An accurate prediction method for surface subsidence in coal mining under thick loose layer

Abstract

The present invention discloses a method for accurately predicting ground subsidence in coal mining under thick loose layers, which is applicable to the field of ground deformation prediction technology. The probability integral prediction parameters of the mining area are obtained, and the adjustment parameters B are obtained by fitting according to the specific ground subsidence measured data and the corresponding working face information. (1,2) ; Then, the information of the working face to be predicted is obtained according to the plan of the mining project, and the main influence radius r' of the outside of the inflection point is calculated; finally, the inflection point is used as the segmentation point, the inside of the inflection point uses the existing main influence radius r of the mining area, and the outside of the inflection point uses the correction value r' instead of the correction r, and the probability integral model is used to predict the surface subsidence value after the working face under the thick loose layer is mined. Its calculation steps are simple, and the parameters are easy to obtain. It can effectively improve the prediction accuracy of the edge area of ​​the mining subsidence basin under the thick loose layer, and has a strong guiding role in practical work.

Description

Technical Field

[0001] The invention relates to a calculation method for accurately predicting surface subsidence, belongs to the technical field of surface deformation prediction, and is an accurate prediction method for surface subsidence in coal mining under thick loose layers. Technical Background

[0002] Loose layer refers to loose sediments that have not been diagenesized in the Quaternary and Neogene, including alluvial layer, residual layer, and diluvial layer. Generally, when the thickness of loose layer is greater than 50m, it can be considered as thick loose layer. The surface subsidence characteristics of mining under thick loose layer are significantly different from those under general geological mining conditions. Specifically, the subsidence amount at the edge of the subsidence basin is large, the extension range is far, and the subsidence curve converges slowly. This leads to the poor applicability of the probability integral model commonly used for mining subsidence in thick loose layer mining areas, and the large prediction error in the basin edge area. The existence of the above problems seriously restricts the urbanization construction of mining areas, and poses a serious threat to the construction and protection of various oil and gas pipelines, railway lines, viaducts and other facilities passing through the mining areas. Therefore, in the process of ensuring the safe construction of surface structures in mining areas and promoting the development of urbanization in mining areas, how to achieve accurate prediction of surface subsidence in thick loose layer mining areas has become a difficult problem that needs to be solved in mining areas.

[0003] Mining under thick loose layers has unique rules: the edge of the subsidence basin has a large amount of subsidence, a long extension range, and a slow convergence of the subsidence curve. Therefore, the existing probability integral model has poor applicability when used for prediction, resulting in large prediction errors in the edge of the basin. Therefore, the existing probability integral model cannot perform good predictions, affecting the subsequent construction and protection planning of surface buildings. Summary of the invention

[0004] In view of the shortcomings in the existing technology, a method for accurately predicting surface subsidence in thick loose layer mining areas is provided, which has simple steps, high accuracy, and takes into account the characteristics of surface subsidence in thick loose layers and the geological characteristics of the mining area.

[0005] In order to achieve the above technical objectives, the present invention provides a method for accurately predicting surface subsidence in coal mining under thick loose layers, and the steps are as follows:

[0006] The probability integral prediction parameters of the mining area are obtained based on the monitoring data of the surface mobile observation station established in the mining area. The probability integral parameters include: subsidence coefficient q, main influence angle tangent tanβ, inflection point offset distance S, and mining influence propagation angle θ;

[0007] The adjustment parameter B is obtained by fitting and inversion using the measured data of the specific surface subsidence in the predicted area and the corresponding working face information. (1,2) ;

[0008] Then, according to the relevant information of the working face to be predicted: working face width a, face length b, mining depth H, mining thickness m, inclination angle α, the subsidence curve of any section of the surface subsidence surface is obtained as needed;

[0009] The inflection point of the sinking curve is used as the segmentation point of the main influencing radius, and the main influencing radius r' corrected outside the inflection point of the sinking curve is calculated; the range where the two inflection points of the sinking curve are close to the working surface is defined as the inner side of the inflection point, and the range where the two inflection points are far from the working surface is defined as the outer side of the inflection point;

[0010] The following formula is used to calculate the existing main influence radius r of the predicted mining area inside the inflection point, and then the probability integral model is used to calculate the predicted value of the surface subsidence inside the inflection point;

[0011]

[0012] The corrected main impact radius r' for surface subsidence prediction outside the inflection point is calculated using the following formula, and then the predicted value of surface subsidence outside the inflection point is calculated using the probability integral model;

[0013]

[0014] Then, the predicted values ​​of surface subsidence calculated inside and outside the inflection point are combined to obtain the final complete predicted value of surface subsidence after mining of the working face under the thick loose layer;

[0015] Where l is the calculated width of the working surface, K (1,2) are the correction coefficients for trend and inclination, B (1,2) are the adjustment parameters of strike and dip respectively; x is the coordinate of the point to be predicted on the main section with the center of the goaf as the origin.

