MT-InSAR adaptive fusion measurement method and device for long-term surface deformation of coal mining subsidence area

Abstract

The invention relates to an MT-InSAR self-adaptive fusion measurement method and device for long-term surface deformation of a coal mining subsidence area, and the method comprises the steps: obtaining surface deformation monitoring data in a plurality of modes, and carrying out the at least one of time-space alignment, noise removal and error correction of the surface deformation monitoring data, so as to obtain surface deformation data; calculating coherence indexes of the earth surface deformation data; generating a space adjustment coefficient by using a double-peak Gaussian function model constructed based on a coal mining subsidence area deformation rule; constructing an adaptive weighting method based on the coherence index and the spatial adjustment coefficient of the earth surface deformation data to calculate the weight of the multi-source InSAR data; and performing weighted fusion on the surface deformation monitoring data by using the weight of the multi-source InSAR data to obtain a final surface deformation result. Therefore, the problems of insufficient monitoring precision, incomplete coverage, poor adaptability and the like due to the fact that a single InSAR technology is difficult to comprehensively reflect spatio-temporal evolution characteristics of long-term surface deformation of the wide-area coal mining subsidence area are solved.

Description

Technical Field

[0001] The present application relates to the technical field of geological disaster monitoring, and in particular to an MT-InSAR (Multi-Temporal-Interferometric Synthetic Aperture Radar) adaptive fusion measurement method and device for long-term surface deformation in coal mining subsidence areas. Background Art

[0002] With the acceleration of global urbanization, surface subsidence in coal mining-induced subsidence areas has become a significant threat to ecological security and social development. InSAR (Interferometric Synthetic Aperture Radar) technology, due to its high precision, wide range, and all-weather capabilities, has been widely used in surface deformation monitoring. This technology has been further developed into multi-temporal InSAR (MT-InSAR) technologies, including D-InSAR (Differential-Interferometric Synthetic Aperture Radar), SBAS-InSAR (Small Baseline Subset-Interferometric Synthetic Aperture Radar), and DS-InSAR (Distributed Scatterers-Interferometric Synthetic Aperture Radar).

[0003] However, among related technologies, D-InSAR is susceptible to atmospheric delay and spatiotemporal incoherence, which can lead to inaccurate monitoring results in complex surface environments (such as vegetation-covered areas and coal mining subsidence areas). SBAS-InSAR and DS-InSAR, while performing well in low-coherence areas and for long-term deformation monitoring, can underestimate deformation or cause data loss in areas with large deformation amplitudes. In summary, a single InSAR technology is unable to fully reflect the spatiotemporal evolution of long-term surface deformation in wide-area coal mining subsidence areas. Problems such as insufficient monitoring accuracy, incomplete coverage, and poor adaptability require urgent resolution. Summary of the Invention

[0004] The present application provides an MT-InSAR adaptive fusion measurement method and device for long-term surface deformation in coal mining subsidence areas, in order to solve the problems in related technologies where a single InSAR technology is unable to fully reflect the spatiotemporal evolution characteristics of long-term surface deformation in wide-area coal mining subsidence areas, resulting in insufficient monitoring accuracy, incomplete coverage, and poor adaptability.

[0005] The first aspect of the present application provides an MT-InSAR adaptive fusion measurement method for long-term surface deformation in a coal mining subsidence area, comprising the following steps: obtaining surface deformation monitoring data using a plurality of methods, and performing at least one of spatiotemporal alignment, noise removal, and error correction on the surface deformation monitoring data to obtain surface deformation data; calculating the coherence index of the surface deformation data; generating a spatial adjustment coefficient using a bimodal Gaussian function model constructed based on the deformation law of the coal mining subsidence area; constructing an adaptive weighting method based on the coherence index of the surface deformation data and the spatial adjustment coefficient to calculate the weight of multi-source InSAR data; and performing weighted fusion on the surface deformation monitoring data using the weight of the multi-source InSAR data to obtain a final surface deformation result.

[0006] Through the above technical means, the weights of multi-source InSAR data are used to perform weighted fusion on the surface deformation monitoring data to obtain the final surface deformation results. This can give full play to the advantages of their respective time density, spatial coverage and coherence stability, and utilize the complementary characteristics of multi-source data to achieve high-precision monitoring of complex geological environments and long-term surface deformation processes. It can significantly alleviate the technical bottleneck of existing single InSAR technology that is prone to data failure, misjudgment or underestimation of deformation in low-coherence areas, large-scale deformation areas and complex surface environments, improve the accuracy, stability and adaptability of the monitoring system, and further provide an efficient, practical and scalable solution for deformation monitoring in complex geological backgrounds.

[0007] Optionally, in one embodiment of the present application, it further includes: verifying the surface deformation result to obtain an analysis error; and using the analysis error to optimize the model parameters of the bimodal Gaussian function model and / or the adaptive weighting method.

[0008] Through the above technical means, the model parameters of the bimodal Gaussian function model and / or adaptive weighting method are optimized by analyzing the error. The center position, peak weight or width parameters of the Gaussian function can be dynamically adjusted based on the error size, and the fusion weights of different data sources can be redistributed. This can significantly improve the model fitting accuracy and spatial consistency of deformation identification, enhance the model's generalization ability under different geological backgrounds and deformation characteristics, and further improve the overall stability and accuracy of surface deformation monitoring.

[0009] Optionally, in one embodiment of the present application, the multiple modes include multiple modes among a D-InSAR mode, a SBAS-InSAR mode and a DS-InSAR mode.

[0010] Through the above technical means, D-InSAR, SBAS-InSAR and DS-InSAR are used to obtain surface deformation monitoring data. The advantages of each method in spatial coverage, temporal resolution and deformation accuracy can be comprehensively utilized to improve the ability to identify surface deformation processes.

