Ensuring mining safety and efficiency relies heavily on identifying the source of mine water inrush. This review article aims to provide a comprehensive overview of standard methods used to pinpoint the origin of mine water inrush, highlighting the development and progress in the research of discrimination methods. These methods are systematically classified into various categories, encompassing hydrochemistry examination, water level and temperature analysis, geostatistical approaches, machine learning and deep learning methods, as well as the utilization of other analytical techniques. The review not only presents a quantitative and visual analysis of the theoretical methods proposed by scholars but also emphasizes their strengths, weaknesses, and applicability to various mining operations. Furthermore, it explores the increasing utilization of artificial neural networks and machine learning algorithms in source discrimination models, indicating the advancement in this area of research. To further advance the field, specific examples of these methods and their effectiveness in identifying the source of mine water inrush are provided, aiming to stimulate further research. The article also offers detailed recommendations for future research directions and emerging trends, underlining the importance of comprehensive multidisciplinary and multi-method analysis. It suggests exploring emerging technologies such as the Internet of Things (IoT) and cloud computing, while emphasizing the need to develop more accurate and reliable models for source identification. The fusion of artificial intelligence (AI), heightened computational capabilities, online programming, and intelligent data collection systems presents the prospect of transforming the way industries respond to these critical events. By providing a comprehensive overview, analyzing the effectiveness of existing methods, and proposing future research directions, this review aims to contribute to the continuous development and progress of discrimination methods for mine water inrush incidents. Ultimately, it seeks to enhance mining safety and efficiency by facilitating the prompt and accurate identification of the sources of mine water inrush.

The purpose of this research was to understand the hydrogeochemical characteristics and assess the quality of phreatic and confined groundwater in southern Hebei Province. A total of 107 groundwater samples were collected, representing different aquifer conditions over the study area. Multivariate statistical analysis, hydrochemical maps, ionic ratio coefficients, geographic information system (GIS) and geochemical simulation were comprehensively and systematically used to reveal the hydrochemical characteristics of groundwater and its controlling mechanism. The results revealed that both phreatic (pH = 7.02–9.08) and confined groundwater (pH = 7.00–10.60) were slightly alkaline. The hydrochemical types were mainly present as the HCO₃-Ca-Mg type in the western premontane area and mixed Ca-Mg-SO₄-Cl and Na-Cl-SO₄ types in the eastern plains. The hydrochemical composition was dominated by water–rock interactions of natural processes, including silicate weathering, dissolution of sulfate minerals (gypsum, anhydrite), and cation-exchange adsorption. Anthropogenic activities were the main factor causing NO₃⁻ content in some groundwater samples to exceed the geochemical baseline. The hydrogeochemistry of groundwater in different aquifers was significantly varied. The average contents of TH, TDS, Na⁺, Ca²⁺, Mg²⁺, Cl⁻ and SO₄²⁻ in phreatic aquifers were significantly higher than those in confined aquifers. The Entropy Weighted Water Quality Index (EWQI) results revealed that 17.78% of phreatic and 50% of confined water samples were meeting the purpose of drinking water. The groundwater samples with EWQI values exceeding 100 were mainly situated in the Handan urban area and the eastern region of Xingtai City, which should be avoided for direct utilization and needs to be improved through protection and management measures, to enhance the quality of groundwater. Correlation analysis showed that groundwater quality was significantly dominated by TH, TDS, Na⁺, Mg²⁺, Cl⁻ and SO₄²⁻ concentrations.