A DInSAR deformation monitoring method and system for mining areas

Abstract

The present application discloses a mining area DInSAR deformation monitoring method and system, which relates to the technical field of mining area settlement monitoring, and the method includes: obtaining SAR images and leveling data of the area to be measured; determining the first LOS deformation according to the SAR images; determining the second LOS deformation according to the leveling data using the BPM‑EKTF settlement prediction model; determining the third LOS deformation of the area to be measured according to the first LOS deformation and the second LOS deformation; the third LOS deformation is the LOS deformation obtained by adding the first non-integer periodic phase corresponding to the first LOS deformation and the second full periodic phase corresponding to the second LOS deformation, and converting the third deformation phase. The present application can improve the accuracy of mining area settlement monitoring by combining SAR images and leveling data to determine the mining area settlement monitoring results.

Description

Technical Field

[0001] The present application relates to the technical field of mining area settlement monitoring, and in particular to a mining area DInSAR deformation monitoring method and system. Background Art

[0002] High-intensity coal mining can cause environmental disasters in mining areas, such as ground collapse and engineering geological damage. Therefore, mining subsidence monitoring in mining areas is of great significance to the prevention and control of environmental disasters in mining areas. At present, the mainstream mining subsidence monitoring method is mainly the leveling observation method. The leveling observation method is to lay two leveling observation lines along the strike and inclination of the working face before coal seam mining; during the mining process of the working face, the leveling measurement of the observation station is carried out regularly to obtain the surface deformation caused by mining. However, this method can only obtain a small amount of discrete deformation along the strike and inclination of the working face, resulting in low accuracy of mining subsidence monitoring. In recent years, with the development of remote sensing technology, new measurement technologies represented by InSAR (Interferometric Synthetic Aperture Radar) have been applied to deformation monitoring in mining areas. InSAR obtains surface deformation information or separates surface deformation through interference processing. DInSAR (Differential Interferometric Synthetic Aperture Radar) is an extension of InSAR and is a common and mature method for mining deformation monitoring. However, the incoherence caused by the large deformation gradient makes the LOS deformation obtained in the non-edge area of ​​the mining influence range differ greatly from the true value, resulting in low accuracy of mining subsidence monitoring. The final mining subsidence monitoring results are inaccurate and unreliable. Summary of the invention

[0003] The purpose of this application is to provide a mining area DInSAR deformation monitoring method and system, which can improve the accuracy of mining area subsidence monitoring.

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

[0005] In a first aspect, the present application provides a mining area DInSAR deformation monitoring method, the mining area DInSAR deformation monitoring method comprising:

[0006] Obtain SAR images and leveling data of the area to be measured.

[0007] A first LOS deformation is determined according to the SAR image; the first LOS deformation is obtained by obtaining a first deformation phase according to the SAR image and converting the first deformation phase.

[0008] According to the leveling data, the BPM-EKTF subsidence prediction model is used to determine the second LOS deformation; the BPM-EKTF subsidence prediction model is a model for predicting three-dimensional surface deformation based on the Boltzmann function prediction algorithm and the Knothe time function, and the second LOS deformation is the LOS deformation obtained by converting the three-dimensional surface deformation predicted by the BPM-EKTF subsidence prediction model, and the three-dimensional surface deformation includes settlement, east-west horizontal movement and north-south horizontal movement.

[0009] According to the first LOS deformation and the second LOS deformation, a third LOS deformation of the area to be measured is determined; the third LOS deformation is obtained by adding a first non-integer cycle phase corresponding to the first LOS deformation and a second full cycle phase corresponding to the second LOS deformation to obtain a third deformation phase, and converting the third deformation phase to obtain the LOS deformation.

[0010] Optionally, determining the first LOS deformation according to the SAR image specifically includes:

[0011] The SAR image is preprocessed to obtain the first deformation phase; the preprocessing includes interference processing, filtering processing and unwrapping processing.

[0012] The first deformation phase is converted to obtain the first LOS deformation.

[0013] Optionally, according to the leveling data, a BPM-EKTF subsidence prediction model is used to determine the second LOS direction deformation, specifically including:

[0014] According to the level data, a fitness function is determined.

[0015] According to the fitness function, a genetic algorithm is used to determine the model parameters of the BPM-EKTF subsidence prediction model.

