## 1 Introduction  
Mountain tunnels often have to cross fracture zones and karst areas, where undesirable geological bodies are hidden and construction conditions in mountainous areas are far more complex than those in urban areas [1]. During construction, the site is frequently exposed to hazards, such as water gushing [2, 3]. In particular, when deeply buried tunnels cross water-rich fracture zones, large amounts of high-pressure groundwater will rush into the tunnel through the fracture zones in a short period of time. The lives and property of workers will be at risk [4], and the local ecology will be negatively affected [5, 6, 7]. Aiming at the problem of water gushing caused by water-rich fracture tectonic zones, scholars have carried out various types of approaches to model predictions for tunnels [1, 8, 9, 10]. [PERSON] et al. [11] believed that the dynamic characteristics of groundwater can be replicated by in-situ monitoring and numerical simulation to predict tunnel gushing. [PERSON] et al. [12] analysed the formation and evolution of water gushing channels on deeply buried faults using Comsol software, which has positive implications for the prediction of water gushing on faults. [PERSON] et al. [13] applied numerical simulations to show the characteristics of water gushing in the excavation phase based on electromagnetic geophysical prospecting. Based on the Darcy-Brinkman flow equation, [PERSON] et al. [14] explores the behaviour of groundwater flow in faults to obtain the evolution of pore pressure and flow velocity at different locations, which provides a reference for the prediction of water gushing in fractured tectonic zones. In addition, it is important to determine the source of water in the process of predicting the amount of water gushing [15; 16]. This supports engineers in predicting the size of the water gushing. The fuzzy clustering method and the hydro-geochemistry method could also be used to identify the source of water gushing, even though they were more commonly employed for groundwater quality monitoring, such as the work performed in reference [17]. [PERSON] et al. [18], [PERSON] et al. [19], and others analysed the data from the above method and identified the source of water gushing in subterranean space. [PERSON] et al. [20] applied a fuzzy clustering method and the hydro-geochemistry method to identify the source of water gushing and provide data support for the prediction of water gushing. In general, various new methods for the prediction of water gushing have progressed considerably in terms of depth of research, but not enough attention has been paid to the identification of sources of water gushing. In practice, the fuzzy clustering method and the hydro-geochemistry method are rarely used in combination to identify the source of water gushing. The results derived from the hydro-geochemistry method rely more on the experience of the engineers to analyse and judge, and do not apply the fuzzy clustering method scientifically. Meanwhile, less research has been carried out on areas of fractured tectonic zones with developing groundwater in igneous rocks, which are hard and stable [21; 22] and, as such, are often considered ideal areas for underground construction. At this stage, the applicability of the new methods is limited by the limitations and complexity of geological investigation techniques [23; 24]. Indeed, there are fewer theoretical studies on bedrock fracture water flow in deep alpine layers, fewer hydrogeological tests conducted in the field [25], and the high cost of trials is an obstacle to their application. In addition, engineers prefer to apply traditional methods of prediction, in which the effective radius of the water gushing is regarded as a fixed value by traditional methods and the flow field in the tunnel site area is also simplified in general terms. Admittedly, they are intuitive and simple, but large deviations are often found.