Assessing the effects of mineral systems-derived exploration targeting criteria for random Forests-based predictive mapping of mineral prospectivity in Ahar-Arasbaran area, Iran
Tài liệu tham khảo
Agard, 2005, Convergence history across Zagros (Iran): constraints from collisional and earlier deformation, International journal of earth sciences, 94, 401, 10.1007/s00531-005-0481-4
Agterberg, 2005, Measuring the performance of mineral-potential maps, Natural Resources Research, 14, 1, 10.1007/s11053-005-4674-0
Aitchison, 1982, The statistical analysis of compositional data, Journal of the Royal Statistical Society: Series B (Methodological), 44, 139
Alavi, 1994, Tectonics of the Zagros orogenic belt of Iran: new data and interpretations, Tectonophysics, 229, 211, 10.1016/0040-1951(94)90030-2
Arndt, 2015, Metals and society: An introduction to economic geology, Springer.
Bonham-Carter, 1989, Weights of evidence modeling: a new approach to mapping mineral potential, Statistical applications in the earth sciences, 171
Brandmeier, 2020, Boosting for mineral prospectivity modeling: A new GIS toolbox, Natural Resources Research, 29, 71, 10.1007/s11053-019-09483-8
Breiman, 1984, Classification and Regression Trees, Chapman & Hall/CRC.
Breiman, 1996, Bagging predictors, Machine Learning., 24, 123, 10.1007/BF00058655
Breiman, 2001, Random forests, Machine Learning., 45, 5, 10.1023/A:1010933404324
Carranza, 2015, Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: Application of Random Forests algorithm, Ore Geology Reviews, 71, 777, 10.1016/j.oregeorev.2014.08.010
Carranza, 2016, Data-driven predictive modeling of mineral prospectivity using random forests: a case study in Catanduanes Island (Philippines), Natural Resources Research, 25, 35, 10.1007/s11053-015-9268-x
Carranza, 2008
Carranza, 2009, Objective selection of suitable unit cell size in data-driven modeling of mineral prospectivity, Computers & Geosciences, 35, 2032, 10.1016/j.cageo.2009.02.008
Chen, T. and Guestrin, C., 2016, August. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).
Chen, 2016, A prospecting cost-benefit strategy for mineral potential mapping based on ROC curve analysis, Ore Geology Reviews, 74, 26, 10.1016/j.oregeorev.2015.11.011
Chen, 2020, A Bat Algorithm-Based Data-Driven Model for Mineral Prospectivity Mapping, Natural Resources Research, 29, 247, 10.1007/s11053-019-09589-z
Chiaradia, 2020, Gold endowments of porphyry deposits controlled by precipitation efficiency, Nature communications, 11, 1, 10.1038/s41467-019-14113-1
Chung, 2003, Validation of spatial prediction models for landslide hazard mapping, Natural Hazards, 30, 451, 10.1023/B:NHAZ.0000007172.62651.2b
Daviran, 2021, A new strategy for spatial predictive mapping of mineral prospectivity: Automated hyperparameter tuning of random forest approach, Computers & Geosciences, 148, 10.1016/j.cageo.2021.104688
Dietterich, 2002, Ensemble learning, The Handbook of Brain Theory and Neural Networks, 2, 110
Egozcue, 2003, Isometric logratio transformations for compositional data analysis, Mathematical Geology, 35, 279, 10.1023/A:1023818214614
Filzmoser, 2009, Robust factor analysis for compositional data, Computers & Geosciences, 35, 1854, 10.1016/j.cageo.2008.12.005
Ford, 2010, The effect of map scale on geological complexity for computer-aided exploration targeting, Ore Geology Reviews, 38, 156, 10.1016/j.oregeorev.2010.03.008
Ford, 2020, Practical implementation of random forest-based mineral potential mapping for porphyry Cu–Au mineralization in the Eastern Lachlan Orogen, NSW, Australia, Natural Resources Research, 29, 267, 10.1007/s11053-019-09598-y
Ford, 2019, Translating expressions of intrusion-related mineral systems into mappable spatial proxies for mineral potential mapping: Case studies from the Southern New England Orogen, Australia. Ore Geology Reviews, 111
Ghezelbash, 2019, Mapping of single-and multi-element geochemical indicators based on catchment basin analysis: Application of fractal method and unsupervised clustering models, Journal of Geochemical Exploration, 199, 90, 10.1016/j.gexplo.2019.01.017
Ghezelbash, 2020, Sensitivity analysis of prospectivity modeling to evidence maps: Enhancing success of targeting for epithermal gold, Takab district, NW Iran. Ore Geology Reviews, 120
Hagemann, 2016, Mineral system analysis: Quo vadis, Ore Geology Reviews, 76, 504, 10.1016/j.oregeorev.2015.12.012
Harris, 2015, Data-and knowledge-driven mineral prospectivity maps for Canada's North, Ore Geology Reviews, 71, 788, 10.1016/j.oregeorev.2015.01.004
Hassanpour, Sh., 2010. Metallogeny and Mineralization of Copper and Gold in Arasbaran zone, NW Iran. Ph.D. Dissertation, Shahid Beheshti University, Tehran, Iran. (In Persian).
