Underground mining subsidence monitoring method and system integrating time series and spatiotemporal deformation

Abstract

The present invention provides an underground mining settlement monitoring method and system that integrates time series and spatiotemporal deformation, and the method includes the following steps: generating an InSAR interference pattern stack based on existing mining area SAR images; obtaining a mining area predicted settlement model and an unwrapped interference pattern stack by a MA-PU assisted model phase unwrapping method based on the InSAR interference pattern stack; removing linear errors and reference points from the unwrapped interference pattern stack; performing pixel classification on the processed unwrapped interference pattern stack, and filling the unwrapped interference pattern stack from the spatial relationship and time relationship of the pixel points based on the pixel type; performing singular value decomposition on the filled unwrapped interference pattern stack to obtain the time deformation sequence of the settlement surface; and completing underground mining settlement monitoring that integrates the time series and spatiotemporal deformation based on the time deformation sequence. The present invention provides more comprehensive deformation data for mining area monitoring.

Description

Technical Field

[0001] The present invention belongs to the technical field of InSAR surface deformation monitoring in actual engineering applications of underground mining, and in particular relates to an underground mining settlement monitoring method and system integrating time series and spatiotemporal deformation. Background Art

[0002] Land subsidence is a global problem that has had a negative impact on more than 50 countries worldwide and has become one of the main geological disasters in my country. Mining subsidence is a phenomenon in which the original stress balance of the surrounding rock mass is destroyed after underground resource exploitation, the geological structure becomes unstable, and eventually a subsidence basin is formed on the surface. This kind of land subsidence caused by underground resource exploitation has become one of the main causes of land subsidence in my country, and its destructiveness is extremely great.

[0003] In recent years, the rapid development of InSAR technology, especially the rapid development of time series InSAR technology, has provided important conditions for the continuous observation of surface deformation in the entire basin, which provides important data support for studying the laws of mining subsidence. To this end, by analyzing the time series InSAR data, accurate deformation monitoring signals are extracted, and the subsidence development trend is predicted based on this information. This is of great significance and practical value for expanding the study of the temporal and spatial variation of subsidence from a small number of discrete monitoring points to the entire subsidence basin, and is crucial for accurately predicting dynamic surface subsidence and accurately assessing the degree of ecological environmental damage.

[0004] Although time series InSAR analysis can provide valuable model parameters, it does not always ensure accurate deformation measurements, which depends on how well the used settlement model matches the actual situation in the field. The degree of accuracy is closely related to how well the settlement model fits the specific location. Therefore, the use of model deformations as a substitute for actual deformation measurements should be minimized. The latter approach solves the large gradient problem by integrating settlement results obtained from different methods, but this integration may lead to reduced measurement accuracy due to the inherent uncertainties of methods such as POT. In this case, MA-PU becomes a very promising solution, which seems to be able to overcome the above challenges and become a potential valid choice for deriving SBAS deformation results.

[0005] It is worth noting that although MA-PU can effectively solve the phase gradient problem, it cannot solve the temporal correlation problem. Strong temporal correlation in the interferometric phase can lead to non-untangling of the phase (low signal-to-noise ratio). The lack of certain interferometric pairs in the interferometric stack can lead to rank deficiency and ill-conditioning problems when deriving the deformation time series. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention integrates the time series and spatiotemporal deformation of underground mining settlement monitoring method and system, overcomes the problem of interference phase aliasing caused by excessive settlement gradient in underground mining and loss of coherence in settlement center leading to the inability to untangle, and processes other types of untangle problems (such as time correlation) through SBAS, spatial interpolation method and time model, so as to correctly obtain the time series of the entire settlement surface of the mining area.

[0007] To achieve the above object, the present invention provides the following solutions:

[0008] The underground mining subsidence monitoring method integrating time series and spatiotemporal deformation includes the following steps:

[0009] Generate InSAR interferogram stacks based on existing mining area SAR images;

[0010] Based on the InSAR interferogram stack, a mining area predicted settlement model and an unwrapped interferogram stack are obtained by using a MA-PU assisted model phase unwrapping method;

[0011] Performing linear error and reference point removal processing on the unwrapped interference pattern stack;

[0012] Classifying pixels of the processed disentangled interference pattern stack, and filling the disentangled interference pattern stack based on the pixel type and the spatial relationship and time relationship of the pixel points;

[0013] Perform singular value decomposition on the filled disentangled interference pattern heap to obtain the time deformation sequence of the settlement surface;

[0014] Based on the time deformation sequence, underground mining settlement monitoring integrating the time series and the spatiotemporal deformation is completed.

