Method for Identifying Hidden Dangers of Bedding Landslides Based on Slope Structure and Multi-Source Remote Sensing Technology

Abstract

The present application discloses a method for identifying bedding landslide hazards based on slope structure and multi-source remote sensing technology, which relates to the field of engineering geological slope disaster prevention and control technology. The method includes: obtaining elevation images and historical landslide data of the study area to divide the study area into units, and obtain each slope unit in the study area; screening according to the rock layer inclination, the angle of the slope direction and the number of free surfaces; extracting the controlling factors of the landslide hazards of the bedding slope; using the controlling factors as the input parameters of the information quantity model, and determining the information quantity value of each controlling factor based on the information quantity model; superimposing all the information quantity values based on the grid calculator to determine the susceptibility index of each grid; obtaining deformation data from the high-susceptibility partition in the susceptibility partition map; and combining the optical remote sensing image and the susceptibility partition map to determine the work points in the study area with bedding landslide hazards. This method can improve the efficiency and accuracy of identifying bedding landslide hazards.

Description

Technical Field

[0001] The present application relates to the technical field of engineering geological slope disaster prevention and control, and specifically to a method for identifying bedding landslide hazards based on slope structure and multi-source remote sensing technology. Background Art

[0002] With the rapid development of infrastructure construction, a large number of rock slope projects have emerged in the fields of transportation, mining, hydropower and other engineering fields, among which the problem of bedding landslides has become increasingly prominent. Bedding landslides refer to the sliding of slopes along the surface whose inclination direction is close to or roughly the same as the inclination of the layered bedrock. Bedding landslides are characterized by being easy to lose stability, large in scale, and difficult to prevent. In particular, red-bed slope rock masses often develop weak interlayers such as interlayer dislocation zones and mud layers, which are typical "easy-to-slide bedding structural surfaces." Frequent bedding landslide disasters pose a serious threat to people's lives and property, engineering construction safety, and the operation of important infrastructure. Research on the identification and evaluation methods of bedding landslide hazards is of great significance to disaster prevention and mitigation work in the region.

[0003] Currently, the engineering community generally considers bedding landslides to be bedding-type landslides, which occur along-bedding slopes where the angle between the rock strata dip and the slope direction is less than 30 degrees and the slope is greater than the rock strata dip. However, during highway construction, it has been discovered that in areas with well-developed surface water systems, erosion occasionally forms free-falling surfaces along the leading edge or sidewalls of bedding slopes. Slope excavation creates new free-falling areas, creating unfavorable spatial structures with free-falling surfaces on two or even three sides. Traditional landslide hazard identification relies heavily on on-site investigations by professional technicians, using a comprehensive assessment of surface cracks or subsidence, slope deformation and sliding, surface vegetation collapse, and groundwater seepage characteristics, relying heavily on engineering experience. In recent years, with the continuous development of computer information technology, advanced methods such as geographic information systems (GIS), remote sensing interpretation (RS), and interferometric synthetic aperture radar (InSar) have been gradually applied to landslide hazard identification and assessment, resulting in relatively systematic research methods and results. However, a single method is limited by complex topographic and geological conditions in dangerous mountainous areas. For example, when the vegetation coverage rate is high, it is difficult for image data to accurately obtain surface information, and the landslide susceptibility assessment method also focuses on the comprehensive topography, stratigraphic rock groups, etc., without considering the structure of the bedding landslide, that is, the combined relationship between the rock layer dip and the geometric characteristics of the slope. Therefore, it is not significantly applicable to the identification and evaluation of bedding landslides in red soft rocks. Summary of the Invention

[0004] This application provides a method for identifying bedding landslide hazards based on slope structure and multi-source remote sensing technology. By integrating multi-source remote sensing data with dynamic analysis of slope structure, accurate identification and graded early warning of bedding landslide hazards can be achieved, thereby improving the efficiency and accuracy of identifying bedding landslide hazards.

