CN113190793A - Dynamic determination method for water-rich property of loose bearing water-containing layer region - Google Patents
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Abstract
The invention provides a dynamic determination method for water-rich property of a loose bearing water-containing layer region, which comprises the following steps: a: determining the classification of the water-rich partition of the loose confined aquifer; b: establishing a hierarchical structure model; c: normalizing the main control factor data of the water pumping and discharging test hole of the loose confined aquifer, and calculating a subjective weight vector W1(ii) a D: calculating the correlation coefficient between every two main control factors, the standard deviation of each main control factor and calculating an objective weight vector W2(ii) a E: obtaining an objective and subjective coupling weight vector W; f: multiplying the normalized water drainage test hole data of the loose confined aquifer by the subjective and objective coupling weight vector to obtain a water-rich value VkFor water-rich value VkDetermining the interruption value of the water-rich subarea category after clustering analysis; g, geological exploration drilling according to aquifer to be evaluatedCalculating water-rich value V by using hole master control factor datakAnd evaluating the aquifer water-rich property according to the interruption value. By applying the embodiment of the invention, the obtained evaluation result is more reasonable and reliable.
Description
Technical Field
The invention relates to the technical field of coal mine water prevention and control, in particular to a dynamic determination method for water-rich property of a loose bearing water-containing layer area.
Background
The fourth series of loose confined aquifers of the hidden coal field in North China cover the coal series strata, and are the hot spots for researches on the prevention and control of water damage of the coal mine roof, the management of underground water resources in mining areas, the protection of ecological environment and the like. The aquifer takes non-cemented sandy soil and gravel as a framework, sand-cement interbedded layers are obvious, pressure bearing performance is achieved, and the difference of the water-rich space distribution is large.
In the prior art, the method for determining the water-rich property of the loose confined aquifer mainly comprises a water drainage test method, a geophysical exploration method and a multi-factor composite evaluation method. The water pumping test operation is time-consuming and labor-consuming, and after the test is finished, the test drilled hole needs to be plugged, so that the test cannot be carried out again. The geophysical prospecting method is used for evaluating the water-rich property, the evaluation result is different according to interpreters, and the evaluation result cannot be corresponding to the water-rich property grade of the aquifer specified in the coal mine control water regulations. The key point of the multi-factor composite evaluation method is to determine the weight of each factor, for example, the Analytic Hierarchy Process (AHP) pays attention to expert opinions, and ignores the original characteristics of data; the CRITIC method (criterion impact Through intercritical Correlation) focuses on the data change rule, but ignores the expert opinion, resulting in incomplete evaluation results.
Therefore, the improved AHP and the CRITIC are coupled to determine the weight of the influence factors of the water-rich property of the loose confined aquifer, the calculated weight not only considers the opinions of experts and scholars, but also accords with the characteristics of hydrogeology real measurement data, and finally a more scientific multi-factor composite water-rich property evaluation model of the loose confined aquifer is provided. The parameters required by the model can be obtained through common geological exploration drilling data, more water pumping tests are not needed, and the construction cost and the time investment of water pumping drilling are reduced. Compared with the method that only a few water pumping test hole data of a coal mine area are used for aquifer water-richness evaluation and partition, the model has higher evaluation accuracy. Meanwhile, the water level of the aquifer is influenced by nature or man-made factors and is changed periodically or semipermanently, so that the water-rich property of the aquifer is changed correspondingly, and the water-rich property evaluation model can accurately evaluate the water-rich property of a research area in a time-space dynamic way.
Disclosure of Invention
The invention aims to solve the technical problem of how to realize dynamic evaluation of the water-rich property of the loose bearing water-containing layer area.
The invention solves the technical problems through the following technical means:
the invention discloses a dynamic determination method for water-rich property of a loose bearing water-containing layer region, which comprises the following steps:
step A: and determining the zoning category of the water richness of the loose confined aquifer according to the appendix I of coal mine control water regulations.