[0015] Preferably, the method for obtaining the InSAR interference pattern stack is:

[0016] Perform image pairing based on the time baseline and space baseline of all mining area SAR images to obtain paired images;

[0017] Oversampling the paired images in a ranging direction to obtain a sampled image;

[0018] Based on the sampled images, the InSAR interference pattern stack is generated by differential interferometry technology; wherein a pair of images generates one interference pattern.

[0019] Preferably, the method for obtaining the mining area predicted settlement model by the MA-PU auxiliary model phase unwrapping method is:

[0020] Based on the branch and cut method and the traditional MCF method, phase unwrapping is performed on the interferograms in the InSAR interferogram stack to obtain an initial unwrapped phase;

[0021] Based on a quadtree algorithm, sub-sampling the initial phase unwrapping result is performed, and the spatial model is inverted to obtain spatial model parameters;

[0022] Based on the spatial model parameters, a spatial model phase is obtained; the spatial model phase is the predicted settlement model of the mining area;

[0023] Calculating a residual phase between the spatial model phase and the initial unwrapped phase, and iterating;

[0024] The sum of the spatial model phases of all iterations is calculated and added to the unwrapped residual phase of the last iteration to obtain a final deformation amount; wherein the final deformation amount is extracted from the unwrapped interference pattern.

[0025] Preferably, the pixel classification results include full-rank points, far-field points, intermediate points, and near-field points; singular value decomposition is performed on each type of pixel to obtain a time deformation sequence of the settlement surface;

[0026] The space model and the time model are used to fill the blank pixels of the intermediate points and the near-field points in the disentangled interference pattern stack; the time model adopts the Logistic model.

[0027] Preferably, the method of filling the blank pixels at the middle points in the unwrapped interference pattern stack is:

[0028] Based on the genetic algorithm and the accumulated deformation of the mining date, inversion is performed to obtain the time model parameters of the time model;

[0029] Based on the time model parameters, the pixel value of the blank pixel in each disentangled interference pattern at the middle point in the disentangled interference pattern stack is calculated.

[0030] The present invention also provides an underground mining settlement monitoring system integrating time series and spatiotemporal deformation, which is used to implement the underground mining settlement monitoring method, including:

[0031] The image pile acquisition module is used to generate InSAR interference image piles based on the existing mining area SAR images;

[0032] An unwrapping module is used to obtain a mining area predicted settlement model and an unwrapped interferogram stack based on the InSAR interferogram stack by using a MA-PU assisted model phase unwrapping method;

[0033] A de-wrapping interference pattern stack processing module, used for removing linear errors and reference points from the de-wrapping interference pattern stack;

[0034] A time deformation sequence acquisition module is used to classify the pixels of the processed disentangled interference pattern pile, and fill the disentangled interference pattern pile from the spatial relationship and time relationship of the pixel points based on the pixel type; perform singular value decomposition on the filled disentangled interference pattern pile to obtain the time deformation sequence of the settlement surface;

[0035] The monitoring module completes underground mining settlement monitoring by integrating the time series with the spatiotemporal deformation based on the time deformation sequence.

[0036] Preferably, the graph stack acquisition module includes:

[0037] A pairing unit is used to pair images according to the time baseline and space baseline of all mining area SAR images to obtain paired images;

[0038] A sampling unit, used for oversampling the paired image in a ranging direction to obtain a sampled image;

[0039] The interference pattern stack generating unit is used to generate the InSAR interference pattern stack based on the sampled images by differential interferometry technology; wherein a pair of images generates one interference pattern.

[0040] Preferably, the disentanglement module comprises:

[0041] An initial phase unwrapping unit is used to perform phase unwrapping on the interferograms in the InSAR interferogram stack based on a branch-cut method and a traditional MCF method to obtain an initial unwrapped phase;

[0042] A spatial model inversion unit, configured to subsample the initial phase unwrapping result based on a quadtree algorithm, and invert the spatial model to obtain spatial model parameters;

[0043] A spatial model phase acquisition unit, used to obtain a spatial model phase based on the spatial model parameters; the spatial model phase is the predicted settlement model of the mining area;

[0044] An iterative unit, used for calculating a residual phase between the spatial model phase and the initial unwrapped phase, and performing iteration;

[0045] The model building unit is used to calculate the sum of all iterated spatial model phases and add it to the unwrapping residual phase of the last iteration to obtain a final deformation amount; wherein the final deformation amount is extracted from the unwrapping interference pattern.

