SOURCE-LINKED INTELLIGENCE

Dynamics of Deep Earthquakes

CORDIS · observation · Publication date unknown

question we need a methodological shift to obtain new observations of deep Earth processes and dynamics, which must be integrated into realistic physical models of slabs. The SODA project introduces deep learning methods to analyse seismological data aiming at improving the detection, characterization and classification of deep events. The outcome will be a new generation of observations offering an unprecedented view of the spatiotemporal evolution (e.g., spatial migrations) of deep events, directly informing us of the physical processes occurring in subduction zones (e.g., fluid movements, aseismic deformation). We will then determine the complex thermal structure, mineralogy, and geometry of subduction zones. Finally, we will use machine learning to unveil the relationships in between our new seismological observations and the modelled physical properties of the slabs, to quantitatively unravel the boundary conditions controlling the occurrence and the dynamics of deep events. The integrated approach of the SODA project will provide an unparalleled quantitative view of the deep E

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recordType

award

status

SIGNED

region

EU

value

1947405

unit

EUR