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Using deep learning to better image earthquakes

Prof. Andy Hooper (SEE), Prof. David Hogg, Dr S. Ebmeier

Contact email: a.hooper@leeds.ac.uk

Summary

Radar Interferometry (InSAR) is a techniques that can provide measurements of surface displacement from Space, with millimetric accuracy. These measurements are used in the natural hazards community, e.g. for earthquake analysis and landslide monitoring, and for monitoring anthroprogenic activities, such as oil and gas extraction, and drawdown of underground water storage. A key step in the InSAR processing chain is that of phase unwrapping, which is the estimation of the integer ambiguities that are inherent with any measurement of phase. Although there are several existing algorithms that exist to do this automatically, they all generally fail when there is movement on faults at the surface, and visual inspection accompanied by manual correction is currently the only failsafe way to achieve this. Artificial Intelligence offers a novel way to solve this problem through the application of deep learning algorithms.

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Related undergraduate subjects:

  • Applied mathematics
  • Civil engineering
  • Computer science
  • Computing
  • Earth science
  • Electrical engineering
  • Geophysical science
  • Geophysics
  • Geoscience
  • Physics