Combining Tectonics and Machine Learning to Constrain Plate Reconstruction Models Through Time

Project description

Neural networks aid plate tectonics modelling

The constraints of the Earth’s lithosphere net rotation with respect to the underlying mantle cannot be determined from oceanic crust, as it is destroyed by plate tectonics. On the other hand, plate tectonics have a self-organising and statistically predictable nature that can be taught to neural networks. The TEMPO project will produce estimates for net rotation by training networks to use current data on the Earth, synthetic data, and the rules of physics. They will thus generate proposals of tectonic motion and mantle convection that will be tested against geological data, contributing to further research and the modelling of plate tectonics.

Fields of science

• natural sciences › computer and information sciences › databases
• natural sciences › earth and related environmental sciences › geology › seismology › plate tectonics
• natural sciences › computer and information sciences › artificial intelligence › machine learning
• natural sciences › computer and information sciences › artificial intelligence › computational intelligence

Programme(s)

• H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions Main Programme
• H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility

Topic(s)

• MSCA-IF-2018 - Individual Fellowships

Call for proposal

H2020-MSCA-IF-2018

See other projects for this call

Funding Scheme

MSCA-IF-EF-ST - Standard EF

Coordinator

ECOLE NORMALE SUPERIEURE

Net EU contribution

€ 196 707,84

Address

45, Rue D'ulm
75230 Paris Cedex 05
France

Region

Ile-de-France › Ile-de-France › Paris

Activity type

Higher or Secondary Education Establishments

Links

Other funding

€ 0,00