CN112241835B - Multi-source information evaluation method for flood disaster in deep shaft engineering - Google Patents
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Abstract
The invention provides a multi-source information evaluation method for a flood disaster in deep shaft engineering, and belongs to the technical field of mine flood risk evaluation. Firstly, establishing a multi-element information evaluation system, then carrying out dimensionless treatment on an original index, then calculating the weight of an influence factor, and finally, constructing a water inrush risk evaluation model. According to the method, a water inrush risk evaluation index system is established according to factors influencing the water inrush risk of the vertical shaft, test methods of different influencing factors are provided, then the weight of different factors is determined by using a hierarchical analysis method, and finally the water inrush risk of the vertical shaft is evaluated based on a multi-source information set model. And selecting a structural fracture zone, a fault fracture zone, water enrichment, water bursting points, water conductivity and ground stress to establish a multi-element information evaluation system. The invention solves the accidental errors in the single factor analysis process, provides a more comprehensive and accurate analysis method, provides reliable basis for construction decision and has stronger practicability.
Description
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 a water inrush disaster of a deep shaft engineering.
Background
With the exhaustion of shallow mineral resources, the metal mine is transferred into deep mining, and a deep vertical shaft is a throat part of deep mining and is a main channel for communicating the earth surface with the underground. When the construction of the vertical shaft passes through different geological structures from top to bottom, when the construction encounters complex geological environments such as aquifers, fracture breaking zones, water guide faults and the like, the water burst accident of the vertical shaft is extremely easy to occur, so that serious consequences such as casualties, equipment damage, construction period delay, well flooding and the like are caused, and huge economic and property losses are brought. Guo Tun the main shaft of the coal mine, the air shaft of the bird mountain of the crane, the No. 2 main shaft of the Longgu coal mine, the air shaft of the new element coal mine and other shaft construction processes all suffer serious water bursting accidents, great economic loss is brought, and the water bursting risk of the shaft becomes the first environmental engineering risk in the shaft construction process. Therefore, in the construction process, accurate judgment of the water inrush point has important significance for decision in advance and effective control measures.
The prediction and forecast is one of important contents of dynamic management and control of the water inrush risk, and also determines whether the possibility of occurrence of accidents and disaster loss of the water inrush risk can be effectively reduced. Most of the current water burst prediction is concentrated on the aspects of top and bottom plates and tunnels, and the water burst prediction method for the vertical shaft in the early stage of well construction is less. The initial evaluation is a basic work of performing risk evaluation on hydrology of a region to be constructed and engineering geological data on the basis of engineering geological survey before excavation construction. The primary evaluation is the evaluation of the risk probability and damage of the pregnancy risk environment, and provides a reliable basis for construction organization design. The current risk evaluation of the water inrush stratum of the vertical shaft stays in the aspect of simple geological analysis, and is carried out through single data. The method is mainly characterized in that data required in the current water inrush risk evaluation method are difficult to obtain through drilling geological exploration, and a method suitable for shaft water inrush prediction needs to be established, so that a corresponding index system is constructed. Therefore, the invention establishes a shaft water inrush risk evaluation method by analyzing key factors influencing the water inrush disaster and providing a measuring and analyzing method of corresponding factors.
Disclosure of Invention
The invention aims to provide a method for evaluating multi-source information of a flood disaster in deep shaft engineering.
The method comprises the following steps:
(1) Establishing a multivariate information evaluation system:
Based on factors of influence of deep shaft water inrush risk and difficulty in data acquisition, six influencing factors of structural fracture zone, fault fracture zone, water enrichment, water inrush point, water conductivity and ground stress are selected as evaluation indexes, and a multi-element information evaluation system is established;
(2) Dimensionless treatment is carried out on the original index:
because the evaluation indexes have different dimensions or evaluation standards, the evaluation indexes are nondimensionally processed according to the following table in order to eradicate the incoordination of the generated indexes and facilitate calculation:
Different evaluation index feature values
(3) Calculating the weight of the influencing factors:
Comparing the scale criteria according to an analytic hierarchy process, and comparing two factors to obtain a judgment matrix:
the method is characterized by weighing according to a root method, and the specific process is as follows:
① Solving n times square roots of the row products of the judgment matrix: Where i=1, 2,3,4,5,6, n is the order; w is a feature vector, a ij is a numerical representation of the relative importance of A i to A j for the target element;
② Normalization: Where i=1, 2,3,4,5,6, the weight vector W is calculated i＝(0.0420,0.1216,0.2412,0.4571,0.0691,0.0691)T
Wherein W i is the influence factor weight coefficient;
the weight values of the influence factors of the water inrush are calculated as shown in the following table:
Water inrush influence factor weight
(4) Constructing a water inrush risk evaluation model:
Evaluating the water burst risk of the vertical shaft by adopting a vulnerability index method, wherein the water burst index is as follows:
Wherein: f is a water burst evaluation index; w i is the influence factor weight coefficient; f i (x, y) is a dimensionless characteristic value of the evaluation index, x, y is a vertical shaft depth coordinate, m is the number of influence factors of the evaluation index, and m=6.