And B: and (4) summarizing main control factors influencing the classification of the water-rich property of the loose confined aquifer, and establishing a hierarchical structure model.
And C: normalizing the main control factor data of the water pumping and discharging test hole of the loose confined aquifer, and calculating a subjective weight vector W1。
Step D: calculating the correlation coefficient between every two main control factors, the standard deviation of each main control factor, and calculating the objective weight vector W2。
Step E: and establishing an optimized first derivative matrix aiming at the subjective weight vector and the objective weight vector, and determining an optimization coefficient to obtain a subjective and objective coupling weight vector W.
Step F: multiplying the normalized data of the water pumping and discharging test holes of the unconsolidated confined aquifer by the subjective and objective coupling weight vector to obtain a water-rich value VkFor water-rich value VkAnd determining the interruption value of the water-rich subarea category after the clustering analysis.
Step G: calculating the water-rich value V according to the geological exploration drilling main control factor data of the aquifer to be evaluatedkFurther, the water-rich property of the water-containing layer was evaluated.
Optionally, step a includes:
according to the first appendix of coal mine water control regulations, the water-rich property of the aquifer is divided into weak water-rich property, medium water-rich property, strong water-rich property and strong water-rich property.
Optionally, the master factors include: hydrohead coefficient, grading coefficient, sedimentary microphase coefficient, water-bearing layer thickness, thickest sand layer thickness and water-resisting coefficient.
Optionally, step C includes:
c1: and normalizing the thickness of the thickest sand layer, the thickness of the water-containing layer, the water head coefficient and the grading coefficient by adopting a maximum value method, and normalizing the deposition microphase coefficient and the water-resisting coefficient by adopting a minimum value method.
C2: performing regression analysis on the main control factors and the evaluation standard values by using EXCEL software, and calculating CORREL correlation coefficient C of the main control factors and the evaluation standard valuesi(ii) a According to CORREL correlation coefficient CiThe importance degree of each main control factor is scaled according to a preset expected scaling method.
C3: calculating the value of unknown element of the first judgment matrix to construct a first judgment matrix B1。
C4: and calculating a sample standard deviation S (i) of each main control factor, and calculating a relative importance degree parameter value according to the sample standard deviation.
C5: constructing a second judgment matrix B according to the relative importance parameter values2。
C6: respectively calculate the firstJudging a first weight vector W corresponding to the matrix01And a second weight vector W corresponding to the second judgment matrix02Obtaining a subjective weight vector W according to an average of the first weight vector and the second weight vector1。
Optionally, the step of calculating a relative importance parameter value according to the sample standard deviation in C4 includes:
by means of the formula (I) and (II),
calculating a relative importance parameter value, wherein bijIs a relative importance degree parameter of the master control factor i relative to the master control factor j; s (i) is the sample standard deviation of master factor i; s (j) is the sample standard deviation of master factor j; bm＝min{9,int[Smax/Smin+0.5]}, and int is a rounding function; smaxThe maximum value of the sample standard difference of the main control factor i; sminIs the minimum value of the standard deviation of the samples of the master factor i.
Optionally, the C6 includes:
respectively calculating a first judgment matrix B1Maximum eigenvalue λ of1And a second decision matrix B2Maximum eigenvalue λ of2。
According to the maximum eigenvalue lambda1With the maximum eigenvalue λ2By using the formula, the method can be used,
calculating a random consistency evaluation index CR, wherein n is the judgment matrix order, namely the number of main control factors; lambda [ alpha ]iTaking the value as the maximum eigenvalue lambda1With the maximum eigenvalue λ2(ii) a RI is a random consistency ratio.
And under the condition that the first judgment matrix and the second judgment matrix both meet the consistency requirement, respectively calculating the eigenvector of the maximum eigenvalue of the first judgment matrix and the eigenvector of the maximum eigenvalue of the second judgment matrix.
