Method and system for predicting dynamic subsidence basin in mining areas based on combined time function

Abstract

The present application discloses a method and system for predicting a dynamic subsidence basin in a mining area based on a combined time function, and relates to the technical field of mining area subsidence prediction. The method comprises acquiring a SAR image set of a region to be measured; determining a mining area surface deformation result of the region to be measured according to the SAR image set; predicting a first surface subsidence result according to the mining area surface deformation result by using a combined Usher time function model; the first surface subsidence result is the surface subsidence amount of the region to be measured at any future time; predicting a second surface subsidence result according to the first surface subsidence result by using an IPIM model; the second surface subsidence result is the surface subsidence amount of the entire basin in 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 and system based on a combined time function. Background Art

[0002] At present, in various prediction methods of dynamic settlement of mining areas, such as the prediction method of dynamic settlement basin of mining areas based on time function and SBAS-InSAR (Small Baseline Subset InSAR, differential interferometry short baseline set time series analysis), it usually includes measured analysis, similar simulation and theoretical analysis. However, in the process of measured analysis, similar simulation and theoretical analysis of dynamic settlement of mining areas, a single time function is usually used to predict the dynamic settlement of the surface, which makes the prediction results of dynamic settlement basin of mining areas inaccurate. Therefore, how to improve the accuracy of prediction of dynamic settlement basin of mining areas has become a technical problem that needs to be solved urgently in this field. Summary of the invention

[0003] The purpose of this application is to provide a method and system for predicting dynamic subsidence basins in mining areas based on a combined time function, which can improve the accuracy of prediction of dynamic subsidence basins in mining areas.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a method for predicting a dynamic subsidence basin in a mining area based on a combined time function, the method comprising:

[0006] A SAR image set of the area to be measured is obtained; the SAR image set includes a plurality of SAR images.

[0007] The mining area surface deformation result of the area to be measured is determined according to the SAR image set; the mining area surface deformation result includes mining area surface deformation information.

[0008] According to the surface deformation results of the mining area, a combined Usher time function model is used to predict the first surface subsidence result; the combined Usher time function model is a model established based on the empirical formula of Usher function, proportional coefficient, thick loose layer and average mining depth, and the first surface subsidence result is the surface subsidence amount of the measured area at any time in the future.

[0009] According to the first surface subsidence result, the IPIM model is used to predict the second surface subsidence result; 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.

[0010] In a second aspect, the present application provides a mining area dynamic subsidence basin prediction system based on a combined time function, the mining area dynamic subsidence basin prediction system based on a combined time function comprises:

[0011] The SAR image set acquisition module is used to acquire a SAR image set of the area to be measured; the SAR image set includes a plurality of SAR images.

[0012] The mining area surface deformation result determination module is used to determine the mining area surface deformation result of the area to be measured according to the SAR image set; the mining area surface deformation result includes mining area surface deformation information.

[0013] The first prediction module is used to predict the first surface settlement result according to the surface deformation result of the mining area by using the combined Usher time function model; the combined Usher time function model is a model established based on the empirical formula of Usher 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.

[0014] The second prediction module is used to predict the second surface subsidence result according to the first surface subsidence result by 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 amount of the entire basin in the measured area.

[0015] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0016] The present application provides a method and system for predicting dynamic subsidence basins in mining areas based on a combined time function. A combined Usher time function model is established according to an empirical formula based on an Usher function, a proportional coefficient, a thick loose layer and an average mining depth, and a first surface subsidence result is predicted by the combined Usher time function model; then a probability integral model, namely an IPIM model, is constructed after parameter correction based on the mining degree coefficient, and a second surface subsidence result is predicted by the IPIM model. Since a combined Usher time function model suitable for thick loose layer conditions is applied in the process of surface subsidence prediction, the combined Usher time function model is constructed on the basis of an empirical formula of an Usher function, a proportional coefficient, a thick loose layer and an average mining depth, and is essentially a combined time function model, which can effectively improve the accuracy of prediction of dynamic subsidence basins in mining areas, and 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 dynamic subsidence basins in mining areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] 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.

[0018] Figure 1 A schematic flow chart of a method for predicting dynamic subsidence basins in mining areas based on a combined time function is provided in one embodiment of the present application.

[0019] Figure 2 A schematic diagram of a time function curve and feature point positions of a combined Usher time function model provided in an embodiment of the present application.

[0020] 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.

[0021] Figure 4 This is a SBAS-InSAR spatiotemporal baseline connection diagram provided in one embodiment of the present application.