[0016] The model parameters are substituted into the BPM-EKTF subsidence prediction model to predict the three-dimensional deformation of the ground surface within a monitoring period.

[0017] According to the LOS projection principle, the three-dimensional deformation of the surface is transformed to obtain the second LOS deformation.

[0018] Optionally, the fitness function is expressed as:

[0019]

[0020] Among them, Fun represents the fitness function, W P , W Mare the predicted settlement and the actual measured settlement, respectively, U P , U M are the predicted horizontal movement and the actually measured horizontal movement, respectively; NW and NU are the amounts of settlement and horizontal movement, respectively.

[0021] Optionally, the three-dimensional deformation of the ground surface is converted using the following formula to obtain the second LOS deformation:

[0022]

[0023] Where LOS2 represents the second LOS deformation, W is the settlement; U NS , U WE are the north-south horizontal movement and the east-west horizontal movement respectively; θ INC ,θ FLI are the incident angle and azimuth of the satellite respectively.

[0024] Optionally, determining a third LOS deformation of the area to be measured according to the first LOS deformation and the second LOS deformation specifically includes:

[0025] According to the radar wavelength, the first LOS deformation is converted into a first deformation phase, and the first deformation phase is split to obtain a first integral cycle phase and a first non-integral cycle phase.

[0026] According to the radar wavelength, the second LOS deformation is converted into a second deformation phase, and the second deformation phase is split to obtain a second integral period phase and a second non-integral period phase.

[0027] The first non-integer cycle phase and the second integer cycle phase are summed to obtain a third deformed phase.

[0028] The third deformation phase is converted to obtain the third LOS deformation.

[0029] Optionally, the first integral cycle phase and the first non-integral cycle phase are determined by the following formula:

[0030]

[0031] φ D =φ D,FCP +φ D,NFCP ;

[0032] Where LOS1 represents the first LOS deformation, λ represents the radar wavelength, is the first deformation phase, floor(·) is the rounding down operation, φ D represents the deformation phase in DInSAR monitoring, φ D,FCPis the phase of the first full cycle, φ D,NFCP is the first non-integer cycle phase.

[0033] Optionally, the second integral cycle phase and the second non-integral cycle phase are determined by the following formula:

[0034]

[0035] φ B =φ B,FCP +φ B,NFCP ;

[0036] Where LOS2 represents the second LOS deformation, λ represents the radar wavelength, is the second deformation phase, floor(·) is the rounding down operation, φ B represents the deformation phase in the BPM-EKTF subsidence prediction model, φ B,FCP is the phase of the second full cycle, φ B,NFCP is the second non-integer cycle phase.

[0037] Optionally, the third deformation phase is determined by the following formula:

[0038]

[0039] in, represents the third deformation phase, φ B,FCP represents the phase of the second full cycle, φ D,NFCP Represents the first non-integer period phase.

[0040] In a second aspect, the present application provides a mining area DInSAR deformation monitoring system, the mining area DInSAR deformation monitoring system comprising:

[0041] The data acquisition module is used to obtain SAR images and leveling data of the area to be measured.

[0042] The first LOS deformation determination module is used to determine a first LOS deformation according to the SAR image; the first LOS deformation is a LOS deformation obtained by obtaining a first deformation phase according to the SAR image and converting the first deformation phase.

[0043] The second LOS deformation determination module is used to determine the second LOS deformation according to the leveling data and the BPM-EKTF settlement prediction model; the BPM-EKTF settlement prediction model is a model for predicting three-dimensional surface deformation based on the Boltzmann function prediction algorithm and the Knothe time function, and the second LOS deformation is the LOS deformation obtained by converting the three-dimensional surface deformation predicted by the BPM-EKTF settlement prediction model, and the three-dimensional surface deformation includes settlement, east-west horizontal movement and north-south horizontal movement.

[0044] The third LOS deformation determination module is used to determine the third LOS deformation of the area to be measured according to the first LOS deformation and the second LOS deformation; the third LOS deformation is the LOS deformation obtained by adding a first non-integer cycle phase corresponding to the first LOS deformation and a second full cycle phase corresponding to the second LOS deformation, and converting the third deformation phase.

