Mineral Prospectivity Prediction via Convolutional Neural Networks Based on Geological Big Data
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Title
Mineral Prospectivity Prediction via Convolutional Neural Networks Based on Geological Big Data
Authors
Keywords
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Journal
Journal of Earth Science
Volume 32, Issue 2, Pages 327-347
Publisher
Springer Science and Business Media LLC
Online
2021-04-12
DOI
10.1007/s12583-020-1365-z
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Note: Only part of the references are listed.- Effects of Random Negative Training Samples on Mineral Prospectivity Mapping
- (2020) Renguang Zuo et al. Natural Resources Research
- Geodata Science-Based Mineral Prospectivity Mapping: A Review
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- 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
- Big Data Analytics of Identifying Geochemical Anomalies Supported by Machine Learning Methods
- (2017) Renguang Zuo et al. Natural Resources Research
- A MaxEnt Model for Mineral Prospectivity Mapping
- (2017) Yue Liu et al. Natural Resources Research
- Random Forest-Based Prospectivity Modelling of Greenfield Terrains Using Sparse Deposit Data: An Example from the Tanami Region, Western Australia
- (2017) Siddharth Hariharan et al. Natural Resources Research
- Recognition of geochemical anomalies using a deep autoencoder network
- (2016) Yihui Xiong et al. COMPUTERS & GEOSCIENCES
- Identifying geochemical anomalies associated with Au–Cu mineralization using multifractal and artificial neural network models in the Ningqiang district, Shaanxi, China
- (2016) Jiangnan Zhao et al. JOURNAL OF GEOCHEMICAL EXPLORATION
- A machine learning approach to geochemical mapping
- (2016) Charlie Kirkwood et al. JOURNAL OF GEOCHEMICAL EXPLORATION
- Using Random Forests to distinguish gahnite compositions as an exploration guide to Broken Hill-type Pb–Zn–Ag deposits in the Broken Hill domain, Australia
- (2015) Joshua J. O'Brien et al. JOURNAL OF GEOCHEMICAL EXPLORATION
- Supervised geochemical anomaly detection by pattern recognition
- (2015) Arman Mohammadi Gonbadi et al. JOURNAL OF GEOCHEMICAL EXPLORATION
- 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
- 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
- Big Data: A Survey
- (2014) Min Chen et al. MOBILE NETWORKS & APPLICATIONS
- Support vector machine for multi-classification of mineral prospectivity areas
- (2012) Maysam Abedi et al. COMPUTERS & GEOSCIENCES
- Support vector machine: A tool for mapping mineral prospectivity
- (2010) Renguang Zuo et al. COMPUTERS & GEOSCIENCES
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- (2010) Chuan-Yu Chang et al. PATTERN RECOGNITION
- 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
- Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation
- (2008) Steven J. Phillips et al. ECOGRAPHY
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