A machine learning method for distinguishing detrital zircon provenance

[1]  Peter A. Cawood,et al.  I-type and S-type granites in the Earth’s earliest continental crust , 2023, Communications Earth & Environment.

[2]  O. Melnik,et al.  The rises and falls of zirconium isotopes during zircon crystallisation , 2022, Geochemical Perspectives Letters.

[3]  Deru Xu,et al.  Application of Machine Learning to Characterizing Magma Fertility in Porphyry Cu Deposits , 2022, Journal of Geophysical Research: Solid Earth.

[4]  M. Kelly,et al.  Allometry-based estimation of forest aboveground biomass combining LiDAR canopy height attributes and optical spectral indexes , 2022, Forest Ecosystems.

[5]  Guofeng Xu,et al.  The Role of Jiningian Pluton in Yanshanian Metallogenic Events in the Dahutang Tungsten Deposit: Evidence from Whole Rock and Zircon Geochemistry , 2022, Minerals.

[6]  H. Zhang,et al.  Apatite and zircon geochemistry for discriminating ore-forming intrusions in the Luming giant porphyry Mo deposit, Northeastern China , 2022, Ore Geology Reviews.

[7]  Daniel J. Smith,et al.  Machine learning for geochemical exploration: classifying metallogenic fertility in arc magmas and insights into porphyry copper deposit formation , 2022, Mineralium Deposita.

[8]  Li Tang,et al.  Geology and genesis of auriferous porphyritic monzogranite and its correlation with the Qiyugou porphyry-breccia system in East Qinling, central China , 2022, Ore Geology Reviews.

[9]  Fok Kar Wai,et al.  Machine learning for encrypted malicious traffic detection: Approaches, datasets and comparative study , 2021, Comput. Secur..

[10]  J. Huizenga,et al.  Rare earth element enrichment in the ion-adsorption deposits associated granites at Mesozoic extensional tectonic setting in South China , 2021 .

[11]  K. Qiu,et al.  Machine Learning Prediction of Quartz Forming‐Environments , 2021, Journal of Geophysical Research: Solid Earth.

[12]  Aijaz Ahmad,et al.  Feature Selection for Electrical Demand Forecasting and Analysis of Pearson Coefficient , 2021, 2021 IEEE 4th International Electrical and Energy Conference (CIEEC).

[13]  D. Trail,et al.  Emergence of peraluminous crustal magmas and implications for the early Earth , 2021 .

[14]  Jie Zhou,et al.  The influence of fractionation of REE-enriched minerals on the zircon partition coefficients , 2021 .

[15]  C. Hart,et al.  RECOGNIZING PORPHYRY COPPER POTENTIAL FROM TILL ZIRCON COMPOSITION: A CASE STUDY FROM THE HIGHLAND VALLEY PORPHYRY DISTRICT, SOUTH-CENTRAL BRITISH COLUMBIA , 2021 .

[16]  San-zhong Li,et al.  Porphyry copper and skarn fertility of the northern Qinghai-Tibet Plateau collisional granitoids , 2021 .

[17]  M. Petrelli,et al.  Machine Learning Thermo‐Barometry: Application to Clinopyroxene‐Bearing Magmas , 2020, Journal of Geophysical Research: Solid Earth.

[18]  Noora Shrestha Detecting Multicollinearity in Regression Analysis , 2020, American Journal of Applied Mathematics and Statistics.

[19]  Eyad Elyan,et al.  Deep learning for symbols detection and classification in engineering drawings , 2020, Neural Networks.

[20]  I. Campbell,et al.  S-type granites: Their origin and distribution through time as determined from detrital zircons , 2020 .

[21]  A. Williams-Jones,et al.  Partial melting, fractional crystallisation, liquid immiscibility and hydrothermal mobilisation – A ‘recipe’ for the formation of economic A-type granite-hosted HFSE deposits , 2020 .

[22]  O. Melnik,et al.  Zircon survival in shallow asthenosphere and deep lithosphere , 2020, 2001.05336.

[23]  Pedro Lara-Velázquez,et al.  A COMPARATIVE CLUSTERING MODEL THAT CONSIDERS FALSE POSITIVES AND FALSE NEGATIVES IN SOME SOCIOECONOMIC APPLICATIONS , 2020 .

[24]  R. Seltmann,et al.  Characterization of the zircon Ce anomaly for estimation of oxidation state of magmas: a revised Ce/Ce* method , 2019, Mineralogy and Petrology.

[25]  Maarten V. de Hoop,et al.  Machine learning for data-driven discovery in solid Earth geoscience , 2019, Science.

[26]  Prabhat,et al.  Deep learning and process understanding for data-driven Earth system science , 2019, Nature.

[27]  Cynthia Rudin,et al.  Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead , 2018, Nature Machine Intelligence.

