Gordon Jonathan E, Akanbi Olatunde D, Bhuvanagiri Deepa C, Omodolor Hope E, Mandayam Vibha, French Roger H, Yarus Jeffrey M, Barcelos Erika I
Materials Data Science for Stockpile Stewardship: Center of Excellence, Case Western Reserve University, Cleveland, OH, USA.
Department of Materials Science, Case Western Reserve University, Cleveland, 44106, USA.
Sci Rep. 2025 Jan 7;15(1):1053. doi: 10.1038/s41598-024-85050-3.
Understanding subsurface temperature variations is crucial for assessing material degradation in underground structures. This study maps subsurface temperatures across the contiguous United States for depths from 50 to 3500 m, comparing linear interpolation, gradient boosting (LightGBM), neural networks, and a novel hybrid approach combining linear interpolation with LightGBM. Results reveal heterogeneous temperature patterns both horizontally and vertically. The hybrid model performed best achieving a root mean square error of 2.61 °C at shallow depths (50-350 m). Model performance generally decreased with depth, highlighting challenges in deep temperature prediction. State-level analyses emphasized the importance of considering local geological factors. This study provides valuable insights for designing efficient underground facilities and infrastructure, underscoring the need for depth-specific and region-specific modeling approaches in subsurface temperature assessment.
了解地下温度变化对于评估地下结构中的材料降解至关重要。本研究绘制了美国本土50至3500米深度范围内的地下温度图，比较了线性插值、梯度提升（LightGBM）、神经网络以及一种将线性插值与LightGBM相结合的新型混合方法。结果揭示了水平和垂直方向上的非均匀温度模式。混合模型表现最佳，在浅深度（50 - 350米）处的均方根误差为2.61°C。模型性能通常随深度降低，凸显了深部温度预测的挑战。州级分析强调了考虑当地地质因素的重要性。本研究为设计高效的地下设施和基础设施提供了有价值的见解，强调了在地下温度评估中采用特定深度和特定区域建模方法的必要性。
