Postgraduate research opportunities

Data-driven micro-seismic signal analysis

The goal of this PhD is to develop a method for optimal acoustic and microseismic sensor placement for accurately detecting microseismic activity. This project will use an alternative, data-driven, approach which will draw on significant recent developments in the area of machine learning.

Number of places

1

Funding

Home fee, Equipment costs, Stipend

Opens

14 June 2019

Deadline

30 September 2019

Duration

4 years

Eligibility

Standard eligibility for UKRI studentships.

https://epsrc.ukri.org/skills/students/help/eligibility/

Eligibility for RCUK studentships

  • Research Council (RC) fees and stipend can only be awarded to UK and EU students and not to EEA or International students.
  • EU students are only eligible for RC stipend if they have been resident in the UK for 3 years, including for study purposes, immediately prior to starting their PhD.
  • If an EU student cannot fulfil this condition then they are eligible for a fees only studentship.
  • International students cannot be funded from RC funds unless they are ‘settled’ in the UK. ‘Settled’ means being ordinarily resident in the UK without any immigration restrictions on the length of stay in the UK. To be ‘settled’ a student must either have the Right to Abode or Indefinite leave to remain in the UK or have the right of permanent residence in the UK under EC law. If the student’s passport describes them as a British citizen they have the Right of Abode.
  • Students with full Refugee status are eligible for fees and stipend.

Project Details

Optimal sensor placement, i.e., determining the locations of sensors that maximise information about the monitored dynamic system, is an active research area with numerous applications. The goal of this PhD is to develop a method for optimal acoustic and microseismic sensor placement for accurately detecting microseismic activity. In contrast to commonly used traditional optimisation approaches this project will use an alternative, data-driven, approach which will draw on significant recent developments in the area of machine learning - deep learning and graph signal processing - to develop supervised or semi-supervised regression algorithms. The project will make use of already available microseismic monitoring data from well stimulation projects in North America. The input data with corresponding cost outputs will be used to train a model which will predict a solution for the test data. The main advantage of this approach is avoiding analytical optimisation methods that could be either too complex or inaccurate due to assumptions required to be introduced in order to make the solution computationally tractable.

This PhD will be based at the University of Strathclyde. Optimal sensor placement is an emerging area of research that, to the best of the supervisors’ knowledge, has not been studied in the context of microseismic monitoring. It is unclear whether past methods developed for structural monitoring are transferrable to the problem in hand. These past methods are mainly based on complex optimisation approaches or proper orthogonal decomposition, which both require model simplifications to ensure computational tractability. Some recent attempts to use data-driven methods rely on random forests algorithms and their adoption to the problem in hand is unclear. The novelty of the work is: (1) novel deep learning network architectures for optimal sensor placement; (2) new combined network for iteratively performing sensor placement and event detection; (3) real-time implementation of the developed algorithms.

Funding Details

Home fee, standard EPSRC stipend plus £5k per year project costs

Non-home students will have to provide the excess fees and may not be eligible for a stipend (see UKRI studentships page)

Number of places

0

Further information

This project will result in an in-depth knowledge of aspects of geophysics, machine learning and associated software design. In combination with gained experience in microseismic and acoustic signal processing, it provides a highly desirable background for any job related to exploration, production and environmental control for oil and gas as well as in Academia.  The researcher will have the opportunity to interact with two research groups (Electronics and Electrical Engineering and Civil Engineering), as well as Industrial partners taking advantage of diverse skills and infrastructure available.

Contact us

Stella Pytharouli - stella.pytharouli@strath.ac.uk