Geological Literature Search (GEOLIS) (Geological Survey of Japan / AIST)

Development of a machine learning approach to estimate temperature distribution for evaluating supercritical geothermal resources (B31)(abs.)

Authors=ISHITSUKA K., KOBAYASHI Y., MOGI T., UGO T., SUZUKI K., WATANABE N., YAMAYA Y., OKAMOTO K., ASANUMA H., KAJIWARA T., SUGIMOTO T., SAITO R.

Journal/Book_names=Annual Meeting, Geothermal Research Society of Japan, Abstracts with Programs

volume=2019

pages=97-97

Publish_year=2019

Publish_Country=JPN

Publisher=Geothermal Research Society of Japan

Language_of_Text=JA

ID=202070087

@id=https://gbank.gsj.jp/ld/resource/geolis/202070087