CN106703883B - A kind of personalized method for determining Water Inrush From Working-faces danger classes - Google Patents
A kind of personalized method for determining Water Inrush From Working-faces danger classes Download PDFInfo
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Abstract
The invention discloses a kind of personalized method for determining Water Inrush From Working-faces danger classes, belongs to safe coal exploitation field.This method considers coal-face bottom plate hydraulic pressure, impermeable layer thickness, seat earth ore deposit and presses breakoff phenomenon, water proof section rock stratum lithology combination, bottom plate artesian groundwater are original to lead the factors such as liter development phenomenon, establish the Implicitly function dependence of these influence factors and gushing water danger classes, by the catastrophe point of the interpolation analysis Implicitly function, the System of Comprehensive Evaluation for evaluating Water Inrush danger classes is established.This method considers to influence the various factors that coal mining Water Inrush threatens comprehensively, establish the functional dependencies of each influence factor and water bursting coefficient, solve conventional method only to consider water pressure and impermeable layer thickness or the problem of influence factor and water bursting coefficient functional dependencies can not be established, calculated for water bursting coefficient and provide new method.
Description
Technical field
The invention belongs to safety of coal mines production technique field, and in particular to a kind of personalized determination coal-face bottom plate is dashed forward
The method of water danger classes.
Background technology
The dangerous method of evaluation safety of coal mines exploitation Water Inrush mainly uses water inrush coefficient method at present.Water bursting coefficient meter
Calculate formula T=p/M be China's Jiaozhuo hydrogeology decisive battle in 1964 during use for reference Hungary engineer Wei Gefulunsi to every
What water thickness (T=M/p) concept proposed, p is seat earth aquifer water pressure in formula, and M is seat earth impermeable layer thickness.Should
After formula proposes, with the inundation mine area Water Inrush case such as Jiaozhuo, Feng Feng, Jingxing, Handan, Feicheng, Zibo and basic data
For foundation, T≤0.06MPa/m is calculated as safety, 0.06MPa/m ＜ T≤MPa/m are 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.
The considerably long period of history afterwards, water bursting coefficient is for instructing safety of coal mines exploitation to play an important role.
But, coal mining progressively turns to deep, and the hydraulic pressure of Ordovician karst water is stepped up, in reality. with shallow-layer coal seam resource exploitation totally
Water bursting coefficient is far longer than in exploitation《Mine geological hazards provide》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 calculation formula of various water bursting coefficients, but proposed
Adjacency coefficient is still using the adjacent value proposed in 1964.Obviously, this is ill-considered.
There are the following problems for current water bursting coefficient computational methods：
1. the factor for influenceing Spray water way considers not comprehensive
Water bursting coefficient calculation formula only accounts for hydraulic pressure and impermeable layer thickness, does not account for seat earth ore deposit and crushes bad journey
The relevant factor such as degree, water proof section rock stratum lithology combination, bottom plate artesian groundwater rising height, aquifer water well index.
2. without the functional dependencies established using machine learning method between Spray water way and influence factor
The water bursting coefficient calculation formula Consideration of early stage is few, and calculation formula is simple.The calculation formula Consideration in later stage
It is more, in the case where collecting sample is less, the functional dependencies between water bursting coefficient and influence factor can not be fitted.
What 3. water bursting coefficient provided is only the danger of gushing water, and unrelated with water inrush quantity
Current water bursting coefficient, which is can be seen that, from its calculation formula T=p/M only considers seat earth aquifer water pressure, coal
The factors such as layer water-resisting floor thickness, it is not related with actual water inrush quantity.If floor water-bearing rock is rich in actual recovery process
It is water-based 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 bottom plate and dashed 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 1：Coal-face integrated data during collection coal production, analysis maximal water*.inrush quality and a variety of influences
The correlation of factor, corresponding species influence factor is selected as the independent variable for influenceing Water Inrush danger classes；
Step 2：According to specific mine comprehensive draining ability and maximal water*.inrush quality data, personalization division 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, establishes independent variable and bottom plate
The Implicitly function dependence of gushing water danger classes；
Step 4：The vector combination x of the independent variable newly collected is substituted into step 3 to the Implicitly function established and relies on pass
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 established using step 3, to each independent variable subdivision interpolation, is dashed forward in each bottom plate
In the range of water danger classes, very big, the minimum of each independent variable are asked in reversely calculation, and it is dangerous with Water Inrush to establish each independent variable
The System of Comprehensive Evaluation of grade；
Step 6：Each independent variable and the System of Comprehensive Evaluation of Water Inrush danger classes established according to step 5,
Field personnel is to the argument data that newly collects, you can determines that Water Inrush is endangered by inquiring about System of Comprehensive Evaluation
Dangerous grade.
