标题
Mineral Potential Mapping Using a Conjugate Gradient Logistic Regression Model
作者
关键词
Logistic regression, Conjugate gradient, Parameter optimization, Youden index, ROC curve analysis, Mineral potential mapping
出版物
Natural Resources Research
Volume -, Issue -, Pages -
出版商
Springer Science and Business Media LLC
发表日期
2019-06-28
DOI
10.1007/s11053-019-09509-1
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。- GIS-based rare events logistic regression for mineral prospectivity mapping
- (2018) Yihui Xiong et al. COMPUTERS & GEOSCIENCES
- Back-propagation neural network and support vector machines for gold mineral prospectivity mapping in the Hatu region, Xinjiang, China
- (2018) Nannan Zhang et al. Earth Science Informatics
- Isolation Forest as an Alternative Data-Driven Mineral Prospectivity Mapping Method with a Higher Data-Processing Efficiency
- (2018) Yongliang Chen et al. Natural Resources Research
- Application of one-class support vector machine to quickly identify multivariate anomalies from geochemical exploration data
- (2017) Yongliang Chen et al. GEOCHEMISTRY-EXPLORATION ENVIRONMENT ANALYSIS
- Application of support vector machines for copper potential mapping in Kerman region, Iran
- (2017) Mahdi Shabankareh et al. JOURNAL OF AFRICAN EARTH SCIENCES
- Maximum entropy modeling for orogenic gold prospectivity mapping in the Tangbale-Hatu belt, western Junggar, China
- (2017) Yue Liu et al. ORE GEOLOGY REVIEWS
- Mapping mineral prospectivity using an extreme learning machine regression
- (2017) Yongliang Chen et al. ORE GEOLOGY REVIEWS
- A MaxEnt Model for Mineral Prospectivity Mapping
- (2017) Yue Liu et al. Natural Resources Research
- Mineral Potential Mapping Via TOPSIS with Hybrid AHP–Shannon Entropy Weighting of Evidence: A Case Study for Porphyry-Cu, Farmahin Area, Markazi Province, Iran
- (2017) Faranak Feizi et al. Natural Resources Research
- Inversion of Source Parameters from Magnetic Anomalies for Mineral/Ore Deposits Exploration Using Global Optimization Technique and Analysis of Uncertainty
- (2017) Arkoprovo Biswas Natural Resources Research
- GIS-based weights of evidence modeling applied to mineral prospectivity mapping of Sn-W and rare metals in Laouni area, Central Hoggar, Algeria
- (2016) Hocine Zeghouane et al. Arabian Journal of Geosciences
- Spatial association analysis between hydrocarbon fields and sedimentary residual magnetic anomalies using Weights of Evidence: An example from the Triassic Province of Algeria
- (2016) Karim Allek et al. JOURNAL OF APPLIED GEOPHYSICS
- Data-driven logistic-based weighting of geochemical and geological evidence layers in mineral prospectivity mapping
- (2016) Mahyar Yousefi et al. JOURNAL OF GEOCHEMICAL EXPLORATION
- A prospecting cost-benefit strategy for mineral potential mapping based on ROC curve analysis
- (2016) Yongliang Chen et al. ORE GEOLOGY REVIEWS
- Combining biomarkers linearly and nonlinearly for classification using the area under the ROC curve
- (2016) Youyi Fong et al. STATISTICS IN MEDICINE
- Fuzzification of continuous-value spatial evidence for mineral prospectivity mapping
- (2015) Mahyar Yousefi et al. COMPUTERS & GEOSCIENCES
- Prediction–area (P–A) plot and C–A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling
- (2015) Mahyar Yousefi et al. COMPUTERS & GEOSCIENCES
- Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines)
- (2015) Emmanuel John M. Carranza et al. COMPUTERS & GEOSCIENCES
- BoostWofE: A New Sequential Weights of Evidence Model Reducing the Effect of Conditional Dependency
- (2015) Qiuming Cheng Mathematical Geosciences
- 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
- Mineral potential mapping with a restricted Boltzmann machine
