A method for identifying potential landslides in permafrost regions that takes into account InSAR deformation trends

Abstract

This invention belongs to the field of landslide hazard identification methods, specifically a method for identifying potential landslides in permafrost regions that takes into account InSAR deformation trends. The method involves: InSAR deformation monitoring; identifying landslides not strongly affected by freeze-thaw cycles based on InSAR deformation rates; calculating the AP value of all deformation points; classifying the time-series deformation trends of all deformation points into three categories: uncorrelated, linearly correlated, and nonlinear; using GIS software to screen deformation points that simultaneously meet the criteria of deformation exceeding a stability threshold, significant periodicity, and linearly or nonlinearly correlated deformation trends; and identifying landslides strongly affected by freeze-thaw cycles based on these screened points. This invention provides a complete technical process for early identification of landslide hazards in permafrost regions using InSAR technology, which can improve the efficiency and economic benefits of identifying potential landslide hazards in permafrost regions.

Description

Technical Field

[0001] This invention belongs to the field of landslide hazard identification methods, specifically a method for identifying potential landslides in permafrost areas that takes into account InSAR deformation trends. Background Technology

[0002] In high-altitude regions worldwide, permafrost is undergoing severe degradation, closely related to global warming and increased human activities such as deforestation and urbanization. With widespread permafrost degradation, it is experiencing accelerated thawing and erosion, further altering the thickness of the active layer, pore water pressure, soil cohesion, and shear strength, directly impacting the stability of plateau and mountain slopes. Under the influence of heavy rainfall, earthquakes, and human activities, landslides, collapses, thermo-thaw landslides, and freeze-thaw mudflows occur frequently, seriously affecting the ecological environment, engineering structures, and the safety of human production activities in permafrost regions. Understanding the spatial and temporal distribution and deformation characteristics of these slope geological hazards is crucial for disaster risk prevention. Therefore, early identification of landslides in permafrost regions is extremely important.

[0003] Traditional methods for landslide identification mainly include early field surveys and mapping, and later developed aerial photogrammetry and visual interpretation of aerial images. However, due to the complexity of topographic and climatic conditions in permafrost regions and the unreliability of remote sensing imagery, these techniques are difficult to use for early landslide identification in permafrost areas. With the continuous development of radar remote sensing technology, Interferometric Synthetic Aperture Radar (InSAR) has become an effective technique for detecting large-area surface deformation and identifying potential landslides. It features all-weather, all-time coverage, high precision, high resolution, and strong penetration, and can cover areas with complex climate, topography, and vegetation conditions, providing an opportunity for monitoring surface deformation in permafrost regions. In recent years, surface deformation rates obtained based on InSAR technology have been widely used to identify potential landslides in the Loess Plateau or high mountain canyons. However, because landslides in permafrost regions are significantly affected by freeze-thaw cycles, it is difficult to effectively identify potential landslides in permafrost regions based on a single deformation rate. Based on the early deformation patterns of landslides in permafrost regions, this study delves into the deformation trend information of InSAR time series data. By combining the deformation rate with the temporal deformation pattern, the settlement-uplift-settlement process caused by freeze-thaw cycles can be revealed. Therefore, a method for identifying potential landslides in permafrost regions that takes into account InSAR deformation trends is proposed. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention proposes a method for identifying potential landslide hazards in permafrost regions that takes into account InSAR deformation trends. This method solves the problem that existing landslide identification methods based on InSAR deformation rates in permafrost regions are subject to freeze-thaw cycles, resulting in poor identification performance.

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

[0006] A method for identifying potential landslides in permafrost regions that takes into account InSAR deformation trends includes:

[0007] InSAR technology was used to acquire line-of-sight surface deformation data of the study area;

[0008] The surface deformation data along the line of sight in the study area includes: deformation rate, deformation amount, and InSAR time series displacement information; and time series deformation curves are plotted using the InSAR time series displacement information to represent the time series deformation trend.

[0009] By utilizing whether the deformation rate is greater than the stability threshold, and combining the characteristics of landslides in optical remote sensing images and topographic features, landslides that have not been strongly affected by freeze-thaw cycles can be identified.

[0010] Determine whether the deformation exceeds the stability threshold;

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

[0012] Based on the determination of whether the linear regression of the time series deformation curve is significant, it is determined whether the time series deformation trend is correlated or uncorrelated; based on the determination of whether the piecewise regression of the correlated time series deformation curve is significant, whether there are discontinuities in the time series, and whether the quadratic regression of the time series deformation curve is significant, it is determined whether the correlated time series deformation trend is linearly correlated or nonlinearly correlated.

[0013] GIS software was used to screen deformation points that simultaneously met the criteria of deformation exceeding the stability threshold, significant periodicity, and deformation trends of both linear and nonlinear correlation. 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 cycles were identified.

