Underground water mixed end member identification method considering influence of seasonal change of river channel water

Abstract

The invention discloses an underground water mixed end member identification method considering the influence of the seasonal change of the river channel water, and belongs to the technical field of water environment engineering.According to the technical scheme, the underground water mixed end member identification method considering the influence of the seasonal change of the river channel water is provided. Comprising the following steps: screening data of river and underground water samples; analyzing main components of the river water sample; screening river water mixing end members; analyzing main components of the underground water sample; screening underground water mixed end members; according to the method, on one hand, the river water mixing end member can be effectively identified, on the other hand, representative physical and chemical parameters can be further screened out, and quantitative estimation of the mixing proportion and physical and chemical parameter data of the mixing end member are achieved; meanwhile, the underground water end member in the mixing process of different water bodies can be effectively identified, the influence of river water infiltration on underground water is visually and quantitatively evaluated, and the accuracy and effectiveness of underground water mixing end member identification are improved.

Description

Technical Field

[0001] The present application belongs to the field of water environment engineering technology, and specifically, relates to a groundwater mixing end-member identification method that takes into account the impact of seasonal changes in river water. Background Art

[0002] In areas with scarce water resources, recycled water or inter-regional water transfer is regarded as a vital available resource. It is not only widely used as a water source for agricultural irrigation, urban industry and greening, but also bears the important task of repairing exhausted river channels and optimizing the river ecological environment. With the large-scale application of surface water and recycled water in rivers across regions, its impact on the groundwater environment has increasingly become a focus of public attention.

[0003] The use of surface water or recycled water can bring ecological benefits to urban rivers in arid environments, but the potential negative impacts on aquatic systems and human health cannot be ignored. Currently, when analyzing the mixing effect in groundwater systems connected to river water or surface water, the spatiotemporal variability of the physicochemical parameters of each end member is not fully considered, which brings uncertainty to the accuracy of calculating the mixing ratio and may not be sufficient to accurately assess the impact of infiltration of different end member water bodies on groundwater. Summary of the invention

[0004] In order to solve the above problems and technical defects, the embodiment of the present application adopts the following technical solution, a groundwater mixing end member identification method considering the influence of seasonal changes in river water, comprising the following steps:

[0005] Step S1, screening data of river and groundwater samples;

[0006] Step S2, analyzing the main components of the river water sample;

[0007] Step S3, screening river water mixing end members;

[0008] Step S4, analyzing the main components of the groundwater sample;

[0009] Step S5, screening groundwater mixing end members.

[0010] Further, the screening data parameters of the water sample in step S1 include: field index, cations, anions, nitrogen, total organic carbon, permanganate index and total dissolved solids;

[0011] The screening method adopts a direct elimination method.

[0012] Preferably, the specific steps of analyzing the main components of the river water sample in step S2 are as follows:

[0013] Step S201, setting a data set X including m samples and n chemical and physical parameters;

[0014] Step S202, calculate the mean vector of the sample, centralize the data set to obtain a centralized data set, and then calculate the covariance matrix of the data;

[0015] Step S203, performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and corresponding eigenvectors;

[0016] Step S204, sorting the eigenvalues ​​according to their size;

[0017] Step S205: Obtain the principal component score according to the eigenvectors corresponding to the sorted eigenvalues.

[0018] Furthermore, the calculation formula of the covariance matrix in step S202 is as follows:

[0019]

[0020] X centered It is the data after centralized processing.

[0021] Furthermore, the covariance matrix is ​​subjected to eigenvalue decomposition to obtain eigenvalues ​​λ1, λ2, λ3...λ n and the corresponding eigenvectors ν1, v2, v3...v n .

[0022] Furthermore, the first eigenvector can be expressed as v1 = [v 11 ,v 21 ,v 31 ...,λ n1 ] T , the eigenvectors correspond to the principal component directions in the data set, and the eigenvalues ​​represent the data variance explained by each principal component.

