Method for identifying potential landslide in permafrost region by considering InSAR deformation trend

Abstract

The invention belongs to the field of landslide disaster identification methods, and particularly relates to a permafrost region potential landslide identification method considering an InSAR deformation trend. Monitoring the deformation of the InSAR; the landslide which is not strongly influenced by the freezing and thawing effect is identified based on the InSAR deformation rate; calculating AP values of all deformation points; dividing the time sequence deformation trends of all deformation points into an irrelevant type, a linear relevant type and a nonlinear type; gIS software is used for screening deformation points which simultaneously meet the conditions that the deformation amount is larger than a stable threshold value, the periodicity is remarkable and the deformation trend is a linear correlation type and a non-linear correlation type, and the landslide which is strongly influenced by the freezing and thawing effect is recognized based on the screening points. According to the invention, a set of complete technical process is provided for carrying out landslide disaster early-stage identification by using the InSAR technology in the permafrost region, and the working efficiency and the economic benefit of identifying the potential landslide disaster in the permafrost region can be improved.

Description

Technical Field

[0001] The invention belongs to the field of landslide hazard identification methods, and in particular to a method for identifying potential landslides in frozen soil areas taking into account InSAR deformation trends. Background Art

[0002] Permafrost is experiencing severe degradation in high-altitude areas around the world, which is closely related to global warming and the increase in human activities such as deforestation and urbanization. With the widespread degradation of permafrost, it is experiencing accelerated melting and erosion, and its active layer thickness, pore water pressure, soil cohesion, and soil shear strength are further changing, which directly affects the stability of plateau mountain slopes. Under the interference of heavy rainfall, earthquakes, human activities, etc., slope geological disasters such as landslides, collapses, thermal melt slides, and freeze-thaw mudslides occur frequently, seriously affecting the ecological environment, engineering buildings, and human production activities in frozen soil areas. Understanding the distribution and deformation characteristics of these slope geological disasters in spatial and temporal dimensions is crucial to disaster risk prevention. Therefore, it is extremely important to carry out early identification of landslides in frozen soil areas.

[0003] Traditional methods for landslide identification mainly include early field investigation and mapping and later developed aerial photogrammetry and visual interpretation of aerial photographs. However, due to the complexity of topographic and climatic conditions in frozen areas and the ineffectiveness of remote sensing images, it is difficult to use these technologies to identify landslides in frozen areas at an early stage. With the continuous development of radar remote sensing technology, synthetic aperture radar interferometry (InSAR) has become an effective technical means to effectively detect large-area surface deformation and identify potential landslides. It has the characteristics of all-day, all-weather, high precision, high resolution, and strong penetration. It can cover areas with complex climate, landforms and vegetation conditions, providing an opportunity for surface deformation monitoring in frozen areas. In recent years, the surface deformation rate obtained based on InSAR technology has been widely used to identify potential landslides in the Loess Plateau or alpine canyon areas. However, since landslides in frozen areas are significantly affected by freeze-thaw cycles, it is difficult to effectively identify potential landslides in frozen areas based on a single deformation rate. Based on the deformation law of landslides in frozen soil areas, the deformation trend information of InSAR time series is deeply mined, and the deformation rate is combined with the time series deformation law. The settlement-uplift-settlement movement process caused by the freeze-thaw cycle of the slope can be displayed. Therefore, a potential landslide identification method in frozen soil areas taking into account the InSAR deformation trend is proposed. Summary of the invention

[0004] In view of the shortcomings of the existing technology, the present invention proposes a method for identifying potential landslide disasters in frozen soil areas taking into account the InSAR deformation trend, which solves the problem that the existing method for identifying landslides in frozen soil areas based on InSAR deformation rate is affected by freeze-thaw effects, resulting in poor identification effect.

[0005] The technical solution of the present invention is as follows:

[0006] A method for identifying potential landslides in frozen soil areas taking into account InSAR deformation trends includes:

[0007] InSAR technology was used to obtain the surface deformation data of the study area in the line of sight direction;

[0008] The surface deformation data of the study area include deformation rate, deformation amount and InSAR time series displacement information. The time series deformation curve is drawn using the InSAR time series displacement information to represent the deformation trend of the time series.

