Interpreting Mine Water Sources and Determining Mixing Ratios Based on the Spatial and Chemical Characteristics of Bedrock Brines in a Coastal Mine

Abstract

Water inrush caused by mining below the seafloor seriously affects the safety and production of mines. Identifying the end element of mine inrush and accurately calculating the mixing ratios of end elements are the basis for a reasonable evaluation of water inrush risk. Based on hydrogeochemical and stable isotope indexes, combined with the spatial distribution characteristics of brine, the classification of brine in the study area was preliminarily determined as follows: shallow brine, middle brine, and deep brine (while previous studies have only classified bedrock brine as one category). Hierarchical multi-index analysis was used to identify the inrush end elements in the different sublevels, and an end-element mixed model was determined according to the analysis results of the four pairs of evaluation indexes (Cl⁻–δ¹⁸ O, Cl⁻–Ca²⁺, Cl⁻–Mg²⁺, and Cl⁻–Na⁺). Through a comparison with the deviation analysis results of previous studies, it was shown that this method is suitable for mine-water-source identification when under complex hydrogeology conditions. According to the calculation results of the mixing ratio, the seawater ratio shows, within the mining process, a trend of first increasing, then decreasing, and finally stabilizing. This trend is controlled by disturbance stress, self-weight stress, and tectonic stress. The vertical zonation of the seawater proportions indicates that seawater mainly recharges mine water through vertical fractures. The difference in the proportion of seawater at the water inrush points of the −600 m sublevels indicates that the F3 fault and the northwest water-conducting fracture zone may be the preferred flow channels for seawater to recharge mine water. The research results are of great significance to promote the safe mining of coastal mines around the world.

1. Introduction

Water inrush is an example of the major disasters that occur in coastal mines, and they pose a huge threat to the safety of underground personnel and to production [1,2,3,4,5]. After years of mining, the main mining sections of coastal mines are mostly located below sea level; thus, once seawater floods into the roadway, it will cause immeasurable losses [6,7]. However, it is very difficult to predict such disasters due to the phenomenon’s complex mechanisms [8]. Hence, to propose reasonable protection suggestions, the most important aspect is to identify the sources of mine water and to calculate the end-member mixing ratio [9]. The end-member mixing ratio refers to the mixing ratio of different water sources at the inrush point.

In recent years, many experts and scholars have carried out a great deal of research work on water source identification and evolution, which is mainly divided into two methods [9,10,11,12,13,14,15]. The first method comprehensively analyzes the water inrush sources by focusing on the main ions and stable isotopes; however, due to the method using only a part of the information of the water sample, it cannot reflect all the characteristics of the water sample, and because it cannot accurately determine the number of water inrush end elements, the method needs to be improved [16,17,18,19,20,21]. The second method is multivariate statistical analysis. Its representative methods are mainly principal component analysis and cluster analysis. Both of these methods can reduce and compress the dimensions of water chemistry data information and extract key information representing the original data [10,16,22]. Principal component analysis replaces the original water sample information with the extracted principal components, which is a linear combination of the original information. Although it contains complete water sample information, it also results in the main information becoming “neutralized” by the secondary information, which weakens the indication role of the key information and may lead to an inability to accurately identify the water source [23].In this study, principal component analysis, cluster analysis, hydrogeochemical analysis, the maximum likelihood method, and other methods were used to identify the water sources in the Xishan mining area [8,16,24]. However, due to the limitation of timescales and the research area, the common problem with the methods was that only one type of brine was considered in all of the sublevels. The key point for water source identification and mixing ratio calculations is to clarify the number and category of the end elements. The determination of bedrock brine end elements is based on extreme points in isotope and ion distribution maps [25,26]. However, when there are multiple bedrock brines, there will be multiple extreme points in the isotope and ion correlation maps; at the same time, in some index plots, there are no extreme values of brine end elements, so further analysis is needed to determine the bedrock brine end elements at different levels. The complex hydrogeological conditions in the coastal mines and the existence of multiple water inrush elements—rainwater, fresh water, Quaternary saline water, seawater, and the three types of bedrock brine—greatly increase the complexity of the problem.

