Method for evaluating impact risk of horizontal section fully mechanized caving face of steeply inclined ultra-thick coal seam

Abstract

The invention discloses a steeply-inclined ultra-thick coal seam horizontal section fully-mechanized caving face impact risk assessment method. A coal rock mass numerical simulation model of a to-be-assessed area is established to obtain a high-stress area; dividing a high-stress area space by using an octree algorithm; calculating whether the space volume of the area and micro-seismic large-energy event distribution are on octree nodes or not, and determining whether the area is a dangerous area or not; calculating dynamic parameters of the dangerous area, drawing a space scanning graph of the dynamic parameters, and superposing to obtain space distribution characteristics of the dangerous area; obtaining static parameters of the dangerous area, and performing weight distribution on the static parameters and the dynamic parameters by using a weight distribution algorithm; obtaining an impact risk division result through a rating model; and determining the impact danger of each dangerous area. According to the method, the to-be-evaluated region is divided into a plurality of sub-regions, the static indexes and the dynamic indexes are comprehensively considered, the impact danger of each sub-region is dynamically evaluated, and finally the impact danger of the sub-regions is confirmed.

Description

Technical Field

[0001] The invention relates to the technical field of mining engineering, and in particular to a method for assessing impact hazard of a horizontal segmented fully mechanized caving face in a steeply inclined and extremely thick coal seam. Background Art

[0002] The geological conditions of steeply inclined and thick coal seams are extremely complex, and the coal seams have a large inclination angle and huge thickness, making roof management particularly difficult during mining. The roof rock strata are prone to inclined fractures under the influence of mining, and there is a nonlinear positive correlation between the fracture step length and the coal seam inclination angle. Especially when the inclination angle is greater than 60°, the roof fracture step length increases significantly, increasing the complexity and danger of roof management. Secondly, during the mining process of steeply inclined and thick coal seams, due to the large inclination angle of the coal seams, the horizontal stress concentration phenomenon is significant, which is easy to induce dynamic disasters such as rock burst. Traditional mining methods are difficult to effectively cope with this complex stress environment, resulting in increased safety risks during the mining process. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method for assessing the impact hazard of horizontally segmented fully-mechanized caving faces in steeply inclined and extremely thick coal seams in response to the deficiencies in the above-mentioned prior art. The method divides the area to be assessed into several sub-areas, and comprehensively considers static and dynamic indicators to dynamically assess the impact hazard of each sub-area, so as to ultimately confirm the impact hazard of the sub-area.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for assessing the impact hazard of a horizontal segmented fully mechanized caving face in a steeply inclined and extremely thick coal seam, comprising the following steps:

[0005] S1: Establish a numerical simulation model of the coal-rock mass in the area to be evaluated and identify the high stress area in the area to be evaluated;

[0006] S2: dividing the high stress area into multiple sub-areas by using an octree, and obtaining microseismic events in each sub-area;

[0007] S3: Calculate the volume of each sub-region respectively, and determine whether each sub-region is a dangerous area according to the volume distribution and microseismic event distribution of each sub-region in the area to be evaluated;

[0008] S4: Calculate the dynamic parameters of each dangerous area respectively, draw a spatial scanning diagram of the dynamic parameters, and superimpose to obtain the spatial distribution characteristics of each dangerous area;

[0009] S5: Obtain static parameters of each dangerous area, and use a weight distribution algorithm to distribute weights to the static parameters and the dynamic parameters;

[0010] S6: Based on the static parameters and the dynamic parameters after weight allocation, the impact risk classification result is obtained through a pre-built rating model;

[0011] S7: comparing the spatial distribution characteristics of each dangerous area with the impact risk classification result to determine the impact risk of each dangerous area;

[0012] The rating model is used to output the impact risk classification result by taking the static parameters and the dynamic parameters after weight allocation of the risk area as output.

[0013] Furthermore, the dynamic parameters include b value, A(b) value and AC value; the static parameters include coal seam thickness, inclination, burial depth and ground stress distribution.

[0014] Furthermore, in step S4, respectively calculating the dynamic parameters of each dangerous area and drawing a spatial scanning diagram of the dynamic parameters includes:

[0015] According to the microseismic events in each dangerous area, the microseismic data of each dangerous area is acquired, and the microseismic data is preprocessed;

[0016] Calculate the b value of each dangerous area and generate a spatial distribution map of the b value;

[0017] Calculate the A(b) value of each hazardous area and generate a spatial distribution map of the A(b) value;

[0018] Calculate the AC value of each hazardous area and generate a spatial distribution map of the AC value;

[0019] The spatial distribution diagram of the b value, the spatial distribution diagram of the A(b) value, and the spatial distribution diagram of the AC value are all spatial scanning diagrams of the dynamic parameters.

