Summary
Earthquakes are a major threat to humankind, causing damage above 500 billion USD and more than 400,000 fatalities within the last 20 years. Nonetheless, the generation of large earthquakes remains poorly understood. Recent research suggests that the key to deciphering this preparatory phase lies in the complex interplay of seismic and aseismic processes. Three event types are of genuine interest: slow slip events (SSEs), which are episodic aseismic deformations; low-frequency earthquakes (LFEs), a type of earthquake depleted in high-frequency energy; and regular earthquakes.
A key factor limiting the understanding of the seismic-aseismic interplay are incomplete LFE catalogs, caused by the difficulty to detect these events. Therefore, in this project I will develop a novel detection method for LFEs building on recent advances in deep learning. Applying this method, I will compile comprehensive catalogs for three regions: Northern Chile, Nankai (Japan) and Nicoya (Costa Rica). These catalogs, in conjunction with continuous geodetic records, SSE catalogs and seismicity catalogs, will allow me to study the seismic-aseismic interplay. This will reveal physical driving mechanisms of the seismic-aseismic interplay and give insights into the preparation of large earthquakes. This will contribute towards the accurate assessment of seismic hazard and the preparedness for seismic events.
I will conduct this project at the Université Grenoble Alpes, with a secondment at the Massachusetts Institute of Technology. My scientific background in interdisciplinary research between deep learning and seismology, is complemented by my supervisiors: Anne Socquet, expert on aseismic processes and subduction zones; and William Frank, expert in the detection and characterisation of LFEs. The project, together with targeted training activities, will refine my scientific profile and extend my skill set, enabling me to define my independent research agenda and pursue a career in research.
A key factor limiting the understanding of the seismic-aseismic interplay are incomplete LFE catalogs, caused by the difficulty to detect these events. Therefore, in this project I will develop a novel detection method for LFEs building on recent advances in deep learning. Applying this method, I will compile comprehensive catalogs for three regions: Northern Chile, Nankai (Japan) and Nicoya (Costa Rica). These catalogs, in conjunction with continuous geodetic records, SSE catalogs and seismicity catalogs, will allow me to study the seismic-aseismic interplay. This will reveal physical driving mechanisms of the seismic-aseismic interplay and give insights into the preparation of large earthquakes. This will contribute towards the accurate assessment of seismic hazard and the preparedness for seismic events.
I will conduct this project at the Université Grenoble Alpes, with a secondment at the Massachusetts Institute of Technology. My scientific background in interdisciplinary research between deep learning and seismology, is complemented by my supervisiors: Anne Socquet, expert on aseismic processes and subduction zones; and William Frank, expert in the detection and characterisation of LFEs. The project, together with targeted training activities, will refine my scientific profile and extend my skill set, enabling me to define my independent research agenda and pursue a career in research.
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| Web resources: | https://cordis.europa.eu/project/id/101104996 |
| Start date: | 01-10-2023 |
| End date: | 30-09-2025 |
| Total budget - Public funding: | - 195 914,00 Euro |
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Original description
Earthquakes are a major threat to humankind, causing damage above 500 billion USD and more than 400,000 fatalities within the last 20 years. Nonetheless, the generation of large earthquakes remains poorly understood. Recent research suggests that the key to deciphering this preparatory phase lies in the complex interplay of seismic and aseismic processes. Three event types are of genuine interest: slow slip events (SSEs), which are episodic aseismic deformations; low-frequency earthquakes (LFEs), a type of earthquake depleted in high-frequency energy; and regular earthquakes.A key factor limiting the understanding of the seismic-aseismic interplay are incomplete LFE catalogs, caused by the difficulty to detect these events. Therefore, in this project I will develop a novel detection method for LFEs building on recent advances in deep learning. Applying this method, I will compile comprehensive catalogs for three regions: Northern Chile, Nankai (Japan) and Nicoya (Costa Rica). These catalogs, in conjunction with continuous geodetic records, SSE catalogs and seismicity catalogs, will allow me to study the seismic-aseismic interplay. This will reveal physical driving mechanisms of the seismic-aseismic interplay and give insights into the preparation of large earthquakes. This will contribute towards the accurate assessment of seismic hazard and the preparedness for seismic events.
I will conduct this project at the Université Grenoble Alpes, with a secondment at the Massachusetts Institute of Technology. My scientific background in interdisciplinary research between deep learning and seismology, is complemented by my supervisiors: Anne Socquet, expert on aseismic processes and subduction zones; and William Frank, expert in the detection and characterisation of LFEs. The project, together with targeted training activities, will refine my scientific profile and extend my skill set, enabling me to define my independent research agenda and pursue a career in research.
Status
SIGNEDCall topic
HORIZON-MSCA-2022-PF-01-01Update Date
31-07-2023
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