[0022] Figure 5 A schematic diagram of SBAS-InSAR monitoring results provided in one embodiment of the present application.

[0023] Figure 6 A schematic diagram of extracting dynamic prediction results provided in one embodiment of the present application.

[0024] Figure 7 A schematic diagram of a complete surface subsidence basin provided in one embodiment of the present application.

[0025] Figure 8 A schematic diagram of the structure of a mining area dynamic subsidence basin prediction system based on a combined time function provided in one embodiment of the present application. DETAILED DESCRIPTION

[0026] 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.

[0027] 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.

[0028] like Figure 1As shown, this embodiment provides a method for predicting dynamic subsidence basins in mining areas based on a combined time function, and the method specifically includes the following steps:

[0029] Step S1: Acquire a SAR (Synthetic Aperture Radar) image set of the area to be measured.

[0030] In this embodiment, a SAR image set of the area to be measured is collected by synthetic aperture radar, and the SAR image set includes several SAR images. The imaging principle of SAR images is to transmit coherent signals and receive echoes, and then form images through coherent processing. The synthetic aperture radar system measures the distance according to the motion trajectory of the platform and performs two-dimensional imaging. Its two-dimensional coordinate information is distance information and azimuth information perpendicular to the distance. The azimuth resolution is proportional to the beam width and inversely proportional to the antenna size. The SAR image can be used in scenarios such as dynamic subsidence basin prediction in mining areas.

[0031] Step S2: determining the surface deformation result of the mining area in the area to be measured according to the SAR image set.

[0032] In this embodiment, the mining area surface deformation result mainly includes mining area surface deformation information. Synthetic aperture radar is used to obtain continuous SAR images of the area to be measured, and the mining area surface deformation information of the area to be measured is analyzed based on these SAR images, so that surface deformation prediction can be performed based on the SAR images and the mining area surface deformation information.

[0033] Step S3: According to the surface deformation result of the mining area, a combined Usher time function model is used to predict a first surface settlement result. The combined Usher time function model is a model established based on an empirical formula of Usher function, proportionality 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 settlement result, the IPIM (Improved Probabilistic Integral Method) model is used to predict the second surface settlement result. The IPIM model is a probability integral model with parameter correction based on the mining degree coefficient, and the second surface settlement result is the surface settlement of the entire basin in the measured area.

[0035] In this embodiment, step S2 determines the surface deformation result of the mining area in the measured area according to the SAR image set, which specifically includes:

[0036] Step S21, setting a time baseline threshold and a space baseline threshold.

[0037] 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.

[0038] 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.

[0039] Step S24: determining the surface deformation result of the mining area in the area to be measured according to the plurality of differential interference patterns.

[0040] In this embodiment, step S24 determines the surface deformation result of the mining area in the measured area according to the plurality of differential interference patterns, and specifically includes:

[0041] 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.

[0042] Step S242: using the spatial coordinate system of the external DEM (Digital Elevation Model) data, geocoding the surface deformation phase information respectively to obtain the surface deformation result of the mining area in the area to be measured.

[0043] In this embodiment, step S3 uses a combined Usher time function model to predict a first surface settlement result based on the surface deformation result of the mining area, specifically including:

[0044] Step S31: According to the surface deformation result of the mining area, the PSO algorithm is used to perform parameter inversion on the combined Usher time function model to determine the model parameters of the combined Usher time function model.

[0045] Step S32: using the combined Usher time function model and the inverted model parameters, predicting the surface subsidence of the area to be tested to obtain the first surface subsidence result.

[0046] In this embodiment, step S4 uses the IPIM model to predict the second surface subsidence result based on the first surface subsidence result, and specifically includes:

[0047] Step S41: According to the first surface subsidence result, a PSO algorithm is used to perform parameter inversion on the IPIM model to determine model parameters of the IPIM model.

[0048] Step S42: using the IPIM model and the inverted model parameters, predicting the surface subsidence of the area to be measured to obtain the second surface subsidence result.

[0049] In this embodiment, after step S4, the mining area dynamic subsidence basin prediction method based on the combined time function may further include the following steps:

[0050] 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 amount of the entire basin in the measured area at each time.

[0051] 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.

[0052] The present embodiment proposes a method for predicting a dynamic subsidence basin in a mining area based on a combined time function, which mainly includes the following contents:

[0053] (1) Select a certain area to be measured and obtain the SAR image covering the mining area during the multi-track working face mining 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:

[0054] 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 acquisition time series is expressed as:

[0055] t=[t0,…,t N ] T (1).