[0005] The method for identifying landslide hazards along the bedding layer based on slope structure and multi-source remote sensing technology provided in the embodiments of the present application may include:

[0006] Obtaining elevation images and historical landslide data of the study area to divide the study area into units, and obtaining each slope unit in the study area;

[0007] In the slope unit, screening is performed according to the rock layer inclination, the angle of slope direction and the number of free faces to obtain the bedding slope;

[0008] Extracting the disaster-controlling factors of the landslide disaster of the bedding slope;

[0009] Taking the disaster control factors as input parameters of an information quantity model, determining the information quantity value of each of the disaster control factors based on the information quantity model;

[0010] Superimposing all the information values based on a grid calculator, determining a susceptibility index of each grid, dividing the susceptibility index into a plurality of grading intervals, and obtaining a susceptibility zoning map of bedding landslides in the study area;

[0011] Acquire deformation data from a high-susceptibility partition in the susceptibility partition map, and verify a partition result of the susceptibility partition based on the deformation data;

[0012] The optical remote sensing image and the susceptibility zoning map are combined to determine the construction sites in the study area where there are hidden dangers of bedding landslides.

[0013] In some embodiments, obtaining elevation images and historical landslide data of the study area to divide the study area into units to obtain each slope unit within the study area may include:

[0014] Obtaining an elevation image and historical landslide data of a study area, and extracting a slope aspect and a hill shadow map from the elevation image and the historical landslide data respectively;

[0015] Determining parameters of a multi-scale segmentation algorithm based on the slope aspect and the hillshade map; the parameters of the multi-scale segmentation algorithm include scale, shape feature weight, and compactness weight parameters;

[0016] Combining the modified trial-and-error method with the parameters of the multi-scale segmentation algorithm, each slope unit in the study area is obtained after all areas are merged through pixel aggregation and heterogeneity threshold merging.

[0017] In some embodiments, the slope unit is screened according to the stratum inclination, the angle of the slope direction, and the number of free faces to obtain the bedding slope, including:

[0018] Slope units with an angle between the rock layer dip and the slope direction less than the set threshold are classified as forward slopes, and the threshold range of the angle is adjusted according to the conditions of single-sided open space, double-sided open space, and three-sided open space.

[0019] In some embodiments, the step of classifying a slope unit whose angle between the stratum dip and the slope direction is less than a set threshold as a forward slope, and adjusting the threshold range of the angle according to the conditions of single-sided open space, double-sided open space, and three-sided open space, includes:

[0020] Slope structures with an inclination angle less than 10 degrees are classified as nearly horizontal structural slopes;

[0021] Generating an interpolated rock layer dip by Kriging interpolation, and performing grid calculation on the interpolated rock layer dip and the slope direction to obtain an angle between the rock layer dip and the slope direction;

[0022] For a single-sided open-air slope, slope work points with an angle of less than 30 degrees are retained; for a double-sided open-air slope, slope work points with an angle of less than 60 degrees are retained; for a three-sided open-air slope, slope work points with an angle of less than 90 degrees are retained.

[0023] In some embodiments, extracting the landslide disaster controlling factors of the bedding slope includes:

[0024] The frequency ratios of topography, stratum lithology, geological structure, hydro-meteorological and engineering disturbance indicators in the bedding slope are calculated, and the five factors with the largest frequency ratios are selected as disaster control factors.

[0025] In some embodiments, the method of using the disaster control factor as an input parameter of an information quantity model and determining the information quantity value of each disaster control factor based on the information quantity model includes:

[0026] Assume that each disaster control factor is X i ( i =1,2,3……), for landslide events H The amount of information provided I ( X i , H ) can be calculated using the sample frequency form:

[0027]

[0028] in, N is the total number of landslide hazards in the study area; T is the total number of evaluation units in the study area; For the i The first of the disaster-controlling factors j The number of landslides of the type; For thei Among the disaster-controlling factors j The number of evaluation units occupied by the class.

[0029] In some embodiments, the grid-based calculator superimposes all the information values to determine a susceptibility index for each grid, divides the susceptibility index into a plurality of grading intervals, and obtains a susceptibility zoning map of bedding landslides in the study area, including:

[0030] The information value of each disaster control factor is superimposed based on the ArcGIS raster calculator to obtain the susceptibility index of each raster.

[0031] In some embodiments, the grid-based calculator superimposes all the information values to determine a susceptibility index for each grid, divides the susceptibility index into a plurality of grading intervals, and obtains a susceptibility zoning map of bedding landslides in the study area, further comprising:

[0032] The natural breakpoint method was used to divide the susceptibility of bedding landslides in the study area into four classification intervals, and a zoning map of the susceptibility of bedding landslides in the study area was obtained.