[0016] Furthermore, for the working face to be predicted, according to its geological mining condition information, a working face with similar geological mining conditions is selected, and the adjustment parameter B in the correction formula for correcting the main influence radius r' is obtained by fitting based on the monitoring data and probability integral parameters of the surface mobile observation station of the similar working face. (1,2) , using the section parameter B (1,2) Prediction of surface subsidence at future mining faces under the same geological mining conditions.

[0017] Furthermore, the predicted values ​​of surface subsidence on the inner side of the inflection point calculated according to the formula include: surface subsidence W1, horizontal movement U1, inclination I1, horizontal deformation ε1 and curvature K1; the predicted values ​​of surface subsidence on the outer side of the inflection point include: surface subsidence W2, horizontal movement U2, inclination I2, horizontal deformation ε2 and curvature K2.

[0018] Furthermore, the range of surface subsidence is divided into two parts, the inner part and the outer part of the inflection point. The probability integral model is used for prediction on the inner part of the inflection point, and its prediction parameters are not changed. On the outer part of the inflection point, the correction formula of the corrected main influence radius r' mentioned above is used to calculate the corrected main influence radius r', which is used to replace the original r, and then the original probability integral model is used for prediction. Finally, the two are combined together to form a complete surface subsidence prediction result.

[0019] Beneficial effects:

[0020] The present application discloses a method for accurately predicting surface subsidence in coal mining under thick loose layers. It aims to solve the problem that the existing probability integral model has poor local applicability in thick loose layer mining areas, resulting in large prediction errors at the edge of the subsidence basin. While ensuring the consistency of the predicted parameters, it can achieve accurate prediction of surface subsidence in thick loose layer mining areas, and provide technical support for achieving safe and efficient mining under thick loose layers and accurate protection of ground buildings (structures). BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram comparing the surface subsidence prediction result of the method of the present invention with the prediction result of the probability integral model;

[0022] Figure 2 A schematic diagram of the upper and lower comparison of a working surface well in an embodiment of the present invention;

[0023] Figure 3 It is a schematic diagram for comparing the prediction results of mining subsidence of a certain working face in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The present invention will be further described in detail below with reference to the figures and specific implementation process:

[0025] like Figure 1 As shown, the present invention provides a method for accurately predicting surface subsidence in coal mining under thick loose layers. First, the probability integral prediction parameters of the mining area are obtained according to the monitoring data of the surface mobile observation station established in the mining area, including the subsidence coefficient q, the main influence angle tangent tanβ, the inflection point offset distance S, and the mining influence propagation angle θ and other parameters. Then, the adjustment parameter B in formula 1 is obtained by fitting according to the specific surface subsidence measured data and the corresponding working face information. (1,2) , the adjustment parameter can be used to predict the future mining subsidence of the working face under the same geological mining conditions; then, according to the mining engineering plan, the width a, length b, mining depth H, mining thickness m, and inclination angle α of the working face to be predicted are obtained; then the adjustment parameter B (1,2)Substituting it into formula 1, the corrected main influence radius r' outside the inflection point can be obtained; finally, the inflection point is used as the segmentation point, the existing main influence radius r of this mining area is used inside the inflection point, and the corrected value r' is used instead of the corrected r outside the inflection point. The probability integral model is used to predict the surface subsidence value after mining of the working face under the thick loose layer.

[0026] Figure 1 The correction curve in the figure refers to the prediction result outside the inflection point after surface subsidence prediction is performed using the method of the present application. The figure is to show the comparison with the prediction result of the probability integral model. The two points where the sinking curve intersects with the vertical dotted line in the figure are the two inflection points, the sinking curve between the two inflection points is the inner range of the inflection point, and the two sides of the two inflection points are the outer range of the inflection point.

[0027] The surface subsidence surface can be realized through the probability integral model. The specific implementation method is to divide it into two directions: the strike main section and the dip main section. Then, the subsidence of any point on the entire surface is calculated according to a specific formula. If the main section is used as the cross section to make a profile, the subsidence curve on the main section is obtained. Then, it can be Figure 1 As shown, the inflection point is the concave-convex dividing point of the subsidence curve, and its position is specifically indicated by the inflection point offset distance (the horizontal distance from the dotted line to the coal pillar in the figure).

