CN112380691A - Method for evaluating risk of water inrush during mining under loose confined aquifer - Google Patents
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Abstract
The invention provides a method for evaluating the danger of water inrush during mining under a loose confined aquifer, which comprises the following steps: step A: primarily screening evaluation factors; and B: establishing an evaluation factor database; and C: importing the evaluation factor database into ArcGIS to obtain a thematic map of each evaluation factor; step D: calculating the information quantity corresponding to each grade of each evaluation factor in each thematic map to obtain the information quantity evaluation result of the single evaluation factor; step E: sequencing all factors according to the fitting accuracy of the information quantity evaluation result of the single evaluation factor from high to low, and sequentially performing superposition calculation of the total information quantity to obtain an information quantity evaluation model of the combined evaluation factor; step F: sequencing the fitting precision of each evaluation model to obtain an optimal evaluation factor combination; step G: and carrying out danger zoning on the water inrush during mining under the loose confined aquifer by using the optimized information quantity evaluation model. By applying the embodiment of the invention, the accuracy of the water inrush risk evaluation is improved.
Description
Technical Field
The invention relates to the technical field of coal mine water prevention and control, in particular to a method for evaluating the water inrush risk of mining under a loose confined aquifer.
Background
The coal-series stratum of the hidden coal field in North China is generally covered by a thick loose layer, and the bottom of the loose layer is provided with a confined aquifer which takes gravel and non-cemented sandy soil and the like as frameworks. Along with the expansion of coal mining scale and the improvement of mining upper limit, mining water burst accidents under a loose confined aquifer frequently occur, which not only leads to the stagnation of coal mining production work, but also causes the injury and death of underground workers. The water damage of mining under a loose confined aquifer is usually solved by means of measures of reserving waterproof coal rock pillars according to the rules of reserving coal pillars and pressing coal mining for buildings, water bodies, railways and main roadways (hereinafter referred to as the rules). However, the calculation formula of the height of the waterproof coal rock pillar given by the regulation only considers the overburden lithology and the coal seam mining thickness, and even some waterproof coal rock pillars reserved in the coal mine according to the regulation are too conservative, but water inrush disasters still occur unpredictably. The reason is considered, whether water inrush disasters occur under a loose confined aquifer is determined, and hydrogeology and engineering geological conditions of the aquifer are comprehensively considered. Therefore, the evaluation factors are reasonably selected, the risk of water inrush during mining under a loose confined aquifer is accurately evaluated, and the method is a problem to be solved urgently in coal mine safe and efficient production.
In the prior art, the main methods for exploiting the water inrush danger subarea under the loose confined aquifer comprise a numerical simulation method, a physical simulation method, a three-diagram-double prediction method, a machine learning method and the like. The zoning evaluation method for the water inrush risk during the water cut under the condition of loose bearing pressure considers the simple superposition of one or more evaluation factors, and can not reflect the real water inrush risk; moreover, the selection of the evaluation factor is also different from person to person, and is subjective, which further causes the evaluation result of the water inrush risk to deviate from the actual water inrush situation. Therefore, the existing evaluation method does not consider the optimization selection of the evaluation factors, and finally the accuracy of the established evaluation model is influenced.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method for optimizing and selecting evaluation factors so as to more accurately evaluate the mining water inrush risk under a loose confined aquifer in a subarea manner.
The invention solves the technical problems through the following technical means:
the invention discloses a method for evaluating the risk of water inrush during mining under a loose confined aquifer, which is based on an information quantity method and ArcGIS software and specifically comprises the following steps:
step A: primarily screening evaluation factors, wherein the evaluation factors at least comprise four types of loose confined aquifer characteristics, bedrock lithology, geological structure and mining influence;
and B: collecting geological drilling data and geological map information of a coal mine, and establishing an evaluation factor database;
and C: importing an evaluation factor database into ArcGIS software, and grading each evaluation factor to obtain a thematic map of each evaluation factor;
step D: calculating the information quantity corresponding to each grade of each evaluation factor in each thematic map to obtain the information quantity evaluation result of the single evaluation factor;
step E: sequencing all factors according to the fitting accuracy of the information quantity evaluation result of the single evaluation factor from high to low, and sequentially performing superposition calculation of the total information quantity to obtain an information quantity evaluation model of the combined evaluation factor;
step F: sequencing the fitting precision of each combined evaluation factor information quantity evaluation model to obtain an optimal evaluation factor combination;
step G: and (4) carrying out danger zoning on the water inrush under the loose confined aquifer by combining the ArcGIS grid data model and the information quantity evaluation model after the evaluation factor optimization.
