A complex terrain mining subsidence prediction method based on additional subsidence compensation

Abstract

The application discloses a complex terrain mining subsidence prediction method based on additional subsidence compensation and belongs to the technical field of mine exploitation damage prediction and protection. Three indexes, i.e., a mining depth and topsoil layer thickness ratio, a slope coupling index and a prediction horizontal strain index, are calculated through historical mining and measured mining data of a mining area; the three indexes are regressed and fitted to obtain a predicted additional subsidence value; the predicted additional subsidence value is combined with a benchmark mining subsidence value obtained by a probability integral method, and thus a mining subsidence prediction value under complex terrain conditions can be obtained. The method can effectively represent the subsidence increasing and subsidence decreasing effects caused by complex terrain, identify the asymmetry and spatial offset of a subsidence basin, improve the prediction precision on the basis of the applicability of the probability integral method and is suitable for shaft mining areas under complex terrain conditions.

Description

Technical Field

[0001] This invention relates to the field of mining damage prediction and protection technology, specifically to a method for predicting subsidence in complex terrain mining based on additional subsidence compensation. Background Technology

[0002] Underground coal mining causes overburden movement and surface subsidence. Subsidence prediction is crucial for safe mining, surface protection, damage assessment, and ecological restoration. Existing methods for predicting mining subsidence are largely based on the assumption of a nearly flat surface, and are relatively mature under flat or gently undulating terrain conditions. However, in actual mining areas, many working faces are distributed in complex terrain regions such as slopes, valleys, hills, and loess gullies. Topographical undulations and differences in overburden thickness affect the manifestation of mining-induced subsidence on the surface, causing subsidence basins to exhibit characteristics such as displacement, asymmetric deformation, and localized increases or decreases in subsidence. If traditional subsidence prediction methods based on flat terrain assumptions are still directly applied, inconsistencies between predicted results and actual subsidence distribution are likely. Currently, research on mining subsidence under complex terrain conditions often employs methods such as empirical correction, slope propagation analysis, or numerical simulation. However, these methods still suffer from insufficient characterization of the additional subsidence effects of complex terrain, inadequate depiction of the coupling effect between topographic factors and subsidence response, and the difficulty in directly applying correction results to engineering predictions.

[0003] Prior art, disclosed in publication number CN116522796A, provides a method, apparatus, electronic device, and storage medium for predicting mining subsidence in a mining area. The method includes: acquiring real-time subsidence data of a target mining area; processing the real-time subsidence data to determine the principal components corresponding to the real-time subsidence data, and constructing new subsidence data based on the principal components; inputting the new subsidence data into a trained prediction model to obtain the predicted subsidence parameters corresponding to the new subsidence data output by the trained prediction model; and predicting the mining subsidence of the target mining area based on the predicted subsidence parameters corresponding to the new subsidence data. This method requires a prediction model constructed using a neural network, necessitating extensive training before use, resulting in high costs and significant computational demands. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this paper provides a simple, convenient, and rapid method for predicting mining subsidence in complex terrain based on additional subsidence compensation. By identifying additional subsidence and constructing compensation relationships, the method can quickly achieve corrective prediction of mining subsidence under complex terrain conditions.

[0005] To achieve the above-mentioned technical objectives, this invention discloses a method for predicting mining subsidence in complex terrain based on additional subsidence compensation. By using historical mining data and measured mining data from the mining area, three indicators are calculated: the ratio of mining depth to topsoil thickness, the slope coupling index, and the predicted horizontal strain index. The three indicators are then fitted by regression to obtain the predicted additional subsidence value. By combining the predicted additional subsidence value with the baseline mining subsidence value obtained by the probability integral method, the predicted mining subsidence value under complex terrain conditions can be obtained.

[0006] The specific steps are as follows: S1. Obtain mining, geological, and rock movement data of the mining area, and use the probability integral method to obtain the benchmark mining subsidence value under ideal flat terrain conditions. and predicted horizontal strain index ; S2. Obtain the measured settlement values ​​of the stabilized area under complex terrain conditions. This allows us to obtain the additional subsidence value resulting from complex terrain relative to ideal flat terrain. ; S3. Based on the surface elevation data, geological data and benchmark subsidence values ​​before mining, extract the topographic-subsidence coupling influence factor. S4. Impact factors of topographic-subsidence coupling and predicted horizontal strain index Regression fitting is performed to establish the additional settlement response relationship. This relationship is then used to predict the additional settlement value under complex terrain conditions; this is the predicted additional settlement value. ; S5, at the benchmark mining subsidence value Introducing predicted additional subsidence values As a compensation term, the predicted value of mining subsidence under complex terrain conditions is obtained. .