[0045] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0046] The present application provides a mining area DInSAR deformation monitoring method and system, which integrates leveling data on the basis of the traditional DInSAR monitoring method, and combines SAR images and leveling data as the basic data for monitoring the surface settlement and deformation of the mining area. On the one hand, the DInSAR monitoring technology is used to obtain the first deformation phase through the SAR image based on DInSAR monitoring, and converted into the first LOS deformation; on the other hand, the three-dimensional surface deformation predicted by the leveling data based on the BPM-EKTF subsidence prediction model is converted into the second LOS deformation. Then, the third LOS deformation is determined according to the first LOS deformation and the second LOS deformation, thereby realizing the fusion of SAR image features and leveling data features to determine the settlement and deformation of the mining area under the premise of combining DInSAR monitoring and model prediction, so that the final third LOS deformation is more real, accurate and reliable, and the accuracy of mining area subsidence monitoring is effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] 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.

[0048] Figure 1A schematic flow chart of a mining area DInSAR deformation monitoring method provided in one embodiment of the present application.

[0049] Figure 2 A schematic diagram of LOS deformation monitored by DInSAR before fusing leveling data provided in an embodiment of the present application.

[0050] Figure 3 A schematic diagram of LOS deformation monitored by DInSAR after fusion of leveling data provided in an embodiment of the present application.

[0051] Figure 4 A structural schematic diagram of a mining area DInSAR deformation monitoring system provided in one embodiment of the present application. DETAILED DESCRIPTION

[0052] 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.

[0053] 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.

[0054] like Figure 1 As shown, this embodiment provides a mining area DInSAR deformation monitoring method, which combines SAR (Synthetic Aperture Radar) image data and leveling data to determine the surface subsidence deformation of the area to be measured. The mining area DInSAR deformation monitoring method specifically includes the following steps:

[0055] Step S1, obtaining SAR images and leveling data of the area to be measured.

[0056] In this embodiment, a DInSAR monitoring method is adopted, and a synthetic aperture radar is used to obtain SAR images. Parameters such as the radar wavelength of the synthetic aperture radar are used in the data processing process.

[0057] Synthetic aperture radar is a high-resolution imaging radar that can obtain high-resolution radar images similar to optical photography under extremely low visibility weather conditions. Synthetic aperture radar is characterized by high resolution, all-weather operation, and the ability to effectively identify camouflage and penetrate cover. The high azimuth resolution obtained is equivalent to the azimuth resolution provided by a large aperture antenna. The SAR image collected by the synthetic aperture radar is a series of continuous SAR images. The imaging principle of SAR images is to transmit coherent signals and receive echoes, and then form images through coherent processing.

[0058] In this embodiment, the leveling data refers to data used to measure the elevation of the earth's surface, and a high-precision level can be used to measure the area to be measured to obtain the leveling data.

[0059] In this embodiment, the area to be measured refers to a target area to be monitored with a mining area. The purpose of this embodiment is to accurately determine the surface subsidence and deformation of the mining area in the area to be measured by combining SAR image data and leveling data to achieve high-precision monitoring.

[0060] Step S2: Determine the first LOS (Line-of-Sight) deformation according to the SAR image.

[0061] In this embodiment, the first LOS deformation is a LOS deformation obtained by obtaining a first deformation phase according to the SAR image and converting the first deformation phase.

[0062] In this embodiment, LOS deformation refers to the position offset or deformation caused by the relative movement between two points on the surface. By analyzing LOS deformation, relevant information such as crustal movement, seismic activity, and geological structure can be obtained, which is of great significance for mining area settlement monitoring, earthquake monitoring, geological disaster early warning, and structural evolution research.

[0063] In this embodiment, step S2 mainly performs pre-processing such as interference processing, filtering processing, and unwrapping processing based on the SAR image covering the mining area during the working face recovery period, and converts the first deformation phase obtained after the unwrapping processing into the first LOS deformation. Specifically, it includes the following steps:

[0064] Step S21, preprocessing the SAR image to obtain the first deformation phase; the preprocessing includes interference processing, filtering processing and unwrapping processing.

[0065] Step S22: convert the first deformation phase to obtain the first LOS deformation.

[0066] In this embodiment, DInSAR monitoring uses only two periods of SAR image data, and the SAR images have measurement dates. Taking two periods of SAR images with measurement dates of 2004.08.12 and 2005.09.22 as examples, from the dimension of measurement time, the SAR image with measurement date of 2004.08.12 is earlier than the SAR image with measurement date of 2005.09.22, that is, the measurement date of the SAR image with measurement date of 2004.08.12 is earlier, and the measurement date of the SAR image with measurement date of 2005.09.22 is later.

