CN106703883A - Method for determining floor water inrush danger level of coal mining working faces in personalized manner - Google Patents

Method for determining floor water inrush danger level of coal mining working faces in personalized manner Download PDF

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CN106703883A
CN106703883A CN201611243211.3A CN201611243211A CN106703883A CN 106703883 A CN106703883 A CN 106703883A CN 201611243211 A CN201611243211 A CN 201611243211A CN 106703883 A CN106703883 A CN 106703883A
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water
inrush
danger classes
water inrush
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CN106703883B (en
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卫文学
赵卫东
何明祥
彭延军
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Shandong University of Science and Technology
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Abstract

The invention discloses a method for determining floor water inrush danger level of coal mining working faces in a personalized manner, and belongs to the field of coal safety mining. Factors such as water pressures of floors of the coal mining working faces, the thicknesses of water-insulation layers, destruction phenomena of floors of coal seams due to mine pressures, lithological combinations of rock strata of water-insulation sections and original guide rise and development phenomena of floor confined underground water are comprehensively considered, implicit functional dependency relationships between the influence factors and the water inrush danger level are established, and comprehensive evaluation index systems for evaluating the floor water inrush danger level can be established by means of interpolation analysis on sudden change points of implicit functions. The method has the advantages that the various factors which can affect coal mining floor water inrush thread are comprehensively considered, the functional dependency relationships between the various influence factors and water inrush coefficients are established, accordingly, the difficult problem that only water pressures and the thicknesses of water-insulation layers are considered in the traditional method or functional dependency relationships between influence factors and water inrush coefficients cannot be established is solved, and the novel method can be provided for computing the water inrush coefficients.

Description

A kind of personalized method for determining Water Inrush From Working-faces danger classes
Technical field
The invention belongs to safety of coal mines production technique field, and in particular to a kind of personalized determination coal-face base plate is dashed forward The method of water danger classes.
Background technology
The dangerous method of safety of coal mines exploitation Water Inrush is evaluated at present mainly uses water inrush coefficient method.Water bursting coefficient meter Calculate used for reference during formula T=p/M is China's hydrogeology decisive battle of Jiaozhuo in 1964 Hungary engineer Wei Gefulunsi to every What water thickness (T=M/p) concept was proposed, p is seat earth aquifer water pressure in formula, and M is seat earth impermeable layer thickness.Should After formula is proposed, with the inundation mine area Water Inrush case such as Jiaozhuo, peak-to-peak, Jingxing, Handan, Feicheng, Zibo and basic data It is foundation, it is safety to calculate T≤0.06MPa/m, and 0.06MPa/m < T≤MPa/m is safer, and 0.1MPa/m < T are danger Danger.Water bursting coefficient reflects the most basic rule of seepage action of ground water in hydrogeology.
Considerably long period of history afterwards, water bursting coefficient plays an important role for instructing safety of coal mines to exploit. But with shallow-layer coal seam resource exploitation totally, coal mining progressively turns to deep, and the hydraulic pressure of Ordovician karst water is stepped up, in reality Water bursting coefficient is far longer than in exploitation《Mine geological hazards specify》0.1MPa/m.Although later stage someone considers Seam Floor Failure Degree, water proof section rock stratum lithology combination and other influence factors, it is proposed that the computing formula of various water bursting coefficients, but proposed The adjacent value that adjacency coefficient was still proposed using 1964.Obviously, this is ill-considered.
There are the following problems for current water bursting coefficient computational methods:
1. the factor of influence Spray water way considers not comprehensive
Water bursting coefficient computing formula only accounts for hydraulic pressure and impermeable layer thickness, does not account for seat earth ore deposit and crushes bad journey The relevant factors such as degree, water proof section rock stratum lithology combination, base plate artesian groundwater rising height, aquifer water well index.
2. the functional dependencies between Spray water way and influence factor are not set up using machine learning method
The water bursting coefficient computing formula Consideration of early stage is few, and computing formula is simple.The computing formula Consideration in later stage It is many, in the case where collecting sample is less, it is impossible to fit the functional dependencies between water bursting coefficient and influence factor.
3. what water bursting coefficient was given is only the danger of gushing water, and unrelated with water inrush quantity
Current water bursting coefficient is can be seen that from its computing formula T=p/M only consider seat earth aquifer water pressure, coal The factors such as layer water-resisting floor thickness, it doesn't matter with actual water inrush quantity.If floor water-bearing rock is rich in actual recovery process It is aqueous poor, even gushing water, because water inrush quantity is smaller, larger harm will not be also brought to coal mining working face.
