基于物理信息神经网络的多孔介质耦合传热参数联合反演OA

[Background]The thermal diffusivity and vertical seepage velocity of porous media are critical to character-izing the mechanisms underlying subsurface heat transfer.These two parameters are typically determined through inver-sion using temperature data since they are difficult to measure directly.Fiber-optic distributed temperature sensing(FO-DTS)can yield high-resolution,continuous temperature profiles,providing data for parameter identification.However,conventional numerical inversion suffers from limitations such as mesh dependency and sensitivity to initial values when used to process continuous monitoring data.[Methods]This study proposed a joint geothermal parameter inversion method that integrates FO-DTS data with a physics-informed neural network(PINN).Specifically,the steady-state con-vection-diffusion equation was embedded as a soft regularization term into the loss function of a PINN to provide a physical constraint.In this manner,the parameter inversion problem was transformed into a physics-guided optimization process,enabling the simultaneous estimation of vertical seepage velocity and thermal diffusivity.[Results and Conclu-sions]The results indicate that the temperature field derived from PINN-based inversion exhibited a root mean square error(RMSE)of 0.20 ℃ compared to observed temperature data.The thermal diffusivity and vertical seepage velocity derived from the inversion were roughly consistent with the results from the upwind finite difference-based inversion.Furthermore,the sensitivity analysis of key hyperparameters confirms the high robustness of the PINN-based inversion framework.The results of this study provide a novel approach characterized by both physical consistency and data adapt-ability for identifying parameters in geothermal-seepage coupling systems.