Respectively comparing the eigenvector of the maximum eigenvalue of the first judgment matrix with the maximum eigenvalue of the second judgment matrixNormalizing the feature vector of the eigenvalue to obtain a first weight vector W corresponding to the first judgment matrix01(ii) a And a second weight vector W corresponding to the second decision matrix02。
According to a first weight vector W01And a second weight vector W02Calculates a subjective weight vector W from the mean of1。
Optionally, step D includes:
d1: and calculating Pearson correlation coefficients between every two main control factors by using Excel software.
D2: by means of the formula (I) and (II),
calculating an objective weight vector using EjIndicating the amount of information contained by the jth factor. Wherein, WjThe weight is the normalized weight corresponding to the main control factor j in the objective weight vector; rhoijThe Pearson correlation coefficient between the main control factor i and the main control factor j; sigmaiIs the standard deviation of the master factor i.
Optionally, step E includes:
e1: by means of the formula (I) and (II),
optimizing 2 linear combination coefficients, whereiniTo optimize the coefficients; w1Is a subjective weight vector; alpha is alpha1Optimizing coefficients for the subjective weights; alpha is alpha2Optimizing coefficients for the objective weights; w2Is an objective weight vector.
E2: and carrying out normalization processing on the optimization coefficient to obtain an objective and subjective coupling weight vector W.
Optionally, step F includes:
f1: and D, multiplying the normalization result of the water drainage test hole data in the step C by the subjective and objective coupling weight vector to obtain a water-rich value Vk。
F2: water enrichment value V by SPSS softwarekSystem with inter-group connection and interval of squared Euclidean distanceClustering analysis, wherein the clustering number is the water-rich subarea category number; and taking the average value of the demarcation points of two adjacent subarea categories as an interruption value.
Optionally, the step of evaluating the water-rich property of the aquifer according to the interruption value comprises:
according to the water-rich value V of the interrupt value relative to the aquifer to be evaluatedkThe water-rich property was evaluated.
The invention has the advantages that:
when the embodiment of the invention is applied to the water-rich evaluation, the water head coefficient (controlled by underground water level) is taken into consideration as one of the main control factors, and the underground water level is dynamically changed, so the embodiment of the invention can dynamically evaluate the water-rich property of the loose confined aquifer.
In addition, when the method determines the water enrichment of the loose confined aquifer, the water enrichment of the loose confined aquifer can be accurately evaluated according to the existing geological exploration drilling information without performing a water pumping and discharging test specified in the coal mine water control regulations, so that the workload is greatly reduced, and the manpower and material resources are saved. And the geological exploration drilling distribution range is wider, and the water-richness of the loose bearing water-containing layer area can be evaluated finely.
Finally, the method is simple to operate and easy to apply practically, and provides a new method and thought for evaluating the water-rich property of the loose confined aquifer.
Drawings
Fig. 1 is a schematic flow chart of a method for dynamically determining water-rich property of a region of a loosely confined water-containing layer according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Fig. 1 is a schematic flow chart of a method for dynamically determining water-rich property of a region of a loosely confined water-containing layer according to an embodiment of the present invention, as shown in fig. 1, the method includes:
s101: according to the stipulation of the supplementary notes of 'coal mine control water thin rules' issued by the national coal mine safety supervision agency in 2018, the aquifer water-rich property is divided into weak rich water (q is less than or equal to 0.1L/(s.m)), medium rich water (0.1L/(s.m) < q is less than or equal to 1L/(s.m)), strong rich water (1L/(s.m) < q is less than or equal to 5L/(s.m)), and extremely strong water-rich property (5L/(s.m) < q).
S102: and (4) summarizing main control factors influencing the classification of the water-rich property of the loose confined aquifer, and establishing a hierarchical structure model. According to data analysis and actual production conditions, the main control factors influencing the water-rich property of the loose pressure-bearing water-containing layer are summarized into 6: water-containing layer thickness, water head coefficient, deposition microphase coefficient, grading coefficient, thickest sand layer thickness and water-resisting coefficient.
