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FAIR principles have become reference criteria for promoting and evaluating openness of scientific data and for improving datasets Findability, Accessibility, Interoperability, and Reusability. This also applies to Research Infrastructures (RIs) in the solid Earth domain committed to provide access to seismological data, ground deformations inferred from terrestrial, and satellite observations, geological maps, and laboratory experiments. Such RIs have been indeed committed for a long time, well before the appearance of FAIR principles, to engage scientific communities involved in data collection, standardization, and quality control as well as in implementing metadata and services for qualification, storage and accessibility. By addressing open science and managing scientific data, they are working to adopt FAIR principles, thus having the onerous task of turning these principles into practices. In this work we argue that although FAIR principles have the merit of creating a common background of knowledge to engage communities in providing data in a standard way thus easing interoperability and data sharing, in order to make the adoption of FAIR principles less onerous there is an urgent need of clear models, reference architectures and technical guidelines which can support RI implementers in the realization of FAIR data provision systems. We therefore discuss the state of the art of FAIR principles ecosystem and open new perspectives by discussing a four-stages roadmap that reorganizes FAIR principles in a way that better fits to the approach of RI implementers, and a FAIR adoption process that relates FAIR principles to technologies for their implementation.

During the last decades, the availability of Synthetic Aperture Radar (SAR) satellite missions, such as the ERS-1/2 and ENVISAT ones operating at C-band who have worked since 1992 to 2011, as well as the X-band COSMOSkyMed and TerraSAR-X constellations, up to the brand new Sentinel-1 mission, have strongly contributed to SAR data diffusion and popularity in the generation of different studies at different scales and in different research fields. One of the most popular SAR technique is the one referred to as Differential SAR Interferometry (DInSAR), which allows measuring with centimeter accuracy the Earth's surface deformation entity related to both natural and man-made hazards. Nowadays, with the increasing of SAR data availability provided by Sentinel-1 constellation of Copernicus European Program, which is composed by two twin satellites operating in C-band since 2014 and 2016, with a repeat pass of 6 days and with a global (i.e. worldwide) data acquisition policy, the SAR EO scenario is becoming more and more operational, thus mainly providing support for natural hazards monitoring. This allows, in theory, and disposing of sufficient computing power, the EO community to monitor, for instance, the deformation of every volcano or to obtain co-seismic displacement maps in a short time frame and anywhere in the world. Accordingly, in this work, we present a fully automatic and fast processing service for the generation of co-seismic displacement maps by using Sentinel-1 data. The implemented system is completely unsupervised and is triggered by the all significant (i.e. larger than a defined magnitude) seismic event registered by the online catalog as those provided by the United States Geological Survey (USGS) and the National Institute of Geophysics and Volcanology of Italy (INGV). The service has been specifically designed to operate for Civil Protection purposes. The generated DInSAR measurements are made available to the geoscience community through the EPOS Research Infrastructure and they will contribute to the creation of a global database of co-seismic displacement maps. Finally, it is worth noting that the developed system relies on widely common IT methods and protocols and is not specifically tied to a defined computing architecture, thus implying its portability, in view also of the European Commission Data and Information Access Services (DIAS) where satellite data (mainly Sentinel) and processing facilities are co-located to reduce the data transfer time during their processing.

Virtual Research Environments (VREs), also known as science gateways or virtual laboratories, assist researchers in data science by integrating tools for data discovery, data retrieval, workflow management and researcher collaboration, often coupled with a specific computing infrastructure. Recently, the push for better open data science has led to the creation of a variety of dedicated research infrastructures (RIs) that gather data and provide services to different research communities, all of which can be used independently of any specific VRE. There is therefore a need for generic VREs that can be coupled with the resources of many different RIs simultaneously, easily customised to the needs of specific communities. The resource metadata produced by these RIs rarely all adhere to any one standard or vocabulary however, making it difficult to search and discover resources independently of their providers without some translation into a common framework. Cross-RI search can be expedited by using mapping services that harvest RI-published metadata to build unified resource catalogues, but the development and operation of such services pose a number of challenges. In this paper, we discuss some of these challenges and look specifically at the VRE4EIC Metadata Portal, which uses X3ML mappings to build a single catalogue for describing data products and other resources provided by multiple RIs. The Metadata Portal was built in accordance to the e-VRE Reference Architecture, a microservice-based architecture for generic modular VREs, and uses the CERIF standard to structure its catalogued metadata. We consider the extent to which it addresses the challenges of cross-RI search, particularly in the environmental and earth science domain, and how it can be further augmented, for example to take advantage of linked vocabularies to provide more intelligent semantic search across multiple domains of discourse.