[0023] Furthermore, the contribution rate of each principal component PC is:

[0024] λ1 / sum(λ1,λ2,λ3...λ n )

[0025] Furthermore, the eigenvalues ​​are sorted by their size, and the eigenvectors corresponding to the first k eigenvalues ​​are selected from large to small; each principal component summarizes the original variable as follows:

[0026] PC K =v 1K X1+v 2K X2+ν 3K X3+...+v nK X m

[0027] Furthermore, the principal components can explain the maximum variance in the data set. The first few principal components are selected to effectively retain the main information of the data. The original water sample data is projected into the principal component coordinate system to obtain the principal component scores of different samples.

[0028] Preferably, the specific steps of screening the mixed end members of river water in step S3 are as follows:

[0029] Step S301, performing mixed analysis on river water samples;

[0030] Step S302: Perform end-member analysis using conservative species to calculate the mixing ratio of each sample.

[0031] Compared with the prior art, the beneficial effects of the embodiments of the present application are:

[0032] (1) On the one hand, the present application can effectively identify the mixing end members of river water, and on the other hand, it can further screen out representative physical and chemical parameters to achieve quantitative estimation of the mixing ratio and the physical and chemical parameter data of the mixing end members; at the same time, it can effectively identify the groundwater end members in the mixing process of different water bodies, intuitively and quantitatively evaluate the impact of river water infiltration on groundwater, and improve the accuracy and effectiveness of groundwater mixing end member identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In the attached picture:

[0034] Figure 1 A flowchart of an embodiment of the present application;

[0035] Figure 2 Schematic diagram of PCA analysis results of river water data and contribution of each variable to the eigenvector in the embodiment of the present application;

[0036] Figure 3 This is a scatter plot of the PC score of the river water sample in the embodiment of the present application;

[0037] Figure 4 Schematic diagram of estimated concentration and measured concentration in a river water sample in an embodiment of the present application;

[0038] Figure 5 Schematic diagram of the PCA analysis results of groundwater data and the contribution of each variable to the eigenvector in the embodiment of the present application;

[0039] Figure 6 This is a schematic diagram of the PCA results of the groundwater samples in the embodiments of this application;

[0040] Figure 7 Schematic diagram of measured concentration and estimated concentration in groundwater sample in an embodiment of the present application. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Generally, the components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0042] Example:

[0043] like Figure 1 As shown, a groundwater mixing end-member identification method considering the influence of seasonal changes in river water includes the following steps:

[0044] Step S1, screening data of river and groundwater samples;

[0045] Step S2, analyzing the main components of the river water sample;

[0046] Step S3, performing river water end member mixing analysis to screen typical river water mixing end members that can represent the influence of characteristic water samples in different seasons;

[0047] Step S4, analyzing the main components of the groundwater sample;

[0048] Step S5, screening groundwater mixing end members.

[0049] The screening data parameters of the water sample in step S1 include: field index, cations, anions, nitrogen, total organic carbon, permanganate index and total dissolved solids;

[0050] The screening method adopts direct elimination method.

[0051] It should be noted that the field index is pH and EC value, cations include (K+, Na+, Ca2+ and Mg2+; anions include Cl-, SO42- and HCO3-; nitrogen includes NH4-N, NO3-N and NO2-N;

[0052] It should also be noted that during the elimination process, the physicochemical parameters of river water and groundwater samples were screened and evaluated, and samples that were clearly beyond a reasonable range and could not be reasonably explained were directly eliminated.

[0053] The specific steps of step S2 for analyzing the main components of the river water sample are as follows:

[0054] Step S201, set a data set X containing m samples and n chemical and physical parameters, where X = [X1, X2, ..., Xm], and each sample Xi is an n-dimensional vector;

[0055] Step S202, calculate the mean vector of the sample, centralize the data set to obtain a centralized data set, and then calculate the covariance matrix of the data;

[0056] Step S203, performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and corresponding eigenvectors;

[0057] Step S204, sorting the eigenvalues ​​according to their size;

[0058] Step S205: Obtain the principal component score according to the eigenvectors corresponding to the sorted eigenvalues.

[0059] The calculation formula of the covariance matrix in step S202 is as follows:

[0060]

[0061] X centered It is the data after centralized processing.

[0062] Perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​λ1, λ2, λ3...λ n and the corresponding eigenvectors v1, v2, v3...v n .

