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NERC GW4+ DTP PhD Studentship for 2024 Entry - Bayesian Sampling Methods for Geophysical Inversion using Multi-component Seismic Data

University of Exeter - Department of Mathematics

Qualification Type: PhD
Location: Cornwall, Penryn
Funding for: UK Students, EU Students, International Students
Funding amount: From £18,622
Hours: Full Time
Placed On: 2nd November 2023
Closes: 9th January 2024
Reference: 4950

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.

Project Background

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.

Project Aims and Methods

This project will explore advanced signal and image processing, machine learning approaches, in particular, deep-learning and Bayesian inference methods for parameter estimation, uncertainty quantification and probabilistic inversion of large-scale geological models fusing multicomponent geophysical/seismic data such as 3 component acceleration from geophones and pressure data from hydrophones and other petrophysical data. Here, the aim is to reduce computational time utilising the recent advancements in deep neural networks and deep Gaussian processes to approximate the physical data generation process, i.e. the seismic wave propagation and reservoir fluid flow simulations. The main target of this project is to explore and compare the Markov Chain Monte Carlo samplers and nested sampling approaches for Bayesian model comparison in seismic imaging. Uncertainty quantification in such geophysical inverse problems in terms of both seismic source properties and unknown elastic geological models (density, compressional and shear wave velocity) and petrophysical parameters like permeability, porosity etc. is a complex problem [4]-[5]. Geophysical inverse problems rely on the travel-time calculation between sources and receivers. However, uncertainties in the velocity model can make these estimates highly erroneous. Alternatively, a full seismic wave-based inversion can be attempted for improved imaging, albeit being computationally challenging. The project will also explore the inversion results of 3-component geophone recordings apart from pressure measurements by hydrophones. Traditional inversion or seismic imaging methods involve a series of heuristic filtering steps that can be more optimally selected using a deep machine learning based expert system [6].

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