[0009] The landslides that are not strongly affected by freeze-thaw are identified by using whether the deformation rate is greater than the stability threshold and combining the characteristics of the landslide in optical remote sensing images and the topographic features.

[0010] Determine whether the deformation is greater than the stability threshold;

[0011] The annual periodicity index AP is used to detect the periodic fluctuation of the displacement time series with a wavelength of 1 year and to determine whether the periodic fluctuation of the InSAR time series displacement information is significant.

[0012] Based on the judgment of whether the linear regression of the time series deformation curve is significant, it is obtained whether the deformation trend of the time series is correlated or uncorrelated; based on the judgment of whether the segmented regression of the correlated time series deformation curve is significant, whether there are breakpoints in the time series, and whether the quadratic regression of the time series deformation curve is significant, it is obtained whether the deformation trend of the correlated time series is linearly correlated or nonlinearly correlated;

[0013] GIS software is used to screen deformation points that meet the requirements of deformation greater than the stability threshold, significant periodicity, and linear and nonlinear deformation trends. Based on the screened deformation points and combined with the characteristics of the landslide in optical remote sensing images and the topographic and geomorphic characteristics, the landslides strongly affected by freeze-thaw are identified.

[0014] The SBAS-InSAR technology is selected in the InSAR technology. In the InSAR technology, the time span of the selected SAR data is ≥ 2 years.

[0015] Whether the deformation rate is greater than a stability threshold, the stability threshold is ±10 mm per year.

[0016] Whether the deformation is greater than a stability threshold, the stability threshold is ±10 mm per year.

[0017] The calculation formula of the annual periodic index AP is:

[0018]

[0019] Where P0 is the peak power of the time series spectrum power in the fundamental frequency f0 segment, and P1 is the peak power of the time series spectrum power in the annual frequency f1 segment.

[0020] The AP index ranges from 0 to 1, that is, from no periodic fluctuations to strong periodic fluctuations.

[0021] The standard for judging whether the periodicity of the InSAR time series displacement information is significant is to use 0.8 as a threshold value. That is, if the AP value is less than 0.8, the periodic fluctuation of the InSAR time series displacement information is not significant, and if the AP value is greater than or equal to 0.8, the periodic fluctuation of the InSAR time series displacement information is significant.

[0022] The method for judging whether the deformation trend of the time series is correlated or uncorrelated is as follows: an ANOVAF test is performed on whether the linear regression of the InSAR time series displacement information is significant, a time series deformation curve is drawn and fitted with a linear regression model, and then the probability P1 that the slope of the regression line is equal to 0 is calculated using the F statistic; if P1 is less than the significance level α1, α1 is 1e-20, then the deformation trend of the InSAR time series displacement information has a significant linear relationship; on the contrary, if p1 is greater than the significance level α1, then the deformation trend of the InSAR time series displacement information is linearly uncorrelated;

[0023] The method of judging whether the segmented regression of the time series deformation curve based on the correlation type is significant and whether there is a breakpoint in the time series is as follows:

[0024] The segmented regression test is based on BIC. By comparing the BIC values ​​of different models, if the minimum BIC value of the segmentation model is lower than the BIC values ​​of other models, it can be inferred that there is a breakpoint in the time series;

[0025] The method for judging whether the quadratic regression of the time series deformation curve of the correlation type is significant is as follows: if the time series deformation curve is linearly correlated and has no breakpoints, an ANOVAF test is performed on the significance of the quadratic regression, and the F statistic is used to calculate the probability P that the quadratic term has no contribution to the regression. 12 , if P 12 Less than the significance level α 12 , indicating that the quadratic term has a significant contribution to the regression and should be retained. At this time, the time series deformation curve is classified as a quadratic curve type. If P12 Greater than the significance level α 12 , indicating that the quadratic term does not contribute significantly to the regression and the time series should be classified as linearly related.

[0026] The α 12 is 0.01, and the time series deformation trend is divided into linear correlation type and quadratic curve type.