The content of the invention
In view of the above-mentioned problems existing in the prior art, the present invention proposes a kind of personalized determination coal-face base plate and dashes forward The method of water danger classes.
The adopted technical solution is that:
A kind of personalized method for determining Water Inrush From Working-faces danger classes, is carried out according to the following steps:
Step one:Coal-face integrated data during coal production is collected, maximal water*.inrush quality and various influences are analyzed The correlation of factor, selects corresponding species influence factor as the independent variable of influence Water Inrush danger classes;
Step 2:According to specific mine comprehensive draining ability and maximal water*.inrush quality data, personalization divides Water Inrush danger Dangerous grade;
Step 3:The vector combination of independent variable is denoted as x, Water Inrush danger classes is denoted as y, sets up independent variable and base plate The Implicitly function dependence of gushing water danger classes;
Step 4:The vector combination x of the independent variable that will newly collect substitutes into the Implicitly function dependence set up in step 3 and closes System, is calculated Water Inrush danger classes y, then determines Water Inrush danger classes according to step 2.
The above method is further comprising the steps of:
Step 5:The Implicitly function dependence set up using step 3, it is prominent in each base plate to each independent variable subdivision interpolation In the range of water danger classes, very big, the minimum of each independent variable are asked in reversely calculation, set up each independent variable dangerous with Water Inrush The System of Comprehensive Evaluation of grade;
Step 6:Each independent variable and the System of Comprehensive Evaluation of Water Inrush danger classes set up according to step 5, Field personnel is to the argument data that newly collects, you can determine that Water Inrush is endangered by inquiring about System of Comprehensive Evaluation Dangerous grade.
Preferably, in step one:The coal-face integrated data includes maximal water*.inrush quality, water bursting coefficient, coal seam bottom Plate ore deposit pressure destructiveness, water proof section rock stratum lithology combination, base plate artesian groundwater rising height and aquifer water well index etc.; Analysis maximal water*.inrush quality presses destructiveness, water proof section rock stratum lithology combination, base plate pressure-bearing with water bursting coefficient, seat earth ore deposit respectively The correlation of the various factors such as underground water rising height and aquifer water well index, and phase is selected according to correlation size Species influence factor such as seat earth ore deposit pressure destructiveness, base plate artesian groundwater rising height etc. are answered as influence Water Inrush The independent variable of danger classes.
Preferably, in step 2:Water Inrush dangerous grade classification is safe, relatively hazardous, dangerous Three Estate;Maximum is prominent Water is set to safe class 1 less than or equal to specific mine comprehensive draining ability 2/3rds;Maximal water*.inrush quality is more than specific ore deposit Mountain comprehensive draining ability 2/3rds and it is set to relatively hazardous grade 2 less than or equal to specific mine comprehensive draining ability;Maximum is prominent Water is set to danger classes 3 more than specific mine comprehensive draining ability.
Preferably, in step 3:The n observation sample (x according to known to specific mine1,y1),(x2,y2)……(xn,yn) Optimal function f (x, a ω are asked in some functions { f (x, ω) }0), unknown dependence is estimated, make formula R (ω)=∫ Expected risk shown in L (y, f (x, ω)) dF (x, y) is minimum.
In above-mentioned steps three, the independent variable of foundation and the Implicitly function dependence of Water Inrush danger classes, by machine The realization of the device theories of learning, specifically:
(1) { f (x, ω) } is anticipation function collection, and ω is referred to as Generalized Parameters, and L (y, f (x, ω)) is loss function;
(2) loss function uses L (y, f (x, ω))=(y-f (x, ω))2
(3) utilizable information only has sample data in the training process, it is therefore desirable for risk
R (ω)=∫ L (y, f (x, ω)) dF (x, y) cannot be calculated, and be usedTo it Estimated;
(4) in calculating process, training is successive value using the danger classes of regression forecasting, the method using rounding up Round.
Preferably, in step 4:The vector combination x of independent variable is actually obtained during construction exploration, coal work;It is right In the location for not carrying out physical prospecting, interpolative prediction is carried out by three-dimensional mine mathematical model, first prediction, rear adjustment.