Hronsky, 2019, Applying spatial prospectivity mapping to exploration targeting: fundamental practical issues and suggested solutions for the future, Ore Geology Reviews, 107, 647, 10.1016/j.oregeorev.2019.03.016
Jamali, 2015, Relationships between arc maturity and Cu–Mo–Au porphyry and related epithermal mineralization at the Cenozoic Arasbaran magmatic belt, Ore Geology Reviews, 65, 487, 10.1016/j.oregeorev.2014.06.017
Jamali, 2010, Metallogeny and tectonic evolution of the Cenozoic Ahar-Arasbaran volcanic belt, northern Iran, International Geology Review, 52, 608, 10.1080/00206810903416323
Knox-Robinson, 1997, Towards a holistic exploration strategy: using geographic information systems as a tool to enhance exploration, Australian journal of earth sciences, 44, 453, 10.1080/08120099708728326
Kreuzer, 2008, Linking mineral deposit models to quantitative risk analysis and decision-making in exploration, Economic Geology, 103, 829, 10.2113/gsecongeo.103.4.829
Kuhn, 2013, vol. 26
Lindsay, 2016, Reducing subjectivity in multi-commodity mineral prospectivity analyses: Modelling the west Kimberley, Australia, Ore Geology Reviews, 76, 395, 10.1016/j.oregeorev.2015.03.022
Lisitsin, 2014, Probabilistic fuzzy logic modeling: quantifying uncertainty of mineral prospectivity models using Monte Carlo simulations, Mathematical Geosciences, 46, 747, 10.1007/s11004-014-9534-1
McCuaig, 2010, Translating the mineral systems approach into an effective exploration targeting system, Ore Geology Reviews, 38, 128, 10.1016/j.oregeorev.2010.05.008
Meshkani, 2013, Recognition of the regional lineaments of Iran: Using geospatial data and their implications for exploration of metallic ore deposits, Ore Geology Reviews, 55, 48, 10.1016/j.oregeorev.2013.04.007
Nykänen, 2015, Receiver operating characteristics (ROC) as validation tool for prospectivity models—A magmatic Ni–Cu case study from the Central Lapland Greenstone Belt, Northern Finland, Ore Geology Reviews, 71, 853, 10.1016/j.oregeorev.2014.09.007
Parsa, 2021, Modulating the Impacts of Stochastic Uncertainties Linked to Deposit Locations in Data-Driven Predictive Mapping of Mineral Prospectivity, Natural Resources Research, 10.1007/s11053-021-09891-9
Parsa, 2021, A simulation-based framework for modulating the effects of subjectivity in greenfield Mineral Prospectivity Mapping with geochemical and geological data, Journal of Geochemical Exploration, 10.1016/j.gexplo.2021.106838
Parsa, 2021, A data augmentation approach to XGboost-based mineral potential mapping: An example of carbonate-hosted ZnPb mineral systems of Western Iran, Journal of Geochemical Exploration, 10.1016/j.gexplo.2021.106811
Parsa, 2016, Decomposition of anomaly patterns of multi-element geochemical signatures in Ahar area, NW Iran: a comparison of U-spatial statistics and fractal models, Arabian journal of Geosciences, 9, 260, 10.1007/s12517-016-2435-5
Parsa, 2017, An improved data-driven fuzzy mineral prospectivity mapping procedure; cosine amplitude-based similarity approach to delineate exploration targets, International journal of applied earth observation and geoinformation, 58, 157, 10.1016/j.jag.2017.02.006
Parsa, 2018, Spatial analyses of exploration evidence data to model skarn-type copper prospectivity in the Varzaghan district, NW Iran, Ore Geology Reviews, 92, 97, 10.1016/j.oregeorev.2017.11.013
Parsa, 2018, A receiver operating characteristics-based geochemical data fusion technique for targeting undiscovered mineral deposits, Natural Resources Research, 27, 15, 10.1007/s11053-017-9351-6
Parsa, 2017, Enhancement and mapping of weak multivariate stream sediment geochemical anomalies in Ahar Area, NW Iran, Natural Resources Research, 26, 443, 10.1007/s11053-017-9346-3
Parsa, 2017, Multifractal interpolation and spectrum–area fractal modeling of stream sediment geochemical data: Implications for mapping exploration targets, Journal of African Earth Sciences, 128, 5, 10.1016/j.jafrearsci.2016.11.021
Parsa, 2016, Prospectivity modeling of porphyry-Cu deposits by identification and integration of efficient mono-elemental geochemical signatures, Journal of African Earth Sciences, 114, 228, 10.1016/j.jafrearsci.2015.12.007
Parsa, 2016, Recognition of significant multi-element geochemical signatures of porphyry Cu deposits in Noghdouz area, NW Iran, Journal of Geochemical Exploration, 165, 111, 10.1016/j.gexplo.2016.03.009
Pirajno, 2012
