H2020Индивидуална стипендия2019–2021

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

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период: 2019-08-25 → 2021-08-24
Финансиране от ЕС: 196 708 €
Участници: 1
Схема: MSCA-IF-EF-ST

Линиите свързват координатора с партньорите.

Накратко на български

Движенията на тектоничните плочи и тяхното въртене спрямо земната мантия се анализират чрез симулации и машинно обучение. Това помага за по-точното моделиране на минали климатични промени и бъдещата реакция на планетата към глобалното затопляне.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

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

The Problem Plate tectonics processes continuously destroy oceanic crust, which contain the most reliable record of plate motion. There is therefore little data to constrain net rotation of the lithosphere with respect to the deep mantle, constraints on which are required to produce accurate reference frames for plate motion. Accurate plate motion models are required for various geoscience applications including mantle, geodynamo and geochemical modelling of the Earth's interior, and ocean, atmospheric and ecosystem modelling at the surface. Uncertainties in the record of net rotation propagate through plate motion models and eventually limit the certainty on estimates of things such as models of climate change in the geological past. Society Plate motion studies have an important role to play in the modelling of climate change and how the Earth will respond to them. Without accurate plate reconstructions, we cannot model the contributions of changing surface configurations to the changes in the temperature of the Earth's surface, sea-level changes and greenhouse gas concentrations. Without accurate knowledge of how the planet responded to changing greenhouse gas concentrations in the geological past, we cannot accurately model how it is likely to respond to anthropogenic global warming. Objectives I want to use mantle dynamic simulations to study what drives net rotation, to find how to constrain it and therefore how to reduce the uncertainties in plate tectonic reconstructions.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

Plate tectonics processes continuously destroy oceanic crust, which contain the most reliable record of plate motion. There is therefore little data to constrain net rotation of the lithosphere with respect to the deep mantle, constraints on which are required to produce accurate reference frames for plate motion, The location of intra-oceanic plate boundaries and bathymetry in the geological past are also lost. I will use state-of-the-art numerical convection simulations combined with state-of-the-art machine learning techniques to put constraints on both net rotation and the location of plate boundaries with uncertainty estimates. This is possible due to the self-organising and statistically predictable nature of plate tectonics. I will develop one set of neural networks to make inferences for net rotation with uncertainties given observation of continent positions and movement. The networks will take both synthetic and real geological observations as training inputs and produce estimates for net rotation. They will be thoroughly tested using synthetic data and benchmarked using present-day Earth data, thereby testing both the networks and the physics behind the convection simulations. The networks will then be applied to the geological past. A second set of networks will treat the lack of information on oceanic plate boundaries as an image completion problem to fill the gaps in geological data. They will be trained to produce proposals for the location and type of oceanic plate boundaries that are consistent with the physics behind tectonic motion and mantle convection. The networks learn about the physics from the database of convection simulations. These proposals can be assessed against geological and palaeo-oceanographic data, provide suggestions for alternative solutions, give an indication of uncertainties and guide future data collection and modelling work.

Оригинален текст от CORDIS (на английски).

Участници

ECOLE NORMALE SUPERIEURE · ParisКоординаторФранция