Hydrogeological parameter inversion method and system based on time sequence InSAR (Interferometric Synthetic Aperture Radar)

Abstract

The invention discloses a hydrogeological parameter inversion method and system based on a time sequence InSAR, and relates to the field of remote sensing geological inversion, and the method comprises the steps: carrying out the interference processing of a synthetic aperture radar image set, and obtaining a time sequence interference image set; carrying out atmospheric correction on the time sequence interference image set by adopting an atmospheric correction model; determining an average coherence coefficient graph according to the corrected time sequence interference image set, extracting high coherence points from the average coherence coefficient graph, and determining the interference correlation of each high coherence point; the interference correlation of each high-coherence point is introduced into an inversion equation to serve as a weight, and the corrected time sequence interference image set is combined to determine a rail ascending deformation quantity and a rail descending deformation quantity; determining the deformation quantity in the vertical direction according to the rail ascending deformation quantity and the rail descending deformation quantity; establishing an earth surface deformation model according to the corrected time sequence interference image set; and inverting hydrogeological parameters based on the deformation quantity in the vertical direction and the earth surface deformation model. According to the invention, the inversion precision of the hydrogeological parameters is improved.

Description

Technical Field

[0001] The present application relates to the field of remote sensing geological inversion, and in particular to a method and system for inversion of hydrogeological parameters based on time-series InSAR. Background Art

[0002] In the field of remote sensing geological inversion, with the deepening of earth science research and the continuous development of technology, the demand for the acquisition and analysis of key information such as hydrogeological parameters is increasing. Although traditional geodetic methods, such as the Global Navigation Satellite System (GNSS) and leveling, can provide surface deformation information to a certain extent, they are limited by factors such as high observation costs, small working range, and low spatial resolution, making it difficult to achieve large-area, high-precision continuous monitoring. Existing technologies often rely on empirical models and assumptions when modeling and inverting deformation mechanisms in complex hydrogeological environments, making it difficult to accurately reflect the actual dynamic changes of groundwater, resulting in low inversion accuracy of hydrogeological parameters. Summary of the invention

[0003] The purpose of this application is to provide a hydrogeological parameter inversion method and system based on time-series InSAR, which can achieve high-precision inversion of hydrogeological parameters.

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

[0005] In a first aspect, the present application provides a method for inversion of hydrogeological parameters based on time-series InSAR, comprising:

[0006] Acquire synthetic aperture radar image sets;

[0007] Performing interference processing on the synthetic aperture radar image set to obtain a time-series interference image set;

[0008] Using an atmospheric correction model, the time-series interferometric image set is atmospherically corrected to obtain a corrected time-series interferometric image set;

[0009] Determine an average coherence coefficient map according to the corrected time-series interference image set, extract high coherence points from the average coherence coefficient map, and determine the interference correlation of each high coherence point;

[0010] Introducing the interference correlation of each high coherence point into the inversion equation as a weight, and combining the corrected time-series interference image set to determine the orbit ascending deformation amount and orbit descending deformation amount;

[0011] Determine a vertical direction deformation amount according to the orbit ascending deformation amount and the orbit descending deformation amount;

[0012] Establishing a surface deformation model based on the corrected time-series interferometric image set;

[0013] The hydrogeological parameters are inverted based on the vertical deformation amount and the surface deformation model.

[0014] In the second aspect, the present application provides a hydrogeological parameter inversion system based on time-series InSAR, comprising:

[0015] A data acquisition module, used for acquiring a synthetic aperture radar image set;

[0016] An interference processing module, used for performing interference processing on the synthetic aperture radar image set to obtain a time-series interference image set;

[0017] An atmospheric correction module, used to perform atmospheric correction on the time-series interferometric image set by using an atmospheric correction model to obtain a corrected time-series interferometric image set;

[0018] A coherent point extraction module, used to determine an average coherence coefficient map according to the corrected time-series interference image set, extract high coherence points from the average coherence coefficient map, and determine the interference correlation of each high coherence point;

[0019] An ascending and descending orbit deformation determination module is used to introduce the interference correlation of each high coherence point into the inversion equation as a weight, and determine the ascending orbit deformation amount and the descending orbit deformation amount in combination with the corrected time-series interference image set;

[0020] A vertical deformation determination module, used for determining a vertical deformation amount according to the orbit ascending deformation amount and the orbit descending deformation amount;

[0021] A deformation model building module, used to build a surface deformation model according to the corrected time-series interferometric image set;

[0022] An inversion module is used to invert hydrogeological parameters based on the vertical deformation amount and the surface deformation model.

