Fault recognition based on principal component analysis and k-nearest neighbor algorithm

Authors

ZOU Guangui, State Key Laboratory of Coal Resource and Safety Mining, China University of Mining and Technology(Beijing), Beijing 100083, China; College of Geoscience and Surveying Engineering, China University of Mining and Technology(Beijing), Beijing 100083, China
REN Ke, College of Geoscience and Surveying Engineering, China University of Mining and Technology(Beijing), Beijing 100083, ChinaFollow
JI Yin, College of Geoscience and Surveying Engineering, China University of Mining and Technology(Beijing), Beijing 100083, China
Ding Jianyu, College of Geoscience and Surveying Engineering, China University of Mining and Technology(Beijing), Beijing 100083, China
ZHANG Shaomin, College of Geoscience and Surveying Engineering, China University of Mining and Technology(Beijing), Beijing 100083, China

Abstract

Fault recognition based on principal component analysis and k-nearest neighbor algorithm

Keywords

seismic attributes, principal component analysis(PCA), k-nearest neighbor(kNN) algorithm, fault identification, Yangdong Coal Mine of Fengfeng Mining Area

DOI

10.3969/j.issn.1001-1986.2021.04.003

Recommended Citation

ZOU Guangui, REN Ke, JI Yin, et al. (2021) "Fault recognition based on principal component analysis and k-nearest neighbor algorithm," Coal Geology & Exploration: Vol. 49: Iss. 4, Article 4.
DOI: 10.3969/j.issn.1001-1986.2021.04.003
Available at: https://cge.researchcommons.org/journal/vol49/iss4/4

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