CN112241835A - Deep shaft project water inrush disaster multi-source information evaluation method - Google Patents

Deep shaft project water inrush disaster multi-source information evaluation method Download PDF

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CN112241835A
CN112241835A CN202011063586.8A CN202011063586A CN112241835A CN 112241835 A CN112241835 A CN 112241835A CN 202011063586 A CN202011063586 A CN 202011063586A CN 112241835 A CN112241835 A CN 112241835A
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water inrush
water
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weight
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纪洪广
张同钊
张月征
由爽
向鹏
刘力源
王轶民
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University of Science and Technology Beijing USTB
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract

The invention provides a multi-source information evaluation method for water inrush disasters in deep shaft engineering, and belongs to the technical field of mine water inrush risk evaluation. The method comprises the steps of firstly establishing a multivariate information evaluation system, then carrying out dimensionless on original indexes, then calculating the weight of influence factors, and finally constructing a water inrush risk evaluation model. The method comprises the steps of establishing a water inrush risk evaluation index system according to factors influencing the water inrush risk of the vertical shaft, providing test methods of different influencing factors, determining the weight of different factors by using an analytic hierarchy process, and finally evaluating the water inrush risk of the vertical shaft based on a multi-source information set model. Wherein, a multi-information evaluation system is established by selecting a structural fracture zone, a fault fracture zone, water-rich property, a water inrush point, water conductivity and ground stress. The invention solves the problem of accidental errors in the single factor analysis process, provides a more comprehensive and accurate analysis method, provides a reliable basis for construction decision making, and has stronger practicability.

Description

Deep shaft project water inrush disaster multi-source information evaluation method
Technical Field
The invention relates to the technical field of mine water inrush risk evaluation, in particular to a multi-source information evaluation method for water inrush disasters of deep shaft engineering.
Background
With the exhaustion of shallow mineral resources, the metal mine is shifted to deep mining, and the deep vertical shaft is a throat part for deep mining and is a main channel for communicating the ground surface and the underground. The shaft construction passes through different geological structures from top to bottom, when the construction encounters complex geological environments such as aquifers, fracture broken zones, water guide faults and the like, water inrush accidents of shafts are easy to happen, serious consequences such as personal casualties, equipment damage, construction period delay and even well flooding are caused, and huge economic and property losses are brought. The shaft construction processes of a main shaft of a Gutun coal mine, an air shaft of a Haemon mountain coal mine, a No. 2 main shaft of a Longgu coal mine, a Xinyuan coal mine air shaft and the like all suffer from serious water inrush accidents, so that huge economic loss is brought, and the water inrush risk of the shaft becomes the first environmental engineering risk in the shaft construction process. Therefore, in the construction process, the accurate judgment of the water inrush point has important significance for making a decision in advance and taking effective control measures.
Prediction and forecast are one of important contents for dynamic management and control of water inrush risks, and whether the possibility of occurrence of water inrush risk accidents and disaster damage can be effectively reduced is also determined. Most of the current water inrush prediction focuses on the top floor and the tunnel, and the water inrush prediction method for the vertical shaft at the initial stage of well building is less. The initial evaluation is the basic work of risk evaluation on hydrological and engineering geological data of the area to be constructed on the basis of engineering geological survey before excavation construction. The initial evaluation is the evaluation of the potential risk probability and damage of the pregnancy risk environment, and provides a reliable basis for the design of construction organization. And the risk evaluation of the current shaft water inrush stratum stays in the aspect of simple geological analysis and is evaluated through single data. The main reason is that data required in the current water inrush risk evaluation method is difficult to obtain through drilling geological exploration, and a method suitable for shaft water inrush prediction needs to be established urgently to construct a corresponding index system. Therefore, the method for evaluating the water inrush risk of the vertical shaft is established by analyzing key factors influencing water inrush disasters and providing a measuring and analyzing method of corresponding factors.
Disclosure of Invention
The invention aims to provide a multi-source information evaluation method for water inrush disasters in deep shaft engineering.
