A data augmentation approach to XGboost-based mineral potential mapping: An example of carbonate-hosted Zn Pb mineral systems of Western Iran
出版年份 2021 全文链接
标题
A data augmentation approach to XGboost-based mineral potential mapping: An example of carbonate-hosted Zn Pb mineral systems of Western Iran
作者
关键词
Data augmentation, XGboost, Mineral potential, Carbonate-hosted mineralization
出版物
JOURNAL OF GEOCHEMICAL EXPLORATION
Volume 228, Issue -, Pages 106811
出版商
Elsevier BV
发表日期
2021-05-21
DOI
10.1016/j.gexplo.2021.106811
参考文献
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注意：仅列出部分参考文献，下载原文获取全部文献信息。- Multi-dataset analysis to assess mineral potential of MVT-type zinc-lead deposits in Malayer-Isfahan metallogenic belt, Iran
- (2021) Hengameh Hosseini-Dinani et al. Arabian Journal of Geosciences
- A Predictive Geospatial Exploration Model for Mississippi Valley Type Pb–Zn Mineralization in the Southeast Missouri Lead District
- (2020) Nathan D. Williams et al. Natural Resources Research
- Multi-geohazards susceptibility mapping based on machine learning—a case study in Jiuzhaigou, China
- (2020) Juan Cao et al. NATURAL HAZARDS
- Translating a mineral systems model into continuous and data-driven targeting models: An example from the Dolatabad chromite district, southeastern Iran
- (2020) Bijan Roshanravan JOURNAL OF GEOCHEMICAL EXPLORATION
- Bootstrap aggregation and cross‐validation methods to reduce overfitting in reservoir control policy search
- (2020) Zachary P. Brodeur et al. WATER RESOURCES RESEARCH
- Modeling of Cu-Au prospectivity in the Carajás mineral province (Brazil) through machine learning: Dealing with imbalanced training data
- (2020) Elias Martins Guerra Prado et al. ORE GEOLOGY REVIEWS
- Random-Drop Data Augmentation of Deep Convolutional Neural Network for Mineral Prospectivity Mapping
- (2020) Tong Li et al. Natural Resources Research
- Deep learning and its application in geochemical mapping
- (2019) Renguang Zuo et al. EARTH-SCIENCE REVIEWS
- Applying spatial prospectivity mapping to exploration targeting: Fundamental practical issues and suggested solutions for the future
- (2019) Jon M.A. Hronsky et al. ORE GEOLOGY REVIEWS
- Boosting for Mineral Prospectivity Modeling: A New GIS Toolbox
- (2019) Melanie Brandmeier et al. Natural Resources Research
- Fluid inclusions, S isotopes, and Pb isotopes characteristics of the Kuh-e-Surmeh carbonate-hosted Zn–Pb deposit in the Zagros Fold Belt, southwest Iran: Implications for the source of metals and sulfur and MVT genetic model
- (2019) Samaneh Fazli et al. ORE GEOLOGY REVIEWS
- Geology, isotope geochemistry, and fluid inclusion investigation of the Robat Zn-Pb-Ba deposit, Malayer-Esfahan metallogenic belt, southwestern Iran
- (2019) Shojaeddin Niroomand et al. ORE GEOLOGY REVIEWS
- Translating expressions of intrusion-related mineral systems into mappable spatial proxies for mineral potential mapping: Case studies from the Southern New England Orogen, Australia
- (2019) A. Ford et al. ORE GEOLOGY REVIEWS
- Exploration information systems – A proposal for the future use of GIS in mineral exploration targeting
- (2019) Mahyar Yousefi et al. ORE GEOLOGY REVIEWS
- Practical Implementation of Random Forest-Based Mineral Potential Mapping for Porphyry Cu–Au Mineralization in the Eastern Lachlan Orogen, NSW, Australia
- (2019) Arianne Ford Natural Resources Research
- A Bat Algorithm-Based Data-Driven Model for Mineral Prospectivity Mapping
- (2019) Yongliang Chen et al. Natural Resources Research
- GIS-based rare events logistic regression for mineral prospectivity mapping
- (2018) Yihui Xiong et al. COMPUTERS & GEOSCIENCES
- Controls on Mississippi Valley-Type Zn-Pb mineralization in Behabad district, Central Iran: Constraints from spatial and numerical analyses
- (2018) Mohammad Parsa et al. JOURNAL OF AFRICAN EARTH SCIENCES
- Selection of an elemental association related to mineralization using spatial analysis
- (2018) Renguang Zuo JOURNAL OF GEOCHEMICAL EXPLORATION
- Dehydration of hot oceanic slab at depth 30–50 km: KEY to formation of Irankuh-Emarat Pb Zn MVT belt, Central Iran
- (2018) Mohammad Hassan Karimpour et al. JOURNAL OF GEOCHEMICAL EXPLORATION
- Spatial analyses of exploration evidence data to model skarn-type copper prospectivity in the Varzaghan district, NW Iran
- (2018) Mohammad Parsa et al. ORE GEOLOGY REVIEWS
- A review of major non-sulfide zinc deposits in Iran
- (2018) Sajjad Maghfouri et al. Geoscience Frontiers
- Multifractal interpolation and spectrum–area fractal modeling of stream sediment geochemical data: Implications for mapping exploration targets
- (2017) Mohammad Parsa et al. JOURNAL OF AFRICAN EARTH SCIENCES
- A Receiver Operating Characteristics-Based Geochemical Data Fusion Technique for Targeting Undiscovered Mineral Deposits
- (2017) Mohammad Parsa et al. Natural Resources Research
- Enhancement and Mapping of Weak Multivariate Stream Sediment Geochemical Anomalies in Ahar Area, NW Iran
- (2017) Mohammad Parsa et al. Natural Resources Research
