Distributed acoustic sensing for complex environments

About the Project

Supervisory team: Dr Michal Kalkowski and Prof Jen Muggleton

This project investigates the fundamental physics of distributed acoustic sensing (DAS) using optical fibres. DAS is an exciting technology enabling large-scale acoustic monitoring. Although it is commercially available, numerous fundamental aspects of how it works remain to be explained. This research will lay the groundwork for physics-based analysis of various DAS configurations and evaluate the performance of array signal processing algorithms applied to DAS signals from complex environments.

DAS is a relatively recent technology that enables continuous acquisition over kilometres of optical fibre from a single location. This setup provides an abundance of data containing signals from various sources, such as people, vehicles, machinery, and transportation. The most common practice in exploiting that data is listening for sound sources (looking for ‘hot spots’) or black-box machine learning. However, grounding these methods in a thorough understanding of wave physics can unlock their full potential.

This project aspires to advance distributed acoustic sensing, especially in complex multi-source environments. Its key contributions will include:

• a rigorous determination of the strengths and limitations of different fibre configurations, that is, the effect of cable design, conduits, installation, and proximity to infrastructure.
• developing imaging workflows for multiple source identification in complex environments
• merging different modalities of optical fibre sensing to increase reliability.

The student will develop analytical and numerical models for wave propagation in the soil-cable systems and cables themselves to capture the fundamental physics. Later in the project, they will be extended to include additional complexities, such as infrastructure, tunnels, foundations, etc. Complemented by experimental measurements and their analysis, they will establish firm foundations for enhancing the interpretation of DAS data or potentially inform machine learning processing algorithms.

Entry requirements:

You must have a UK 2:1 honours degree, or its international equivalent.

We are looking for candidates with backgrounds in engineering, applied mathematics, applied physics, or computer science. The project requires using Python and its scientific computing modules, so knowledge of these is beneficial. Willingness to combine experimental work with computation, including high-performance computing, is essential.

Fees and funding:

Full scholarships include tuition fees, a tax-free stipend at the UKRI rate for up to 3.5 years (totalling £20,780 for 2025/26, rising annually). UK, EU and Horizon Europe students are eligible for scholarships. Chinese Scholarship Council-funded students are eligible for fee waivers. Funding for other international applicants is very limited and highly competitive. Overseas students who have secured or are seeking external funding are welcome to apply.

How to apply:

Apply now

Please apply via the online portal and select:

• programme type: research
• academic year: 2026/27
• if you will be full time or part time
• faculty: Engineering and Physical Sciences
• search for programme PhD Engineering & the Environment (7175)
• please add the name of the supervisor in section 2 of the application.

Applications should include:

• your CV (resumé)
• 2 academic references
• degree transcripts/ certificates to date
• English language qualification (if applicable)