Back to search results

Bayesian Machine Learning and Sampling Methods for Geophysical Inversion using Large Scale Seismic and Reservoir Simulations, NERC GW4+ DTP PhD studentship for 2022 Entry, PhD in Mathematics

University of Exeter - Department of Mathematics

Qualification Type: PhD
Location: Exeter
Funding for: UK Students, EU Students
Funding amount: Tuition fees and stipend for 3.5 years (currently £15,609 p.a. for 2021/22)
Hours: Full Time
Placed On: 22nd October 2021
Closes: 10th January 2022
Reference: 4257

Funding: Tuition fees and stipend for 3.5 years (currently £15,609 p.a. for 2021/22). Also covers research budget of £11,000 for an international conference, lab, field and research expenses and a training budget of £3,250 for specialist training courses and expenses.

Lead Supervisor

Dr Saptarshi Das, Department of Mathematics, University of Exeter, Penryn Campus, Cornwall

Additional Supervisors

Dr Xi Chen, Department of Computer Science, University of Bath 

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.  It aims to provide a broad training in earth and environmental sciences, designed to train tomorrow’s leaders in earth and environmental science http://nercgw4plus.ac.uk/ .

Project Background

In earth science 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. Both the FWI and 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 deep Gaussian process models. Stress accumulation and fluid flow movement monitoring in reservoir needs complex geophysical and petrophysical simulations using known velocity models, permeability, permittivity etc. More information is available on the University’s website.

Project Aims and Methods

This project will explore advanced signal/image processing, machine learning approaches, in particular, deep-learning and Bayesian inference for parameter/uncertainty estimation and probabilistic inversion of large-scale geological models fusing geophysical/seismic and 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 concepts from seismic interferometry will also be used for turning highly noisy traces into useful interpretable signals using various correlation-based methods. Quantification of the uncertainties 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. 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. More information is available on the University’s website.

We value your feedback on the quality of our adverts. If you have a comment to make about the overall quality of this advert, or its categorisation then please send us your feedback
Advert information

Type / Role:

Subject Area(s):

Location(s):

PhD tools
 

PhD Alert Created

Job Alert Created

Your PhD alert has been successfully created for this search.

Your job alert has been successfully created for this search.

Ok Ok

PhD Alert Created

Job Alert Created

Your PhD alert has been successfully created for this search.

Your job alert has been successfully created for this search.

Manage your job alerts Manage your job alerts

Account Verification Missing

In order to create multiple job alerts, you must first verify your email address to complete your account creation

Request verification email Request verification email

jobs.ac.uk Account Required

In order to create multiple alerts, you must create a jobs.ac.uk jobseeker account

Create Account Create Account

Alert Creation Failed

Unfortunately, your account is currently blocked. Please login to unblock your account.

Email Address Blocked

We received a delivery failure message when attempting to send you an email and therefore your email address has been blocked. You will not receive job alerts until your email address is unblocked. To do so, please choose from one of the two options below.

Max Alerts Reached

A maximum of 5 Job Alerts can be created against your account. Please remove an existing alert in order to create this new Job Alert

Manage your job alerts Manage your job alerts

Creation Failed

Unfortunately, your alert was not created at this time. Please try again.

Ok Ok

Create PhD Alert

Create Job Alert

When you create this PhD alert we will email you a selection of PhDs matching your criteria.When you create this job alert we will email you a selection of jobs matching your criteria. Our Terms and Conditions and Privacy Policy apply to this service. Any personal data you provide in setting up this alert is processed in accordance with our Privacy Notice

Create PhD Alert

Create Job Alert

When you create this PhD alert we will email you a selection of PhDs matching your criteria.When you create this job alert we will email you a selection of jobs matching your criteria. Our Terms and Conditions and Privacy Policy apply to this service. Any personal data you provide in setting up this alert is processed in accordance with our Privacy Notice

 
 
 
More PhDs from University of Exeter

Show all PhDs for this organisation …

More PhDs like this
Join in and follow us

Browser Upgrade Recommended

jobs.ac.uk has been optimised for the latest browsers.

For the best user experience, we recommend viewing jobs.ac.uk on one of the following:

Google Chrome Firefox Microsoft Edge