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Bayesian and Neural Network Approaches to Estimate Deep Temperature Distribution for Assessing a Supercritical Geothermal System: Evaluation Using a Numerical Model

Crossref DOI link: https://doi.org/10.1007/s11053-021-09874-w

Published Online: 2021-06-19

Published Print: 2021-10

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Ishitsuka, Kazuya https://orcid.org/0000-0003-4462-4598
Kobayashi, Yosuke https://orcid.org/0000-0003-3959-4347
Watanabe, Norihiro https://orcid.org/0000-0003-2993-3755
Yamaya, Yusuke https://orcid.org/0000-0002-7239-4366
Bjarkason, Elvar https://orcid.org/0000-0003-0616-3268
Suzuki, Anna https://orcid.org/0000-0001-8330-2175
Mogi, Toru

Asanuma, Hiroshi https://orcid.org/0000-0002-1836-5839
Kajiwara, Tatsuya

Sugimoto, Takeshi

Saito, Ryoichi
Funding

Funding for this research was provided by:

the New Energy and Industrial Technology Development Organization (Technology for estimation of deep geothermal structures and drilling bit life using artificial intelligence)

the Japan Society for the Promotion of Science (20K15219)
License Information

Text and Data Mining valid from 2021-06-19

Version of Record valid from 2021-06-19
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Article History

Received: 15 November 2020

Accepted: 14 May 2021

First Online: 19 June 2021

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