A method, device, medium and product for predicting dynamic subsidence basin in mining area

Abstract

The present application discloses a method, device, medium and product for predicting a dynamic subsidence basin in a mining area, and relates to the technical field of mining area subsidence prediction. The method comprises acquiring a SAR image of a region to be measured; determining the mining area surface deformation information of the region to be measured based on the SAR image; inverting the model parameters of a combined Weibull time function model based on the mining area surface deformation information, and using the combined Weibull time function model to predict a first surface subsidence result, wherein the first surface subsidence result is the surface subsidence amount of the region to be measured at any future time; inverting the model parameters of an IPIM model based on the first surface subsidence result, and using the IPIM model to predict a second surface subsidence result, wherein the second surface subsidence result is the surface subsidence amount of the entire basin of the region to be measured. The present application can improve the accuracy of prediction of a dynamic subsidence basin in a mining area.

Description

Technical Field

[0001] The present application relates to the technical field of mining area subsidence prediction, and in particular to a mining area dynamic subsidence basin prediction method, equipment, medium and product. Background Art

[0002] Coal resources still account for more than 50% of the current energy. However, the high-intensity mining of coal resources will cause the redistribution of overburden stress in the goaf area, and the rock strata will move and deform, resulting in geological disasters such as cracks and collapse on the surface, which will damage surrounding buildings. The surface subsidence caused by coal mining is a process of rebalancing the overburden stress and long-term dynamic changes in the surface. It is insufficient to only estimate the mining damage after the surface settles. The degree of damage to surface buildings caused by surface mining subsidence depends not only on the final results of various indicators (static values), but also on the change process of various indicators (dynamic values). The damage assessment of surface buildings currently mainly uses indicators such as inclination, curvature and horizontal deformation, and the damage level is judged based on the observed inclination, curvature and horizontal deformation data. Therefore, restoring the complete dynamic subsidence basin on the surface has important theoretical and practical significance for studying the dynamic change law of the surface caused by mining subsidence.

[0003] The dynamic surface subsidence caused by coal mining is a complex surface deformation process. Under the condition of uniform mining speed, the dynamic surface settlement curve presents a Sigmoid shape, which can be divided into three stages: initial settlement period, main settlement period and residual settlement period. In view of this feature, Knothe proposed the Knothe time function to describe the dynamic settlement process of mining, but the shape of its settlement-time curve is not "S" shaped, and it cannot fully reflect the three stages of surface settlement. Based on this, some scholars have proposed improved models such as power Knothe and segmented Knothe, as well as growth curve models such as Weibull, Usher, MMF (Multi-Model Fusion), and Logistic (logistic regression). Based on limited GPS (Global Positioning System) and leveling measured data, the time function parameter inversion is carried out to predict the future surface settlement trend, which has been widely used in the field of mining subsidence. However, due to the limited measured data, this method requires a lot of manpower and material resources to predict large-scale settlement caused by mining.

[0004] InSAR (Interferometric Synthetic Aperture Radar) technology can be used for deformation monitoring over large areas with short revisit periods, which is impossible with traditional leveling and GPS measurements. Technologies such as D-InSAR (Differential InSAR) and TS-InSAR (Time-Series InSAR) have been widely used to monitor surface deformation and have played an important role in monitoring earthquakes, volcanic activity, landslides, glacial movement, mining deformation caused by underground mining, and urban deformation. Compared with traditional measurement methods, SBAS-InSAR (Small Baseline Subset InSAR) technology has the characteristics of high accuracy, all-weather, and large coverage in a short period of time, providing an effective monitoring method for large-scale subsidence prediction research during coal mining.

[0005] In recent years, relevant scholars have studied the prediction methods of dynamic subsidence basins in mining areas based on time functions and SBAS-InSAR by using methods such as field analysis, similarity simulation, and theoretical analysis, and have made some progress. However, most current studies use a single time function to predict dynamic surface subsidence. Studies have shown that the subsidence caused by coal mining in thick loose layer mining areas is mainly composed of mining-induced subsidence and additional soil subsidence, which is the result of the combined effect of the overlying bedrock and loose layer. A single time function cannot accurately describe the superimposed influence of bedrock and soil, which ultimately leads to inaccurate prediction results of dynamic subsidence basins in mining areas. Summary of the invention

[0006] The purpose of this application is to provide a method, equipment, medium and product for predicting dynamic subsidence basins in mining areas, which can improve the accuracy of prediction of dynamic subsidence basins in mining areas.