[0020] Furthermore, the preprocessing method includes one or more of the following: removing noise and removing errors.

[0021] Further, in step S6, the method of allocating the static parameters and the dynamic parameters based on weights includes any one of the following: a hierarchical analysis method, a fuzzy comprehensive evaluation method, a machine learning algorithm or a weighted regression algorithm.

[0022] Compared with the prior art, the present invention has the following advantages:

[0023] The present invention provides a method for assessing the impact hazard of a horizontal segmented fully mechanized caving face in a steeply inclined and extremely thick coal seam. The method uses numerical simulation to analyze high stress areas and calculates the distribution volume of microseismic high energy events in coal and rock mass through an octree to determine the crack extension area. The spatial distribution characteristics such as b value, A(b) value, and AC value are used as dynamic characteristics, and combined with static characteristics to perform multi-parameter impact hazard assessment. The weight distribution should be based on the contribution of each indicator to the rock burst to ensure that the model can accurately reflect the impact of different factors. A rating model is then established to divide the impact hazard level, so as to assess the impact hazard through multi-parameter indicators.

[0024] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A schematic diagram of the overall process of a method for assessing impact hazards of a horizontal segmented fully mechanized caving face in a steeply inclined and extremely thick coal seam provided by the present invention. DETAILED DESCRIPTION

[0026] like Figure 1 As shown, the present invention provides a method for assessing impact risk of a horizontal segmented fully mechanized caving face in a steeply inclined and extremely thick coal seam, comprising the following steps:

[0027] S1: Establish a numerical simulation model of the coal-rock mass in the area to be evaluated and identify the high stress area in the area to be evaluated;

[0028] S2: dividing the high stress area into multiple sub-areas by using an octree, and obtaining microseismic events in each sub-area;

[0029] S3: Calculate the volume of each sub-region respectively, and determine whether each sub-region is a dangerous area according to the volume distribution and microseismic event distribution of each sub-region in the area to be evaluated;

[0030] S4: Calculate the dynamic parameters of each dangerous area respectively, draw a spatial scanning diagram of the dynamic parameters, and superimpose to obtain the spatial distribution characteristics of each dangerous area;

[0031] S5: Obtain static parameters of each dangerous area, and use a weight distribution algorithm to distribute weights to the static parameters and the dynamic parameters;

[0032] S6: Based on the static parameters and the dynamic parameters after weight allocation, the impact risk classification result is obtained through a pre-built rating model;

[0033] S7: comparing the spatial distribution characteristics of each dangerous area with the impact risk classification result to determine the impact risk of each dangerous area;

[0034] The rating model is used to output the impact risk classification result by taking the static parameters and the dynamic parameters after weight allocation of the risk area as output.

[0035] The above steps use numerical simulation to analyze high stress areas and calculate the distribution volume of microseismic high energy events in coal and rock mass through octree to determine the crack extension area, and then use the spatial distribution characteristics such as b value, A(b) value, AC value as dynamic characteristics, combined with static characteristics to perform multi-parameter impact hazard assessment. The weight distribution should be based on the contribution of each indicator to the impact rock pressure to ensure that the model can accurately reflect the impact of different factors. Then establish a rating model to divide the impact hazard level, so as to evaluate the impact hazard through multi-parameter indicators.

[0036] In the present invention, the impact risk results of the assessment are no impact risk, weak impact risk, medium impact risk and strong impact risk. The standard level is: the impact risk dynamic and static evaluation comprehensive index Wd calculated separately is less than 0.25 for no impact risk, 0.25-0.50 for weak impact risk, 0.50-0.75 for medium impact risk, and >0.75 for strong impact risk.

[0037] In step S1, based on the geological conditions of the coal seam and the mining process, a numerical simulation model of the coal rock mass is established and the high stress area is identified and analyzed. Through the numerical simulation results, the stress distribution diagram of each stage is generated. Areas with high stress concentration are identified, which are usually prone to microseismicity and crack propagation.