(1) Head coefficient T: the confined water head of aquifer directly characterizes the water pressure of the aquifer, and the large difference of the burial depths of different aquifers is considered, so that a water head coefficient T is constructed:
T＝A/X
wherein A is the confined water head of the aquifer, and the unit is m; x is the buried depth of the top plate of the aquifer and the unit m.
(2) Thickness of the aqueous layer S: the larger the thickness of the water-bearing layer is, the more the water-bearing space is, and under the same condition, the larger the water-bearing capacity is, the unit m is.
(3) Deposition microphase coefficient R: the phenomenon of sand-soil and clay interbed can appear in the loose confined aquifer soil body deposition process, and interbed quantity and interbed inside sand-clay proportion have great influence to rich water nature. According to the quantification of the contribution of the clay thickness specific gravity to the water-resisting effect in the interbed, table 1 assigns values to the clay thickness specific gravity level, as shown in table 1.
TABLE 1
| Clay thickness/interbed layer thickness | ＜0.1 | 0.1～0.25 | 0.25～0.5 | 0.5～0.75 | 0.75～1 |
| Quantized value ri | 1 | 2 | 3 | 4 | 5 |
The sedimentary microphase coefficient R was thus constructed:
R＝∑rihi/S
wherein r isiIs an assignment of the proportion of the clay thickness in the interbed, hiIs the thickness of the respective interlayer, in m.
(4) Grading coefficient D: the water-rich property is different when the grain sizes of the soil bodies of the aquifer are different and the gaps among the grains are different. The contribution of the soil body particle size combination to the water-rich property of the aquifer is quantified, and the value of the loose confined aquifer is assigned according to the soil body particle size grade in table 2, as shown in table 2. And then constructing a class distribution coefficient D by using a formula:
TABLE 2
| Particle size classification | Clay | Clay sand | Silt | Fine sand | Medium sand | Coarse sand | Gravel |
| Quantized value di | 1 | 1.5 | 2 | 3 | 4 | 5 | 6 |
D＝∑disi/S
Wherein s isiThe thickness of the soil layer with each grain size is unit m.
(5) Thickness M of the thickest sand layer: the thickness of the gravel layer with the largest thickness in the water-containing layer is m. Because there is not the waterproof influence of clay layer in its middle part, the water storage effect is better.
(6) Water separation coefficient P: a certain amount of overflow supply exists between aquifers, and the phenomenon is more remarkable in a region with a water-resisting layer missing. The magnitude of the overflow replenishment is mainly determined by the thickness of the overlying water barrier, thereby establishing a water barrier coefficient P:
P＝(h1+...+hn)/X
wherein h is1+…+hnIs the sum of the thicknesses of the overlying water-resisting layers of the aquifer, and the unit is m.
The highest layer of the structural hierarchical model is a target layer which is a loose confined aquifer rich water; the middle layer is a master control factor layer and comprises the six master control factors; the bottom layer is the evaluation layer in the water-rich zone classification: weak, medium, strong and extremely strong.
S103: carrying out normalization processing on main control factor data of the water pumping and discharging test hole of the loose confined aquifer, and calculating a subjective weight vector W1。
C1: and normalizing the thickness of the thickest sand layer, the thickness of the water-containing layer, the water head coefficient and the grading coefficient by adopting a maximum value method, and normalizing the deposition microphase coefficient and the water-resisting coefficient by adopting a minimum value method.
And table 3 summarizes the normalized data of the main control factors of the water drainage test drilling in a certain mining area.
TABLE 3
In the embodiment of the present invention, different normalization methods are used, including maximum value normalization and minimum value normalization, in order to ensure that the data variation and the water-rich strength variation have the same direction.