[0007] To achieve the above objectives, this application provides the following solutions:

[0008] In a first aspect, the present application provides a method for predicting a dynamic subsidence basin in a mining area, the method comprising:

[0009] Acquire SAR images of the area to be measured;

[0010] Determining the surface deformation information of the mining area in the area to be measured according to the SAR image;

[0011] According to the surface deformation information of the mining area, the model parameters of the combined Weibull time function model are inverted, and the first surface settlement result is predicted by using the combined Weibull time function model; the combined Weibull time function model is a model established based on the empirical formula of WeiBull function, proportional coefficient, thick loose layer and average mining depth, and the first surface settlement result is the surface settlement amount of the measured area at any time in the future;

[0012] According to the first surface subsidence result, the model parameters of the IPIM model are inverted, and the second surface subsidence result is predicted using the IPIM model; the IPIM model is a probability integral model with parameter correction based on the mining degree coefficient, and the second surface subsidence result is the surface subsidence of the entire basin in the measured area.

[0013] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described methods for predicting dynamic subsidence basins in mining areas.

[0014] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described methods for predicting dynamic subsidence basins in mining areas.

[0015] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for predicting dynamic subsidence basins in mining areas.

[0016] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0017] The present application provides a method, device, medium and product for predicting a dynamic subsidence basin in a mining area, which combines a combined Weibull time function model and an IPIM model to predict a dynamic subsidence basin in a mining area. First, a combined Weibull time function model is established based on the empirical formula of the Weibull function, the proportional coefficient, the thick loose layer and the average mining depth, and the model parameters of the combined Weibull time function model are inverted according to the surface deformation information of the mining area determined by the SAR image, so that a first surface subsidence result can be predicted by the combined Weibull time function model; then, a probability integral model, namely, an IPIM model, is constructed after parameter correction based on the mining degree coefficient, and on the basis of the first surface subsidence result, the model parameters of the IPIM model are inverted, and then the second surface subsidence result is predicted by the IPIM model, so that the accuracy of the prediction of the dynamic subsidence basin in the mining area can be effectively improved, and a more accurate and reliable prediction result of the dynamic subsidence basin in the mining area can be obtained, which can solve the problem that the current single time function cannot accurately describe the superimposed influence of bedrock and soil, resulting in inaccurate prediction results of the dynamic subsidence basin in the mining area. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 A schematic flow chart of a method for predicting dynamic subsidence basins in a mining area provided in one embodiment of the present application.

[0020] Figure 2 A schematic diagram of a time function curve and characteristic point positions of a combined Weibull time function model provided in an embodiment of the present application.

[0021] Figure 3 A schematic diagram of the trend and tendency prediction principle of the probability integral method provided in one embodiment of the present application.

[0022] Figure 4 This is a SBAS-InSAR spatiotemporal baseline connection diagram provided in one embodiment of the present application.

[0023] Figure 5 A schematic diagram of SBAS-InSAR monitoring results provided in one embodiment of the present application.

[0024] Figure 6 A schematic diagram of extracting dynamic prediction results provided in one embodiment of the present application.

[0025] Figure 7A schematic diagram of a complete surface subsidence basin provided in one embodiment of the present application.

[0026] Figure 8 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0028] In order to make the purpose, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0029] like Figure 1 As shown, this embodiment provides a method for predicting a dynamic subsidence basin in a mining area, and the method specifically includes the following steps:

[0030] Step S1, obtaining a SAR image of the area to be measured.

[0031] Step S2: determining the surface deformation information of the mining area in the area to be measured based on the SAR image.

[0032] Step S3: invert the model parameters of the combined Weibull time function model according to the surface deformation information of the mining area, and use the combined Weibull time function model to predict and obtain a first surface settlement result.

[0033] In this embodiment, the combined Weibull time function model is a model established based on the empirical formula of WeiBull function, proportional coefficient, thick loose layer and average mining depth, and the first surface settlement result is the surface settlement amount of the measured area at any time in the future.

