A correction and screening method for time-series InSAR interferograms of interseismic deformation

Abstract

The present invention provides a time-series InSAR interseismic deformation interferogram correction and screening method, which belongs to the field of measurement technology and specifically includes: step 1, using the GACOS method to perform atmospheric correction on the original interferogram set, then using the improved CANDIS method to perform perturbation phase correction, and performing line-of-sight plate motion phase correction based on the plate model to obtain a long-term corrected interferogram set; step 2, using the Pearson correlation coefficient between the interferogram and the interseismic deformation model and the standard deviation of the interferogram to screen the long-term corrected interferogram set to obtain a target interferogram set; step 3, based on the target interferogram set, using the intermittent stacking method to set the intermittent coherence pixel number threshold to generate the InSAR average interseismic deformation velocity field. The solution of the present invention improves the accuracy and adaptability of interseismic deformation signal extraction.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of measurement technology, and in particular to a method for correcting and screening time-series InSAR interseismic deformation interferograms. Background Art

[0002] Interferometric Synthetic Aperture Radar (InSAR) technology, which uses SAR image collections to extract surface deformation at high temporal and spatial resolution across a study area, has been widely used for monitoring interseismic tectonic deformation, providing a rich data source for understanding continental deformation, earthquake cycles, and seismic hazards. However, compared with other types of tectonic deformation, interseismic deformation signals are lower in magnitude and cover hundreds to thousands of kilometers. They are often confounded by orbital errors and atmospheric phase delays, making them difficult to effectively extract from interferograms and the resulting velocity field, thus hindering subsequent tectonic interpretation.

[0003] To overcome this problem, researchers at home and abroad have primarily used various methods to correct the interferometry phase and screen high-quality interferograms for time series analysis and deformation field extraction. Phase correction often involves joint inversion correction models, external meteorological data correction, and common master image stacking correction. However, in joint inversion correction, the spatial coupling of interseismic deformation and long-wavelength errors such as orbits can cause the model to overfit and absorb the interseismic deformation phase in some areas. External meteorological data correction is effective for tropospheric correction in complex regions but is still affected by turbulence. While common master image stacking is highly effective for correcting errors such as turbulence and orbits that are independent of the SAR acquisition time, it can result in a loss of coherent points in vegetated areas and temporally inhomogeneous coherence regions, leading to a decrease in the atmospheric point density estimated within the region. Interferogram screening strategies primarily include manual visual screening and quantitative screening based on structure functions. While manual screening is straightforward and effective, it requires a high level of human-computer interaction, which increases the instability of the results. Furthermore, the structure function estimate can be affected by the presence of slow gradients of interseismic deformation within the region, which in turn affects the interferogram selection.

[0004] It can be seen that there is an urgent need for a time-series InSAR interseismic deformation interferogram correction and screening method that can extract high-precision interseismic deformation signals. Summary of the Invention

[0005] In view of this, an embodiment of the present invention provides a time-series InSAR interseismic deformation interferogram correction and screening method, which at least partially solves the problem of poor extraction accuracy in the prior art.

[0006] In a first aspect, an embodiment of the present invention provides a method for correcting and screening a time-series InSAR interferogram of interseismic deformation, comprising:

[0007] Step 1: Use the GACOS method to perform atmospheric correction on the original interferogram, then use the improved CANDIS method to perform disturbance phase correction, and perform line-of-sight plate motion phase correction based on the plate model to obtain a long-term corrected interferogram.

[0008] Step 2: Use the Pearson correlation coefficient between the interferogram and the interseismic deformation model and the standard deviation of the interferogram to screen the long-term correction interferogram set to obtain the target interferogram set;

[0009] Step 3: Based on the target interferogram set, the intermittent stacking method is used to set the threshold of the number of intermittent coherence pixels to generate the average interseismic deformation velocity field.

[0010] According to a specific implementation of an embodiment of the present invention, the step of performing atmospheric correction on the original interferogram using the GACOS method includes:

[0011] The GACOS method is used to perform difference and projection on the original interferogram set and the interferogram set is divided into short-time baseline interferogram and long-time baseline interferogram according to the time baseline of the interferogram.