[0063] The first eigenvector can be expressed as v1 = [v 11 ,v 21 ,v 31 ...,λ n1 ] T , the eigenvectors correspond to the principal component directions in the data set, and the eigenvalues ​​represent the data variance explained by each principal component.

[0064] The contribution rate of each principal component PC is:

[0065] λ1 / sum(λ1,λ2,λ3...λ n )

[0066] Sort by the size of the eigenvalues, and select the eigenvectors corresponding to the first k eigenvalues ​​from large to small; each principal component summarizes the original variable as follows:

[0067] PC K =v 1K X1+v 2K X2+v 3K X3+...+ν nK X m

[0068] The principal component can explain the maximum variance in the data set. The first few principal components are selected to effectively retain the main information of the data. The original water sample data is projected into the principal component coordinate system to obtain the principal component scores of different samples. For example, the kth principal component score of the i-th sample is Fik:

[0069] F ik =v 1K X 1i +v 2K X 2i +v 3K X 3i +...+v nK X ni

[0070] Quantitative analysis of the mixing effects of different end members and the screening of mixed end members is an optimization process for estimating end member components and mixing ratios. For example, the existing sample data X is a data set containing m samples and n physicochemical parameters, where X = [X1, X2, ..., Xm], and each sample Xi is an n-dimensional vector.

[0071] When it is necessary to quantitatively analyze the mixing ratio of m samples from w end-member water bodies, it is necessary to estimate the concentration or value of n physicochemical parameters in each end-member water body. When all the types of physicochemical parameters used are conservative parameters, the multi-end-member mixing process can be simply summarized as follows:

[0072] X=YΔ+ε

[0073] Where X represents the n×m dimensional matrix of all physicochemical parameters of all samples:

[0074]

[0075] Y represents an n×w dimensional matrix of w mixing end members and n physicochemical parameters:

[0076]

[0077] Δ represents the mixing ratio of different end members and is a w×m-dimensional matrix:

[0078]

[0079] ε represents the error of the measurement value and is an n×m dimensional matrix.

[0080] In addition, the mixing ratio needs to satisfy [0,1], and for any column, its sum is 1.

[0081] Under the assumption that the concentrations of the end-member components are known, the least squares method can be used to calculate the matrix of the mixing ratio; under the assumption of a certain (for example, 5%) measurement error, the problem of estimating the mixing ratio and the mixed end-member components becomes a problem of solving the minimum value, min{X-(YΔ+ε)}; considering the [0,1] condition that the mixing ratio needs to satisfy, and the condition that the sum of any column of Δ is 1, the minimum problem is iteratively calculated until convergence.

[0082] The specific steps of screening the mixed end members of river water in step S3 are as follows:

[0083] Step S301, performing mixed analysis on river water samples;

[0084] Step S302: Perform end-member analysis using conservative species to calculate the mixing ratio of each sample.

[0085] In areas affected by river water infiltration, groundwater in the aquifer system can be considered a mixture of river water and initial groundwater. In order to distinguish these effects, it is necessary to consider the PCA scores of groundwater samples and the loadings of multiple variables. By quantitatively comparing the loadings of physicochemical parameters in different principal components PC, the effects of different processes (such as mineral dissolution, river water infiltration, etc.) on the chemical properties of groundwater are analyzed.

[0086] The selection of principal components and the calculation process of principal component scores of samples refer to step S2.

[0087] Based on the multiple river water end-members determined as the final components in step S3, and after eliminating the groundwater samples shown to be affected by human sources, a two-dimensional scatter plot of the principal component analysis results of the groundwater is generated, and the principal component scores of the river water end-members are superimposed. The groundwater samples are selected as new initial groundwater mixing end-members according to the distribution of the two-dimensional scatter plot.

[0088] When quantitatively analyzing the mixing effect of groundwater samples, it is necessary to select multiple physical and chemical parameters as conservative species and set the groundwater mixing end member EM groundwater standard deviation. In the present invention, it is assumed that the species concentration in the river water mixing end member EM river water remains unchanged in the multi-end member mixing model.

[0089] According to the multi-endmember mixing analysis process in step S3, the estimated values ​​of different physical and chemical parameters of EM groundwater endmembers are finally obtained by performing least squares analysis on different physical and chemical parameters. According to the multi-endmember mixing calculation results, the degree of influence of river water infiltration on groundwater samples at different depths can be analyzed. By comparing the results with the known on-site geological conditions, the EM of river water in different seasons can be evaluated. 河道水 and groundwater EM 地下水 The effectiveness of mixed modes.