Therefore, in order to identify the water sources of tunnel inrush and to ensure the safe mining of coastal mines, this paper takes the Sanshandao Gold mine, a typical coastal mine, as the research object, and puts forward a coastal mine water source identification model. In this study, the self-finiteness of water chemical data was used to clarify the end elements of water inrush at different levels through hierarchical multi-index analysis. Coastal mines may contain a variety of brines, and through the literature research, it was found that most of the previous studies only considered one type of bedrock brine, which is not consistent with the actual situation and will cause errors in water source identification. In the presence of only a single bedrock brine, both hydrogeochemical analysis and principal component analysis methods are commonly used to identify mine water sources and to calculate inrush mixing ratios. However, for the complex hydrogeological conditions present in the study area, this method cannot accurately calculate the mixing ratio. Therefore, hierarchical multi-index analysis and principal component analysis methods were used to identify the water inrush patterns and to calculate the mixing ratios. Duan et al. demonstrated that hierarchical multi-index analysis is an effective method in complex water inrush pattern recognition [23]. Based on the results of water chemistry and stable isotope analysis, combined with the spatial distribution of brine, three different bedrock brines were identified: shallow brine, central brine, and deep brine; these are the possible end elements. In this study, the effects of various bedrock brines will be considered to determine the mixing modes of the different sublevels in a coastal mine. In addition, the mixing ratio will be calculated, based on the conservation of mass method, to assess the risk of seawater inrush in coastal mines.

2. Study Area

2.1. Geological Background

The Xishan Gold Mine is located in the Laizhou Bay coastal zone of Laizhou City, Shandong Province, Eastern China. It is located between geographical coordinates 37°22′30″ N and 37°24′15″ N, and between 119°54′30″ E and 119°57′45″ E. Additionally, there are three main faults in the mining area, namely, F1, F2, and F3 (Figure 1a). The fault F1 is spread in an S-shaped pattern, with an overall strike of northeast (40°); it is southeast leaning and has an inclination in the range 45–75°. Fault F1 is a compression-shear fault, its oblique extension depth exceeds −1200 m, and the fault plane develops 10 cm−20 cm gray-black fault mud, which has good water insulation performance (

Table 1. Main characteristics of the faults in the mining area.

Fault	Trend	Inclination	Dip	Extension Depth/m	Cementation	Hydrological Properties
F1	40°	SE	45~75°	>1200	Good cementation	Impermeable
F2	70°	NW	85°	—	—	Hydraulic
F3	310°	Upper NE Down SW	90°	>850	No cementation	Hydraulic

). The ore body occurs in the alteration zone of the footwall of fault F1, and the occurrence is basically consistent with fault F1 (Figure 1b).

2.2. Hydrologic Conditions

The underground water system in the mining area mainly includes a Quaternary aquifer and bedrock fissure aquifer. The vertical direction of the aquifer changes significantly and is roughly divided into four layers: the first layer; the third layer, which is the aquifer from the top to the bottom; the second layer and fourth layer, which are the relative aquifuge layer and aquifuge layer, respectively. The first aquifer (I₁), with a thickness of 3.5~17.29 m, is mainly composed of coarse sand and sand gravel. It has the characteristics of marine sedimentation and good water permeability. It is recharged by atmospheric precipitation and seawater. The first aquiclude (A1) is a sea mud layer, which is located below the first aquifer. The lithology is mainly sandy clay and clayey sand. This layer has small viscosity and relatively poor water resistance, so it is a relatively water-resisting layer. The thickness of this layer is generally 7–8 m, and the distribution is relatively continuous. The second aquifer (I₂) is a sand gravel layer located between the sea mud layer and the bottom aquiclude. The lithology is medium sand, coarse sand, and gravel. It contains pore-confined water generally with a thickness of 3–4 m, which can accept the recharge of the first aquifer and seawater. According to its effect on the water inrush of the tunnel, the above three layers are uniformly divided in the strong Quaternary water-rich aquifer. Below the aquifer (I₂) is an aquiclude (A2) with a thickness of 3 m–5 m, which is above the weathered crust of bedrock. The lithology of the area is mainly clay and sandy clay, with high viscosity and good water insulation, and it is continuously distributed throughout the mining area (see Figure 2).

The bedrock fissure aquifer system is located below the aquiclude (A2). The bedrock aquifer can be divided into two parts (hanging wall bedrock fissure aquifer II and footwall bedrock fissure aquifer III), with the F1 aquiclude as the dividing line. The weathering fissures and structural fissures developed in the granite and metamorphic rocks of the hanging wall F1 contain confined water. The total dissolved solids of the fissure water are between 39.1 and 92.7 g/L. The F1 fault gouge and mylonite in the lower part have a good blocking effect on the groundwater in the water-bearing zone entering the mine pit. The F1 footwall-bedrock-fractured aquifer III can be divided into three sublayers: (1) the occurrence of the altered structure aquifer (III₁) is roughly the same as that of the ore body; influenced by other aquifers, the distribution of the fissure water is uneven, and the total dissolved solids are between 24.4 and 68.9 g/L; (2) the aquifer (III₂) of the F3 fault structure is the F3 fault zone, which staggers the aquiclude of the F1 and is perpendicular to each roadway; this creates the hydraulic connection between the seawater, Quaternary aquifer, upper fissure water, and roadway; and (3) the structural fissure aquifer (III₃) is distributed in the northern half of the mining area, and the total dissolved solids are 56.32–61.06 g/L; due to the shallow extension depth of the fissures, it mainly contributes to the water inrush in the shallow tunnel.