[0027] The beneficial effects of the present invention are as follows: (1) The present invention provides a method for identifying potential landslide disasters in frozen soil areas taking into account the InSAR deformation trend, thereby solving the problem that the identification method of landslides in frozen soil areas based on InSAR deformation rate in the prior art is affected by the freeze-thaw cycle and leads to poor identification effect; (2) The present invention provides a complete technical process for early identification of landslide disasters in frozen soil areas using InSAR technology, which can improve the work efficiency and economic benefits of identifying potential landslide disasters in frozen soil areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a technical flow chart for early identification of landslide hazards in frozen soil areas based on synthetic aperture radar interferometry;

[0029] Figure 2 Flowchart for time series trend partitioning;

[0030] Figure 3 Identification of potential landslides based on deformation rates;

[0031] Figure 4 AP value classification;

[0032] Figure 5 Classification of deformation trends in time series;

[0033] Figure 6 Deformation;

[0034] Figure 7 Identification results of potential landslides based on AP values, time series trend division and deformation. 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] Figure 1 This is a technical flow chart of early identification of landslide hazards in frozen soil areas based on synthetic aperture radar interferometry. The specific steps are as follows:

[0037] S100: InSAR technology is used to obtain the surface deformation data of the study area, including deformation rate, deformation amount and InSAR time series displacement information. InSAR time series displacement information is used to draw a time series deformation curve to show the deformation trend of the time series

[0038] Interferometric synthetic aperture radar (InSAR) is a remote sensing technology that uses radar beams to detect the surface and analyze echo signals to obtain surface deformation information. Line-of-sight surface deformation data refers to the surface displacement data in the direction of the radar beam, including:

[0039] Deformation rate: The rate of change of surface displacement over time, used to analyze the speed of deformation.

[0040] Deformation: The total amount of surface displacement, used to assess the magnitude of deformation.

[0041] InSAR time series displacement information: multiple sets of surface displacement data collected over long time intervals are used to analyze deformation trends and periodicity.

[0042] In a possible implementation, the InSAR technology selects SBAS-InSAR technology. Compared with D-InSAR and PS-InSAR, this technology increases the spatiotemporal sampling rate, has higher spatiotemporal coherence, and is easier to obtain high-coherence pixel points densely distributed on the ground. In areas with complex terrain, SBAS-InSAR has a stronger nonlinear displacement capture capability.

[0043] Among them, in InSAR technology, the time span of the selected SAR data is ≥ 2 years. The longer the time span, the better the deformation trend of the InSAR time series can be analyzed.

[0044] S200: Using whether the deformation rate is greater than the stability threshold, combined with the characteristics of the landslide in the optical remote sensing image and the topographic features, the landslide that is not strongly affected by freeze-thaw is identified.

[0045] The deformation rate obtained from the InSAR data is compared with the preset stability threshold. If the deformation rate is greater than the threshold, it means that the surface deformation in the area is active and there may be potential landslides. Combined with the unique color, texture, brightness and other characteristics of landslides in optical remote sensing images and topographic features, the landslides in the study area that are not strongly affected by freeze-thaw are identified and delineated.

[0046] In a possible implementation, whether the deformation rate is greater than a stability threshold, the stability threshold is ±10 mm per year.

[0047] S300: Determine whether the deformation amount is greater than a stability threshold.

[0048] In a possible implementation manner, whether the deformation is greater than a stability threshold, the stability threshold is ±10 mm per year.

[0049] S400: Use the annual periodicity index AP to detect the periodic fluctuation of the displacement time series with a wavelength of 1 year, and determine whether the periodic fluctuation of the InSAR time series displacement information is significant;

[0050] The AP value is calculated based on the power spectrum generated by Fast Fourier Transform (FFT), and the annual periodicity index AP calculation formula is:

[0051]

[0052] Where P0 is the peak power of the time series spectrum power in the fundamental frequency f0 segment, and P1 is the peak power of the time series spectrum power in the annual frequency f1 segment.

[0053] The AP index ranges from 0 to 1, that is, from no periodic fluctuations to strong periodic fluctuations.

