Preprints
https://doi.org/10.5194/hess-2022-403
https://doi.org/10.5194/hess-2022-403
 
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 Ouyang1, Xiao-Wei Jiang1,2, Gang Mei3, Hong-Bin Yan4, Ran Niu1, Li Wan1, and Yijian Zeng5 Kai-Gao Ouyang et al.
  • 1MWR Key Laboratory of Groundwater Conservation, China University of Geosciences, Beijing, China
  • 2MNR Key Laboratory of Shallow Geothermal Energy, China University of Geosciences, Beijing, China
  • 3School of Engineering and Technology, China University of Geosciences, Beijing, China
  • 4Yungang Research Institute, Datong, Shanxi, China
  • 5Department of Water Resources, ITC Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, the Netherlands

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: open (extended)

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  • RC1: 'Comment on hess-2022-403', Anonymous Referee #1, 19 Dec 2022 reply

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 https://doi.org/10.5281/zenodo.7382895

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.