottom seismometers that were deployed in Pozzuoli Bay (Vassallo et al. 2008), CUMAS was deployed in a site that was selected to improve the performance of the present seismic network in terms of the detection threshold of the local seismicity and of hypocenter errors. CUMAS will provide long time-series data that will allow, for the first time, the study of the evolution of the volcanic activity and related phenomena in the marine sector of the Campi Flegrei caldera, which to date has only been investigated on the basis of land data. CUMAS is the first node of a marine network that is at present the subject of a feasibility study that will cover most of the submerged Campi Flegrei volcanic area and will be integrated into the local monitoring systems.

The paper gives a review of the open source and commercial seismic network monitoring tools currently available, and discusses in detail the development of SeismNet Manager used for the Irpinia Seismic Network (ISNet). An overview of the network is also given, with the flow of data from station to data center. The SeismNet Manager features are discussed in relation to the operation of this network, before going into specifics about how the application actually works. The paper gives a description of the application and its use in data acquisition of the Irpinia Seismic Network.