[0046] Compared with the prior art, the beneficial effects of the present invention are as follows: starting from the subsidence model, spatial relationship and time characteristics of underground mining deformation, the present invention obtains the time deformation sequence of the entire settlement surface under the influence of factors such as large gradient deformation and time incoherence, thereby providing more comprehensive deformation data for mining area monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0048] Figure 1 This is a flow chart of an underground mining subsidence monitoring method integrating time series and spatiotemporal deformation according to an embodiment of the present invention;

[0049] Figure 2 : This is a comparison diagram of the results of the traditional MCF unwrapping method and the MA-PU method in an embodiment of the present invention; wherein (a) is the original interference map after filtering, (b) is the map directly unwrapped using MCF, (c) is the sedimentation funnel map restored by the method of the present invention, and (d) is the difference between (c) and (b);

[0050] Figure 3 48 pairs of interference graphs of the West Cliff mining area in an embodiment of the present invention;

[0051] Figure 4 This is a time series diagram of the deformation of the entire settlement surface in the West Cliff mining area. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0054] Embodiment 1

[0055] like Figure 1 As shown in FIG. 1 , the underground mining subsidence monitoring method integrating time series and spatiotemporal deformation includes the following steps:

[0056] S1: Generate InSAR interferogram stack based on existing mining area SAR images;

[0057] A further implementation method is that the method for obtaining the InSAR interference pattern stack is:

[0058] Perform image pairing based on the time baseline and space baseline of all mining area SAR images to obtain paired images;

[0059] Oversampling the paired images in the ranging direction to obtain a sampled image;

[0060] Based on the sampled images, a stack of InSAR interferograms is generated by differential interferometry, where a pair of images generates one interferogram.

[0061] In this embodiment, if Figure 1 As shown in part (A) of the figure, in order to obtain the time series information of the deformation of the entire subsidence surface in a certain area, the SAR images of the corresponding time period of the area are first publicly or purchased. If the images are paired according to the time baseline and spatial baseline of all images, the paired images are oversampled in the ranging direction, and then the interference pattern stack is generated by differential interferometry technology (a pair of images generates one interference pattern).

[0062] S2: Based on the InSAR interference pattern stack, the mining area predicted settlement model and the unwrapped interference pattern stack are obtained by the MA-PU assisted model phase unwrapping method; that is, each interference pattern in the interference pattern stack is unwrapped by the MA-PU method, such as Figure 1 In part (B), the MA-PU method refers to the entire process from the interference graph to the disentanglement graph. In this process, the mining area prediction settlement model (i.e. Figure 1 (see Section C of the spatial model for sedimentation).

[0063] A further implementation method is that the method for obtaining a mining area predicted settlement model by using the MA-PU assisted model phase unwrapping method is:

[0064] (1) Based on the branch-cut method and the traditional MCF method (minimum cost flow unwrapping method), the interferograms in the InSAR interferogram stack are phase unwrapped to obtain the initial unwrapped phase;

[0065] (2) Based on the quadtree algorithm, the initial phase unwrapping results are subsampled to reduce the number of far-field measurement points. The spatial model is then inverted to obtain the spatial model parameters. The spatial model parameters are inverted using the genetic algorithm (GA) from the data obtained from the quadtree, and then the spatial model is established using the parameters and the corresponding model formula.

[0066] (3) Based on the spatial model parameters, a spatial model phase is obtained; the spatial model phase is the predicted settlement model of the mining area;

[0067] (4) Calculate the residual phase between the spatial model phase and the initial unwrapped phase (subtract the initial unwrapped phase from the model phase, i.e., the residual phase), and iterate (1)-(4). In this embodiment, iterate (1) to (4) and use the output of (4) as the input of (1) until no further improvement is observed, that is, no residual phase map with a smaller phase value and a smoother spatial distribution can be obtained.