C2: performing correlation analysis on each main control factor and the evaluation standard value by using a CORREL function in Excel software, and calculating a CORREL correlation coefficient C of the main control factors and the evaluation standard valuei(ii) a According to CORREL correlation coefficient CiThe importance degree of each main control factor is scaled according to a preset expected scaling method.
Table 4 is a table of importance ratings for each master factor for the expected scaling, as shown in table 4:
TABLE 4
| Degree of importance | Proportional scaling |
| Are identical to each other | 1 |
| Of little importance | 1.3 |
| Of obvious importance | 1.77 |
| Of absolute importance | 3.63 |
C3: calculating the value of unknown element of the first judgment matrix to construct a first judgment matrix B1. The values of the unknown elements of the first decision matrix are calculated and, if present in the first decision matrix,
a12＝c1,a23＝c2,…,a(n-1)n＝c(n-1)then, then
Thereby calculating the value of the unknown element in the first judgment matrix; further, a complete judgment matrix B can be constructed1。
C4: and calculating a sample standard deviation S (i) of each main control factor, and calculating a relative importance degree parameter value according to the sample standard deviation.
By means of the formula (I) and (II),
calculating a relative importance parameter value, wherein bijIs a relative importance degree parameter of the master control factor i relative to the master control factor j; s (i) is the sample standard deviation of master factor i; s (j) is the sample standard deviation of master factor j; bm＝min{9,int[Smax/Smin+0.5]}, and int is a rounding function; smaxThe maximum value of the sample standard difference of the main control factor i; sminIs the minimum value of the standard deviation of the samples of the master factor i.
C5: constructing a second judgment matrix B according to the relative importance parameter values2。
For example, the second judgment matrix B may be constructed by arranging horizontally the sedimentary microphase coefficients, the water cut layer thickness, the thickest sand layer thickness, the grading coefficients, the head coefficients, and the water-blocking coefficients in this order, and arranging vertically the sedimentary microphase coefficients, the water cut layer thickness, the thickest sand layer thickness, the grading coefficients, the head coefficients, and the water-blocking coefficients in this order2Comprises the following steps:
c6: respectively calculating first weight vectors W corresponding to the first judgment matrixes01And a second weight vector W corresponding to the second judgment matrix02Obtaining a subjective weight vector W according to an average of the first weight vector and the second weight vector1。
First, a first judgment matrix B is calculated respectively1And a second decision matrix B2Maximum eigenvalue λ1、λ2。
According to λ1And λ2By using the formula, the method can be used,
calculating the random consistency evaluation index CR of two judgment matrixes, wherein n is the judgment matrixThe order is the number of main control factors; lambda [ alpha ]iIs the maximum eigenvalue lambda1With the maximum eigenvalue λ2(ii) a RI is a random consistency ratio; when n is 6 and RI is 1.25, CR less than 0.1 is considered to satisfy the consistency requirement.
And under the condition that the first judgment matrix and the second judgment matrix both meet the consistency requirement, respectively calculating the eigenvector of the maximum eigenvalue of the first judgment matrix and the eigenvector of the maximum eigenvalue of the second judgment matrix.
Respectively carrying out normalization processing on the eigenvector of the maximum eigenvalue of the first judgment matrix and the eigenvector of the maximum eigenvalue of the second judgment matrix to obtain a first weight vector W corresponding to the first judgment matrix01(ii) a And a second weight vector W corresponding to the second decision matrix02。
According to a first weight vector W01And a second weight vector W02Calculates a subjective weight vector W from the mean of1W obtained1＝(0.1477，0.2320，0.1332，0.1126，0.1525，0.2220)。
S104: calculating the correlation coefficient between every two main control factors, the standard deviation of each main control factor and calculating an objective weight vector W2。
D1: pearson correlation coefficients between every two main control factors are calculated by utilizing a Pearson function in Excel software, and a table 5 shows the Pearson correlation coefficients between the main control factors.
