SOURCE-LINKED INTELLIGENCE

Learning the Earth with Neural Operators

CORDIS · observation · Publication date unknown

ts. Calculations are slow and often miss small-scale features that are crucial for hazard assessment, limiting our ability to exploit the growing volume of seismic data worldwide. Recent advances in artificial intelligence offer a pathway to overcome these barriers. Machine learning can capture the relationship between Earth structure and seismic wave propagation, enabling accurate waves simulations orders of magnitude faster than traditional methods. This makes it possible to explore thousands of Earth models and quantify uncertainty in imaging results, providing confidence levels that were previously too costly to compute. In this project, I will advance AI-based techniques for seismic imaging and apply them to dense datasets at multiple scales. A key target is the MACIV experiment, the largest volcanological deployment to date, with 150 broadband and 600 short-period stations, offering a unique chance to probe intraplate volcanic systems in Europe. I will also test the framework on other networks, including USArray and AlpArray to apply it across diverse geological context. I w

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

446228.88

unit

EUR