[0034] Step S4: According to the first surface subsidence result, invert the model parameters of the IPIM (Improved Probabilistic Integral Method) model, and use the IPIM model to predict and obtain the second surface subsidence result.

[0035] In this embodiment, the IPIM model is a probability integral model after parameter correction based on the mining degree coefficient, and the second surface settlement result is the surface settlement amount of the entire basin in the measured area.

[0036] In this embodiment, step S2 determines the surface deformation information of the mining area in the measured area according to the SAR image, which specifically includes the following steps:

[0037] Step S21, setting a time baseline threshold and a space baseline threshold.

[0038] Step S22: select an image from all the SAR images as a main image, perform image registration between the main image and other images in pairs, and obtain a plurality of freely combined differential interference pairs.

[0039] Step S23: selecting the freely combined differential interference pairs that meet the time baseline threshold and the space baseline threshold from all the freely combined differential interference pairs, and obtaining a corresponding plurality of differential interference graphs.

[0040] Step S24: determining the surface deformation information of the mining area in the area to be measured based on the plurality of differential interference patterns.

[0041] In this embodiment, step S24 determines the surface deformation information of the mining area in the area to be measured according to the plurality of differential interference patterns, and specifically includes the following steps:

[0042] Step S241: perform SBAS-InSAR processing on the differential interferograms to obtain surface deformation phase information of the area to be measured in the radar image coordinate system.

[0043] Step S242: using the spatial coordinate system of external DEM (Digital Elevation Model) data, geocoding the surface deformation phase information respectively to obtain the surface deformation information of the mining area in the area to be measured.

[0044] In this embodiment, after the step of inverting the model parameters of the IPIM model according to the first surface subsidence result and predicting the second surface subsidence result using the IPIM model in step S4, the mining area dynamic subsidence basin prediction method may further include the following steps:

[0045] Step S5: Use the IDW (Inverse Distance Weighting) interpolation method to perform IDW interpolation fusion on the second surface subsidence result and the first surface subsidence result to obtain a third surface subsidence result. The third surface subsidence result is the surface subsidence of the entire basin in the measured area at each time.

[0046] In order to make the technical solution of this embodiment clearer, the specific implementation process of the method is described in detail below in the form of examples.

[0047] The present embodiment provides a method for predicting a dynamic subsidence basin in a mining area, which mainly includes the following contents:

[0048] (1) Select a certain area to be measured and obtain the SAR image covering the mining area during the multi-track working face recovery period. Set the temporal and spatial baseline threshold, obtain the free combination differential interferometer pair, and perform SBAS-InSAR processing to obtain the final surface deformation information of the mining area. Specifically include the following:

[0049] In this embodiment, for the area to be measured, it is assumed that there are N+1 SAR images covering the same area acquired in time series, and the image acquisition time series is expressed as:

[0050] t=[t0,…,t N ] T (1).

[0051] Where t represents time t, t N Indicates the date of the Nth SAR image.

[0052] Then, one of the images is selected as the main image for registration, and the free combination differential interference pairs that meet the time baseline and space baseline thresholds are selected from all the free combination differential interference pairs. Assuming that M differential interference patterns are obtained, we have:

[0053]

[0054] Wherein, M represents the number of differential interference patterns, and N represents the number of SAR images. In this embodiment, N+1 SAR images are acquired.

[0055] Based on the above-obtained free combination differential interferometer pairs, SBAS-InSAR processing is performed, which includes two inversion processes. The first inversion process is to estimate the displacement rate and residual terrain, and the second phase unwrapping is performed to optimize the results. The second inversion process is to calculate the displacement in the time series. Based on the obtained deformation rate, customized atmospheric filtering is performed to estimate and remove the atmospheric phase to obtain a purer time series displacement.

[0056] In this embodiment, the surface deformation phase information obtained after SBAS-InSAR processing is in the radar image coordinate system. After obtaining the line-of-sight surface deformation in the surface radar coordinate system, it is necessary to geocode the obtained surface deformation phase information according to the spatial coordinate system of the external DEM data, so as to obtain the surface deformation information of the mining area.