[0012] According to a specific implementation of an embodiment of the present invention, the step of performing disturbance phase correction using the improved CANDIS method includes:

[0013] Modeling short-time baseline interferometry phase;

[0014] The intermittent coherence method is used to perform phase estimation based on common superposition of the interference pixels, and the disturbance error of each phase is obtained.

[0015] According to a specific implementation of an embodiment of the present invention, the expression for the short-time baseline interference phase modeling is:

[0016] Δφ i,j =Δτ i,j +α j -α i +ε ij

[0017] where Δτ i,j is the temporally stable deformation phase between acquisition times i and j, α is the perturbation phase in each interference image, and ε is other noise.

[0018] According to a specific implementation of an embodiment of the present invention, the step of performing phase correction of line-of-sight motion of a plate based on a plate model to obtain a long-term interferogram includes:

[0019] The plate motion is derived from the plate model and projected into the LOS direction according to the satellite imaging geometry to correct the residual long wavelength in the interferometric phase, resulting in a long-time corrected interferometric atlas.

[0020] According to a specific implementation of the embodiment of the present invention, before step 2, the method further includes:

[0021] Based on the geological fault surface trace geometry, microseismic data and GNSS observation data in the area, the interseismic deformation model is obtained using the Bayesian inversion method.

[0022] According to a specific implementation of an embodiment of the present invention, the expression of the Pearson correlation coefficient is:

[0023]

[0024] Among them, φ mn and φ mn Represent the interference phase value and model phase value of the mth row and nth column respectively, and represent their average values ​​respectively.

[0025] The time-series InSAR interseismic deformation interferogram correction and screening scheme in an embodiment of the present invention includes: step 1, using the GACOS method to perform atmospheric correction on the original interferogram set, then using the improved CANDIS method to perform disturbance phase correction, and based on the line-of-sight plate motion phase correction of the plate model, to obtain a long-term corrected interferogram set; step 2, using the Pearson correlation coefficient between the interferogram and the interseismic deformation model and the standard deviation of the interferogram to screen the long-term corrected interferogram set to obtain a target interferogram set; step 3, based on the target interferogram set, using the intermittent stacking method to set the intermittent coherence pixel number threshold to generate an average interseismic deformation velocity field.

[0026] The beneficial effects of the embodiments of the present invention are as follows: 1) a phase correction method for interferograms that integrates GACOS and CANDIS is proposed, which can more effectively remove the troposphere and turbulent atmospheric delay in the region, thereby compensating for the difference in correction performance when using GACOS and CANDIS methods alone;

[0027] 2) An improved CANDIS method is proposed to replace the traditional CANDIS method, which solves the problem of low atmospheric estimation sampling density of the traditional CANDIS in vegetation areas and inhomogeneous coherent areas;

[0028] 3) A method for simulating the residual long-wavelength signal in the interferogram using a plate model is proposed, which avoids the problem of linear polynomial fitting absorbing interseismic deformation;

[0029] 4) A method using the correlation coefficient and standard deviation of the interseismic deformation model as interference pattern screening indicators is proposed. This method takes into account the spatial distribution of the interseismic deformation signal and the overall noise level in the interference pattern, solving the problem that the structural function of the traditional method does not consider the spatial distribution of interseismic deformation. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0031] Figure 1 A schematic flow chart of a method for correcting and screening time-series InSAR interseismic deformation interferograms provided by an embodiment of the present invention;

[0032] Figure 2 A schematic diagram of a specific implementation process of a time-series InSAR interseismic deformation interferogram correction and screening method provided by an embodiment of the present invention;

[0033] Figure 3 A schematic diagram of an atmospheric phase estimation embodiment based on an intermittent coherence method provided by an embodiment of the present invention;

[0034] Figure 4 A schematic diagram of interferogram correction of different phase components provided by an embodiment of the present invention;

[0035] Figure 5 An interseismic model of the Haiyuan Fault and the Gulang Fault and a schematic diagram of the depth distribution of microseismic events provided in an embodiment of the present invention;

[0036] Figure 6 A schematic diagram of comparison and verification of interseismic deformation using different methods provided by an embodiment of the present invention;

[0037] Figure 7 A schematic diagram of an embodiment of the present invention providing an inversion method using a two-dimensional elastic dislocation model using a cross section of a region. DETAILED DESCRIPTION

[0038] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0039] The following describes the embodiments of the present invention through specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0040] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present invention, those skilled in the art will appreciate that an aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement an apparatus and / or practice a method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this apparatus and / or practice this method.

