Environmental Seismology: Using Large-N seismology to image the critical zone

Soil moisture is critical for production of food, ecosystem function and can play a controlling factor in some hazards, such as flooding and wildfires. However, to date, monitoring of this region relies on point source or satellite observations. This PhD will use data from a recent large-N passive seismology nodal deployment in the UK, where 1200 nodes were deployed in a farm in Dumfries, Scotland. Recent analysis of these data shows that changes in seismic velocity at high frequency (50 Hz) are driven by soil moisture changes in the critical zone, providing a new tool for imaging and understanding this crucial bit of the Earth at temporal and spatial scales not previously available. 

The PhD will develop this further by inverting this large dataset for temporally evolving models of velocity, density and other elastic parameters and relate this to changes in soil moisture in the subsurface. Exploring new methods that leverage machine learning and can account for uncertainties (e.g., Bayesian inversions, Zhao & Curtis, 2024), the student will determine how robustly soil saturation can be determined from seismic velocity anomalies, thus understand the usability of this method in agricultural settings. There is scope to include fieldwork to collect more seismic node data in other UK environments.

Potential CASE partners are seismic node manufacturer Stryde, where students can explore novel uses for rapidly evolving seismic node technology.