Preferably, in step 5 specifically:
(1) very big, the minimum of each component of the vector combination x of analysis independent variable;
(2) subtracted by maximum minimum difference 1 percent in units of, subdivision interpolation;
(3) the Water Inrush danger classes of each interpolation point is asked for, danger classes now does not carry out the treatment that rounds up;
(4) ask gushing water danger classes it is interval [0,1.5), [1.5,2.5), [2.5,3.5) when, each independent variable it is very big, Minimum, sets up the System of Comprehensive Evaluation of each independent variable and Water Inrush danger classes.
The method have the benefit that:
Compared with prior art, the present invention considers specific mine drainage ability, the various factors of influence water inrush quantity, builds Implicitly function dependence between the dangerous grading of vertical gushing water and influence factor, using parameter region interpolation agriculture products framework, solution The technical barriers such as gushing water data of having determined Small samples modeling, Implicitly function application difficult so that gushing water danger classes evaluates more section Learn reasonable, meet mining production reality.
Brief description of the drawings
Fig. 1 is a kind of personalized flow chart for determining Water Inrush From Working-faces danger classes method of the present invention.
Specific embodiment
The invention provides a kind of personalized method for determining Water Inrush From Working-faces danger classes.The method synthesis Consider coal-face base plate hydraulic pressure, impermeable layer thickness, seat earth ore deposit pressure breakoff phenomenon, water proof section rock stratum lithology combination, bottom Plate artesian groundwater is original to lead the factors such as liter development phenomenon, set up the Implicitly function of these influence factors and gushing water danger classes according to The relation of relying, by the catastrophe point of the interpolation analysis Implicitly function, sets up the comprehensive evaluation index for evaluating Water Inrush danger classes System.The method considers the various factors for influenceing coal mining Water Inrush to threaten comprehensively, sets up each influence factor and gushing water system Several functional dependencies, solve conventional method and only consider water pressure and impermeable layer thickness or cannot set up influence factor and dash forward The problem of water coefficient functional dependencies, new method is provided for water bursting coefficient is calculated.
Below in conjunction with the accompanying drawings and specific embodiment is described in further detail to the present invention:
As shown in figure 1, a kind of method that Water Inrush From Working-faces danger classes is determined for personalization, according to as follows Step is carried out:
Step one:Coal-face maximal water*.inrush quality, water bursting coefficient, seat earth ore deposit are crushed during collection coal production The integrated datas such as bad degree, water proof section rock stratum lithology combination, base plate artesian groundwater rising height, aquifer water well index, Analysis maximal water*.inrush quality presses the correlation of other influence factors such as destructiveness with water bursting coefficient, seat earth ore deposit, and selection is related Property influence factor higher as influence maximal water*.inrush quality independent variable.
Step 2:According to specific mine comprehensive draining ability and maximal water*.inrush quality data, personalization divides each group of data Water Inrush danger classes.Generally it is divided into safe, relatively hazardous, dangerous Three Estate.Maximal water*.inrush quality is less than or waits It is set to safe class 1 in specific mine comprehensive draining ability 2/3rds;Maximal water*.inrush quality is more than specific mine comprehensive draining energy Power 2/3rds and it is set to relatively hazardous grade 2 less than specific mine comprehensive draining ability;Maximal water*.inrush quality is more than or equal to specific Mine comprehensive draining ability is set to danger classes 3.
Step 3:The vector combination of independent variable is denoted as x, Water Inrush danger classes is denoted as y.According to known to specific mine N observation sample (x1,y1),(x2,y2)……(xn,yn) asked in some functions { f (x, ω) } an optimal function f (x, ω0), unknown dependence is estimated, make expected risk shown in formula R (ω)=∫ L (y, f (x, ω)) dF (x, y) most It is small.
The functional dependencies of each influence factor and Water Inrush danger classes that step 3 is set up be implicit function according to The relation of relying, can realize by machine Learning Theories such as SVMs theories.
Specifically:
(1) { f (x, ω) } is anticipation function collection, and ω is referred to as Generalized Parameters, and L (y, f (x, ω)) is loss function.
(2) loss function uses L (y, f (x, ω))=(y-f (x, ω))2
(3) utilizable information only has sample data in the training process, it is therefore desirable for risk R (ω)=∫ L (y, f (x, ω)) dF (x, y) cannot calculate, usesIt is estimated.
(4) in calculating process, training is successive value using the danger classes of regression forecasting, the method using rounding up Round.