Preferably, in step 1：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, bottom plate artesian groundwater rising height and aquifer water well index etc.；
Analyze maximal water*.inrush quality and press destructiveness, water proof section rock stratum lithology combination, bottom 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, bottom 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, makes 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) can not be calculated, and be usedTo it
Estimated；
(4) in calculating process, training is successive value using the danger classes of regression forecasting, using the method to round 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 the vector combination x of independent variable each component are analyzed；
(2) in units of 1 percent that the difference of minimum is subtracted by maximum, subdivision interpolation；
(3) the Water Inrush danger classes of each interpolation point is asked for, danger classes now is without the processing that rounds up；
(4) ask gushing water danger classes section [0,1.5), [1.5,2.5), [2.5,3.5) when, each independent variable it is very big,
Minimum, establish 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, influences the various factors of water inrush quantity, builds
Vertical gushing water is dangerous to grade the Implicitly function dependence between influence factor, using parameter region interpolation agriculture products framework, solves
The technical barriers such as gushing water data of having determined Small samples modeling, Implicitly function application difficult so that gushing water danger classes evaluation more section
It is reasonable to learn, and meets 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.
Embodiment
The invention provides a kind of personalized method for determining Water Inrush From Working-faces danger classes.This method integrates
Consider coal-face bottom 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, establish 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, establish the comprehensive evaluation index for evaluating Water Inrush danger classes
System.This method considers the various factors that influence coal mining Water Inrush threatens comprehensively, establishes each influence factor and gushing water system
Several functional dependencies, solve conventional method and only consider water pressure and impermeable layer thickness or influence factor can not be established with dashing forward
The problem of water coefficient functional dependencies, calculated for water bursting coefficient and provide new method.
Below in conjunction with the accompanying drawings and embodiment is described in further detail to the present invention：
As shown in figure 1, a kind of be used for the personalized method for determining Water Inrush From Working-faces danger classes, according to as follows
Step is carried out：
Step 1：Coal-face maximal water*.inrush quality, water bursting coefficient, seat earth ore deposit crush during collection coal production
The integrated datas such as bad degree, water proof section rock stratum lithology combination, bottom plate artesian groundwater rising height, aquifer water well index,
The correlation that maximal water*.inrush quality presses other influence factors such as destructiveness with water bursting coefficient, seat earth ore deposit is analyzed, selection is related
Property higher influence factor as the independent variable for influenceing maximal water*.inrush quality.
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 waited
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, makes 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 established for implicit function according to
The relation of relying, can be realized 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) can not calculate, usesIt is estimated.
(4) in calculating process, training is successive value using the danger classes of regression forecasting, using the method to round up
Round.
Step 4：The Implicitly function dependence established according to step 3, 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 obtain during construction exploration, coal work.Location for not carrying out physical prospecting, can pass through three
Tie up mine mathematical model and carry out interpolative prediction, first prediction, rear adjustment.
Step 5：For convenience of live practical application, the Implicitly function dependence established 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, are established each
Independent variable and the System of Comprehensive Evaluation of Water Inrush danger classes.
Specifically：
(1) very big, the pole of each component of Water Inrush danger classes influence factor (the vector combination x of independent variable) are analyzed
Small value.
(2) in units of 1 percent that the difference of minimum is subtracted by maximum, subdivision interpolation.
(3) the Water Inrush danger classes of each interpolation point is asked for, danger classes now is without the processing that rounds up.
(4) ask gushing water danger classes section [0,1.5), [1.5,2.5), [2.5,3.5) when, each independent variable it is very big,
Minimum, establish the System of Comprehensive Evaluation of each independent variable and Water Inrush danger classes.
Step 6：It is existing according to each independent variable that step 5 is established and the System of Comprehensive Evaluation of Water Inrush danger classes
Staff is to newly collecting the data of influence factor, you can determines that Water Inrush is endangered by inquiring about System of Comprehensive Evaluation
Dangerous grade.