3. Materials and Methods

The surrounding rock of the tunnel is mainly granite or granodiorite, and the groundwater is mainly stored in and flows along the fissures. Monitoring and sampling all of the inrush fissures revealed the presence of multiple bedrock brine in the study area. The basic assumptions of the study are as follows: (1) the chemical characteristics of the inrush end elements are constant; (2) the mine water is controlled by the mixing of different end elements; and (3) (as explained by the reactions) the ions and isotopes used as tracers are stable [8].

Sampling and Analysis

The water samples can be divided into two types: potential end-element water samples and mine water inrush water samples. The sampling work was carried out in accordance with the standard procedure, the sample vial was a 600 mL brown polyethylene bottle, and the water samples were stored in an environment of 5 °C. The potential end-member samples included seawater samples, rainwater samples, Quaternary saline water samples, and freshwater samples. Two bottles of seawater samples were obtained from one meter below sea level in the Bohai Sea on the south side of the mine; rainwater samples were collected on rainy days in April and August; five bottles of Quaternary saline water samples were pumped into five wells on the south side of the mine, which were charged by seawater and freshwater; four bottles of freshwater samples came from the Wang River on the south side of the mine; and, from 2019 to 2022, the water inrush points in the sublevels of −375 m, −510 m, −600 m, −960 m, −1005 m, −1035 m, −1050 m, −1065 m, −1095 m, and −1140 m were sampled every August, and two water samples were collected at each sampling site. One water sample was used to detect the concentration of the major ions (including K⁺, Na^+, Ca²⁺, Mg²⁺, Cl⁻, SO₄²⁻, HCO₃⁻, and NO₃⁻) (unit mg/L). At the same time, to detect the total dissolved solids, the PH value and conductivity of the sample was determined. The testing standards were based on the national standards of the People’s Republic of China (GB11904-89, GB7477-87, GB7476-87, and GB 11899-89). The other water sample was analyzed with an MT-253 mass spectrometer, and the hydrogen-oxygen isotope level was expressed based on the Vienna mean standard seawater (VSMOW). Bedrock brine refers to the ancient seawater that is concentrated by strong evaporation and stored in rock, and its total dissolved solids are >50 g/L. According to the analysis results, three types of bedrock brine were obtained in the sublevels of −375 m, −960 m, and −1140 m, of which the shallow brine number is 375-20, the middle brine number is 960-DX, and the deep brine number is 1140-4. A total of 108 water samples were collected, including potential end-member water samples, which were obtained in 2006. The chemical analysis of the water samples was performed by the State Key Laboratory of Institute of Geology and Geophysics, Chinese Academy of Sciences.

Figure 3 shows the location of the water inrush points at different sublevels. As shown in the figure, most of the inrush points are distributed in the main roadway, parallel to the ore body and F1 fault, and only a few of the inrush points are distributed in the branch roadway (perpendicular to the main roadway) (for example, see −1095 m). Since the ore body is located at the footwall of the ore-controlling fault, the water inrush points are concentrated in the footwall of fault F1, and the aquifer type is shown in Figure 2. Fault F3 and fissures in the northwest of the mine area create a hydraulic connection between the overlying seawater and the roadway, increasing the risk of mine water inrush.

According to the results of the spatial distribution and ion concentration analysis of the brine, the existence of three kinds of bedrock brine in the Xishan mining area was preliminarily determined. For this complex situation, traditional methods such as hydrochemical analysis and principal component analysis were insufficient to determine the mixing mode. Thus, five pairs of analysis indexes were selected, and the hierarchical multi-index analysis method was used to identify the inrush end elements and mixing modes at different sublevels. Then, according to the mixing model, the mixing ratio of the water inrush point was calculated based on the principle of mass conservation. Thus, the risk of mine water inrush was evaluated.

4. Results

4.1. Origin of Mine Water

Piper and Gibbs diagrams are often used to determine the type and source of groundwater [27,28]. In Figure 4a, the different color dots represent different mine water samples. And the mine water was concentrated at the right apex of the diamond-shaped area. According to the Piper three-line plot zone, seawater and many types of brine are located at the right apex of the diamond-shaped area, so it can be concluded that the chemical composition of mine water, seawater, and brine are similar. As shown in Figure 4, evaporation is the dominant process of mine water evolution, rather than rock and precipitation. This indicates that mine water has undergone strong evaporative concentration, which is consistent with the formation mechanism of brine. In summary, brine and seawater have a significant impact on the formation of mine water.