- Prospectivity modeling of porphyry-Cu deposits by identification and integration of efficient mono-elemental geochemical signatures
- (2016) Mohammad Parsa et al. JOURNAL OF AFRICAN EARTH SCIENCES
- Recognition of significant multi-element geochemical signatures of porphyry Cu deposits in Noghdouz area, NW Iran
- (2016) Mohammad Parsa et al. JOURNAL OF GEOCHEMICAL EXPLORATION
- Mapping mineral prospectivity for Cu polymetallic mineralization in southwest Fujian Province, China
- (2016) Yuan Gao et al. ORE GEOLOGY REVIEWS
- Fuzzification of continuous-value spatial evidence for mineral prospectivity mapping
- (2015) Mahyar Yousefi et al. COMPUTERS & GEOSCIENCES
- A comparative study of trend surface analysis and spectrum–area multifractal model to identify geochemical anomalies
- (2015) Haicheng Wang et al. JOURNAL OF GEOCHEMICAL EXPLORATION
- Statistical analysis of soil geochemical data to identify pathfinders associated with mineral deposits: An example from the Coles Hill uranium deposit, Virginia, USA
- (2015) Denise M. Levitan et al. JOURNAL OF GEOCHEMICAL EXPLORATION
- Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: Application of Random Forests algorithm
- (2015) Emmanuel John M. Carranza et al. ORE GEOLOGY REVIEWS
- Data- and knowledge-driven mineral prospectivity maps for Canada's North
- (2015) J.R. Harris et al. ORE GEOLOGY REVIEWS
- Mineral potential mapping with a restricted Boltzmann machine
- (2015) Yongliang Chen ORE GEOLOGY REVIEWS
- Receiver operating characteristics (ROC) as validation tool for prospectivity models — A magmatic Ni–Cu case study from the Central Lapland Greenstone Belt, Northern Finland
- (2015) Vesa Nykänen et al. ORE GEOLOGY REVIEWS
- Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines
- (2015) V. Rodriguez-Galiano et al. ORE GEOLOGY REVIEWS
- A comparative study of fuzzy weights of evidence and random forests for mapping mineral prospectivity for skarn-type Fe deposits in the southwestern Fujian metallogenic belt, China
- (2015) ZhenJie Zhang et al. Science China-Earth Sciences
- Comparison of the Data-Driven Random Forests Model and a Knowledge-Driven Method for Mineral Prospectivity Mapping: A Case Study for Gold Deposits Around the Huritz Group and Nueltin Suite, Nunavut, Canada
- (2015) G. McKay et al. Natural Resources Research
- Data-Driven Predictive Modeling of Mineral Prospectivity Using Random Forests: A Case Study in Catanduanes Island (Philippines)
- (2015) Emmanuel John M. Carranza et al. Natural Resources Research
- Jurassic to Cenozoic tectonics of the Zagros Orogen in northwestern Iran
- (2013) M. Mohajjel et al. INTERNATIONAL GEOLOGY REVIEW
- Recognition of the regional lineaments of Iran: Using geospatial data and their implications for exploration of metallic ore deposits
- (2013) Seyed Ahmad Meshkani et al. ORE GEOLOGY REVIEWS
- Support vector machine for multi-classification of mineral prospectivity areas
- (2012) Maysam Abedi et al. COMPUTERS & GEOSCIENCES
- Metallogeny of Cretaceous carbonate-hosted Zn–Pb deposits of Iran: geotectonic setting and data integration for future mineral exploration
- (2012) Abdorrahman Rajabi et al. INTERNATIONAL GEOLOGY REVIEW
- Identification of hydrothermal alteration minerals for exploring of porphyry copper deposit using ASTER data, SE Iran
- (2011) Amin Beiranvnd Pour et al. JOURNAL OF ASIAN EARTH SCIENCES
- The application of ASTER remote sensing data to porphyry copper and epithermal gold deposits
- (2011) Amin Beiranvand Pour et al. ORE GEOLOGY REVIEWS
- Support vector machine: A tool for mapping mineral prospectivity
- (2010) Renguang Zuo et al. COMPUTERS & GEOSCIENCES
- A Review of Ensemble Methods in Bioinformatics
- (2010) Pengyi Yang et al. Current Bioinformatics
- Sediment-Hosted Lead-Zinc Deposits in Earth History
- (2010) D. L. Leach et al. ECONOMIC GEOLOGY
- Exploratory factor analysis revisited: How robust methods support the detection of hidden multivariate data structures in IS research
- (2010) Horst Treiblmaier et al. INFORMATION & MANAGEMENT
- Translating the mineral systems approach into an effective exploration targeting system
- (2010) T. Campbell McCuaig et al. ORE GEOLOGY REVIEWS
- Significance of Nain-Baft ophiolitic belt (Iran): Short-lived, transtensional Cretaceous back-arc oceanic basins over the Tethyan subduction zone
- (2009) Hadi Shafaii Moghadam et al. COMPTES RENDUS GEOSCIENCE
- Objective selection of suitable unit cell size in data-driven modeling of mineral prospectivity
- (2009) Emmanuel John M. Carranza COMPUTERS & GEOSCIENCES
- Principal component analysis for compositional data with outliers
- (2009) Peter Filzmoser et al. ENVIRONMETRICS
- Emarat carbonate-hosted Zn–Pb deposit, Markazi Province, Iran: A geological, mineralogical and isotopic (S, Pb) study
- (2009) Farhad Ehya et al. JOURNAL OF ASIAN EARTH SCIENCES
- Linking Mineral Deposit Models to Quantitative Risk Analysis and Decision-Making in Exploration
- (2008) O. P. Kreuzer et al. ECONOMIC GEOLOGY
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