[0033] In some embodiments, obtaining deformation data from a high-susceptibility partition in the susceptibility partition map, and verifying the partitioning result of the susceptibility partition based on the deformation data, includes:

[0034] InSAR deformation monitoring is performed on the bedding slope in the high-susceptibility zone, and deformation time series data is obtained from the high-susceptibility zone in the susceptibility zone map.

[0035] In some embodiments, the InSAR deformation monitoring includes:

[0036] The interference pairs of the bedding slope in the high-prone area are differentially processed, and the nonlinear deformation and noise phase are separated by spatiotemporal filtering. The deformation time series is reconstructed by combining the weighted least squares method and the temporal coherence coefficient, and the temporal coherence coefficient is used to screen the coherent points.

[0037] Compared with the existing technology, the beneficial effects of this application are: by integrating multi-source remote sensing data and dynamic analysis of slope structure, accurate identification and graded early warning of bedding landslide hazards can be achieved, and bedding slopes can be quickly locked by screening based on terrain segmentation and rock formation occurrence. The spatial differences in susceptibility are quantified by combining the disaster control factor information model, and a closed-loop verification mechanism is formed by deformation monitoring and optical image feature verification. This effectively solves the problems of traditional methods relying on manual experience, ignoring the particularity of slope structure, and insufficient identification accuracy in complex mountainous environments. It improves the efficiency and accuracy of identifying bedding landslide hazards, and provides data-driven technical support for engineering construction and disaster prevention and control in dangerous mountainous areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 Schematic diagram of the steps of the method for identifying bedding landslide hazards based on slope structure and multi-source remote sensing technology provided in an embodiment of the present application.

[0039] Figure 2 This is a schematic diagram of the process of identifying bedding landslide hazards based on slope structure and multi-source remote sensing technology in an embodiment of the present application.

[0040] Figure 3 This is an image of the study area provided in the examples of this application.

[0041] Figure 4 A schematic diagram of the slope unit division provided in an embodiment of the present application.

[0042] Figure 5 This is a slope structure zoning diagram provided in an embodiment of the present application.

[0043] Figure 6 This is a susceptibility zoning map for bedding landslides in the study area provided in the embodiments of this application.

[0044] Figure 7 Schematic diagram of InSAR deformation rate provided in the embodiment of this application.

[0045] Figure 8 Schematic diagram of the final determination of a bedding landslide provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] The present application is further described in detail below in conjunction with test examples and specific implementation methods. However, this should not be understood as limiting the scope of the above-mentioned subject matter of the present application to the following embodiments. All technologies implemented based on the content of the present application fall within the scope of protection of the present application.

[0047] Unless otherwise specified, in the description of the specific embodiments of this application, the terms indicating the orientation or position relationship such as "up", "down", "left", "right", "center", "inside", "outside", and "side" are based on the expression of the orientation or position relationship shown in the accompanying drawings, or the orientation or position relationship in which the product / device / apparatus is placed when it is usually used. These terms of orientation or position relationship are only for the convenience of describing the scheme of this application or simplifying the description in the specific embodiments to facilitate the technicians to quickly understand the scheme, and do not indicate or imply that a specific device / component / element must have a specific orientation, or be constructed and operated in a specific position relationship, and therefore should not be understood as limiting this application.

[0048] In the description of the embodiments of this application, the technical terms "first," "second," etc., merely distinguish one entity or operation from another and are not to be understood as indicating or implying relative importance or implicitly specifying the quantity, specific order, or primary-secondary relationship of the indicated technical features. In the description of the embodiments of this application, "plurality" means two or more, unless otherwise specifically defined.

[0049] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0050] Please see Figure 1 , Figure 1 A schematic diagram of the steps of a method for identifying landslide hazards along the bedding layer based on slope structure and multi-source remote sensing technology provided in an embodiment of the present application. The method for identifying landslide hazards along the bedding layer based on slope structure and multi-source remote sensing technology may include:

[0051] S1. Obtain elevation images and historical landslide data of the study area to divide the study area into units and obtain each slope unit in the study area.

[0052] S2. Within the slope unit, screening is performed based on the rock layer inclination, slope angle and number of free surfaces to obtain the layer slope.

