Genetic Algorithm Optimized Light Gradient Boosting Machine for 3D Mineral Prospectivity Modeling of Cu Polymetallic Skarn-Type Mineralization, Xuancheng Area, Anhui Province, Eastern China

While geological data gathering expands and advances along with mineral exploration, there is still a need for more innovation and enrichment because there are few complete analytical tools for these data. The light gradient boosting machine (LightGBM) technique is used in this study to estimate the...

Full description

Saved in:

Bibliographic Details
Published in:	Natural resources research (New York, N.Y.) Vol. 32; no. 5; pp. 1897 - 1916
Main Authors:	Li, He, Li, Xiaohui, Yuan, Feng, Zhang, Mingming, Li, Xiangling, Ge, Can, Wang, Zhiqiang, Guo, Dong, Lan, Xueyi, Tang, Minhui, Lu, Sanming
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.10.2023 Springer Nature B.V
Subjects:	Accuracy Algorithms Chemistry and Earth Sciences China Computer Science Copper Earth and Environmental Science Earth Sciences Fossil Fuels (incl. Carbon Capture) Genetic algorithms Geography Global optimization Machine learning Mathematical Modeling and Industrial Mathematics Mathematical models Mineral exploration Mineral Resources Mineralization Model accuracy Modelling Original Paper Parameters Physics Statistics for Engineering Support vector machines Sustainable Development Three dimensional models China Anhui China GA–LightGBM 3D mineral prospectivity modeling Ensemble learning Skarn-type mineralization
ISSN:	1520-7439, 1573-8981
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!