Bayesian mass anomaly estimation with measurements of gravity

The paper describes the implementation of a Bayesian model to assess novel gravimeters and gradiometers, which employ recent advances in cold matter wave interferometry to accurately measure micro-gravity. This has application to the resolution of underground structures, as an array of measurements at the surface can be used to infer what lies beneath. Potential applications of this technology include: improved identification of oil reservoirs; the detection of sink-holes to support civil engineering; detection of underground buildings; and geophysical mapping as a means to navigate in GPS denied environments. For the challenge of detecting underground voids at depths 10m beneath the Earth, the utility of the posterior distribution for computing probability of excavation maps is described and the impact of different sampling configurations and densities is investigated.