Quantum sensing for gravity cartography

Ben Stray, Andrew Lamb, Aisha Kaushik, Jamie Vovrosh, Anthony Rodgers, Jonathan Winch, Farzad Hayati, Daniel Boddice, Artur Stabrawa, Alexander Niggebaum, Mehdi Langlois, Yu-Hung Lien, Samuel Lellouch, Sanaz Roshanmanesh, Kevin Ridley, Geoffrey Villiers, Gareth Brown, Trevor Cross, George Tuckwell, Asaad Faramarzi, Nicole Metje, Kai Bongs and Michael Holynski ()
Additional contact information
Ben Stray: University of Birmingham
Andrew Lamb: University of Birmingham
Aisha Kaushik: University of Birmingham
Jamie Vovrosh: University of Birmingham
Anthony Rodgers: University of Birmingham
Jonathan Winch: University of Birmingham
Farzad Hayati: University of Birmingham
Daniel Boddice: University of Birmingham
Artur Stabrawa: University of Birmingham
Alexander Niggebaum: University of Birmingham
Mehdi Langlois: University of Birmingham
Yu-Hung Lien: University of Birmingham
Samuel Lellouch: University of Birmingham
Sanaz Roshanmanesh: University of Birmingham
Kevin Ridley: University of Birmingham
Geoffrey Villiers: University of Birmingham
Gareth Brown: Dstl, Porton Down
Trevor Cross: Teledyne e2v
George Tuckwell: University of Birmingham
Asaad Faramarzi: University of Birmingham
Nicole Metje: University of Birmingham
Kai Bongs: University of Birmingham
Michael Holynski: University of Birmingham

Nature, 2022, vol. 602, issue 7898, 590-594

Abstract: Abstract The sensing of gravity has emerged as a tool in geophysics applications such as engineering and climate research1–3, including the monitoring of temporal variations in aquifers4 and geodesy5. However, it is impractical to use gravity cartography to resolve metre-scale underground features because of the long measurement times needed for the removal of vibrational noise6. Here we overcome this limitation by realizing a practical quantum gravity gradient sensor. Our design suppresses the effects of micro-seismic and laser noise, thermal and magnetic field variations, and instrument tilt. The instrument achieves a statistical uncertainty of 20 E (1 E = 10−9 s−2) and is used to perform a 0.5-metre-spatial-resolution survey across an 8.5-metre-long line, detecting a 2-metre tunnel with a signal-to-noise ratio of 8. Using a Bayesian inference method, we determine the centre to ±0.19 metres horizontally and the centre depth as (1.89 −0.59/+2.3) metres. The removal of vibrational noise enables improvements in instrument performance to directly translate into reduced measurement time in mapping. The sensor parameters are compatible with applications in mapping aquifers and evaluating impacts on the water table7, archaeology8–11, determination of soil properties12 and water content13, and reducing the risk of unforeseen ground conditions in the construction of critical energy, transport and utilities infrastructure14, providing a new window into the underground.

Date: 2022
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DOI: 10.1038/s41586-021-04315-3

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