[0090] In summary, the above method was applied in the following experiments, and the experimental data were recorded during the experimental application process, as follows:

[0091]

[0092] Table 1

[0093] Descriptive statistics of in situ parameters such as pH and major ions in groundwater and river water samples are listed in Table 1. The average concentrations of K+, Na+, Cl-, SO42-, NO3-N, CODMn and TOC in river water samples were higher than those in groundwater, while the average values ​​of Ca2+, Mg2+ and HCO3- in the latter were higher than those in the former. From the standard deviation values ​​of groundwater and river water samples, the concentration changes of Ca2+, HCO3-, Na+, Cl-, SO42- and TDS were greater than those of other parameters.

[0094] Reference Figure 2 , principal component analysis was performed on river water samples, and the 11 species selected by PCA were K+, Na+, Cl-, SO42-, NO3-N, Ca2+, Mg2+, HCO3-, CODMn, TDS and TOC, while pH and NH4-N were excluded due to small data changes (Table 1). Three principal components were selected. The first component (EG1, accounting for 63.9% of the variance) had high positive loadings for Cl-, Mg2+, Na+, K+ and TDS. The second component (EG2, accounting for 17.1% of the variance) had high positive loadings for NO3-N, Ca2+ and HCO3-, and high negative loadings for CODMn and TOC. The third component (EG3, accounting for 8.6% of the variance) had a high positive loading for SO42-.

[0095] Reference Figure 3 During the experiment, river water is affected by seasonal factors such as evaporation, river recharge water, and rainfall; the first principal component distinguishes river water in the rainy season (September samples) and the dry season (May samples), while samples collected from recharge water sources (such as SP1-09 and SP1-11) and near the outlet have higher second principal component scores; based on the scatter plots of the first and second principal component scores, three samples (SP1-09, SP1-11, and SP6-05) can be defined as the initial end members of the end member mixing analysis.

[0096] The first end molecule (EM1) corresponds to the SP5-09 sample, whose TDS value (310.93 mg / L) is the smallest among all river water samples, and the concentrations of Ca2+, Mg2+, Na+, K+ and Cl- (17.6 mg / L, 12.9 mg / L, 40.1 mg / L, 8.2 mg / L and 53.1 mg / L, respectively) are the lowest among all river water samples.

[0097] This terminal molecule may be river water diluted by rainwater. The second initial terminal molecule (EM2) was preliminarily defined as the SP1-11 sample, which had the smallest CODMn and TOC values ​​(3.11 mg / L and 3.30 mg / L); the third initial terminal molecule (EM3) was selected as the SP6-05 sample, which had the largest Cl- value (104.0 mg / L) among the river water samples; given the conservative nature of Cl-, the high concentration of Cl- in the SP6-05 sample can be attributed to the water sample collected during the dry season.

[0098]

[0099] Table 2

[0100] Reference Figure 4 , a series of mixing analyses were performed on the river water samples using a least squares optimization approach. First, a mixing analysis was performed using the same dataset selected for the river water PCA. This step was performed to qualitatively assess the conservative behavior of all selected species, as the estimated versus measured concentrations of the conserved elements tended to coincide with a 1:1 mixing line. End-member analyses were then performed using only the conservative species to calculate the mixing ratio for each sample.

[0101] Standard deviation values ​​were required as input for all end-member species and water samples during the estimation process using the least squares optimization method to represent the uncertainty associated with the unknown true end-members and seasonal and spatial variability. The standard deviations of non-conservative species were generally higher than those of conservative species; therefore, we set the standard deviation of Cl- in end-members and river water samples to 1% and the standard deviation of other species to 5%.

[0102] Figure 4 The estimated concentrations of river water samples and final components were plotted against the measured concentrations. From the scatter plot of the estimated concentrations versus the measured concentrations, it can be seen that NO3-N, HCO3-, and SO42- in the water samples deviate significantly from the 1:1 mixing line, indicating that other processes are involved in addition to simple mixing.

