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Digital data, services and infrastructure

Highlights

Fait marquant

Opening up science at BRGM

Two major initiatives were started in 2019 to increase the visibility, accessibility and usability of BRGM's science data. By linking data from different information systems using shared terminology in accordance with semantic web good practices, 40 registries were configured on several BRGM themes, including groundwater, geology and mineral resources, and are accessible on https://data.geoscience.fr/ncl/.
BRGM has also simplified the way in which data are downloaded on InfoTerre, with harmonised geological vector maps and BSS data available across whole French départements.

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The semantic web: 40 registries (for groundwater, geology, etc.) are accessible on data.geosciences.fr. © BRGM

Call for innovative projects

How can we imagine BRGM’s future digital services at the intersection of information technologies and geological and science challenges? This is the goal of the internal competition organised by the DISN, BRGM’s division for digital services and infrastructure, to spark innovation in our products and services by putting together teams of developers and thematic experts. The projects selected for 2019 include: “how to better communicate on risks using the internet and interactive 3D”; “improving computing performance in a big data environment”; “inventing DIY economic sensors to better understand outdoor natural phenomena”; “designing a single software package to speed up the more common dating processes”; and “how to disseminate BRGM’s geoscience information in ways that are fun and interactive to reach the wider public and encourage participation”.

Closing of the EPOS project for solid Earth observation

Co-funded by the European Commission's Horizon 2020 programme, the European Plate Observing System (EPOS) platform provides coordinated access to geoscience data and services for researchers, decision makers and the general public in areas such as geology, seismology, volcanology, fault observation and hazards caused by human activity. By hosting the platform, BRGM led the implementation of geological data and 3D/4D modelling services.

Continuous improvement of the subsurface database

As a major public policy support tool, the subsurface database (BSS) benefits from continuously improved data collection processes, thereby increasing data quality. BSS contains data on France’s underground structures collected over more than 50 years (e.g. boreholes, excavation works, wells and springs) and is managed by BRGM on behalf of the French government. It provides information on more than 860,000 underground structures built over the past century and contains more than 2,700,000 digitised documents (e.g. site drawings and thematic maps, geological and technical cross-sections, logs, analysis results, and water quality and quantity measurements). To improve data quality, BRGM offers input applications with strict management rules for raw data collected by the general public, and automated processes for checking the consistency of imported data. The database complies with the most recent standardisation and confidentiality requirements.

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The subsurface database (BSS) is a national database for maintaining and providing access to technical and science data on underground structures in France. © BRGM

Launch of the GeoERA European project

BRGM was selected to design the architecture of the joint European Information Platform as part of the GIP project. This platform will structure, disseminate and maintain the results of the 15 research projects funded by GeoERA in geo-energy, underground water and raw materials. The projects promote more integrated, efficient, responsible and publicly acceptable subsurface management, exploitation and use.

AI to support water table level prediction

Can we predict water table levels in the same way that we provide weather forecasts? Measurement data on groundwater levels are expected to be published with increasing frequency, and even daily. Artificial intelligence can support the improvement of existing predictive models. The MétéEau des Nappes project uses a platform providing real-time sensor measurement data and predictions based on machine learning systems.