The method comprises the following steps:
(1) establishing a multivariate information evaluation system:
based on factors influenced by the water inrush risk of the deep shaft and the difficulty degree of data acquisition, selecting six influencing factors of a structural fracture zone, a fault fracture zone, water richness, a water inrush point, water conductivity and ground stress as evaluation indexes, and establishing a multi-information evaluation system;
(2) carrying out dimensionless on the original indexes:
because the evaluation indexes have different dimensions or evaluation standards, the evaluation indexes are subjected to non-dimensionalization treatment according to the following table in order to eradicate the generated indexes and facilitate calculation:
characteristic value of different evaluation indexes
Figure BDA0002713111110000021
(3) Calculating the weight of the influence factors:
comparing scale criteria according to an analytic hierarchy process, and comparing two factors to obtain a judgment matrix:
Figure BDA0002713111110000022
solving the weight according to a root method, which comprises the following specific steps:
solving the n power root of the row product of the judgment matrix:
Figure BDA0002713111110000023
wherein i is 1, 2, 3, 4, 5, 6, n is the order; w is a feature vector, and W is a feature vector,aijfor the target element, AiTo AjNumerical embodiment of relative importance;
normalization:
Figure BDA0002713111110000031
wherein i is 1, 2, 3, 4, 5, 6, and a weight vector W is obtained by calculationi＝(0.0420，0.1216，0.2412，0.4571，0.0691，0.0691)T
Wherein, WiIs an influencing factor weight coefficient;
the weight values of the influence factors of the water inrush are obtained through calculation and are shown in the following table:
water inrush effect factor weight
Figure BDA0002713111110000032
(4) Constructing a water inrush risk evaluation model:
evaluating the water inrush risk of the vertical shaft by adopting a vulnerability index method, wherein the water inrush index is as follows:
Figure BDA0002713111110000033
in the formula: f is a water inrush evaluation index; wiIs an influencing factor weight coefficient; f. ofiAnd (x, y) is a dimensionless characteristic value of the evaluation index, (x, y) is a vertical shaft depth coordinate, m is the number of influencing factors of the evaluation index, and m is 6.
And obtaining evaluation indexes of strata at different depths according to the evaluation model, generating a frequency statistical histogram, and dividing the evaluation indexes into 5 intervals by using a Natural Breaks method to obtain 5-level results, namely high water inrush risk, medium water inrush risk, low water inrush risk and low water inrush risk.
The method for identifying the water inrush point in the step (1) comprises a fluid diffusion method and temperature abnormal region identification.
The method is based on the basic principle that a water inrush risk evaluation index system is established according to factors influencing the water inrush risk of the vertical shaft, testing methods of different influencing factors are provided, then the weights of the different factors are determined by an analytic hierarchy process, and finally the water inrush risk of the vertical shaft is evaluated based on a multi-source information set model.
The evaluation indexes in the step (1) are explained in detail as follows:
constructing a fissure zone: the formation of the fractured zone is easy to form a water guide channel, and is one of the important conditions for water inrush accidents of the vertical shaft. The distinguishing method is to obtain the sound velocity characteristic for recognition through the geological characteristic revealed by the drilling core and the acoustic logging technology.
Fault fracture zone: the deep fault fracture zone is a main underground water breeding zone, and meanwhile, the fault fracture zone is poor in surrounding rock quality, cracks grow, the whole fault fracture zone is easy to destabilize, difficult to support, poor in water resistance and strong in water conductivity, and serious water inrush accidents are easily caused. The fault fracture zone can be identified through core characteristics.
Water-rich: abundant groundwater is a prerequisite for water inrush in deep shafts. The water-rich stratum has the advantages that the strength of the rock mass is reduced due to the softening effect of water, the deformation is increased, the permeability is increased, and the probability of water inrush accidents is increased. The water-rich nature of the formation may be characterized by a well pump test.
Water inrush point: through the well logging technology, the water inrush point is accurately judged, and the position of the water inrush stratum is facilitated to be clear. Currently, water inrush formations can be identified by a combination of methods. The water inrush stratum judged by multiple methods at the same time is judged as a high water inrush point. The identification method comprises a fluid diffusion method and temperature abnormal region identification.
Wherein, the fluid diffusion method specifically comprises the following steps: the resistivity of the fresh water is relatively high (10 ohm-m) and the resistivity of the salt water is low (3.5 ohm-m) in combination with the resistivity characteristics of the fluid, the well fluid is changed from the fresh water to the salt water, and then the position of the fresh water is changed to show high resistance characteristics, which is the basic principle of determining the position of the water outlet in the well by the salt diffusion method. The well is thoroughly washed before the method is applied, then the fluid resistivity in a clear water state is tested, the fluid resistivity is relatively stable under normal conditions, if saline water is discharged from a certain position, the fluid resistivity is reduced, low-resistance abnormity occurs, and obviously, the discharge position of the saline water can be judged by using the fluid resistivity tested after hole washing.
The identification of the temperature abnormal area is specifically as follows: due to the action of the water head pressure, water in the rock stratum cannot be expanded into the drilled hole, and the water temperature in the drilled hole is kept stable. After water is pumped through the wash hole, water in the water-rich formation will expand into the borehole, resulting in an increase in temperature of the formation compared to the formation before pumping, and if an abnormal increase in formation temperature is measured before and after pumping a formation, the formation can be determined to be a water inrush point.
