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Deep Machine Learning for Probabilistic Seismic Inversion and Imaging


Project Description

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/

Location: University of Exeter, Penryn Campus, Penryn, Cornwall TR10 9FE

Supervisory team:
Dr Saptarshi Das, Department of Mathematics, College of Engineering, Mathematics and Physical Sciences, University of Exeter
Dr James Hickey, Department of Geology, College of Engineering, Mathematics and Physical Sciences, University of Exeter
Dr James Verdon. School of Earth Sciences, University of Bristol.

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.

Funding Notes

NERC GW4+ funded studentship available for September 2019 entry. For eligible students, the studentship will provide funding of fees and a stipend which is currently £14,777 per annum for 2018-19.

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.

References

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.

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