[0067] First, a suitable SAR image interference pair is obtained based on the SAR image, and the SAR image with an earlier measurement date is used as the main image, and the SAR image with a later measurement date is used as the auxiliary image. The auxiliary image is aligned with the main image and resampled to the same coordinate space to complete the alignment and resampling. Since the SAR image contains SLC (Single Look Comple) data, the SLC data of the SAR image is the original highest resolution data. The SLC data complex matrices of the main and auxiliary images after the above alignment are conjugate multiplied to obtain the initial interference map. The reference ellipsoid phase and terrain phase are removed from the interference map, and filtering is performed to weaken the noise phase. The first deformation phase obtained after the unwrapping process is converted into the first LOS deformation.

[0068] Step S3: According to the leveling data, the second LOS deformation is determined by using the BPM-EKTF (Boltzmann function prediction algorithm-exponential Knott time function) settlement prediction model.

[0069] In this embodiment, the BPM-EKTF subsidence prediction model is a model for predicting three-dimensional surface deformation based on the Boltzmann function prediction algorithm and the exponential Knothe time function. The second LOS deformation is the LOS deformation obtained by converting the three-dimensional surface deformation predicted by the BPM-EKTF subsidence prediction model. The three-dimensional surface deformation includes settlement, east-west horizontal movement, and north-south horizontal movement.

[0070] The BPM-EKTF subsidence prediction model uses the Boltzmann function prediction algorithm (i.e., the BPM algorithm) to obtain the final deformation caused by mining, and uses the exponential Knothe time function (i.e., EKTF) to describe the subsidence process. Among them, BPM uses the Boltzmann function as the influence function, and obtains the deformation calculation formula according to the derivation principle and process of PIM (Probability Integration Method). BPM can better predict the final deformation caused by working face mining, and the prediction result of BPM at the edge of the subsidence basin is better than PIM. EKTF is based on the assumption of Knothe time function (KTF, a time function proposed by scholar Knott and widely used to describe the subsidence process of mining areas). By adding an exponent to the KTF formula to control the changes in subsidence speed and acceleration. Compared with KTF, EKTF has only one more variable, but it can describe the subsidence law very well.

[0071] In this embodiment, step S3 uses the BPM-EKTF subsidence prediction model to determine the second LOS deformation based on the leveling data. Specifically, it includes the following steps:

[0072] Step S31, determining a fitness function according to the level data.

[0073] In this embodiment, the expression of the fitness function is:

[0074]

[0075] Among them, Fun represents the fitness function, W P , W M are the predicted settlement and the actual measured settlement, respectively, U P , U M are the predicted horizontal movement and the actually measured horizontal movement, respectively; NW and NU are the amounts of settlement and horizontal movement, respectively.

[0076] Step S32: According to the fitness function, a genetic algorithm is used to determine the model parameters of the BPM-EKTF subsidence prediction model.

[0077] Step S33: Substitute the model parameters into the BPM-EKTF subsidence prediction model to predict the three-dimensional deformation of the ground surface within a monitoring period.

[0078] Step S34: According to the LOS projection principle, the three-dimensional deformation of the ground surface is transformed to obtain the second LOS deformation.

[0079] In this embodiment, the following formula is used to transform the three-dimensional deformation of the ground surface to obtain the second LOS deformation:

[0080]

[0081] Where LOS2 represents the second LOS deformation, W is the settlement; U NS , U WE are the north-south horizontal movement and the east-west horizontal movement respectively; θ INC ,θ FLI are the incident angle and azimuth of the satellite respectively.

[0082] Step S3 of this embodiment is mainly to solve the BPM-EKTF subsidence prediction model based on leveling data, and then use the model to predict the three-dimensional surface deformation within the DInSAR deformation monitoring period: settlement, east-west horizontal movement, north-south horizontal movement, and convert the three-dimensional surface deformation into the second LOS deformation.

[0083] In this embodiment, for the BPM-EKTF subsidence prediction model, the calculation formulas for the subsidence and horizontal movement of the ground surface caused by coal seam mining on the tth day are respectively shown in formulas (3)-(9). The combined formulas (3)-(12) are the calculation formulas of the BPM-EKTF subsidence prediction model.