TABLE 5
| Master control factor | Deposition of microphaseCoefficient of performance | Thickness of water-containing layer | Grading coefficient | Coefficient of water resistance | Thickness of thickest sand layer | Coefficient of head |
| Coefficient of deposition microphase | 1 | -0.04081 | 0.373614 | -0.15734 | 0.398826 | 0.018741 |
| Thickness of water-containing layer | -0.040814 | 1 | 0.39688 | 0.747268 | 0.609259 | 0.288336 |
| Grading coefficient | 0.373614 | 0.39688 | 1 | 0.11079 | 0.586026 | 0.245776 |
| Coefficient of water resistance | -0.157345 | 0.747268 | 0.11079 | 1 | 0.361565 | 0.055538 |
| Thickness of thickest sand layer | 0.3988258 | 0.609259 | 0.586026 | 0.361565 | 1 | 0.339062 |
| Coefficient of head | 0.0187407 | 0.288336 | 0.245776 | 0.055538 | 0.339062 | 1 |
D2: by means of the formula (I) and (II),
calculating an objective weight vector using EjIndicating the amount of information contained by the jth factor; wherein, WjThe weight is the normalized weight corresponding to the main control factor j in the objective weight vector; rhoijThe Pearson correlation coefficient between the main control factor i and the main control factor j; sigmaiIs the standard deviation of the master factor i.
Table 6 shows the standard deviation of each master control factor.
TABLE 6
| Master control factor | Coefficient of head | Thickness of water-containing layer | Coefficient of water resistance | Thickness of thickest sand layer | Grading coefficient | Coefficient of deposition microphase |
| Standard deviation of | 0.174399 | 0.251882 | 0.169849 | 0.208770 | 0.194185 | 0.274835 |
The objective weight vector W is obtained by using the above formula2＝(0.1892，0.1440，0.1288，0.1624， 0.1863，0.1892)。
S105: and establishing an optimized first-order derivative matrix aiming at the subjective weight vector and the objective weight vector, and determining an optimization coefficient to obtain a subjective and objective coupling weight vector W.
E1: and according to the obtained subjective weight vector and objective weight vector, the deviation between the sum of random combinations of the two groups of weight vectors and any combination is minimized by using an optimization coefficient alpha, thereby establishing the most optimized first derivative condition matrix.
By means of the formula (I) and (II),
optimizing the 2 linear combination coefficients to obtain an optimized coefficient alphaiI is 1,2, wherein αiTo optimize the coefficients; w1Is a subjective weight vector; alpha is alpha1Optimizing coefficients for the subjective weights; alpha is alpha2Optimizing coefficients for the objective weights; w2Is an objective weight vector.
Optimization coefficient alpha1＝0.8047，α2＝0.2085
E2: normalizing the optimized coefficient to obtain
And
and multiplying the vector by the subjective and objective weight vectors respectively to obtain a subjective and objective coupling weight vector W.
Therefore, the subjective weight vector and the optimization coefficient corresponding to the objective weight vector can be obtained, and further, by using a formula,
and calculating subjective and objective coupling weight W. Water-stop coefficient (0.2152), aquifer thickness (0.2139), thickest sand layer thickness (0.1323), grading coefficient (0.1228), head coefficient (0.1595) and sedimentary microphase coefficient (0.1562). Namely:
W＝(0.1562，0.2139，0.1323，0.1228，0.1595，0.2152)
s106: multiplying the normalized data of the water pumping and discharging test holes of the unconsolidated confined aquifer by the subjective and objective coupling weight vector to obtain a water-rich value VkFor water-rich value VkThe break value for the water-rich partition class is determined after cluster analysis.
F1: and D, multiplying the normalization result of the water drainage test hole data in the step C by the subjective and objective coupling weight vector to obtain a water-rich value Vk。
F2: with SPSS (Statistical Product and Service Sol)Solutions) software for the water-rich value VkPerforming interclass connection, performing systematic clustering analysis with the interval of squared Euclidean distance, and obtaining the clustering number as the class number of the water-rich subareas. The average of the demarcation points of the two partition categories is taken as the interrupt value.