The present invention in step 1 propose Spray water way key be water inrush quantity, rather than just water inrush coefficient with
The ratio of impermeable layer thickness.And considered in step 1 influence the 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, bottom 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 can not 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, establishes the System of Comprehensive Evaluation of each independent variable and Water Inrush danger classes.
The present invention propose it is a kind of be used for the personalized method for determining Water Inrush From Working-faces danger classes, it is and existing
Technology is compared, and the present invention considers specific mine drainage ability, influences the various factors of water inrush quantity, establishes the dangerous grading of gushing water
Implicitly function dependence between influence factor, using parameter region interpolation agriculture products framework, it is small to solve gushing water data
The technical barriers such as sample, Implicitly function application difficult so that the evaluation of gushing water danger classes is more scientific and reasonable, meets mine
Produce reality.
Take or use for reference prior art and can be achieved in the part do not addressed in aforesaid way.
It is limitation of the present invention that described above, which is not, and the present invention is also not limited to the example above, the art
The variations, modifications, additions or substitutions that technical staff is made in the essential scope of the present invention, it should also belong to the protection of the present invention
Scope.
Claims (7)
- A kind of 1. personalized method for determining Water Inrush From Working-faces danger classes, it is characterised in that enter according to the following steps OK：Step 1：Coal-face integrated data during collection coal production, analyzes maximal water*.inrush quality and various factors Correlation, select corresponding species influence factor as influence Water Inrush danger classes independent variable；Step 2：According to specific mine comprehensive draining ability and maximal water*.inrush quality data, personalization division 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, establishes independent variable and Water Inrush The Implicitly function dependence of danger classes；Step 4：The vector combination x of the independent variable newly collected is substituted into step 3 to the Implicitly function dependence established, meter Calculation obtains Water Inrush danger classes y, then determines Water Inrush danger classes according to step 2；Step 5：The Implicitly function dependence established using step 3, to each independent variable subdivision interpolation, endangered in each Water Inrush In dangerous rate range, very big, the minimum of each independent variable are asked in reversely calculation, establish each independent variable and Water Inrush danger classes System of Comprehensive Evaluation；Step 6：According to each independent variable that step 5 is established and the System of Comprehensive Evaluation of Water Inrush danger classes, scene Staff is to the argument data that newly collects, you can determines Water Inrush danger etc. by inquiring about System of Comprehensive Evaluation Level.
- 2. a kind of personalized method for determining Water Inrush From Working-faces danger classes according to claim 1, it is special Sign is, in step 1：The coal-face integrated data includes maximal water*.inrush quality, water bursting coefficient, seat earth ore deposit and crushed Bad degree, water proof section rock stratum lithology combination, bottom 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, bottom 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.
- 3. a kind of personalized method for determining Water Inrush From Working-faces danger classes according to claim 1, it is special Sign is, 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 more than specific mine synthesis row 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.
- 4. a kind of personalized method for determining Water Inrush From Working-faces danger classes according to claim 1, it is special Sign is, 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.
- 5. a kind of personalized method for determining Water Inrush From Working-faces danger classes according to claim 4, it is special Sign is：The independent variable of foundation and the Implicitly function dependence of Water Inrush danger classes, are realized 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) can not 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 to round up It is whole.
- 6. a kind of personalized method for determining Water Inrush From Working-faces danger classes according to claim 1, it is special Sign is, in step 4：The vector combination x of independent variable is actually obtained during construction exploration, coal work；For not carrying out The location of physical prospecting, interpolative prediction is carried out by three-dimensional mine mathematical model, first prediction, rear adjustment.
- 7. a kind of personalized method for determining Water Inrush From Working-faces danger classes according to claim 1, it is special Sign is in step 5, specifically：(1) very big, the minimum of the vector combination x of independent variable each component are analyzed；(2) in units of 1 percent that the difference of minimum is subtracted by maximum, subdivision interpolation；(3) the Water Inrush danger classes of each interpolation point is asked for, danger classes now is without the processing that rounds up；(4) ask gushing water danger classes section [0,1.5), [1.5,2.5), [2.5,3.5) when, each independent variable it is very big, minimum Value, establish the System of Comprehensive Evaluation of each independent variable and Water Inrush danger classes.
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| PCT/CN2017/108619 WO2018121035A1 (en) | 2016-12-29 | 2017-10-31 | Customized method for determining coal mining face floor water inrush risk level |
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