[0028] Furthermore, if Figure 1 As shown in the figure, when mining under thick loose layers, the surface sinks as indicated by the "surface" in the figure. The surface sinking outside the inflection point is large and wide. In actual engineering, when the probability model is used for prediction, the prediction results inside the inflection point are often good, which is basically consistent with the actual sinking. However, outside the inflection point, as it gradually moves away from the goaf, the probability integral model converges too quickly (the predicted sinking value reaches 10mm), resulting in an increasing relative error between it and the actual sinking. The most sensitive parameter in the probability integral model for predicting the influence range of subsidence is the main influence radius r (r = H / tanβ, because r is related to the mining depth, so r is actually used as an intermediate parameter in the model, and the main influence angle tangent tanβ is used as a parameter). Therefore, inside the inflection point, since the probability integral model can achieve good prediction accuracy, there is no need to make any changes to it; outside the inflection point, starting from the most sensitive parameter, the main influence radius, the main influence radius is corrected to achieve accurate prediction outside the inflection point. At the same time, when correcting r, it is not simply to change its value during prediction (this will lead to discontinuous prediction results at the inflection point, and the improvement of the prediction accuracy of the final result will be relatively limited). Instead, it is to make a dynamic change with distance through the above formula while ensuring that the original prediction parameters remain unchanged. This not only ensures the consistency of the prediction parameters (the existing parameters in the mining area do not need to be changed and can be used directly), but also ensures the continuity of the prediction results at the segmentation point (inflection point) (the calculation results inside and outside this point are the same).

[0029] The specific steps are:

[0030] Step 1: First, for the mining area where the working face to be predicted is located, the monitoring data of the surface mobile observation stations that have been established in the mining area in the past are collected, and the probability integral prediction parameters of the mining area are obtained by fitting, including the subsidence coefficient q, the main influence angle tangent tanβ, the inflection point offset distance S, and the mining influence propagation angle θ parameter;

[0031] Step 2: For the working face to be predicted, according to its geological mining condition information, select a working face with similar geological mining conditions, and obtain the adjustment parameter B in the correction formula through fitting based on the monitoring data of its surface mobile observation station and probability integral parameters. (1,2) , then this parameter can be used to predict the surface subsidence of future mining faces under the same geological mining conditions.

[0032] Step 3: Combined with the mining plan, mining engineering plan and drilling column chart data of the mining area, collect and organize the relevant information of the working face to be predicted, including the working face width a, face length b, mining depth H, mining thickness m, and inclination angle α;

[0033] Step 4: Using the above-prepared probability integral parameters and the information of the working face to be predicted, the probability integral model is used to calculate the predicted value of the surface subsidence inside the inflection point, including surface subsidence W1, horizontal movement U1, inclination I1, horizontal deformation ε1 and curvature K1;

[0034] Step 5: Use the determined adjustment parameter B (1,2) , substituting it into formula 1, we can obtain the corrected main influence radius r' outside the inflection point, and then use the corrected main influence radius r' to replace the original r, and use the same method as step 4 to calculate the predicted value of surface subsidence outside the inflection point, including surface subsidence W2, horizontal movement U2, inclination I2, horizontal deformation ε2 and curvature K2;

[0035] Step 6: Combine the predicted surface subsidence value inside the inflection point obtained in step 4 and the predicted surface subsidence value outside the inflection point obtained in step 5 according to their positions to obtain a complete surface subsidence prediction result including the inside and outside of the inflection point.

[0036]

[0037] Where r is the main influence radius used inside the inflection point during prediction; r' is the main influence radius used outside the inflection point; H is the mining depth; tanβ is the tangent of the main influence angle; l is the calculated width of the working face; K (1,2) are the correction coefficients for strike and dip, respectively, and are given by B (1,2) Calculated; B (1,2)are the adjustment parameters of strike and dip respectively; x is the coordinate of the point to be predicted on the main section with the center of the goaf as the origin.

[0038] Example

[0039] like Figure 2 The figure shows a comparison diagram of the upper and lower parts of a working face in a thick loose layer mining area in my country. It is necessary to predict the sinking of a pipeline on the surface after the working face is mined.

