|
Near Fault Observatories (NFO) are natural laboratories undergoing active, and complex geophysical processes at, or in proximity to, densely populated urban areas. NFOs bound relatively small areas and are ideal sites for in-depth monitoring and advanced research. Six NFOs in Europe have been identified by the European Plate Observing System as long-term Research Infrastructures (including the Valais in Switzerland); three additional NFOs are in observer status. NFOs target the enhanced understanding of the mechanics of earthquakes to unravel the anatomy of complex seismogenic faults. This can only be achieved by the acquisition of continuous, long-term, high-resolution, high-quality, and high-density multidisciplinary data, and the application of consistent, high quality and state-of-the-art data processing. TRANSFORM² has the ambitious goal of improving and transforming the existing NFOs, by integrating cutting-edge methodological and technological solutions, paving the road for the next generation NFOs across Europe. This will be achieved through the development of four ‘concepts’:
A focus will be on bringing transformative concepts such as Machine Learning and fiber optic cable sensing to the NFO community. Breakthrough ideas will be explored and addressed with focus on analytical and feasibility control detecting weaknesses and missing elements that will be resolved within the project. Awareness and engagement of stakeholders as well as societal needs will be a priority. Issues concerning NFO funding and sustainability will be addressed. |
|---|---|
| Chef de projet au SED | John Clinton |
| Membres du projet au SED | Maren Böse, Thomas Planes, Dario Jozinovic, Lukas Heiniger, Nikolaj Dahman, Michèle Marti, Tonja Iten |
| Partenaires de recherche | UPAT, INGV, NKUA, IPGP, NOA, UNINA, NIEP, CU, OGS, BOUN, PLEAD, UPWr, ZRC,UniTS, EPPO, RWG, ETHZ |
| Source de financement | SBFI and HORIZON EUROPE - RIA (HORIZON-INFRA-2024-DEV-01–01) |
| Durée | 2025-2027 |
| Mots-clef | NFOs, AI, EEW, realtime monitoring |
| Domaine de recherche | Monitoring, alerting and analysis (Data processing, products and alerting, real-time seismology); Support (Communication, IT and technology) |
| Website | https://www.transform2-project.eu/ |
Dahmen, N., Clinton, J. F., Meier, M.-A., & Scarabello, L. (2026). Toward Operational Earthquake Seismogram Denoising. Bulletin of the Seismological Society of America, https://doi.org/10.1785/0120250198
Dahmen, N. L. (2026). Earthquake Seismogram Denoising Across Time, Time-Frequency, and Hybrid Domain Approaches. JGR: Machine Learning and Computation