Coal mine goaf collapse range prediction method

Abstract

The invention relates to a coal mine goaf collapse range prediction method, relates to the technical field of coal mine safety monitoring, comprehensively applies various advanced monitoring technologies, can comprehensively and accurately obtain geometric, mechanical and deformation information of a goaf and surrounding rock masses, and provides rich and reliable data support for collapse range prediction. The adopted multi-model coupling prediction method gives full play to respective advantages of a probability integral method and numerical simulation, through multi-source data fusion decision, the accuracy and reliability of collapse range prediction are remarkably improved, prediction errors are greatly reduced, and compared with a traditional single model, the precision improvement effect is remarkable. And secondly, the implementation process and the verification link ensure that the prediction method can track the geological condition change of the goaf in real time, update model parameters in time and realize dynamic and accurate prediction, powerful technical guarantee is provided for coal mine safety production, the occurrence risk of goaf collapse accidents is effectively reduced, and the method has important economic and social values.

Description

Technical Field

[0001] This invention relates to the field of coal mine safety monitoring technology, and more particularly to a method for predicting the extent of coal mine goaf collapse. Background Technology

[0002] Coal mine goaf subsidence poses a serious threat to safe production in mining areas, the surrounding ecological environment, and the lives and property of residents. With the continuous operation of coal mining, the area of ​​goafs is constantly expanding, and subsidence accidents occur frequently. Traditional methods for predicting the extent of coal mine goaf subsidence have many shortcomings. For example, prediction methods based on empirical formulas do not fully consider complex geological conditions and the coupling effects of multiple factors, leading to significant deviations between predicted results and actual conditions. Single monitoring methods, such as relying solely on surface displacement monitoring, cannot comprehensively obtain information on the mechanical state and deformation of the rock mass inside and around the goaf, making accurate prediction difficult. Currently, there is an urgent need for a method for predicting the extent of coal mine goaf subsidence that comprehensively considers multiple factors, utilizes various monitoring technologies, and employs advanced prediction models to improve the accuracy and reliability of predictions and provide strong protection for safe coal mine production.

[0003] In conclusion, there is an urgent need for a method to predict the extent of coal mine goaf subsidence in order to solve the above problems. Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide a scientifically optimized method for predicting the extent of coal mine goaf collapse. By constructing a multi-dimensional monitoring system and a multi-model coupled prediction method, the method can accurately predict the extent of coal mine goaf collapse, effectively improve the level of coal mine safety production, and reduce the risk of collapse accidents.

[0005] To achieve the above objectives, this invention provides a method for predicting the subsidence range of coal mine goaf areas, comprising the following steps: S1. Determine the basic parameters of the goaf; Monitoring of geometric and mechanical parameters of the goaf: Spatial morphology of the goaf, including parameters such as length L, width B, height H, and burial depth D, is obtained using borehole radar and 3D seismic exploration techniques; rock mechanics tests are conducted indoors to determine the uniaxial compressive strength σ. c The mechanical parameters of elastic modulus E, Poisson's ratio μ, and rock mass unit weight γ were obtained, and in-situ parameters were obtained in conjunction with sonic logging.

[0006] S2. Set up monitoring points to obtain real-time data; S2.1. Using time-series InSAR technology, perform SBAS-InSAR processing on no fewer than 28 Sentinel-1 images to obtain the surface subsidence field W with millimeter-level accuracy. InSAR (x,y), with a coverage area of ​​up to 100km. 2Simultaneously, more than 30 monitoring points were set up along the main cross-section, and high-precision surface settlement W was obtained by using a combination of GPS and leveling measurements. GPS (x,y) and horizontal displacement U GPS (x,y); S2.2 Monitoring of underground rock strata movement: Six layers of multi-point displacement gauges are arranged within a 30m range above the roof of the goaf to monitor the vertical displacement w(z) and horizontal displacement u(z); Three borehole inclinometers are arranged along the dip to obtain the rock strata dip angle change rate di / dz. S2.3 Microseismic and Stress Field Monitoring: A microseismic monitoring system consisting of eight three-component accelerometers is deployed to locate the spatiotemporal distribution (x, y, z, t) of microseismic events in real time. Four sets of anchor stress gauges were installed on the coal pillar to monitor the changes in principal stresses σ1, σ2, and σ3, and to determine the maximum principal stress.

