基于机器学习的地下温度预测方法研究现状OA

Underground temperature prediction serves as a critical component for efficient geothermal resource exploitation and sustainable utilization.Conventional prediction methods remain constrained by the nonlinear characteristics of complex geological data and challenges in multi-source information integration,often failing to meet precision requirements.Machine learning algorithms,leveraging their robust data mining and nonlinear modeling capabilities,offer novel approaches for subsurface thermal forecasting.This paper systematically reviews recent advancements in machine learning applications for underground temperature prediction,with particular emphasis on analyzing the performance of neural networks and their variants in multi-source data fusion and the optimization of indirect geothermometers.A comparative evaluation of prediction accuracy and adaptability across different algorithms is presented.Research demonstrates that innovative methodologies,including neural networks and clustering analysis,significantly enhance the reliability of temperature prediction in deep geothermal reservoirs.Nevertheless,current studies still face challenges,including inadequate representation of spatiotemporal dynamics and limited model generalizability.The study further proposes optimized pathways integrating spatiotemporal sequence analysis,transfer learning,and 3D geological modeling,providing theoretical and technical references for geothermal resource exploration and development.