Optionally, the characteristics of the loose confined aquifer in the evaluation factors include: one or a combination of the thickness of the loose layer, the thickness of the loose confined aquifer, the unit water inflow of the aquifer, the load transmission coefficient of the aquifer and the water pressure of the aquifer;
the lithology of the bedrock comprises one or a combination of the thickness of the bedrock, the thickest sandstone ratio of the bedrock, the brittle plastic rock thickness ratio of the bedrock and the thickness of a weathered zone of the bedrock;
the geological structure comprises one or a combination of a distance to fault and a fault density;
the mining effect includes the height of development of the water-conducting fractured zone.
Optionally, step B includes:
b1: a formula for calculating the development height of the water flowing fractured zone:
in the formula, HiThe development height of the water flowing fracture is shown; and M is the thickness of the coal bed.
B2: the aquifer load transfer coefficient calculation formula is as follows:
kz＝[γwH′+k(γH+γsath-γwH′)]/γH
in the formula, kzThe aquifer load transfer coefficient; h is the thickness of the topsoil layer; gamma is the average volume weight of the surface soil layer; h' is the height of the aquifer pressure measuring head; gamma raysatThe pore level mean volume weight; h is the thickness of the loose bearing water-containing layer; gamma raywIs the average bulk weight of the aquifer; k is the effective stress transfer coefficient.
Optionally, step D includes:
d1: and processing each evaluation factor thematic layer by using the functions of ArcGIS (geographic information System) coordinate projection, picture vectorization, grid calibration and the like, and converting each thematic layer into a grid layer with the same projection coordinate.
D2: and (4) carrying out cell division on each grid layer by using a reclassification tool of ArcGIS, and counting the number of the cells under each grade of the evaluation factors and the number of the corresponding water inrush point cells.
D3: computing formula using information amount
And calculating the information amount of each grade of the single evaluation factor, wherein,
Iigrading x for evaluation factorsiThe amount of information of (a); n is a radical ofiGrading x for evaluation factorsiThe number of water inrush point units in the unit (d); n is the total number of water inrush point units in the research area; siThe number of units containing evaluation factors xi in the research area; s is the total unit number of the research area.
Optionally, step E includes:
e1: and (3) sequencing the fitting degrees of all the evaluation factors according to a success rate Curve method, wherein the horizontal axis represents the percentage of the Area of the information quantity values from high to low in the evaluation result, the vertical axis represents the percentage of the water inrush disaster occurring in the corresponding information quantity values, and the larger the Area Under the line (AUC) of the success rate Curve is, the higher the fitting precision of the evaluation factors is.
E2: and assigning the cells of the raster image layers of the single evaluation factors according to the corresponding information quantity by applying the reclassification function of ArcGIS.
E3: and sequentially arranging and combining the single evaluation factors according to the fitting precision to obtain a plurality of combined evaluation factors, superposing each single evaluation factor raster image layer by applying ArcGIS to each combined evaluation factor to obtain a combined evaluation factor information amount superposition image, and taking the evaluation factor information amount superposition image as a combined evaluation factor information amount evaluation model.
Optionally, the step E1 includes:
the vertical axis represents the proportion of water inrush disasters in the corresponding information values to the total water inrush frequency, the horizontal axis represents the area percentage of the information values from high to low in the evaluation results, and a success rate curve graph is drawn.