[0007] S3. Establish a geometric morphological model; Based on geometric and mechanical parameters, the equivalent stiffness coefficient of the goaf is calculated as follows: ; Used for setting boundary conditions in subsequent numerical simulations; The Kalman filter algorithm is used to fuse InSAR and GPS data, correct for errors such as atmospheric delay, and obtain the final accurate surface subsidence field W(x,y); By analyzing underground rock strata movement monitoring data, the depth of delamination development was determined to be... ; Used to determine the location of overburden instability; Based on data from the microseismic monitoring system, the energy E was determined. r ; When the frequency of microseismic events increases suddenly and the stress drop exceeds 15%, an orange alert is triggered, indicating that there are significant safety hazards in the goaf area.

[0008] S4. Construct a multi-model coupling system; Parameter inversion using the probability integral method: The probability integral method is used to calculate surface subsidence values. The core formula is as follows: , in, W(x,y): The subsidence value (m) of any point (x,y) on the Earth's surface; m: Coal seam mining thickness (m); q: Sinking coefficient; r: radius of influence (m); x, y: Coordinates (m) of the calculation point relative to the center of the goaf; Radius of influence ; Using the Dung Beetle Optimization (DBO) algorithm, with the InSAR subsidence field as the objective function, the subsidence coefficient q and the influence angle tangent tanβ are inverted.

[0009] The optimization goal is , And set the constraints 0.2≤q≤0.8, 1.0≤tanβ≤2.5; Inversion results: subsidence coefficient q and influence angle tangent tanβ; Substitute the boundary conditions into the core formula to solve for the surface subsidence value (i.e., the collapse radius R1).

[0010] Numerical simulation verification: A three-dimensional geological model was established using FLAC 3D software, divided into 100,000+ elements, and a Mohr-Coulomb constitutive model was set up, with a self-weight stress field P0=γ·D applied. The collapse radius is then... .

[0011] S5. Output the prediction results; Multi-source data fusion decision-making: The prediction results of the distance squared weighted method and the probability integral method (weight 0.5) and numerical simulation (weight 0.5) are fused to obtain the final collapse range radius. R final =0.5R1+0.5R plas .

[0012] S6. Compare the actual monitoring results with the forecast results; The prediction error of the fusion model was verified by comparing it with the measured data.

[0013] S7. Optimize model parameters and iteratively predict; Data acquisition: InSAR image data is updated regularly every quarter, and microseismic and stress data are transmitted to the cloud platform in real time to ensure the timeliness and completeness of the data; Parameter inversion: Based on the latest collected monitoring data, the DBO algorithm is run weekly to update the parameters of the probability integral method to adapt to the constantly changing geological conditions of the goaf. Dynamic prediction: Outputs a daily prediction map of the collapse range, when R... final When the threshold (H / 5) is exceeded, a red alert is immediately triggered, reminding relevant departments to take emergency measures.

[0014] Compared with the prior art, the technical solution of this application has the following beneficial effects: The coal mine goaf collapse range prediction method constructed in this invention comprehensively utilizes multiple advanced monitoring technologies to obtain comprehensive and accurate information on the geometry, mechanics, and deformation of the goaf and surrounding rock mass, providing rich and reliable data support for collapse range prediction. Its multi-model coupled prediction method fully leverages the advantages of probability integral methods and numerical simulation, significantly improving the accuracy and reliability of collapse range prediction through multi-source data fusion decision-making, and drastically reducing prediction errors. Compared to traditional single models, the accuracy improvement is significant. Furthermore, the implementation process and verification steps ensure that the prediction method can track changes in geological conditions in the goaf in real time, achieving dynamic and accurate prediction. This provides strong technical support for safe coal mine production, effectively reducing the risk of goaf collapse accidents and possessing significant economic and social value. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating a method for predicting the extent of coal mine goaf collapse in one embodiment of this application. Detailed Implementation

[0016] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0017] Example 1:

[0018] A method for predicting the extent of coal mine goaf subsidence includes the following steps:

[0019] S1. Determine the basic parameters of the goaf; Monitoring of geometric and mechanical parameters of goaf: Spatial morphology of the goaf, including parameters such as length L, width B, height H, and burial depth D, is obtained using borehole radar and 3D seismic exploration techniques; rock mechanics tests are conducted indoors to determine the uniaxial compressive strength σ. c Mechanical parameters such as elastic modulus E and Poisson's ratio μ were obtained, along with in-situ parameters acquired through sonic logging. Taking Linhuan Coal Mine as an example, spatial morphology parameters of its goaf area—5000 meters long, 2000 meters wide, 5 meters high, and 300 meters deep—were obtained using borehole radar and 3D seismic exploration techniques. Indoor rock mechanics tests were conducted to determine the uniaxial compressive strength up to σ. c The mechanical parameters are 80 MPa, elastic modulus 30 GPa, and Poisson's ratio 0.25, and γ = 25 kN / m obtained by sonic logging. 3 In-situ parameters. Based on these parameters, the formula is: The equivalent stiffness coefficient of the goaf is calculated for use in setting boundary conditions in subsequent numerical simulations. Here, K is the equivalent stiffness coefficient of the goaf, E is the elastic modulus, H is the height of the goaf, L is the length of the goaf, and B is the width of the goaf. In the case of Linhuan Coal Mine, the equivalent stiffness coefficient of the goaf is calculated to be 1.5 × 10⁻⁶. 4 N / m.

[0020] S2. Set up monitoring points to obtain real-time data; S2.1. Using time-series InSAR technology, perform SBAS-InSAR processing on no fewer than 28 Sentinel-1 images of the mining area to obtain WInSAR(x,y) surface subsidence field data with millimeter-level accuracy, covering an area of ​​up to 100 km. 2 To obtain surface subsidence field data with millimeter-level accuracy, the maximum subsidence rate in the mining area reached 166.25 mm / a, and the maximum cumulative subsidence was 188.75 mm. Simultaneously, more than 30 monitoring points were deployed along the main cross-section, and high-precision surface subsidence data (W) was obtained using a combined GPS and leveling measurement method. GPS (x,y) and horizontal displacement U GPS (x,y). The Kalman filter algorithm is used to fuse InSAR and GPS data, correcting errors such as atmospheric delay, to obtain the final accurate surface subsidence field W(x,y). S2.2 Monitoring of underground rock strata movement: Six layers of multi-point displacement gauges are arranged within a 30m range above the roof of the goaf to monitor the vertical displacement w(z) and horizontal displacement u(z); Three borehole inclinometers are arranged along the dip to obtain the rock strata dip angle change rate di / dz. By analyzing underground rock strata movement monitoring data, the depth of delamination development was determined to be... ; ΔZ: The difference between vertical displacement w(z) and horizontal displacement u(z); Used to determine the location of overburden instability. In this coal mine case, the depth of the delamination was calculated to be 12 meters.

[0021] S2.3 Microseismic and Stress Field Monitoring: A microseismic monitoring system consisting of eight three-component accelerometers was deployed in the coal mine to locate the spatiotemporal distribution (x, y, z, t) of microseismic events in real time; based on the data from the microseismic monitoring system, the energy is E r =1200J; Four sets of anchor stress gauges were installed on the coal pillar to monitor the changes in principal stresses σ1, σ2, and σ3, with the maximum principal stress σ1 being 35 MPa. When the frequency of microseismic events suddenly increased and the stress drop exceeded 15%, an orange alert was triggered, indicating a significant safety hazard in the goaf.

[0022] S4. Construct a multi-model coupling system; Parameter inversion using the probability integral method: The probability integral method is used to calculate surface subsidence values. The core formula is as follows: ; in, W(x,y): The subsidence value (m) of any point (x,y) on the Earth's surface; m: Coal seam mining thickness (m); q: Sinking coefficient; r: Radius of influence (m); x, y: Coordinates (m) of the calculation point relative to the center of the goaf; Radius of influence ; Using the Dung Beetle Optimization (DBO) algorithm, with the InSAR subsidence field as the objective function, the subsidence coefficient q and the influence angle tangent tanβ are inverted.