[0068] The sum of the spatial model phases of all iterations is calculated and added to the unwrapped residual phase of the last iteration to obtain the final deformation; wherein the final deformation is extracted from the unwrapped interference pattern. Specifically, the first iteration obtains the spatial model d1, the second iteration obtains d2..., then the final spatial model is d=d1+d2+..., and the final deformation is: d+the unwrapped residual phase of the last iteration.

[0069] Since multiple deformation model inversions are performed, the starting point of the inversion parameters is not important and a wider range of constraint boundaries can be used. Therefore, the impact of imperfect models and inaccurate geological parameters is greatly reduced.

[0070] The method was applied to West Cliff Colliery, Australia, using 23 ALOS-1 data and validated with GPS data. The results showed that the method improved accuracy and correlation compared with the traditional method, with the measurement accuracy increasing from 30 mm and correlation of 0.97 to 20 mm and 0.99 respectively.

[0071] S3: Remove linear errors and reference points from the unwrapped interference pattern stack; in this embodiment, the linear trend is removed by least squares plane fitting; remove reference points: select an area with almost no change in time, regard it as a reference point, and subtract the average phase of the reference point from the entire unwrapped pattern. This operation is performed on each image in the unwrapped pattern stack.

[0072] S4: performing pixel classification on the processed disentangled interference pattern stack, and filling the disentangled interference pattern stack from the spatial relationship and temporal relationship of the pixel points based on the pixel type; performing singular value decomposition on the filled disentangled interference pattern stack to obtain the temporal deformation sequence of the settlement surface.

[0073] The goal of the present invention is to monitor the settlement of the entire mining face. In fact, due to the large gradient settlement and decoherence problems in the mining area, the generated interference map has some areas that cannot be untangled (i.e., the deformation cannot be obtained). The MA-PU method and pixel classification are based on the spatial relationship of the pixels (spatial model + spatial interpolation) and the temporal settlement characteristics (temporal model) to simulate the deformation values ​​of these areas that cannot be untangled.

[0074] Specifically, a time series settlement value is generated based on the type of settlement point in the mining area. The settlement points are classified according to full rank, rank deficiency, and spatial position of the settlement points to obtain the time deformation sequence of the entire settlement surface.

[0075] A further implementation method is that the pixel classification results include full-rank points, far-field points, intermediate points and near-field points; singular value decomposition is performed on each type of pixel to obtain the time deformation sequence of the settlement surface; the specific process design is as follows Figure 1 The near-field point refers to the point near the center of the subsidence, the middle point refers to the point at the edge of the subsidence, and the far-field point refers to the point far away from the subsidence area.

[0076] Among them, the space model and the time model are used to fill the blank pixels of the middle points and near-field points in the disentangled interference pattern pile; the time model adopts the Logistic model.

[0077] In this embodiment, for each pixel, a design matrix is ​​constructed based on the successfully unwrapped interference pattern to calculate the deformation time series. Specifically, based on the successfully unwrapped interference pattern, the deformation at the time point is the variable x, and all equations Ax=b are listed. The variable coefficient on the left side of the equation is the design matrix A, and the right side of the equation is the deformation b obtained by unwrapping each interference pair.

[0078] In the case of full-rank pixels, the design matrix is ​​full-rank and the deformation time series can be directly inverted. For far-field points, the deformation time series is inverted using singular value decomposition (SVD), similar to the SBAS algorithm. As mentioned above, for Ax = b, calculate x = A -1 b, since A is an m*n matrix, a non-invertible matrix, it is necessary to use SVD to find the pseudo-inverse (A -1 ); For far-field points, the deformation is small, and the deformation in the missing time period can be filled by calculating the deformation rate of adjacent time points.

[0079] For the intermediate and near-field points, due to the nonlinear nature of mine settlement, both spatial and temporal models are required to fill the blank pixels in the unwrapped interferograms before applying the time series inversion.

[0080] A further implementation method is to fill the blank pixels at the middle points in the unwrapped interference pattern stack by:

[0081] Based on the genetic algorithm and the accumulated deformation of the mining date, the time model parameters of the time model are obtained by inversion;

[0082] Based on the temporal model parameters, the pixel value of the blank pixel in each disentangled interferogram at the middle point in the disentangled interferogram stack is calculated.

