Characterisation of background seismicity in the UK: putting induced seismicity into perspective
Dr S Pytharouli
Prof R Lunn
No more applications being accepted
Competition Funded PhD Project (European/UK Students Only)
The UK has committed to a net zero carbon emission target for 2050. With industry being one of the largest emitters and heating being the most challenging area to decarbonise, technologies such as CO2 capture & storage and geothermal energy will play a crucial role in the transition to a low carbon economy. However, their development is prohibited by their association with induced seismicity and it has become clear that environmental assurance is required for any technology to become socially accepted. Increasing the public confidence in regulatory control, requires to adequately predict and distinguish between induced and natural microseismicity. The distinction between the two remains a challenge, especially in translating this learning into new low-carbon technologies. Whilst prediction of the frequency of large events is well understood, there are no published studies exploring the frequency of natural microseismic events that include data for events with magnitude ML < 0. This project aims to characterise background seismicity in the UK and develop a methodology that would make the occurrence frequency prediction model transferable to other areas in the world. There are three specific objectives: (1) collect microseismic data at a site in Scotland, a second site at a naturally high seismically area in Europe (e.g. Italy) and at a site with high induced seismicity rates, e.g. Oklahoma, (N. America), (2) use machine learning algorithms to detect and classify microseismic events and (3) derive the frequency-magnitude distribution for seismic events with M<2 for the selected sites. The University of Strathclyde owns three short-period microseismic arrays that can detect events down to M-3 at distances up to 10 km, which will be deployed for this project. Detection of microseismicity will be based on in-house detection and classification algorithms (Li et al., IEEE Trans. Geosci. Remote Sens, in review) that incorporate machine learning, currently applied for analysis of outstanding recordings from the Alberta Geological Survey. A background in Geosciences with basic knowledge of computer programming are desirable for delivery of this research. The successful PhD student will benefit from a visit to Virginia Tech for training and discussions with Prof M. Chapman (assessment and mitigation of earthquake hazards) and Dr Pollyea (geofluids and energy resources) within the Department of Geosciences, Virginia Tech (US) with whom the supervisory team has strong collaboration links.
Studentships are fully funded for 4 years and cover tuition fees and stipend at the UK Research & Innovation recommended levels for each year of study. For the 2020/21 academic session, this is £4,327 for fees and £15,009 for stipend. Studentships also provide a generous £20,000 individual allowance to cover costs associated with pursuing the PhD over the 4-year study period e.g. conference travel, data collection, equipment purchase, travel to and from CDT training courses.
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FTE Category A staff submitted: 20.20
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