[0007] Furthermore, the mining, geological, and rock movement data obtained in S1 include: working face location, mining thickness, mining depth, coal seam dip angle, loose layer or topsoil layer thickness, main influence angle tangent, subsidence coefficient, horizontal movement coefficient, inflection point offset distance, and mining boundary; benchmark mining subsidence value. This is the theoretical benchmark subsidence value calculated by the classical probability integral method without considering the influence of complex terrain undulations.

[0008] Furthermore, the measured subsidence values ​​under complex terrain conditions in S2 Obtained by the difference in surface elevation data before and after mining in the mining area; Using the formula: Calculate the additional subsidence value caused by complex terrain relative to ideal flat terrain. ;like When, it indicates that the actual settlement under complex terrain conditions has an increasing settlement effect relative to the ideal flat terrain benchmark settlement; if When, it indicates that the actual settlement under the action of complex terrain has a settlement reduction effect relative to the benchmark settlement of ideal flat terrain.

[0009] Furthermore, the topographic-subsidence coupling influencing factors in S3 include the ratio of mining depth to topsoil thickness. Slope coupling index between original topographic slope and baseline subsidence slope Two indicators; The ratio of mining depth to topsoil thickness is used to characterize the geometric effect controlled by topsoil thickness during the transmission of overburden deformation to the surface, denoted as . , where H is the mining depth and the subscript h indicates the thickness of the topsoil layer; Using the original terrain slope Compared with the benchmark settlement slope The directional relationship is used to construct a slope coupling index between the original terrain slope and the benchmark subsidence slope. : , In the formula, σ is the direction sign coefficient; it is calculated by the angle difference between the original topographic slope aspect and the reference settlement slope aspect. Determine the directional relationship between the original topographic slope aspect and the reference settlement slope aspect. When the original topographic dip is in the same direction as the reference settlement slope aspect... When the original topographic dip is approximately lateral to the baseline subsidence slope aspect, When the original topographic dip is opposite to the baseline subsidence slope aspect, ;when When, it indicates that the original topography and the baseline settlement have a unidirectional coupling relationship; when When, it indicates that the two have an inverse coupling relationship; when When the time is specified, it indicates that the original topography and the baseline settlement are not coupled.

[0010] Furthermore, in S5, an additional settlement response relationship is added with additional settlement values. With as the dependent variable and key influencing factors as independent variables, the regression fit is expressed as: , The additional subsidence response relationship was obtained through fitting: , In the formula For the intercept term; , , The parameters of the additional settlement response relationship obtained by fitting measured data are as follows: , , , .

[0011] Furthermore, in S5, the additional subsidence value will be predicted. The baseline mining subsidence value obtained by superimposing a complex terrain correction term onto the probability integral method is used as a basis for the complex terrain correction term. Thus, predicted values ​​of mining subsidence under complex terrain conditions can be obtained. ;in .

[0012] Furthermore, under complex terrain conditions, the predicted value of mining subsidence was obtained. This also includes the predicted value of mining subsidence. Compared with the measured subsidence value Error evaluation was conducted, and the root mean square error, mean absolute error, and coefficient of determination were used to evaluate the prediction accuracy of mining subsidence in complex terrain.

[0013] A computer device includes a processor and a memory, the processor being electrically connected to the memory for storing instructions and data, and the processor for executing a method for predicting mining subsidence in complex terrain based on additional subsidence compensation.

[0014] Beneficial Effects: This method uses the baseline settlement results for flat terrain obtained by the probability integral method as a reference, and reflects the influence of complex terrain in the form of additional settlement. By quantitatively characterizing the influence of topographic factors such as slope and aspect, as well as topographic-settlement coupling factors, on additional settlement, an additional settlement response relationship is established. The predicted additional settlement value is then introduced as a compensation term into the baseline settlement results, enabling the prediction of mining settlement under complex terrain conditions. This method can effectively reflect the asymmetry, spatial offset, and local increase or decrease characteristics of the subsidence basin. While retaining the engineering applicability and baseline prediction capability of the probability integral method, this method introduces an additional settlement compensation mechanism. The method flow is clear, the parameters are easy to obtain, and it is suitable for subsidence prediction and damage assessment in underground mining areas with complex terrain. Attached Figure Description

[0015] Figure 1 This is a flowchart of the method for predicting mining subsidence in complex terrain based on additional subsidence compensation, according to the present invention.