[0011] Optionally, in one embodiment of the present application, the weight calculation formula is:

[0012] ω i (x,y)=γ i (x,y)·α i (x,y),

[0013] Among them, ω i (x,y) represents the weight of the i-th InSAR data at pixel (x,y); γ i (x,y) represents the coherence of the data source at (x,y); α i (x,y) represents the spatial correlation adjustment coefficient; x and y represent the coordinates of any pixel point in the InSAR data; the subscript i represents different InSAR data.

[0014] Through the above technical means, combined with the coherence index and spatial adjustment coefficient of multi-source InSAR data, a weighting function is constructed to determine the fusion weight of each data source in different regions. Regional adaptive allocation of fusion weights can be achieved, which can retain more high-quality data in high-coherence areas, while relying on supplementary data with more stable spatial structure in low-coherence or severely deformed areas, thereby improving the accuracy and continuity of the surface deformation information after fusion.

[0015] Optionally, in one embodiment of the present application, the calculating the coherence index of the surface deformation data includes: acquiring complex signals of pixel points; and calculating the coherence index of the surface deformation data according to the complex signals.

[0016] By using the above technical means, the coherence index of surface deformation data is calculated based on complex signals, and the surface deformation data can be quality screened and weighted, thereby improving the accuracy and robustness of subsequent deformation monitoring and data fusion. This has obvious practical value, especially in low-coherence areas such as vegetation cover, drastic terrain changes, or building obstructions.

[0017] Optionally, in one embodiment of the present application, the calculation formula of the bimodal Gaussian function model is:

[0018]

[0019] Among them, α i (x, y) represents the spatial correlation adjustment coefficient; λ represents the peak value; a1 and a2 represent the main section distribution; b1 and b2 represent the vertical section distribution; (x1, y1) and (x2, y2) represent the center position.

[0020] Through the above technical means, the main section distribution, vertical section distribution and deformation center position of the deformation area are analyzed, and then a three-dimensional deformation profile model is constructed. This can effectively characterize the "W"-shaped or asymmetric "U"-shaped deformation characteristics, improve the adaptability and fitting accuracy of the model in actual engineering scenarios, and is particularly suitable for settlement patterns caused by double working faces or non-uniform mining structures.

[0021] Optionally, in one embodiment of the present application, the fusion formula of the surface deformation monitoring data is:

[0022]

[0023] Among them, D Fusion represents the result after fusion; ω i (x,y) represents the weight of the i-th InSAR data at pixel (x,y); D i (x, y) represents the surface deformation monitoring results of different InSAR data at the spatial position (x, y); n represents the number of InSAR methods involved in the fusion.

[0024] Through the above technical means, the surface deformation monitoring results of different InSAR data are utilized, and combined with the confidence weights of each data source in the target area, and then weighted fusion is performed. The complementary advantages of multi-source data in spatial coverage, temporal resolution and coherence can be fully utilized to achieve high-precision, wide-range and long-term continuous monitoring of surface deformation information. It can effectively overcome the problem of data failure or deformation underestimation that is prone to occur with single InSAR technology in low-coherence areas, large deformation areas or complex surface environments, thereby improving the accuracy, stability and adaptability of monitoring results.

[0025] The second aspect of the present application provides an MT-InSAR adaptive fusion measurement device for long-term surface deformation in a coal mining subsidence area, comprising: an acquisition module for acquiring surface deformation monitoring data in a plurality of ways, and performing at least one of spatiotemporal alignment, noise removal, and error correction on the surface deformation monitoring data to obtain surface deformation data; a calculation module for calculating the coherence index of the surface deformation data; a generation module for generating a spatial adjustment coefficient using a bimodal Gaussian function model constructed based on the deformation law of the coal mining subsidence area; a construction module for constructing an adaptive weighting method based on the coherence index of the surface deformation data and the spatial adjustment coefficient to calculate the weight of multi-source InSAR data; and a measurement module for performing weighted fusion on the surface deformation monitoring data using the weight of the multi-source InSAR data to obtain a final surface deformation result.

[0026] Through the above technical means, the weights of multi-source InSAR data are used to perform weighted fusion on the surface deformation monitoring data to obtain the final surface deformation results. This can give full play to the advantages of their respective time density, spatial coverage and coherence stability, and utilize the complementary characteristics of multi-source data to achieve high-precision monitoring of complex geological environments and long-term surface deformation processes. It can significantly alleviate the technical bottleneck of existing single InSAR technology that is prone to data failure, misjudgment or underestimation of deformation in low-coherence areas, large-scale deformation areas and complex surface environments, improve the accuracy, stability and adaptability of the monitoring system, and further provide an efficient, practical and scalable solution for deformation monitoring in complex geological backgrounds.

[0027] Optionally, in one embodiment of the present application, it also includes: a verification module for verifying the surface deformation results to obtain an analysis error; an optimization module for using the analysis error to optimize the model parameters of the bimodal Gaussian function model and / or the adaptive weighting method.

[0028] Through the above technical means, the model parameters of the bimodal Gaussian function model and / or adaptive weighting method are optimized by analyzing the error. The center position, peak weight or width parameters of the Gaussian function can be dynamically adjusted based on the error size, and the fusion weights of different data sources can be redistributed. This can significantly improve the model fitting accuracy and spatial consistency of deformation identification, enhance the model's generalization ability under different geological backgrounds and deformation characteristics, and further improve the overall stability and accuracy of surface deformation monitoring.

[0029] Optionally, in one embodiment of the present application, the acquisition method includes multiple methods selected from the group consisting of a D-InSAR method, a SBAS-InSAR method, and a DS-InSAR method.

[0030] Through the above technical means, D-InSAR, SBAS-InSAR and DS-InSAR are used to obtain surface deformation monitoring data. The advantages of each method in spatial coverage, temporal resolution and deformation accuracy can be comprehensively utilized to improve the ability to identify surface deformation processes.

