Near-Fault Observatories (NFO) continuously integrate data, products, and services within the EPOS community. The NFO community relies on different Thematic Core Services (TCS) for access to a wide range of raw data and data products. 

Seismological data (waveforms, earthquake parameters and strainmeters), geodetic data (information from GNSS stations), geological maps, satellite data, and geochemical data have distinct data formats and metadata. The TCS NFO works to link the varied data collected by individual NFOs into a network of observatories with common monitoring standards, protocols for observation and data access and distribution channels. These multidisciplinary data and products are then integrated, stored and distributed within TCS NFO services, thus facilitating research on the formation of earthquakes. 


The TCS NFO manages various data, products, and services, which need specific formats and metadata descriptions and services. FRIDGE is the common portal for all the different NFOs’ data and products, where users can discover and download the NFO specific data and high-level data products. 

In addition, this virtual environment, common to all the EU Near Fault Observatories, hosts simple visualisation tools for multidisciplinary data. It allows users to understand the anatomy of faults and compare data from different time intervals and locations. 



NFOs are important facilities in the effective risk mitigation of earthquakes since they continuously monitor seismic activity near faults. One of the Early Warning System services developed by the NFOs community is CREW, the EU Testing Center for Earthquake Early Warning and Source Characterization. This testing facility is based on the Near Fault Observatory in Irpinia, southern Italy, and supported by other NFOs’ seismic networks. 

CREW displays Early Warning seismic alerts, in the form of a map or table, according to specific search criteria, such as location, magnitude, lead-time and ground motion estimation. Additionally, CREW compares these alerts with those from authoritative sources, such as INGV or ISNET, and shows the statistics about how fast and accurate the Early Warning Systems alerts were. This information will help the community to build the next generation of real-time Early Warning  Systems for earthquake events.