[0053] S3. Extract the controlling factors of landslide disasters on the bedding slope.

[0054] S4. Using the disaster control factor as an input parameter of the information quantity model, the information quantity value of each disaster control factor is determined based on the information quantity model.

[0055] S5. Based on the grid calculator, all information values are superimposed to determine the susceptibility index of each grid, and the susceptibility index is divided into multiple classification intervals to obtain the susceptibility zoning map of bedding landslides in the study area.

[0056] S6. Obtain deformation data from the high-susceptibility partition in the susceptibility partition map, and verify the partition result of the susceptibility partition based on the deformation data.

[0057] S7. Combine optical remote sensing images and susceptibility zoning maps to determine the construction sites in the study area that have the risk of bedding landslides.

[0058] Please Figure 1 Based on the reference Figure 2 , Figure 2This is a schematic diagram of the process of identifying bedding landslide hazards based on slope structure and multi-source remote sensing technology in an embodiment of the present application.

[0059] The specific implementation of step S1 may include:

[0060] Obtaining an elevation image and historical landslide data of a study area, and extracting a slope aspect and a hill shadow map from the elevation image and the historical landslide data respectively;

[0061] Determining parameters of a multi-scale segmentation algorithm based on the slope aspect and the hillshade map; the parameters of the multi-scale segmentation algorithm include scale, shape feature weight, and compactness weight parameters;

[0062] Combining the modified trial-and-error method with the parameters of the multi-scale segmentation algorithm, each slope unit in the study area is obtained after all areas are merged through pixel aggregation and heterogeneity threshold merging.

[0063] First, we collected basic elevation images and a historical landslide database of the study area. We extracted the slope direction reflecting the topography and the hillshade map that enhances the topography from the data elevation (DEM) and the historical landslide database, as well as the landslide shape and size characteristics as the basic inputs for the Multi-Scale Segmentation (MSS) method. We also counted the morphological and scale characteristics of all historical landslides, including stratum lithology, waviness, slope direction, etc. Figure 3 , Figure 3 This is an image of the study area provided in the examples of this application.

[0064] The slope was then divided into units using a multiscale segmentation method. The parameters of the multiscale segmentation algorithm, including scale, shape feature weights, and compactness weights, were determined through a trial-and-error approach combined with the morphological and scale characteristics of historical landslides in the study area (a modified trial-and-error approach). The scale parameter controls the minimum heterogeneity threshold for segmented units, while the shape weights and compactness weights constrain the geometric regularity of the segmented objects.

[0065] On this basis, the algorithm starts from the pixel layer, and first aggregates pixels with similar features in adjacent areas into small image areas based on the modified trial and error method and the set scale, shape and compact weight parameters; merges similar small image areas into large image areas based on the principle of minimum heterogeneity, and calculates whether the heterogeneity f of the merged area of the two areas is greater than the scale threshold s for each merger; if it is greater than the scale threshold s, the two areas are not merged; if it is less than the scale threshold s, the two areas are merged to generate a new larger image area; for example, during the first merger, calculate whether the heterogeneity f1 of the merged area of the two areas is greater than the scale threshold s; until the nth merger is performed based on the above results, calculate the heterogeneity f of the merged area of the two areas after the merger.n Is it greater than the scale threshold? If so, perform the n+1th merge based on the above results. At this time, if the heterogeneity of all regions is greater than the scale threshold, or all regions have been merged, then stop merging. Perform optimization processing such as segmentation, merging, and smoothing on the image objects to eliminate objects with local "island effects" and finally obtain the slope units in the study area. Please refer to Figure 4 , Figure 4 A schematic diagram of the slope unit division provided in an embodiment of the present application.

[0066] By evaluating the slope structure and susceptibility of hidden dangers of slope units, using InSAR technology to quantitatively identify landslide hazards based on temporal and spatial characteristics such as deformation rate, cumulative deformation magnitude, and deformation area boundaries, and finally combining optical remote sensing images to determine the work points in the study area with hidden dangers of bedding landslides, the bedding landslide identification results were obtained.

[0067] The specific implementation of step S2 may include:

[0068] Slope units with an angle between the rock layer dip and the slope direction less than the set threshold are classified as forward slopes, and the threshold range of the angle is adjusted according to the conditions of single-sided open space, double-sided open space, and three-sided open space.