[0041] It should also be noted that the illustrations provided in the following embodiments are merely schematic illustrations of the basic concept of the present invention. The illustrations only show components related to the present invention and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the type, quantity, and proportion of each component may be changed arbitrarily, and the component layout may also be more complex.

[0042] Additionally, in the following description, specific details are provided to provide a thorough understanding of the examples. However, one skilled in the art will appreciate that the aspects described can be practiced without these specific details.

[0043] An embodiment of the present invention provides a time-series InSAR interseismic deformation interferogram correction and screening method, which can be applied in the process of surface deformation detection.

[0044] See also Figure 1 , is a flow chart of a method for correcting and screening time-series InSAR interseismic deformation interferograms provided by an embodiment of the present invention. Figure 1 and Figure 2 As shown, the method mainly includes the following steps:

[0045] Step 1: Use the GACOS method to perform atmospheric correction on the original interferogram, then use the improved CANDIS method to perform disturbance phase correction, and perform line-of-sight plate motion phase correction based on the plate model to obtain a long-term corrected interferogram.

[0046] Furthermore, the step of performing atmospheric correction on the original interferogram using the GACOS method includes:

[0047] The GACOS method is used to perform difference and projection on the original interferogram set and the interferogram set is divided into short-time baseline interferogram and long-time baseline interferogram according to the time baseline of the interferogram.

[0048] Furthermore, the step of performing disturbance phase correction using the improved CANDIS method includes:

[0049] Modeling short-time baseline interferometry phase;

[0050] The intermittent coherence method is used to perform phase estimation based on common superposition of the interference pixels, and the disturbance error of each phase is obtained.

[0051] Furthermore, the expression for the short-time baseline interference phase modeling is:

[0052] Δφ i,j =Δτ i,j +α j -α i +ε ij

[0053] where Δτ i,j is the temporally stable deformation phase between acquisition times i and j, α is the perturbation phase in each interference image, and ε is other noise.

[0054] Furthermore, the step of correcting the phase of the plate motion based on the line of sight of the plate model to obtain a long-term interference atlas includes:

[0055] The plate motion is derived from the plate model and projected into the LOS direction according to the satellite imaging geometry to correct the residual long wavelength in the interferometric phase, resulting in a long-time corrected interferometric atlas.

[0056] In practice, phase correction involves using the improved CANDIS method and GACOS for perturbation phase correction (including tropospheric delay, turbulent atmosphere, and longwave errors), and using a plate model to remove the regional longwave plate motion phase. This can be done in three main steps: (a) GACOS atmospheric correction; (b) perturbation phase correction based on the improved CANDIS method; and (c) line-of-sight plate motion phase correction based on the plate model.

[0057] (a) GACOS atmospheric correction

[0058] GACOS is primarily used for tropospheric atmospheric delay correction. For each interferogram, the GACOS zenith atmospheric delay product, after differencing and projection, is used to correct the tropospheric phase within the region. Interferograms are categorized into short-baseline interferograms and long-baseline interferograms based on their time baseline. Specifically, interferograms with a time baseline of less than 120 days are classified as short-baseline interferograms, while those with a time baseline of more than 300 days are classified as long-baseline interferograms.