Polikar, 2006, Ensemble based systems in decision making, IEEE Circuits and Systems Magazine, 6, 21, 10.1109/MCAS.2006.1688199
Porwal, 2003, Knowledge-driven and data-driven fuzzy models for predictive mineral potential mapping, Natural Resources Research, 12, 1, 10.1023/A:1022693220894
Reimann, 2002, Factor analysis applied to regional geochemical data: problems and possibilities, Applied geochemistry, 17, 185, 10.1016/S0883-2927(01)00066-X
Rodriguez-Galiano, 2015, Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines, Ore Geology Reviews, 71, 804, 10.1016/j.oregeorev.2015.01.001
Roshanravan, 2020, Translating a mineral systems model into continuous and data-driven targeting models: An example from the Dolatabad chromite district, southeastern Iran, Journal of Geochemical Exploration, 215, 10.1016/j.gexplo.2020.106556
Roshanravan, 2020, Structural and non-structural statistical methods: implications for delineating geochemical anomalies, Applied Earth Science, 129, 111, 10.1080/25726838.2020.1801109
Sadr, 2018, Random forests algorithm in podiform chromite prospectivity mapping in Dolatabad area, SE Iran, Journal of Mining and Environment, 9, 403
Sillitoe, 2010, Porphyry copper systems. Economic geology, 105, 3, 10.2113/gsecongeo.105.1.3
Spadoni, 2006, Geochemical mapping using a geomorphologic approach based on catchments, Journal of Geochemical Exploration, 90, 183, 10.1016/j.gexplo.2005.12.001
Swets, 1996
Tessema, 2017, Mineral systems analysis and artificial neural network modeling of chromite prospectivity in the Western Limb of the Bushveld Complex, South Africa, Natural Resources Research, 26, 465, 10.1007/s11053-017-9344-5
Thompson, 1976, Duplicate analysis in geochemical practice. Part I. Theoretical approach and estimation of analytical reproducibility, Analyst, 101, 690, 10.1039/an9760100690
Tosdal, 2001, Magmatic and structural controls on the development of porphyry Cu±Mo±Au deposits, Rev. in Econ. Geol., 14, 157
Treiblmaier, 2010, Exploratory factor analysis revisited: How robust methods support the detection of hidden multivariate data structures in IS research, Information & management, 47, 197, 10.1016/j.im.2010.02.002
Van Helvoort, 2005, Sequential Factor Analysis as a new approach to multivariate analysis of heterogeneous geochemical datasets: An application to a bulk chemical characterization of fluvial deposits (Rhine–Meuse delta, The Netherlands), Applied geochemistry, 20, 2233, 10.1016/j.apgeochem.2005.08.009
Vigneresse, 2019, How do metals escape from magmas to form porphyry-type ore deposits?, Ore Geology Reviews, 105, 310, 10.1016/j.oregeorev.2018.12.016
Wang, 2020, A Monte Carlo-based framework for risk-return analysis in mineral prospectivity mapping, Geoscience Frontiers, 11, 2297, 10.1016/j.gsf.2020.02.010
Yilmaz, 2019, Singularity mapping of bulk leach extractable gold and− 80# stream sediment geochemical data in recognition of gold and base metal mineralization footprints in Biga Peninsula South, Turkey, Journal of African Earth Sciences, 153, 156, 10.1016/j.jafrearsci.2019.02.015
Zhang, 2021, Recognition of multivariate geochemical anomalies associated with mineralization using an improved generative adversarial network, Ore Geology Reviews
Zhang, 2021, Mineral Prospectivity Mapping based on Isolation Forest and Random Forest: Implication for the Existence of Spatial Signature of Mineralization in Outliers, Natural Resources Research, 1
Zuo, 2011, Support vector machine: a tool for mapping mineral prospectivity, Computers & Geosciences, 37, 1967, 10.1016/j.cageo.2010.09.014
Zuo, 2011, Identifying geochemical anomalies associated with Cu and Pb–Zn skarn mineralization using principal component analysis and spectrum–area fractal modeling in the Gangdese Belt, Tibet (China), Journal of Geochemical Exploration, 111, 13, 10.1016/j.gexplo.2011.06.012
Zuo, 2018, Selection of an elemental association related to mineralization using spatial analysis, Journal of Geochemical Exploration, 184, 150, 10.1016/j.gexplo.2017.10.020
Zuo, 2021, Uncertainties in GIS-based mineral prospectivity mapping: Key types, potential impacts and possible solutions, Natural Resources Research, 10.1007/s11053-021-09871-z
Zuo, 2015, Evaluation of uncertainty in mineral prospectivity mapping due to missing evidence: a case study with skarn-type Fe deposits in Southwestern Fujian Province, China, Ore Geology Reviews, 71, 502, 10.1016/j.oregeorev.2014.09.024