Bayesian signal processing techniques for the detection of highly localised gravity anomalies using quantum interferometry technology

Gareth Brown; Kevin Ridley; Anthony Rodgers; Geoffrey de Villiers

doi:10.1117/12.2240933

25 October 2016 Bayesian signal processing techniques for the detection of highly localised gravity anomalies using quantum interferometry technology

Gareth Brown, Kevin Ridley, Anthony Rodgers, Geoffrey de Villiers

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Proceedings Volume 9992, Emerging Imaging and Sensing Technologies; 99920M (2016) https://doi.org/10.1117/12.2240933
Event: SPIE Security + Defence, 2016, Edinburgh, United Kingdom

Abstract

Recent advances in the field of quantum technology offer the exciting possibility of gravimeters and gravity gradiometers capable of performing rapid surveys with unprecedented precision and accuracy. Measurements with sub nano-g (a billionth of the acceleration due to gravity) precision should enable the resolution of underground structures on metre length scales. However, deducing the exact dimensions of the structure producing the measured gravity anomaly is known to be an ill-posed inversion problem. Furthermore, the measurement process will be affected by multiple sources of uncertainty that increase the range of plausible solutions that fit the measured data. Bayesian inference is the natural framework for accommodating these uncertainties and providing a fully probabilistic assessment of possible structures producing inhomogeneities in the gravitational field. Previous work introduced the probability of excavation map as a means to convert the high-dimensional space belonging to the posterior distribution to an easily interpretable map. We now report on the development of the inference model to account for spatial correlations in the gravitational field induced by variations in soil density.

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Gareth Brown, Kevin Ridley, Anthony Rodgers, and Geoffrey de Villiers "Bayesian signal processing techniques for the detection of highly localised gravity anomalies using quantum interferometry technology", Proc. SPIE 9992, Emerging Imaging and Sensing Technologies, 99920M (25 October 2016); https://doi.org/10.1117/12.2240933

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