[0056] Where t represents time t, t N Indicates the date of the Nth SAR image.

[0057] Then, one of the SAR images is selected as the main image for registration, and the free combination differential interferogram that meets the time baseline and space baseline thresholds is selected from all the free combination differential interferograms. Assuming that M differential interferograms are obtained, we have:

[0058]

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] (2) Based on the Usher function model, the empirical formula of the proportional coefficient, thick loose layer and average depth, a combined Usher time function model is established. Specifically, it includes the following contents:

[0064] The Usher 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 Usher model is:

[0065]

[0066] Where W(t) represents the surface settlement of the traditional Usher model at time t, a, b, and c 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.

[0067] 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:

[0068]

[0069] Among them, ρ is the proportionality coefficient, h represents the thickness of the loose layer, and H0 represents the average mining depth.

[0070] 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.

[0071] Based on the above proportional relationship, this embodiment describes the dynamic surface settlement process by combining two Usher time functions with different parameters in a certain proportion, thereby constructing a combined Usher time function model, referred to as the combined Usher Model. The combined Usher time function model is a surface settlement dynamic prediction model, which is theoretically not only suitable for dynamic model prediction under thick loose layer mining conditions, but also for general mining conditions. The established surface settlement dynamic prediction model is expressed as:

[0072]

[0073] 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 Usher time function model at time t. Since two Usher time functions with different parameters are used, w1(t) represents the first Usher time function model and w2(t) represents the second Usher time function model.

[0074] (3) Based on the SBAS-InSAR subsidence monitoring results of the mining area surface in (1), that is, the surface deformation information of the mining area, the model parameters of the combined Usher 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:

[0075] 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, and the combined Usher 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 Usher time function model are calculated and inverted pixel by pixel, and the combined Usher 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 each pixel of a SAR image, so the model parameters of the combined Usher time function model can be inverted by extracting each time series data, and after having the model parameters of the combined Usher time function model, the model can be used to predict the surface subsidence at any time.

[0076] (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:

[0077] 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:

[0078]

[0079] 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.

[0080] 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:

[0081]

[0082] Among them, D1 is the mining length of the dip, D3 is the mining length of the strike, θ 0 is the mining impact propagation angle, 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.

[0083] The prediction formula for settlement and horizontal movement of any point on the surface is:

[0084]

[0085] Among them, w0 is the maximum settlement in the main section, D v is the settlement value of any point on the surface.

[0086] 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:

[0087]

[0088] Among them, n is the mining degree coefficient, q d is the settlement coefficient during incomplete mining, and q is the settlement coefficient.

[0089] According to formula (9), the prediction model based on the probability integral method can be modified as follows:

[0090]

[0091] 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.

[0092] 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:

[0093]

[0094] 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.

[0095] 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:

[0096] D v =f(GP,P) (12).

[0097] 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.

[0098] 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.

[0099] In this embodiment, based on the IPIM model and the combined Usher time function model established above, a mining area dynamic settlement extraction method is constructed in combination with SBAS-InSAR technology. 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:

[0100]

[0101] 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.

[0102] 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 Usher time function model can be integrated using the IDW interpolation method, and the principle is shown in the following formula.

[0103]

[0104] Among them, P(x i,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.

[0105] The weight of each point λ i The calculation formula is:

[0106]

[0107] Among them, d i Indicates the absolute distance from each valid pixel to the solution pixel.

[0108] In this embodiment, the prediction result W2(t n ) and the prediction results of the combined Usher 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:

[0109] W(t n )=IDW(W1(t n ),W2(t n )) (16).

[0110] 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.

[0111] 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 combined Usher model parameters pixel by pixel, and the combined Usher 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 IPIM prediction results of the decoherent area and the combined Usher model prediction results are combined for IDW interpolation fusion 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 7This 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)".

[0112] In an exemplary embodiment, a mining area dynamic subsidence basin prediction system based on a combined time function is provided, and the mining area dynamic subsidence basin prediction system based on a combined time function includes the following functional modules:

[0113] The SAR image set acquisition module M1 is used to acquire a SAR image set of the area to be measured; the SAR image set includes a plurality of SAR images.

[0114] The mining area surface deformation result determination module M2 is used to determine the mining area surface deformation result of the area to be measured according to the SAR image set; the mining area surface deformation result includes mining area surface deformation information.