[0057] In this embodiment, the spatial coordinate system can select the WGS-84 (World Geodetic System-1984 Coordinate System) geodetic coordinate system, and the surface deformation phase information obtained is geocoded according to the WGS-84 geodetic coordinate system to obtain the surface deformation information of the mining area under the geodetic coordinate system.

[0058] (2) Based on the WeiBull function model, the proportional coefficient, the thick loose layer and the empirical formula of the average depth, a combined Weibull time function model is established. Specifically, it includes the following contents:

[0059] The Weibull model has central symmetry and contains three characteristic dividing points, namely the maximum acceleration point, the maximum velocity point and the minimum acceleration point, which can describe the dynamic settlement process caused by mining, such as Figure 2 This embodiment uses the coverage exceeding the minimum acceleration point as the predictable condition of the logic model, and the expression of the Weibull model is:

[0060]

[0061] Where W(t) represents the surface settlement at time t in the traditional Weibull model, a and b are model parameters related to the properties of the overlying strata on the mining face, and W m is the maximum settlement, and t represents time t.

[0062] In this embodiment, considering that the subsidence caused by coal mining in a deep-buried thick loose layer mining area is mainly composed of subsidence caused by mining and additional settlement of the soil body, it is the result of the combined action of the overlying bedrock and alluvial layer. The unit subsidence basin caused by mining is represented by a combination of two unit subsidence basins with different main influence radii in a certain proportion. According to the superposition principle, a settlement and deformation prediction model for any point in the surface movement basin is established, and based on the measured data of multiple observation stations, an empirical formula of proportional coefficient, thick loose layer and average depth is given, which is expressed as:

[0063]

[0064] Among them, ρ is the proportionality coefficient, h represents the thickness of the loose layer, and H0 represents the average mining depth.

[0065] In this embodiment, the proportionality coefficient ρ is mainly related to the loose layer thickness h and the average mining depth H0. Under the condition of a certain mining depth (mining depth for short), the greater the loose layer thickness, that is, the greater the ratio of the loose layer thickness to the bedrock thickness (H0-h), the greater the proportionality coefficient ρ; when the ratio of the loose layer thickness to the bedrock thickness is less than 0.2, the proportionality coefficient ρ increases exponentially with the increase of the ratio; when the ratio of the loose layer thickness to the bedrock thickness is greater than 0.2, ρ increases approximately linearly with the increase of the ratio of the loose layer thickness to the bedrock thickness.

[0066] Based on the above proportional relationship, this embodiment describes the dynamic surface settlement process by combining two Weibull time functions with different parameters in a certain proportion, thereby constructing a combined Weibull time function model, referred to as a combined Weibull (Combined Weibull Model) model. As a surface settlement dynamic prediction model, the combined Weibull time function model is theoretically not only suitable for dynamic model prediction under thick loose layer mining conditions, but also suitable for general mining conditions. The established combined Weibull time function model is expressed as:

[0067]

[0068] Among them, h represents the thickness of the loose layer, H0 represents the average mining depth, and w(t) represents the surface settlement of the combined Weibull time function model at time t. Since two Weibull time functions with different parameters are used, w1(t) represents the first Weibull time function model and w2(t) represents the second Weibull time function model.

[0069] (3) Based on the SBAS-InSAR subsidence monitoring results of the mining area surface in (1), i.e., the mining area surface deformation information, the model parameters of the combined Weibull time function model are inverted to forward predict the surface subsidence at a certain moment in the future as the first surface subsidence result. Specifically, it includes the following contents:

[0070] Based on the principle of SBAS-InSAR technology, the acquired SAR images are processed by SBAS-InSAR to obtain the cumulative LOS deformation of the surface of the goaf area; then, based on the radar incident angle, the cumulative LOS deformation of the surface of the goaf area is converted into subsidence data; then, the prediction parameter P is calculated based on the geological conditions of the working face, a combined Weibull time function model is constructed, and the SSA (Singular Spectrum Analysis) algorithm is used to remove the error data in the subsidence data obtained based on the SAR image; finally, the model parameters of the combined Weibull time function model are calculated and inverted pixel by pixel, and the combined Weibull time function model is used to forward predict the surface subsidence at time t. Since SAR image data is a multidimensional matrix, there is a time series data for a pixel of a SAR image, so the model parameters of the combined Weibull time function model can be inverted by extracting each time series data, and after having the model parameters of the combined Weibull time function model, the model can be used to predict the surface subsidence at any time.

