CN113848551B - Landslide depth inversion method using InSAR lifting rail deformation data - Google Patents

Abstract

The invention discloses a landslide depth inversion method using InSAR lift rail deformation data, comprising the following steps: S1, using time-series InSAR technology to process the lift-rail radar image of the landslide area, to obtain the time-series deformation data of the landslide area; S2, According to the time-series deformation data, the spatial geometric relationship of the landslide area and the DEM data, the two-dimensional deformation data of the landslide surface along the slope direction and the normal direction are obtained through calculation; S3. Constructing a landslide depth inversion model based on two-dimensional deformation under the principle of mass conservation , and calculate the landslide depth inversion model according to the two-dimensional deformation data, and obtain the inversion calculation results of the landslide depth. The method of the present invention is applicable to the landslide body that can successfully extract the InSAR deformation field of the satellite lifting orbit. In addition, the rheological parameters of the slope body material can significantly invert the depth of the landslide, and the accuracy of this parameter significantly improves the reliability and applicability of the method. sex.

Description

A Landslide Depth Inversion Method Using InSAR Lift Rail Deformation Data

technical field

The invention belongs to the technical field of landslide depth detection, and in particular relates to a landslide depth inversion method using InSAR lifting rail deformation data.

Background technique

Landslide disasters are characterized by strong suddenness, great harm, and difficulties in early identification, monitoring and early warning. Since the 1950s, at least 22 provinces, municipalities, and autonomous regions in my country have suffered landslide disasters of varying degrees, causing huge disasters. Casualties and property losses, and a direct threat to the safety of major human engineering facilities such as roads, railways, water conservancy and hydropower, and mines. Therefore, early identification, monitoring and early warning, and risk assessment of landslide disasters have become important tasks in the field of disaster prevention and mitigation in my country. Among them, landslide surface deformation velocity, landslide depth, and landslide volume are all important factors in landslide risk assessment. The accurate acquisition of the above landslide characteristic parameters directly affects the accurate assessment of landslide hazard degree, hazard range, and instability risk.

With the rapid development of modern geodetic technology, GNSS technology has been widely used in landslide deformation monitoring. High-precision GNSS equipment can provide reliable single-point three-dimensional deformation data of slope surface for researchers and engineers, but limited Due to the high cost of layout and monitoring, GNSS technology often only obtains the deformation of a few discrete points on the surface of the slope, which is not conducive to the recognition of the spatial distribution characteristics of landslide deformation. The rapid development of InSAR technology in recent years has well made up for the problem of conventional GNSS single-point measurement and sparse observation data. Especially with the significant improvement of the temporal and spatial resolution of modern SAR satellites, InSAR technology is becoming an important means of landslide deformation monitoring, and has significantly Facilitated early identification of landslide hazards. However, as the demand for landslide monitoring continues to increase, researchers have found that geodetic deformation data only reflect the movement state of the landslide surface, and cannot effectively reveal the depth distribution of the landslide body, making it difficult to accurately estimate the landslide volume and the scope of landslide instability. and degree of influence.

Landslide depth detection is currently mainly divided into two types: contact type and non-contact type. The contact type landslide depth detection methods include deep displacement detection and ground penetrating radar detection. Deep displacement detection of landslide depth requires drilling in the landslide body and placing deep displacement. Detector, this method can accurately obtain the depth of the landslide, but it is limited by the high detection cost and time-consuming. This method is often only used for the detection of single-point detection depth, which is not conducive to reflecting the depth of the entire landslide. Ground penetrating radar equipment is often used to detect the boundary range and depth of landslides, and can obtain the depth of landslides within the range of motion of landslides relatively completely. At the same time, the efficiency is extremely low, and it is difficult to apply to large and giant landslides. Giant landslides; in addition, the above-mentioned contact detection method is less practical for landslides with high and steep terrain, difficult climbing, and greater danger.

Non-contact landslide depth detection methods mainly include: balanced section method, elastic dislocation method, and mass conservation method. The balanced section method considers that the landslide material is an incompressible rigid material, and does not consider the rheological properties of the landslide material, which will lead to estimation The reliability of the landslide depth is not high; the elastic dislocation method significantly simplifies the landslide body from the geometric and physical models, so that the method can generally only be applied to the initial stage of landslide development, or the landslide body has no obvious inelastic deformation. This significantly limits the scope of application of the elastic dislocation method. The mass conservation method is a new method developed in recent years to calculate the depth of deformed bodies such as glaciers and landslides in a non-contact manner. Thickness inversion provides a reliable technical approach. However, using the mass conservation method to invert the landslide depth requires a three-dimensional deformation field on the landslide surface. Among the existing deformation observation methods, only GNSS technology can effectively provide the three-dimensional deformation field on the slope surface, but the sparse observation density of GNSS obviously cannot meet the overall depth detection requirements of the landslide. need. However, the InSAR technology for surface monitoring is limited by the insensitivity to deformation in the north-south direction, and it is often difficult to extract the three-dimensional deformation of the slope even if the multi-track data is combined.

