학술논문
Development of temperature prediction method for supercritical geothermal resources using neural networks / ニューラルネットを用いた超臨界地熱資源評価手法の開発
Document Type
Journal Article
Author
Source
Proceedings of the Annual Conference of JSAI. 2020, :1
Subject
Language
Japanese
Abstract
We propose an subsurface temperature structure prediction model using a neural network with the aim of predicting a distribution of a supercritical geothermal resources. In our proposed model, three-dimensional coordinates, specific resistance by magnetotelluric, D95, gravity anomaly value, and mineral isograds were calculated from measurement data as input features. This model training procedure was applied to the Kakkonda geothermal field, Japan. As a result of evaluation using actual measurement data, the RMSE was shown 39.3 ℃ when optimized input features.