And obtaining evaluation indexes of stratum with different depths according to the evaluation model, generating a frequency statistical histogram, and dividing the stratum into 5 intervals by using a Natural break-point method Natural break, so as to obtain 5-level results, namely high water inrush risk, higher water inrush risk, medium water inrush risk, lower water inrush risk and low water inrush risk.
The method for identifying the water burst point in the step (1) comprises a fluid diffusion method and temperature anomaly area identification.
The basic principle of the method is that a water inrush risk evaluation index system is established according to factors influencing the water inrush risk of the vertical shaft, a test method of different influencing factors is provided, then the weight of the different factors is determined by using an analytic hierarchy process, and finally the water inrush risk of the vertical shaft is evaluated based on a multisource information set model.
The evaluation indexes in the step (1) are described in detail as follows:
Constructing a fracture zone: the construction of the fracture belt is easy to form a water guide channel, and is one of important conditions for water burst accidents of the vertical shaft. The distinguishing method is to obtain sound velocity characteristics through geological characteristics and sound wave logging technology disclosed by the drilling core for recognition.
Fault breaking belt: the deep fault fracture zone is a main inoculation area of underground water, and meanwhile, the fault fracture zone has poor surrounding rock quality, cracks develop, the whole is easy to be unstable, the support is difficult, the water blocking capability is poor, the water conductivity is strong, and serious water burst accidents are easy to be caused. Fault zone may be identified by core characteristics.
Rich water: abundant groundwater is a precondition for causing deep shaft water burst. The water-rich stratum has the advantages that the strength of the rock body is reduced due to the softening effect of water, the deformation is increased, the permeability is increased, and the probability of water burst accidents is also increased. The water-rich nature of the formation can be characterized by pumping tests of the well.
Water burst point: through logging technology, accurately judge water burst point, help definitely water burst stratum position. The water inrush stratum can be distinguished in a mode that a plurality of methods want to be combined. The water bursting stratum which is simultaneously discriminated by a plurality of methods is discriminated as a high water bursting point. The identification method comprises a fluid diffusion method and temperature anomaly identification.
The fluid diffusion method specifically comprises the following steps: in combination with the characteristic of fluid resistivity, the resistivity of fresh water is relatively high (10 omega m), the resistivity of brine is low (3.5 omega m), well fluid is changed from fresh water to brine, and the position of the fresh water is displayed with high resistance, which is the basic principle of determining the position of the water in the well by using a salt diffusion method. The method is used for thoroughly washing the well before the method is applied, then the fluid resistivity in the clean water state is tested, the fluid resistivity at the moment is relatively stable under normal conditions, if the salty water is discharged from a certain position, the fluid resistivity can reduce the occurrence of low-resistance abnormality, and obviously, the outlet position of the salty water can be judged by using the fluid resistivity tested after the hole washing.
The temperature anomaly area is specifically identified as follows: due to the action of the water head pressure, water in the rock stratum cannot spread into the drill hole, and the water temperature in the drill hole is kept stable. After water is pumped through the borehole, water in the water-rich formation will expand into the borehole, causing the formation to rise in temperature compared to before water is pumped, and if an abnormal rise in the temperature of the formation measured before and after water is pumped from a certain formation, the formation can be judged to be a water burst point.