4.2. Hydrochemical Analysis and Principal Component Analysis

Table 2. Hydrochemical analysis results of mine water and potential end members.

	Mine Water			Sea Water	Fresh Water	Saline Water	Rain Water
	Min	Max	Mean
Sample Numbers	95			2	2	5	2
δ18O (‰)	−8.22	−0.48	−2.48	−0.20	−7.96	−2.32	−10.09
δD (‰)	−62.98	−8.57	−20.55	−5.41	−56.9	−24.5	−75.24
Na+ (mg/L)	6656	20,600	10,066	8930	192	10,300	15.1
Ca2+ (mg/L)	264	2379	842	387	132	455	6.2
Mg2+ (mg/L)	353	3030	1120	1125	24.8	1130	2.3
Cl− (mg/L)	14,590	40,196	19,687	16,400	285	18,000	10.6
SO42− (mg/L)	454	4446	2504	2350	217	2440	9.6
total dissolved solids (g/L)	24.4	69.7	35.8	29.7	1.1	32.8	0.1

shows the results of the water chemistry testing of the mine inrush samples and potential end elements (including δ¹⁸O, δD, Na⁺, Ca²⁺, Mg²⁺, Cl⁻, SO₄²⁻, and total dissolved solids). The analysis results in Table 2 show that the concentration of water ions and hydroxide isotopes in the mine has a large range, indicating that there are large differences between the water inrush points in the mines. The total dissolved solids value of the mine inrush far exceeded that of the seawater sample, indicating that the mine inrush contained a certain proportion of bedrock brine, which had an impact on the groundwater in the study area. Stable isotopes have a tracer effect, and from the level of hydrogen-oxygen isotopes, seawater has the largest mean value of hydrogen-oxygen isotopes, while rainwater has the lowest average of hydrogen-oxygen isotopes. This is because rainwater is formed by the evaporation of seawater and fresh water, which directly recharges freshwater, so rainwater is not used as a potential inrush end member. Furthermore, the Quaternary saline brine was recharged by seawater and freshwater, and its chemical characteristics were controlled by the mixing of seawater and freshwater. Thus, when seawater and fresh water are used as potential end elements, the contribution of Quaternary brine to the mixed model cannot be considered. By comprehensive analysis of the test results and the hydrogeological conditions of the study area, it was inferred that there were four potential inrush end elements of mine water: bedrock brine, seawater, freshwater, and Quaternary saline brine. It was found that the composition of seawater, freshwater, and saline water was relatively stable, and the ion concentration basically did not change. Many transgressions and regressions occurred in the study area, forming a variety of bedrock brines. Due to the similar formation conditions of bedrock brine in the study area, the characteristics of the three kinds of bedrock brine were not obvious, and further analysis was required to identify the source of mine inrush water. Mining needs to be considered as an important influencing factor. Mining causes stress redistributions of the surrounding rock, closing the original water-conducting fractures and creating new water-conducting fractures. Thus, even at the same sampling point, the inrush end element will change as the mining work progresses, so it is necessary to consider the brine evolution in the identification of mine water sources.

For the principal component analysis, the Kaiser–Meyer–Olkin (KMO) test and Bartlett test were used to measure the correlation of the data. The Kaiser–Meyer–Olkin value, calculated with the SPSS statistical analysis software, was 0.61, which is greater than 0.6. The p-value of the Bartlett test was 0.000, which was less than 0.005, indicating that the data used in the paper are suitable for principal component analysis. Figure 5 shows the results of the hydrochemical analysis and principal component analysis. The circle represents mine water, and the triangle represents potential end members. Figure 5a shows the correspondence between Cl⁻ and δ¹⁸O in all water samples. As the figure shows, seawater, fresh water, and bedrock brine are the apex of the area, which can be judged to be potential inrush end elements. However, it is not possible to distinguish the types of bedrock brines that play a major role in different regions. As bedrock brine with a low Cl⁻ concentration and low δ¹⁸O is replaced by bedrock brine with a high Cl⁻ concentration and high δ¹⁸O during the inrush source identification process, deep brine (for example) will be obscured by middle brine, and central brine will be obscured by shallow brine (the blue triangle and red triangle, respectively). In the process of bedrock brine identification, three factors need to be comprehensively considered: (1) the difference in the ion concentration of brine end members; (2) the spatial location and dominant flow channel of brine; and (3) the water temperature and its evolution at the sampling point. Figure 5b shows the principal component analysis results of the main ions in the water sample, and the two principal components, principal component 1 and principal component 2, were obtained by reducing the dimensionality of the main ions and hydrogen-oxygen isotope analysis results. Since the characteristics of many key indicators will be “neutralized” in the process of dimensionality reduction, even though the main contributing end elements of the water inrush point can be preliminarily determined, it is impossible to accurately identify the brine end elements in the different sublevels. For example, the Euclidean distance between the inrush points of the −375 m, −510 m, and −600 m sublevels and the seawater is the closest, indicating that seawater has a high proportion in these water samples. However, the type of brine cannot be determined, so further analysis is required.