[0083] In this embodiment, for the intermediate points, the missing deformation values ​​in each individual disentangled interference graph can be estimated based on the time model. A mining activity (e.g., a longwall mining face) can be expressed in the conventional form of the Logistic model:

[0084] Where D(t) is the displacement at time t, D max represents the maximum displacement, k is the rate constant, c is the scale constant, t 0 is the time when the maximum sedimentation rate occurs. When a point is affected by multiple mining activities in time:

[0085]

[0086] Among them, D total (t) is the total displacement at time t, and m represents the specific mth mining activity. The model allows each longwall to contribute differently to the total displacement, reflecting the uniqueness of the settlement at each site. Assuming that InSAR paired data are available, the residual displacement of any observation point can be determined. The residual sum of squares for X observation points is:

[0087]

[0088] in, represents the satellite line of sight (LOS) deformation derived from InSAR, t x is the acquisition time of the primary image and the auxiliary image. The primary image refers to the base image used as a reference, and the auxiliary image refers to the second image compared with the primary image.

[0089] The inversion process of the present invention uses a genetic algorithm (GA) to obtain the model parameters of the combined Logistic model. It should be noted that the Logistic model is not suitable for processing situations with lifting phenomena. In order to avoid incorrect inversion due to this problem, the cumulative deformation of each acquisition date is added in the inversion process, and the deformation is calculated by adding all continuous data pairs. Subsequently, according to the inverted Logistic model parameters, the pixel value of the missing pixel of the middle point in the pile in each disentangled interference graph is calculated, the deformation value of the middle point of the full rank is obtained, and the deformation time series is inverted. Specifically, according to the Logistic parameters and their formulas, the deformation prediction value of all time points of the point can be obtained. If the deformation value of the point is missing in the time period of 368 to 414 days (corresponding to the deformation value of the point in a certain disentangled graph), the deformation value in the missing time period is obtained by subtracting the deformation of the 368th day from the deformation of the 414th day obtained by Logistic.

[0090] For near-field points, since there is no data corresponding to the peak period of deformation, it may not be possible to accurately derive the correct model parameters by relying solely on a time-based model. Therefore, it is necessary to first use spatial interpolation to fill these missing data in time. In this study, the residual phase of each unwrapped interferogram is first calculated according to the corresponding spatial model in the spatial model (Okada or PIM, Mogi, etc.) (the residual phase is obtained by subtracting the simulated deformation from the interferogram). The image filling technique is applied to the unwrapped residual phase image, and then combined with the simulated deformation to generate a filled unwrapped interferogram. After updating the phase values ​​of the near-field points in the filled unwrapped interferogram, a filled unwrapped interferogram stack can be obtained. Then the filled unwrapped image stack is inverted, and the model parameters of the Logistic model are obtained from the filled unwrapped deformation data through a genetic algorithm. After obtaining the time model parameters, the deformation time series can be obtained by using the same method as the intermediate point (i.e., combining with the successfully unwrapped pixels).

[0091] S5: Based on the time deformation sequence, complete the underground mining subsidence monitoring by integrating the time series and spatiotemporal deformation.

[0092] Embodiment 2

[0093] The present invention also provides an underground mining settlement monitoring system integrating time series and spatiotemporal deformation, which is used to implement an underground mining settlement monitoring method, including:

[0094] The image pile acquisition module is used to generate InSAR interference image piles based on the existing mining area SAR images;

[0095] An unwrapping module is used to obtain a mining area predicted settlement model and an unwrapped interferogram stack based on the InSAR interferogram stack by using a MA-PU assisted model phase unwrapping method;

[0096] A de-wrapping interference pattern stack processing module, used for removing linear errors and reference points from the de-wrapping interference pattern stack;

[0097] A time deformation sequence acquisition module is used to classify the pixels of the processed disentangled interference pattern pile, and fill the disentangled interference pattern pile from the spatial relationship and time relationship of the pixel points based on the pixel type; perform singular value decomposition on the filled disentangled interference pattern pile to obtain the time deformation sequence of the settlement surface;

[0098] The monitoring module completes underground mining settlement monitoring by integrating the time series with the spatiotemporal deformation based on the time deformation sequence.

[0099] A further implementation manner is that the graph stack acquisition module includes:

[0100] A pairing unit is used to pair images according to the time baseline and space baseline of all mining area SAR images to obtain paired images;

[0101] A sampling unit, used for oversampling the paired image in a ranging direction to obtain a sampled image;

[0102] The interference pattern stack generating unit is used to generate the InSAR interference pattern stack based on the sampled images by differential interferometry technology; wherein a pair of images generates one interference pattern.