[0040] Firstly, according to the subsidence monitoring data of the relevant surface movement observation station in the mining area, the probability integral prediction parameters were fitted, which were the subsidence coefficient q = 0.88, the main influence angle tangent tanβ = 1.78, the inflection point offset distance S = 30.9m and the mining influence propagation angle θ = 87.6°;

[0041] Then, for the predicted working face, the adjustment parameters in the correction model were determined according to its geological mining conditions, namely, strike direction B (1) 1450, tending towards direction B (2) is 4940;

[0042] Then, combined with the mining plan, mining engineering plan and drilling column chart data of the mining area, the relevant information of the working face to be predicted was collected and sorted out, which were the working face width a=361.9m, face length b=805.3m, average mining depth H=574.3m, average mining thickness m=8.09m, and inclination angle α=5°;

[0043] Then, the collected working face information parameters and probability integral parameters are substituted into the probability integral model to predict the surface subsidence and obtain the predicted value of the surface subsidence. In this implementation case, the probability integral model and the method for accurately predicting surface subsidence in coal mining under loose layer of this application are respectively used for the L observation line of a certain oil and gas pipeline. The predicted results of the method for accurately predicting surface subsidence of the surface subsidence are shown in Table 1, and the curve is shown in Table 1. Figure 3 As shown in the figure, the accurate prediction model of the present application is basically consistent with the measured value of surface subsidence, and the prediction accuracy is better than the prediction result of the probability integral model.

[0044] Table 1 Sinking prediction results of implementation case 1

[0045]

[0046]

Claims

1. A method for accurately predicting surface subsidence in coal mining under thick loose layers, characterized in that Here are the steps: The probability integral prediction parameters of the mining area are obtained based on the monitoring data of the surface mobile observation station established in the mining area. The probability integral parameters include: subsidence coefficient q, main influence angle tangent tanβ, inflection point offset distance S, and mining influence propagation angle θ; The adjustment parameter B is obtained by fitting and inversion using the measured data of the specific surface subsidence in the predicted area and the corresponding working face information. (1,2) ; Then, according to the relevant information of the working face to be predicted: working face width a, face length b, mining depth H, mining thickness m, inclination angle α, the subsidence curve of any section of the surface subsidence surface is obtained as needed; The inflection point of the sinking curve is used as the segmentation point of the main influencing radius, and the main influencing radius r' corrected outside the inflection point of the sinking curve is calculated; the range where the two inflection points of the sinking curve are close to the working surface is defined as the inner side of the inflection point, and the range where the two inflection points are far from the working surface is defined as the outer side of the inflection point; The following formula is used to calculate the existing main influence radius r of the predicted mining area inside the inflection point, and then the probability integral model is used to calculate the predicted value of the surface subsidence inside the inflection point; The corrected main impact radius r' for surface subsidence prediction outside the inflection point is calculated using the following formula, and then the predicted value of surface subsidence outside the inflection point is calculated using the probability integral model; Then, the predicted values ​​of surface subsidence calculated inside and outside the inflection point are combined to obtain the final complete predicted value of surface subsidence after mining of the working face under the thick loose layer; Where l is the calculated width of the working surface, K (1,2) are the correction coefficients for trend and inclination, B (1,2) are the adjustment parameters of strike and dip respectively; x is the coordinate of the point to be predicted on the main section with the center of the goaf as the origin.

2. The method for accurately predicting surface subsidence in coal mining under thick loose layers according to claim 1 is characterized in that: For the working face to be predicted, according to its geological mining conditions information, a working face with similar geological mining conditions is selected. According to the monitoring data and probability integral parameters of the surface mobile observation station of the similar working face, the adjustment parameter B in the correction formula for correcting the main influence radius r' is obtained by fitting. (1,2) , using the section parameter B (1,2) Prediction of surface subsidence at future mining faces under the same geological mining conditions.

3. The method for accurately predicting surface subsidence in coal mining under thick loose layers according to claim 2 is characterized in that: The predicted values ​​of surface subsidence on the inner side of the inflection point calculated according to the formula include: surface subsidence W1, horizontal movement U1, inclination I1, horizontal deformation ε1 and curvature K1; the predicted values ​​of surface subsidence on the outer side of the inflection point include: surface subsidence W2, horizontal movement U2, inclination I2, horizontal deformation ε2 and curvature K2.

4. The method for accurately predicting surface subsidence in coal mining under thick loose layers according to claim 3 is characterized in that: The range of surface subsidence includes two parts, the inner part and the outer part of the inflection point. The probability integral model is used for prediction on the inner part of the inflection point without making any changes to its prediction parameters. On the outer part of the inflection point, the revised formula for the revised main influence radius r' mentioned above can be used to calculate the revised main influence radius r', which is used to replace the original r, and then the original probability integral model is used for prediction. Finally, the two are combined together to form a complete surface subsidence prediction result.

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