[0031] Optionally, in one embodiment of the present application, the weight calculation formula is:

[0032] ω i (x,y)=γ i (x,y)·α i (x,y),

[0033] Among them, ω i (x,y) represents the weight of the i-th InSAR data at pixel (x,y); γ i (x,y) represents the coherence of the data source at (x,y); α i (x,y) represents the spatial correlation adjustment coefficient; x and y represent the coordinates of any pixel point in the InSAR data; the subscript i represents different InSAR data.

[0034] Through the above technical means, combined with the coherence index and spatial adjustment coefficient of multi-source InSAR data, a weighting function is constructed to determine the fusion weight of each data source in different regions. Regional adaptive allocation of fusion weights can be achieved, which can retain more high-quality data in high-coherence areas, while relying on supplementary data with more stable spatial structure in low-coherence or severely deformed areas, thereby improving the accuracy and continuity of the surface deformation information after fusion.

[0035] Optionally, in one embodiment of the present application, the calculation module includes: an acquisition unit for acquiring complex signals of pixel points; and a calculation unit for calculating a coherence index of the surface deformation data based on the complex signals.

[0036] By using the above technical means, the coherence index of surface deformation data is calculated based on complex signals, and the surface deformation data can be quality screened and weighted, thereby improving the accuracy and robustness of subsequent deformation monitoring and data fusion. This has obvious practical value, especially in low-coherence areas such as vegetation cover, drastic terrain changes, or building obstructions.

[0037] Optionally, in one embodiment of the present application, the calculation formula of the bimodal Gaussian function model is:

[0038]

[0039] Among them, α i(x, y) represents the spatial correlation adjustment coefficient; λ represents the peak value; a1 and a2 represent the main section distribution; b1 and b2 represent the vertical section distribution; (x1, y1) and (x2, y2) represent the center position.

[0040] Through the above technical means, the main section distribution, vertical section distribution and deformation center position of the deformation area are analyzed, and then a three-dimensional deformation profile model is constructed. This can effectively characterize the "W"-shaped or asymmetric "U"-shaped deformation characteristics, improve the adaptability and fitting accuracy of the model in actual engineering scenarios, and is particularly suitable for settlement patterns caused by double working faces or non-uniform mining structures.

[0041] Optionally, in one embodiment of the present application, the fusion formula of the surface deformation monitoring data is:

[0042]

[0043] Among them, D Fusiin represents the result after fusion; ω i (x,y) represents the weight of the i-th InSAR data at pixel (x,y); D i (x, y) represents the surface deformation monitoring results of different InSAR data at the spatial position (x, y); n represents the number of InSAR methods involved in the fusion.

[0044] Through the above technical means, the surface deformation monitoring results of different InSAR data are utilized, and combined with the confidence weights of each data source in the target area, and then weighted fusion is performed. The complementary advantages of multi-source data in spatial coverage, temporal resolution and coherence can be fully utilized to achieve high-precision, wide-range and long-term continuous monitoring of surface deformation information. It can effectively overcome the problem of data failure or deformation underestimation that is prone to occur with single InSAR technology in low-coherence areas, large deformation areas or complex surface environments, thereby improving the accuracy, stability and adaptability of monitoring results.

[0045] The third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the MT-InSAR adaptive fusion measurement method for long-term surface deformation in coal mining subsidence areas as described in the above embodiment.

[0046] The fourth aspect of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above-mentioned MT-InSAR adaptive fusion measurement method for long-term surface deformation in coal mining subsidence areas.

[0047] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0049] Figure 1 This is a schematic diagram of the system architecture of the MT-InSAR adaptive fusion measurement method for long-term surface deformation in coal mining subsidence areas according to one embodiment of the present application;

[0050] Figure 2 This is a flow chart of an MT-InSAR adaptive fusion measurement method for long-term surface deformation in coal mining subsidence areas provided in accordance with an embodiment of the present application;

[0051] Figure 3 This is a schematic diagram of surface monitoring results using three InSAR technologies according to an embodiment of the present application;

[0052] Figure 4 This is a schematic diagram of the coherence results of the InSAR technology according to one embodiment of the present application;

[0053] Figure 5 This is a schematic diagram of the spatial adjustment coefficient results based on D-InSAR calculation according to one embodiment of the present application;

[0054] Figure 6 This is a schematic diagram of the spatial adjustment coefficient results calculated based on SBAS-InSAR and DS-InSAR according to one embodiment of the present application;

[0055] Figure 7 This is a schematic diagram of the MT-InSAR adaptive fusion data results according to one embodiment of the present application;

[0056] Figure 8 This is a schematic diagram of the comparison results of the main section and vertical section of the single InSAR technology and fusion data in one embodiment of the present application;

[0057] Figure 9 Schematic diagram of a block diagram of an MT-InSAR adaptive fusion measurement device for long-term surface deformation in a coal mining subsidence area according to an embodiment of the present application;

[0058] Figure 10 The figure is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application.

[0059] Reference numerals:

[0060] 10-MT-InSAR adaptive fusion measurement device for long-term surface deformation in coal mining subsidence areas; 100-acquisition module, 200-calculation module, 300-generation module, 400-construction module, 500-measurement module; 1001-memory, 1002-processor, 1003-communication interface. DETAILED DESCRIPTION

[0061] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0062] The following describes the MT-InSAR adaptive fusion measurement method and device for long-term surface deformation in coal mining subsidence areas according to the embodiments of the present application with reference to the accompanying drawings. In view of the fact that the single InSAR technology mentioned in the above background technology is difficult to fully reflect the spatiotemporal evolution characteristics of long-term surface deformation in a wide area of ​​coal mining subsidence areas, and there are technical problems such as insufficient monitoring accuracy, incomplete coverage, and poor adaptability, the present application provides a MT-InSAR adaptive fusion measurement method for long-term surface deformation in coal mining subsidence areas. In this method, three types of InSAR measurement data, D-InSAR, SBAS-InSAR, and DS-InSAR, are coupled, their coherence index is calculated, and a bimodal Gaussian function model is constructed. Then, an adaptive fusion strategy is adopted to perform dynamic weighted integration of multi-source InSAR data, which can give full play to the complementary advantages of multi-source InSAR data, realize continuous and high-precision monitoring of surface deformation processes, significantly improve monitoring accuracy and spatial coverage, effectively address the limitations of different interference methods in spatial scale and temporal density, and is particularly suitable for deformation identification and assessment under complex terrain conditions such as coal mining subsidence areas and areas prone to geological disasters. It can provide an efficient and practical technical means for geological disaster warning and safety decision-making. This solves the problem that a single InSAR technology is unable to fully reflect the spatiotemporal evolution characteristics of long-term surface deformation in wide-area coal mining subsidence areas, leading to problems such as insufficient monitoring accuracy, incomplete coverage, and poor adaptability.