[0023] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0024] The present application provides a method and system for inversion of hydrogeological parameters based on time-series InSAR, which uses an atmospheric correction model to perform atmospheric correction on a time-series interferometric image set, effectively reducing the influence of atmospheric delay errors, and by calculating an average coherence coefficient map and extracting high coherence points from the average coherence coefficient map, determining the interference correlation of each high coherence point, thereby improving the objectivity and accuracy of coherence point selection, and using the phase coherence of the interferometric map as a weight for fusion and decomposition, further improving the accuracy and resolution of the inversion results, making the inverted hydrogeological parameters closer to the actual situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] 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.

[0026] Figure 1 This is an application environment diagram of a hydrogeological parameter inversion method based on time-series InSAR in one embodiment of the present application;

[0027] Figure 2 A schematic flow chart of a method for inverting hydrogeological parameters based on time-series InSAR provided in one embodiment of the present application;

[0028] Figure 3 A schematic diagram of a temporal interference image generation process provided in an embodiment of the present application;

[0029] Figure 4 A schematic diagram of fusion decomposition of high and low coherence points provided in an embodiment of the present application;

[0030] Figure 5 A schematic diagram of various parameters in a ground deformation model provided in an embodiment of the present application;

[0031] Figure 6 A schematic diagram of adjusting the inversion process through an artificial neural network provided in an embodiment of the present application;

[0032] Figure 7 An overall schematic diagram of the hydrogeological parameter inversion process provided in one embodiment of the present application;

[0033] Figure 8 A schematic diagram of functional modules of a hydrogeological parameter inversion system based on time-series InSAR provided in another embodiment of the present application;

[0034] Fig. 9 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0035] 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.

[0036] Interferometric Synthetic Aperture Radar (InSAR) technology has shown great potential in geological disaster monitoring and surface deformation research due to its advantages of all-day, all-weather, high resolution, and continuous spatial coverage.

[0037] In recent years, InSAR technology has been widely used in the monitoring and early warning of natural disasters such as earthquakes, volcanic activities, landslides, and mudslides, especially in the indirect inversion of hydrogeological parameters. However, traditional InSAR technology, such as D-InSAR, still faces many challenges in dealing with complex surface deformation, atmospheric delay errors, and long time series data. For this reason, time series InSAR technology came into being. By combining SAR data from multiple time series, it improves the accuracy and reliability of deformation monitoring and provides a new solution for the inversion of hydrogeological parameters.

[0038] Although time-series InSAR technology has made significant progress in surface deformation monitoring, there are still some limitations in practical applications. First, atmospheric delay error is one of the main factors affecting the accuracy of InSAR deformation monitoring, and traditional correction methods often fail to completely eliminate its influence. Second, the phase decorrelation problem caused by factors such as surface cover and vegetation changes limits the effective selection of high coherence points, which in turn affects the accuracy of deformation monitoring.

[0039] This application achieves high-precision inversion of hydrogeological parameters by optimizing the time-series processing flow of InSAR interferograms, combining advanced atmospheric correction models, high coherence point selection algorithms and displacement modeling technology. It not only effectively reduces the impact of atmospheric delay errors and improves the accuracy of deformation monitoring, but also can further improve the reliability and accuracy of the inversion results through multi-source data fusion and neural network correction. It is of great significance to solve the current technical difficulties in the inversion of hydrogeological parameters.

[0040] In order to make the above-mentioned objects, 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.

[0041] The hydrogeological parameter inversion method based on time-series InSAR provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, or it can be integrated on the server 104, or it can be placed on the cloud or other servers. The terminal 102 can send a set of synthetic aperture radar images to be processed to the server 104, and the server 104 performs hydrogeological parameter inversion after receiving the set of synthetic aperture radar images to be processed. The server 104 can feed back the obtained inversion results to the terminal 102. In addition, in some embodiments, the hydrogeological parameter inversion method based on time-series InSAR can also be implemented by the server 104 or the terminal 102 alone.

