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Summary
Summary
Multiscale study of seismogenic processes in Campania-Lucania Apennines using machine learning algorithms and multiparametric observations (FRACTURES)
Most of the earthquakes occurring worldwide result from a slip along faults under tectonic stress field. Crustal stress at regional and local scales may also be affected by changes in density, rigidity and rock rheology, fluid flow in rocks, pore-pressure increase, and gravitational loading/unloading. Considering the crust to be in a self-organized critical state at a stress level close to failure, such perturbations (having either natural or anthropogenic origin) can be locally sufficient to exceed the strength of faults thus nucleating an earthquake.
The main objective of FRACTURES is to study the seismogenic processes in Campania-Lucania Apennines related to both natural and anthropogenic seismicity and to map the 3D fault zone architecture by detecting, accurately locating, and characterizing background seismicity and sequences. The seismogenic belt of the Campania-Lucania Apennines consists of different fault segments along which normal faulting earthquakes occur, such as the catastrophic 1980 M 6.9 Irpinia and the 1857 M7.0 Basilicata events. The complex geological-structural setting, the high seismic hazard of the region, the presence of anthropic activities capable of inducing earthquakes (i.e. High Agri Valley area), and the availability of high-quality continuous data stream from a dense virtual seismic network (including also a constellation of seismic arrays), make the Campania-Lucania Apennine an advanced laboratory for developing such a research. The aim is to provide a significant progress in the understanding of the driving processes of natural and anthropogenic earthquakes, from the regional to the local scale, and contribute to new insights on the spatial and time-dependent distribution of seismicity along active faults during the interseismic period as well as on their 3D architecture and possible interactions among them.
The seismic big data (approximately about 30 TB) will be rapidly processed by applying fully automated, advanced methodologies incorporating deep neural network and supervised/unsupervised machine learning algorithms. Multi-resolution ground displacement time-series will be obtained by processing EU Copernicus SAR data characterized by a sensor revisiting time of the same scene from 6 to 12 days. A multiphysics modelling to reproduce the effects of different forcing scenarios for the reservoir induced seismicity observed in the High Agri Valley will be developed as well. As a further outcome, FRACTURES will also contribute to developing a data-driven “best practice” protocol to discriminate and manage induced seismicity.
Project data will be compliant with Open Data/Open Science FAIR (Findable, Accessible Interoperable, Reusable) guiding principles and will be standardized and integrated into the EPOS framework, the data infrastructure for Earth Sciences, offering new research opportunities to the international scientific community.
The main objective of FRACTURES is to study the seismogenic processes in Campania-Lucania Apennines related to both natural and anthropogenic seismicity and to map the 3D fault zone architecture by detecting, accurately locating, and characterizing background seismicity and sequences. The seismogenic belt of the Campania-Lucania Apennines consists of different fault segments along which normal faulting earthquakes occur, such as the catastrophic 1980 M 6.9 Irpinia and the 1857 M7.0 Basilicata events. The complex geological-structural setting, the high seismic hazard of the region, the presence of anthropic activities capable of inducing earthquakes (i.e. High Agri Valley area), and the availability of high-quality continuous data stream from a dense virtual seismic network (including also a constellation of seismic arrays), make the Campania-Lucania Apennine an advanced laboratory for developing such a research. The aim is to provide a significant progress in the understanding of the driving processes of natural and anthropogenic earthquakes, from the regional to the local scale, and contribute to new insights on the spatial and time-dependent distribution of seismicity along active faults during the interseismic period as well as on their 3D architecture and possible interactions among them.
The seismic big data (approximately about 30 TB) will be rapidly processed by applying fully automated, advanced methodologies incorporating deep neural network and supervised/unsupervised machine learning algorithms. Multi-resolution ground displacement time-series will be obtained by processing EU Copernicus SAR data characterized by a sensor revisiting time of the same scene from 6 to 12 days. A multiphysics modelling to reproduce the effects of different forcing scenarios for the reservoir induced seismicity observed in the High Agri Valley will be developed as well. As a further outcome, FRACTURES will also contribute to developing a data-driven “best practice” protocol to discriminate and manage induced seismicity.
Project data will be compliant with Open Data/Open Science FAIR (Findable, Accessible Interoperable, Reusable) guiding principles and will be standardized and integrated into the EPOS framework, the data infrastructure for Earth Sciences, offering new research opportunities to the international scientific community.
Start date: September 28, 2023
End date: September 27, 2025
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Conferences presentations
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International
Stabile, T.A., Panebianco, S., Izzi, F., La Scaleia, G., Amato, L., Maio, D., Serlenga, V. “Analysis of seismic activity in the High Agri Valley (southern Italy) from 2016 to 2023 using machine learning algorithms and high-performance computing”, ESC-2024 39th General Assembly, Session 41, Abstract Number 566, Corfu, Greece, 2021, September 22-27 (oral presentation) – SPEAKER
Panebianco, S., Serlenga, V., Tizzani, P., Castaldo, R., Telesca, L., Fat-Helbary, E.R., El-Amin, E.M., Ahmed, H., Stabile, T.A. "Analysis of spatiotemporal evolution of the 1982-2016 Aswan seismicity (south Egypt) using machine learning algorithms and FEM models", ESC-2024 39th General Assembly, Session 24, Abstract Number 670, Corfu, Greece, 2021, September 22-27 (poster)
Panebianco, S., Moratto, L., Satriano, C., Priolo, E., Stabile, T.A. "Using κo estimation to improve microearthquakes source parameters assessment in the High Agri Valley (southern Italy)", ESC-2024 39th General Assembly, Session 41, Abstract Number 566, Corfu, Greece, 2021, September 22-27 (oral presentation)
Serlenga, V., Amoroso, O., Capuano, P., Ferreri, A.P., Filippucci, M., Mungiello, G., Napolitano, F., Panebianco, S., Stabile, T.A., Tallarico, A. "Automatic earthquake detection and location procedures: is all that glitters gold? Two case studies in Southern Italy", ESC-2024 39th General Assembly, Session 11, Abstract Number 596, Corfu, Greece, 2021, September 22-27 (poster)
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