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6
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Introduction
I'm studying the use of machine learning to increase the resolution of geophysical data.
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Current institution
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January 2015 - December 2017
Publications
Publications (6)
The recent use of spatial coordinate features in multilayer perceptron (MLP) neural networks provides opportunities for novel applications in potential field geophysics. So-called coordinate MLP networks allow for learning a representative function of potential fields from their surveyed samples. We present a novel method for implicit neural repres...
Densely sampled geophysical surveys are a key driver for mineral exploration, but sample density, and therefore grid resolution, is limited by survey cost. Consequently, computational methods are resorted to for upsampling, or ‘super-resolving’, of gridded geophysical survey data. However, existing approaches such as interpolation filters do not le...
Optical tracking systems typically trade off between astrometric precision and field of view. In this work, we showcase a networked approach to optical tracking using very wide field-of-view imagers that have relatively low astrometric precision on the scheduled OSIRIS-REx slingshot manoeuvre around Earth on 22 Sep 2017. As part of a trajectory des...
Optical tracking systems typically trade-off between astrometric precision and field-of-view. In this work, we showcase a networked approach to optical tracking using very wide field-of-view imagers that have relatively low astrometric precision on the scheduled OSIRIS-REx slingshot manoeuvre around Earth on September 22nd, 2017. As part of a traje...
The principle aim of the research was to overcome the challenges faced by modern geophysical data analysts, particularly those working with large multivariate datasets using Self Organising Maps (SOM). SOM is an unsupervised learning technique for multivariate data, which works by taking multiple geophysical datasets for an area of interest, and in...