[0023] The optimization goal is , And set the constraints 0.2≤q≤0.8, 1.0≤tanβ≤2.5; Inversion results: subsidence coefficient q and influence angle tangent tanβ; In the case, m=5 meters, and q=0.6 is obtained from DBO inversion, where D=300m (burial depth) and tanβ=1.5 (influence angle tangent); since the goaf is symmetrically distributed, y=0 is taken to simplify it into a one-dimensional problem; The maximum subsidence value Wmax occurs at the center of the goaf (x=0, y=0), at which point the exponential term exp(0)=1, therefore: Wmax=m·q; In industry standards, the collapse boundary is defined as the position where the subsidence value reaches 10% of the maximum subsidence value, i.e.: W=0.1Wmax; Substituting the boundary conditions into the core formula, we can solve for the surface subsidence value x (i.e., the collapse radius R1): ; In this coal mine case, the collapse radius R1 using the probability integral method is 285m. Numerical simulation verification: A three-dimensional geological model was established using FLAC 3D software, divided into 100,000+ elements, and a Mohr-Coulomb constitutive model was set up, with a self-weight stress field P0=γ·D applied. ;

[0024] Where: γ is the unit weight of the rock mass (kN / m³) 3 ); H: Equivalent to D, is the depth of the goaf (m), which is the vertical distance from the ground surface to the top of the goaf. In this coal mine case, numerical simulation verifies the collapse radius R. plas =290m;

[0025] S5. Output the prediction results; Multi-source data fusion decision-making: The prediction results of the distance squared weighted method and the probability integral method (weight 0.5) and numerical simulation (weight 0.5) are fused to obtain the final collapse range radius. R final =0.5R1+0.5R plas =287.5m.

[0026] S6. Compare the actual monitoring results with the forecast results; By comparing the measured data of 280m, the prediction error of the fusion model was verified to be 2.67%.

[0027] By comparing the measured data of the mine, it was verified that the prediction error of the fusion model was reduced and the prediction accuracy was improved compared with the single model.

[0028] S7. Optimize model parameters and iteratively predict; Data Acquisition: InSAR imagery data is updated quarterly, and microseismic and stress data are transmitted to the cloud platform in real time to ensure data timeliness and completeness. For example, a coal mine, through a stable data acquisition process, can promptly obtain the latest InSAR imagery, providing a data foundation for subsequent analysis. Parameter inversion: Based on the latest collected monitoring data, the DBO algorithm is run weekly to update the parameters of the probability integral method to adapt to the constantly changing geological conditions of the goaf. For example, during the mining process of a certain mine, as the goaf expands, the DBO algorithm is run weekly to continuously update parameters such as the subsidence coefficient and the influence angle tangent. Dynamic prediction: A daily prediction map of the subsidence area is output. When the area exceeds a warning threshold, a red alert is immediately triggered, reminding relevant departments to take emergency measures. In practical application, a coal mine used the daily prediction map to provide early warnings of potential subsidence areas, effectively preventing accidents.

[0029] The advantages of this application compared to the prior art are as follows: The coal mine goaf collapse range prediction method constructed in this invention comprehensively utilizes multiple advanced monitoring technologies to obtain comprehensive and accurate information on the geometry, mechanics, and deformation of the goaf and surrounding rock mass, providing rich and reliable data support for collapse range prediction. Its multi-model coupled prediction method fully leverages the advantages of probability integral methods and numerical simulation, significantly improving the accuracy and reliability of collapse range prediction through multi-source data fusion decision-making, and drastically reducing prediction errors. Compared to traditional single models, the accuracy improvement is significant. Furthermore, the implementation process and verification steps ensure that the prediction method can track changes in geological conditions in the goaf in real time, achieving dynamic and accurate prediction. This provides strong technical support for safe coal mine production, effectively reducing the risk of goaf collapse accidents and possessing significant economic and social value.

