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Steve Kuhn
Steve Kuhn
CODES, TMVC Hub, UTas
在 utas.edu.au 的电子邮件经过验证
标题
引用次数
引用次数
年份
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
522018
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
442019
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
222019
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
122016
Identification of intrusive lithologies in volcanic terrains in British Columbia by machine learning using random forests: The value of using a soft classifierMapping …
S Kuhn, MJ Cracknell, AM Reading, S Sykora
Geophysics 85 (6), B249-B258, 2020
72020
Inverse modeling constrained by potential field data, petrophysics, and improved geologic mapping: A case study from prospective northwest Tasmania
E Eshaghi, AM Reading, M Roach, M Duffett, D Bombardieri, ...
Geophysics 85 (5), K13-K26, 2020
22020
The utility of machine learning in identification of key geophysical and geochemical datasets: a case study in lithological mapping in the Central African copper belt
S Kuhn, M Cracknell, A Reading
ASEG Extended Abstracts 2018 (1), 1-4, 2018
22018
A comparison of random forests and cluster analysis to identify ore deposits type using LA-ICPMS analysis of pyrite
DD Gregory, MJ Cracknell, RR Large, P McGoldrick, S Kuhn, MJ Baker, ...
15th SGA Biennial Meeting 3, 1274-1277, 2019
12019
Random Forest classification of pyrite trace element composition to identify ore deposit type in far-field exploration and vectoring
D Gregory, R Large, M Cracknell, S Kuhn, V Maslennikov, I Belousov, ...
Society of Economic Geologists Conference 2017, ., 2017
12017
Machine Learning for Mineral Exploration: Prediction and Quantified Uncertainty at Multiple Exploration Stages
S Kuhn
CODES / ARC TMVC Hub - University of Tasmania, 2021
2021
Case History 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), 2020
2020
Summary and final report on pyrite, magnetite and hematite mineral geochemistry, South Australia
JA Steadman, RR Large, DD Gregory, S Meffre, M Cracknell, S Kuhn
2018
Summary report on South Australia pyrite and magnetite geochemistry studies (including Mineral Systems Drilling Program pyrite project)
JA Steadman, RR Large, DD Gregory, S Meffre, M Cracknell, SD Kuhn
2017
Big Data Techniques for Applied Geoscience: Compute and Communicate
AM Reading, MJ Cracknell, S Kuhn
ASEG Extended Abstracts 2016 (1), 1-5, 2016
2016
Data Driven Knowledge Discovery for Earth Sciences: Aims and Actions
A Reading, M Cracknell, S Kuhn, S Hardy
Australian Earth Sciences Convention, 372, 2016
2016
Annual report: South Australian pyrite, hematite and magnetite fingerprint database
D Gregory, S Meffre, R Large, M Cracknell, S Kuhn
2015
Towards user friendly data-driven minerals exploration: lithological mapping in an orogenic gold setting
S Kuhn, MJ Cracknell, AM Reading, M Roach
SEG 2015 Conference, -, 2015
2015
Phase 1 report of trace elements in titanite, hematitic sediments, magnetite and chlorite vectoring study
D Gregory, R Large, M Cracknell, S Kuhn
2015
Potential Field Geophysical Interpretation of the Cave Rocks Region. Kambalda, Western Australia
S Kuhn
University of Tasmania, 2008
2008
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