[0014] The selected InSAR technology is SBAS-InSAR, where the selected SAR data spans at least 2 years.

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

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

[0017] The formula for calculating the annual cyclical index AP is as follows:

[0018]

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

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

[0021] The standard for determining whether the periodicity of InSAR time series displacement information is significant is based on a threshold of 0.8. 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.

[0022] The method for determining whether the time series deformation trend is correlated or uncorrelated is as follows: Perform an ANOVAF test to determine the significance of the linear regression of the InSAR time series displacement information; plot and fit the time series deformation curve using a linear regression model; 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 = 1e-20), then the deformation trend of the InSAR time series displacement information has a significant linear relationship; conversely, 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 for determining whether there are breakpoints in the time series is based on whether the piecewise regression of the correlated time series deformation curve is significant.

[0024] Piecewise regression testing 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 are discontinuities in the time series.

[0025] The method for determining the significance of the quadratic regression on the time series deformed curve is as follows: if the time series deformed curves are linearly correlated and have no discontinuities, then the significance of the quadratic regression is assessed using an ANOVAF test, and the F-statistic is used 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 This indicates that the quadratic term makes a significant contribution to the regression and should be retained. In this case, the time series distortion curve is classified as a quadratic curve. If P12 Greater than the significance level α 12 This indicates that the quadratic term does not contribute significantly to the regression, and the time series should be classified as linearly correlated.

[0026] The α 12 With a value of 0.01, 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 hazards in permafrost areas that takes into account the deformation trend of InSAR, which solves the problem that the identification method for landslides in permafrost areas based on InSAR deformation rate is affected by the freeze-thaw cycle and the identification effect is not good in the prior art; (2) The present invention provides a complete technical process for early identification of landslide hazards in permafrost areas using InSAR technology, which can improve the efficiency and economic benefits of identifying potential landslide hazards in permafrost areas. Attached Figure Description

[0028] Figure 1 This is a flowchart of an early identification technology for landslide disasters in permafrost regions based on synthetic aperture radar interferometry.

[0029] Figure 2 Flowchart for defining time series trends;

[0030] Figure 3 Potential landslides are identified based on deformation rate results;

[0031] Figure 4 AP value classification;

[0032] Figure 5 Classification of time series distortion trends;

[0033] Figure 6 Deformation amount;

[0034] Figure 7 Potential landslides are identified based on AP values, time series trend analysis, and deformation data. Detailed Implementation

[0035] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0036] Figure 1 The flowchart of the early identification technology for landslide disasters in permafrost areas based on synthetic aperture radar interferometry is as follows:

[0037] S100: InSAR technology was used to acquire line-of-sight surface deformation data of the study area, including deformation rate, deformation amount, and InSAR time-series displacement information. Time-series deformation curves were then plotted using the InSAR time-series displacement information to represent the time-series deformation trend.

[0038] Interferometric Synthetic Aperture Radar (InSAR) is a remote sensing technology that uses radar beams to detect the Earth's surface and analyze the echo signals to obtain information about surface deformation. Line-of-sight surface deformation data refers to surface displacement data along the radar beam direction, including:

[0039] Deformation rate: The rate of change of surface displacement over time, used to analyze how fast or slow the deformation occurs.

[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, used to analyze deformation trends and periodicity.

[0042] In one possible implementation, the InSAR technology chosen is SBAS-InSAR, which, compared to D-InSAR and PS-InSAR, increases the spatiotemporal sampling rate, has higher spatiotemporal coherence, and is more likely to acquire densely distributed high-coherence pixels on the ground. In areas with complex terrain, SBAS-InSAR has a stronger nonlinear displacement capture capability.

[0043] In InSAR technology, the selected SAR data should have a time span of ≥2 years. The longer the time span, the better the InSAR time series deformation trend can be analyzed.

[0044] S200: By utilizing whether the deformation rate is greater than the stability threshold, and combining the characteristics of landslides in optical remote sensing images and topographic features, landslides that have not been strongly affected by freeze-thaw cycles are identified.

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

[0046] In one possible implementation, the deformation rate is considered to be greater than a stability threshold, which is ±10 mm per year.

[0047] S300: Determines whether the deformation is greater than the stability threshold.

[0048] In one possible implementation, the deformation amount is considered to be greater than a stability threshold, which is ±10 mm per year.

[0049] S400: The annual periodicity index AP is used to detect the periodic fluctuations of the displacement time series with a wavelength of 1 year, and to determine whether the periodic fluctuations of the InSAR time series displacement information are significant.

[0050] The AP value is calculated based on the power spectrum generated by Fast Fourier Transform (FFT) analysis. The formula for calculating the annual periodicity index AP is as follows:

[0051]

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

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

[0054] In one possible implementation, the standard for determining whether the periodicity of InSAR time series displacement information is significant is a threshold of 0.8. 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.