[0054] In a possible implementation, the standard for judging whether the periodicity of the InSAR time series displacement information is significant is to use 0.8 as a threshold value. That is, if the AP value is less than 0.8, the periodic fluctuation of the InSAR time series displacement information is not significant, and if the AP value is greater than or equal to 0.8, the periodic fluctuation of the InSAR time series displacement information is significant.

[0055] S500: Based on the judgment of whether the linear regression of the time series deformation curve is significant, whether the deformation trend of the time series is correlated or uncorrelated is obtained; based on the judgment of whether the segmented regression of the correlated time series deformation curve is significant, whether there are breakpoints in the time series, and whether the quadratic regression of the time series deformation curve is significant, whether the deformation trend of the correlated time series is linearly correlated or nonlinearly correlated is obtained.

[0056] like Figure 2 As shown, the specific steps include:

[0057] S510: Perform an ANOVA F test on whether the linear regression of the InSAR time series displacement information is significant, draw and fit the time series deformation curve with a linear regression model, and then use the F statistic to calculate the probability P1 that the slope of the regression line is equal to 0; if P1 is less than the significance level α1, α1 is 1e-20, then the deformation trend of the InSAR time series displacement information has a significant linear relationship; on the contrary, if p1 is greater than the significance level α1, then the deformation trend of the InSAR time series displacement information is linearly unrelated.

[0058] S520: If the linear regression is significant, perform a segmented regression test on the time series deformation curve. Segmentation is introduced to see if there is a sudden change in the slope of the data and to determine the location of the change. The segmented regression test is based on the Bayesian Information Criterion (BIC), which solves the overfitting problem by introducing a penalty term related to the number of model parameters, and gives priority to models that can fit the data well (low RSS) with a small number of parameters. By comparing the BIC values ​​of different models, especially in time series analysis, if the minimum BIC value of the segmentation model is lower than the BIC values ​​of other models, it can be inferred that there are breakpoints in the time series.

[0059] S530: If the time series deformation curve is linearly related and has no breakpoints, perform an ANOVAF test on the significance of the quadratic regression. Use the F statistic to calculate the probability p that the quadratic term does not contribute to the regression. 12 , if p 12 Less than the significance level α 12 , α 12 is 0.01, indicating that the quadratic term has a significant contribution to the regression and should be retained. In this case, the time series deformation curve is classified as a quadratic curve. 12 Greater than the significance level α 12 , indicating that the quadratic term does not contribute significantly to the regression and the time series should be classified as linearly related.

[0060] Because linear correlation and nonlinear correlation deformation are often used to reflect the development process of landslides, they can serve as an important basis for identifying potential landslides. Therefore, the deformation trends are divided into three types: uncorrelated type, linear correlation type, and nonlinear correlation type (quadratic curve type and type with significant breakpoints).

[0061] S600: GIS software is used to screen deformation points that meet the requirements of deformation greater than the stability threshold, significant periodicity, and linear and nonlinear deformation trends; based on the screened deformation points and combined with the characteristics of landslides in optical remote sensing images and topographic features, landslides strongly affected by freeze-thaw are identified.

[0062] Example 2

[0063] Select a specific experimental area. The experimental area is mainly composed of high-altitude hills and medium-undulating mountains, and most areas are above 4,500 meters above sea level. The experimental area has a semi-arid continental climate, with an average annual temperature between -10℃ and 4℃ and precipitation between 70.5 mm and 291.4 mm. Affected by global warming and human activities, the thickness of the active layer in the area has gradually thickened, and permafrost has continued to degrade, leading to frequent slope geological disasters such as landslides, collapses, thermal melt landslides, and freeze-thaw mudslides.

[0064] 1. Basic data acquisition

[0065] In this study, 119 Sentinel-1A descending orbit images covering the entire study area were collected (Table 1) to calculate the surface deformation. The optical remote sensing images used for visual interpretation were from Google Earth with a resolution of 0.5 m and the time period was from August 2017 to July 2022.

[0066] Table 1 Basic information of SAR data used in the study

[0067]

[0068] 2. InSAR deformation monitoring

[0069] Firstly, SBAS-InSAR was used to obtain the line-of-sight deformation rate, deformation amount and InSAR time series displacement information of Heka Mountain.

