[S-CG51] 機械学習による固体地球科学の牽引

[SCG51-P06] Physics-informedニューラルネットワークを用いた深部の温度推定：2次元数値データを用いた精度評価

*石塚 師也¹ (1.京都大学大学院 工学研究科)

キーワード：温度推定、物理法則を考慮したニューラルネットワーク、熱対流

Estimation of temperatures at depth is important for understanding physical processes in the Earth. Especially for geothermal applications, neural network approach has been used to estimate temperatures based on existing temperature logs. This approach trains the relationship between locations and temperatures at existing wells, and then predicts the temperatures at other locations. Although this approach has shown the effectiveness, the estimated temperatures do not necessarily follow physical laws. Recently, physics-informed neural network has been proposed, and evaluated the accuracy in groundwater applications. As this method constrains the predicted quantities using physical laws described by partial derivative functions, the predictions can follow the basic physical equations. In this study, I extended the proposed method for temperature estimation at depth and examined the prediction accuracy using 2D geothermal simulation data. For 2D simulation data, I considered a water saturated domain with the depth of 1 km and a constant heat flux at the bottom boundary. As a result, the RMSE of the estimated temperature was 7.7% using the physics-informed neural network, while the RMSE by a standard neural network was 25.2%. In addition, the loss of the mass conservation decreased by using the physics-informed neural network.

The author acknowledges the supports by JST ACT-X "AI powered research innovation" (grant no. JPMJAX20A1).