[0069] Specifically, slopes with an inclination angle of less than 10 degrees are classified as near-horizontal structural slopes. This is because the rock formation dips less than 10 degrees, which is close to horizontal. These slopes, due to their near-horizontal rock formations, have a lower sliding risk and can be directly excluded from the bedding landslide analysis.

[0070] The interpolated stratum dip is generated through Kriging interpolation, and the interpolated stratum dip and the slope aspect are rasterized to obtain the angle between the stratum dip and the slope aspect. For a single-sided open slope, slope work points with an angle less than 30 degrees are retained; for a double-sided open slope, slope work points with an angle less than 60 degrees are retained; for a three-sided open slope, slope work points with an angle less than 90 degrees are retained.

[0071] Among them, the kriging interpolation method can be used to estimate the occurrence between data points using ArcGIS software. When predicting the slope structure data of unknown points based on the rock formation occurrence and slope aspect, slope gradient and other slope structure data of existing slope data points, the covariance function of formula (1) and formula (2) is used:

[0072]

[0073]

[0074] Where, is the Euclidean distance between data points i and j, represents the spatial correlation between data points i and j, ( , )and( , ) is the plane coordinate of the i-th slope data point and the j-th slope data point; Z( , ) and Z( , ) are the slope structure characteristic values of the i-th data point and the j-th data point, such as rock layer inclination, slope direction, slope, etc.

[0075] The spatial correlation of the slope structure characteristic values at different locations is obtained by and The Gaussian function relationship between them is used to measure, as shown in formula (3):

[0076] (3)

[0077] Where: C0, C, and a are the nugget constant, arch height, and range, respectively; h is the lag distance, representing the spatial distance between two points, used to calculate the covariance; and e is the base of the natural logarithm. Based on this, the covariance function relationship between the predicted points of the slope structure characteristic value and the existing numerical points is as shown in formula (4):

[0078] (4)

[0079] Where: n is the total number of known data points, which is determined based on the data from actual slope hazard identification applications; represents the spatial correlation between data points i and j, This is the special case when i=j=n, which usually reflects the inherent variability of the data; is the weighted contribution of the i-th slope structure characteristic value to the slope structure characteristic value of the prediction point. According to the known point eigenvalues Z1~Z n Calculation is as follows (5):

[0080]

[0081] Where k is an index variable, representing the kth data point, and the value range of k is k=1, 2,…, n; is the slope structure characteristic value of the kth known data point, It is the weight coefficient calculated in Kriging interpolation, which represents the k-th known data point to the predicted point Contribution weight; for different slope structure characteristic value data of the prediction point, such as rock layer inclination, slope direction, slope gradient, etc., formula (1) to formula (5) are implemented respectively to obtain the slope structure characteristic value of the prediction point.

[0082] The interpolated rock layer dip and slope direction are rasterized to obtain the angle β between them. The slope structure is further divided into three types according to the β value: forward slope (β < 45 degrees), oblique slope (45 degrees < β < 135 degrees), and reverse slope (β > 135 degrees).

[0083] For the selected slopes, according to the existing open space conditions of the slope and the excavation conditions of highway construction, the slopes are divided into three types: single-sided open space (such as slope foot excavation), double-sided open space (such as slope foot excavation and river cutting) and three-sided open space (such as slope foot excavation, river cutting and structural erosion). For the single-sided open space slopes, only β Slope work site <30 degrees; for the forward slope with no opening on both sides, retain β Slope work site <60 degrees; for the forward slope with three sides facing the open, retain β All down slopes with a slope angle of <90 degrees are classified as down slopes after screening in this step. The slope structure zoning diagram of the study area is obtained. Figure 5 , Figure 5 This is a slope structure zoning diagram provided in an embodiment of the present application.

[0084] The specific implementation of step S3 may include:

[0085] The frequency ratios of topography, stratum lithology, geological structure, hydro-meteorological and engineering disturbance indicators in the bedding slope are calculated, and the five factors with the largest frequency ratios are selected as disaster control factors.