[0070] 3. Identify landslides that are not strongly affected by freeze-thaw

[0071] Based on the deformation rate, combined with the unique color, texture, brightness and topographic features of landslides in optical remote sensing images, the landslides in Heka Mountain that were not strongly affected by freeze-thaw were identified and delineated, and a total of 27 potential landslides were identified. Figure 3 shown.

[0072] 4. Identify landslides that are strongly affected by freeze-thaw

[0073] AP value: Calculate the AP value of all deformation points in the study area, and use 0.8 as the threshold to divide the deformation points into two types: those with significant periodic fluctuations and those with insignificant periodic fluctuations. Figure 4 shown.

[0074] Time series deformation trend classification: ANOVAF test is performed on the significance of linear regression, and the selected significance level α1 is 1e-20. If the linear regression is not significant, it is classified as unrelated. If the linear regression is significant, the second step is to perform a piecewise regression test on the time series deformation trend according to the BIC criterion. If the time series deformation trend is linearly related and has no breakpoints, the ANOVAF test is used to perform quadratic fitting to test the significance of quadratic fitting being better than linear fitting. The selected significance level α12 is 0.01, and the time series deformation trend can be divided into linear correlation type and quadratic curve type. If the piecewise regression is significant, the third step is to perform a discontinuity test on the time series deformation trend characterized by significant breakpoints, and the confidence interval is set to 95%. If there is no discontinuous breakpoint, it is classified as a bilinear correlation type; otherwise, the ANOVA F test is finally used to evaluate whether there is a significant difference in speed before and after the breakpoint. The selected significance level α is 0.05, and the time series deformation trend can be divided into uniform discontinuity type and variable speed discontinuity type. Because linear correlation and nonlinear correlation deformation are often used to reflect the development process of landslides, they can serve as an important basis for identifying potential landslides. Therefore, the deformation trend is divided into three types: uncorrelated, linearly correlated, and nonlinearly correlated. Figure 5 shown.

[0075] Screening points: Use GIS software to screen deformation points that meet the requirements of deformation greater than ±40 mm, significant periodicity, and linear and nonlinear deformation trends. Figure 4 , 5 , 6, and 7.

[0076] Finally, based on the deformation points obtained by screening and combined with the unique color, texture, brightness and topographic features of landslides in optical remote sensing images, the landslides strongly affected by freeze-thaw in the study area were identified and delineated, and a total of 19 potential landslides were identified. Figure 7 shown.

[0077] A total of 33 potential landslides were identified in the study area, of which 11 were identified by the deformation rate-based identification method and the deformation trend-based identification method. The deformation rate-based identification method identified 15 that the deformation trend-based identification method failed to identify, while the deformation trend-based identification method identified 7 that the deformation rate-based identification method failed to identify. Therefore, the combination of the two identification methods can effectively identify landslides in frozen soil areas.

[0078] The basic principles of the present application are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present application. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, rather than limitation, and the above details do not limit the present application to being implemented by adopting the above specific details.

[0079] The block diagrams of the devices, apparatuses, equipment, and systems involved in this application are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0080] It should also be noted that in the apparatus, device and method of the present application, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0081] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features of the present invention.

[0082] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for identifying potential landslides in frozen soil areas taking into account InSAR deformation trends, characterized in that: include: InSAR technology was used to obtain the surface deformation data of the study area in the line of sight direction; The surface deformation data of the study area include deformation rate, deformation amount and InSAR time series displacement information. The time series deformation curve is drawn using the InSAR time series displacement information to represent the deformation trend of the time series. The landslides that are not strongly affected by freeze-thaw are identified by using whether the deformation rate is greater than the stability threshold and combining the characteristics of the landslide in optical remote sensing images and the topographic features. Determine whether the deformation is greater than the stability threshold; The annual periodicity index AP is used to detect the periodic fluctuation of the displacement time series with a wavelength of 1 year and to determine whether the periodic fluctuation of the InSAR time series displacement information is significant. Based on the judgment of whether the linear regression of the time series deformation curve is significant, it is obtained whether the deformation trend of the time series is correlated or uncorrelated; based on the judgment of whether the segmented regression of the correlated time series deformation curve is significant, whether there are breakpoints in the time series, and whether the quadratic regression of the time series deformation curve is significant, it is obtained whether the deformation trend of the correlated time series is linearly correlated or nonlinearly correlated; GIS software is used to screen deformation points that meet the requirements of deformation greater than the stability threshold, significant periodicity, and linear and nonlinear deformation trends. Based on the screened deformation points and combined with the characteristics of the landslide in optical remote sensing images and the topographic and geomorphic characteristics, the landslides strongly affected by freeze-thaw are identified.

