LisAlps - Explorer la lithosphère alpine en 3D par inversion de formes d'ondes des données télésismiques AlpArray - ANR 2021
Probing the 3D Alpine lithosphere by Full Waveform Inversion of the AlpArray teleseismic data
NILAFAR - Quantifier les fluctuations hydrologiques, documenter leurs conséquences sur les communautés humaines passées - ANR PRC 2021
The NILe and AFAR regions: hydrologic changes and impact on human adaptation in the last 20,000 years
EARLI - Détection de signaux sismiques précoces en utilisant l'intelligence artificielle - ERC 2021
Detection od Early seismic signal using ARtificial Intelligence
WIND - Inversion de formes d'ondes - Consortium Pétrolier 2020
Waveform Inversion of Node Data
S5 - Séismes Lents & Essaims Sismiques - ANR 2019
Synchronous Slow Slip & Seismic Swarm
MARACAS - Les terrasses marines comme proxy pour l’appréhension de l’aléa sismique - ANR JC 2018
MARine terraces alonf the northern Andean Coast as a proxy for seismic hazard ASsessment
All the labs former projects
Large subduction earthquakes occur just below densely populated coastal areas and are therefore the source of very high risks for these regions. Among them, South America is one of the most seriously threatened. Its western coast is formed all along by the longest and most active known subduction zone. This project focuses on the northern section of this subduction zone from Northern Ecuador to Northern Peru.
Anticipating the location of the next ruptures and their magnitude is of commensurate importance to properly anticipate the risks and hence to protect the populations. Until recently, the critical questions posed to the scientific community of ‘when, where, and how large will the next earthquakes be?’ have been addressed using characteristics of large past earthquakes as a proxy for the possible future events. In this project, we propose a complementary approach to modern strain and stress monitoring of active tectonic zones.
While the past forty years have been affected by a significant increase in the number of disasters, they have also proved the intricacy of the events when numerous causes mingled together (physical, biological, technological and human causes). Those trends should not reverse over the coming years, for numerous risk factors remain: climate change, geopolitical strains, risks associated with technological development and the needs of human societies, population growth and poverty, environment degradation and urban pressure, etc. (URD, 2010). Modern societies, whatever their stages of development, are still inadequately prepared to cope with the intricacy and suddenness of disaster events, and become resilient. Populations often do not know how they should react or take action to protect themselves against a threat or danger (CEPRI, 2013). If some behaviors prove to be appropriate, some other ones, unfortunately more numerous (Boyd, 1981; ISI, 2012), turn out to be inappropriate (stunning, escape towards the danger zone, etc.) or clearly insane (curiosity, goods protection, etc.), as compared to the behaviors expected by the operational stakeholders (Quarantelli, 2008) and that are recommended in prevention tools. This partial misconception is not confined solely to populations and policy-makers; it also relates to the difficulties encountered by the research community in identifying the range of behavior patterns actually triggered in the face of a disaster (Crocq, 1994), their sequence, dynamics, and interdependence (Provitolo et al. 2015).
Decades of research on earthquakes have yielded meager prospects for earthquake predictability: we cannot predict the time, location and magnitude of a forthcoming earthquake with sufficient accuracy for immediate societal value. Therefore, the best we can do is to mitigate their impact by anticipating the most “destructive properties” of the largest earthquakes to come: longest extent of rupture zones, largest magnitudes, amplitudes of displacements, accelerations of the ground. This topic has motivated many studies in last decades. Yet, despite these efforts, major discrepancies still remain between available model outputs and natural earthquake behaviors. Here we argue that an important source of discrepancy is related to the incomplete integration of actual geometrical and mechanical properties of earthquake causative faults in existing rupture models.