[0084]

[0085] Among them, x and y are the horizontal and vertical coordinates of the ground point in the working face coordinate system, t is the number of days of mining at the working face, and W(x,y,i) is the final ground settlement caused by the basic unit mined on the i-th day. In fact, the final deformation is not instantaneous, and the settlement of the basic unit on the t-th day needs to be calculated through the time function f(ti).

[0086]

[0087] Where, v is the mining speed of the working face, W 0 (xv·i+v) is the settlement function of the main section along the working face, W 0 (y) is the settlement function of the main section of the working face.

[0088] W0=m·q·cosα (5).

[0089] Among them, m is the mining thickness of the coal seam; q is the settlement coefficient; α is the inclination of the coal seam.

[0090]

[0091] Among them, R is the main influencing radius, R = H / tanβ, H is the mining depth, β is the main influencing angle; S1 and S2 are the inflection point offsets, and l is the mining length of the working face.

[0092]

[0093] Among them, S3 and S4 are the inflection point offset distances, and L is the inclined mining length of the working face.

[0094] f(ti)=(1-e -c·(t-i) ) k (8).

[0095] Among them, c and k are time influence coefficients.

[0096]

[0097] Among them, U(x, y, t) is the final horizontal movement of the ground caused by the basic unit mined on the i-th day.

[0098]

[0099] Among them, B is the horizontal movement coefficient, l is the mining length of the working face, It is the angle from the positive direction of the x-axis to the specified direction counterclockwise, U 0 (x) is the horizontal movement function of the main section of the working face.

[0100]

[0101] Among them, U 0 (y) is the horizontal movement function of the working face inclination main section.

[0102] In this embodiment, based on the relevant calculation formula of the above-mentioned BPM-EKTF subsidence prediction model, when solving the parameters of the BPM-EKTF subsidence prediction model according to the leveling data, the fitness function constructed is shown in formula (1). Based on the fitness function, the model parameters of the BPM-EKTF subsidence prediction model [q, tanβ, B, θ, S1, S2, S3, S4, c, k] can be obtained using a genetic algorithm, where q is the subsidence coefficient; tanβ is the main influence angle tangent; B is the horizontal movement coefficient; θ is the mining influence propagation angle; S1, S2, S3 and S4 are all inflection point offset distances, and c and k are time influence coefficients.

[0103] Substituting the above-solved parameters into the BPM-EKTF subsidence prediction model, given the time of the DInSAR monitoring cycle, the three-dimensional surface deformation within the time period of the DInSAR monitoring cycle can be predicted: settlement, east-west horizontal movement, and north-south horizontal movement. According to the LOS projection principle of DInSAR, the three-dimensional surface deformation can be converted into the second LOS deformation by formula (2).

[0104] Step S4: determining a third LOS deformation of the area to be measured according to the first LOS deformation and the second LOS deformation.

[0105] In this embodiment, the third LOS deformation is obtained by adding a first non-integer cycle phase corresponding to the first LOS deformation and a second full cycle phase corresponding to the second LOS deformation to obtain a third deformation phase, and converting the third deformation phase to obtain the LOS deformation.

[0106] In this embodiment, step S4 mainly converts the first LOS deformation obtained by the DInSAR monitoring method and the second LOS deformation predicted by the BPM-EKTF subsidence prediction model into deformation phases according to the radar wavelength, and splits them into an integral cycle phase and a non-integral cycle phase; then, the non-integral cycle phase obtained by the DInSAR monitoring is added to the periodic phase obtained by the BPM-EKTF subsidence prediction model to obtain the third deformation phase of the mining area and convert it into the third LOS deformation. Specifically, the following steps are included:

[0107] Step S41: convert the first LOS deformation into a first deformation phase according to the radar wavelength, and split the first deformation phase to obtain a first integral cycle phase and a first non-integral cycle phase.

[0108] Step S42: convert the second LOS deformation into a second deformation phase according to the radar wavelength, and split the second deformation phase to obtain a second integral cycle phase and a second non-integral cycle phase.

[0109] Step S43: sum the first non-integer periodic phase and the second integer periodic phase to obtain a third deformed phase.

[0110] Step S44: convert the third deformation phase to obtain the third LOS deformation.

[0111] In this embodiment, the first integral cycle phase and the first non-integral cycle phase are determined by the following formula:

[0112]

[0113] φ D =φ D,FCP +φ D,NFCP (15).

