Intelligent Earth Seminar: Sacha Lapins (University of Bristol)

7 May 14:00
Atmospheric, Oceanic and Planetary Physics Department: Dobson Lecture Theatre
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Sacha Lapins is a Leverhulme Early Career Fellow at the University of Bristol. He specialises in the application of machine learning and advanced signal processing techniques to geophysical monitoring. His research focuses on developing innovative methods to enhance the detection and analysis of seismic signals, particularly in challenging environments such as volcanoes, glaciers and offshore carbon storage sites. Through projects funded by the Royal Society and EPSRC, his current work includes collaborations with international monitoring agencies, research institutions, and industry/government partners, translating advances in seismic sensing technologies and AI into practical solutions for environmental/hazard monitoring and resource sustainability.

 

Distributed Acoustic Sensing (DAS) converts ordinary fibre optic cables into extensive arrays of seismic sensors by measuring tiny strain changes along the fibre using backscattered light from laser pulses. This technology offers unprecedented spatial resolution at sub-metre scales over distances of tens of kilometres, enabling continuous monitoring and high-resolution subsurface imaging in harsh, remote or built-up environments. However, DAS data are often contaminated by strong random noise processes that make identifying or exploiting the underlying signals of interest challenging. In this talk, I present a weakly-supervised machine learning method (DAS-N2N) for suppressing strong random noise in DAS data. By exploiting the availability of multiple, redundant fibre cores within a single cable jacket, this approach uses only raw noisy data for training, mapping random noise to a chosen summary statistic, such as the distribution mean, median or mode, whilst retaining the true underlying signal. I demonstrate how this approach provides improved performance and efficiency over conventional filtering techniques using data from cables deployed on the Rutford Ice Stream in Antarctica and the Pacific Ocean floor. Additionally, I discuss how this technique can be extended to other sensing technologies, and how AI can further the utility of DAS for characterising, monitoring and managing natural and urban environments more broadly.