[0103] Referring to Table 2, the deviation analysis results of the estimated values ​​and observed values ​​of all parameters and all samples are listed, among which the correlation coefficients of Cl-, Ca2+, Mg2+, Na+, TDS, CODMn and TOC are all above 0.9, and the root mean square error is significantly lower than that of other parameters. These results prove the effectiveness of the least squares optimization method in identifying end members.

[0104] In addition, the selected end members can well characterize the spatial and seasonal variation characteristics of river water samples. In the subsequent analysis process, since CODMn and TOC parameters are easily affected by the hydrogeochemical reactions during the infiltration of river water, they were not included in the calculation of the mixing ratio.

[0105] Through this step of analysis, the feasibility of using Cl-, Ca2+, Mg2+, Na+ and TDS as conservative species parameters for terminal molecular analysis of river water is fully demonstrated.

[0106] It should be noted that Figure 4 The estimated concentrations of end-member components in mid-stream water are shown in Table 3:

[0107]

[0108] Table 3

[0109] Reference Figure 5 , principal component analysis was performed on groundwater samples. For groundwater, 12 species were selected for PCA analysis, and three principal components were retained.

[0110] The first component (EG1, accounting for 44.4% of the variance) has high positive loadings for Ca2+, Mg2+, HCO3-, and TDS. The second component (EG2, accounting for 23.0% of the variance) has high positive loadings for Cl-, Na+, and K+. The third component (EG3, accounting for 12.6% of the variance) has high positive loadings for NO3-N, NH4-N, and TOC.

[0111] Above Figure 5 and Figure 2 The contribution patterns of the variables to the principal components are different, indicating that the hydrochemical controlling factors are different between the river water and groundwater samples.

[0112] Reference Figure 6 ,At the sampling points in the demonstration area, the groundwater in the multi-layer aquifer system can be regarded as a mixture of river water and environmental groundwater, showing the PCA scores of all groundwater samples. The high positive loadings of Ca2+, Mg2+, HCO3- and TDS in the first PC indicate that mineral dissolution has an impact on the chemical properties of groundwater, while the high positive loadings of K+, Na+ and Cl- in the second PC clearly indicate that the infiltration of river water has an impact on the chemical properties of groundwater.

[0113] exist Figure 6The three river water samples (SP6-05, SP1-11 and SP5-09) identified as final components in step S2 are also included. Groundwater samples (such as 13(30), 13(50), 46(30) and 46(80)) were shown to be affected by anthropogenic sources and were therefore not included in the final component estimation.

[0114] according to Figure 6 Based on the sample score distribution pattern in the , in addition to the three river water end members, samples 32 (50) and 31 (80) were selected as two new end members EM4 and EM5 for mixed analysis.

[0115] Reference Figure 7 , when analyzing groundwater samples using the least squares optimization method, Cl-, Ca2+, Mg2+, Na+, and TDS were considered conservative species, and the standard deviations of Cl- and other species for groundwater samples and terminal molecules EM4 and EM5 were 1% and 5%, respectively. The species concentrations in EM1, EM2, and EM3 remained unchanged in the five terminal molecule mixing models.

[0116] Mixed analysis is also performed by incorporating data from five categories, Cl-, Ca2+, Mg2+, Na+, and TDS, into the least squares optimization method. The mixed analysis results are shown in Figure 7 shown.

[0117] from Figure 7 The scatter plot of the estimated and measured concentrations for all mixing endmembers and groundwater samples shows that the estimated and observed values ​​in the water samples are very close to the 1:1 mixing line, which indicates the validity of the mixing model.

[0118]

[0119] Table 4

[0120] The results of the deviation analysis between the estimated and measured concentrations in groundwater samples are listed in Table 4. The correlation coefficient and root mean square error (normalized) show that the mixed model with five end members can well characterize the variation characteristics of the five physicochemical parameters of groundwater samples.

[0121]

[0122] Table 5

[0123] Table 5 lists the estimated and measured concentrations in groundwater samples.

[0124] In general, measured concentrations in groundwater samples from aquifers at depths of 30, 50, and 80 m were in good agreement with estimated concentrations, indicating that mixing analyses using these species are reliable.