Formation water conductivity: the water guide channel is a main path for underground water migration, the speed and flow rate of the underground water migration are determined by the difference of different lithologic strata, and the water inrush risk is higher when the water conductivity is high; the water resistance is poor, the water blocking energy is stronger, and the possibility that underground water breaks through a water-resisting layer is lower. The formation water conductivity can be judged by analyzing the lithology characteristics and the thickness of the water-resisting layer.
Ground stress: in the process of excavating rock masses in strata with higher ground stress, the energy generated by the rock masses is higher, so that the expansion area of rock mass fractures caused by the energy release of surrounding rocks is wider, underground water is spread along the disturbed open fractures, the water burst pressure in the areas with higher ground stress is higher, and the water burst disaster is more serious. Identification of the ground stress can be obtained by deep hole hydraulic fracturing tests or nonlinear elastic recovery tests.
The technical scheme of the invention has the following beneficial effects:
in the scheme, the problems that the existing shaft water inrush prediction method is lack and low in accuracy are solved. Compared with the existing geological analysis and drilling analysis technologies, the method adopts a more mature and effective technology, solves accidental errors in the single factor analysis process, provides a more comprehensive and accurate analysis method, provides a reliable basis for construction decision making, and has stronger practicability.
Drawings
FIG. 1 is a schematic flow chart of a multi-source information evaluation method for water inrush disaster in deep shaft engineering according to the present invention;
FIG. 2 is a schematic structural diagram of a single-level model according to an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
The invention provides a deep shaft project water inrush disaster multi-source information evaluation method aiming at the problem of insufficient prediction accuracy in the current shaft water inrush evaluation single index evaluation.
As shown in fig. 1, the method comprises the steps of:
(1) establishing a multivariate information evaluation system:
based on factors influenced by the water inrush risk of the deep shaft and the difficulty degree of data acquisition, selecting six influencing factors of a structural fracture zone, a fault fracture zone, water richness, a water inrush point, water conductivity and ground stress as evaluation indexes, and establishing a multi-information evaluation system;
(2) carrying out dimensionless on the original indexes:
because the evaluation indexes have different dimensions or evaluation standards, the evaluation indexes are subjected to non-dimensionalization treatment according to the following table 1 in order to eliminate the generated indexes and facilitate calculation:
TABLE 1 evaluation of different index characteristics
Figure BDA0002713111110000051
(3) Calculating the weight of the influence factors:
comparing scale criteria according to an analytic hierarchy process, and comparing two factors to obtain a judgment matrix:
Figure BDA0002713111110000061
solving the weight according to a root method, which comprises the following specific steps:
solving the n power root of the row product of the judgment matrix:
Figure BDA0002713111110000062
wherein i is 1, 2, 3, 4, 5,6, n is the order; w is a feature vector, aijFor the target element, AiTo AjNumerical embodiment of relative importance;
normalization:
Figure BDA0002713111110000063
wherein i is 1, 2, 3, 4, 5, 6, and a weight vector W is obtained by calculationi＝(0.0420，0.1216，0.2412，0.4571，0.0691，0.0691)T
Wherein, WiIs an influencing factor weight coefficient;
the weight values of the influence factors of the water inrush are obtained through calculation and are shown in the following table 2:
TABLE 2 Water inrush Effect factor weights
Figure BDA0002713111110000064
(4) Constructing a water inrush risk evaluation model:
evaluating the water inrush risk of the vertical shaft by adopting a vulnerability index method, wherein the water inrush index is as follows:
Figure BDA0002713111110000065
in the formula: f is a water inrush evaluation index; wiIs an influencing factor weight coefficient; f. ofiAnd (x, y) is a dimensionless characteristic value of the evaluation index, (x, y) is a vertical shaft depth coordinate, m is the number of influencing factors of the evaluation index, and m is 6.
Wherein, the analytic hierarchy process in the step (3) is specifically as follows:
1) establishing hierarchical structure, adopting single-level model structure (shown in FIG. 2), wherein the model comprises a target C and n evaluation elements A belonging to the target C1… … An and a decision maker. The decision maker evaluates the n elements in the target sense, orders the advantages and the disadvantages of the n elements, makes a balance of relative importance, and compares the degrees of the advantages and the disadvantages of the two elements.
Adopt 1 &The 9-scale method (see table 3) performs a relative comparison between each two elements to construct a decision matrix a ═ aij) And max is calculated, and the characteristic root of the judgment matrix A is solved. AW ═ λmaxW, calculating the maximum feature root λmaxFinding out the corresponding characteristic vector W, namely the sorting weight of each factor of the same layer equivalent to the relative importance of a certain factor of the previous layer, and then carrying out consistency check.