[0016] Figure 2 This is a schematic diagram of the principle of the mining subsidence prediction method for complex terrain based on additional subsidence compensation according to the present invention.

[0017] Figure 3 This diagram illustrates the accuracy comparison between the method of this invention and existing methods. The left side shows the test results of the traditional probability integral method, and the right side shows the test results of this method. Detailed Implementation

[0018] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0019] like Figure 1As shown, this invention discloses a method for predicting mining subsidence in complex terrain based on additional subsidence compensation. The method uses the benchmark mining subsidence value obtained by the probability integral method under ideal flat terrain conditions as a reference to identify additional subsidence caused by complex terrain. The method then compensates for the benchmark subsidence result by constructing an additional subsidence response relationship, thereby obtaining the predicted value of mining subsidence under complex terrain conditions.

[0020] This embodiment takes a tunneling face in an underground coal mine in a loess gully region as the research object. The working face advance distance is 1023.0m, the working face length is 301.5m, the coal thickness is 2.38m, the mining height is 2.2m, the average mining depth is 180.0m, and the average dip angle of the coal seam is 0-1°. The study area has obvious surface gully incision and strong slope undulation, exhibiting typical complex terrain features.

[0021] S1. Calculate the benchmark mining subsidence value under ideal flat terrain conditions: First, the mining parameters, geological parameters, and predicted rock movement parameters of the study area are obtained. These parameters include the working face location, working face length, advance distance, mining height, mining depth, coal seam dip angle, mining boundary, topsoil thickness, main influence angle tangent, subsidence coefficient, horizontal movement coefficient, and inflection point offset distance, etc.

[0022] Based on this, the benchmark mining subsidence value under the assumption of ideal flat terrain is calculated using the probability integral method. Meanwhile, the predicted horizontal strain index is calculated simultaneously based on the probability integral method.

[0023] The formula for calculating mining subsidence using the probability integral method is as follows: , In the formula, The maximum surface subsidence is given by the formula: The calculations show that m is the mining height and q is the subsidence coefficient. Let d be the coal seam dip angle, d be the advancing distance, and L be the working face length. The main influencing angle is tangent. For mining depth; The probability integral method is used to calculate the predicted horizontal strain index. The calculation formula is as follows: , In the formula, b is the horizontal movement coefficient, and r is the main influence radius. Calculated.

[0024] S2. Obtain measured subsidence values ​​and construct an additional subsidence sample set: Surface elevation data of the study area before and after mining were acquired. This surface elevation data was obtained through UAV laser point cloud measurements to generate high-precision surface elevation data. Differential calculations were performed based on the surface elevation data before and after mining to obtain measured subsidence values ​​under complex terrain conditions. .

[0025] To ensure the comparability of the measured subsidence values ​​with the benchmark mining subsidence values, the measured subsidence data underwent coordinate unification, spatial registration, outlier removal, and necessary smoothing. Subsequently, the measured subsidence values ​​were... Compared with benchmark mining subsidence value By performing difference calculations at the same spatial location, the additional subsidence value generated by complex terrain relative to ideal flat terrain is obtained. : , Among them, when When, it indicates that the actual settlement under complex terrain is greater than the benchmark settlement under ideal flat terrain, which manifests as a settlement enhancement effect; when When the actual settlement under complex terrain is less than the benchmark settlement under ideal flat terrain, it indicates a settlement reduction effect.

[0026] In this embodiment, a 5m×5m regular fishing net was constructed within the study area, and gully areas were removed. The center point of the fishing net was used as equally spaced sampling points, and the measured additional subsidence value of each sampling point was extracted as an additional subsidence sample set. After spatial overlay analysis, a total of 6319 original sampling points were obtained; after removing sampling points outside the boundary, sampling points with abnormal elevation changes, and sampling points with missing data, 5401 valid sampling points were obtained. Statistical results show that... Both positive and negative value areas exist. Among them, 3059 points showed increased settlement, accounting for 56%, while 2342 points showed decreased settlement, accounting for 44%. This indicates that the complex terrain of the Loess Gully region has a significant local increase and decrease effect on mining subsidence, and it is appropriate to model it as an additional subsidence term for compensation.