[0059] (b) Disturbance phase correction based on improved CANDIS

[0060] Based on the conventional CANDIS method, an improved I-CANDIS method is proposed to improve the point density of perturbation phase estimation in non-uniform coherent regions. The short-time baseline interferometric phase Δφ can be modeled as follows:

[0061] Δφ i,j =Δτ i,j +α j -α i +ε ij #(1)

[0062] where Δτ i,j is the temporally stable deformation phase between acquisition times i and j, α is the perturbation phase in each SAR image, and ε is other noise. Assuming that the error associated with α is randomly distributed over time, it can be estimated and removed by common point superposition, as shown in the following equation:

[0063]

[0064] Where N is the number of SAR images. As can be seen from Equation (2), when the interferometric pixels used for stacking have non-uniform coherence, the phase estimation of the pixels will fail, thereby reducing the spatial resolution of the estimated phase. To solve this problem, we use the intermittent coherence method for phase estimation. Before stacking together, we further divide all interferometric pixel pairs {Δφ i，i-j ,Δφ i+j，i |j=1~N,j≠i} is used for phase estimation; when there are incoherent pixels in a pixel pair (i.e. Δφ i，i-j *Δφ i+j，i =NaN), then this pair of pixels does not participate in the common stacking. Therefore, each pixel may have a different superposition equation. We can set a specific threshold for the number of coherent pixel pairs to obtain perturbation phase estimates with different distributions. The size of the number threshold affects the sampling density of the point estimate and the accuracy of the phase estimate. We call this method intermittent CANDIS (I-CANDIS), and Equation (2) can be modified as follows:

[0065]

[0066] Where k is the number of incoherent pixel pairs in the interferogram set. This method can effectively increase the number and distribution of estimated disturbance phase pixels, significantly improving the ability to detect disturbance phase in vegetation areas and non-uniform temporally coherent regions.

[0067] Although the random perturbation phase of the common scene can be more accurately estimated by increasing the number of observations, more observation stacks impose stronger linear constraints on the deformation. Therefore, to avoid this problem, this method uses a short-time baseline interferogram to estimate the perturbation phase observed at each moment. And because the phase estimate cannot be well resolved at both ends of the time series image, we discard the first and last three moments and other moments that have not been estimated. Based on the observation data of the remaining moments, a long-time baseline interferogram is constructed, and the estimated perturbation error is applied to the long-time baseline interferogram to further estimate the interseismic velocity.

[0068] (c) Phase correction of line-of-sight plate motion based on plate model

[0069] After GACOS and I-CANDIS correction, the residual long wavelength signal in the long-term baseline interferogram is mainly contributed by plate motion. This is due to the projection of plate motion in the satellite reference coordinate system onto the satellite line of sight. p The plate motion velocity V p It can be represented by the Euler level Ω(V p =Ω×X p ), which can be determined by GNSS stations at a certain distance from the plate boundary. Therefore, the method proposed in this paper infers plate motion from the plate model and projects it into the LOS direction based on satellite imaging geometry to correct for residual long wavelengths in the interferometric phase. Because of the use of an external, independent physical model, the long-wavelength correction for plate motion does not affect the magnitude and distribution of interseismic deformation.

[0070] Step 2: Use the Pearson correlation coefficient between the interferogram and the interseismic deformation model and the standard deviation of the interferogram to screen the long-term correction interferogram set to obtain the target interferogram set;

[0071] Based on the above embodiment, before step 2, the method further includes:

[0072] Based on the geological fault surface trace geometry, microseismic data and GNSS observation data in the area, the interseismic deformation model is obtained using the Bayesian inversion method.

[0073] Furthermore, the expression of Pearson correlation coefficient is

[0074]

[0075] Among them, φ mn and φ mn Represent the interference phase value and model phase value of the mth row and nth column respectively, and represent their average values ​​respectively.

[0076] In practice, this method uses the Pearson correlation coefficient (COR) between the interferogram and the interseismic model and the interferogram standard deviation (STD) to select interferograms during the interferogram screening process. After correlation coefficient screening, the STD of the interferogram varies with the tectonic signal and spatial noise. Lower STDs indicate lower spatial noise, and the remaining phase variation is primarily caused by interseismic deformation gradients. Therefore, interferogram selection based on both COR and STD can suppress spatial noise while also accounting for interseismic deformation gradient signals within the interferogram. This method involves three substeps: (a) interseismic fault modeling based on regional GNSS data; (b) calculation of the Pearson correlation coefficient (COR) between the interferogram and the interseismic model; and (c) calculation of the interferogram standard deviation (STD).