[0063] Before describing the MT-InSAR adaptive fusion measurement method for long-term surface deformation in coal mining subsidence areas proposed in the embodiment of the present application, the application scenarios and system architecture involved in the embodiment of the present application are first described.

[0064] The embodiment of this application takes the mining subsidence area of ​​Nangou Coal Mine in Xinzhou City, Shanxi Province as the research object, including three mined working faces 20201, 20202, and 50501. There is a potential impact of the mining subsidence area on the surface deformation in the study area. In order to improve the accuracy of deformation monitoring and solve the problem of data failure or underestimation of deformation in low coherence areas and large deformation areas with a single technology, the embodiment of this application proposes a method of adaptive fusion of a bimodal Gaussian function model and MT-InSAR observation data to monitor surface deformation. Its system architecture can be as follows: Figure 1 As shown in the figure, by coupling the three InSAR measurement data of D-InSAR, SBAS-InSAR and DS-InSAR, the complementary characteristics of multi-source data are fully utilized to achieve high-precision monitoring of complex geological environments and long-term surface deformation.

[0065] Specifically, Figure 2 This is a flow chart of an MT-InSAR adaptive fusion measurement method for long-term surface deformation in coal mining subsidence areas provided in an embodiment of the present application.

[0066] like Figure 2 As shown in FIG, the MT-InSAR adaptive fusion measurement method for long-term surface deformation in coal mining subsidence areas includes the following steps:

[0067] In step S201, surface deformation monitoring data are acquired by using a plurality of methods, and at least one of spatiotemporal alignment, noise removal, and error correction is performed on the surface deformation monitoring data to obtain surface deformation data.

[0068] Among them, surface deformation monitoring data refers to data used to reflect spatial changes such as displacement, settlement, uplift or deformation of the surface within a certain time range, which may include but is not limited to InSAR phase data, deformation time series, GNSS (Global Navigation Satellite System) coordinate changes, leveling data and structural deformation data.

[0069] Optionally, in one embodiment of the present application, the multiple methods may include but are not limited to multiple methods among a D-InSAR method, a SBAS-InSAR method and a DS-InSAR method.

[0070] It should be noted that the embodiments of the present application can perform temporal and spatial alignment on the surface deformation monitoring data obtained by D-InSAR, SBAS-InSAR and DS-InSAR technologies, ensuring that different technologies monitor surface deformation at the same time reference and spatial resolution; remove noise from the data, including but not limited to removing atmospheric delay, orbit error and the impact of noise on deformation monitoring; and adopt an error correction model to correct the systematic deviation of each data source to improve monitoring accuracy.

[0071] As a possible implementation method, the embodiment of the present application can obtain the SLC (Single-look Complex) of the Sentinel-1A satellite once every 12 days, and generate a registered SAR (Synthetic Aperture Radar) time series dataset through basic preprocessing such as data cropping, geocoding, image registration, slant range correction, and addition of precise orbit data.

[0072] Specifically, this embodiment of the application uses Sentinel-1A image data covering the study area from August 27, 2023, to May 5, 2024. The imaging mode can be IW (Interferometric Wide Swath Mode) and the polarization mode (referring to the electromagnetic vibration direction of the radar wave) can be VV (Dual Polarization (VV polarization)). After screening, the SAR image data of the study area can be shown in Table 1. Table 1 is a detailed table of Sentinel-1A satellite data.

[0073] Table 1

[0074] Serial number Imaging date Imaging mode Polarization Data Type 1 20230827 IW VV SLC 2 20230908 IW VV SLC 3 20230920 IW VV SLC 4 20231002 IW VV SLC 5 20231014 IW VV SLC 6 20231026 IW VV SLC 7 20231107 IW VV SLC 8 20231119 IW VV SLC 9 20231201 IW VV SLC 10 20231213 IW VV SLC 11 20231225 IW VV SLC 12 20240106 IW VV SLC 13 20240118 IW VV SLC 14 20240130 IW VV SLC 15 20240211 IW VV SLC 16 20240223 IW VV SLC 17 20240306 IW VV SLC 18 20240318 IW VV SLC 19 20240330 IW VV SLC 20 20240411 IW VV SLC 21 20240423 IW VV SLC 22 20240505 IW VV SLC

[0075] The embodiment of the present application can update the orbital state vector of the SAR image by applying the high-precision satellite position and velocity information provided in the precise orbit file, which can effectively reduce the deviation problem caused by satellite orbit error. At the same time, the introduction of precise orbit data can significantly improve the orbit accuracy of the SAR image, thereby providing a more reliable foundation for subsequent image processing.

[0076] Furthermore, in order to reduce image offset caused by terrain changes, the embodiment of the present application can combine open source DEM (Digital Elevation Model) data for auxiliary calculation to compensate for the errors caused by terrain fluctuations.

[0077] Specifically, in an embodiment of the present application, when the vertical baseline between the master image and the slave image is large, the undulating terrain may cause additional offsets between the images, and a local offset of about 2 meters may be generated for every 1000-meter change in elevation. The embodiment of the present application can achieve accurate image registration by using NASA DEM (NASA Digital Elevation Model) data with a resolution of 30 meters and correcting the offset caused by the terrain based on the DEM model. Among them, the spatial reference coordinate system of the DEM data adopts the GCS_WGS_1984 (Geographic Coordinate System_World Geographic System_1984) standard.