[28]  R. Seltmann,et al.  Can magmatic zircon be distinguished from hydrothermal zircon by trace element composition? The effect of mineral inclusions on zircon trace element composition , 2018, Lithos.

[29]  W. Collins,et al.  Origin of postcollisional magmas and formation of porphyry Cu deposits in southern Tibet , 2018, Earth-Science Reviews.

[30]  A. Schmitt,et al.  Stability of Zircon and Its Isotopic Ratios in High-Temperature Fluids: Long-Term (4 months) Isotope Exchange Experiment at 850°C and 50 MPa , 2018, Front. Earth Sci..

[31]  O. Melnik,et al.  Modeling of trace elemental zoning patterns in accessory minerals with emphasis on the origin of micrometer-scale oscillatory zoning in zircon , 2018 .

[32]  C. Allen,et al.  Use and abuse of zircon-based thermometers: A critical review and a recommended approach to identify antecrystic zircons , 2018 .

[33]  Calvin F. Miller,et al.  Zircon as Magma Monitor , 2017 .

[34]  D. Rubatto Zircon: The Metamorphic Mineral , 2017 .

[35]  Scott Lundberg,et al.  A Unified Approach to Interpreting Model Predictions , 2017, NIPS.

[36]  A. Berry,et al.  Formation of Hadean granites by melting of igneous crust , 2017 .

[37]  P. Boehnke,et al.  Aluminum in zircon as evidence for peraluminous and metaluminous melts from the Hadean to present , 2017 .

[38]  M. Petrelli,et al.  Combining machine learning techniques, microanalyses and large geochemical datasets for tephrochronological studies in complex volcanic areas: new age constraints for the Pleistocene magmatism of Central Italy , 2017, 1701.06375.

[39]  R. Tay Correlation, variance inflation and multicollinearity in regression model , 2017 .

[40]  M. Kubát An Introduction to Machine Learning , 2017, Springer International Publishing.

[41]  J. Brenan,et al.  Magmatic oxygen fugacity estimated using zircon-melt partitioning of cerium , 2016 .

[42]  G. Gehrels,et al.  A new method for estimating parent rock trace element concentrations from zircon , 2016, Chemical Geology.

[43]  T. M. Harrison,et al.  Recovering the primary geochemistry of Jack Hills zircons through quantitative estimates of chemical alteration , 2016 .

[44]  Diego Perugini,et al.  Solving petrological problems through machine learning: the study case of tectonic discrimination using geochemical and isotopic data , 2016, Contributions to Mineralogy and Petrology.

[45]  E. Carranza,et al.  Spatial analysis and visualization of exploration geochemical data , 2016 .

[46]  Yongjun Lu,et al.  Zircon Compositions as a Pathfinder for Porphyry Cu ± Mo ± Au Deposits , 2016 .

[47]  Nora El-Gohary,et al.  Semantic Text Classification for Supporting Automated Compliance Checking in Construction , 2016, J. Comput. Civ. Eng..

[48]  B. John,et al.  “Fingerprinting” tectono-magmatic provenance using trace elements in igneous zircon , 2015, Contributions to Mineralogy and Petrology.

[49]  Michael I. Jordan,et al.  Machine learning: Trends, perspectives, and prospects , 2015, Science.

[50]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[51]  P. Sossi,et al.  Fe isotopes and the contrasting petrogenesis of A-, I- and S-type granite , 2015 .

[52]  Francisco Herrera,et al.  On the use of MapReduce for imbalanced big data using Random Forest , 2014, Inf. Sci..

[53]  M. Novák,et al.  Trace element composition of quartz from different types of pegmatites: A case study from the Moldanubian Zone of the Bohemian Massif (Czech Republic) , 2014, Mineralogical Magazine.

[54]  J. Lindsay,et al.  Zircon trace element chemistry at sub-micrometer resolution for Tarawera volcano, New Zealand, and implications for rhyolite magma evolution , 2014, Contributions to Mineralogy and Petrology.

[55]  Kang Tai,et al.  Comparison of statistical and machine learning methods in modelling of data with multicollinearity , 2013, Int. J. Model. Identif. Control..

[56]  K. Jarvis,et al.  Zircon/rock partition coefficients of REEs, Y, Th, U, Nb, and Ta in granitic rocks Uses for provenance and mineral exploration purposes , 2013 .

[57]  Damaris Zurell,et al.  Collinearity: a review of methods to deal with it and a simulation study evaluating their performance , 2013 .

[58]  Peter A. Cawood,et al.  The continental record and the generation of continental crust , 2013 .

[59]  A. Berry,et al.  An experimental study of trace element partitioning between zircon and melt as a function of oxygen fugacity , 2012 .

