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High arsenic concentrations are common in the water and soils in Tibet. In this study, arsenic concentrations were found to be below detection limits in natural river water, although the high background concentrations of HCO3− and the pH all favor arsenic release. Lake water had the highest arsenic concentrations due to intense evaporation. The impact of mining on arsenic release was assessed using a mixing model composed of three end-members: KS02 (a post-mining sample), R07 (a river water sample), and S12 (spring water that contains arsenic). The results indicated that mining operations are likely responsible for a small proportion (2.0%) of direct release of arsenic from deep thermal water, and that most arsenic was released by desorption from Fe oxyhydroxides. The mixing model also revealed that so far, mining operations have accelerated carbonate dissolution, but have not led to arsenopyrite oxidation. Therefore, HCO3− concentrations increased during mining, which along with the pH (> 7.0), led to desorption of arsenic from Fe oxyhydroxide.

Identification of water sources is a key issue of water inrush. This study applied a mixing model based on hydrochemical data to identify water sources and proportions. This study highlighted (1) the importance of model scale and reaction evaluation before using the mixing model, (2) a newly proposed criterion based on eigenvalue analysis to identify the number of end-members, and (3) linear mixing model based on PCA (principal component analysis). 2.5 km2 area was an appropriate scale to mixing model because tectonics and lithology were simple. Ion activity, ion exchange, and cycle time of water were evaluated, indicating that groundwater components were dominated by the mixing process. Tracers, such as K, Na, Ca, Mg, Cl, SO4, δ18O, δD, EC, TH, and TDS, were used as tracers in the mixing model. Five end-members (representing seawater, Quaternary water, freshwater, Ca-rich water, and Mg-rich water) were identified based on eigenvalue analysis and hydrochemical evolution analysis. A linear mixing algorithm was programmed using Matlab to compute the ratio of each end-member. The results showed that seawater was the dominated water sources (70% at most) threatening the mining operations, especially at the deep levels. Quaternary water mainly recharged the middle level and made up 50% at − 420 m level. Freshwater recharged the shallow level and made up to 40% at − 150 m level. Ca-rich water and Mg-rich water decreased with time. Finally, cross test and extension test of this method showed a high precision in reconstructing ion concentrations, low sensitivity to noise data, and highly extendible to future data.

High-temperature geothermal systems are widely used to produce electricity and reduce atmospheric carbon emissions. The Zhaxikang geothermal field, located in the southeast of the Tibetan Plateau, is a typical high-temperature geothermal field related to the orogenic rift system. The hydrochemical type of the hot springs is predominantly HCO3–Cl–Na. In this study, the primary hydrochemical processes, including mixing and degassing, were identified and qualified based on hydrogeochemical and isotopic data. The results indicate that the deep geothermal fluid consists of meteoric water (80%) mixed with magmatic water (20%). Mixing with magmatic water and weak water–rock interactions might be responsible for the inapparent oxygen isotope shift. Comprehensive analyses of the Na–K and quartz geothermometers, the Na–K–Ca ternary diagram, as well as the enthalpy–silica and enthalpy–chloride mixing model yield reliable temperature estimates ranging from 180 to 210 °C. The equilibrium temperature of the multi-minerals converges at a narrow range from 185 to 205 °C, which was determined using geothermometrical modeling after correcting for the dilution and degassing processes. Different degrees of CO2 degassing (0.1–0.7 mol/L) were identified during the upward migration of the geothermal fluid through geothermometrical modeling. The SiO2 concentration (292 mg/L) and chloride concentration (490 mg/L) of the deep geothermal fluid under the Zhaxikang geothermal field were estimated through a comprehensive analysis of the enthalpy–silica and enthalpy–chloride diagrams, respectively.

A linear mixing model method based on principal component analysis (PCA) in three-dimensional space was used to assess groundwater dynamics. PCA was performed on a series of hydrochemical datasets collected from 2009 to 2014 (except in 2010). The results of PCA and a prior conceptual model were used to identify the evolution and potential end-members of water. Then, a mixing calculation code was applied to compute the mixing proportions, and the results were used to reconstruct the mixing process. Deviations were evaluated by comparing the computed and measured concentrations of ions. The accuracy of this method was compared to that of a 2D model that was based on only conservative ions and a 3D model developed in this study that does not consider the water’s physical parameters. The results indicated that the method that considered all of the measured ions, stable isotopes, and physical parameters, performed well. Its accuracy was demonstrated by good agreement between its measured and simulated values. The mean values of deviation for δ18O, δD, K, Na, Ca, Mg, Cl, and SO4 were 0.26, 0.51, 0.19, 0.08, 0.21, 0.15, 0.05, and 0.08, respectively. Five water sources and their groundwater dynamics were interpreted using this model; the results demonstrated that mining has had a substantial influence on the groundwater flow system in both the vertical and lateral directions. Above a depth of -375 m, freshwater is the dominant source, and its proportions in most sites exceeds 40%. Seawater has reached a depth of − 510 m, and its maximum proportion of 82% can be observed at 510-2a. Quaternary water recharged the area between F3 and the prospecting line 2230. Its proportion exceeded 45% at most sites. The recharge depth reached − 510 m at most sites and − 600 m at some sites. Calcium-rich and Mg-rich water were distributed above and below − 510 m, respectively. These distinguishing features indicate that induced ground deformation broke through the Quaternary aquifuge and increased the vertical recharge in the tensional zone, while preventing vertical recharge in the compressive zone at the subsidence center.

