A Machine Learning Methodology for Predicting Geothermal Heat Flow in the Bohai Bay Basin, China-Reference-Cited by-同舟云学术

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A Machine Learning Methodology for Predicting Geothermal Heat Flow in the Bohai Bay Basin, China

Published:2022-01-11 Issue:1 Volume:31 Page:237-260
ISSN:1520-7439
Container-title:Natural Resources Research
language:en
Short-container-title:Nat Resour Res

Author:

He Jifu,Li Kewen,Wang Xinwei,Gao Nanan,Mao Xiaoping,Jia Lin

Funder

Sinopec Star Petroleum Corporation Limited

Publisher

Springer Science and Business Media LLC

Subject

General Environmental Science

Link

https://link.springer.com/content/pdf/10.1007/s11053-021-10002-x.pdf

Reference58 articles.

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2. Assouline, D., Mohajeri, N., Gudmundsson, A., & Scartezzini, J. L. (2019). A machine learning approach for mapping the very shallow theoretical geothermal potential. Geothermal Energy, 7(1), 1–50.

3. Balmino, G., Vales, N., Bonvalot, S., & Briais, A. (2012). Spherical harmonic modelling to ultra-high degree of Bouguer and isostatic anomalies. Journal of Geodesy, 86(7), 499–520.

4. Barazideh, R., Niknam, S., & Natarajan, B. (2019). Impulsive noise detection in OFDM-based systems: A deep learning perspective. In 2019 IEEE 9th annual computing and communication workshop and conference, CCWC 2019, January, pp. 937–942. https://doi.org/10.1109/CCWC.2019.8666489

5. Bodri, L., & Bodri, B. (1985). On the correlation between heat flow and crustal thickness. Tectonophysics, 120(1–2), 69–81.

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