Optionally, the step E3 includes:
by means of the formula (I) and (II),
the information amount of the combined evaluation factor is calculated, wherein,
Iigrading x for evaluation factorsiThe amount of information of (a); i is the total information content of a certain unit in the evaluation area; n is a radical ofiGrading x for evaluation factorsiThe number of water inrush point units in the unit (d); n is the total number of water inrush point units in the research area; siGrading x for evaluation factors in the study areaiThe number of units of (a); s is the total unit number of the research area; n is the total number of evaluation factors.
Optionally, step F includes:
f1: uniformly dividing the information quantity superposition graph of the combined evaluation factor into 5 grades according to the information quantity by using the natural breakpoint method of ArcGIS;
f2: counting the number of units under each grade of the combined evaluation factor information amount superposed graph and the corresponding number of units of the water inrush points;
f3: and drawing a success rate curve graph according to a success rate curve method, sequencing the fitting accuracy of each combination evaluation factor, and determining the best evaluation factor combination.
Optionally, the step G includes:
and (3) partitioning an information amount superposition graph under the optimal factor combination according to the information amount by using a natural breakpoint method in ArcGIS, wherein the partitioned graph comprises a water inrush mining dangerous area, a medium dangerous area, a safe area and a safe area under a loose confined aquifer.
The invention has the advantages that:
the engineering geology and hydrogeology conditions of the North China coal field are complex, and whether the evaluation factor is selected reasonably can greatly influence the accuracy of the risk zoning evaluation of water inrush under the loose confined aquifer. According to the method, an information quantity model and ArcGIS software are combined, the evaluation factors are optimized and selected before a loose confined aquifer water inrush model is built, and the evaluation model built based on the optimal factor combination directly carries out water inrush risk regional evaluation compared with the evaluation model built by artificially selecting the evaluation factors in the prior art, so that the objectivity of evaluation factor selection in the process of model building is improved, and test data shows that the method is effective; therefore, the method and the device improve the accuracy of the risk evaluation of water inrush during mining under the loose confined aquifer.
Drawings
FIG. 1 is a schematic flow chart of a method for evaluating the risk of water inrush during mining in a loose confined aquifer according to an embodiment of the invention;
FIG. 2 is a diagram of a result of ordering of fitting accuracy of a single-evaluation-factor information quantity model provided by an embodiment of the present invention;
FIG. 3 is a diagram of a result of ranking of fitting accuracy of a combined evaluation factor information quantity model according to an embodiment of the present invention;
FIG. 4 is a diagram showing the evaluation result distribution of the risk of water inrush during mining under a loose confined aquifer according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to solve the problem that the evaluation result of the water inrush risk in the prior art is not accurate enough due to the fact that the optimization selection of the evaluation factors is not considered in the prior art and the subjectivity of the selection of the artificial evaluation factors is added, and the final partitioning result is close to the actual water inrush situation as much as possible, an evaluation model capable of optimizing and selecting the evaluation factors is urgently needed at present. Therefore, the applicant proposes a flow chart of a method for evaluating the risk of water inrush during mining under a loose confined aquifer, as shown in fig. 1, wherein the method comprises the following steps:
s101: primarily screening evaluation factors, wherein the evaluation factors at least comprise four types of loose confined aquifer characteristics, bedrock lithology, geological structure and mining influence;
the evaluation factors primarily screened in the step are all evaluation factors which can influence the water inrush risk evaluation result of the loose confined aquifer, and specifically, the characteristics of the loose confined aquifer in the evaluation factors comprise: one or a combination of the thickness of the loose layer, the thickness of the loose confined aquifer, the unit water inflow of the aquifer, the load transmission coefficient of the aquifer and the water pressure of the aquifer;
the lithology of the bedrock comprises one or a combination of the thickness of the bedrock, the thickest sandstone ratio of the bedrock, the brittle plastic rock thickness ratio of the bedrock and the thickness of a weathered zone of the bedrock;
the geological structure comprises one or a combination of a distance to fault and a fault density;
the mining effect includes the height of development of the water-conducting fractured zone.