[0114] Where LOS1 represents the first LOS deformation, λ represents the radar wavelength, is the first deformation phase, floor(·) is the rounding down operation, φ D represents the deformation phase in DInSAR monitoring, φ D,FCP is the phase of the first full cycle, φ D,NFCP is the first non-integer cycle phase.

[0115] In this embodiment, the second integral cycle phase and the second non-integral cycle phase are determined by the following formula:

[0116]

[0117] φ B =φ B,FCP +φ B,NECP (18).

[0118] Where LOS2 represents the second LOS deformation, λ represents the radar wavelength, is the second deformation phase, floor(·) is the rounding down operation, φ B represents the deformation phase in the BPM-EKTF subsidence prediction model, φ B,FCP is the phase of the second full cycle, φ B,NFCP is the second non-integer cycle phase.

[0119] In this embodiment, the third deformation phase is determined by the following formula:

[0120]

[0121] in, represents the third deformation phase, φ B,FCP represents the phase of the second full cycle, φ D,NFCP Represents the first non-integer period phase.

[0122] In this embodiment, the first LOS deformation obtained based on the DInSAR monitoring method and SAR image data, the second LOS deformation obtained based on the BPM-EKTF subsidence prediction model and leveling data, and the third LOS deformation fused with SAR image features and leveling data features are essentially LOS deformations, and can realize the conversion between the deformation phase and the corresponding LOS deformation. Among them, the conversion between the deformation phase and the LOS deformation is not the focus of this embodiment, which belongs to the prior art and will not be repeated here.

[0123] The conversion of the LOS deformation to the deformation phase in step S4 can be implemented by formula (13) and formula (16), and the splitting of the deformation phase into the whole cycle phase and the non-whole cycle phase can be implemented by formula (14) and formula (17). The first LOS deformation obtained by the DInSAR monitoring method is converted into the first deformation phase and split into the first whole cycle phase and the first non-whole cycle phase, see formula (15). The second LOS deformation obtained by the BPM-EKTF subsidence prediction model is converted into the second deformation phase and split into the second whole cycle phase and the second non-whole cycle phase, see formula (18). The second whole cycle phase obtained by the BPM-EKTF subsidence prediction model is added to the first non-whole cycle phase obtained by DInSAR monitoring to obtain the third deformation phase, see formula (19). Finally, the third deformation phase can be converted into the third LOS deformation by formula (20) as the final mining area surface deformation monitoring result.

[0124]

[0125] Wherein, LOS3 represents the third LOS deformation, λ represents the radar wavelength, It is the third deformation phase.

[0126] This embodiment uses DInSAR monitoring technology to monitor the surface subsidence caused by coal mining and obtain the LOS deformation of the mining area. Based on the leveling data, after solving the BPM-EKTF subsidence prediction model, the model is used to predict the three-dimensional deformation within the DInSAR deformation monitoring period: subsidence, east-west horizontal movement, north-south horizontal movement, and convert the three-dimensional deformation into LOS deformation. According to the radar wavelength, the LOS deformation predicted by DInSAR monitoring and the BPM-EKTF subsidence prediction model is converted into a deformation phase and split into an integer cycle phase and a non-integer cycle phase; then the integer cycle phase obtained by the BPM-EKTF subsidence prediction model is added to the non-integer cycle phase obtained by DInSAR monitoring to obtain the mining area deformation phase and convert it into LOS deformation. Figure 2 and Figure 3 These are schematic diagrams of LOS deformation monitored by DInSAR before and after fusion of leveling data. It can be seen that fusing leveling data on the basis of DInSAR monitoring, that is, combining SAR images and leveling data to monitor deformation in mining areas, can make the deformation monitoring results more accurate and the deformation monitoring effect more prominent.

[0127] In an exemplary embodiment, a mining area DInSAR deformation monitoring system is provided, such as Figure 4 As shown in the figure, the DInSAR deformation monitoring system for the mining area specifically includes the following functional modules:

[0128] The data acquisition module M1 is used to acquire SAR images and leveling data of the area to be measured.

[0129] The first LOS deformation determination module M2 is used to determine a first LOS deformation according to the SAR image; the first LOS deformation is a LOS deformation obtained by obtaining a first deformation phase according to the SAR image and converting the first deformation phase.

