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Machine-supported mineralogical interpretations from hyperspectral and geochemical datasets

thesis
posted on 28.07.2022, 23:38 authored by ANGELA AFONSO RODRIGUES

Geologists face the challenge of analysing large volumes of complex, multivariate and uncertain datasets. This contribution focusses on the creation of machine supported workflows for the analysis of multivariate datasets in order to identify mineralogy, and to support geologists to objectively analyse these datasets.

History

Campus location

Australia

Principal supervisor

Laurent Ailleres

Additional supervisor 1

Robin Armit

Additional supervisor 2

Lachlan Grose

Additional supervisor 3

Mehrtash Harandi

Year of Award

2022

Department, School or Centre

Earth, Atmosphere and Environment

Additional supervisor 4

Matthew Cracknell

Course

Doctor of Philosophy

Faculty

Faculty of Science