[0077] (a) Interseismic fault modeling based on regional GNSS data

[0078] Based on the geological fault surface trace geometry, microseismic data, and regional GNSS observations, a Bayesian inversion method was used to obtain an interseismic deformation model. The surface trace geometry was used to determine the spatial location and geometric distribution of the fault; the microseismic data were used to determine the fault's seismogenic depth. The seismogenic depth of the fault was calculated by counting the microseismic depths within a 15 km radius of the fault and calculating the maximum depth of 96% of the microseismic activity as the seismogenic depth. GNSS observations were used in the Bayesian inversion to constrain the deep slip rate of the fault. During the Bayesian inversion, the input observations were GNSS horizontal velocities, and the inversion model was the Okada rectangular dislocation model. The Bayesian inversion method was used to obtain the three-dimensional deformation components caused by fault slip, and the interseismic fault model deformation field along the line of sight was obtained through satellite image geometric projection.

[0079] (b) Calculate the Pearson correlation coefficient (COR) between the interference pattern and the interseismic model

[0080] By setting the Pearson correlation coefficient threshold, the interference pattern related to the interseismic model is selected to ensure that there is a clear deformation gradient. The Pearson correlation coefficient can be expressed as:

[0081]

[0082] where φ mn and φmn Represent the interference phase value and model phase value of the mth row and nth column respectively, and represent their average values ​​respectively.

[0083] (c) Calculate the standard deviation (STD) of the interference pattern

[0084] To select interferograms with low noise levels, the STD of the interseismic deformation model (i.e., interseismic velocity model × interferogram maximum time baseline) was used as a second threshold. To retain more interferograms with high signal-to-noise ratios and ensure the stability of the interferogram network, when the proportion of selected interferograms was less than 60%, approximately 60% of the corrected interferograms were retained as the target interferogram set based on the order of the interferogram standard deviation.

[0085] Step 3: Based on the target interferogram set, the intermittent stacking method is used to set the threshold of the number of intermittent coherence pixels to generate the average interseismic deformation velocity field.

[0086] In specific implementation, based on the target interferogram set formed by the screened high-quality interferograms, the intermittent stacking method can be used to set the threshold of the number of intermittent coherence pixels to generate a high-precision average interseismic deformation velocity field.

[0087] The time-series InSAR interseismic deformation interferogram correction and screening method provided in this embodiment proposes a phase correction method for the interferogram by integrating GACOS and CANDIS. This method can more effectively remove the tropospheric and turbulent atmospheric delays in the region, making up for the difference in correction performance between the GACOS and CANDIS methods used alone. An improved CANDIS method is proposed to replace the traditional CANDIS, solving the problem of low atmospheric estimation sampling density of the traditional CANDIS in vegetation areas and inhomogeneous coherent areas. A method using a plate model to simulate the residual long-wave signal in the interferogram is proposed, avoiding the problem of linear polynomial fitting absorbing interseismic deformation. A method using the correlation coefficient and standard deviation of the interseismic deformation model as interferogram screening indicators is proposed. This method takes into account the spatial distribution and overall noise level of the interseismic deformation signal in the interferogram, solving the problem that the structural function of the traditional method does not consider the spatial distribution of interseismic deformation, and improving the accuracy and adaptability of interseismic deformation signal extraction.

[0088] The present invention is further illustrated below with reference to a specific example. Comprehensive testing using Sentinel-1 SAR imagery covering the Haiyuan Fault Zone validates the effectiveness of our workflow for measuring interseismic deformation. We also demonstrate that the proposed joint InSAR-GNSS workflow can be extended to studying subtle interseismic deformation across major fault systems in Tibet and globally.