- (2015) Yongliang Chen 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
- 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 Index Overlay and Boolean Logic Mineral Prospectivity Modeling in Greenfields Exploration
- (2015) Mahyar Yousefi 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
- A modified Wald interval for the area under the ROC curve (AUC) in diagnostic case-control studies
- (2014) Martina Kottas et al. BMC Medical Research Methodology
- Predictive modelling of gold potential with the integration of multisource information based on random forest: a case study on the Rodalquilar area, Southern Spain
- (2014) V.F. Rodriguez-Galiano et al. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
- Alteration mineral mapping with ASTER data by integration of coded spectral ratio imaging and SOM neural network model
- (2014) Mohammad Hassan TAYEBI et al. TURKISH JOURNAL OF EARTH SCIENCES
- Spectral delineation of albite zone using ASTER data in Khetri Copper Belt
- (2013) Poonam S. Tiwari et al. Arabian Journal of Geosciences
- Application of singularity analysis for mineral potential identification using geochemical data — A case study: Nanling W–Sn–Mo polymetallic metallogenic belt, South China
- (2013) Yue Liu et al. JOURNAL OF GEOCHEMICAL EXPLORATION
- A modified area under the ROC curve and its application to marker selection and classification
- (2013) WenBao Yu et al. Journal of the Korean Statistical Society
- A Comparison of Modified Fuzzy Weights of Evidence, Fuzzy Weights of Evidence, and Logistic Regression for Mapping Mineral Prospectivity
- (2013) Daojun Zhang et al. Mathematical Geosciences
- Detection of hydrothermal mineralized zones associated with listwaenites in Central Oman using ASTER data
- (2013) Sankaran Rajendran et al. ORE GEOLOGY REVIEWS
- Support vector machine for multi-classification of mineral prospectivity areas
- (2012) Maysam Abedi et al. COMPUTERS & GEOSCIENCES
- Singularity theory and methods for mapping geochemical anomalies caused by buried sources and for predicting undiscovered mineral deposits in covered areas
- (2012) Qiuming Cheng JOURNAL OF GEOCHEMICAL EXPLORATION
- A classification problem of credit risk rating investigated and solved by optimisation of the ROC curve
- (2011) Efsun Kürüm et al. Central European Journal of Operations Research
- Support vector machine: A tool for mapping mineral prospectivity
- (2010) Renguang Zuo et al. COMPUTERS & GEOSCIENCES
- Detecting areas of high-potential gold mineralization using ASTER data
- (2010) Safwat Gabr et al. ORE GEOLOGY REVIEWS
- Application of Artificial Neural Network for Gold–Silver Deposits Potential Mapping: A Case Study of Korea
- (2010) Hyun-Joo Oh et al. Natural Resources Research
- Artificial neural networks applied to mineral potential mapping for copper-gold mineralizations in the Carajás Mineral Province, Brazil
- (2009) Emilson Pereira Leite et al. GEOPHYSICAL PROSPECTING
- Combined conceptual/empirical prospectivity mapping for orogenic gold in the northern Fennoscandian Shield, Finland
- (2008) V. Nykänen et al. AUSTRALIAN JOURNAL OF EARTH SCIENCES
- Singularity analysis of ore-mineral and toxic trace elements in stream sediments
- (2008) Qiuming Cheng et al. COMPUTERS & GEOSCIENCES
- Probabilistic neural networks applied to mineral potential mapping for platinum group elements in the Serra Leste region, Carajás Mineral Province, Brazil
- (2008) Emilson Pereira Leite et al. COMPUTERS & GEOSCIENCES
- Regional Probabilistic and Statistical Mineral Potential Mapping of Gold–Silver Deposits Using GIS in the Gangreung Area, Korea
- (2008) Hyun-Joo Oh et al. RESOURCE GEOLOGY
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