[0027] S3. Extract the topographic-subsidence coupling influencing factors: The factors influencing the topographic-subsidence coupling include the ratio of mining depth to topsoil thickness. Slope coupling index between original topographic slope and baseline subsidence slope Two indicators are used to characterize the coupling effect between complex terrain conditions and mining-induced subsidence, including the ratio of mining depth to topsoil thickness and the slope coupling index. These are obtained as follows: Using benchmark mining subsidence values Irregular triangular network was constructed to calculate the baseline settlement slope. The original terrain slope is denoted as The original topographic slope aspect is denoted as The baseline subsidence slope is denoted as By calculating the angle difference between the original topographic slope aspect and the reference settlement slope aspect. This indicates the directional relationship between the original topographic slope aspect and the reference settlement slope aspect: , Using the direction sign coefficient The directional relationships are divided as follows: , The following formula is used to construct a slope coupling index between the original terrain slope and the reference settlement slope. : , In the formula, when When, it indicates that the original topography and the baseline settlement have a unidirectional coupling relationship; when When, it indicates that the two have an inverse coupling relationship; when When the two are in a lateral coupling or the coupling effect is weak, it indicates that the two are relatively well coupled. S4. Establish the additional settlement response relationship: Based on the additional subsidence sample set and the ratio of mining depth to topsoil thickness Slope coupling index between original topographic slope and baseline subsidence slope Predicting horizontal strain index Three indicators were used to establish an additional subsidence response relationship through regression fitting.

[0028] Additional settlement response formula with additional settlement value With as the dependent variable and key influencing factors as independent variables, the regression fit is expressed as: .

[0029] The additional subsidence response relationship is obtained after fitting: , In the formula For the intercept term; , , The additional subsidence response parameters obtained by fitting the sample dataset in S2 are respectively: , , , .

[0030] In determining the parameters of the additional subsidence response equation, the sample dataset is divided into training and testing samples. The training samples are used to fit the parameters of the additional subsidence response equation, while the testing samples are used to evaluate the predictive performance of the equation. Evaluation metrics include the coefficient of determination. The root mean square error (RMSE) and mean absolute error (MAE) were used to measure the subsidence. By comparing the errors between the baseline subsidence prediction results and the prediction results after additional subsidence compensation, the effectiveness of the additional subsidence response formula for subsidence correction in complex terrain was verified.

[0031] S5. Obtain predicted values ​​of mining subsidence under complex terrain conditions. After obtaining the predicted additional subsidence value Then, it is added as a complex terrain compensation term to the baseline mining subsidence value obtained in step S1. Additional settlement compensation principle as follows Figure 2 As shown, the predicted value of mining subsidence under complex terrain conditions is obtained. , .

[0032] In this embodiment, while retaining the ability of the probability integral method to predict the basic morphology of subsidence basins under ideal flat terrain, the subsidence increase or decrease effect caused by complex terrain is expressed as additional subsidence, and the reference subsidence field is corrected by using the additional subsidence response formula.

[0033] like Figure 3 As shown, the sample dataset was divided into training and testing samples in a 7:3 ratio, with 3780 training samples and 1620 testing samples. The model fitting results show that the coefficient of determination on the testing samples... The accuracy was 0.873, RMSE was 172.6 mm, and MAE was 136.2 mm. This indicates that the established model can well express the response relationship between the terrain-subsidence coupling factor and the additional subsidence. Comparing the accuracy of the obtained results with those predicted by the traditional probability integral method, it can be found that the overall accuracy of this method is improved by 24.1%. The value increased from 0.632 to 0.873, and the RMSE and MAE decreased by 121.4 mm and 77.7 mm, respectively. This indicates that the method can better reflect the displacement, asymmetry, and local subsidence or subsidence characteristics of subsidence basins under complex terrain conditions in the Loess Gully Region, and can be used for subsidence prediction, damage assessment, and ecological restoration design in underground mining areas with complex terrain.