[0038] In steps S2-S3, a three-dimensional spatial coordinate system is established for the mining area, and the space is divided using the Octree algorithm. The volume of the microseismic events distributed in space is calculated through the segmentation of the Octree. By analyzing the distribution density and volume changes of microseismic events in the octree division units, the areas where microseismic events are concentrated and the volume is expanded are identified. That is, the spatial volume of each sub-area and whether the distribution of microseismic high-energy events is on the octree node are calculated to determine whether the area is a dangerous area. Among them, the microseismic events in the present invention refer to microseismic high-energy events. Microseismic high-energy events refer to the rock burst mine engineering practice, where the single energy is generally greater than 1x10 5 J's microseismic events.

[0039] The present invention calculates the volume distribution of the high stress area through an octree and obtains the distribution information of microseismic events in the area, and then determines whether the area is a dangerous area according to the number of microseismic high energy events.

[0040] The present invention establishes a three-dimensional spatial coordinate system for the coal and rock mining area, and divides the space using an octree algorithm. The octree recursively divides the three-dimensional space into multiple smaller sub-areas, each of which is called a node. The monitored high-energy microseismic events are assigned to different nodes of the octree. Each node contains information about all microseismic events in the area, including the number of events, energy release, and location. For each octree node, its spatial volume is calculated. According to the depth of segmentation and the size of the node, the volume of each node is accumulated to obtain the distribution volume of the high-energy microseismic events.

[0041] At the same time, based on the volume distribution and event density distribution of the octree nodes, areas with dense microseismic events and high energy are identified. These areas are usually areas of crack extension. The high stress areas and crack extension areas are visualized in three dimensions to intuitively mark the potential dangerous areas in the coal and rock mass.

[0042] Combined with the stress simulation results, the crack propagation area calculated by octree is compared and superimposed with the high stress area of ​​numerical simulation to determine the most dangerous area where the coal and rock mass is most likely to be damaged.

[0043] In step S4, a multi-parameter impact hazard assessment is performed through spatial distribution characteristics such as b value, A(b) value, AC value, etc. The present invention divides the mining area into several small blocks and calculates the b value distribution of each area. Low b value areas often represent high stress concentration areas with high rock burst risk; by calculating the spatial change rate A(b) value based on the b value distribution, the area with significant b value changes is identified. The A(b) value of these areas is large, which usually indicates that crack extension or stress change is more active, and it is a potential high-risk area; according to the volume change and distribution of microseismic events, the spatial distribution of AC value is calculated. Areas with higher AC values ​​often reflect significant volume changes in local areas and represent areas of crack extension. The distribution of AC value can identify high-risk areas for coal and rock mass damage. Finally, a multi-parameter superposition analysis is performed to superimpose the spatial distribution of b value, A(b) value and AC value to identify areas with multi-parameter high-risk characteristics. Through this multi-parameter superposition evaluation, high-risk areas can be identified more comprehensively so as to carry out targeted safety management and preventive measures.

[0044] The present invention performs hazard zoning, and performs impact hazard zoning on the mining area based on spatial distribution characteristics such as b value, A(b) value and AC value. Areas with low b value, high A(b) value and significant AC value are usually high-risk areas, and these areas should become key monitoring areas, and then determine a multi-parameter weight analysis model, assign weights to b value, A(b) value and AC value, and construct a multi-parameter impact hazard assessment model. The comprehensive risk level of each area is analyzed by the model to generate a more scientific and reasonable zoning result. The result is compared with the crack extension evaluated by the octree to determine the final impact hazard area.

[0045] In the present invention, the weight is determined according to the hierarchical analysis method, fuzzy comprehensive evaluation or machine learning method (such as weighted regression), etc. The establishment of the structural model, the judgment matrix structure, the weight matrix calculation and one-time inspection, and the weight confirmation are completed.

[0046] The specific method is as follows:

[0047] (1) Collect microseismic monitoring data, conduct preliminary screening of the collected microseismic data, remove noise and error data, and ensure the accuracy and effectiveness of the data.

[0048] (2) Calculate the b value, divide the mining area into several small areas, calculate the b value in each area, and form a spatial distribution map of the b value. Areas with lower b values ​​often represent areas of high stress concentration and are potentially high-risk areas.

