Optimization of Critical Parameters of Deep Learning for Electrical Resistivity Tomography to Identifying Hydrate
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- Sun, Yi-Fei & Zhong, Jin-Rong & Chen, Guang-Jin & Cao, Bo-Jian & Li, Rui & Chen, Dao-Yi, 2021. "A new approach to efficient and safe gas production from unsealed marine hydrate deposits," Applied Energy, Elsevier, vol. 282(PB).
- Jinhuan Zhao & Changling Liu & Qiang Chen & Changchun Zou & Yang Liu & Qingtao Bu & Jiale Kang & Qingguo Meng, 2022. "Experimental Investigation into Three-Dimensional Spatial Distribution of the Fracture-Filling Hydrate by Electrical Property of Hydrate-Bearing Sediments," Energies, MDPI, vol. 15(10), pages 1-12, May.
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- Francesco Grimaccia & Marco Montini & Alessandro Niccolai & Silvia Taddei & Silvia Trimarchi, 2022. "A Machine Learning-Based Method for Modelling a Proprietary SO 2 Removal System in the Oil and Gas Sector," Energies, MDPI, vol. 15(23), pages 1-15, December.
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Keywords
deep learning; electrical resistivity tomography; hydrate distribution; numerical simulation; optimization;All these keywords.
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