Formation water conductivity: the water guide channel is a main path of groundwater migration, and the difference of different lithology strata determines the speed and flow of groundwater migration, so that the water burst risk faced by strong water guide is higher; the lower the water conductivity, the stronger the water blocking energy, and the lower the possibility that the groundwater breaks through the water-blocking layer. And the water conductivity of the stratum can be judged by analyzing the lithology characteristics and the thickness of the water-resisting layer.
Ground stress: in the rock mass excavation process of the stratum with higher ground stress, due to higher energy of the rock mass, the expansion area of rock mass cracks is wider due to energy release of surrounding rock, groundwater spreads along disturbance expansion cracks, water surge pressure is higher in the area with higher ground stress, and water surge disaster is more serious. The identification of the ground stress can be obtained through a deep hole hydraulic fracturing test or a nonlinear elastic recovery test.
The technical scheme of the invention has the following beneficial effects:
According to the scheme, the problems that a current vertical shaft water burst prediction method is lack and low in accuracy are solved. Compared with the existing geological analysis and drilling analysis technology, the method provided by the invention adopts a more mature and effective technology, solves the accidental error in the single factor analysis process, provides a more comprehensive and accurate analysis method, provides a reliable basis for construction decision, and has stronger practicability.
Drawings
FIG. 1 is a flow chart of a method for evaluating multi-source information of a flood disaster in a deep shaft engineering;
FIG. 2 is a schematic diagram of a single-layer sub-model structure in an embodiment of the invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages to be solved more apparent, the following detailed description will be given with reference to the accompanying drawings and specific embodiments.
Aiming at the problem of insufficient prediction accuracy of single index evaluation of current shaft water inrush evaluation, the invention provides a multi-source information evaluation method for deep shaft engineering water inrush disaster.
As shown in fig. 1, the method comprises the steps of:
(1) Establishing a multivariate information evaluation system:
Based on factors of influence of deep shaft water inrush risk and difficulty in data acquisition, six influencing factors of structural fracture zone, fault fracture zone, water enrichment, water inrush point, water conductivity and ground stress are selected as evaluation indexes, and a multi-element information evaluation system is established;
(2) Dimensionless treatment is carried out on the original index:
because the evaluation indexes have different dimensions or evaluation standards, the evaluation indexes are nondimensionally processed according to the following table 1 in order to eradicate the generated index incoordination and facilitate calculation:
TABLE 1 different evaluation index feature values
(3) Calculating the weight of the influencing factors:
Comparing the scale criteria according to an analytic hierarchy process, and comparing two factors to obtain a judgment matrix:
the method is characterized by weighing according to a root method, and the specific process is as follows:
① Solving n times square roots of the row products of the judgment matrix: Where i=1, 2,3,4,5,6, n is the order; w is a feature vector, a ij is a numerical representation of the relative importance of A i to A j for the target element;
② Normalization: Where i=1, 2,3,4,5,6, the weight vector W is calculated i＝(0.0420,0.1216,0.2412,0.4571,0.0691,0.0691)T
Wherein W i is the influence factor weight coefficient;
the calculated weight values of the influence factors of the water inrush are shown in the following table 2:
TABLE 2 Water burst influencing factor weight
(4) Constructing a water inrush risk evaluation model:
Evaluating the water burst risk of the vertical shaft by adopting a vulnerability index method, wherein the water burst index is as follows:
Wherein: f is a water burst evaluation index; w i is the influence factor weight coefficient; f i (x, y) is a dimensionless characteristic value of the evaluation index, x, y is a vertical shaft depth coordinate, m is the number of influence factors of the evaluation index, and m=6.
Wherein, the analytic hierarchy process in the step (3) is specifically:
1) A hierarchical structure is established, and a single-level model structure (shown in fig. 2) is adopted, wherein the model consists of a target C, n evaluation elements a 1 … … An belonging to the target C and decision makers. The decision maker evaluates the n elements in the target sense, sorts the elements better and worse, makes a relative importance trade-off, and compares the better and worse degrees of the two elements.
And (3) carrying out relative comparison between every two elements by adopting a 1-9 scale method (see table 3), constructing a judgment matrix A= (a ij) max, calculating, and solving characteristic roots of the judgment matrix A. AW=lambada max W, calculating the maximum characteristic root lambada max, finding out the corresponding characteristic vector W, namely the ordering weight of each factor of the same layer corresponding to the relative importance of a certain factor of the previous layer, and then carrying out consistency test.