[0071] (4) Based on the surface subsidence predicted in (3), the model parameters of the IPIM model are inverted to forward predict the surface subsidence of the entire basin as the second surface subsidence result. Specifically, it includes the following contents:

[0072] The Probability Integration Method (PIM) is a widely used subsidence prediction method in coal mine subsidence prediction. Usually, the calculation formula of subsidence in the prediction model based on the probability integration method is:

[0073]

[0074] Among them, w(x) is the predicted settlement value of the main section, m c is the thickness of the coal seam, q is the sedimentation coefficient, θ is the inclination of the coal seam, x is the strike coordinate in the mining coordinate system, and r is the mining influence radius.

[0075] like Figure 3 As shown, in this embodiment, the prediction formulas for the predicted settlement of the strike main section and the predicted settlement of the dip main section are:

[0076]

[0077] Among them, D1 is the mining length of the dip, D3 is the mining length of the strike, θ 0 is the propagation angle of mining influence, y is the dip coordinate in the mining coordinate system, and w 0 (x) is the predicted settlement value of the main section, w 0(y) is the predicted settlement value of the dip main section, l1 is the calculated length of the dip working face, l3 is the calculated length of the strike working face, S is the offset of the strike turning point, S1 is the offset of the turning point in the downhill direction, and S2 is the offset of the turning point in the uphill direction.

[0078] The prediction formula for settlement and horizontal movement of any point on the surface is:

[0079]

[0080] Among them, w0 is the maximum settlement in the main section, D v is the settlement value of any point on the surface.

[0081] In this embodiment, considering that most coal mines have been transferred to deep mining, insufficient mining is more common. The probability integral method is applicable to sufficient mining conditions, but it is not applicable to subsidence prediction under insufficient mining conditions. In this embodiment, according to the insufficient mining prediction parameter correction method, the mining degree coefficient is introduced to correct the prediction parameters of the model, and the surface subsidence prediction parameters applicable to insufficient mining conditions are established, which are expressed as:

[0082]

[0083] Among them, n is the mining degree coefficient, q d is the settlement coefficient during incomplete mining, and q is the settlement coefficient.

[0084] According to formula (9), the prediction model based on the probability integral method can be modified as follows:

[0085]

[0086] Among them, w(x) is the predicted settlement value of the main section, m c is the thickness of the coal seam, q is the settlement coefficient, θ is the inclination of the coal seam, x is the strike coordinate in the mining coordinate system, r is the mining influence radius, and n is the mining degree coefficient.

[0087] In this embodiment, after the prediction model based on the probability integral method is corrected, the mining degree coefficient will be used as one of the prediction parameters of the probability integral method. The expressions of the corrected geological parameter GP and the prediction parameter P are as follows:

[0088]

[0089] Among them, H is the strike mining depth; H1 is the downhill mining depth; H2 is the uphill mining depth; D1 is the dip mining length; D3 is the strike mining length; θ is the coal seam inclination; m c is the thickness of the coal seam; q is the sedimentation coefficient; tanβ is the tangent value of the main influencing angle; θ 0is the propagation angle of mining impact; n is the mining degree coefficient; S is the offset of the strike turning point; S1 is the offset of the downhill turning point; S2 is the offset of the uphill turning point.

[0090] In this embodiment, the settlement value D of any point on the surface corrected by the probability integral method is obtained according to equations (6) to (8), (9) and (10): v The functional relationship between the prediction parameter GP and the prediction parameter P is expressed as:

[0091] D v =f(GP,P) (12).

[0092] Among them, D v is the settlement value of any point on the surface, f(·) represents the functional relationship, and GP and P are prediction parameters.

[0093] In this embodiment, the modified prediction model based on probability integral method (ie, modified PIM model) is called IPIM model. IPIM model is applicable to subsidence prediction under non-sufficient mining and sufficient mining conditions.