[0078] In addition, after completing the image registration, the embodiment of the present application can be cropped to the study area, and the cropped image range covers the study area. The above method can ensure the geometric accuracy and positioning accuracy of the image, thereby laying a high-quality data foundation for further analysis within the study area.

[0079] Combine Figure 3 , taking a specific example, the use of D-InSAR, SBAS-InSAR and DS-InSAR technologies to obtain surface deformation monitoring data is explained in detail.

[0080] (1) D-InSAR technology is used to monitor the surface deformation in the study area.

[0081] In an embodiment of the present application, D-InSAR can extract surface deformation information by interferometrically processing SAR images from two time phases. The core of this method is to calculate the interferometric phase of the image and eliminate the terrain phase through differential processing, thereby obtaining deformation information of the target area. During the processing, the embodiment of the present application corrects satellite orbit errors by introducing precise orbit data, and uses an open source digital elevation model (DEM) to eliminate the impact of terrain undulations on the interferometric phase, and corrects for atmospheric delay and noise, thereby improving monitoring accuracy.

[0082] (2) SBAS-InSAR technology is used to accurately monitor the surface deformation in the study area.

[0083] The embodiment of the present application reduces the impact of atmospheric delay, orbital error and coherence degradation on monitoring results by screening interferometric image pairs with small spatial baselines and time baselines, thereby improving the accuracy and reliability of deformation monitoring. Specific processing steps may include: geocoding and coherence analysis of pre-processed images, screening out interferometric image pairs that meet the conditions, and using time series analysis methods to extract surface deformation information of the study area. At the same time, the embodiment of the present application eliminates and corrects noise and errors in data processing to ensure the accuracy of monitoring results.

[0084] (3) DS-InSAR technology is used to monitor the surface deformation in the study area.

[0085] By analyzing the phase information of DS (Distributed Scatterers), DS-InSAR technology can effectively identify and utilize stable scatterers in areas with low coherence (such as vegetation-covered areas, bare land, etc.), thereby expanding the scope of application of traditional PS-InSAR (Persistent Scatterer Interferometric Synthetic Aperture Radar) methods. Specifically, this technology performs interferometric processing on multi-temporal SAR images, combining coherence analysis, filtering, and time series modeling methods to extract the phase stability information of distributed scatterers and then invert the surface deformation of the target area. During the processing process, by combining coherence indicators and high-resolution digital elevation models (DEMs), atmospheric delays, orbit errors, and terrain errors can be corrected, further improving the accuracy of deformation monitoring.

[0086] In step S202 , the coherence index of the surface deformation data is calculated.

[0087] Among them, the coherence index is a core parameter in InSAR technology. It can be used to evaluate the coherence degree or phase stability between two radar images at a certain pixel position, reflecting whether the area can be reliably interferometrically processed and deformation extracted.

[0088] Optionally, in one embodiment of the present application, calculating the coherence index of the surface deformation data includes: acquiring complex signals of pixel points; and calculating the coherence index of the surface deformation data according to the complex signals.

[0089] Specifically, the embodiment of the present application can calculate the coherence index based on the complex signal of the pixel point, which can be used to measure the reliability of the monitoring data. The formula for calculating the coherence index can be as follows:

[0090]

[0091] Among them, γ(x,y) is the coherence value of the pixel point (x,y), and are the complex values ​​of the primary and secondary images in the jth interference pattern, N is the number of interference patterns, and * represents the complex conjugate.

[0092] It should be noted that if Figure 4 As shown in the figure, the coherence calculation results can be used as the weight basis for monitoring data quality. The coherence value is between 0 and 1. The higher the coherence value, the greater the contribution of the monitoring data in the fusion process. For data in low coherence areas, a threshold can be set to eliminate them to avoid the influence of invalid or low-precision data on the results.

[0093] In step S203, a spatial adjustment coefficient is generated using a bimodal Gaussian function model constructed based on the deformation law of the coal mining subsidence area.

[0094] For example, the law of surface deformation can be manifested as a phased evolution law of "gradual-violent-stable" in the time dimension; it can also be manifested as a "W"-shaped or "U"-shaped distribution feature of the surface settlement curve in the spatial dimension.

[0095] It should be noted that the spatial adjustment coefficient refers to a correction parameter introduced during the surface deformation, geological structural response, or data modeling process to account for differences in geological conditions, mining intensity, subsidence response, and other factors at different spatial locations. This coefficient can be set based on the sensitivity or response level of different regions and can be used to improve model accuracy or the adaptability of results.

[0096] In the specific implementation of this application, a bimodal Gaussian model can be constructed to accurately describe the long-term deformation patterns of the surface in coal mining subsidence areas. In some cases, the surface deformation patterns in coal mining subsidence areas generally conform to the deformation characteristics of a "W" shape in the main section and a "U" shape in the vertical section. To this end, the embodiments of this application use a bimodal Gaussian function model to define the deformation center and its influence range.

[0097] Optionally, in one embodiment of the present application, the calculation formula of the bimodal Gaussian function model can be expressed as:

[0098]

[0099] Among them, α i (x, y) represents the spatial correlation adjustment coefficient; λ represents the peak value, which is used to control the height of the double peak; a1 and a2 both represent the main section distribution, which are used to control the range of the double peak in the main section direction; b1 and b2 both represent the vertical section distribution, which are used to control the range of the double peak in the vertical section direction; (x1, y1) and (x2, y2) represent the center position, which are used to control the position of the double peak.

[0100] Further, combined with Figure 5 and Figure 6 , and based on the actual geological characteristics and surface deformation laws of the coal mining subsidence area, the steps of determining the parameters of the bimodal Gaussian model are described. The embodiment of the present application may include:

[0101] (1) Geological data analysis.

[0102] This embodiment of the present application firstly obtains characteristic data of the mining area, such as the mining scope and time, stratum thickness, and geological structure. Furthermore, based on an analysis of the mining history of the mining area, this embodiment of the present application finds that the 20202 working face was the last working face to be stopped, and it plays a major role in the long-term surface deformation.