AbstractThe ability to predict the magnitude of an earthquake caused by deep fluid injections is an important factor for assessing the safety of the reservoir storage and the seismic hazard. Here, we propose a new approach to evaluate the seismic energy released during fluid injection by integrating injection parameters, induced aseismic deformation, and the distance of earthquake sources from injection. We use data from ten injection experiments performed at a decameter scale into fault zones in limestone and shale formations. We observe that the seismic energy and the hydraulic energy similarly depend on the injected fluid volume (V), as they both scale as V3/2. They show, however, a large discrepancy, partly related to a large aseismic deformation. Therefore, to accurately predict the released seismic energy, aseismic deformation should be considered in the budget through the residual deformation measured at the injection. Alternatively, the minimal hypocentral distance from injection points and the critical fluid pressure for fault reactivation can be used for a better prediction of the seismic moment in the total compilation of earthquakes observed during these experiments. Complementary to the prediction based only on the injected fluid volume, our approach opens the possibility of using alternative monitoring parameters to improve traffic-light protocols for induced earthquakes and the regulation of operational injection activities.

We present an unsupervised and automatic system for volcano deformation monitoring via the Copernicus Sentinel-1 data. The system relies on the Parallel Small BAseline Subset (P-SBAS) approach, permitting us to generate updated displacement time series at every new Sentinel-1 acquisition over a selected area of interest in a fast and accurate way. The service is currently operative to monitor the main active Italian volcanoes in the framework of cooperation with the Italian Department of Civil Protection. The system is potentially extendable to every area on the Earth, thus making it suitable for surface displacement monitoring of a large variety of phenomena. Finally, the obtained results are made available to the scientific community through the EPOS Research Infrastructure.

Geothermal exploration will help move towards a low-carbon economy and provide a basis for green and sustainable growth. The development of new practical, reliable methods for geophysical characterization of a reservoir has the potential to facilitate a broader application of deep geothermal energy. At the Groß Schönebeck in situ laboratory, a unique vertical seismic profiling (VSP) dataset was recorded in two 4.3 km deep geothermal boreholes using fibre-optic cables in early 2017. The experiment set-up consisted of 61 vibrator points organized in a spiral pattern around the well site to ensure a proper offset and azimuth distribution in the target reservoir section. Data were processed using a standard workflow for VSP. As a result, a detailed three-dimensional 0.75km×1km×4.5 km image around the existing boreholes was created using the Kirchhoff migration algorithm with restricted aperture. The imaging resolved small-scale features in the reservoir essential for the future exploration of the geothermal research site. Borehole data with vertical resolution up to 16 m revealed the existing depth variations of the Elbe reservoir sandstone horizon at 4.08–4.10 km depth and indications of an unconformity in the area where we expect volcanic rocks. In addition, in the borehole data a complex interlaying with numerous pinch-outs in the Upper Rotliegend reservoir section (3.8 to 4 km depth) was discovered. Thereby, we demonstrate that wireline fibre-optic data can significantly contribute to exploration by providing an efficient and reliable method for deep geothermal reservoir imaging.

A cycle of four webinars on Open Science and Open Access for earth and environmental sciences, with discipline-specific tools and practical resources. Course outline: Module 1: - Introduction and motivations - Open Science in Solid Earth Science Module 2: - Research Data Management - OS in solid Earth sciences: the EPOS research infrastructure experience Module 3: - FAIR principles and Open Data - Implementing FAIR. Considerations from the solid Earth domain Module 4: - The Data Management Plan - The adoption of Open Science Paradigm at INGV - Practical Tips Scientific committee: Maria Silvia Giamberini, IGG/CNR Gina Pavone, ISTI/CNR