For example, the interrupt values are: 0.4446, 0.6472. I.e. VkLess than or equal to 0.4446 is weak water-rich, 0.4446<VkLess than or equal to 0.6472 is medium water-rich, Vk>0.6472 is strong water-rich, and the water-drawing test holes in the working examples have no strong water-rich, so that the interruption value of strong water-rich and strong water-rich is not obtained.
S107: calculating the water-rich value V according to the geological exploration drilling main control factor data of the aquifer to be evaluatedkAnd evaluating the water-rich property of the aquifer on the basis of the cut-off value.
In practical application, aiming at a drill hole which cannot know the buried depth of the underground water level, 6 main control factor values can be determined according to the description of a drill hole histogram and the measured data of a nearby hydrological long sight hole, and the water-rich value V of the drill hole is calculated according to the subjective and objective coupling weight vectorkAccording to the VkThe water-rich values were evaluated in accordance with the water-rich ranges.
The above examples are only for illustrating the technical solutions of the present invention, and are not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the present invention as defined by the appended claims.
Claims (10)
1. A method for dynamically determining the water-rich property of a region containing water under loose pressure, which is characterized by comprising the following steps:
step A: determining the classification of the water-rich partition of the loose confined aquifer according to appendix one of coal mine water control rules;
and B: main control factors influencing the classification of the water-rich property of the loose confined aquifer are summarized, and a hierarchical structure model is established;
and C: carrying out normalization processing on main control factor data of the water pumping and discharging test hole of the loose confined aquifer, and calculating a subjective weight vector W1；
Step D: calculating the correlation coefficient between every two main control factors, the standard deviation of each main control factor and calculating an objective weight vector W2；
Step E: establishing an optimized first derivative matrix aiming at the subjective weight vector and the objective weight vector, and determining an optimization coefficient to obtain a subjective and objective coupling weight vector W;
step F: multiplying the normalized data of the water pumping and discharging test holes of the unconsolidated confined aquifer by the subjective and objective coupling weight vector to obtain a water-rich value VkFor water-rich value VkDetermining the interruption value of the water-rich subarea category after clustering analysis;
step G: calculating the water-rich value V according to the geological exploration drilling main control factor data of the aquifer to be evaluatedkAnd evaluating the water-rich property of the aquifer on the basis of the cut-off value.
2. The method for dynamically determining the water-rich property of the loose bearing water-containing layer area according to claim 1, wherein the step A: the method comprises the following steps:
according to appendix one in coal mine water control regulations, the water-rich property of the aquifer is divided into weak water-rich property, medium water-rich property, strong water-rich property and strong water-rich property.
3. The method of claim 1, wherein the main control factors comprise: hydrohead coefficient, grading coefficient, sedimentary microphase coefficient, water-bearing layer thickness, thickest sand layer thickness, and water-resisting coefficient.