[0086] For the screened bedding slopes, the frequency ratio method was used to analyze the action mechanism of landslide hazard factors on the bedding slopes in the study area, taking topography (including surface undulation, elevation, slope, slope direction, etc.), stratum lithology (including stratum age, soft and hard rock combination, rock formation occurrence, etc.), geological structure (distance from the tectonic belt), hydro-meteorological conditions (distance from the surface water system, rainfall, rainfall duration, etc.), and engineering disturbance (distance from the highway) as indicators.

[0087] Specifically: Assume that a certain influencing factor of landslide is divided into i categories (for example, the elevation factor is divided into 0~1000m, 1000m~2000m, 2000m~3000m... a total of i categories), B i is the landslide area of the i-th category of a certain influencing factor, A i is the research area of the i-th category of a certain impact factor, B is the total landslide area in the study area, A is the total area of the study area, then the frequency ratio of the influencing factors of the bedding slope is FR i Frequency Ratio is defined as:

[0088]

[0089] Based on the frequency calculation results of each factor, we compared and determined the main causative factors for landslides on bedding slopes in the study area. Assuming the ranking of causative factors for landslides on bedding slopes in a particular study area is: slope > rock formation dip > soft and hard rock combination > engineering disturbance > rainfall > ... > surface relief, we selected the five causative factors with the highest frequency ratios as the controlling factors and conducted a susceptibility assessment for landslides on bedding slopes.

[0090] The specific implementation of step S4 may include:

[0091] According to the obtained controlling factors of bedding slope landslide disaster, such as "slope, rock formation, soft and hard rock combination, engineering disturbance, rainfall", the information volume model is used to evaluate the susceptibility of bedding slope landslide disaster. X i ( i =1,2,3……), for landslide events H The amount of information provided I ( X i , H ) can be calculated using the sample frequency form:

[0092]

[0093] in, N is the total number of landslide hazards in the study area; T is the total number of evaluation units in the study area; For the i The first of the disaster-controlling factors j The number of landslides of the type; For the i Among the disaster-controlling factors j The number of evaluation units occupied by the class.

[0094] The specific implementation of step S5 may include:

[0095] The information value of each disaster control factor is superimposed based on the ArcGIS raster calculator to obtain the susceptibility index of each raster.

[0096] The natural breakpoint method was used to divide the susceptibility of bedding landslides in the study area into four classification intervals, and a zoning map of the susceptibility of bedding landslides in the study area was obtained.

[0097] Among them, according to the information value I of each disaster control factor calculated, the I value can be superimposed using the ArcGIS raster calculator to obtain the susceptibility index of each raster. The natural breaks method is used to divide the susceptibility of bedding landslides in the study area into four classification intervals: high, medium, low, and non-, and the susceptibility zoning map of bedding landslides in the study area is obtained. Please refer to Figure 6 Figure 6 This is a susceptibility zoning map for bedding landslides in the study area provided in the embodiments of this application.

[0098] The specific implementation of step S6 may include:

[0099] InSAR deformation monitoring is performed on the bedding slope in the high-susceptibility zone, and deformation time series data is obtained from the high-susceptibility zone in the susceptibility zone map.

[0100] InSAR deformation monitoring specifically includes:

[0101] The interferometric pairs of slopes along the bedding in high-prone areas are differentially processed, and the nonlinear deformation and noise phases are separated by spatiotemporal filtering. The deformation time series is reconstructed by combining the weighted least squares method and the temporal coherence coefficient, and the temporal coherence coefficient is used to screen the coherent points.

[0102] For example, for areas with high and extremely high risk of landslides along the layers, interference pairs within the appropriate space-time baseline threshold are selected. First, differential interference processing and phase unwrapping are performed to obtain low-frequency deformation phase, terrain error phase, and terrain residual phase; then, the space-time filtering method is used to separate the nonlinear deformation, atmosphere, and noise phases in the residual phase, eliminate terrain error, atmospheric delay error, and noise, retain low-frequency deformation and nonlinear deformation, and obtain the error-corrected deformation data set; finally, the weighted least squares method is used to reconstruct the deformation time series, and the temporal coherence coefficient is used to screen high-quality coherent points to improve monitoring accuracy. Through this step, InSAR technology is used to quantitatively identify landslide hazards based on temporal and spatial characteristics such as deformation rate, cumulative deformation magnitude, and deformation area boundaries. Please refer to Figure 7 , Figure 7 Schematic diagram of InSAR deformation rate provided in the embodiment of this application.

