Computer science

Geology

Physics

Astronomy

Image (mathematics)

Environmental science

Artificial intelligence

Remote sensing

Noise (video)

Metre

Nature

The sensing of gravity has emerged as a tool in geophysics applications such as engineering and climate research 1–3 , including the monitoring of temporal variations in aquifers 4 and geodesy 5 . 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 noise 6 . 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 table 7 , archaeology 8–11 , determination of soil properties 12 and water content 13 , and reducing the risk of unforeseen ground conditions in the construction of critical energy, transport and utilities infrastructure 14 , providing a new window into the underground.

Quantum sensing for gravity cartography