Deep lEarning foRecasting of Induced Seismicity for risK management operations

HORIZON.1.2HORIZON-TMA-MSCA-PF-EFID: 101105516
EC Contribution
€1,728
Consortium Size
1 orgs
Summary

Climate change mitigation requires a fast and efficient transition to clean and sustainable energy production.Enhanced Geothermal Systems (EGS) play a key Climate change mitigation requires a fast and efficient transition to clean and sustainable energy production. In this context, Enhanced Geothermal Systems (EGS) can play a key role in facing this challenge since, with this technology, clean energy production from the Earth's heat is no longer confined to volcanic or hydrothermal regions. Despite this potential, EGS presents society and economy-related problems that need to be solved to ensure operational safety, continuity, and public acceptance of such industrial projects. Induced seismicity is the major obstacle to the development and social acceptance of EGS projects. In the last years, several damaging earthquakes have been associated with EGS, leading to the definitive closure of the involved projects and raising social concerns against this form of energy production. With DERISK we aim to develop new paradigms for induced seismicity analysis, combining the latest available data acquisition technologies, such as distributed acoustic sensing (DAS), with innovative deep-learning techniques. To characterize induced seismicity with unprecedented resolution and accuracy, we want to develop a next-generation data analysis framework combining deep-learning and waveform-based seismic imaging techniques. Our final goal is to produce deep-learning-based enhanced microseismicity catalogs that will be used to test the performance of new induced seismicity forecasting models exploiting the recent research advances in the field of physics-informed machine learning. The techniques developed within DERISK will be tested and validated with high-quality induced seismicity datasets collected at different EGS sites. If successful, DERISK will open the way to a safer and more widespread development of EGS projects, contributing to the transition to sustainable energy production.

Consortium (1)

Project Results (8)

Source: CORDIS, the EU research results database.

Publications (5)
A Waveform-Based Graph Neural Network Approach for Microseismic Monitoring
· 2025DOI
Matteo Bagagli, Francesco Grigoli, Davide Bacciu
Leveraging Deep-Learning Methods for Operational Analysis at Enhanced Geothermal Systems
· 2025DOI
Matteo Bagagli, Francesco Grigoli, Davide Bacciu
Local earthquake tomography of the Alpine region from 24 years of data
Geophysical Journal International· 2025DOI
M Bagagli; I Molinari; T Diehl; E Kissling
Effects on a Deep-Learning, Seismic Arrival-Time Picker of Domain-Knowledge Based Preprocessing of Input Seismograms
Seismica· 2024DOI
Anthony Lomax; Matteo Bagagli; Sonja Gaviano; Spina Cianetti; Dario Jozinović; Alberto Michelini; Christopher Zerafa; Carlo Giunchi
HEIMDALL: a grapH-based sEIsMic Detector And Locator for microseismicity
· 2024DOI
Matteo Bagagli, Francesco Grigoli, Davide Bacciu
Deliverables (2)
Other Results (1)
Periodic Reporting for period 1 - DERISK (Deep lEarning foRecasting of Induced Seismicity for risK management operations)