[0049] (3) Calculate the A(b) value. By calculating the A(b) value, identify areas where the b value changes significantly. The A(b) value in these areas is large, which usually means that the stress state changes rapidly, the crack propagation and energy release are more active, and there is a higher risk of rock burst. Visualize the A(b) value results and generate its spatial distribution map. Areas with high A(b) values ​​usually correspond to rupture propagation and high-risk areas.

[0050] (4) Calculate the AC value and divide the mining area into multiple small units. According to the spatial position of the microseismic events, calculate the cumulative volume change of each unit and obtain the distribution of AC values. Areas with larger AC values ​​usually correspond to stronger energy release and volume change, which are potential crack propagation areas. Visualize the distribution results of AC values ​​and generate an AC value spatial distribution map to clearly show the dangerous areas with high AC values.

[0051] (5) Multi-parameter overlay analysis: According to the influence of b value, A(b) value and AC value on rock burst, weights are assigned to each parameter using the analytic hierarchy process (AHP), fuzzy comprehensive evaluation or other multi-parameter weighted methods. Combining the spatial distribution characteristics of the three, a comprehensive assessment model is constructed through weighted summation or fuzzy logic. The model is used to generate a total score for each small area, indicating the rock burst risk level in the area. The weighted overlay results of b value, A(b) value and AC value are used to generate a multi-parameter rock burst hazard distribution map. Areas with higher scores indicate that the area has a higher risk of rock burst and is an area that requires special attention.

[0052] Therefore, in step S4, respectively calculating the dynamic parameters of each dangerous area and drawing a spatial scanning diagram of the dynamic parameters includes:

[0053] According to the microseismic events in each dangerous area, the microseismic data of each dangerous area is acquired, and the microseismic data is preprocessed;

[0054] Calculate the b value of each dangerous area and generate a spatial distribution map of the b value;

[0055] Calculate the A(b) value of each hazardous area and generate a spatial distribution map of the A(b) value;

[0056] Calculate the AC value of each hazardous area and generate a spatial distribution map of the AC value;

[0057] The spatial distribution diagram of the b value, the spatial distribution diagram of the A(b) value, and the spatial distribution diagram of the AC value are all spatial scanning diagrams of the dynamic parameters.

[0058] The preprocessing method includes one or more of the following: removing noise and removing errors.

[0059] In step 5-6, a dynamic-static multi-parameter index comprehensive evaluation method for rock burst is used. The dynamic parameters in the present invention include b value, A(b) value and AC value; the static parameters include coal seam thickness, inclination, burial depth and ground stress distribution.

[0060] In the present invention, dynamic indicators reflect the real-time changes in the stress state and energy release of coal rock mass, including microseismic event parameters (such as b value, AC value, A(b) value), magnitude frequency, energy release rate, etc. These dynamic indicators are obtained through real-time monitoring data, which can reflect the dynamic characteristics of rock burst in time. Static indicators indicate the initial geological and mechanical conditions of coal rock mass, which usually do not change significantly over time. Including coal seam thickness, inclination, burial depth, ground stress distribution, rock mechanical parameters (such as compressive strength, elastic modulus), etc. These indicators are used to evaluate the foundation rock burst danger in coal mine areas. Based on the analysis of the rock burst mechanism of coal rock mass, dynamic and static indicators that have an important impact on the rock burst risk are selected. Dynamic indicators may include b value, AC value, frequency of microseismic events, etc.; static indicators include coal seam thickness, rock compressive strength, burial depth, etc. The weights of each indicator are assigned by using techniques such as hierarchical analysis method (AHP), fuzzy comprehensive evaluation or machine learning methods (such as weighted regression). The weight distribution should be based on the contribution of each indicator to rock burst, ensuring that the model can accurately reflect the impact of different factors. Then a rating model is established to classify rock burst hazards.

[0061] The specific method is as follows:

[0062] (1) Select dynamic-static evaluation indicators.

[0063] Dynamic indicators: Select dynamic parameters that can reflect the stress state and energy changes of coal and rock mass in real time. Commonly used dynamic indicators include: frequency of microseismic events (frequency, number of events), b value, A(b) value and AC value of microseismic events, energy release rate, stress change rate, etc.

[0064] Static indicators: static parameters that reflect the geological characteristics and initial stress state of the coal rock mass are selected. Commonly used static indicators include: coal seam thickness, inclination, burial depth, ground stress distribution, rock mechanics parameters (such as compressive strength, elastic modulus), etc.

