Investigating local noise sources and background seismicity in London

Project Details

This project is co-funded by the Leverhulme prize

Key Questions

How much seismicity?

Background

Local geology and seismic hazards are generally investigated through the analysis of seismic data. However, in urban environments, estimating background seismicity and seismic imaging is complicated by high levels of anthropogenic seismic noise, i.e. noise generated by our daily lives and activities. As a consequence, we only have a poor understanding of subsurface structures and their potential seismic risk under metropoles such as London, even though these may affect the lives of millions of people.

Aims of the Project

This project aims to better characterise the sources of seismic noise and the background seismicity levels in London. Although London is not known for having significant seismicity, Mason et al. (2015) observed surface deformation that could be linked to relatively recent activity on subsurface fault structures. This project will take advantage of current lower anthropogenic noise levels to investigate background noise and seismicity and compare the findings to these recent ground deformation results.

Project Description

In recent years, urban seismology has become an active research field, not only for seismology (e.g. Green et al., 2015), but also for social science and engineering purposes (e.g. Díaz et al., 2017, 2020; Badcoe et al., 2020). Although the deployment of professional instruments has not significantly increased, the development of citizen science instruments such as Raspberry Shakes has led to a wealth of seismic data in urban environments. Despite being a fraction of the cost and easy to install, these instruments are able to capture teleseismic and local earthquakes, with background noise levels similar as on professional instruments.

 

During the Covid-19 pandemic, lockdowns resulted in decreased anthropogenic seismic noise, which was reliably detectable around the globe (Lecocq et al., 2020a). Data from this period have significantly improved our understanding of anthropogenic noise sources and, accordingly, our ability to isolate and detect natural seismicity (De Plaen et al., 2021). Combined with the finding that Raspberry Shakes are capable of recording meaningful information regarding seismic noise sources, this provides an unprecedented opportunity to better estimate background seismicity in urban environments.

background seismicity levels in London. Although London is not usually considered to be tectonically active (and thus no professional seismometers are installed), subsurface movements have recently been observed on previously unknown fault structures, with implications for engineering projects and seismic hazard maps (Morgan et al., 2021). This project will take advantage of our new understanding of anthropogenic seismic noise to isolate and investigate these seismogenic structures.

 

Methodology

The project will combine seismic data from existing instruments as well as new deployments. Particularly, we will install an array of Raspberry Shakes and other seismic instruments around London. These will be installed both underground and above ground (using existing contacts of the supervisors) to optimise station coverage and obtain a larger seismic data set.

 

Existing Python packages will be used to detect and analyse anthropogenic signals and natural seismicity. Noise sources will be investigated using SeismoRMS (Lecocq et al., 2020b), which analyses seismic noise as a function of time and frequency. Data from existing instruments will be incorporated and will be useful for identifying dominant sources of seismic noise by comparing data from before, during and after Covid-19 lockdowns.

 

Stacking techniques will be used to search for small local seismic events, with power law relationships subsequently utilised to characterise background seismicity. Depending on time and data quality, seismic velocity may be developed using noise correlations or microseismicity. The outcomes of these endeavours will be compared to observations of subsurface faults and recent surface deformation (Morgan et al., 2021).

 

Training & skills gained

The successful candidate will join the seismology group at the University of Oxford, and benefit from interactions with existing PhD students, postdocs and faculty who work on similar topics.

 

The PhD student will receive training in computational methods and the processing of large seismic data sets, as well as the analysis of seismic noise and seismicity. In addition, they will be mentored on how to prepare scientific results at (inter)national conferences, how to write manuscripts for publication in international journals and how to communicate their science to a general audience.

 

In addition to the training in these transferable skills and research skills, the student will be provided with advice on funding applications and career support.

Methods to be used

The PhD candidate will analyse seismic data from existing instruments as well as be involved in new deployments. Particularly, the project aims to install an array of Raspberry Shakes around London, some of which will be co-located with reflectors for radar tracking. Stacking techniques will be used to search for small local seismic events, with power law relationships subsequently utilised to characterise background seismicity. Using noise correlations, seismic velocity models will be developed and compared to local geology constraints and deformation data. Of particular interest will be the analysis of seismic data before, during and after the governmental lockdowns in 2020, which will aid in investigating the dominant sources of seismic noise.

Specialised skills required

This project is suitable for a numerate candidate with an interest in seismology and seismic risk. They should have a background in Physics or Geophysics, preferably with knowledge of seismology and programming experience. 

Please contact Dr Paula Koelemeijer (at Oxford from May 2022: paula.koelemeijer@rhul.ac.uk) or Prof Mike Kendall (mike.kendall@earth.ox.ac.uk) if you are interested in this project.