Figure 6 shows the correlation between Cl⁻, Mg²⁺, and Ca²⁺ in all water samples. The Cl⁻ and Mg²⁺ in the mine water of the shallow sublevels showed a good linear relationship, but the mine water in the deep sublevels deviated from this linear relationship, and its Mg²⁺ concentration was lower than that in the shallow sublevels (as shown in Figure 6a). Figure 6b shows that the mine water in the deep sublevels has a high Ca²⁺ concentration. From this, it can be inferred that the brine with a low Mg²⁺ concentration and high Ca²⁺ concentration (deep brine represented by 1140-4 and 1140-5) being mixed into the mine water, or ion exchange, occurs in the mine water during the seepage process, so the strength of the water–rock reaction needs to be evaluated. The red triangle and blue triangle in Figure 6 are the middle brine represented by 960-DX and the shallow brine represented by 375-20, respectively. As shown in Figure 6, the ion concentration and total dissolved solids of the shallow brine and middle brine are much higher than the deep brine, so deep brine has obvious differences from the other two brines. Thus, deep brine can be identified according to characteristic parameters such as ion concentration. The shallow brine is similar to the middle brine with little difference in the main characteristic parameters, but the shallow brine has a higher total dissolved solids, Mg²⁺ concentration, and a lower SO₄²⁻concentration, so it can be distinguished according to this characteristic and spatial location. In Figure 6b, there is a good linear relationship between the Cl⁻ and Ca²⁺ of shallow and middle mine water, and the mine water in the deep sublevels that is affected by deep brine deviates from this mixing line (i.e., the mixing line determined by seawater, saline water, middle brine, and shallow brine). Figure 6b preliminarily identified the inrush end elements of mine water in the sublevels but was limited by the number of analysis indicators. This meant it was impossible to accurately identify the inrush end elements. Thus, the hierarchical multi-index analysis method was proposed to identify the mine water source.

4.3. Hierarchical Multi-Index Analysis

According to the distribution of the mine water, the study area was divided into three levels: shallow sublevels (−375 m and −510 m), middle sublevels (−600 m, −1005 m, −1035 m, −1050 m, and −1065 m), and deep sublevels (−1095 m and −1140 m). Under the influence of mining, the brine end elements of the same sampling point may change over time, so the mine water in middle and deep sublevels are plotted on the same map for comprehensive analysis. To identify the mine water sources, four pairs of indicators (Cl⁻–δ¹⁸O, Cl⁻–Ca²⁺, Cl⁻–Mg²⁺, Cl⁻–Na⁺) were analyzed by the hierarchical multi-index analysis method.

4.3.1. The Shallow Sublevel

In Figure 6, Figure 7 and Figure 8, the triangle and circle represent the potential end element and mine water, respectively, and the difference in color indicates the type of water sample. The mixing lines and mixing boundaries in the figure indicate the mixing mode. When the potential end element is not clear, the dashed line is the mixing boundary, as is shown in Figure 7a. When multiple end elements are on a straight line, the dashed line is a mixing line, as shown in Figure 7b. In Figure 7a, the δ¹⁸O of the seawater, freshwater, shallow brine, and middle brine are all regional extremes, which are judged to be potential end elements. Meanwhile, the possibility of deep brine and saline water is excluded, but the type of brine cannot be determined. Figure 7d shows that shallow brine is more likely a potential end element. In addition, when combined with the spatial location and dominant seepage channel of brine, it can be determined that shallow brine is the inrush end element. In Figure 7c,d, the mine water in the red circle (375-4 and 375-15) deviates from the mixing line and is presumed to have undergone ion exchange. There are three main possible ion exchange methods in the mining area: CaX + 2Na = Na2X + Ca, MgX + 2Na = Na2X + Mg, and CaX + Mg = MgX + Ca. A good linear relationship between Cl⁻ and Na⁺ (Figure 7b) proves that no ion exchange occurs between Na⁺ and Mg²⁺, and Ca²⁺. Thus, high Ca²⁺ and low Mg²⁺ concentrations in the 375-4 and 375-15 mine water indicate that ion exchange occurs between Mg²⁺ and Ca²⁺ (Figure 7c,d). As shown in Figure 7, the Euclidean distance between the mine water and the seawater in shallow sublevels was the smallest, so it can be assumed that the proportion of seawater is the highest in the end members. In summary, the mixed model of the shallow sublevels is a ternary hybrid model of seawater, freshwater, and shallow brine.