Step 4:According to the Implicitly function dependence that step 3 is set up, the data vector to newly collecting influence factor That is the vector combination x of independent variable, you can substitute into Implicitly function and calculate its Water Inrush danger classes.Water Inrush danger classes shadow The factor of sound can actually be obtained during construction exploration, coal work.For the location for not carrying out physical prospecting, can be by three Dimension mine mathematical model carries out interpolative prediction, first prediction, rear adjustment.
Step 5:For convenience of live practical application, the Implicitly function dependence set up using step 3, to each independent variable Subdivision interpolation, in the range of each Water Inrush danger classes, very big, the minimum of each independent variable are asked in reversely calculation, set up each The System of Comprehensive Evaluation of independent variable and Water Inrush danger classes.
Specifically:
(1) very big, the pole of each component of analysis Water Inrush danger classes influence factor (the vector combination x of independent variable) Small value.
(2) subtracted by maximum minimum difference 1 percent in units of, subdivision interpolation.
(3) the Water Inrush danger classes of each interpolation point is asked for, danger classes now does not carry out the treatment that rounds up.
(4) ask gushing water danger classes it is interval [0,1.5), [1.5,2.5), [2.5,3.5) when, each independent variable it is very big, Minimum, sets up the System of Comprehensive Evaluation of each independent variable and Water Inrush danger classes.
Step 6:Each independent variable and the System of Comprehensive Evaluation of Water Inrush danger classes set up according to step 5, it is existing Staff is to newly collecting the data of influence factor, you can determine that Water Inrush is endangered by inquiring about System of Comprehensive Evaluation Dangerous grade.
What the present invention proposed Spray water way in step one it is critical only that water inrush quantity, and not exclusively water inrush coefficient with The ratio of impermeable layer thickness.And considered in step one the influence water bursting coefficient of water inrush quantity, seat earth ore deposit crush it is bad The factors such as degree, water proof section rock stratum lithology combination, base plate artesian groundwater rising height, aquifer water well index.
According to the personalized division mine coal-face gushing water danger classes of specific mine drainage ability in step 2, and It is not all mine sudden flooding danger classes of universal formulation.
Under conditions of it cannot provide gushing water influence factor and maximal water*.inrush quality explicit function dependence, in step 3 The functional dependencies of maximal water*.inrush quality and each influence factor are described using Implicitly function dependence, the functional dependencies will Ask:For data sample (x1,y1),(x2,y2),(x3,y3)……(xn,yn), ask one most in some functions { f (x, ω) } Major function f (x, ω0), unknown dependence is estimated so that the expected risk shown in following formula is minimum.
R (ω)=∫ L (y, f (x, ω)) dF (x, y)
To each influence factor subdivision interpolation in step 5, in the range of each Water Inrush danger classes, reversely calculation is asked Very big, the minimum of each independent variable are taken, the System of Comprehensive Evaluation of each independent variable and Water Inrush danger classes is set up.
The present invention proposes a kind of method that Water Inrush From Working-faces danger classes is determined for personalization, and existing Technology is compared, and the present invention considers specific mine drainage ability, the various factors of influence water inrush quantity, sets up the dangerous grading of gushing water Implicitly function dependence between influence factor, using parameter region interpolation agriculture products framework, solve gushing water data small The technical barriers such as sample, Implicitly function application difficult so that gushing water danger classes evaluates more scientific and reasonable, meets mine Produce reality.
Take or use for reference prior art to be capable of achieving in the part do not addressed in aforesaid way.
Described above is not limitation of the present invention, and the present invention is also not limited to the example above, the art Change, remodeling, addition or replacement that technical staff is made in essential scope of the invention, should also belong to protection of the invention Scope.

Claims (8)

1. a kind of method that personalization determines Water Inrush From Working-faces danger classes, it is characterised in that enter according to the following steps OK:
Step one:Coal-face integrated data during coal production is collected, maximal water*.inrush quality and various factors are analyzed Correlation, select corresponding species influence factor as the independent variable of influence Water Inrush danger classes;
Step 2:According to specific mine comprehensive draining ability and maximal water*.inrush quality data, personalization divides Water Inrush danger etc. Level;
Step 3:The vector combination of independent variable is denoted as x, Water Inrush danger classes is denoted as y, sets up independent variable and Water Inrush The Implicitly function dependence of danger classes;
Step 4:The vector combination x of the independent variable that will newly collect substitutes into the Implicitly function dependence set up in step 3, meter Calculation obtains Water Inrush danger classes y, then determines Water Inrush danger classes according to step 2.