[0115] The first prediction module M3 is used to predict the first surface settlement result according to the surface deformation result of the mining area by using the combined Usher time function model; the combined Usher time function model is a model established based on the empirical formula of Usher 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.

[0116] The second prediction module M4 is used to predict the second surface settlement result according to the first surface settlement result by 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 settlement result is the surface settlement amount of the entire basin in the measured area.

[0117] The IDW interpolation fusion module M5 is used to use the IDW 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 amount of the entire basin of the measured area at each time.

[0118] 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.

[0119] 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 based on combined time functions, characterized in that: The mining area dynamic subsidence basin prediction method based on the combined time function includes: Acquire a SAR image set of the area to be measured; the SAR image set includes a plurality of SAR images; Determine the mining area surface deformation result of the area to be measured according to the SAR image set; the mining area surface deformation result includes mining area surface deformation information; According to the surface deformation result of the mining area, a combined Usher time function model is used to predict a first surface settlement result; the combined Usher time function model is a model established based on an empirical formula of Usher function, proportionality 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; According to the first surface subsidence result, the IPIM model is used to predict the second surface subsidence result; 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 based on combined time functions according to claim 1 is characterized in that: Determining the surface deformation result of the mining area in the area to be measured according to the SAR image set specifically includes: Set time baseline threshold and space baseline threshold; Selecting one 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; According to the plurality of differential interference patterns, the surface deformation result of the mining area in the area to be measured is determined.

3. The method for predicting dynamic subsidence basins in mining areas based on combined time functions according to claim 2 is characterized in that: Determining the surface deformation result 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 result of the mining area in the area to be measured.

4. The method for predicting dynamic subsidence basins in mining areas based on combined time functions according to claim 1, characterized in that: According to the surface deformation results of the mining area, the combined Usher time function model is used to predict the first surface settlement results, including: According to the surface deformation results of the mining area, the PSO algorithm is used to perform parameter inversion on the combined Usher time function model to determine the model parameters of the combined Usher time function model; The combined Usher time function model and the inverted model parameters are used to predict the surface subsidence of the area to be tested, and the first surface subsidence result is obtained.

5. The method for predicting dynamic subsidence basins in mining areas based on combined time functions according to claim 1, characterized in that: The expression of the combined Usher time function model is: Where W(t) represents the surface settlement at time t of the traditional Usher model, a, b and c 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 proportional coefficient, h represents the thickness of the loose layer, H0 represents the average mining depth, w(t) represents the surface settlement of the combined Usher time function model at the time t, w1(t) represents the first Usher time function model, and w2(t) represents the second Usher time function model.

6. The method for predicting dynamic subsidence basins in mining areas based on combined time functions according to claim 1, characterized in that: According to the first surface subsidence result, the IPIM model is used to predict the second surface subsidence result, which specifically includes: According to the first surface subsidence result, a PSO algorithm is used to perform parameter inversion on the IPIM model to determine model parameters of the IPIM model; The IPIM model and the inverted model parameters are used to predict the surface subsidence of the area to be measured to obtain the second surface subsidence result.

7. The method for predicting dynamic subsidence basins in mining areas based on combined time functions according to claim 1, characterized in that: The expression of the IPIM model is: D v =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.

8. The method for predicting dynamic subsidence basins in mining areas based on combined time functions according to claim 1, characterized in that: After the step of predicting the second surface subsidence result by using the IPIM model according to the first surface subsidence result, the method for predicting the dynamic subsidence basin of the mining area based on the combined time function 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.

9. The method for predicting dynamic subsidence basins in mining areas based on combined time functions according to claim 8, characterized in that: The expression of the third surface settlement result is: W(t n )=IDW(W1(t n ),W2(t n )); 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.

10. A mining area dynamic subsidence basin prediction system based on combined time function, characterized in that: The mining area dynamic subsidence basin prediction system based on the combined time function includes: A SAR image set acquisition module is used to acquire a SAR image set of the area to be measured; the SAR image set includes a plurality of SAR images; A mining area surface deformation result determination module is used to determine the mining area surface deformation result of the area to be measured according to the SAR image set; the mining area surface deformation result includes mining area surface deformation information; A first prediction module is used to predict a first surface settlement result according to the surface deformation result of the mining area by using a combined Usher time function model; the combined Usher time function model is a model established based on an empirical formula of an Usher function, a proportionality coefficient, a thick loose layer and an 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 second prediction module is used to predict the second surface subsidence result according to the first surface subsidence result by 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 amount of the entire basin in the measured area.

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