[0103] Among them, based on the preset spatiotemporal baseline thresholds (usually controlling the spatial baseline to <200 meters and the temporal baseline to <30 days to reduce the risk of loss of correlation), high-quality Synthetic Aperture Radar (SAR) image interferometry pairs are screened to ensure data coherence; then DInSAR (Differential Interferometric Synthetic Aperture Radar) is implemented. DInSAR is a remote sensing method that uses the phase difference information of Synthetic Aperture Radar (SAR) images to detect small surface deformations (such as earthquakes, volcanic activity, ground subsidence, etc.) through differential interferometric processing technology. Phase unwrapping is used to convert the wrapped phase into absolute phase difference, separating the low-frequency deformation phase (reflecting slow landslide movement), terrain error phase (caused by digital elevation model (DEM) error) and residual phase (including atmospheric delay, noise and nonlinear deformation). Then, spatiotemporal filtering techniques (such as wavelet decomposition or principal component analysis) are used to remove atmospheric disturbances (such as ionospheric delay, water vapor change) and random noise from the residual phase, while retaining low-frequency deformations (such as creep) and sudden nonlinear deformations (such as local collapse) related to landslides, forming a high-confidence deformation dataset after error correction. The weighted least squares method is further used to calculate the residual phase. The InSAR technology was used to fuse multi-temporal SAR data with the World Wide Web (WLS) to reconstruct the surface deformation time series. Stable coherent points were screened using the temporal coherence coefficient (γ>0.7), and low-quality pixels (such as vegetation-covered areas) were excluded. Finally, key parameters such as deformation rate (such as millimeters / year), cumulative displacement (such as centimeters-level mutations), and deformation spatial boundaries (such as the outline of continuous deformation areas) were extracted. Combined with the landslide susceptibility zoning, the deformation anomaly area was delineated, and a coordinated analysis of InSAR technology and slope structural characteristics was achieved, thereby accurately identifying the hidden dangers of bedding landslides in the creeping or acceleration stages.

[0104] The specific implementation of step S7 may include:

[0105] For bedding landslide sites, we select high-resolution images without cloud cover to display the 3D geomorphological features of the corresponding area. Through optical remote sensing (RS) image analysis, we can identify the landslide hazard characteristics of the bedding slope, such as drumlins at the front edge, river diversion, and steep slopes behind the landslide. Finally, we can determine the sites in the area where there are potential bedding landslide risks. Figure 8 , Figure 8 Schematic diagram of the final determination of a bedding landslide provided in an embodiment of the present application.

[0106] During the above implementation process, by integrating multi-source remote sensing data with dynamic analysis of slope structure, accurate identification and graded early warning of bedding landslide hazards can be achieved. By quickly locking the bedding slope based on terrain segmentation and rock formation occurrence screening, the spatial differences in susceptibility are quantified by combining the disaster control factor information model, and a closed-loop verification mechanism is formed by deformation monitoring and optical image feature verification. This effectively solves the problems of traditional methods relying on manual experience, ignoring the particularity of slope structure, and insufficient identification accuracy in complex mountainous environments, improves the efficiency and accuracy of identifying bedding landslide hazards, and provides data-driven technical support for engineering construction and disaster prevention and control in dangerous mountainous areas.