[0089] Step 1: Phase Correction

[0090] This example uses Sentinel-1 data collected from March 19, 2015 to November 30, 2021 in the Haiyuan Fault Zone to construct long-term (Group A) and short-term (Group B) baseline interferograms for monitoring deformation and estimating disturbance phase, respectively. The temporal and spatial baselines are: Group A (Group B p <90m,400d T <500d), Group B (B p <200m,B T <120d). Since the study area is located in the Eurasian Plate, the Euler pole w = [-0.085, -0.531, 0.770] is used to derive the plate velocity field. In the estimation of the perturbation phase, the pixel pair threshold of I-CANDIS is set to 1, such as Figure 3 As shown, (a) represents standard CANDIS, (b) represents I-CANDIS, and (c) represents the average STD of the interferograms of group B obtained by multiple iterations. Figure 3 The results demonstrate the sampling density advantage of I-CANDIS over CANDIS in atmospheric phase estimation, as well as the advantage of the combined GACOS and CANDIS corrections in reducing interferogram standard deviation compared to CANDIS correction alone. I-CANDIS improves the spatial coverage of atmospheric estimation by approximately 28% compared to the traditional CANDIS method. The integrated GACOS and I-CANDIS corrections stabilize after two iterations compared to CANDIS correction alone, so we estimate the number of atmospheric iterations to be two. The integrated corrections also reduce the average interferogram standard deviation from 0.7 rad to 0.6 rad, a reduction of approximately 14%.

[0091] like Figure 4 As shown, a) represents the atmospheric phase estimated by GACOS. b) represents the original interferogram. c) represents the interferogram corrected by GACOS. d) represents the disturbance phase estimated by I-CANDIS. e) represents the plate motion phase derived from the plate model. f) represents the interferogram corrected by GACOS+I-CANDIS, g) represents the interferogram corrected by GACOS+I-CANDIS+plate model, h) represents the phase slope obtained by first-order polynomial fitting, and i) represents the interferogram corrected by GACOS+I-CANDIS+polynomial slope. The upper right corner of the interferograms g) and i) is the COR value between the interferogram and the interseismic model. In Figure 4 The correction of the example interferogram shows the advantages of the plate model over the traditional polynomial fitting. The COR between the interferogram obtained by polynomial fitting correction and the interseismic model is 0.03. Figure 4 ​As shown in (i), the COR of the interferogram after plate motion correction is 0.49. Figure 4 This shows that using the plate model to correct the residual longwave signal effectively avoids the problem of traditional polynomial correction absorbing interseismic deformation.

[0092] Step 2: Interference pattern screening

[0093] Based on the regional geological structure, the interseismic fault geometry model was established using the geological fault traces of the two active fault zones, the Haiyuan Fault Zone and the Gulang Fault Zone. Figure 5 As shown in Figure 1, (a) shows the three-dimensional fault geometry. (b) shows the microseismic depth histogram, with the dashed line indicating the cutoff depth (96%). (c) shows the interseismic deformation model predicted in the LOS direction. To avoid boundary effects, the width and length of the fault are set to 3000 km and 500 km, respectively. Figure 5 According to the cutoff depth of microseismicity (96%), we set the locking depths of the Haiyuan fault and Gulang fault to 20 km and 18 km, respectively. Figure 5 As shown in (b). The interseismic model deformation field of the region is obtained through Bayesian inversion as follows Figure 5 As shown in (c), the correlation coefficient threshold is set to 0.45 for interference pattern screening.

[0094] Step 3: Average deformation velocity field solution

[0095] The number of intermittent coherent pixels is set to 100, and intermittent stacking is used to obtain the regional interseismic deformation velocity field based on the screened high-quality interferograms.