S102: and collecting geological drilling data and geological map information of the coal mine, and establishing an evaluation factor database.
In an exemplary case of a Kendong coal mine, in the evaluation factors selected in the embodiment of the invention, the thickness of a loose layer, the thickness of a loose confined water layer, the unit water inflow of an aquifer, the water pressure of the aquifer, the thickness of a bed rock, the thickest sandstone ratio of the bed rock, the thickness ratio of brittle plastic rock of the bed rock, the thickness of a weathering zone of the bed rock, the distance from a fault and the fault density can be accurately calculated by collecting geological drilling data and geological map information of the Kendong coal mine.
In addition, the development height of the water flowing fracture can be calculated according to a water flowing fracture zone height formula in evaluation factors given by geophysical prospecting, drilling or rules for reserving and pressing coal for buildings, water bodies, railways and main roadway coal pillars:
in the formula, HiThe development height of the water flowing fracture is shown; and M is the thickness of the coal bed.
And the aquifer load transfer coefficient in the evaluation factor is the ratio of the loads of the base rock top interface and the aquifer top interface, and the adopted calculation formula is as follows:
kz＝[γwH′+k(γH+γsath-γwH′)]/γH
in the formula, kzThe aquifer load transfer coefficient; h is the thickness of the topsoil layer; gamma is the average volume weight of the surface soil layer; h' is the height of the aquifer pressure measuring head; gamma raysatThe pore level mean volume weight; h is the thickness of the loose bearing water-containing layer; gamma raywIs the average bulk weight of the aquifer; k is the effective stress transfer coefficient.
S103: and importing the evaluation factor database into ArcGIS software, and grading each evaluation factor to obtain a thematic map of each evaluation factor.
And uniformly dividing each evaluation factor into 5 grades by applying a natural breakpoint method in ArcGIS.
It should be noted that, a process of classifying data by using the natural breakpoint method in ArcGIS is the prior art, and the embodiment of the present invention is not described herein again.
S104: and calculating the information quantity corresponding to each grade of each evaluation factor in each thematic map to obtain the information quantity evaluation result of the single evaluation factor.
D1: aiming at the thematic map of each evaluation factor, firstly, the thematic map layers of each evaluation factor are processed by sequentially applying the functions of ArcGIS coordinate projection, picture vectorization, grid calibration and the like, and each thematic map layer is converted into a grid map layer with the same projection coordinate.
D2: and (4) carrying out cell division on each grid layer by using a reclassification tool of ArcGIS, and counting the number of the cells under each grade of the evaluation factors and the number of the corresponding water inrush point cells.
It can be understood that the D1-D2 process is implemented by the own function of ArcGIS software.
D3: and (3) calculating a formula by using the information amount of the single evaluation factor:
Iigrading x for evaluation factorsiThe amount of information of (a); ln is a logarithmic function with natural index as the base; n is a radical ofiGrading x for evaluation factorsiThe number of water inrush point units in the unit (d); n is water inrush point sheet in research areaThe total number of elements; siThe number of units containing evaluation factor grades xi in the research area; s is the total unit number of the research area.
Table 1 shows the calculation results of the evaluation factor information amounts of a certain coal mine in a qidong coal mine:
TABLE 1
S105: sequencing the fitting degree of each evaluation factor according to the fitting precision of the single evaluation factor model from high to low, and sequentially performing superposition calculation of total information quantity to obtain a combined evaluation factor information quantity model;
e1: fig. 2 is a diagram of a result of ranking the fitting accuracy of the single-evaluation-factor information quantity model according to an embodiment of the present invention, and as shown in fig. 2, the fitting degrees of the evaluation factors are ranked according to a success rate Curve method, where the horizontal axis represents the percentage of Area of the information quantity values from high to low in the evaluation result, the vertical axis represents the percentage of water inrush disaster in the coal mine occurring in the corresponding information quantity values, and the greater the Area Under the line (AUC) of the success rate Curve, the higher the fitting accuracy of the single evaluation factor.