[0042] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0043] In an exemplary embodiment, Figure 2 As shown, a method for inversion of hydrogeological parameters based on time series InSAR is provided. The method is executed by a computer device, and specifically can be executed by a computer device such as a terminal or a server alone, or can be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the method, which includes the following steps 201 to 208. Among them:

[0044] Step 201, obtain a synthetic aperture radar image set. The synthetic aperture radar image set is a synthetic aperture radar (SAR) data set, which meets the following conditions:

[0045] 1) The time range span is greater than 300 days, and the revisit period is less than 14 days;

[0046] 2) The spatial resolution is higher than 5 meters in azimuth and higher than 20 meters in range;

[0047] 3) Contains both ascending and descending data.

[0048] Specifically, the generation process of the time-series interference image is as follows: Figure 3 As shown, the SAR image needs to be preprocessed (including geometric registration and atmospheric correction) first.

[0049] Step 202: performing interference processing on the synthetic aperture radar image set to obtain a time-series interference image set.

[0050] In a specific example, the obtained SAR data set is subjected to interference processing using satellite interferometric measurement software GMTSAR to obtain an InSAR interferogram. Step 202 includes the following steps 301 to 306 .

[0051] Step 301, selecting interference pairs from the synthetic aperture radar image set to obtain a plurality of basic interference patterns. Specifically, the vertical baseline threshold is set to 150m, and the time baseline threshold is set to 50 days to obtain basic interference patterns.

[0052] Step 302: Use a digital elevation model to remove the terrain phase contribution in each basic interferogram. Specifically, use digital elevation model data with a resolution of 30m to remove the terrain phase contribution.

[0053] Step 303, multi-view averaging in azimuth and range is performed on the basic interferogram after removing the terrain phase contribution, and then phase filtering is performed to obtain an interferogram after phase filtering.

[0054] Specifically, the interference pattern is subjected to 8×2 multi-view averaging in azimuth and range directions, and then the interference pattern is subjected to phase filtering using the Goldstein method to suppress the interference phase error. In addition, multi-view averaging and phase filtering processing may also be performed before step 301 .

[0055] Step 304: perform two-dimensional phase unwrapping on each phase-filtered interference image to obtain a preliminary time-series interference image set. Specifically, use the SNAPHU algorithm to perform two-dimensional phase unwrapping on each interference image.

[0056] For the acquired SAR data set, this application uses the satellite interferometry software GMTSAR to parse the phase information in the SAR image, records the subtle deformation characteristics of the surface through phase unwrapping technology, and generates a high-quality InSAR interferogram; the interferogram has the function of displaying the surface deformation by describing the phase difference between the radar images at two time points, and the fringes reflect the displacement changes.

[0057] Step 305, resample the intensity of each interference pattern in the preliminary time-series interference image set so that each interference pattern maintains the same spatial resolution, and obtain a resampled time-series interference image set. By resampling the intensity, errors in deformation recording of the same position by different images are reduced and avoided.

[0058] Step 306 , performing two-dimensional Gaussian filtering on each interference pattern in the resampled time-series interference image set to obtain a time-series interference image set.

[0059] The resampled interference pattern is still affected by noise, which can cause periodic stripes and spots in the image. Therefore, this application uses a two-dimensional Gaussian filter method to remove noise. Specifically, the image processing software MATLAB is combined with a two-dimensional Gaussian filter algorithm to filter out the influence of noise on image quality, thereby achieving the effect of filtering out the interference of noise on image quality. The formula for Gaussian filtering is as follows:

[0060]

[0061] Among them, x is the horizontal coordinate of the pixel, y is the vertical coordinate of the pixel, G(x,y) is the new pixel value, and σ is the standard deviation of the image.

[0062] Step 203: Use an atmospheric correction model to perform atmospheric correction on the time-series interferometric image set to obtain a corrected time-series interferometric image set.

[0063] In a specific example, an atmospheric correction model is used to calculate the atmospheric phase delay during synthetic aperture radar data imaging, and each interference pattern of the time-series interference image set is subtracted from the atmospheric phase delay to obtain a corrected time-series interference image set.

