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GEO-4D

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Geodetic data assimilation Forecasting Deformation with InSAR

Recent space-based geodetic measurements of ground deformation suggest a paradigm shift is required in our understanding of the behaviour of active tectonic faults. The classic view of faults classified in two groups – the locked... ver más

31/12/2023

ENS

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

ECOLE NORMALE SUPERIEURE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

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Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2023-12-31

ERC-2017-STG: ERC Starting Grant

I+D

Cerrada hace 8 años

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2 Participantes

ENS

1.50M€ | Lider

BIOMET SPAIN ORTHOPAEDICS,S....

583.09K€ | Participante

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Duración del proyecto: 74 meses Fecha Inicio: 2017-10-09
Fecha Fin: 2023-12-31

Líder del proyecto

ECOLE NORMALE SUPERIEURE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 1M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Recent space-based geodetic measurements of ground deformation suggest a paradigm shift is required in our understanding of the behaviour of active tectonic faults. The classic view of faults classified in two groups – the locked faults prone to generate earthquakes and the creeping faults releasing stress through continuous aseismic slip – is now obscured by more and more studies shedding light on a wide variety of seismic and aseismic slip events of variable duration and size. What physical mechanism controls whether a tectonic fault will generate a dynamic, catastrophic rupture or gently release energy aseismically? Answering such a fundamental question requires a tool for systematic and global detection of all modes of slip along active faults. The launch of the Sentinel 1 constellation is a game changer as it provides, from now on, systematic Radar mapping of all actively deforming regions in the world with a 6-day return period. Such wealth of data represents an opportunity as well as a challenge we need to meet today. In order to expand the detection and characterization of all slip events to a global scale, I will develop a tool based on machine learning procedures merging the detection capabilities of all data types, including Sentinel 1 data, to build time series of ground motion. The first step is the development of a geodetic data assimilation method with forecasting ability toward the first re-analysis of active fault motion and tectonic phenomena. The second step is a validation of the method on three faults, including the well-instrumented San Andreas (USA) and Longitudinal Valley faults (Taiwan) and the North Anatolian Fault (NAF, Turkey). I will deploy a specifically designed GPS network along the NAF to compare with outputs of our method. The third step is the intensive use of the algorithm on a global scale to detect slip events of all temporal and spatial scales for a better understanding of the slip behaviour along all active continental faults.

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