4.3.2. The Middle Sublevel

The middle brine represented by 960-DX is distributed in the middle sublevels of −960 m. Figure 8a indicates that seawater and freshwater are potential end members, but due to the similar chemical characteristics of shallow brine and middle brine, it is impossible to determine the type of brine. In Figure 8b–d, except for the mine water (1050-1, 1065-1) in the red circle, a good linear relationship between Cl⁻ and Na⁺, Ca²⁺, and Mg²⁺ is presented; thus, the dashed lines in the figures are all mixing lines. It can be seen from Figure 8c that the middle brine is more likely a potential end element. Moreover, the early mine water in the−600 m sublevels was high-temperature hot water (deep hot water from the F3 fault), which prompted speculation that F3 created a hydraulic link between the mine water of the −600 m sublevels and the middle brine [25,29]. According to the above analysis, it was determined that the end element in the middle sublevels is seawater, middle brine, and freshwater, but the mine water in the red circle does not conform to this law and needs to be further analyzed. The mine water in the red triangle and the red circle are water samples collected from the same sampling point at different times. The water samples in the red triangle were sampled in March 2022, and the water samples in the red circle were sampled in June and September 2022. Thus, the possibility of ion exchange was excluded; the essential reason for this was that the brine changed over time. The analysis results of Figure 8c,d show that the brine type of the mine water in the red circle is deep brine. Under the action of mining disturbances, stress redistribution was generated in the surrounding rock, and the stress concentration and stress state changed to cause the original water-conducting fracture to close and the new water-conductive fracture to occur, which further led to the change in brine type. Therefore, in the process of identifying the water sources, the impact of mining activities should be fully considered. In summary, the mixed model of the middle sublevels is a ternary hybrid model of seawater, freshwater, and middle brine.

4.3.3. The Deep Sublevel

The deep mine-water data distribution is more discrete than those of shallow and middle mine water. There was no tendency toward the concentrated distribution of individual end elements, and most of them were distributed discretely along the mixing line (Figure 9). A comprehensive analysis of Figure 9 shows that freshwater, seawater, and deep brine are potential inrush end elements. In Figure 9c,d, the mine water (from the −1095 m sublevel) in the red circle deviates from the mixing line. The mine water in the −1095 m sublevels has a high Mg²⁺ concentration and a low Ca²⁺ concentration, but its sum is constant. It was thus inferred that ion exchange occurs between Mg²⁺ and Ca²⁺, but the direction of ion exchange is opposite to the shallow sublevels. This may be due to differences in the lithology between the shallow sublevels and deep sublevels, or because the high-temperature conditions in the deep sublevels change the direction of ion exchange. In addition, the Euclidean distance between deep brine and mine water reflects that deep brine is the main source of the mine water. The mixed model of the deep middle mine water mainly includes deep brine, seawater, and freshwater.

The water source identification results show that the shallow, middle, and deep sublevels are all ternary mixing models. Seawater, brine, and freshwater are potential end members of mine water, and only the type of brine differs. The Euclidean distance between the mine water and the three end elements preliminarily indicates that the main sources of mine water are seawater and brine, which is consistent with the analysis results of Figure 4.

4.4. Analysis on Water–Rock Reaction

In the process of water source identification, it was found that the mine water had a water–rock reaction during the seepage process. The water–rock reaction methods were, mainly, divided into three ways: rock dissolution, mineral precipitation, and ion exchange. Many experts have analyzed the ion activity of the mine water in the Sanshandao Gold Mine at different temperatures, and they have proved that there is no mineral precipitation and rock dissolution in the mine water [30]. The possibility of ion exchange is evaluated by analyzing the correspondence between the ions (Figure 10). Cl⁻ is stable in most environments, so changes in Cl⁻ concentration are controlled by mine water mixing. Figure 10a indicates that no ion exchange has occurred between the Na⁺ and other ions. In Figure 10b, there was a good linear relationship between the Cl⁻ and the sum (molar concentration) of Ca²⁺ and Mg²⁺ in the mine water. However, the Ca²⁺ and Mg²⁺ of some mine water in the shallow and deep sublevels deviated from the mixing line. This phenomenon indicates that ion exchange occurs between the Ca²⁺ and Mg²⁺ in mine water, which is consistent with the research results of other studies [8].