[0064] Specifically, the Generic Atmospheric Correction Online Service (GACOS) atmospheric correction model is used to reduce the tropospheric delay error in the interferogram. The atmospheric correction model uses meteorological data and atmospheric models to estimate and correct the atmospheric effects in InSAR observations, thereby improving the accuracy of surface deformation measurements.

[0065] The steps of GACOS atmospheric correction are:

[0066] 1) Import the metadata of the image data (including the time-series interferometric image set and the GACOS atmospheric correction model downloaded from the Internet);

[0067] 2) Input various parameters: study area coordinates, international standard time, SAR data imaging time, SAR data exterior orientation elements, GACOS data path, atmospheric correction algorithm parameters, phase unwrapping and filtering parameters, and data output format;

[0068] 3) Load the GACOS atmospheric correction model to calculate the atmospheric phase delay during SAR data imaging;

[0069] 4) Subtract the atmospheric phase delay from the SAR interferogram to reduce the influence of the atmosphere on the interferogram;

[0070] 5) Generate the phase image of the interference pattern, which is expressed by the following formula:

[0071]

[0072] Among them, ΔΦ i is the phase image of the interference pattern, m i is the surface deformation, subscript i represents the i-th interference pattern, λ is the radar wavelength, B v is the vertical baseline, R is the slant range, θ is the radar incident angle, h err is the elevation error, Φ atm,res is the residual atmospheric error after GACOS correction, ΔΦ noise is the noise phase.

[0073] Step 204 , determining an average coherence coefficient map according to the corrected time-series interference image set, extracting high coherence points from the average coherence coefficient map, and determining the interference correlation of each high coherence point.

[0074] Specifically, the coherence coefficient of each pixel point in the average coherence coefficient map is an average value of the coherence coefficients of the corresponding pixel points of all interference maps in the corrected time-series interference image set.

[0075] like Figure 4 As shown, the process of extracting high coherence points from the average coherence coefficient map in step 204 and determining the interference correlation of each high coherence point specifically includes the following steps 401 to 404.

[0076] Step 401: Pixel points whose coherence coefficients in the average coherence coefficient map are greater than a preset coherence coefficient threshold are used as preliminary high coherence points to obtain a differential phase interference map. The differential phase interference map includes all preliminary high coherence points.

[0077] Step 402, the differential phases of adjacent preliminary high coherence points of the differential phase interferogram are differentiated to obtain the secondary differential phases of each preliminary high coherence point. Specifically, the differential phases of adjacent preliminary high coherence points of the differential phase interferogram are differentiated again to obtain the secondary differential phases of adjacent preliminary high coherence points.

[0078] Step 403, determine the amplitude dispersion index according to the intensity of each pixel point of all interference images in the corrected time-series interference image set. The amplitude dispersion index is expressed by the following formula:

[0079]

[0080] Among them, D A is the amplitude dispersion index, σ A is the standard deviation of the amplitude value sequence, μ A is the average value of the amplitude value sequence, and the amplitude value sequence is the intensity of each pixel point of all interference patterns in the corrected time-series interference image set.

[0081] Step 404, screening the preliminary high coherence points according to the amplitude dispersion index, determining the final high coherence points, and using the secondary differential phase of the final high coherence points as the interference correlation of the corresponding high coherence points.

[0082] Step 205: introduce the interference correlation of each high coherence point into the inversion equation as a weight, and determine the orbit ascending deformation amount and orbit descending deformation amount in combination with the corrected time-series interference image set.

[0083] In a specific example, the phase coherence of the interferogram is introduced into the inversion equation as a weight to suppress the error caused by low-coherence target points. By incorporating phase coherence into the inversion equation as a weight factor, the contribution of data in areas with low coherence to the final inversion results can be automatically reduced, avoiding the data errors in these areas from having a significant impact on the global results.