[0107] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for identifying landslide hazards along the bedding layer based on slope structure and multi-source remote sensing technology, characterized in that: include: Obtaining elevation images and historical landslide data of the study area to divide the study area into units, and obtaining each slope unit within the study area; In the slope unit, screening is performed according to the rock layer inclination, the angle of slope direction and the number of free faces to obtain the bedding slope; Extracting the disaster-controlling factors of the landslide disaster of the bedding slope; Taking the disaster control factors as input parameters of an information quantity model, determining the information quantity value of each of the disaster control factors based on the information quantity model; Superimposing all the information values based on a grid calculator, determining a susceptibility index of each grid, dividing the susceptibility index into a plurality of grading intervals, and obtaining a susceptibility zoning map of bedding landslides in the study area; Acquire deformation data from a high-susceptibility partition in the susceptibility partition map, and verify a partition result of the susceptibility partition based on the deformation data; Determine the construction sites in the study area that have the potential for bedding landslides by combining the optical remote sensing image and the susceptibility zoning map; In the slope unit, screening is performed according to the rock layer inclination, the angle of the slope direction and the number of free surfaces to obtain the bedding slope, including: Slope units with an angle between the rock layer dip and the slope direction smaller than the set threshold are classified as down-slopes, and the threshold range of the angle is adjusted according to the conditions of single-sided open space, double-sided open space, and three-sided open space. The slope units whose angle between the rock layer dip and the slope direction is less than the set threshold are classified as down-slopes, and the threshold range of the angle is adjusted according to the conditions of single-sided open space, double-sided open space, and three-sided open space, including: Slope structures with an inclination angle less than 10 degrees are classified as nearly horizontal structural slopes; Generating an interpolated rock layer dip by Kriging interpolation, and performing grid calculation on the interpolated rock layer dip and the slope direction to obtain an angle between the rock layer dip and the slope direction; For a single-sided open-air slope, slope work points with an angle of less than 30 degrees are retained; for a double-sided open-air slope, slope work points with an angle of less than 60 degrees are retained; for a three-sided open-air slope, slope work points with an angle of less than 90 degrees are retained.

2. The method according to claim 1, characterized in that The elevation image and historical landslide data of the study area are obtained to divide the study area into units, and each slope unit in the study area is obtained, including: Obtaining an elevation image and historical landslide data of a study area, and extracting a slope aspect and a hill shadow map from the elevation image and the historical landslide data respectively; Determining parameters of a multi-scale segmentation algorithm based on the slope aspect and the hillshade map; the parameters of the multi-scale segmentation algorithm include scale, shape feature weight, and compactness weight parameters; Combining the modified trial-and-error method with the parameters of the multi-scale segmentation algorithm, each slope unit in the study area is obtained after all areas are merged through pixel aggregation and heterogeneity threshold merging.

3. The method according to claim 1, characterized in that The step of extracting the landslide disaster controlling factors of the bedding slope includes: The frequency ratios of topography, stratum lithology, geological structure, hydro-meteorological and engineering disturbance indicators in the bedding slope are calculated, and the five factors with the largest frequency ratios are selected as disaster control factors.

4. The method according to claim 1, wherein The method of using the disaster control factor as an input parameter of an information quantity model and determining the information quantity value of each disaster control factor based on the information quantity model includes: Assume that each disaster control factor is X i ( i =1,2,3……), for landslide events H The amount of information provided I ( X i , H ) is calculated using the sample frequency form: in, N is the total number of landslide hazards in the study area; T is the total number of evaluation units in the study area; For the i The first of the disaster-controlling factors j The number of landslides of this type; For the i Among the disaster-controlling factors j The number of evaluation units occupied by the class.

5. The method according to claim 1, characterized in that The grid-based calculator superimposes all the information values to determine the susceptibility index of each grid, divides the susceptibility index into a plurality of grading intervals, and obtains a susceptibility zoning map of bedding landslides in the study area, including: The information value of each disaster control factor is superimposed based on the ArcGIS raster calculator to obtain the susceptibility index of each raster.

6. The method according to claim 5, characterized in that The grid calculator is used to superimpose all the information values to determine the susceptibility index of each grid, and the susceptibility index is divided into a plurality of classification intervals to obtain a susceptibility zoning map of bedding landslides in the study area, further comprising: The natural breakpoint method was used to divide the susceptibility of bedding landslides in the study area into four classification intervals, and a zoning map of the susceptibility of bedding landslides in the study area was obtained.

7. The method according to claim 1, characterized in that The acquiring deformation data from the high-susceptibility partition in the susceptibility partition map, and verifying the partition result of the susceptibility partition based on the deformation data, includes: InSAR deformation monitoring is performed on the bedding slope in the high-susceptibility zone, and deformation time series data is obtained from the high-susceptibility zone in the susceptibility zone map.

8. The method according to claim 7, characterized in that The InSAR deformation monitoring includes: The interference pairs of the bedding slope in the high-prone area are differentially processed, and the nonlinear deformation and noise phase are separated by spatiotemporal filtering. The deformation time series is reconstructed by combining the weighted least squares method and the temporal coherence coefficient, and the temporal coherence coefficient is used to screen the coherent points.

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