Water inrush is a potential disaster in the Sanshandao Gold Mine, which is located on the coast of the BoHai Sea. A conceptual model based on the water chemistry and hydrogeological setting was established to identify potential water sources. Then, a mixing pattern was developed based on the δ18O and Cl− values. Finally, a linear mixing model was used to estimate the mixing ratios of water sources at each site. Four sources were identified: freshwater: (F), quaternary water (Q), seawater (S), and brine (B), and four mixing patterns were developed: B-F-Q, F-Q-S, B-Q-S, and B-S. The mixing ratios showed that brine was the main water type when mining began. However, seawater (mean = 48.3%) and quaternary water (mean = 36.04%) came to dominate the groundwater composition over a short period of time. The field investigations and hydrochemical analysis indicated that the main flow paths of these waters were along NW-oriented fractures and the F3 fault. Freshwater mainly recharged the shallow fractures (above −285 m) and represented a small proportion of the groundwater (mean = 11.2%). The main freshwater flow paths were bare rock fractures in the mountains.

This study aimed to identify the potential water sources of the Sanshandao Gold Mine and determine the end-member mixing ratios, to prevent seawater intrusion and water inrush disasters. Based on the hydrogeological setting, an end-member model of mine water based on the hydrogeochemical and isotopic analysis was established. Then, the maximum likelihood method was used to estimate the mixing ratios of water sources at each site and analyze the evolution rules of mine water. The results indicated that this method can effectively identify the water sources and calculate the mixing ratios. The fitting results between the calculated and measured values of the stable isotopes and ion concentrations were good. The mean values of deviation for δ18O, δD, K+, Na+, Ca2+, Mg2+, Cl−, SO42−, and Ca2++Mg2+ were 0.01, 0.02, 0.17, 0.00, − 0.23, 0.38, 0.00, 0.04, and − 0.02, respectively. The mixing ratio results demonstrate that seawater is the main component of the mixed water and the proportion varies with the mining activities; especially in 2011 and 2014, the seawater had a high proportion in the entire mine. The effect of mining on mixing was studied by dividing the study area. Both horizontal and longitudinal mixing were analyzed. The water sites located in the south of F3 (this area is less affected by mining) had a low proportion of seawater. The main range affected by fresh water was at the 465-m sublevel and above. The water flow around F3 was greatly affected by mining, and the proportion of seawater around F3 fluctuates greatly every year; so F3 should be monitored more frequently.

Twice-repeated stationary-state temperature measurements have been carried out for prospecting drilling at a 4000-m depth (119°59′42.7″E, 37°24′11.6″N; starting on September 18, 2010 and ending on May 29, 2013; borehole depth is 4006.2 m) using the geophysical well logging method and the new DS2000 temperature continuous data acquisition system. Temperature measurements were obtained on June 25, 2013, and December 6, 2013, at 28 and 192 days after drilling had ended, respectively. The first measurement used the traditional logging method after the drilling was completed; and the second measurement was taken upon completion of drilling using the DS2000 temperature continuous data acquisition system started. Both results are highly consistent, and the change in trends with depth are linear. The results show that the temperature reaches 107.8 °C at a depth of 4004 m. The average geothermal gradient is 2.3 °C/100 m. The geothermal gradient is controlled by the lithology distribution: the geothermal gradients of monzonite granite are 2.0 °C/100 m at a depth range of 30.0–1753.0 m and 2.5 °C/100 m below 1753 m for granitic protomylonite. An intense fluctuation of geothermal gradients is occurred at the fracture zone at a depth of 3190.0–3530.0 m (the fracture zone will not cause the fluctuation, here I think it should be the fluid flow in the fracture zone). Thermal conductivity was tested for 42 core samples at depths of 0–4003.6 m. The results show that the average thermal conductivity is 2.1 W/(m K). Combined with the average gradient, the average terrestrial heat flow is calculated at 48.1 mW/m2. These measurements have provided basic and reliable data for further studies of the deep geothermal field and geodynamics that are characteristic of the study area as well as direct geological information for deep mineral resource potential prediction and resource exploitation.

Prediction of bursting water in a submarine mine (Sanshandao Gold Mine) is very important. The aim of this study was to understand the water sources, mixing ratios, and flow paths in a mining region by using hydrochemistry, principal component analysis (PCA), and rock mechanics. Field observations reveal that there are unusual geothermal gradients and complicated geological conditions. Samples collected from the surface and every depth of the tunnel were analyzed for various ions and isotopes (18O and D). PCA was used to identify the most likely water sources on the foundation of hydrochemistry and a conceptual model. Four kinds of water sources were identified: (1) water mixture of surface freshwater and seawater, (2) brine derived from a deep closed environment, (3) thermal brine derived from the northwestern fault (F3), and (4) Quaternary water. The water sources were variable with time at a sampling site. The combination of water sources in a specific sampling campaign can be decided by the positive scores on principal components (PCs). The PCA results indicate that the first and the fourth kind of water source appear in the deep tunnel with the continual mining activity. Mixing ratio calculations for each sampling campaign show that the first and the fourth kind of water source constitute a small proportion of the total water. The main water sources are the second and third kinds of water. Finally, water flow paths were determined based on the PCA, and rock mechanics is used to explain the change in the water flow paths after excavation. The results show the following: (1) There are several strong water diversion zones that distribute at a certain spacing interval on the large scale and (2) “zonal disintegration” is also found in the tunnel. The fractured zone and the non-fractured zone appear in turn around the tunnel. The rock mass in these two zones represents the tensile and compressive properties, respectively. Thus, volumes of water and permeability of joints and fractures in these two zones are great different.