[0084] The inversion equation is expressed as follows:

[0085]

[0086] Where W is the weight matrix, W = diag[r1,r2,…,r n ] is the weight matrix, r n is the interference correlation of pixel n, β is the intermediate quantity, B v,i is the vertical baseline of the i-th SAR image, ρ is the smoothing factor, Δt i is the time increment between two SAR images, dm i is the displacement increment between the two SAR images that make up the interferogram, d i is the phase observation value of the i-th SAR image, the coefficient matrix size is [n×s+1], n is the width of the interference pattern, and s is the height of the interference pattern.

[0087] Step 206: Determine the vertical direction deformation amount according to the orbit ascending deformation amount and the orbit descending deformation amount.

[0088] In a specific example, the satellite ascending and descending orbit deformation images are fused and decomposed by the following formula to obtain the horizontal deformation and vertical deformation, making full use of complementary information sources, reducing the limitations that may be brought about by single orbit observation, improving the deformation measurement accuracy, realizing multi-dimensional analysis of surface deformation characteristics, and separating and extracting the horizontal deformation and vertical deformation components of the surface:

[0089]

[0090] Among them, d asc is the ascending orbit deformation, d desis the orbital deformation, here it refers to the LOS deformation, d E is the deformation in the east-west direction, d N is the deformation in the north-south direction, d V is the vertical deformation, θ asc is the satellite orbit raising incident angle, α asc is the satellite orbit raising heading angle, θ des is the satellite orbit-dropping incident angle, α des is the satellite descending orbit heading angle. The incident angle and heading angle are obtained through the metadata of each SAR image.

[0091] Step 207: Establishing a surface deformation model based on the corrected time-series interferometric image set.

[0092] In a specific example, the formation deformation information in the time-series InSAR is used as a constraint condition, and the Geertsma analytical method is used to model the surface displacement of the elastic medium caused by the change in the formation pore pressure, and a surface deformation model is established. The model reservoir is regarded as a uniform, isotropic, elastic half-space with Poisson's ratio, and the hydrogeological parameters are inverted. The surface deformation model is:

[0093]

[0094] Among them, u(r) is the hydrogeological parameter of different central formation depths, v is Poisson's ratio, ΔH is the change in formation height, R1 is the radius of the lower formation, D is the formation depth, α is the variable of the integral operator, r is the distance from the surface to the center of the upper formation, J0 is the zero-order Bessel function, and J1 is the first-order Bessel function. The relationship between the parameters is as follows Figure 5 shown.

[0095] Step 208: Invert hydrogeological parameters based on the vertical deformation amount and the surface deformation model.

[0096] In a specific example, other parameters in the surface deformation model are fixed, the stratum depth of the surface deformation model is adjusted, and the stratum height change corresponding to different stratum depths is determined. The difference between the stratum height change corresponding to different stratum depths and the vertical deformation amount is calculated. The hydrogeological parameter corresponding to the minimum difference is used as the final hydrogeological parameter. Finally, the hydrogeological parameter of each coherent point is obtained.

[0097] In order to further improve the inversion accuracy of hydrogeological parameters, the hydrogeological parameter inversion method based on time-series InSAR also includes step 209 and step 210.

[0098] Step 209, based on the time-series interference image set and the hydrogeological parameters, an artificial neural network (ANN) is used to establish a nonlinear mapping relationship between the surface deformation and the hydrogeological parameters.

[0099] Step 210, based on the nonlinear mapping relationship between the surface deformation and the hydrogeological parameters, determine whether the inversion results of the hydrogeological parameters meet the set error threshold; if not, adjust the high coherence points in the average coherence coefficient diagram, and re-invert the hydrogeological parameters until the inversion results meet the set error threshold, and obtain the final hydrogeological parameters.

[0100] In a specific example, if Figure 6 As shown in the figure, the hydrogeological parameters obtained by inversion are subjected to Geertsma analysis to obtain the measured deformation values ​​of the strata. The ANN is constructed by combining the time series InSAR data set and the hydrogeological parameters u(r) at different central stratum depths. The nonlinear mapping relationship between surface deformation and hydrogeological parameters is established to assist in checking the inversion accuracy. If the error is less than or equal to the adaptive threshold, it remains unchanged. If it is greater than the threshold, the relevant points are deleted and the data gaps are filled by spatial interpolation.