Table 3 comparative scale
And constructing a judgment matrix according to the comparison of every two elements of the comparison scale. In the single-layer structure model, the target element is assumed to be C, and has a dominant relationship with the related element A 1…An connected with the target element. The decision matrix is constructed by inquiring the decision maker about the comparison of the quality of the element A under the principle C by taking the target element C of the above layer as the criterion.
Table 4 judgment matrix
Where a ij represents the numerical representation of the relative importance of A i to A j for C, typically a ij can be scaled by 1, 2, … …,9 and their reciprocal.
2) Calculation of λmax and W
Generally, a power method or a root method can be adopted. The root method comprises the following calculation steps:
i.A elements are multiplied by rows;
respectively dividing the obtained products by the power of n;
normalizing the square root vector to obtain a sequencing weight W;
Calculating λmax according to the following formula
3) Consistency check of judgment matrix
I. Computing a consistency index CI
Wherein: n represents the order of the average judgment matrix.
Calculation of the coherence ratio CR
Wherein: RI represents the average random uniformity index, looked up by table 5:
table 5 evaluation of random uniformity index
When CR <0.1, the consistency of the judgment matrix is generally considered acceptable.
After the weight value of each influence factor of the water inrush is obtained in the step (3), the maximum characteristic value is further calculated:
Wherein lambda max is the maximum eigenvalue, A is the judgment matrix, W is the eigenvector, and lambda max = 6.1597 is obtained by calculation.
And further, consistency test is carried out:
Consistency index Calculating a consistency ratioThe evaluation factor weight calculated by the judgment matrix is reasonable according to the requirements.
The method solves the problems of lack and low accuracy of the current vertical shaft water burst prediction method. Compared with the existing geological analysis and drilling analysis technology, the method provided by the invention adopts a more mature and effective technology, solves the accidental error in the single factor analysis process, provides a more comprehensive and accurate analysis method, provides a reliable basis for construction decision, and has stronger practicability.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the present invention.
Claims (2)
1. A deep shaft engineering water inrush disaster multisource information evaluation method is characterized in that: the method comprises the following steps:
(1) Establishing a multivariate information evaluation system:
Based on factors of influence of deep shaft water inrush risk and difficulty in data acquisition, six influencing factors of structural fracture zone, fault fracture zone, water enrichment, water inrush point, water conductivity and ground stress are selected as evaluation indexes, and a multi-element information evaluation system is established;
(2) Dimensionless treatment is carried out on the original index:
For easy calculation, the evaluation index is subjected to dimensionless treatment according to the following table:
Different evaluation index feature values
(3) Calculating the weight of the influencing factors:
Comparing the scale criteria according to an analytic hierarchy process, and comparing two factors to obtain a judgment matrix:
the method is characterized by weighing according to a root method, and the specific process is as follows:
① Solving n times square roots of the row products of the judgment matrix: Where i=1, 2,3,4,5,6, n is the order; w is a feature vector, a ij is a numerical representation of the relative importance of A i to A j for the target element;
② Normalization: Where i=1, 2,3,4,5,6, the weight vector W is calculated i＝(0.0420,0.1216,0.2412,0.4571,0.0691,0.0691)T
Wherein W i is the influence factor weight coefficient;
the weight values of the influence factors of the water inrush are calculated as shown in the following table:
Water inrush influence factor weight
(4) Constructing a water inrush risk evaluation model:
Evaluating the water burst risk of the vertical shaft by adopting a vulnerability index method, wherein the water burst index is as follows:
Wherein: f is a water burst evaluation index; wi is an influence factor weight coefficient; f i (x, y) is a dimensionless characteristic value of the evaluation index, x, y is a vertical shaft depth coordinate, m is the number of influence factors of the evaluation index, and m=6;
According to the evaluation model, the evaluation indexes of the stratum with different depths are obtained, a frequency statistic histogram is generated, and a Natural break point method Natural break is used for dividing the stratum into 5 sections, so that a 5-level result is obtained: high water burst risk, higher water burst risk, medium water burst risk, lower water burst risk, low water burst risk.
2. The deep shaft engineering water bursting disaster multi-source information evaluation method according to claim 1, wherein the method comprises the following steps: the method for identifying the water burst point in the step (1) comprises a fluid diffusion method and temperature anomaly area identification.
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