[0130] The second LOS deformation determination module M3 is used to determine the second LOS deformation according to the leveling data and the BPM-EKTF settlement prediction model; the BPM-EKTF settlement prediction model is a model for predicting three-dimensional surface deformation based on the Boltzmann function prediction algorithm and the Knothe time function, and the second LOS deformation is the LOS deformation obtained by converting the three-dimensional surface deformation predicted by the BPM-EKTF settlement prediction model, and the three-dimensional surface deformation includes settlement, east-west horizontal movement and north-south horizontal movement.

[0131] The third LOS deformation determination module M4 is used to determine the third LOS deformation of the area to be measured according to the first LOS deformation and the second LOS deformation; the third LOS deformation is the LOS deformation obtained by adding the first non-integer cycle phase corresponding to the first LOS deformation and the second full cycle phase corresponding to the second LOS deformation to obtain a third deformation phase, and converting the third deformation phase.

[0132] This embodiment integrates leveling data on the basis of the traditional DInSAR monitoring method. By combining SAR images and leveling data, the two data are used together as the basic data for monitoring the surface settlement and deformation of the mining area. On the one hand, the DInSAR monitoring technology is used to obtain the first deformation phase through the SAR image based on DInSAR monitoring, and converted into the first LOS deformation; on the other hand, the three-dimensional surface deformation predicted by the leveling data based on the BPM-EKTF subsidence prediction model is converted into the second LOS deformation. Then, the third LOS deformation is determined based on the first LOS deformation and the second LOS deformation, thereby realizing the fusion of SAR image features and leveling data features to determine the settlement and deformation of the mining area under the premise of combining DInSAR monitoring and model prediction. Since linear distribution deformation can be obtained based on leveling data, surface distribution deformation is obtained through the BPM-EKTF subsidence prediction model, and then corrected to the LOS deformation monitored by DInSAR. The correction result retains the difference in LOS deformation at the edge of the DInSAR mining influence range, and restores the LOS deformation lost by DInSAR at the center, making the final LOS deformation more real, accurate and reliable, effectively improving the accuracy of mining subsidence monitoring.

[0133] 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.

[0134] 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 mining area DInSAR deformation monitoring method, characterized in that: The mining area DInSAR deformation monitoring method comprises: Obtain SAR images and leveling data of the area to be measured; Determine a first LOS deformation according to the SAR image; the first LOS deformation is a LOS deformation obtained by converting a first deformation phase obtained according to the SAR image; According to the leveling data, the second LOS deformation is determined by using the BPM-EKTF subsidence prediction model; the BPM-EKTF subsidence prediction model is a model for predicting three-dimensional surface deformation based on the Boltzmann function prediction algorithm and the Knothe time function, and the second LOS deformation is the LOS deformation obtained by converting the three-dimensional surface deformation predicted by the BPM-EKTF subsidence prediction model, and the three-dimensional surface deformation includes settlement, east-west horizontal movement and north-south horizontal movement; Determining a third LOS deformation of the area to be measured according to the first LOS deformation and the second LOS deformation; the third LOS deformation is a LOS deformation obtained by adding a first non-integer cycle phase corresponding to the first LOS deformation and a second full cycle phase corresponding to the second LOS deformation, and converting the third deformation phase; determining the third LOS deformation of the area to be measured according to the first LOS deformation and the second LOS deformation, specifically includes: According to the radar wavelength, the first LOS deformation is converted into a first deformation phase, and the first deformation phase is split to obtain a first integral cycle phase and a first non-integral cycle phase; According to the radar wavelength, the second LOS deformation is converted into a second deformation phase, and the second deformation phase is split to obtain a second integral cycle phase and a second non-integral cycle phase; Summing the first non-integer periodic phase and the second integer periodic phase to obtain a third deformed phase; The third deformation phase is converted to obtain the third LOS deformation.

2. The mining area DInSAR deformation monitoring method according to claim 1, characterized in that: Determining the first LOS deformation according to the SAR image specifically includes: Preprocessing the SAR image to obtain the first deformation phase; the preprocessing includes interference processing, filtering processing and unwrapping processing; The first deformation phase is converted to obtain the first LOS deformation.