4. The method for dynamically determining the water-rich property of a region containing water under loose pressure according to claim 3, wherein the step C: the method comprises the following steps:
c1: carrying out normalization processing on the thickness of the thickest sand layer, the thickness of the water-containing layer, the water head coefficient and the grading coefficient by adopting a maximum value method, and carrying out normalization processing on the deposition microphase coefficient and the water-resisting coefficient by adopting a minimum value method;
c2: performing regression analysis on each main control factor and the evaluation standard value by using a CORREL function in Excel software, and calculating CORREL correlation coefficient C of the main control factors and the evaluation standard valuei(ii) a According to CORREL correlation coefficient CiThe importance degree of each main control factor is scaled according to a preset expected scaling method;
c3: calculating the value of unknown element of the first judgment matrix to construct a first judgment matrix B1；
C4: calculating a sample standard deviation S (i) of each main control factor, and calculating a relative importance degree parameter value according to the sample standard deviation;
c5: constructing a second judgment matrix B according to the relative importance parameter values2；
C6: respectively calculating first weight vectors W corresponding to the first judgment matrixes01And a second weight vector W corresponding to the second decision matrix02Obtaining a subjective weight vector W according to an average of the first weight vector and the second weight vector1。
5. The method of claim 4, wherein the step of calculating the relative importance parameter value according to the sample standard deviation in C4 comprises:
by means of the formula (I) and (II),
calculating a relative importance parameter value, wherein bijIs a relative importance degree parameter of the master control factor i relative to the master control factor j; s (i) is the sample standard deviation of master factor i; s (j) is the sample standard deviation of master factor j; bm＝min{9,int[Smax/Smin+0.5]}, and int is a rounding function; smaxThe maximum value of the standard deviation of the sample of the main control factor i; sminIs the minimum value of the standard deviation of the samples of the master factor i.
6. The method of claim 4, wherein the step C6 comprises:
respectively calculating a first judgment matrix B1Maximum eigenvalue λ of1And a second decision matrix B2Maximum eigenvalue λ of2(ii) a According to the maximum eigenvalue lambda1With the maximum eigenvalue λ2By using the formula, the method can be used,
calculating a random consistency evaluation index CR, wherein n is the number of judging matrix orders, namely the number of main control factors; lambda [ alpha ]iIs the maximum eigenvalue lambda1With the maximum eigenvalue λ2(ii) a RI is a random consistency ratio;
under the condition that the first judgment matrix and the second judgment matrix both meet the consistency requirement, respectively calculating the eigenvector of the maximum eigenvalue of the first judgment matrix and the eigenvector of the maximum eigenvalue of the second judgment matrix;
respectively carrying out normalization processing on the eigenvector of the maximum eigenvalue of the first judgment matrix and the eigenvector of the maximum eigenvalue of the second judgment matrix to obtain a first weight vector W corresponding to the first judgment matrix01(ii) a And a second weight vector W corresponding to the second decision matrix02；
According to a first weight vector W01And a second weight vector W02Calculates a subjective weight vector W from the mean of1。
7. The method for dynamically determining the water-rich property of a region containing water under loose pressure according to claim 1, wherein the step D comprises:
d1: calculating Pearson correlation coefficients between every two main control factors by using Excel software;
d2: by means of the formula (I) and (II),
calculating an objective weight vector W2By EjIndicating the amount of information contained by the jth factor. Wherein, WjThe weight is the normalized weight corresponding to the main control factor j in the objective weight vector; rhoijThe Pearson correlation coefficient between the main control factor i and the main control factor j; sigmaiIs the standard deviation of the master factor i.
8. The method for dynamically determining the water-rich property of a region containing water under loose pressure according to claim 1, wherein the step E comprises:
e1: by means of the formula (I) and (II),
optimizing the 2 linear combination coefficients, whereiniTo optimize the coefficients; w1Is a subjective weight vector; alpha is alpha1Optimizing coefficients for the subjective weights; alpha is alpha2Optimizing coefficients for the objective weights; w2Is an objective weight vector;
e2: and carrying out normalization processing on the optimization coefficient to obtain an objective and subjective coupling weight vector W.
9. The method of claim 1, wherein the step F comprises:
f1: multiplying the normalization result of the water drainage test hole data in the step C by the coupling weight vector to obtain a water-rich value Vk；
F2: water enrichment value V by SPSS softwarekPerforming inter-group connection and systematic clustering analysis with an interval of squared Euclidean distance, wherein the clustering number is the water-rich partition category number; and taking the average value of the demarcation points of two adjacent subarea categories as an interruption value.
10. The method of claim 1, wherein the step of evaluating the water-rich property of the aquifer based on the discontinuity value comprises:
according to the interruption value, the water-rich value V of the aquifer to be evaluatedkThe water-rich property was evaluated.
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