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Abbaszadeh, Maliheh, Hezarkhani, Ardeshir, Soltani-Mohammadi, Saeed (2016) Proposing drilling locations based on the 3D modeling results of fluid inclusion data using the support vector regression method. Journal of Geochemical Exploration, 165. 23-34 doi:10.1016/j.gexplo.2016.02.005

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Reference TypeJournal (article/letter/editorial)
TitleProposing drilling locations based on the 3D modeling results of fluid inclusion data using the support vector regression method
JournalJournal of Geochemical Exploration
AuthorsAbbaszadeh, MalihehAuthor
Hezarkhani, ArdeshirAuthor
Soltani-Mohammadi, SaeedAuthor
Year2016 (June)Volume165
PublisherElsevier BV
URL
DOIdoi:10.1016/j.gexplo.2016.02.005Search in ResearchGate
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Mindat Ref. ID718539Long-form Identifiermindat:1:5:718539:1
GUID0
Full ReferenceAbbaszadeh, Maliheh, Hezarkhani, Ardeshir, Soltani-Mohammadi, Saeed (2016) Proposing drilling locations based on the 3D modeling results of fluid inclusion data using the support vector regression method. Journal of Geochemical Exploration, 165. 23-34 doi:10.1016/j.gexplo.2016.02.005
Plain TextAbbaszadeh, Maliheh, Hezarkhani, Ardeshir, Soltani-Mohammadi, Saeed (2016) Proposing drilling locations based on the 3D modeling results of fluid inclusion data using the support vector regression method. Journal of Geochemical Exploration, 165. 23-34 doi:10.1016/j.gexplo.2016.02.005
In(2016) Journal of Geochemical Exploration Vol. 165. Elsevier BV

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Abbaszadeh, Maliheh, Hezarkhani, Ardeshir, Soltani-Mohammadi, Saeed (2015) Classification of alteration zones based on whole-rock geochemical data using support vector machine. Journal of the Geological Society of India, 85 (4) 500-508 doi:10.1007/s12594-015-0242-3
Abbaszadeh, Maliheh, Hezarkhani, Ardeshir, Soltani-Mohammadi, Saeed (2013) An SVM-based machine learning method for the separation of alteration zones in Sungun porphyry copper deposit. Geochemistry, 73 (4) 545-554 doi:10.1016/j.chemer.2013.07.001
Soltani-Mohammadi, Saeed, Abbaszadeh, Maliheh, Hezarkhani, Ardeshir, Carranza, Emmanuel John M. (2022) Uncertainty Analysis of Thermodynamic Variables of Fluid Inclusions: A Deposit-Scale Spatial Exploratory Data Modeling through Fuzzy Kriging. Natural Resources Research, 31 (1) 51-65 doi:10.1007/s11053-021-09969-4
Abbaszadeh, Maliheh, Soltani-Mohammadi, Saeed, Ahmed, Ali Najah (2022) Optimization of support vector machine parameters in modeling of Iju deposit mineralization and alteration zones using particle swarm optimization algorithm and grid search method. Computers & Geosciences, 165. 105140 doi:10.1016/j.cageo.2022.105140
Hezarkhani, Ardeshir (2009) Hydrothermal fluid geochemistry at the Chah-Firuzeh porphyry copper deposit, Iran: Evidence from fluid inclusions. Journal of Geochemical Exploration, 101 (3) 254-264 doi:10.1016/j.gexplo.2008.09.002
Ghannadpour, Seyyed Saeed, Hezarkhani, Ardeshir (2016) Introducing 3D U-statistic method for separating anomaly from background in exploration geochemical data with associated software development. Journal of Earth System Science, 125 (2). 387-401 doi:10.1007/s12040-016-0657-2
Abbaszadeh, Maliheh, Hezarkhani, Ardeshir (2013) Enhancement of hydrothermal alteration zones using the spectral feature fitting method in Rabor area, Kerman, Iran. Arabian Journal of Geosciences, 6 (6) 1957-1964 doi:10.1007/s12517-011-0495-0
Zaremotlagh, S., Hezarkhani, A. (2016) A geochemical modeling to predict the different concentrations of REE and their hidden patterns using several supervised learning methods: Choghart iron deposit, bafq, Iran. Journal of Geochemical Exploration, 165. 35-48 doi:10.1016/j.gexplo.2016.02.001
Soltani-Mohammadi, Saeed, Hezarkhani, Ardeshir (2013) A Simulated Annealing-Based Algorithm to Locate Additional Drillholes for Maximizing the Realistic Value of Information. Natural Resources Research, 22 (3) 229-237 doi:10.1007/s11053-013-9212-x
Soltani-Mohammadi, Saeed, Hoseinian, Fatemeh Sadat, Abbaszadeh, Maliheh, Khodadadzadeh, Mahdi (2022) Grade estimation using a hybrid method of back-propagation artificial neural network and particle swarm optimization with integrated samples coordinate and local variability. Computers & Geosciences, 159. 104981 doi:10.1016/j.cageo.2021.104981
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