05 Dec 2022
 | 05 Dec 2022
Status: this preprint is currently under review for the journal HESS.

Physics-informed machine learning for understanding rock moisture dynamics in a sandstone cave

Kai-Gao Ouyang, Xiao-Wei Jiang, Gang Mei, Hong-Bin Yan, Ran Niu, Li Wan, and Yijian Zeng

Abstract. Rock moisture, which is considered as a hidden component of the terrestrial hydrological cycle, has received little attention. In this study, the frequency-domain reflectometry (FDR) is used to obtain fluctuating rock water content in a sandstone cave of the Yungang Grottoes, China. We identified two major cycles of rock moisture addition and depletion, one in the summer and the other in the winter. By using the LSTM (Long Short-Term Memory) network and the SHAP (SHapley Additive exPlanations) method, relative humidity, air temperature and wall temperature are found to have contributions to rock moisture in the summer. By using vapor concentration and the difference between dew point temperature and wall temperature as two input variables of the LSTM network, the predicted rock water content has a very good agreement with the measured rock water content, with the Nash–Sutcliffe efficiency coefficient (NSE) being as high as 0.978. Because the two new input variables are factors directly controlling vapor condensation, they provide informative priors to the deep learning model and improved prediction performance. After identifying the causal factors of rock water content fluctuations, we also identified the mechanism controlling the multi diurnal vapor condensation. The increased vapor concentration accompanying a precipitation event leads to transport of water vapor into rock pores, which is subsequently adsorbed onto the surface of rock pores and then condensed into liquid water. With the aid of the deep learning model, this study increases understanding of sources of water in caves, which would contribute to future strategies of alleviating weathering in caves.

Kai-Gao Ouyang et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-403', Anonymous Referee #1, 19 Dec 2022
    • AC1: 'Reply on RC1', Xiao-Wei Jiang, 17 Feb 2023
  • RC2: 'Comment on hess-2022-403', Oliver Sass, 06 Mar 2023
    • AC2: 'Reply on RC2', Xiao-Wei Jiang, 07 Mar 2023
  • RC3: 'Comment on hess-2022-403', Anonymous Referee #3, 09 Mar 2023

Kai-Gao Ouyang et al.

Data sets

Data of rock water content and atmospheric conditions Kai-Gao Ouyang, Xiao-Wei Jiang, Gang Mei, Hong-Bin Yan, Ran Niu, Li Wan, Yijian Zeng

Kai-Gao Ouyang et al.


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Short summary
Our knowledge on sources and dynamics of rock moisture is limited. By using the frequency-domain reflectometry (FDR), we monitored rock moisture in a cave. The results of an explainable deep learning model reveal that the direct source of rock moisture responsible for weathering in the studied cave is vapor, not infiltrating precipitation. Using of variables controlling vapor condensation as model input, which is a form of physics-informed deep learning, leads to improved accuracy of prediction.