[0094] In this embodiment, based on the IPIM model and the combined Weibull time function model established above, the SBAS-InSAR technology is combined to construct a mining area dynamic settlement extraction method. n The dynamic settlement W1(t n )(n=1,2,3…) as the fitting data of IPIM, and then use the PSO algorithm to invert the model parameters of the IPIM model, and use the IPIM model to forward predict the subsidence basin, which is expressed as:

[0095]

[0096] Among them, t n (GP, P) is the model parameter of the IPIM model obtained by inversion, PSO represents the PSO algorithm, W1(t n ) is the first surface settlement result, W2(t n ) is the second surface subsidence result.

[0097] In this embodiment, considering that the IDW interpolation method assumes that each input point has a local influence, this influence weakens with the increase of distance, which is basically consistent with the distribution of surface settlement contour lines. The prediction results of the IPIM model and the combined Weibull time function model can be integrated using the IDW interpolation method, and the principle is shown in the following formula.

[0098]

[0099] Among them, P(xi ,y i ) represents the regional pixels that need to be solved, P i (x i ,y i ) represents the i-th regional pixel that needs to be solved, λ i Represents weight.

[0100] The weight of each point λ i The calculation formula is:

[0101]

[0102] Among them, d i Indicates the absolute distance from each valid pixel to the solution pixel.

[0103] In this embodiment, the prediction result W2(t n ) and the forecast results of the combined Weibull time function model W1(t n ) to perform IDW interpolation fusion to restore the mining area t n The complete dynamic subsidence basin W(t n ), as the third surface subsidence result, is expressed as:

[0104] W(t n )=IDW(W1(t n ),W2(t n )) (16).

[0105] Among them, W(t n ) is the third surface settlement result, W1(t n ) is the first surface settlement result, W2(t n ) is the second surface subsidence result, and IDW(·) represents the IDW interpolation fusion operation.

[0106] This embodiment uses SBAS-InSAR technology to monitor the surface subsidence caused by coal mining and obtain the time series subsidence data of the mining area. The spatiotemporal baseline connection of SBAS-InSAR technology is as follows: Figure 4 The monitoring results are shown in Figure 5 Then, the PSO algorithm is used to calculate the parameters of the combined Weibull model pixel by pixel, and the combined Weibull model is used to dynamically predict the surface subsidence. The dynamic prediction results are shown in Figure 6 As shown in the figure. The dynamic prediction results at a certain moment are predicted, the prediction parameters of the IPIM model are inverted, and the settlement value of the decoherent area is predicted forward. The IDW interpolation fusion is performed on the combined IPIM prediction results of the decoherent area and the combined WeiBull model prediction results to restore the complete dynamic settlement basin on the surface of the mining area. The complete settlement basin on the surface is shown in the figure. Figure 7 This method is not only applicable to dynamic model prediction under thick loose layer mining conditions, but also to general mining conditions. When there is no thick loose layer in the overburden, the method provided in this embodiment can also be used to predict the dynamic subsidence basin in the mining area, which can effectively improve the accuracy of the prediction of the dynamic subsidence basin in the mining area and provide a useful reference for the dynamic prediction and subsidence control of coal subsidence under "three (under buildings, under water bodies, and under railways)".

[0107] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data required for the dynamic subsidence basin prediction of the mining area, such as SAR image data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for predicting a dynamic subsidence basin in a mining area is implemented.

[0108] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0109] In an exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of the above-mentioned method for predicting dynamic subsidence basins in mining areas are implemented.

[0110] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for predicting a dynamic subsidence basin in a mining area.

[0111] In an exemplary embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for predicting a dynamic subsidence basin in a mining area.