[0103] (2) Historical deformation monitoring.

[0104] In the embodiment of the present application, the historical deformation law of the coal mining subsidence area is analyzed in combination with the InSAR monitoring data, and the distribution range and the center position are extracted. The embodiment of the present application analyzes the D-InSAR monitoring data, and it can be found that the maximum surface deformation occurs near the 20202 working face. It can be verified that the 20202 working face plays a major role in the long-term deformation of the surface. Therefore, the surface deformation curve of its main section is extracted. The curve conforms to the "W" type feature, so the maximum deformation point can be considered to be the center position. Furthermore, the embodiment of the present application analyzes the SBAS-InSAR and DS-InSAR data to determine the boundary position, and uses ArcGIS9 software to calculate the distance from the boundary to the center position, that is, to obtain a i with b i .

[0105] (3) Parameter inversion.

[0106] As a specific example, based on the geological data of the coal mining subsidence area, historical deformation characteristics and GNSS monitoring data, the embodiment of the present application can invert the peak parameters of the bimodal Gaussian model. Specifically, the embodiment of the present application mainly minimizes the sum of squares of the error between the GNSS monitoring data and the fused settlement value to the model parameter λ i For optimization, the optimization objective function can be defined as:

[0107]

[0108] Where N is the number of GNSS monitoring points; D GNSS (x i ,y i ) is the actual settlement value of the i-th GNSS monitoring point; D fusion (x i ,y i ) is the settlement value of the i-th point predicted by the model.

[0109] To achieve the above optimization goal, the present embodiment can use MATLAB's fminunc function to optimize the parameters. This function can obtain the optimal parameter λ through quasi-Newton iterative search. D and λ SBAS , so that the sum of squared errors is minimized. In the embodiment of the present application, the spatial adjustment coefficients of the bimodal Gaussian model are finally determined to be Figure 5 The adjustment coefficients and Figure 6 The adjustment coefficients calculated based on SBAS-InSAR and DS-InSAR data.

[0110] The embodiment of the present application can inversely determine the parameters of the bimodal Gaussian model through historical deformation data and geological conditions of the coal mining subsidence area, thereby improving the adaptability of the model to actual deformation laws.

[0111] In step S204, an adaptive weighting method is constructed based on the coherence index and spatial adjustment coefficient of the surface deformation data to calculate the weight of the multi-source InSAR data.

[0112] It should be noted that weight refers to the coefficient used to indicate the influence of each data point on the final result when fusion is performed. The weighting strategy dynamically adjusts the contribution of each data source in different regions, ensuring that high-quality data dominates the fusion process. In some cases, D-InSAR data can be given a higher weight in areas with strong deformation, while SBAS-InSAR and DS-InSAR data can be given a higher weight in areas with less deformation but better stability.

[0113] In the embodiment of the present application, an adaptive weighting method can be used to dynamically assign weights to the three types of InSAR data according to the geological characteristics of different regions and the coherence quality of InSAR data, thereby improving the accuracy and reliability of surface deformation monitoring results.

[0114] Optionally, in one embodiment of the present application, the weight calculation formula is:

[0115] ω i (x,y)=γ i (x,y)·α i (x,y),

[0116] Among them, ω i (x,y) represents the weight; γ i (x,y) represents the coherence of the data source at (x,y); α i (x,y) represents the spatial correlation adjustment coefficient; x and y represent the coordinates of any pixel point in the InSAR data; the subscript i represents different InSAR data.

[0117] It is understandable that γ i (x, y) can reflect the data quality of the pixel point. The higher the coherence value, the more reliable the data. i (x,y) and the adjustment coefficient α generated by the bimodal Gaussian model i (x,y), the weight of each InSAR data can be calculated using the adaptive weighting method.

[0118] In step S205, the surface deformation monitoring data is weightedly fused using the weights of the multi-source InSAR data to obtain the final surface deformation result.

[0119] The embodiment of the present application can calculate the final surface deformation result by weighted fusion of the monitoring results of different acquired InSAR data and the corresponding weights.

[0120] Optionally, in one embodiment of the present application, the fusion formula for surface deformation monitoring data is:

[0121]

[0122] Among them, D Fusion represents the result after fusion; ω i (x,y) represents the weight of the i-th InSAR data at pixel (x,y); D t (x, y) represents the surface deformation monitoring results of different InSAR data at the spatial position (x, y); n represents the number of InSAR methods involved in the fusion.

[0123] In the actual implementation process, the fusion process can adopt partitioning processing for different areas, so as to fully consider the spatial heterogeneity of deformation characteristics in the coal mining subsidence area.

[0124] like Figure 7 As shown, in the embodiments of the present application, the results obtained after fusion are improved in terms of recognition accuracy of surface deformation, spatial coverage integrity, and boundary continuity compared to the results of single InSAR technology. The fused results can more accurately reflect the surface deformation characteristics and can significantly enhance the reliability and adaptability of surface deformation monitoring.

[0125] Optionally, in one embodiment of the present application, it also includes: verifying the surface deformation results to obtain analysis errors; and using the analysis errors to optimize the model parameters and / or adaptive weighting method of the bimodal Gaussian function model.

[0126] Combine Figure 8The following is a detailed description of the process of using GNSS verification using a specific example. The embodiment of the present application may include the following steps:

[0127] (1) The fusion results are verified by GNSS monitoring data, and the 3D deformation data of GNSS is projected to the LOS (Line of Sight) direction.

[0128] The projection formula of the embodiment of the present application can be as follows:

[0129] D LOs =D U cosθ-D E sinθcosα+D N sinθsinα,

[0130] Among them, D LOS Denotes the deformation along the radar line of sight; D U 、D E 、D N They represent the easting, northing and vertical displacements provided by GNSS data respectively; θ represents the satellite incidence angle; and α represents the satellite flight azimuth.