[0103] In a further embodiment, the disentanglement module comprises:

[0104] An initial phase unwrapping unit is used to perform phase unwrapping on the interferograms in the InSAR interferogram stack based on a branch-cut method and a traditional MCF method to obtain an initial unwrapped phase;

[0105] A spatial model inversion unit, configured to subsample the initial phase unwrapping result based on a quadtree algorithm, and invert the spatial model to obtain spatial model parameters;

[0106] The quadtree algorithm divides the interference pattern into four parts and calculates the root mean square (RMS) in each quadrant. If the RMS exceeds a given threshold, the quadrant is further subdivided, otherwise, the mean of the quadrant is accepted and output. This process is repeated until the size of the quadrant is equal to the given minimum RMS threshold. The purpose is to downsample and reduce the subsequent spatial model inversion operation time.

[0107] A spatial model phase acquisition unit, used to obtain a spatial model phase based on the spatial model parameters; the spatial model phase is the predicted settlement model of the mining area;

[0108] An iterative unit, used for calculating a residual phase between the spatial model phase and the initial unwrapped phase, and performing iteration;

[0109] The model building unit is used to calculate the sum of the spatial model phases of all iterations and add it to the unwrapped residual phase of the last iteration to obtain the final deformation amount; specifically, the first iteration obtains the spatial model d1, the second iteration obtains d2..., then the final spatial model is d=d1+d2+..., and the final deformation amount is: d+the unwrapped residual phase of the last iteration. The final deformation amount is extracted from the unwrapped interference pattern.

[0110] Embodiment 3

[0111] The study area of ​​this example: LW32, LW33, LW34 working faces in the West Cliff mining area

[0112] from Figure 2((a) The original interference pattern after filtering, (b) is the direct unwrapping pattern using MCF, (c) is the sedimentation funnel pattern restored by the method of the present invention, and (d) is the difference between (c) and (b);) It can be seen that due to the influence of noise, filtering and other factors, the area pointed by the red arrow in sub-image (a) has a phenomenon of stripe disconnection. The maximum value of the traditional direct unwrapping method using the minimum cost flow method is 25.8 rad, while the maximum value of the model iteration recovery method is 30.31 rad. The difference between the two is concentrated in the range where the interference fringes are disconnected, and the average difference is about 6.35 rad. It can be clearly seen from the size and range of the difference that this is exactly the range where the interference fringes are disconnected. It is worth affirming that these factors can be effectively eliminated through this method and the correct results can be restored. This shows that this method has not only improved the accuracy of sedimentation monitoring, but also demonstrated strong adaptability and robustness in practical applications.

[0113] like Figure 3 As shown in the figure, due to the influence of time incoherence and large gradient settlement in the mining area, the settlement surface information of some areas is partially missing during the mining process. However, in other time periods, the area is not affected by the large gradient settlement and the settlement information of this time period is correctly obtained.

[0114] Experimental results: The time series deformation results obtained by this method are compared with GPS data. The average absolute error is 0.016m and the root mean square error is 0.02m. The deformation time series diagram of the entire settlement surface is shown in Figure 4 shown.

[0115] exist Figure 3 In the case where deformation data of some subsidence areas are severely missing, the patented method is applied to obtain the deformation time series of the entire subsidence surface of the mining area.

[0116] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. An underground mining settlement monitoring method integrating time series and spatiotemporal deformation, characterized in that: The following steps are involved: Generate InSAR interferogram stacks based on existing mining area SAR images; Based on the InSAR interferogram stack, a mining area predicted settlement model and an unwrapped interferogram stack are obtained by using a MA-PU assisted model phase unwrapping method; Performing linear error and reference point removal processing on the unwrapped interference pattern stack; Classifying pixels of the processed disentangled interference pattern stack, and filling the disentangled interference pattern stack based on the pixel type and the spatial relationship and time relationship of the pixel points; Perform singular value decomposition on the filled disentangled interference pattern heap to obtain the time deformation sequence of the settlement surface; Based on the time deformation sequence, underground mining settlement monitoring integrating the time series and the spatiotemporal deformation is completed; The method of obtaining the predicted settlement model of the mining area by the MA-PU auxiliary model phase unwrapping method is as follows: Based on the branch and cut method and the traditional MCF method, phase unwrapping is performed on the interferograms in the InSAR interferogram stack to obtain an initial unwrapped phase; Based on the quadtree algorithm, the initial unwrapped phase is sub-sampled, and the spatial model is inverted to obtain the spatial model parameters; Based on the spatial model parameters, a spatial model phase is obtained; the spatial model phase is the predicted settlement model of the mining area; Calculating a residual phase between the spatial model phase and the initial unwrapped phase, and iterating; The sum of the spatial model phases of all iterations is calculated and added to the unwrapped residual phase of the last iteration to obtain a final deformation amount; wherein the final deformation amount is extracted from the unwrapped interference pattern.

