Groundwater is a valuable water source for drinking and irrigation purposes in semiarid regions. Groundwater pollution may affect human health if it is not pretreated and provided for human use. This study investigated the hydrochemical characteristics driving groundwater quality for drinking and irrigation purposes and potential human health risks in the Xinzhou Basin, Shanxi Province, North China. More specifically, we first investigated hydrochemical characteristics using a descriptive statistical analysis method. We then classified the hydrochemical types and analyzed the evolution mechanisms of groundwater using Piper and Gibbs diagrams. Finally, we appraised the groundwater quality for drinking and irrigation purposes using the entropy water quality index (EWQI). We assessed the associated human health risks for different age and sex groups through drinking intake and dermal contact pathways. Overall, we found that (1) Ca-HCO₃ and Ca·Mg-HCO₃ were the dominant hydrochemical types and were mainly governed by rock weathering and water–rock interactions. (2) Based on the EWQI classifications, 67.74% of the groundwater samples were classified as medium quality and acceptable for drinking purpose. According to the values of sodium adsorption ratio (SAR), residual sodium carbonate (RSC) and soluble sodium percentage (%Na), 90.32% of the samples were suitable for irrigation, while the remaining samples were unfit for irrigation because of the high salinity in the groundwater. (3) Some contaminants in the groundwater, such as NO₃⁻, NO₂⁻ and F⁻, exceeded the standard limits and may cause potential risks to human health. Our work presented in this paper could establish reasonable management strategies for sustainable groundwater quality protection to protect public health.

Accurate estimation of S-wave velocity from well logs is relevant for processing seismic data from hydrocarbon reservoirs, CO2 storage sites, and geothermal systems. Standard methods rely on empirical relationships and basic rock-physics models (RPMs), such as the Gassmann equation, which are often inaccurate because they cannot account for the complex factors that influence the relationship between S-wave velocity and other properties. Machine-learning techniques, such as deep neural networks (DNNs), have been shown to be effective methods for the indirect estimation of S-wave logs and other rock properties. In this work, we develop a novel prediction method that combines RPMs (Xu-White and Biot-Rayleigh [BR] models) and DNNs based on pseudolabels, i.e., a class of algorithms that use model outputs to assign labels to unlabeled samples, which are then used as labeled samples during training. The BR model takes into account the volume ratio of inclusions and treats it as a spatially varying parameter, allowing for a better description of the pore microstructure. Data from five boreholes demonstrate the applicability of our model. A single well is used for training, and the remaining four wells are used as a blind test. Compared with DNNs without physical poroelasticity models, the method driven by models indicates a significant improvement in prediction accuracy. By incorporating pseudolabels into the BR model, the prediction performance of the network model is improved. The method proposed in this work is validated and applied in other work areas.