PhD Studentship (NERC GW4+ DTP) - Bayesian Sampling Methods for Geophysical Inversion using Multi-component Seismic Data (September 2025 Entry)

About the Partnership

This project is one of a number that are in competition for funding from the NERC Great Western Four+ Doctoral Training Partnership (GW4+ DTP). The GW4+ DTP consists of the Great Western Four alliance of the University of Bath, University of Bristol, Cardiff University and the University of Exeter plus five Research Organisation partners: British Antarctic Survey, British Geological Survey, Centre for Ecology and Hydrology,  the Natural History Museum and Plymouth Marine Laboratory. The partnership aims to provide a broad training in earth and environmental sciences, designed to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see http://nercgw4plus.ac.uk/

Project details

For information relating to the research project please contact the lead Supervisor via s.das3@exeter.ac.uk

Project Aims and Methods

In earth sciences for hydrocarbon and mineral exploration, determining subsurface and source properties from seismic traces are challenging tasks, commonly known as the full-waveform inversion (FWI) and seismic source inversion, respectively. Often, seismic data are buried under significant amounts of ambient noise and combined with uncertainties in the geological model which complicates the inversion process. The geophysical source inversion are important aspects of subsurface monitoring to constrain changing material properties and evolving stress-fields of large geological models. Such inverse problems usually employ Monte Carlo simulation frameworks, requiring thousands of forward simulations on large complex geological models, which demand significant computing time and resource.  

This project will aim to accelerate this process using recent advances in Bayesian inference and machine learning, especially utilizing deep learning and Gaussian process models [1]-[3]. Stress accumulation and fluid flow movement monitoring in reservoir needs complex geophysical and petrophysical simulations using known velocity models, permeability, density etc. Efficient management and processing of such large volumes of synthetic seismic and petrophysical data in a probabilistic geophysical inversion, seismic imaging and uncertainty quantification is an open challenge, with outcomes that will benefit both industrial and academic research. 

Training

The DTP offers funding to undertake specialist training relating to the student’s specialist area of research.

To apply, please click on the ‘Apply’ button above

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