[60]  W. Fan,et al.  A-type granite belts of two chemical subgroups in central eastern China: Indication of ridge subduction , 2012 .

[61]  Peter A. Cawood,et al.  Detrital zircon record and tectonic setting , 2012 .

[62]  Zhidan Zhao,et al.  Magmatic zircons from I-, S- and A-type granitoids in Tibet: Trace element characteristics and their application to detrital zircon provenance study , 2012 .

[63]  K. Qin,et al.  The role of crustal contamination in the formation of Ni–Cu sulfide deposits in Eastern Tianshan, Xinjiang, Northwest China: Evidence from trace element geochemistry, Re–Os, Sr–Nd, zircon Hf–O, and sulfur isotopes , 2012 .

[64]  G. Stevens,et al.  The enigmatic sources of I-type granites: The peritectic connexion , 2011 .

[65]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[66]  Chao Lan,et al.  Anomaly Detection , 2018, Encyclopedia of GIS.

[67]  Xiaoming Sun,et al.  Nb/Ta fractionation observed in eclogites from the Chinese Continental Scientific Drilling Project , 2009 .

[68]  B. Stewart,et al.  Continental margin volcanism at sites of spreading ridge subduction: Examples from southern Alaska and western California , 2009 .

[69]  I. Bindeman Oxygen Isotopes in Mantle and Crustal Magmas as Revealed by Single Crystal Analysis , 2008 .

[70]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[71]  P. Kelemen,et al.  Trace element chemistry of zircons from oceanic crust: A method for distinguishing detrital zircon provenance , 2007 .

[72]  D. Rubatto,et al.  Experimental zircon/melt and zircon/garnet trace element partitioning and implications for the geochronology of crustal rocks , 2007 .

[73]  L. A. Coogan,et al.  Do the trace element compositions of detrital zircons require Hadean continental crust , 2006 .

[74]  Dimitris Kanellopoulos,et al.  Handling imbalanced datasets: A review , 2006 .

[75]  Nitesh V. Chawla,et al.  Data Mining for Imbalanced Datasets: An Overview , 2005, The Data Mining and Knowledge Discovery Handbook.

[76]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[77]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[78]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[79]  Juha Reunanen,et al.  Overfitting in Making Comparisons Between Variable Selection Methods , 2003, J. Mach. Learn. Res..

[80]  U. Schaltegger,et al.  The Composition of Zircon and Igneous and Metamorphic Petrogenesis , 2003 .

[81]  I. Campbell,et al.  Relative oxidation states of magmas inferred from Ce(IV)/Ce(III) in zircon: application to porphyry copper deposits of northern Chile , 2002 .

[82]  W. Griffin,et al.  Igneous zircon: trace element composition as an indicator of source rock type , 2002 .

[83]  B. Chappell,et al.  Two contrasting granite types: 25 years later , 2001 .

[84]  I. Buick,et al.  Zircon and monazite response to prograde metamorphism in the Reynolds Range, central Australia , 2001 .

[85]  T. Ireland,et al.  Rare earth element chemistry of zircon and its use as a provenance indicator , 2000 .

[86]  Barth,et al.  Rutile-bearing refractory eclogites: missing link between continents and depleted mantle , 2000, Science.

[87]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[88]  J. C. BurgesChristopher A Tutorial on Support Vector Machines for Pattern Recognition , 1998 .

[89]  P. Blevin,et al.  Chemistry, origin, and evolution of mineralized granites in the Lachlan fold belt, Australia; the metallogeny of I- and S-type granites , 1995 .

[90]  G. Eby Chemical subdivision of the A-type granitoids:Petrogenetic and tectonic implications , 1992 .

[91]  P. Blevin,et al.  The role of magma sources, oxidation states and fractionation in determining the granite metallogeny of eastern Australia , 1992, Earth and Environmental Science Transactions of the Royal Society of Edinburgh.

[92]  B. Chappell,et al.  I- and S-type granites in the Lachlan Fold Belt , 1992, Earth and Environmental Science Transactions of the Royal Society of Edinburgh.

[93]  G. Eby The A-type granitoids: A review of their occurrence and chemical characteristics and speculations on their petrogenesis , 1990 .

[94]  Geoffrey E. Hinton Connectionist Learning Procedures , 1989, Artif. Intell..

[95]  J. Whalen,et al.  A-type granites: geochemical characteristics, discrimination and petrogenesis , 1987 .

[96]  W. Collins,et al.  Nature and origin of A-type granites with particular reference to southeastern Australia , 1982 .

[97]  M. Loiselle,et al.  Characteristics and origin of anorogenic granites , 1979 .

[98]  I. Tomek,et al.  Two Modifications of CNN , 1976 .

[99]  R. Flood,et al.  A cordierite-bearing granite suite from the New England Batholith, N.S.W., Australia , 1975 .