2. A method for identifying potential landslides in frozen soil areas taking into account InSAR deformation trends according to claim 1, characterized in that: The SBAS-InSAR technology is selected in the InSAR technology. In the InSAR technology, the time span of the selected SAR data is ≥ 2 years.

3. The method for identifying potential landslides in frozen soil areas taking into account InSAR deformation trends according to claim 1, characterized in that: Whether the deformation rate is greater than a stability threshold, the stability threshold is ±10 mm per year.

4. The method for identifying potential landslides in frozen soil areas taking into account InSAR deformation trends according to claim 1, characterized in that: Whether the deformation is greater than a stability threshold, the stability threshold is ±10 mm per year.

5. The method for identifying potential landslides in frozen soil areas taking into account InSAR deformation trends according to claim 1, characterized in that: The calculation formula of the annual periodic index AP is: Where, P0 is the peak power of the time series spectrum power in the fundamental frequency f0 segment, and P1 is the peak power of the time series spectrum power in the annual frequency f1 segment; The AP index ranges from 0 to 1, that is, from no periodic fluctuations to strong periodic fluctuations.

6. A method for identifying potential landslides in frozen soil areas taking into account InSAR deformation trends according to claim 5, characterized in that: The standard for judging whether the periodicity of the InSAR time series displacement information is significant is to use 0.8 as the threshold; That is, if the AP value is less than 0.8, the periodic fluctuation of the InSAR time series displacement information is not significant; if the AP value is greater than or equal to 0.8, the periodic fluctuation of the InSAR time series displacement information is significant.

7. The method for identifying potential landslides in frozen soil areas taking into account InSAR deformation trends according to claim 1, characterized in that: Whether the time series deformation trend is correlated or uncorrelated is determined by: performing an ANOVA F test on whether the linear regression of the InSAR time series displacement information is significant, drawing and fitting the time series deformation curve with a linear regression model, and then using the F statistic to calculate the probability P1 that the slope of the regression line is equal to 0; if P1 is less than the significance level α1, α1 is 1e-20, then the deformation trend of the InSAR time series displacement information has a significant linear relationship; On the contrary, if p1 is greater than the significance level α1, the deformation trend of the InSAR time series displacement information is linearly uncorrelated; The method of judging whether the segmented regression of the time series deformation curve based on the correlation type is significant and whether there is a breakpoint in the time series is as follows: The segmented regression test is based on BIC. By comparing the BIC values ​​of different models, if the minimum BIC value of the segmentation model is lower than the BIC values ​​of other models, it can be inferred that there is a breakpoint in the time series; The method for judging whether the quadratic regression of the time series deformation curve of the correlation type is significant is as follows: if the time series deformation curve is linearly correlated and has no breakpoints, an ANOVAF test is performed on the significance of the quadratic regression, and the F statistic is used to calculate the probability P that the quadratic term has no contribution to the regression. 12 , if P 12 Less than the significance level α 12 , indicating that the quadratic term has a significant contribution to the regression and should be retained. At this time, the time series deformation curve is classified as a quadratic curve type. If P 12 Greater than the significance level α 12 , indicating that the quadratic term does not contribute significantly to the regression and the time series should be classified as linearly related.

8. The method for identifying potential landslides in frozen soil areas taking into account InSAR deformation trends according to claim 1, characterized in that: The α 12 is 0.01, and the time series deformation trend is divided into linear correlation type and quadratic curve type.

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