4.5. Mixing Ratio Calculation and Deviation Analysis

This article adopts a ternary mixing ratio calculation model based on mass conservation, which considers that the water inflow in the tunnel is composed of three types of end elements and assumes that the concentration of the three end elements is constant. The calculation formula is as follows:

$${\delta }_1{a}_1+{\delta }_2{a}_2+{\delta }_3{a}_3=a_{sp}$$

(1)

$${\delta }_1{b}_1+{\delta }_2{b}_2+{\delta }_3{b}_3=b_{sp}$$

(2)

$${\delta }_1+{\delta }_2+{\delta }_3=1$$

(3)

Here, a₁, a₂, and a₃ are the concentrations of analysis indicators a for water inrush end element 1, end element 2, and end element 3, respectively; b₁, b₂, and b₃ are the concentrations of analysis indicators b for water inrush end element 1, end element 2, and end element 3, respectively; a_sp is the concentration of indicator a for the p-th water inrush sample; b_sp is the concentration of indicator b for the p-th water inrush sample; and δ₁, δ₂, and δ₃ are the proportion of each water inrush end-element of the p-th inrush sample.

The end-member mixing ratio of the mine water in the Sanshandao gold mine was calculated based on the water source identification result. As Cl⁻ and δ¹⁸O were conserved under most conditions, and as the ion exchange had little effect on their concentration, Cl⁻ and δ¹⁸O are used as the analytical indicators in the calculation of mixing ratios. The ternary calculation model was used to calculate the end-element mixing ratio of mine water, and the reliability and accuracy of the new method were evaluated based on the results of the deviation analysis. The model bias can be calculated according to the following formula:

$$D=\frac{C_c-C_m}{C_m}$$

where $C_c$ represents the calculated concentration of the factor and $C_m$ represents the measured concentration of the factor. The factor calculation concentration is obtained by linear weighted superposition according to the end element type and mixing ratio. A positive deviation indicates that the calculated value is greater than the measured value, while a negative deviation indicates that the calculated value is less than the measured value.

Gu et al. calculated the mixing ratio and deviation of the mine water in the −375 m, −510 m, and −600 m sublevels of the Xishan mining area [25]. The reliability of the hierarchical multi-index analysis method was evaluated by comparing the deviation of the method adopted in this paper with a general method (

Table 3. Comparison of the deviation values in the two studies.

		δ18O	δ2H	K+	Na+	Ca2+	Mg2+	Cl−	SO42−
Gu et al. [25]	Max	0.55	4.93	2.34	9.55	0.93	18.83	0.00	3.23
	Min	−0.02	−0.48	−0.23	−0.29	−0.78	−0.43	−0.32	−0.46
	Mean	0.02	0.45	0.26	0.14	0.39	0.34	0.01	0.13
This study	Max	0.00	0.23	1.14	0.15	1.10	3.63	0.00	0.12
	Min	0.00	−0.25	−0.24	−0.13	−0.56	−0.02	0.00	−0.10
	Mean	0.00	0.03	0.12	0.00	0.31	0.19	0.00	0.02

). As shown in Table 3, the hierarchical multi-index analysis method greatly reduces the calculation bias of mine water, which proves that the hierarchical multi-index analysis method can effectively identify water sources and optimize the mixing model. Due to the large number of sampling points in the −600 m sublevels and the continuous sampling, the sublevels of −600 m were selected as the research object. The evolution trends of seawater proportion over time between the two research methods were compared. As shown in Figure 11a, the proportion of the seawater calculated decreases with the mining process. However, the rate of reduction gradually slows down, and finally, the proportion of seawater is stable at 0.6~0.7. Other experts have analyzed the changes in the proportion of seawater in the early stages of mining in the Xinli mining area and found that the proportion of seawater showed a trend of increasing year by year [23].

Thus, the analysis results of the two papers show that the proportion of seawater first increases and then decreases with the progress of mining. The reason for this is that in the early stages of mining, water conduction fractures germinated in the surrounding rock under the action of disturbance stress, while in the later stage of mining, some of the original water conduction fractures connected with seawater were closed under the action of ground stress. In addition, the proportion of seawater calculated by this study method is lower than that which was calculated by [25]. Moreover, it can accurately reflect the state of water inrush points and can reasonably assess the risk of water inrush in mines. Two continuous monitoring points in the −375 m sublevels were selected to analyze the proportion change in the seawater. In 2019, the proportion of seawater in the −375 m sublevels were much higher, reaching 0.95, than those in the −600 m sublevels. However, following the progress of mining, the proportion of seawater fluctuated in the range of 0.6~0.8.

Figure 12a shows the changing trends of the mean seawater proportion in the sublevels of −375 m, −510 m, and −600 m. The variation trend of seawater proportion is the same in the three sublevels, which all gradually decrease with time. The curves of the −375 m and −600 m sublevels almost coincide, and the average seawater proportion of the −510 m sublevel is much lower than that of the other two sublevels. Since the water inrush points in the sublevels of −375 m and −600 m are affected by the F3 fault and the northwest water-conducting fissure zone, they are subject to the vertical recharge of seawater (Figure 3). As the F3 fault is the largest water-conducting fault in the mining area, it is necessary to analyze its impact on mine water inrush. The inrush points in the −600 m sublevels were divided into five categories according to their distance from the F3 fault, and the typical inrush points in each category were selected to analyze the changing trends of the seawater proportions in 2022. With the increase in distance from the F3 fault, the overall trend of seawater proportion decreases. This indicates that the F3 fault has a significant impact on the proportion of seawater at the inrush point; thus, the monitoring of the inrush point near the F3 fault should be strengthened (Figure 12b).