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

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

[0057] S510: Perform an ANOVA F test to determine the significance of the linear regression of InSAR time series displacement information. Plot and fit the time series deformation curve using a linear regression model. 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, where α1 is 1e-20, then the deformation trend of the InSAR time series displacement information has a significant linear relationship. Conversely, if P1 is greater than the significance level α1, then the deformation trend of the InSAR time series displacement information is linearly uncorrelated.

[0058] S520: If the linear regression is significant, perform a piecewise regression test on the distorted time series curve. The piecewise segmentation is introduced to check for sudden changes in the slope of the data and to pinpoint the locations of these changes. The piecewise regression test is based on the Bayesian Information Criterion (BIC). BIC addresses overfitting by introducing a penalty term related to the number of model parameters, prioritizing models that fit the data well with fewer parameters (low RSS). By comparing the BIC values ​​of different models, especially in time series analysis, if the minimum BIC value of the segmented model is lower than that of other models, it can be inferred that a breakpoint exists in the time series.

[0059] S530: If the time series deformed curves are linearly correlated and have no discontinuities, then 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 A value of 0.01 indicates that the quadratic term makes a significant contribution to the regression and should be retained. In this case, the time series distortion curve is classified as a quadratic curve. If p 12 Greater than the significance level α 12 This indicates that the quadratic term does not contribute significantly to the regression, and the time series should be classified as linearly correlated.

[0060] Because linearly and nonlinearly correlated deformations are often used to reflect the development process of landslides, they can serve as an important basis for identifying potential landslides. Therefore, deformation trends are classified into three types: uncorrelated, linearly correlated, and nonlinearly correlated (quadratic curve type and type with significant discontinuities).

[0061] S600: GIS software is used to screen deformation points that simultaneously meet the criteria of deformation amount greater than the stability threshold, significant periodicity, and deformation trends of linear and nonlinear correlation. 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 cycles are identified.

[0062] Example 2

[0063] A specific experimental area was selected. This area mainly consists of high-altitude hills and moderately undulating mountains, with most areas above 4,500 meters in altitude. 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 active layer in this region is gradually thickening, and permafrost is continuously degrading, leading to frequent slope geological disasters such as landslides, collapses, thermo-thaw landslides, and freeze-thaw mudflows.

[0064] 1. Basic Data Acquisition

[0065] This study collected 119 Sentinel-1A down-orbit images covering the entire study area (Table 1) for calculating surface deformation. The optical remote sensing images used for visual interpretation were obtained from Google Earth, with a resolution of 0.5m, and dated from August 2017 to July 2022.

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

[0067]

[0068] 2. InSAR Deformation Monitoring

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

[0070] 3. Identify landslides not severely affected by freeze-thaw cycles.

[0071] Based on deformation rate, and combined with the unique color, texture, brightness, and topographic features of landslides in optical remote sensing images, landslides in Hekashan that were not strongly affected by freeze-thaw cycles were identified and delineated, resulting in the identification of 27 potential landslides. For example... Figure 3 As shown.

[0072] 4. Identify landslides severely affected by freeze-thaw cycles.

[0073] AP value: The AP values ​​of all deformation points in the study area were calculated, and with a threshold of 0.8, the deformation points were divided into two types: those with significant periodic fluctuations and those with insignificant periodic fluctuations. For example... Figure 4 As shown.

[0074] Classification of Time Series Deformation Trends: First, an ANOVAF test is performed on the significance of linear regression, with a selected significance level α1 of 1e-20. If the linear regression is not significant, it is classified as uncorrelated. If the linear regression is significant, the second step involves piecewise regression testing of the time series deformation trend according to the BIC criterion. If the time series deformation trend is linearly correlated and without discontinuities, a quadratic fit is performed using the ANOVAF test to test the significance of the quadratic fit being superior to the linear fit, with a selected significance level α12 of 0.01. This classifies the time series deformation trend into linearly correlated and quadratic curve types. If the piecewise regression is significant, the third step involves a discontinuity test on the time series deformation trend characterized by significant discontinuities, with a confidence interval of 95%. If there are no discontinuous discontinuities, it is classified as bilinearly correlated; otherwise, the ANOVA F test is used to assess whether there is a significant difference in velocity before and after the discontinuity, with a selected significance level α of 0.05. This classifies the time series deformation trend into uniform discontinuous and variable discontinuous types. Because linearly and nonlinearly correlated deformations are often used to reflect the development process of landslides and can serve as important criteria for identifying potential landslides, deformation trends are therefore classified into three types: uncorrelated, linearly correlated, and nonlinearly correlated. For example... Figure 5 As shown.