[0101] ANN is used to assist in verifying and optimizing the inversion results to obtain the measured deformation value of the formation as the label data. The prediction results of ANN are compared with the inversion results of the traditional physical model to give the uncertainty confidence space. In addition, there is no NoData value in the ANN network when the SAR data set is used as the training sample. The activation function uses the Sigmoid function to adapt to the classification problem of high and low coherence points in the interference pattern.

[0102] In another specific example, Figure 7 As shown, the present application first obtains a SAR data set that meets the specified conditions, then uses GMTSAR for interference processing to obtain an interference pattern, performs geometric alignment, resampling and noise filtering, uses the GACOS atmospheric correction model to weaken the delay error of the interference pattern, selects high coherence points through the coherence coefficient diagram and the amplitude discrete index, and finally uses the phase coherence of the interference pattern as the weight, combines the ascending and descending orbit images for fusion decomposition, uses the Geertsma method for displacement modeling, inverts the hydrogeological parameters, and corrects the results through ANN, which can achieve high-precision modeling of the hydrogeological environment, and has certain scientific research and ecological value.

[0103] Compared with the prior art, the beneficial effects of this application include at least the following points:

[0104] (1) The introduction of GMTSAR interferometry processing software and GACOS atmospheric correction model significantly improved the quality of interferogram generation and the accuracy of subsequent processing. GMTSAR ensured the accuracy and stability of interferogram generation with its efficient algorithm and precise processing capabilities. The application of GACOS atmospheric correction model effectively reduced the impact of atmospheric delay error on interferogram. The combination of the two generated higher quality interferograms, laying a solid foundation for the subsequent inversion of hydrogeological parameters.

[0105] (2) In order to solve the problem of strong subjectivity in the selection of coherent points and poor fusion and decomposition effect, an intelligent high-coherence point selection method based on the coherence coefficient map and the amplitude discrete index is used in a targeted manner, and fusion and decomposition are performed in combination with ascending and descending orbit images. This not only improves the objectivity and accuracy of the coherent point selection, but also enhances the reliability of the inversion results by fusing multi-source data. At the same time, the phase coherence of the interference pattern is used as the weight for fusion and decomposition, which further improves the accuracy and resolution of the inversion results, making the inverted hydrogeological parameters closer to the actual situation.

[0106] (3) By training ANN, the hydrogeological parameters obtained from the preliminary inversion are corrected and optimized, which effectively eliminates the systematic errors and random errors in the inversion process. This not only improves the accuracy of the inversion results, but also makes the inversion results more consistent with the complexity and dynamics of the actual hydrogeological environment. Combined with the Geertsma method for displacement modeling, the scientificity and practicality of the inversion results are further enhanced, providing strong support for the high-precision modeling of the hydrogeological environment.

[0107] Based on the same inventive concept, the embodiment of the present application also provides a hydrogeological parameter inversion system based on time series InSAR for implementing the hydrogeological parameter inversion method based on time series InSAR involved above. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments provided below can refer to the limitations of the hydrogeological parameter inversion method based on time series InSAR above, and will not be repeated here.

[0108] In an exemplary embodiment, Figure 8 As shown, a hydrogeological parameter inversion system based on time-series InSAR is provided, including: a data acquisition module 801, an interference processing module 802, an atmospheric correction module 803, a coherent point extraction module 804, an ascending and descending orbit deformation determination module 805, a vertical deformation determination module 806, a deformation model establishment module 807 and an inversion module 808.

[0109] The data acquisition module 801 is used to acquire a synthetic aperture radar image set.

[0110] The interference processing module 802 is used to perform interference processing on the synthetic aperture radar image set to obtain a time-series interference image set.

[0111] The atmospheric correction module 803 is used to perform atmospheric correction on the time-series interferometric image set by using an atmospheric correction model to obtain a corrected time-series interferometric image set.

[0112] The coherence point extraction module 804 is used to determine an average coherence coefficient map according to the corrected time-series interference image set, extract high coherence points from the average coherence coefficient map, and determine the interference correlation of each high coherence point.

[0113] The ascending and descending orbit deformation determination module 805 is used to introduce the interference correlation of each high coherence point into the inversion equation as a weight, and determine the ascending orbit deformation amount and the descending orbit deformation amount in combination with the corrected time-series interference image set.