[0030] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for predicting the extent of coal mine goaf subsidence, characterized in that: Includes the following steps: S1. Determine the basic parameters of the goaf; Specifically, this includes: monitoring of geometric and mechanical parameters of the goaf; S2. Set up monitoring points to obtain real-time data; Specifically, this includes: S2.1, using time-series InSAR technology, performing SBAS-InSAR processing on no fewer than 28 Sentinel-1 images to obtain the surface subsidence field W with millimeter-level precision. InSAR (x,y); Simultaneously, more than 30 monitoring points are set up along the main cross section, and high-precision surface settlement W is obtained by using a combination of GPS and leveling measurements. GPS (x,y) and horizontal displacement U GPS (x,y); S2.2 Monitoring of underground rock strata movement: Six layers of multi-point displacement gauges are arranged within a 30m range above the roof of the goaf to monitor the vertical displacement w(z) and horizontal displacement u(z); Three borehole inclinometers are arranged along the dip to obtain the rock strata dip angle change rate di / dz. S2.3 Microseismic and Stress Field Monitoring: A microseismic monitoring system consisting of eight three-component accelerometers is deployed to locate the spatiotemporal distribution (x, y, z, t) of microseismic events in real time. Four sets of anchor stress gauges were installed on the coal pillar to monitor the changes in principal stresses σ1, σ2, and σ3, and to determine the maximum principal stress. S3. Establish a geometric morphological model; Specific implementation method: Based on geometric and mechanical parameters, the equivalent stiffness coefficient of the goaf is calculated as follows: , Used for setting boundary conditions in subsequent numerical simulations; The Kalman filter algorithm is used to fuse InSAR and GPS data, correct the atmospheric delay error, and obtain the final accurate surface subsidence field W(x,y); By analyzing underground rock strata movement monitoring data, the depth of delamination development was determined to be... ; Used to determine the location of overburden instability; Based on data from the microseismic monitoring system, the energy E was determined. r ; When the frequency of microseismic events suddenly increases and the stress drop exceeds 15%, an orange alert is triggered, indicating that there are significant safety hazards in the goaf area; S4. Construct a multi-model coupling system; Specific implementation method: Parameter inversion using the probability integral method: The probability integral method is used to calculate the surface subsidence value. The core formula is as follows: , in, W(x,y): The subsidence value (m) of any point (x,y) on the Earth's surface; m: Coal seam mining thickness (m); q: Sinking coefficient; r: radius of influence (m); x, y: Coordinates (m) of the calculation point relative to the center of the goaf; Radius of influence ; Using the dung beetle optimization algorithm, with the InSAR subsidence field as the objective function, the subsidence coefficient q and the influence angle tangent tanβ are inverted; The optimization goal is , The constraints are set as follows: 0.2≤q≤0.8, 1.0≤tanβ≤2.5; Inversion results: subsidence coefficient q and influence angle tangent tanβ; Substituting the boundary conditions into the core formula, we solve for the surface subsidence value x, i.e., the collapse radius R1: ; Numerical simulation verification: A three-dimensional geological model was established using FLAC 3D software, divided into 100,000+ elements, and a Mohr-Coulomb constitutive model was set up, with a self-weight stress field P0=γ·D applied. ； Where: γ is the unit weight of the rock mass (kN / m³) 3 ); H is equivalent to D, which is the depth of the goaf (m), that is, the vertical distance from the ground surface to the top of the goaf. S5. Output the prediction results; Specific implementation method: Multi-source data fusion decision-making: The prediction results of the distance squared weighted method and the probability integral method and numerical simulation are fused to obtain the final collapse range radius. R final =0.5R1+0.5R plas 。 2. The method for predicting the subsidence range of coal mine goaf areas according to claim 1, characterized in that: The specific implementation method of step S1 is as follows: The spatial morphology of the goaf, including parameters such as length L, width B, height H, and burial depth D, is obtained through technologies such as borehole radar and 3D seismic exploration; rock mechanics tests are conducted indoors to determine the uniaxial compressive strength σ. c The mechanical parameters of elastic modulus E, Poisson's ratio μ, and rock mass unit weight γ were obtained, and in-situ parameters were obtained in conjunction with sonic logging.

3. The method for predicting the subsidence range of coal mine goaf areas according to claim 1, characterized in that: It also includes S6, comparing actual monitoring and forecast results.

4. The method for predicting the subsidence range of coal mine goaf areas according to claim 3, characterized in that: It also includes S7, optimizing model parameters, and iterative prediction.

5. The method for predicting the subsidence range of coal mine goaf areas according to claim 4, characterized in that: The specific implementation method of step S7 is as follows: Data acquisition: InSAR image data is updated regularly every quarter, and microseismic and stress data are transmitted to the cloud platform in real time to ensure the timeliness and completeness of the data; Parameter inversion: Based on the latest collected monitoring data, the DBO algorithm is run weekly to update the parameters of the probability integral method to adapt to the constantly changing geological conditions of the goaf. Dynamic prediction: Outputs a daily prediction map of the collapse range, when R... final When the threshold is exceeded, a red alert is immediately triggered, reminding relevant departments to take emergency measures.

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