[0125] from Figure 4It can also be seen from the Cl- ion mixing line in that the groundwater sample of the 80-meter deep aquifer is more affected by the groundwater end member EM5 than the samples at 30 and 50 meters. According to the fact of vertical infiltration of groundwater, the groundwater sample of the 80-meter deep aquifer should be less affected by the infiltration of river water end members (EM1, EM2, EM3) than the samples at 30 and 50 meters. The mixed analysis results of the present invention are very consistent with the known on-site geological conditions, indicating that the five end component mixing mode is effective.

[0126] In summary, the groundwater mixing endmember identification technology of the coupled principal component analysis and least squares optimization method proposed in the present invention can fully consider the influence of seasonal changes in mixing endmembers, effectively explain the mixing process of groundwater by infiltration of river water such as surface water or recycled water, and quantitatively identify multiple endmembers and their estimated endmember concentrations.

[0127] The above-mentioned embodiments only express the preferred implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for ordinary technicians in this field, several modifications, improvements and substitutions can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. A method for identifying groundwater mixing end members considering the seasonal changes of river water, characterized in that: The following steps are involved: Step S1, screening data of river and groundwater samples; Step S2, analyzing the main components of the river water sample; Step S3, screening river water mixing end members; Step S4, analyzing the main components of the groundwater sample; Step S5, screening groundwater mixing end members.

2. A groundwater mixing end-member identification method considering the influence of seasonal changes in river water according to claim 1, characterized in that: The screening data parameters of the water sample in step S1 include: field index, cations, anions, nitrogen, total organic carbon, permanganate index and total dissolved solids; The screening method adopts a direct elimination method.

3. A groundwater mixing end-member identification method considering the influence of seasonal changes in river water according to claim 1, characterized in that: The specific steps of step S2 for analyzing the main components of the river water sample are as follows: Step S201, setting a data set X including m samples and n chemical and physical parameters; Step S202, calculate the mean vector of the sample, centralize the data set to obtain a centralized data set, and then calculate the covariance matrix of the data; Step S203, performing eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​and corresponding eigenvectors; Step S204, sorting the eigenvalues ​​according to their size; Step S205: Obtain the principal component score according to the eigenvectors corresponding to the sorted eigenvalues.

4. A method for identifying groundwater mixing end members considering the influence of seasonal changes in river water according to claim 3, characterized in that: The calculation formula of the covariance matrix in step S202 is as follows: X centered It is the data after centralized processing.

5. A groundwater mixing end-member identification method considering the influence of seasonal changes in river water according to claim 4, characterized in that: Perform eigenvalue decomposition on the covariance matrix to obtain eigenvalues ​​λ1, λ2, λ3...λ n and the corresponding eigenvectors ν1, v2, v3...v n .

6. A method for identifying groundwater mixing end members considering the seasonal changes of river water according to claim 5, characterized in that the first The eigenvector can be expressed as v1=[v 11 ,v 21 ,v 31 ...,λ n1 ] T , the eigenvectors correspond to the principal component directions in the data set, and the eigenvalues ​​represent the data variance explained by each principal component.

7. A method for identifying groundwater mixing end members considering the seasonal changes of river water according to claim 6, characterized in that: The contribution rate of each principal component PC is: λ1 / sum(λ1,λ2,λ3...λ n ) 8. A method for identifying groundwater mixing end members considering the seasonal changes of river water according to claim 7, characterized in that: Sort by the size of the eigenvalues, and select the eigenvectors corresponding to the first k eigenvalues ​​from large to small; each principal component summarizes the original variable as follows: PC K =v 1K X1+v 2K X2+v 3K X3+...+ν nK X m 9. A method for identifying groundwater mixing end members considering the seasonal changes of river water according to claim 8, characterized in that: The principal components can explain the maximum variance in the data set. The first few principal components are selected to effectively retain the main information of the data. The original water sample data is projected into the principal component coordinate system to obtain the principal component scores of different samples.

10. A groundwater mixing end-member identification method considering the influence of seasonal changes in river water according to claim 1, characterized in that: The specific steps of screening the mixed end members of river water in step S3 are as follows: Step S301, performing mixed analysis on river water samples; Step S302: Perform end-member analysis using conservative species to calculate the mixing ratio of each sample.

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