Deep Machine Learning for Probabilistic Seismic Inversion and Imaging, NERC GW4+, PhD in Mathematics studentship Funded) Ref: 3332

About the award

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/

For eligible successful applicants, the studentships comprises:

  • An index-linked stipend for 3.5 years (currently £14,777 p.a. for 2018/19);
  • Payment of university tuition fees;
  • A research budget of £11,000 for an international conference, lab, field and research expenses;
  • A training budget of £4,000 for specialist training courses and expenses.

Up to 30 fully-funded studentships will be available across the partnership.

Eligibility
Students from EU countries who do not meet the residency requirements may still be eligible for a fees-only award but no stipend.  Applicants who are classed as International for tuition fee purposes are not eligible for funding.

Project details

In earth science 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 significantly accelerate this process using recent advances in deep machine learning. Efficient management and processing of such large volumes of real and synthetic seismic data in a probabilistic seismic inversion and imaging process is an open challenge, with outcomes that will benefit both industrial and academic research.

Project Aims and Methods

This project will explore advanced signal/image processing and machine learning approaches, in particular, deep-learning and Bayesian inference for parameter estimation and probabilistic inversion of large-scale geological models. The aim is to reduce computational time utilising the recent advancements in deep neural networks to approximate the physical data generation process, i.e. the seismic wave propagation simulation. Concept 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 inverse problems in terms of both seismic source properties and unknown elastic geological models (density, compressional and shear wave velocity) is a complex problem. Traditional 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 in a marine environment. The 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.

Training

The student will receive the required training to pursue fundamental and applied research in this project and will have the opportunity to attend some of the departmental modules in Mathematics. The student will exchange their research findings and methods with their peers and other researchers through regular presentations and conference attendance. The project is inter-disciplinary in nature, so the student will have the opportunity to learn and discuss with other researchers both from Mathematics and Geology. It is expected that the student will also strengthen the existing collaborations of the supervisors with the Oil and Gas and Geoscience industries, and other stakeholders.

CASE or Collaborative Partner 
The student will be manly based in the University of Exeter, Penryn Campus and will also closely collaborate with the University of Bristol. This will be in the form of sharing seismic datasets, communicating the research findings with the researchers in Bristol, develop better understanding and interpretations of seismic data analysis in a broader industrial and academic context, and writing joint collaborative publications.

NERC

Fig.1 Example of a 3D heterogeneous elastic geological model

  NERC
Fig.2 Synthetic seismograms for random seismic events in the subsurface
 

References / Background reading list

1. Tarantola, A., 2005. Inverse problem theory and methods for model parameter estimation (Vol. 89). SIAM.
2. Das, S., Chen, X. and Hobson, M.P., 2017. Fast GPU-Based Seismogram Simulation from Microseismic Events in Marine Environments Using Heterogeneous Velocity Models. IEEE Transactions on Computational Imaging, 3(2), pp.316-329.
3. Das, S., Chen, X., Hobson, M.P., Phadke, S., van Beest, B., Goudswaard, J. and Hohl, D., 2018. Surrogate regression modelling for fast seismogram generation and detection of microseismic events in heterogeneous velocity models. Geophysical Journal International, 215(2), pp.1257-1290.
4. Schuster, G.T., 2017. Seismic inversion. Society of Exploration Geophysicists.
5. Araya-Polo, M., Jennings, J., Adler, A. and Dahlke, T., 2018. Deep-learning tomography. The Leading Edge, 37(1), pp.58-66.
6. Yilmaz, Ö., 2001. Seismic data analysis (Vol. 1, pp. 74170-2740). Tulsa, OK: Society of Exploration Geophysicists.
7. Schuster, G., 2009. Seismic interferometry. Cambridge University Press.
8. Huang, L., Dong, X. and Clee, T.E., 2017. A scalable deep learning platform for identifying geologic features from seismic attributes. The Leading Edge, 36(3), pp.249-256.
9. Wang, Z., Di, H., Shafiq, M.A., Alaudah, Y. and AlRegib, G., 2018. Successful leveraging of image processing and machine learning in seismic structural interpretation: A review. The Leading Edge, 37(6), pp.451-461.
10. Xiong, W., Ji, X., Ma, Y., Wang, Y., BenHassan, N.M., Ali, M.N. and Luo, Y., 2018. Seismic fault detection with convolutional neural network. Geophysics, 83(5), pp.1-28.
11. Bugge, A.J., Clark, S.R., Lie, J.E. and Faleide, J.I., 2018. A case study on semiautomatic seismic interpretation of unconformities and faults in the southwestern Barents Sea. Interpretation, 6(2), pp.SD29-SD40.

Entry requirements

Applicants should have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK.   Applicants with a Lower Second Class degree will be considered if they also have Master’s degree.  Applicants with a minimum of Upper Second Class degree and significant relevant non-academic experience are encouraged to apply.

Candidate Requirements
The candidate will have a good undergraduate and/or master’s degree in any of the following disciplines – Mathematics/Statistics, Computer Science, Physics, Engineering, Geophysics or Earth-sciences. Good analytical, computational skills and in particular, some prior experience in Matlab/Python/R programming is necessary. Some previous research experience in either data analytics, mathematical/statistical computing is also desirable. Prior experience on high-performance computing will be advantageous for this project.

All applicants would need to meet our English language requirements by the start of the  project http://www.exeter.ac.uk/postgraduate/apply/english/.

 

How to apply

In the application process you will be asked to upload several documents.  Please note our preferred format is PDF, each file named with your surname and the name of the document, eg. “Smith – CV.pdf”, “Smith – Cover Letter.pdf”, “Smith – Transcript.pdf”.

  • CV
  • Letter of application outlining your academic interests, prior research experience and reasons for wishing to undertake the project.
  • Transcript(s) giving full details of subjects studied and grades/marks obtained.  This should be an interim transcript if you are still studying.
  • If you are not a national of a majority English-speaking country you will need to submit evidence of your current proficiency in English.
  • Two References (applicants are recommended to have a third academic referee, if the two academic referees are within the same department/school).

Reference information
You will be asked to name two referees as part of the application process.  It is your responsibility to ensure that your two referees email their references to pgrenquiries@exeter.ac.uk, as we will not make requests for references directly; you must arrange for them to be submitted by 7 January 2019

References should be submitted to us directly in the form of a letter. Referees must email their references to us from their institutional email accounts. We cannot accept references from personal/private email accounts, unless it is a scanned document on institutional headed paper and signed by the referee.

All application documents must be submitted in English. Certified translated copies of academic qualifications must also be provided.

The closing date for applications is midnight on 7 January 2019.  Interviews will be held between 4 and 15 February 2019.

If you have any general enquiries about the application process please email pgrenquiries@exeter.ac.uk.  Project-specific queries should be directed to the supervisor.


Data Sharing
During the application process, the University may need to make certain disclosures of your personal data to third parties to be able to administer your application, carry out interviews and select candidates.  These are not limited to, but may include disclosures to:

  • the selection panel and/or management board or equivalent of the relevant programme, which is likely to include staff from one or more other HEIs;
  • administrative staff at one or more other HEIs participating in the relevant programme.

Such disclosures will always be kept to the minimum amount of personal data required for the specific purpose. Your sensitive personal data (relating to disability and race/ethnicity) will not be disclosed without your explicit consent.

Summary

Application deadline:7th January 2019
Value:£14,777 per annum for 2018-19
Duration of award:per year
Contact: PGR Enquiries pgrenquiries@exeter.ac.uk