2. The underground mining subsidence monitoring method integrating time series and spatiotemporal deformation according to claim 1 is characterized in that: The method to obtain the InSAR interferogram stack is: Perform image pairing based on the time baseline and space baseline of all mining area SAR images to obtain paired images; Oversampling the paired images in a ranging direction to obtain a sampled image; Based on the sampled images, the InSAR interference pattern stack is generated by differential interferometry technology; wherein a pair of images generates one interference pattern.

3. The underground mining subsidence monitoring method integrating time series and spatiotemporal deformation according to claim 1 is characterized in that: The pixel classification results include full-rank points, far-field points, intermediate points, and near-field points. Singular value decomposition is performed on each type of pixel to obtain the time deformation sequence of the subsidence surface. The space model and the time model are used to fill the blank pixels of the intermediate points and the near-field points in the disentangled interference pattern stack; the time model adopts the Logistic model.

4. The underground mining subsidence monitoring method integrating time series and spatiotemporal deformation according to claim 3 is characterized in that: The method of filling the blank pixels at the middle points in the unwrapped interference pattern stack is: Based on the genetic algorithm and the accumulated deformation of the mining date, inversion is performed to obtain the time model parameters of the time model; Based on the time model parameters, the pixel value of the blank pixel in each disentangled interference pattern at the middle point in the disentangled interference pattern stack is calculated.

5. An underground mining settlement monitoring system integrating time series and spatiotemporal deformation, characterized in that: The method for monitoring underground mining subsidence according to any one of claims 1 to 4 comprises: The image pile acquisition module is used to generate InSAR interference image piles based on the existing mining area SAR images; An unwrapping module is used to obtain a mining area predicted settlement model and an unwrapped interferogram stack based on the InSAR interferogram stack by using a MA-PU assisted model phase unwrapping method; A de-wrapping interference pattern stack processing module, used for removing linear errors and reference points from the de-wrapping interference pattern stack; A time deformation sequence acquisition module is used to classify the pixels of the processed disentangled interference pattern pile, and fill the disentangled interference pattern pile from the spatial relationship and time relationship of the pixel points based on the pixel type; perform singular value decomposition on the filled disentangled interference pattern pile to obtain the time deformation sequence of the settlement surface; A monitoring module, based on the time deformation sequence, completes underground mining settlement monitoring by integrating the time series and the spatiotemporal deformation; The disentanglement module comprises: An initial phase unwrapping unit is used to perform phase unwrapping on the interferograms in the InSAR interferogram stack based on a branch-cut method and a traditional MCF method to obtain an initial unwrapped phase; A spatial model inversion unit, used for sub-sampling the initial unwrapped phase based on a quadtree algorithm, and inverting the spatial model to obtain spatial model parameters; A spatial model phase acquisition unit, used to obtain a spatial model phase based on the spatial model parameters; the spatial model phase is the predicted settlement model of the mining area; An iterative unit, used for calculating a residual phase between the spatial model phase and the initial unwrapped phase, and performing iteration; The model building unit is used to calculate the sum of all iterated spatial model phases and add it to the unwrapping residual phase of the last iteration to obtain a final deformation amount; wherein the final deformation amount is extracted from the unwrapping interference pattern.

6. The underground mining subsidence monitoring system integrating time series and spatiotemporal deformation according to claim 5 is characterized in that: The graph stack acquisition module includes: A pairing unit is used to pair images according to the time baseline and space baseline of all mining area SAR images to obtain paired images; A sampling unit, used for oversampling the paired image in a ranging direction to obtain a sampled image; The interference pattern stack generating unit is used to generate the InSAR interference pattern stack based on the sampled images by differential interferometry technology; wherein a pair of images generates one interference pattern.

Images