Project description
Computers for seismic hazards prediction
Predicting ground shaking and classifying its impact is fundamental to assess seismic danger. However, the high cost associated with full 3D viscoelastic seismic wavefield simulations puts this out of reach. Researchers rely on empirical relations that cannot capture the path effects of wave propagation that could predict factors that increase ground shaking. The EU-funded TerraPINN project will use an innovation in scientific machine learning, the physics informed neural networks (PINNs), to address the 3D viscoelastic wavefield propagation issue through deep learning. The project relies on a fully trained network to significantly reduce the time required to compute the seismic response for arbitrary sources and receivers, permitting seismic danger prediction in a probabilistic framework.
Objective
In regions of high seismicity, it is essential for society to understand the associated seismic hazard. A cornerstone of seismic hazard assessment is the ability to predict what kind of ground shaking occurs from a particular type of source at some given location; however, given the immense expense of full 3D viscoelastic seismic wavefield simulations, researchers typically rely on empirical relations that do not capture the path effects of wave propagation, which can significantly increase ground shaking by waveguiding and other effects. I propose to use a novel development in scientific machine learning - Physics Informed Neural Networks (PINNs) - to solve the 3D viscoelastic wavefield propagation problem. A fully trained network will drastically reduce the time required to compute the seismic response for arbitrary sources and receivers, enabling fully physics based seismic hazard in a probabilistic framework. PINNs utilize our knowledge of the physics, in this case the equations of motion for continuous media, to regularize learning, which reduces the required amount of training data by many orders of magnitude. We will utilize the PINN wavefield solver in a testbed study of physics based seismic hazard assessment for Southern California, with the goal of producing a framework that is computationally accessible to apply across the world.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- social sciences sociology governance crisis management
- natural sciences computer and information sciences artificial intelligence machine learning
- natural sciences computer and information sciences artificial intelligence computational intelligence
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
MAIN PROGRAMME
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H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-MSCA-IF-2020
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
OX1 2JD Oxford
United Kingdom
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.