E2: and assigning the cells of each raster layer according to the corresponding information quantity by applying a reclassification function of ArcGIS software. This value is assigned to the result calculated in step S104.
E3: and sequentially arranging and combining the single evaluation factors according to the fitting precision to obtain a plurality of combined evaluation factors, superposing each single evaluation factor raster image layer by applying ArcGIS to each combined evaluation factor to obtain a combined evaluation factor information amount superposition image, and taking the evaluation factor information amount superposition image as a combined evaluation factor information amount evaluation model.
In particular, a formula may be used,
calculating the information quantity of the combined evaluation factor, namely obtaining a combined evaluation factor information quantity evaluation model, wherein,
Iigrading x for evaluation factorsiThe amount of information of (a); i is the total information content of a certain unit in the evaluation area; n is a radical ofiGrading x for evaluation factorsiThe number of water inrush point units in the unit (d); n is the total number of water inrush point units in the research area; siGrading x for evaluation factors in the study areaiThe number of units of (a); s is the total unit number of the research area; n is the total number of evaluation factors.
S106: and sequencing the fitting precision of the information quantity models of the evaluation factors of all the combinations to obtain the best evaluation factor combination.
F1: and uniformly dividing the information quantity superposed graph of the combined evaluation factor into 5 grades according to the information quantity by using a natural breakpoint method in ArcGIS. It should be emphasized that the specific classification process is prior art, and the embodiment of the present invention is not described herein.
F2: and counting the number of units under each grade of the combined evaluation factor information quantity superposed graph and the corresponding number of units of the water inrush point.
F3: and drawing a success rate curve graph according to a success rate curve method, sequencing the fitting precision of each combination evaluation factor, and determining the best evaluation factor combination, wherein the success rate, namely the fitting precision, is the accuracy of the number of the water inrush point units predicted in the step F2.
Fig. 3 is a diagram showing the result of ranking the fitting accuracy of the combined evaluation factor information quantity model according to the embodiment of the present invention, and as shown in fig. 3, the fitting accuracy of each combined evaluation factor is ranked according to the success rate curve method, and the factor combination with the largest area under the line (AUC) of the success rate curve is the best evaluation factor combination. It can be seen from fig. 3 that when the evaluation factors involved in the final information quantity calculation are less than 9, the fitting accuracy of the model is improved when one evaluation factor is added. When the evaluation factors involved in the final information amount calculation exceed 9, the fitting accuracy of the model starts to decrease as the evaluation factors increase. Therefore, the evaluation factors of the selection single evaluation factor AUC from high to low, the first 9 evaluation factors are considered to participate in the calculation of the final information quantity value as the optimal selection, and the fitting degree is the highest.
S107: and (4) combining the ArcGIS grid data model and the information quantity model after the evaluation factor optimization to carry out danger zoning on the water inrush during mining under the loose confined aquifer.
Fig. 4 is a diagram of a coal mining water inrush risk evaluation result distribution provided in an embodiment of the present invention, and as shown in fig. 4, by using a natural breakpoint method in ArcGIS, an information amount overlay under an optimal factor combination is partitioned into the same partition according to the size of the information amount, and the partition risk degree is higher when the information amount is larger. The zoning map comprises a water inrush danger zone, a dangerous zone, a medium danger zone, a safe zone and a safe zone below the loose confined aquifer.
