Application of unlabelled big data and deep semi-supervised learning to significantly improve the logging interpretation accuracy for deep-sea gas hydrate-bearing sediment reservoirs
- DOI
- 10.1016/j.egyr.2022.01.139
- Published
- 11
- Container
- Energy Reports
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1016/j.egyr.2022.01.139,
title = {Application of unlabelled big data and deep semi-supervised learning to significantly improve the logging interpretation accuracy for deep-sea gas hydrate-bearing sediment reservoirs},
author = {Linqi Zhu and Jiangong Wei and Shiguo Wu and Xueqing Zhou and Jin Sun},
year = {2022},
journal = {Energy Reports},
doi = {10.1016/j.egyr.2022.01.139},
url = {https://doi.org/10.1016/j.egyr.2022.01.139}
}RIS
TY - JOUR TI - Application of unlabelled big data and deep semi-supervised learning to significantly improve the logging interpretation accuracy for deep-sea gas hydrate-bearing sediment reservoirs AU - Linqi Zhu AU - Jiangong Wei AU - Shiguo Wu AU - Xueqing Zhou AU - Jin Sun PY - 2022 JO - Energy Reports DO - 10.1016/j.egyr.2022.01.139 UR - https://doi.org/10.1016/j.egyr.2022.01.139 ER -
APA
Zhu, L., Wei, J., Wu, S., Zhou, X., & Sun, J. (2022). Application of unlabelled big data and deep semi-supervised learning to significantly improve the logging interpretation accuracy for deep-sea gas hydrate-bearing sediment reservoirs. Energy Reports. https://doi.org/10.1016/j.egyr.2022.01.139
Source records
- doaj · retrieved 2026-09-26T04:24:26.116Z