Matthew J. Cracknell

Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information

Matthew J. Cracknell, Anya M. Reading · Computers & Geosciences · 2013 · 616 citations · Full text

Distinguishing Ore Deposit Type and Barren Sedimentary Pyrite Using Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry Trace Element Data and Statistical Analysis of Large Data Sets

Daniel D. Gregory, Matthew J. Cracknell, Ross R. Large et al. · Economic Geology · 2019 · 164 citations · Full text

Lithologic mapping using Random Forests applied to geophysical and remote-sensing data: A demonstration study from the Eastern Goldfields of Australia

Stephen Kuhn, Matthew J. Cracknell, Anya M. Reading · Geophysics · 2018 · 115 citations · Full text

Ore Exploration, Engineering, Machine Learning +28

The upside of uncertainty: Identification of lithology contact zones from airborne geophysics and satellite data using Random Forests and Support Vector Machines

Matthew J. Cracknell, Anya M. Reading · Geophysics · 2013 · 108 citations

Mapping geology and volcanic-hosted massive sulfide alteration in the Hellyer–Mt Charter region, Tasmania, using Random Forests™ and Self-Organising Maps

Matthew J. Cracknell, Anya M. Reading, Andrew W. McNeill · Australian Journal of Earth Sciences · 2013 · 83 citations

Self-organising Maps, Volcanology, Engineering +23