Normalising Flows for Bayesian Gravity Inversion

Gravity inversion is a commonly applied data analysis technique in the field of geophysics. While machine learning methods have previously been explored for the problem of gravity inversion, these are deterministic approaches returning a single solution deemed most appropriate by the algorithm. The method presented here takes a different approach, where gravity inversion is reformulated as a Bayesian parameter inference problem. Samples from the posterior probability distribution of source model parameters are obtained via the implementation of a generative neural network architecture known as Normalising Flows. Due to its probabilistic nature, this framework provides the user with a range of source parameters and uncertainties instead of a single solution, and is inherently robust against instrumental noise. The performance of the Normalising Flow is compared to that of an established Bayesian method called Nested Sampling. It is shown that the new method returns results with comparable accuracy 200 times faster than standard sampling methods, which makes Normalising Flows a suitable method for real-time inversion in the field. When applied to data sets with high dimensionality, standard sampling methods can become impractical due to long computation times. It is shown that inversion using Normalising Flows remains tractable even at 512 dimensions and once the network is trained, the results can be obtained in $O(10)$ seconds.Comment: 14 pages, 6 figures, submitted for publication in Computers & Geosciences Journa

An Update to the Development of the Wee-g: A High-Sensitivity MEMS-Based Relative Gravimeter for Multi-Pixel Application

The measurement of tiny variations of gravity over long time-scales or across the landscape has been of interest for geophysicists and various industries since the development of the first modern gravimeter. The manufacturing cost and overall survey time required with commercial gravimeters, however, limit their potential application. The MEMS gravimeter developed at the University of Glasgow, Wee-g, is a small form-factor, high-sensitivity relative gravimeter under development, with its low cost enabling the potential to be used in a multi-pixel setting, such as the network planned to be installed around Mount Etna under the NEWTON-g project. Since the previous reporting of the development and assembly of a MEMS based high-sensitivity relative gravimeter for multi-pixel imaging applications (Toland, K et al, EGU2021-13167), significant progress has been achieved towards the goal of achieving multi-pixel imaging. Wee-g field prototypes have been delivered to end users for various projects, including one currently deployed on Mount Etna since summer 2021. The field prototype running on Mount Etna is running in parallel with an iGrav commercial gravimeter to help understand the characteristics of the Wee-g and allow for comparisons with a commercial device. Currently, multiple final design Wee-g devices are being manufactured for delivery, such as for the multi-pixel array as part of NEWTON-g and for various outdoor field trials. This presentation will report on the analysis of the field prototype Wee-g device that is currently running on Mount Etna, as well as the progress that has been made in manufacturing multiple Wee-g devices, and the outlook for activities that will be running throughout 2022, paving the way to a more effective and detailed method of gravity surveying