Under mining conditions, the type of brine at the same sampling point may change. The deviation calculation results show that the brine evolution model can effectively improve the calculation accuracy of the end-element mixing ratio (

Table 4. Comparison of the deviation values in the −1050 m and −1065 m sublevels of the two models.

		δ18O	δ2H	K+	Na+	Ca2+	Mg2+	Cl−	SO42−
Single brine	Mean	0.00	−0.02	0.22	−0.09	0.26	0.14	0.00	0.70
Brine evolution	Mean	0.00	−0.02	0.09	−0.09	0.03	0.24	0.00	0.70

).

5. Discussion

In this article, we used a hierarchical multi-indicator analysis method to identify the water source of the study area. We then introduced a ternary mixing ratio calculation model to calculate the end-element mixing ratio of the water inflow point, and we conducted a deviation analysis. Moreover, the effects of mining, depth, and faults on the water circulation were analyzed based on the mixed ratio calculation.

Since previous studies were limited by the scope and duration of the study, the accurate hydrogeological conditions of the mining area were not obtained, so only the contribution of a single brine was considered. Compared with previous studies, this study identified three types of bedrock brine in the Xishan mining area based on the results of the hydrochemical analysis and spatial location characteristics. Since the late Pleistocene, three large-scale marine intrusion events have occurred in the study area, and the subsequent retreat has led to a strong evaporative concentration of seawater in the rock fractures, resulting in the formation of bedrock brine at different depths. The results of isotopic and hydrogeochemical analysis showed that these brines originated from ancient seawater, which was the product of the evaporation and concentration of ancient seawater, and its ion ratio was basically the same as that of modern seawater. The hierarchical multi-index analysis method was used to determine the mixing models in the different sublevels, and the impact of mining on the mixing model was considered. The deviation analysis shows that the research method adopted in this paper effectively improves the calculation accuracy of the mixing ratio, which can provide a data and theoretical basis for the risk assessment of the water inrush in mines. Meanwhile, the analysis results indicated that the seawater mainly recharged the mine water through vertical fissures; furthermore, the F3 fault was found to be the main water diversion channel in the mining area. In future research, 3D laser scanning technology could be used to establish a 3D stereoscopic model of the water inrush monitoring network to study the relationship between fracture propagation law and end-element mixing ratios when under the influence of mining.

6. Conclusions

In this study, the bedrock brine was classified and, on this basis, the hierarchical multi-index analysis method was used to determine the mixed models of the different sublevels. Based on the mixing model, a ternary graph was established, and the end-element mixing ratio was calculated. The deviation analysis results show that this method can be used for mine water source identification in complex situations, and the specific conclusions are as follows:

(1) Based on the chemical composition of water, stable isotopes, and the spatial position of brine, the brine water in the study area was divided into three categories: shallow brine, middle brine, and deep brine. The hierarchical multi-index analysis method was used to establish the end-element mixing model of the different sublevels, and the influence of mining on brine evolution was considered. Long-term mining has changed the original circulation patterns of groundwater, resulting in changes in the type of brine end elements.

(2) The results of the mixing ratio calculation show that the proportion of seawater in the mine does not increase all the time with the progress of mining, but that it shows a state of first increasing, then decreasing, and finally stabilizing. The reason for this is that a series of water-conducting fractures were formed due to the redistribution of surrounding rock stress in the early stages of mining, and then certain water-conducting fractures were closed under the action of self-weight stress and tectonic stress. Seawater has a greater impact on the shallow sublevels (−375 m and −510 m) and the middle sublevels (−600 m), and the proportion of seawater in the deep sublevels is much lower than those found in the shallow and middle sublevels.

(3) From 2019 to 2022, the proportion of the seawater in the −375 m sublevel was higher than those found in the −510 m sublevel, thus indicating that seawater mainly recharged the mine water through vertical fissures. The difference in the seawater proportion at the −600 m sublevel indicates that the F3 fault and the northwest water-conducting fracture zone may be the priority flow channels for seawater to recharge groundwater. The seawater proportion of inrush points near the F3 fault was found to be generally higher than that in the other inrush points; thus, the monitoring of the water inrush point near the F3 fault should be strengthened.

The research results can provide a data and theoretical basis for water inrush risk assessment and can also provide a reference for the water source identification of other coastal mines.