| 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 … MJ Cracknell, AM Reading Computers & Geosciences 63, 22-33, 2014 | 672 | 2014 |
| Distinguishing ore deposit type and barren sedimentary pyrite using laser ablation-inductively coupled plasma-mass spectrometry trace element data and statistical analysis of … DD Gregory, MJ Cracknell, RR Large, P McGoldrick, S Kuhn, ... Economic Geology 114 (4), 771-786, 2019 | 135 | 2019 |
| The upside of uncertainty: Identification of lithology contact zones from airborne geophysics and satellite data using random forests and support vector machines MJ Cracknell, AM Reading Geophysics 78 (3), WB113-WB126, 2013 | 113 | 2013 |
| Lithologic mapping using Random Forests applied to geophysical and remote-sensing data: A demonstration study from the Eastern Goldfields of Australia S Kuhn, MJ Cracknell, AM Reading Geophysics 83 (4), B183-B193, 2018 | 109 | 2018 |
| Mapping geology and volcanic-hosted massive sulfide alteration in the Hellyer–Mt Charter region, Tasmania, using Random Forests™ and Self-Organising Maps MJ Cracknell, AM Reading, AW McNeill Australian Journal of Earth Sciences 61 (2), 287-304, 2014 | 88 | 2014 |
| Geological mapping in Western Tasmania using radar and random forests DDG Radford, MJ Cracknell, MJ Roach, GV Cumming IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2018 | 51 | 2018 |
| Lithological mapping in the Central African Copper Belt using Random Forests and clustering: Strategies for optimised results S Kuhn, MJ Cracknell, AM Reading Ore Geology Reviews 112, 103015, 2019 | 44 | 2019 |
| Revealing the multi-stage ore-forming history of a mineral deposit using pyrite geochemistry and machine learning-based data interpretation R Zhong, Y Deng, W Li, LV Danyushevsky, MJ Cracknell, I Belousov, ... Ore Geology Reviews 133, 104079, 2021 | 34 | 2021 |
| Automated core logging technology for geotechnical assessment: A study on core from the Cadia East porphyry deposit CL Harraden, MJ Cracknell, J Lett, RF Berry, R Carey, AC Harris Economic Geology 114 (8), 1495-1511, 2019 | 32 | 2019 |
| Multiple influences on regolith characteristics from continental-scale geophysical and mineralogical remote sensing data using Self-Organizing Maps MJ Cracknell, AM Reading, P De Caritat Remote Sensing of Environment 165, 86-99, 2015 | 28 | 2015 |
| Machine learning for geological mapping: Algorithms and applications M Cracknell University of Tasmania, 2014 | 28 | 2014 |
| Linking protolith rocks to altered equivalents by combining unsupervised and supervised machine learning SB Hood, MJ Cracknell, MF Gazley Journal of Geochemical Exploration 186, 270-280, 2018 | 26 | 2018 |
| Quantitative mineral mapping of drill core surfaces II: Long-wave infrared mineral characterization using μXRF and machine learning RD Barker, SLL Barker, MJ Cracknell, ED Stock, G Holmes Economic Geology 116 (4), 821-836, 2021 | 24 | 2021 |
| Combining machine learning and geophysical inversion for applied geophysics AM Reading, MJ Cracknell, DJ Bombardieri, T Chalke ASEG Extended Abstracts 2015 (1), 1-5, 2015 | 23 | 2015 |
| Estimating bedding orientation from high-resolution digital elevation models MJ Cracknell, M Roach, D Green, A Lucieer IEEE Transactions on Geoscience and Remote Sensing 51 (5), 2949-2959, 2012 | 23 | 2012 |
| Supervised machine learning for predicting and interpreting dynamic drivers of plantation forest productivity in northern Tasmania, Australia LN Sotomayor, MJ Cracknell, R Musk Computers and Electronics in Agriculture 209, 107804, 2023 | 20 | 2023 |
| Element mobility and spatial zonation associated with the Archean Hamlet orogenic Au deposit, Western Australia: Implications for fluid pathways in shear zones SB Hood, MJ Cracknell, MF Gazley, AM Reading Chemical Geology 514, 10-26, 2019 | 19 | 2019 |
| Identification of intrusive lithologies in volcanic terrains in British Columbia by machine learning using random forests: The value of using a soft classifier S Kuhn, MJ Cracknell, AM Reading, S Sykora Geophysics 85 (6), B249-B258, 2020 | 17 | 2020 |
| Sampling forest canopy arthropod biodiversity with three novel minimal‐cost trap designs YD Bar‐Ness, PB McQuillan, M Whitman, RR Junker, M Cracknell, ... Australian Journal of Entomology 51 (1), 12-21, 2012 | 17 | 2012 |
| Lithological mapping via random forests: information entropy as a proxy for inaccuracy S Kuhn, MJ Cracknell, AM Reading ASEG Extended Abstracts 2016 (1), 1-4, 2016 | 16 | 2016 |
Anya M. ReadingUniversity of TasmaniaEmail confirmado em utas.edu.au