[0131] (2) Linear interpolation is performed on the GNSS data to generate data that matches the time of the InSAR data.

[0132] In an embodiment of the present application, the interpolation relationship may be as follows:

[0133]

[0134] in, Indicates GNSS data at the same time as InSAR; and Respectively represent the GNSS data of the next scene and the previous scene within the InSAR data time; T b With T a Indicates the corresponding time.

[0135] (3) Calculate the RMSE (Root Mean Square Error) between the GNSS monitoring data and the fusion result to evaluate the accuracy of the fusion result.

[0136] In this embodiment of the present application, the RMSE can be calculated by comparing the projected and interpolated GNSS deformation data with the InSAR fusion results point by point. The formula can be expressed as:

[0137]

[0138] Where N represents the number of monitoring points involved in the calculation; DLOS,GNSS,i represents the GNSS deformation value (LOS direction) of the i-th monitoring point; D LOS,InSAR,i Indicates the InSAR fusion deformation value (LOS direction) of the i-th monitoring point.

[0139] (4) Based on the verification results, the parameters and weight distribution strategy of the bimodal Gaussian model are optimized to further improve the accuracy and stability of the fusion results.

[0140] like Figure 8 As shown, the embodiment of the present application provides RMSE comparison results of GNSS data with three InSAR technologies and fusion data. Based on the GNSS verification results, the error distribution in the monitoring results can be analyzed, and the bimodal Gaussian model parameters and weight distribution strategy can be dynamically adjusted to further improve the monitoring accuracy.

[0141] According to the MT-InSAR adaptive fusion measurement method for long-term surface deformation in coal mining subsidence areas proposed in the embodiment of the present application, three types of InSAR measurement data, D-InSAR, SBAS-InSAR, and DS-InSAR, are coupled, their coherence index is calculated, and a bimodal Gaussian function model is constructed. Then, an adaptive fusion strategy is used to dynamically weighted integrate multi-source InSAR data to realize surface deformation monitoring. This can make up for the problem that existing single InSAR technology is prone to data failure or underestimation of deformation in low-coherence areas, large deformation areas, and complex surface environments, and can improve the accuracy, reliability, and adaptability of surface deformation monitoring in coal mining subsidence areas. At the same time, it provides an efficient and practical technical means for surface deformation monitoring in complex geological environments.

[0142] Next, the MT-InSAR adaptive fusion measurement device for long-term surface deformation in coal mining subsidence areas proposed in accordance with an embodiment of the present application will be described with reference to the accompanying drawings.

[0143] Figure 9 It is a block diagram of the MT-InSAR adaptive fusion measurement device for long-term surface deformation in coal mining subsidence areas according to an embodiment of the present application.

[0144] like Figure 9 As shown, the MT-InSAR adaptive fusion measurement device 10 for long-term surface deformation in coal mining subsidence areas includes: an acquisition module 100, a calculation module 200, a generation module 300, a construction module 400 and a measurement module 500.

[0145] The acquisition module 100 is configured to acquire surface deformation monitoring data in a variety of ways, and perform at least one of spatiotemporal alignment, noise removal, and error correction on the surface deformation monitoring data to obtain surface deformation data.

[0146] The calculation module 200 is used to calculate the coherence index of the surface deformation data.

[0147] The generation module 300 is used to generate a spatial adjustment coefficient using a bimodal Gaussian function model constructed based on the deformation law of the coal mining subsidence area.

[0148] The construction module 400 is used to construct an adaptive weighting method based on the coherence index and spatial adjustment coefficient of the surface deformation data to calculate the weight of the multi-source InSAR data.

[0149] The measurement module 500 is used to perform weighted fusion on the surface deformation monitoring data using the weights of the multi-source InSAR data to obtain the final surface deformation result.

[0150] Optionally, in one embodiment of the present application, it further includes: a verification module and an optimization module.

[0151] Among them, the verification module is used to verify the surface deformation results to obtain the analysis error.

[0152] An optimization module is used to optimize model parameters and / or an adaptive weighting method of a bimodal Gaussian function model using analysis errors.

[0153] Optionally, in one embodiment of the present application, the acquisition method includes multiple methods of D-InSAR method, SBAS-InSAR method and DS-InSAR method.

[0154] Optionally, in one embodiment of the present application, the weight calculation formula is:

[0155] ω i (x,y)=γ i (x,y)·α i (x,y),

[0156] Among them, ω i (x,y) represents the weight of the i-th InSAR data at pixel (x,y); γ i (x,y) represents the coherence of the data source at (x,y); α i (x,y) represents the spatial correlation adjustment coefficient; x and y represent the coordinates of any pixel point in the InSAR data; the subscript i represents different InSAR data.

[0157] Optionally, in one embodiment of the present application, the calculation module 200 includes: an acquisition unit and a calculation unit.

[0158] The acquisition unit is used to acquire the complex signal of the pixel point.

[0159] The calculation unit is used to calculate the coherence index of the surface deformation data according to the complex signal.

[0160] Optionally, in one embodiment of the present application, the calculation formula of the bimodal Gaussian function model is:

[0161]

[0162] Among them, α i (x, y) represents the spatial correlation adjustment coefficient; λ represents the peak value; a1 and a2 represent the main section distribution; b1 and b2 represent the vertical section distribution; (x1, y1) and (x2, y2) represent the center position.

[0163] Optionally, in one embodiment of the present application, the fusion formula for surface deformation monitoring data is:

[0164]

[0165] Among them, D Fusion represents the result after fusion; ω i (x,y) represents the weight of the i-th InSAR data at pixel (x,y); D i (x, y) represents the surface deformation monitoring results of different InSAR data at the spatial position (x, y); n represents the number of InSAR methods involved in the fusion.

[0166] It should be noted that the above explanation of the embodiment of the MT-InSAR adaptive fusion measurement method for long-term surface deformation in coal mining subsidence areas is also applicable to the MT-InSAR adaptive fusion measurement device for long-term surface deformation in coal mining subsidence areas of this embodiment, and will not be repeated here.