2. the method that a kind of personalization according to claim 1 determines Water Inrush From Working-faces danger classes, it is special It is further comprising the steps of to levy:
Step 5:The Implicitly function dependence set up using step 3, to each independent variable subdivision interpolation, in each Water Inrush danger In dangerous rate range, very big, the minimum of each independent variable are asked in reversely calculation, set up each independent variable and Water Inrush danger classes System of Comprehensive Evaluation;
Step 6:Each independent variable and the System of Comprehensive Evaluation of Water Inrush danger classes set up according to step 5, scene Staff is to the argument data that newly collects, you can determine Water Inrush danger etc. by inquiring about System of Comprehensive Evaluation Level.
3. the method that a kind of personalization according to claim 1 determines Water Inrush From Working-faces danger classes, it is special Levy and be, in step one:The coal-face integrated data includes that maximal water*.inrush quality, water bursting coefficient, seat earth ore deposit are crushed Bad degree, water proof section rock stratum lithology combination, base plate artesian groundwater rising height and aquifer water well index;Analysis is maximum prominent Water leads liter with water bursting coefficient, seat earth ore deposit pressure destructiveness, water proof section rock stratum lithology combination, base plate artesian groundwater respectively The correlation of height and aquifer water well index, and corresponding species influence factor is selected as influence bottom according to correlation size The independent variable of plate gushing water danger classes.
4. the method that a kind of personalization according to claim 1 determines Water Inrush From Working-faces danger classes, it is special Levy and be, in step 2:Water Inrush dangerous grade classification is safe, relatively hazardous, dangerous Three Estate;Maximal water*.inrush quality is less than Or it is set to safe class 1 equal to specific mine comprehensive draining ability 2/3rds;Maximal water*.inrush quality is comprehensively arranged more than specific mine Outlet capacity 2/3rds and it is set to relatively hazardous grade 2 less than or equal to specific mine comprehensive draining ability;Maximal water*.inrush quality is more than Specific mine comprehensive draining ability is set to danger classes 3.
5. the method that a kind of personalization according to claim 1 determines Water Inrush From Working-faces danger classes, it is special Levy and be, in step 3:The n observation sample (x according to known to specific mine1,y1),(x2,y2)……(xn,yn) in some functions Optimal function f (x, a ω are asked in { f (x, ω) }0), unknown dependence is estimated, make formula R (ω)=∫ L (y, f (x, ω)) expected risk shown in dF (x, y) is minimum.
6. the method that a kind of personalization according to claim 5 determines Water Inrush From Working-faces danger classes, it is special Levy and be:The independent variable of foundation and the Implicitly function dependence of Water Inrush danger classes, realize by machine Learning Theory, Specifically:
(1) { f (x, ω) } is anticipation function collection, and ω is referred to as Generalized Parameters, and L (y, f (x, ω)) is loss function;
(2) loss function uses L (y, f (x, ω))=(y-f (x, ω))2
(3) utilizable information only has sample data in the training process, it is therefore desirable for risk R (ω)=∫ L (y, f (x, ω)) dF (x, y) cannot be calculated, and be usedIt is estimated;
(4) in calculating process, training is successive value using the danger classes of regression forecasting, is taken using the method for rounding up It is whole.
7. the method that a kind of personalization according to claim 1 determines Water Inrush From Working-faces danger classes, it is special Levy and be, in step 4:The vector combination x of independent variable is actually obtained during construction exploration, coal work;For do not carry out The location of physical prospecting, interpolative prediction is carried out by three-dimensional mine mathematical model, first prediction, rear adjustment.
8. the method that a kind of personalization according to claim 1 determines Water Inrush From Working-faces danger classes, it is special Levy in being step 5, specifically:
(1) very big, the minimum of each component of the vector combination x of analysis independent variable;
(2) subtracted by maximum minimum difference 1 percent in units of, subdivision interpolation;
(3) the Water Inrush danger classes of each interpolation point is asked for, danger classes now does not carry out the treatment that rounds up;
(4) ask gushing water danger classes it is interval [0,1.5), [1.5,2.5), [2.5,3.5) when, each independent variable it is very big, minimum Value, sets up the System of Comprehensive Evaluation of each independent variable and Water Inrush danger classes.
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