[0112] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0113] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0114] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for predicting dynamic subsidence basins in mining areas, characterized in that: The mining area dynamic subsidence basin prediction method comprises: Acquire SAR images of the area to be measured; Determining the surface deformation information of the mining area in the area to be measured according to the SAR image; According to the surface deformation information of the mining area, the model parameters of the combined Weibull time function model are inverted, and the first surface settlement result is predicted by using the combined Weibull time function model; the combined Weibull time function model is a model established based on the empirical formula of WeiBull function, proportional coefficient, thick loose layer and average mining depth, and the first surface settlement result is the surface settlement amount of the measured area at any time in the future; The expression of the combined Weibull time function model is: Where W(t) represents the surface settlement at time t in the traditional Weibull model, a and b are model parameters related to the properties of the overlying strata on the mining face, and W m is the maximum settlement, t represents the time t, ρ is the proportionality coefficient, h represents the thickness of the loose layer, H0 represents the average mining depth, w(t) represents the surface settlement of the combined Weibull time function model at the time t, w1(t) represents the first Weibull time function model, and w2(t) represents the second Weibull time function model; According to the first surface subsidence result, the model parameters of the IPIM model are inverted, and the second surface subsidence result is predicted using the IPIM model; the IPIM model is a probability integral model with parameter correction based on the mining degree coefficient, and the second surface subsidence result is the surface subsidence of the entire basin in the measured area.

2. The method for predicting dynamic subsidence basins in mining areas according to claim 1, characterized in that: Determining the surface deformation information of the mining area in the area to be measured according to the SAR image specifically includes: Set time baseline threshold and space baseline threshold; Selecting an image from all the SAR images as a main image, performing image registration between the main image and other images in pairs, and obtaining a plurality of freely combined differential interference pairs; Selecting the free combination differential interference pairs that meet the time baseline threshold and the space baseline threshold from all the free combination differential interference pairs, and obtaining a corresponding plurality of differential interference graphs; The surface deformation information of the mining area in the area to be measured is determined based on the plurality of differential interference patterns.

3. The method for predicting dynamic subsidence basins in mining areas according to claim 2, characterized in that: Determining the surface deformation information of the mining area in the area to be measured according to the plurality of differential interference patterns specifically includes: Performing SBAS-InSAR processing on the differential interferograms respectively to obtain surface deformation phase information of the area to be measured in the radar image coordinate system; The surface deformation phase information is geocoded by using the spatial coordinate system of the external DEM data to obtain the surface deformation information of the mining area in the area to be measured.

4. The method for predicting dynamic subsidence basins in mining areas according to claim 1, characterized in that: The expression of the IPIM model is: Dv=f(GP,P); Among them, w(x) is the predicted settlement value of the main section, m c is the coal seam thickness, n is the mining degree coefficient, q is the settlement coefficient, θ is the coal seam inclination, x is the strike coordinate in the mining coordinate system, r is the mining influence radius, GP represents the corrected geological parameters, P represents the corrected prediction parameters, H is the strike mining depth; H1 is the downhill mining depth; H2 is the uphill mining depth; D1 is the inclined mining length; D2 is the strike mining length, tanβ is the tangent value of the main influencing angle, θ 0 is the mining impact propagation angle, S is the strike inflection point offset, S1 is the downhill inflection point offset, S2 is the uphill inflection point offset, and D v is the settlement value of any point on the surface, f(·) represents the functional relationship, t n (GP, P) is the model parameter of the IPIM model obtained by inversion, PSO represents the PSO algorithm, W1(t n ) is the first surface settlement result, W2(t n ) is the second surface subsidence result.

5. The method for predicting dynamic subsidence basins in mining areas according to claim 1, characterized in that: After the steps of inverting the model parameters of the IPIM model according to the first surface subsidence result, and using the IPIM model to predict the second surface subsidence result, the method for predicting the dynamic subsidence basin in the mining area further includes: The IDW interpolation method is used to perform IDW interpolation fusion on the second surface subsidence result and the first surface subsidence result to obtain a third surface subsidence result; the third surface subsidence result is the surface subsidence amount of the entire basin of the measured area at each time.

6. The method for predicting dynamic subsidence basins in mining areas according to claim 5, characterized in that: The expression of the third surface settlement result is: W(tn)=IDW(W1(tn),W2(tn)); Among them, W(t n ) is the third surface settlement result, W1(t n ) is the first surface settlement result, W2(t n ) is the second surface subsidence result, and IDW(·) represents the IDW interpolation fusion operation.

7. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for predicting a dynamic subsidence basin in a mining area as described in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for predicting a dynamic subsidence basin in a mining area as described in any one of claims 1 to 6 is implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for predicting a dynamic subsidence basin in a mining area as described in any one of claims 1 to 6 is implemented.

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