[0034] The above description is merely one embodiment of the present invention and is not intended to limit the present invention. Any minor modifications, equivalent substitutions, and improvements made to the above embodiment based on the technical essence of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for predicting mining subsidence in complex terrain based on additional subsidence compensation, characterized in that, Based on historical mining data and measured mining data from the mining area, three indicators were calculated: the ratio of mining depth to topsoil thickness, the slope coupling index, and the predicted horizontal strain index. Regression fitting was performed on these three indicators to obtain the predicted additional settlement value. Combining this predicted additional settlement value with the baseline mining settlement value obtained through the probability integral method yields the predicted mining settlement value under complex terrain conditions. The specific steps are as follows: S1. Obtain mining, geological, and rock movement data of the mining area, and use the probability integral method to obtain the benchmark mining subsidence value under ideal flat terrain conditions. and predicted horizontal strain index ; S2. Obtain the measured settlement values ​​of the stabilized area under complex terrain conditions. This allows us to obtain the additional subsidence value resulting from complex terrain relative to ideal flat terrain. ; S3. Based on pre-mining surface elevation data, geological data, and baseline subsidence values, extract the topographic-subsidence coupling influencing factor; the topographic-subsidence coupling influencing factor includes the ratio of mining depth to topsoil thickness. Slope coupling index between original topographic slope and baseline subsidence slope Two indicators; The ratio of mining depth to topsoil thickness is used to characterize the geometric effect controlled by topsoil thickness during the transmission of overburden deformation to the surface, denoted as . , where H is the mining depth and the subscript h is the thickness of the topsoil layer; Using the original terrain slope Compared with the benchmark settlement slope The directional relationship is used to construct a slope coupling index between the original terrain slope and the benchmark subsidence slope. : ， In the formula, σ is the direction sign coefficient; it is calculated by the angle difference between the original topographic slope aspect and the reference settlement slope aspect. Determine the directional relationship between the original topographic slope aspect and the reference settlement slope aspect. When the original topographic dip is in the same direction as the reference settlement slope aspect... When the original topographic dip is approximately lateral to the baseline subsidence slope aspect, ; When the original topographic dip is opposite to the baseline subsidence slope aspect ;when When, it indicates that the original topography and the baseline settlement have a unidirectional coupling relationship; when When, it indicates that the two have an inverse coupling relationship; when When the original topography and the baseline settlement are not coupled, it indicates that there is no coupling effect between them. S4. Impact factors of topographic-subsidence coupling and predicted horizontal strain index Regression fitting is performed to establish the additional settlement response relationship. This relationship is then used to predict the additional settlement value under complex terrain conditions; this is the predicted additional settlement value. Additional settlement response formula with additional settlement value With as the dependent variable and key influencing factors as independent variables, the regression fit is expressed as: , The additional subsidence response relationship was obtained through fitting: , In the formula For the intercept term; , , These are the additional subsidence response parameters obtained by fitting measured data. S5, at the benchmark mining subsidence value Introducing predicted additional subsidence values As a compensation term, the predicted value of mining subsidence under complex terrain conditions is obtained. .

2. The method for predicting mining subsidence in complex terrain based on additional subsidence compensation according to claim 1, characterized in that, The mining, geological, and rock movement data obtained in S1 include: working face location, mining thickness, mining depth, coal seam dip angle, loose layer or topsoil layer thickness, main influence angle tangent, subsidence coefficient, horizontal movement coefficient, inflection point offset distance, and mining boundary; benchmark mining subsidence value. This is the theoretical benchmark subsidence value calculated by the classical probability integral method without considering the influence of complex terrain undulations.

3. The method for predicting mining subsidence in complex terrain based on additional subsidence compensation according to claim 2, characterized in that, Measured subsidence values ​​under complex terrain conditions in S2 Obtained by the difference in surface elevation data before and after mining in the mining area; Using the formula: Calculate the additional subsidence value caused by complex terrain relative to ideal flat terrain. ;like When, it indicates that the actual settlement under complex terrain conditions has an increasing settlement effect relative to the ideal flat terrain benchmark settlement; if When, it indicates that the actual settlement under the action of complex terrain has a settlement reduction effect relative to the benchmark settlement of ideal flat terrain.

4. The method for predicting mining subsidence in complex terrain based on additional subsidence compensation according to claim 3, characterized in that, In S4, the specific parameters are as follows: , , , .

5. The method for predicting mining subsidence in complex terrain based on additional subsidence compensation according to claim 4, characterized in that, In S5, the additional subsidence value will be predicted. The baseline mining subsidence value obtained by superimposing a complex terrain correction term onto the probability integral method is used as a basis for the complex terrain correction term. Thus, predicted values ​​of mining subsidence under complex terrain conditions can be obtained. ;in .

6. The method for predicting mining subsidence in complex terrain based on additional subsidence compensation according to claim 5, characterized in that, Obtaining predicted values ​​of mining subsidence under complex terrain conditions This also includes the predicted value of mining subsidence. Compared with the measured settlement value Error evaluation was conducted, and the root mean square error, mean absolute error, and coefficient of determination were used to evaluate the prediction accuracy of mining subsidence in complex terrain.

7. A computer device, characterized in that, It includes a processor and a memory, the processor being electrically connected to the memory, the memory being used to store instructions and data, and the processor being used to execute the complex terrain mining subsidence prediction method based on additional subsidence compensation as described in any one of claims 1-6.

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