Daniel Sefton
- Email: eedts@leeds.ac.uk
- Thesis title: Detecting and modelling transient crustal deformation using Sentinel-1 InSAR and Machine Learning
- Supervisors: Professor Tim Wright, Dr Susanna Ebmeier, Dr Laura Gregory, Dr Gordon Ross
Profile
I am PhD Student based within the Institute of Geophysics and Tectonics within the School of Earth and Environment. My research, which is funded by the SENSE CDT, aims to use InSAR and machine learning to characterise time-dependent interseismic deformation with a particular focus on automatically detecting aseismic creep transients along continental faults. This will allow for an improved understanding of the way in which faults behave and will also have potential for improving seismic hazard asssements.
I graduated from the University of Cambridge with an MSci in Natural Sciences which enabled me to study a wide range of areas including physics, materials science, geology and geophysics. My masters project involved extracting and characterising ambient noise using a large dataset obtained by a dense array of seismometers in north-west Turkey. This sparked my interest for exploring large and interesting datasets, such as the one being acquired by Sentinel-1 and other SAR satellites currently.
Research interests
- Geodesy
- Fault Deformation and Tectonics
- Interferometric Synthetic Aperture Radar (InSAR)
- Machine Learning
- Earth Observation
Qualifications
- MSci Natural Sciences, University of Cambridge
Research groups and institutes
- Institute of Geophysics and Tectonics