TABLE 3 comparative Scale
Figure BDA0002713111110000071
And comparing every two elements according to the comparison scale to construct a judgment matrix. In the single-layer structure model, the target element is assumed to be C, and the related element A connected with the target element is assumed to be C1…AnThere is a dominating relationship. And constructing a judgment matrix by inquiring the decision maker about the quality comparison of the element A under the principle C by taking the primary target element C as a criterion.
TABLE 4 decision matrix
Figure BDA0002713111110000072
Wherein a isijDenotes for C, AiTo AjNumerical manifestation of relative importance, typically aij1, 2, … …, 9 and their inverse may be taken as scales.
2) Calculation of λ max and W
In general, either the power law or the root law may be used. The root method comprises the following calculation steps:
i.A by row;
opening the obtained products by the power of n respectively;
normalizing the square root vector to obtain an ordering weight W;
calculating λ max according to
Figure BDA0002713111110000081
3) Consistency check of decision matrix
i. Calculating a consistency index CI
Figure BDA0002713111110000082
In the formula: n denotes the order of the average decision matrix.
Calculating the consistency ratio CR
Figure BDA0002713111110000083
In the formula: RI represents the average random consistency index, which is found in table 5:
TABLE 5 evaluation of random consistency index
Figure BDA0002713111110000084
When CR <0.1, it is generally considered that the consistency of the decision matrix is acceptable.
After the weight values of the influence factors of the water inrush are obtained in the step (3), the maximum characteristic value is further obtained:
Figure BDA0002713111110000085
in the formula ofmaxIs the maximum eigenvalue, A is the judgment matrix, W is the eigenvector, and lambda can be obtained by calculationmax＝6.1597。
And further carrying out consistency check:
index of consistency
Figure BDA0002713111110000086
Calculating a consistency ratio
Figure BDA0002713111110000087
Meets the requirement, and the evaluation factor weight calculated by the judgment matrixIs reasonable.
The method solves the problems that the existing shaft water inrush prediction method is lack and low in accuracy. Compared with the existing geological analysis and drilling analysis technologies, the method adopts a more mature and effective technology, solves accidental errors in the single factor analysis process, provides a more comprehensive and accurate analysis method, provides a reliable basis for construction decision making, and has stronger practicability.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (2)

1. A deep shaft project water inrush disaster multi-source information evaluation method is characterized by comprising the following steps: the method comprises the following steps:
(1) establishing a multivariate information evaluation system:
based on factors influenced by the water inrush risk of the deep shaft and the difficulty degree of data acquisition, selecting six influencing factors of a structural fracture zone, a fault fracture zone, water richness, a water inrush point, water conductivity and ground stress as evaluation indexes, and establishing a multi-information evaluation system;
(2) carrying out dimensionless on the original indexes:
for the calculation, the evaluation indexes were subjected to dimensionless processing according to the following table:
characteristic value of different evaluation indexes
Figure FDA0002713111100000011
(3) Calculating the weight of the influence factors:
comparing scale criteria according to an analytic hierarchy process, and comparing two factors to obtain a judgment matrix:
Figure FDA0002713111100000012
solving the weight according to a root method, which comprises the following specific steps:
solving the n power root of the row product of the judgment matrix:
Figure FDA0002713111100000013
wherein i is 1, 2, 3, 4, 5, 6, n is the order; w is a feature vector, aijFor the target element, AiTo AjNumerical embodiment of relative importance;
normalization:
Figure FDA0002713111100000014
wherein i is 1, 2, 3, 4, 5, 6, and a weight vector W is obtained by calculationi＝(0.0420，0.1216，0.2412，0.4571，0.0691，0.0691)T
Wherein, WiIs an influencing factor weight coefficient;
the weight values of the influence factors of the water inrush are obtained through calculation and are shown in the following table:
water inrush effect factor weight
Figure FDA0002713111100000021
(4) Constructing a water inrush risk evaluation model:
evaluating the water inrush risk of the vertical shaft by adopting a vulnerability index method, wherein the water inrush index is as follows:
Figure FDA0002713111100000022
in the formula: f is a water inrush evaluation index; wiIs an influencing factor weight coefficient; f. ofiAnd (x, y) is a dimensionless characteristic value of the evaluation index, (x, y) is a vertical shaft depth coordinate, m is the number of influencing factors of the evaluation index, and m is 6.
2. The deep shaft project water inrush disaster multi-source information evaluation method according to claim 1, characterized in that: the method for identifying the water inrush point in the step (1) comprises a fluid diffusion method and temperature abnormal region identification.
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