[0075] Screening points: GIS software is used to screen deformation points that simultaneously meet the following criteria: deformation greater than ±40 mm, significant periodicity, and deformation trends exhibiting both linear and non-linear correlation. For example... Figure 4 , 5 As shown in Figures 6 and 7.

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

[0077] A total of 33 potential landslides were identified in the study area. Of these, 11 were identified using a combination of methods based on deformation rate and those considering deformation trend. The deformation rate-based method identified 15 landslides that the deformation trend-considered method failed to detect, while the deformation trend-considered method identified 7 landslides that the deformation rate-based method failed to detect. Therefore, combining both methods is necessary for effective landslide identification in permafrost regions.

[0078] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0079] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0080] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

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

[0082] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications or equivalent substitutions made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for identifying potential landslides in permafrost regions that takes into account InSAR deformation trends, characterized in that, include: InSAR technology was used to acquire line-of-sight surface deformation data of the study area; The surface deformation data along the line of sight in the study area includes: deformation rate, deformation amount, and InSAR time series displacement information; and time series deformation curves are plotted using the InSAR time series displacement information to represent the time series deformation trend. By utilizing whether the deformation rate is greater than the stability threshold, and combining the characteristics of landslides in optical remote sensing images and topographic features, landslides that have not been strongly affected by freeze-thaw cycles can be identified. Determine whether the deformation exceeds the stability threshold; The annual periodicity index AP is used to detect the periodic fluctuations of displacement time series with a wavelength of 1 year, and to determine whether the periodic fluctuations of InSAR time series displacement information are significant. Based on the determination of whether the linear regression of the time series deformation curve is significant, it is determined whether the time series deformation trend is correlated or uncorrelated; based on the determination of whether the piecewise regression of the correlated time series deformation curve is significant, whether there are discontinuities in the time series, and whether the quadratic regression of the time series deformation curve is significant, it is determined whether the correlated time series deformation trend is linearly correlated or nonlinearly correlated. GIS software was used to screen deformation points that simultaneously met the criteria of deformation exceeding the stability threshold, significant periodicity, and deformation trends of both linear and nonlinear correlation. 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 cycles were identified.

2. The method for identifying potential landslides in permafrost regions considering InSAR deformation trends according to claim 1, characterized in that, The selected InSAR technology is SBAS-InSAR, where the selected SAR data spans at least 2 years.

3. The method for identifying potential landslides in permafrost regions considering InSAR deformation trends according to claim 1, characterized in that, Whether the deformation rate is greater than a stability threshold, which is ±10 mm per year.

4. The method for identifying potential landslides in permafrost regions considering InSAR deformation trends according to claim 1, characterized in that, Whether the deformation is greater than a stability threshold, which is ±10 mm per year.

5. The method for identifying potential landslides in permafrost regions considering InSAR deformation trends according to claim 1, characterized in that, The formula for calculating the annual cyclical index AP is as follows: In the formula, P0 is the peak power of the time series spectral power in the fundamental frequency f0 segment, and P1 is the peak power of the time series spectral power in the annual frequency f1 segment; The AP index ranges from 0 to 1, which is the range from no periodic fluctuations to strong periodic fluctuations.

6. The method for identifying potential landslides in permafrost regions considering InSAR deformation trends according to claim 5, characterized in that, The standard for determining whether the periodicity of InSAR time series displacement information is significant is based on a threshold of 0.8; 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 permafrost regions considering InSAR deformation trends according to claim 1, characterized in that, The method for determining whether the time series deformation trend is correlated or uncorrelated is as follows: perform an ANOVAF test to determine whether the linear regression of the InSAR time series displacement information is significant, plot 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, where α1 is 1e-20, then the deformation trend of the InSAR time series displacement information has a significant linear relationship. Conversely, if p1 is greater than the significance level α1, the deformation trend of the InSAR time series displacement information is linearly uncorrelated. The method for determining whether there are breakpoints in the time series is based on whether the piecewise regression of the correlated time series deformation curve is significant. Piecewise regression testing 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 are discontinuities in the time series. The method for determining the significance of the quadratic regression on the time series deformed curve is as follows: if the time series deformed curve is linearly correlated and has no discontinuity, then the significance of the quadratic regression is assessed using an ANOVAF test, and the F-statistic is used 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 This indicates that the quadratic term makes a significant contribution to the regression and should be retained. In this case, the time series distortion curve is classified as a quadratic curve. If P 12 Greater than the significance level α 12 This indicates that the quadratic term does not contribute significantly to the regression, and the time series should be classified as linearly correlated.

8. A method for identifying potential landslides in permafrost regions considering InSAR deformation trends, as described in claim 7, is characterized in that... The α 12 With a value of 0.01, the time series deformation trend is divided into linear correlation type and quadratic curve type.

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