[0096] In the final process of obtaining the velocity field, we compared the velocity fields obtained by intermittent stacking of all uncorrected, all corrected, and corrected and screened interferogram sets to verify the effectiveness of the phase correction and interferogram screening in this proposed method. Figure 6 As shown, (a) and (b) represent the stacking velocity results of all uncorrected and corrected interferograms. The STD and COR of the results are shown in the upper right corner. (c) represents the results obtained after using the screening method. (d) represents the comparison of GNSS observations with the average results of InSAR observation points within 300m. (e) represents the velocity along the profile QQ', and the solid square represents the average value divided every 1.5 kilometers along the profile. The uncorrected stacking results are obviously disturbed by the strong atmospheric phase, and the interseismic strain accumulation on the fault is covered by these phase noises, such as Figure 6As shown in (a) in the figure, the corrected interferogram stacking results show that the STD and correlation are improved, and the interseismic deformation is preserved. This shows that the phase correction method can effectively restore the deformation and suppress the regional atmospheric disturbance. However, there are still long-wave disturbances in the corrected velocity field, such as Figure 6 The results obtained using the proposed screening method are shown in (b). Figure 6 As shown in (c), the remaining long-wave phase and spatial noise are effectively suppressed, further improving the consistency with GNSS observations. The STD is reduced by about 10%, and the correlation COR with the interseismic model is improved by about 6%. The QQ' profile also shows that the slope along the fault strike is further suppressed from 1.48 mm / a / 100 km to 0.25 mm / a / 100 km. Figure 6 As shown in (e); the cross-validation between InSAR and GNSS also shows that the continuous improvement can reach 30% from 0.9mm / a to 0.6mm / a. Figure 6 As shown in (d).

[0097] In order to further verify the reliability of the velocity field obtained by the proposed method, we also used the cross section of the region to perform inversion using a two-dimensional elastic dislocation model, such as Figure 7 Figure 2 shows the interseismic velocity map determined using this method, and (b) the velocity profile (PP′) across the Haiyuan fault zone. The inverted fault slip rates are generally consistent with those reported in previous geological studies within the uncertainty range.

[0098] The units involved in the embodiments of the present invention may be implemented in software or hardware.

[0099] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware or a combination thereof.

[0100] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for correcting and screening time-series InSAR interferograms of interseismic deformation, characterized by: include: Step 1: Use the GACOS method to perform atmospheric correction on the original interferogram, then use the improved CANDIS method to perform disturbance phase correction, and perform line-of-sight plate motion phase correction based on the plate model to obtain a long-term corrected interferogram. The step of performing disturbance phase correction using the improved CANDIS method comprises: Modeling short-time baseline interferometry phase; The intermittent coherence method is used to perform phase estimation based on common superposition on the interference pixels to obtain the disturbance error of each phase. Step 2: Use the Pearson correlation coefficient between the interferogram and the interseismic deformation model and the standard deviation of the interferogram to screen the long-term correction interferogram set to obtain the target interferogram set; Step 3: Based on the target interferogram set, the intermittent stacking method is used to set the threshold of the number of intermittent coherence pixels to generate the average interseismic deformation velocity field.

2. The method according to claim 1, characterized in that The step of using the GACOS method to perform atmospheric correction on the original interferogram includes: The GACOS method is used to perform difference and projection on the original interferogram set and the interferogram set is divided into short-time baseline interferogram and long-time baseline interferogram according to the time baseline of the interferogram.

3. The method according to claim 2, characterized in that , the expression of the short-time baseline interference phase modeling is Df i , j =Dt i,j +a j -a i +e ij where Δτ i,j is the temporally stable deformation phase between acquisition times i and j, α is the perturbation phase in each interference image, and ε is other noise.

4. The method according to claim 3, characterized in that The step of correcting the plate motion phase based on the line of sight of the plate model to obtain a long-term interference atlas includes: The plate motion is derived from the plate model and projected into the LOS direction according to the satellite imaging geometry to correct the residual long wavelength in the interferometric phase, resulting in a long-time corrected interferometric atlas.

5. The method according to claim 4, characterized in that Before step 2, the method further includes: Based on the geological fault surface trace geometry, microseismic data and GNSS observation data in the area, the interseismic deformation model is obtained using the Bayesian inversion method.

6. The method according to claim 5, characterized in that , the expression of Pearson correlation coefficient is in, and Represent the interference phase value and model phase value of the mth row and nth column respectively, and represent their average values ​​respectively.

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