[0065] (2) Indicator weight allocation: According to the degree of influence of different indicators on rock burst risk, weights are allocated to each indicator using the analytic hierarchy process (AHP), entropy weight method, fuzzy comprehensive evaluation method or machine learning technology. Weights are allocated to dynamic and static indicators respectively, and then the relative weights of the two types of indicators in the overall evaluation are determined according to the needs of the specific model; a weight matrix is ​​constructed using the analytic hierarchy process (AHP), and a consistency test is performed to ensure that the indicator weight allocation is reasonable and effective.

[0066] Establish a dynamic-static comprehensive evaluation model. Fusion of dynamic-static indicators: using weighted sum method or fuzzy logic to fuse dynamic-static indicators and form a comprehensive score. The higher the comprehensive score, the greater the risk of rock burst.

[0067] Therefore, in step S6, the method of weighting the static parameters and the dynamic parameters includes any one of the following: hierarchical analysis method, fuzzy comprehensive evaluation method, machine learning algorithm or weighted regression algorithm.

[0068] In summary, the present invention dynamically evaluates the impact hazard by comprehensively considering multiple factors such as coal seam inclination, mining technology, geological conditions, etc., and adjusts mining parameters and support measures according to the evaluation results, thereby achieving effective control of dynamic disasters such as rock burst. The proposed method not only helps to improve the mining efficiency and safety of steeply inclined and thick coal seams, but also provides important technical reference and reference for coal mining under similar geological conditions.

[0069] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent structural change made to the above embodiment based on the technical essence of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for assessing impact risk of horizontal segmented fully mechanized caving face in steeply inclined and extremely thick coal seams, characterized in that: The following steps are involved: S1: Establish a numerical simulation model of the coal-rock mass in the area to be evaluated and identify the high stress area in the area to be evaluated; S2: dividing the high stress area into multiple sub-areas by using an octree, and obtaining microseismic events in each sub-area; S3: Calculate the volume of each sub-region respectively, and determine whether each sub-region is a dangerous area according to the volume distribution and microseismic event distribution of each sub-region in the area to be evaluated; S4: Calculate the dynamic parameters of each dangerous area respectively, draw a spatial scanning diagram of the dynamic parameters, and superimpose to obtain the spatial distribution characteristics of each dangerous area; S5: Obtain static parameters of each dangerous area, and use a weight distribution algorithm to distribute weights to the static parameters and the dynamic parameters; S6: Based on the static parameters and the dynamic parameters after weight allocation, the impact risk classification result is obtained through a pre-built rating model; S7: comparing the spatial distribution characteristics of each dangerous area with the impact risk classification result to determine the impact risk of each dangerous area; The rating model is used to output the impact risk classification result by taking the static parameters and the dynamic parameters after weight allocation of the risk area as output.

2. A method for assessing impact risk of horizontal segmented fully mechanized caving face in steeply inclined and extremely thick coal seams according to claim 1, characterized in that: The dynamic parameters include b value, A(b) value and AC value; the static parameters include coal seam thickness, inclination, burial depth and ground stress distribution.

3. A method for assessing impact risk of horizontal segmented fully mechanized caving face in steeply inclined and extremely thick coal seams according to claim 2, characterized in that: In step S4, respectively calculating the dynamic parameters of each dangerous area and drawing a spatial scanning diagram of the dynamic parameters includes: According to the microseismic events in each dangerous area, the microseismic data of each dangerous area is acquired, and the microseismic data is preprocessed; Calculate the b value of each dangerous area and generate a spatial distribution map of the b value; Calculate the A(b) value of each hazardous area and generate a spatial distribution map of the A(b) value; Calculate the AC value of each hazardous area and generate a spatial distribution map of the AC value; The spatial distribution diagram of the b value, the spatial distribution diagram of the A(b) value, and the spatial distribution diagram of the AC value are all spatial scanning diagrams of the dynamic parameters.

4. A method for assessing impact risk of horizontal segmented fully mechanized caving face in steeply inclined and extremely thick coal seams according to claim 3, characterized in that: The preprocessing method includes one or more of the following: removing noise and removing errors.

5. A method for assessing impact risk of horizontal segmented fully mechanized caving face in steeply inclined and extremely thick coal seams according to claim 1, characterized in that: In step S6, the method of weighting the static parameters and the dynamic parameters includes any one of the following: a hierarchical analysis method, a fuzzy comprehensive evaluation method, a machine learning algorithm or a weighted regression algorithm.

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