[0114] The vertical deformation determination module 806 is used to determine the vertical deformation amount according to the ascending track deformation amount and the descending track deformation amount.

[0115] The deformation model building module 807 is used to build a surface deformation model according to the corrected time-series interferometric image set.

[0116] The inversion module 808 is used to invert the hydrogeological parameters based on the vertical deformation amount and the surface deformation model.

[0117] Furthermore, the hydrogeological parameter inversion system based on time-series InSAR also includes: a neural network construction module 809 and an error judgment module 810.

[0118] The neural network construction module 809 is used to establish a nonlinear mapping relationship between surface deformation and hydrogeological parameters using an artificial neural network based on the time-series interference image set and the hydrogeological parameters;

[0119] The error judgment module 810 is used to judge whether the inversion result of the hydrogeological parameter meets the set error threshold based on the nonlinear mapping relationship between the surface deformation and the hydrogeological parameter; if not, the high coherence points in the average coherence coefficient diagram are adjusted, and the hydrogeological parameters are re-inverted until the inversion result meets the set error threshold, thereby obtaining the final hydrogeological parameters.

[0120] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Fig. 9As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store a set of synthetic aperture radar images to be processed. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a hydrogeological parameter inversion method based on time-series InSAR is implemented.

[0121] Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0122] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0123] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0124] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0125] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0126] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0127] The technical features of the above embodiments may be arbitrarily combined. 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.

[0128] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for inversion of hydrogeological parameters based on time series InSAR, characterized in that: The hydrogeological parameter inversion method based on time series InSAR includes: Acquire synthetic aperture radar image sets; Performing interference processing on the synthetic aperture radar image set to obtain a time-series interference image set; Using an atmospheric correction model, the time-series interferometric image set is atmospherically corrected to obtain a corrected time-series interferometric image set; Determine an average coherence coefficient map according to the corrected time-series interference image set, extract high coherence points from the average coherence coefficient map, and determine the interference correlation of each high coherence point; Introducing the interference correlation of each high coherence point into the inversion equation as a weight, and combining the corrected time-series interference image set to determine the orbit ascending deformation amount and orbit descending deformation amount; Determine a vertical direction deformation amount according to the orbit ascending deformation amount and the orbit descending deformation amount; Establishing a surface deformation model based on the corrected time-series interferometric image set; The hydrogeological parameters are inverted based on the vertical deformation amount and the surface deformation model.

2. The method for inversion of hydrogeological parameters based on time series InSAR according to claim 1, characterized in that: Performing interference processing on the synthetic aperture radar image set to obtain a time-series interference image set specifically includes: Selecting interference pair combinations from the synthetic aperture radar image set to obtain a plurality of basic interference patterns; Digital elevation model is used to remove the terrain phase contribution in each basic interferogram; The basic interferogram after removing the terrain phase contribution is subjected to multi-view averaging in azimuth and range directions, and then phase filtering is performed to obtain the interferogram after phase filtering; Perform two-dimensional phase unwrapping on each phase-filtered interferogram to obtain a preliminary time-series interferogram set; Resampling the intensity of each interference pattern in the preliminary time-series interference image set so that each interference pattern maintains the same spatial resolution, thereby obtaining a resampled time-series interference image set; A two-dimensional Gaussian filter is performed on each interference image in the resampled time-series interference image set to obtain a time-series interference image set.

3. The method for inversion of hydrogeological parameters based on time series InSAR according to claim 1, characterized in that: Using an atmospheric correction model to perform atmospheric correction on the time series interferometric image set to obtain a corrected time series interferometric image set specifically includes: The atmospheric correction model is used to calculate the atmospheric phase delay during synthetic aperture radar data imaging. Each interference pattern of the time-series interference image set is subtracted from the atmospheric phase delay to obtain a corrected time-series interference image set.

4. The method for inversion of hydrogeological parameters based on time series InSAR according to claim 1, characterized in that: The coherence coefficient of each pixel point in the average coherence coefficient map is the average value of the coherence coefficients of the corresponding pixel points of all interference maps in the corrected time-series interference image set.

