Preparation of Subduction Earthquakes Slow Deep Large scale trigger
Subduction zones host the world’s largest earthquakes and tsunamis. Understanding how such earthquakes initiate and interact is a first-order challenge in earth sciences. A puzzling and unexplained observation is that megathrust e...
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Información proyecto DEEP-trigger
Duración del proyecto: 73 meses
Fecha Inicio: 2020-07-09
Fecha Fin: 2026-08-31
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Descripción del proyecto
Subduction zones host the world’s largest earthquakes and tsunamis. Understanding how such earthquakes initiate and interact is a first-order challenge in earth sciences. A puzzling and unexplained observation is that megathrust earthquakes seem to be clustering at the scale of the plate boundary. This suggests that the subducting slab plays an important role, albeit downplayed, in the triggering of megathrust earthquakes.
I propose to study subduction zones recently affected by megathrust events or earthquakes sequences, with an enlarged perspective, considering the mechanisms of deformation in the larger subduction system, including the slab, and their potential role in pushing the megathrust to failure:
- At the scale of the seismic asperity, how aseismic slip can trigger earthquakes and how this triggering depends on depth, duration, migration, periodicity, and amplitude will be examined, notably by integrating new observations of small, short or long-lived slow slip events.
- At the scale of the subduction zone, how distant changes transfer into large-scale deformation, potentially initiate slow slip and eventual rupture on the plate interface will be analyzed. The role of metamorphic fluids in the softening of the mantle surrounding the slab, which may contribute to distant triggering of earthquakes, and recently observed deep-shallow interactions will be explored.
Multi-scale observations of deformation and seismicity will be extracted from the great amount of geophysical data available in South America, Japan and Sumatra, complemented by in-situ GPS monitoring at a promising area in south Peru. Machine Learning will serve to systematize these complementary observables, to characterize how their empirical relationships evolve with time and space, and to isolate the key processes hidden in these large datasets. Physical mechanisms driving the plate interface destabilization will be explored through mechanical and fluid modeling, and tested against the data.