3. The mining area DInSAR deformation monitoring method according to claim 1, characterized in that: According to the leveling data, the BPM-EKTF subsidence prediction model is used to determine the second LOS deformation, including: Determining a fitness function according to the level data; According to the fitness function, a genetic algorithm is used to determine the model parameters of the BPM-EKTF subsidence prediction model; Substituting the model parameters into the BPM-EKTF subsidence prediction model to predict the three-dimensional deformation of the ground surface within a monitoring period; According to the LOS projection principle, the three-dimensional deformation of the surface is transformed to obtain the second LOS deformation.

4. The mining area DInSAR deformation monitoring method according to claim 3, characterized in that: The expression of the fitness function is: Among them, Fun represents the fitness function, W P , W M are the predicted settlement and the actual measured settlement, respectively, U P , U M are the predicted horizontal movement and the actually measured horizontal movement, respectively; NW and NU are the amounts of settlement and horizontal movement, respectively.

5. The mining area DInSAR deformation monitoring method according to claim 3, characterized in that: The following formula is used to transform the three-dimensional deformation of the ground surface to obtain the second LOS deformation: Where LOS2 represents the second LOS deformation, W is the settlement; U NS , U WE are the north-south horizontal movement and the east-west horizontal movement respectively; θ INC ,θ FLI are the incident angle and azimuth of the satellite respectively.

6. The mining area DInSAR deformation monitoring method according to claim 1, characterized in that: The first integral cycle phase and the first non-integral cycle phase are determined by the following formula: f D =φ D,FCP +φ D,NFCP ; Where LOS1 represents the first LOS deformation, λ represents the radar wavelength, is the first deformation phase, floor(·) is the rounding down operation, φ D represents the deformation phase in DInSAR monitoring, φ D,FCP is the phase of the first full cycle, φ D,NFCP is the first non-integer cycle phase.

7. The mining area DInSAR deformation monitoring method according to claim 1, characterized in that: The second integral cycle phase and the second non-integral cycle phase are determined by the following formula: f B =φ B,FCP +φ B,NFCP ; Where LOS2 represents the second LOS deformation, λ represents the radar wavelength, is the second deformation phase, floor(·) is the rounding down operation, φ B represents the deformation phase in the BPM-EKTF subsidence prediction model, φ B,FCP is the phase of the second full cycle, φ B,NFCP is the second non-integer cycle phase.

8. The mining area DInSAR deformation monitoring method according to claim 1, characterized in that: The third deformation phase is determined by the following formula: in, represents the third deformation phase, φ B,FCP represents the phase of the second full cycle, φ D,NFCP Represents the first non-integer period phase.

9. A mining area DInSAR deformation monitoring system, characterized in that: The mining area DInSAR deformation monitoring system includes: A data acquisition module is used to acquire SAR images and leveling data of the area to be measured; A first LOS deformation determination module is used to determine a first LOS deformation according to the SAR image; the first LOS deformation is a LOS deformation obtained by converting a first deformation phase obtained according to the SAR image; A second LOS deformation determination module is used to determine a second LOS deformation according to the leveling data using a BPM-EKTF subsidence prediction model; the BPM-EKTF subsidence prediction model is a model for predicting three-dimensional surface deformation based on a Boltzmann function prediction algorithm and a Knothe time function, the second LOS deformation is a LOS deformation obtained by converting the three-dimensional surface deformation predicted by the BPM-EKTF subsidence prediction model, and the three-dimensional surface deformation includes settlement, east-west horizontal movement, and north-south horizontal movement; A third LOS deformation determination module is used to determine the third LOS deformation of the area to be measured according to the first LOS deformation and the second LOS deformation; the third LOS deformation is the LOS deformation obtained by adding a first non-integer cycle phase corresponding to the first LOS deformation and a second full cycle phase corresponding to the second LOS deformation, and converting the third deformation phase; determining the third LOS deformation of the area to be measured according to the first LOS deformation and the second LOS deformation, specifically including: According to the radar wavelength, the first LOS deformation is converted into a first deformation phase, and the first deformation phase is split to obtain a first integral cycle phase and a first non-integral cycle phase; According to the radar wavelength, the second LOS deformation is converted into a second deformation phase, and the second deformation phase is split to obtain a second integral cycle phase and a second non-integral cycle phase; Summing the first non-integer periodic phase and the second integer periodic phase to obtain a third deformed phase; The third deformation phase is converted to obtain the third LOS deformation.

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