5. The method for inversion of hydrogeological parameters based on time series InSAR according to claim 1, characterized in that: Extracting high coherence points from the average coherence coefficient map and determining the interference correlation of each high coherence point specifically includes: Pixel points whose coherence coefficients in the average coherence coefficient map are greater than a preset coherence coefficient threshold are taken as preliminary high coherence points to obtain a differential phase interference map; the differential phase interference map includes all preliminary high coherence points; Differentiating the differential phases of adjacent preliminary high coherence points of the differential phase interference pattern to obtain a secondary differential phase of each preliminary high coherence point; Determine the amplitude dispersion index according to the intensity of each pixel point of all interference images in the corrected time-series interference image set; According to the amplitude dispersion index, the preliminary high coherence points are screened to determine the final high coherence points, and the secondary differential phase of the final high coherence points is used as the interference correlation of the corresponding high coherence points.

6. The method for inversion of hydrogeological parameters based on time series InSAR according to claim 1, characterized in that: The vertical deformation is determined using the following formula: Among them, d asc is the ascending orbit deformation, d des is the descending orbit deformation, d N is the deformation in the north-south direction, d V is the vertical deformation, θ asc is the satellite orbit raising incident angle, θ asc is the satellite orbit raising heading angle, θ des is the satellite orbit-dropping incident angle, α des is the satellite descending orbit heading angle.

7. The method for inversion of hydrogeological parameters based on time series InSAR according to claim 1, characterized in that: The surface deformation model is: Among them, u(r) is the hydrogeological parameter at different central formation depths, v is Poisson's ratio, ΔH is the change in formation height, R is the radius of the lower formation, D is the formation depth, α is the variable of the integral operator, r is the distance from the surface to the center of the upper formation, J0 is the zero-order Bessel function, and J1 is the first-order Bessel function.

8. The method for inversion of hydrogeological parameters based on time series InSAR according to claim 7, characterized in that: Inverting hydrogeological parameters based on the vertical deformation amount and the surface deformation model specifically includes: Adjusting the stratum depth of the surface deformation model to determine the stratum height change corresponding to different stratum depths; Calculating the difference between the change in stratum height corresponding to different stratum depths and the vertical deformation amount; The hydrogeological parameter corresponding to the minimum difference is taken as the final hydrogeological parameter.

9. The method for inversion of hydrogeological parameters based on time series InSAR according to claim 1, characterized in that: The hydrogeological parameter inversion methods based on time series InSAR also include: According to the time-series interference image set and hydrogeological parameters, an artificial neural network is used to establish a nonlinear mapping relationship between surface deformation and hydrogeological parameters. Based on the nonlinear mapping relationship between the surface deformation and the hydrogeological parameters, it is determined whether the inversion results of the hydrogeological parameters meet the set error threshold; if not, the high coherence points in the average coherence coefficient diagram are adjusted, and the hydrogeological parameters are re-inverted until the inversion results meet the set error threshold, thereby obtaining the final hydrogeological parameters.

10. A hydrogeological parameter inversion system based on time series InSAR, applied to the hydrogeological parameter inversion method based on time series InSAR according to any one of claims 1 to 9, characterized in that: The hydrogeological parameter inversion system based on time-series InSAR includes: A data acquisition module, used for acquiring a synthetic aperture radar image set; An interference processing module, used for performing interference processing on the synthetic aperture radar image set to obtain a time-series interference image set; An atmospheric correction module, used to perform atmospheric correction on the time-series interferometric image set by using an atmospheric correction model to obtain a corrected time-series interferometric image set; A coherent point extraction module, used to determine an average coherence coefficient map according to the corrected time-series interference image set, extract high coherence points from the average coherence coefficient map, and determine the interference correlation of each high coherence point; An ascending and descending orbit deformation determination module is used to introduce the interference correlation of each high coherence point into the inversion equation as a weight, and determine the ascending orbit deformation amount and the descending orbit deformation amount in combination with the corrected time-series interference image set; A vertical deformation determination module, used for determining a vertical deformation amount according to the orbit ascending deformation amount and the orbit descending deformation amount; A deformation model building module, used to build a surface deformation model according to the corrected time-series interferometric image set; An inversion module is used to invert hydrogeological parameters based on the vertical deformation amount and the surface deformation model.

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