Referring to 'coal mine control water regulation' issued by the national coal mine safety supervision agency in 2018, the water inrush points under the loose confined aquifer of the coal mine can be divided into three levels, namely a small water inrush point, a medium water inrush point and a large water inrush point according to the water inrush quantity Q value: small water inrush point Q less than or equal to 60m3H; middle water inrush point 60m3/h＜Q≤600m3H; the large water inrush point Q is more than 600m3/h。
Table 2 shows the results of the examples of the present invention in comparison with the actual results:
TABLE 2
As shown in table 2, statistics is performed on water inrush points under the loose confined aquifer under each danger zone level, wherein 83.78% of the water inrush points are distributed in the danger zone, and 2 large water inrush points account for 100% of the coal mine large water inrush points; 9 medium water inrush points which account for 90 percent of the medium water inrush points in the coal mine; the number of the small water inrush points is 20, and accounts for 80% of the number of the small water inrush points of the coal mine. 16.13% of water inrush points are distributed in the dangerous area, wherein 1 medium water inrush point and 4 small water inrush points respectively account for 10% and 16% of the medium water inrush point and the small water inrush point in the coal mine. The safe and safer zones have no water inrush points, with only 2.7% of the water inrush points being distributed in the medium risk zone.
The ratio of the actual water inrush point in the dangerous area to the dangerous area is up to 97.3% in the prediction, and the applicability of the method in the exploitation of the water inrush danger subarea under the loose confined aquifer is further demonstrated.
By applying the embodiment of the invention, the water inrush during the exploitation under the loose confined aquifer is controlled by various factors such as the characteristics of the loose confined aquifer, the lithology of bedrock, the geological structure and the mining influence, and the risks of water inrush disaster are different under different factors and combinations thereof. The information quantity method can analyze the influence of each evaluation factor and the combination thereof on water burst mining under the loose confined aquifer, and the larger the information quantity is, the stronger the influence of the factors and the combination thereof is, and the higher the possibility of water burst accidents is. The invention provides a new regional evaluation method for the risk of water inrush mining under a loose confined aquifer based on evaluation factor optimization by combining an information quantity method and ArcGIS software.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (9)
1. The method for evaluating the risk of water inrush during mining under a loose confined aquifer is characterized by comprising the following steps of:
step A: primarily screening evaluation factors, wherein the evaluation factors at least comprise four types of loose confined aquifer characteristics, bedrock lithology, geological structure and mining influence;
and B: collecting geological drilling data and geological map information of a coal mine, and establishing an evaluation factor database;
and C: importing an evaluation factor database into ArcGIS software, and grading each evaluation factor to obtain a thematic map of each evaluation factor;
step D: calculating the information quantity corresponding to each grade of each evaluation factor in each thematic map to obtain the information quantity evaluation result of the single evaluation factor;
step E: sequencing all factors according to the fitting accuracy of the information quantity evaluation result of the single evaluation factor from high to low, and sequentially performing superposition calculation of the total information quantity to obtain an information quantity evaluation model of the combined evaluation factor;
step F: sequencing the fitting precision of each combined evaluation factor information quantity evaluation model to obtain an optimal evaluation factor combination;
step G: and (4) combining the ArcGIS grid data model and the information quantity evaluation model after the evaluation factor optimization to carry out danger zoning on the water inrush during mining under the loose confined aquifer.
2. The method for evaluating the risk of water inrush from underground mining of a loose confined aquifer according to claim 1, wherein the characteristics of the loose confined aquifer in the evaluation factor include: one or a combination of the thickness of the loose layer, the thickness of the loose confined aquifer, the unit water inflow of the aquifer, the load transmission coefficient of the aquifer and the water pressure of the aquifer;
the lithology of the bedrock comprises one or a combination of the thickness of the bedrock, the thickest sandstone ratio of the bedrock, the brittle plastic rock thickness ratio of the bedrock and the thickness of a weathered zone of the bedrock;
the geological structure comprises one or a combination of a distance to fault and a fault density;
the mining effect includes the height of development of the water-conducting fractured zone.
3. The method for evaluating the risk of water inrush during underground mining of a unconsolidated confined aquifer according to claim 2, wherein the step B comprises:
b1: by means of the formula (I) and (II),
calculating the development height of the water flowing fractured zone, wherein,
Hithe development height of the water flowing fracture is shown; m is the thickness of the coal bed;
b2: using the formula, kz＝[γwH′+k(γH+γsath-γwH′)]Calculating the aquifer load transfer coefficient, wherein,
kzthe aquifer load transfer coefficient; h is the thickness of the topsoil layer; gamma is the average volume weight of the surface soil layer; h' is the head height of the aquiferDegree; gamma raysatThe pore level mean volume weight; h is the thickness of the loose bearing water-containing layer; gamma raywIs the average bulk weight of the aquifer; k is the effective stress transfer coefficient.
