Flagship Research Projects

LISALPS IconThe Alpine chain has historically been one of the main natural laboratories to study orogenesis due to its outstanding outcrop conditions. Despite countless investigations, the deep structures imaged by geophysical methods and related tectonic processes remain controversial, due to the high complexity of the chain but also from the lack of high-resolution structural information at depth. To fill this gap, the European AlpArray consortium deployed 628 broadband stations, spaced less than 52 km apart, across the whole chain, and 30 ocean-bottom seismometers in the Ligurian basin, hence providing a unique opportunity for a step change in the 3D imaging of the Alpine lithosphere and asthenosphere. The LisAlps project proposes to apply Full Waveform Inversion (FWI) on the teleseismic data recorded during AlpArray to build:
(1) a new reference high-resolution multi-parametric (1500x700 km) model of the alpine lithosphere and asthenosphere down to 700km depth from the entire network and a catalogue of ~300 teleseismic earthquakes (periods: 5s-20s);
(2) a high-resolution model of the lithosphere in the western Alps around the structurally-complex Ligurian knot.


The NILAFAR program proposes to study the impact of hydrological fluctuations on societies in NE Africa over the last 20,000 years and in particular on the development of pastoralism. NILAFAR also aims to better understand the climatic mechanisms at the origin of the fluctuation intensity of the African monsoon (external forcing and internal feedbacks of the climate system), and at the origin of short hyperarid episodes. During the African Humid Period (AHP) these episodes saw the retraction or dissemination of particularly mobile human groups. In an environment that may have been as limiting as it was stimulating due to the alternance of these hyperarid and wet phases, populations innovated using new economic strategies, shifting from a predation to a production economy.

logo EARLINatural hazards such as earthquakes are difficult to predict. Dramatic developments in the field of artificial intelligence (AI), however, are paving the way for anticipating destructive events. The EU-funded EARLI project will use AI to identify weak, early seismic signals to both speed up early warning and explore the possibility of earthquake prediction. Specifically, it will implement an early-warning approach based on a newly identified signal, caused by the perturbation of the gravity field generated by an earthquake, which is ~6 orders of magnitude smaller than seismic waves (strongly limiting its detection with standard techniques), but precedes them. The second, more exploratory, objective will be to adapt the developed AI algorithm to search for even earlier signals preceding the origin of large earthquakes.