Estimation of Spatial Distribution Using the Gaussian Mixture Model with Multivariate Geoscience Data-Reference-Cited by-同舟云学术

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Estimation of Spatial Distribution Using the Gaussian Mixture Model with Multivariate Geoscience Data

Published:2022-08-30 Issue:4 Volume:55 Page:353-366
ISSN:1225-7281
Container-title:Economic and Environmental Geology
language:en
Short-container-title:Econ. Environ. Geol.

Author:

Kim Ho-Rim¹,Yu Soonyoung²,Yun Seong-Taek²,Kim Kyoung-Ho³,Lee Goon-Taek⁴,Lee Jeong-Ho¹,Heo Chul-Ho¹,Ryu Dong-Woo¹

Affiliation:

1. Korea Institute of Geoscience and Mineral Resources, Republic of Korea

2. Korea University, Republic of Korea

3. Korea Environment Institute, Republic of Korea

4. National Instrumentation Center for Environmental Management, Seoul National University, Republic of Korea

Publisher

The Korean Society of Economic and Environmental Geology

Subject

Economic Geology,Geology,Environmental Science (miscellaneous)

Link

http://pdf.medrang.co.kr/kseeg/2022/055/kseeg055-04-353.pdf

Reference36 articles.

1. Collocated Cokriging Based on Merged Secondary Attributes

2. Multivariate Imputation of Unequally Sampled Geological Variables

3. Machine learning for data-driven discovery in solid Earth geoscience

4. Probability density estimation via an infinite Gaussian mixture model: application to statistical process monitoring

5. Influence of geological setting on geochemical baselines of trace elements in soils. Application to soils of South–West Spain

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Spatial Prediction of Soil Contaminants Using a Hybrid Random Forest–Ordinary Kriging Model;Applied Sciences;2024-02-19

2. Optimization of Soil Contamination Distribution Prediction Error using Geostatistical Technique and Interpretation of Contributory Factor Based on Machine Learning Algorithm;Economic and Environmental Geology;2023-06-30

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