4. The method for evaluating the risk of water inrush from underground mining of a unconsolidated confined aquifer according to claim 1, wherein the step D comprises:
d1: processing each evaluation factor thematic layer by using the functions of ArcGIS (geographic information System) coordinate projection, picture vectorization, grid calibration and the like, and converting each thematic layer into a grid layer with the same projection coordinate;
d2: carrying out cell division on each grid layer by using an ArcGIS re-classification tool, and counting the number of cells under each classification of the evaluation factors and the number of corresponding water inrush point cells;
d3: computing formula using single evaluation factor information amount
And calculating the information amount of each grade of the single evaluation factor, wherein,
Iigrading x for evaluation factorsiThe amount of information of (a); n is a radical ofiGrading x for evaluation factorsiThe number of water inrush point units in the unit (d); n is the total number of water inrush point units in the research area; siThe number of units containing evaluation factor grades xi in the research area; s is the total unit number of the research area; n is the number of evaluation factors.
5. The evaluation factor-based optimized risk evaluation method for water inrush during mining under unconsolidated confined aquifer according to claim 1, wherein the step E comprises the following steps:
e1: drawing a success rate curve graph according to a success rate curve method, and sequencing the fitting degree of each evaluation factor;
e2: assigning the cells of each single evaluation factor raster layer according to the corresponding information quantity by applying the reclassification function of ArcGIS;
e3: and sequentially arranging and combining the single evaluation factors according to the fitting precision to obtain a plurality of combined evaluation factors, superposing each single evaluation factor raster image layer by applying ArcGIS to each combined evaluation factor to obtain a combined evaluation factor information amount superposition image, and taking the evaluation factor information amount superposition image as a combined evaluation factor information amount evaluation model.
6. The method for evaluating the risk of water inrush from underground mining of a unconsolidated confined aquifer according to claim 5, wherein the step E1 comprises:
the vertical axis represents the proportion of water inrush disasters in the corresponding information values to the total water inrush frequency, the horizontal axis represents the area percentage of the information values from high to low in the evaluation results, and a success rate curve graph is drawn.
7. The method for evaluating the risk of water inrush from underground mining of a unconsolidated confined aquifer according to claim 5, wherein the step E3 comprises:
by means of the formula (I) and (II),
the information amount of the combined evaluation factor is calculated, wherein,
Iigrading x for evaluation factorsiThe amount of information of (a); i is the total information content of a certain unit in the evaluation area; n is a radical ofiGrading x for evaluation factorsiThe number of water inrush point units in the unit (d); n is the total number of water inrush point units in the research area; siGrading x for evaluation factors in the study areaiThe number of units of (a); s is the total unit number of the research area; n is the total number of evaluation factors.
8. The method for evaluating the risk of water inrush from underground mining of a unconsolidated confined aquifer according to claim 1, wherein the step F comprises:
f1: dividing the information quantity superposition graph of the combined evaluation factor into 5 grades according to the information quantity by using the natural breakpoint method of ArcGIS;
f2: counting the number of units under each grade of the combined evaluation factor information amount superposed graph and the corresponding number of units of the water inrush points;
f3: and drawing a success rate curve graph according to a success rate curve method, sequencing the fitting accuracy of each combination evaluation factor, and determining the best evaluation factor combination.
9. The method for evaluating the risk of water inrush from underground mining of a unconsolidated confined aquifer according to claim 1, wherein the step G comprises:
and (3) partitioning an information content superposition graph under the optimal factor combination according to the information content by using a natural breakpoint method in ArcGIS, wherein the partitioned graph comprises a coal seam roof water inrush risk area, a dangerous area, a medium risk area, a safe area and a safe area.
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