[0167] According to the MT-InSAR adaptive fusion measurement device for long-term surface deformation in coal mining subsidence areas proposed in the embodiment of the present application, three types of InSAR measurement data, D-InSAR, SBAS-InSAR, and DS-InSAR, are coupled, their coherence index is calculated, and a bimodal Gaussian function model is constructed. Then, an adaptive fusion strategy is adopted to perform dynamic weighted integration of multi-source InSAR data to realize surface deformation monitoring. This can make up for the problem that existing single InSAR technology is prone to data failure or underestimation of deformation in low-coherence areas, large deformation areas, and complex surface environments, and can improve the accuracy, reliability, and adaptability of surface deformation monitoring in coal mining subsidence areas. At the same time, it provides an efficient and practical technical means for surface deformation monitoring in complex geological environments.

[0168] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0169] A memory 1001 , a processor 1002 , and a computer program stored in the memory 1001 and executable on the processor 1002 .

[0170] When the processor 1002 executes the program, the MT-InSAR adaptive fusion measurement method for long-term surface deformation in coal mining subsidence areas provided in the above embodiment is implemented.

[0171] Furthermore, the electronic device further includes:

[0172] The communication interface 1003 is used for communication between the memory 1001 and the processor 1002 .

[0173] The memory 1001 is used to store computer programs that can be run on the processor 1002 .

[0174] The memory 1001 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0175] If the memory 1001, processor 1002, and communication interface 1003 are implemented independently, the communication interface 1003, memory 1001, and processor 1002 can be connected to each other via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 10 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0176] Optionally, in a specific implementation, if the memory 1001, the processor 1002 and the communication interface 1003 are integrated on a chip, the memory 1001, the processor 1002 and the communication interface 1003 can communicate with each other through an internal interface.

[0177] The processor 1002 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0178] This embodiment also provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the above-mentioned MT-InSAR adaptive fusion measurement method for long-term surface deformation in coal mining subsidence areas is implemented.

[0179] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0180] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this application, "N" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0181] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing a custom logical function or process step, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed in a different order than shown or discussed, including performing functions in a substantially simultaneous manner or in a reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0182] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or N wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program can be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing it in other suitable ways as necessary, and then storing it in a computer memory.

[0183] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented using hardware, as in another embodiment, it can be implemented using any one or a combination of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0184] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0185] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0186] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A MT-InSAR adaptive fusion measurement method for long-term surface deformation in coal mining subsidence areas, characterized by: The following steps are involved: Acquiring surface deformation monitoring data using a plurality of methods, and performing at least one of spatiotemporal alignment, noise removal, and error correction on the surface deformation monitoring data to obtain surface deformation data; calculating a coherence index of the surface deformation data; The spatial adjustment coefficient is generated by using a bimodal Gaussian function model based on the deformation law of coal mining subsidence areas. Constructing an adaptive weighting method based on the coherence index of the surface deformation data and the spatial adjustment coefficient to calculate the weight of the multi-source InSAR data; The surface deformation monitoring data is weightedly fused using the weights of the multi-source InSAR data to obtain a final surface deformation result.

2. The method according to claim 1, characterized in that Also includes: Verifying the surface deformation result to obtain an analysis error; The analysis error is utilized to optimize model parameters of the bimodal Gaussian function model and / or the adaptive weighting method.

3. The method according to claim 1, characterized in that The multiple modes include multiple ones of the D-InSAR mode, the SBAS-InSAR mode and the DS-InSAR mode.

4. The method according to claim 3, characterized in that The calculation formula of the weight is: oh i (x,y)=γ i (x,y)·a i (x,y), Among them, ω i (x,y) represents the weight of the i-th InSAR data at pixel (x,y); γ i (x,y) represents the coherence of the data source at (x,y); α i (x,y) represents the spatial correlation adjustment coefficient; x and y represent the coordinates of any pixel point in the InSAR data; the subscript i represents different InSAR data.

5. The method according to claim 1, wherein The calculating of the coherence index of the surface deformation data includes: Get the complex signal of the pixel point; A coherence index of the ground surface deformation data is calculated based on the complex signal.

6. The method according to claim 1, characterized in that The calculation formula of the bimodal Gaussian function model is: Among them, α i (x, y) represents the spatial correlation adjustment coefficient; λ represents the peak value; a1 and a2 represent the main section distribution; b1 and b2 represent the vertical section distribution; (x1, y1) and (x2, y2) represent the center position.

7. The method according to claim 1, characterized in that The fusion formula of the surface deformation monitoring data is: Among them, D Fusion represents the result after fusion; ω i (x, t) represents the weight of the i-th InSAR data at the pixel point (x, y); D i (x, y) represents the surface deformation monitoring results of different InSAR data at the spatial position (x, t); n represents the number of InSAR methods involved in the fusion.

8. A MT-InSAR adaptive fusion measurement device for long-term surface deformation in coal mining subsidence areas, characterized by: include: an acquisition module, configured to acquire surface deformation monitoring data using a plurality of methods, and perform at least one of spatiotemporal alignment, noise removal, and error correction on the surface deformation monitoring data to obtain surface deformation data; A calculation module, configured to calculate a coherence index of the surface deformation data; A generation module for generating a spatial adjustment coefficient using a bimodal Gaussian function model constructed based on the deformation law of the coal mining subsidence area; A construction module is used to construct an adaptive weighting method based on the coherence index of the surface deformation data and the spatial adjustment coefficient to calculate the weight of the multi-source InSAR data; The measurement module is used to perform weighted fusion on the surface deformation monitoring data using the weights of the multi-source InSAR data to obtain a final surface deformation result.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the MT-InSAR adaptive fusion measurement method for long-term surface deformation in coal mining subsidence areas as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the MT-InSAR adaptive fusion measurement method for long-term surface deformation in coal mining subsidence areas as described in any one of claims 1 to 7.

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