Therefore, the traditional landslide depth detection method can only determine the depth of sparse single points on the slope, and cannot accurately reflect the depth distribution of the entire landslide.

Contents of the invention

In view of the above-mentioned deficiencies in the prior art, the landslide depth inversion method using InSAR lifting rail deformation data provided by the present invention solves the problem that the traditional landslide depth detection method cannot accurately reflect the depth distribution of the entire landslide body.

In order to achieve the above-mentioned purpose of the invention, the technical solution adopted in the present invention is: a landslide depth inversion method utilizing InSAR lifting rail deformation data, comprising the following steps:

S1. Use the time-series InSAR technology to process the radar images of the ascending and descending rails in the landslide area, and obtain the time-series deformation data of the landslide area;

S2. According to the time-series deformation data, the spatial geometric relationship of the landslide area and the DEM data, obtain the two-dimensional deformation data of the landslide surface along the slope direction and the normal direction;

S3. Construct a landslide depth inversion model based on two-dimensional deformation under the principle of mass conservation, and calculate the landslide depth inversion model according to the two-dimensional deformation data, and obtain the inversion calculation result of landslide depth.

Further, the step S1 is specifically:

S11. Obtain the SAR image of the lifting rail in the landslide area, perform clipping and image registration processing on it, and obtain the registered SAR image;

S12. Perform interference processing on the registered SAR image, and then obtain a corresponding differential interferogram according to the DEM data of the landslide area, and perform adaptive filtering processing on it to obtain a high-quality differential interferogram;

S13. Perform phase unwrapping on the obtained differential interferogram, obtain the unwrapped image, and use a filter to remove the atmospheric phase;

S14. Using the singular value decomposition algorithm to solve the time-series deformation of the target point on the unwrapped graph with the atmospheric phase removed;

S15. Geocoding the calculated time-series deformation data of the target point, the high-quality differential interferogram, and the unwrapped image without the atmospheric phase to obtain the time-series deformation data of the LOS direction of the ascending and descending rails in the landslide area.

Further, the step S2 is specifically:

S21. According to the spatial geometric relationship of the landslide area, determine the imaging parameters of the SAR image of the lifting rail, and then construct a two-dimensional deformation calculation model;

S22, process the DEM data of the landslide area, and obtain the average slope and slope aspect of the landslide area;

S23. Based on the time series deformation data, determine the LOS direction deformation data of the ascending rail and the LOS direction deformation data of the descending rail, and perform deformation sampling of the ascending and descending rails;

S24. According to the sampling data of the lifting rail deformation, the average slope and slope direction of the landslide area, solve the constructed two-dimensional deformation calculation model, and obtain the two-dimensional deformation field of the landslide surface along the slope direction and the normal direction.

Further, the two-dimensional deformation calculation model in the step S21 is:

分别为雷达卫星的入射方向角和飞行方向角。In the formula, a and b are the projection coefficients of the deformation along the slope direction and along the normal direction, respectively, D _K and D _I are the deformation values along the OK direction and the OI direction, respectively, and α and β are the average slope and aspect of the landslide mass, respectively angle, θ and

are the incident direction angle and flight direction angle of the radar satellite, respectively.

Further, the method for constructing the landslide depth inversion calculation model in the step S3 is specifically:

Assuming that the rate of change of landslide thickness during the deformation observation period of the landslide sliding base is equal to the normal deformation velocity of the landslide, the vertical integral mass conservation equation between the landslide sliding base and the slope surface is:

为坡体表面运动速度，f为流变参数，v_z为滑坡法向形变速度；In the formula,

is the velocity of the surface of the slope, f is the rheological parameter, and _vz is the normal deformation velocity of the landslide;

The finite difference form of the vertical integration mass conservation equation is:

In the formula, Δx and Δy are the data sampling intervals along the aspect and vertical aspect, respectively, and v _x (i,j) and v _y (i,j) are the aspect deformation rate and Vertical aspect deformation, h _{i, j} is the landslide depth of the slope body at position (i, j);

Based on v _z (i, j), the landslide depth inversion model in matrix form is obtained as follows:

为坡体法向速度，

为对角占优的参数矩阵，包括采样间距、流变参数和表面形变速率，

为整体坡体滑坡深度。In the formula,

is the slope normal velocity,

is a diagonally dominant parameter matrix, including sampling spacing, rheological parameters, and surface deformation rates,

is the landslide depth of the overall slope.

The beneficial effects of the present invention are:

The method of the present invention is applicable to the landslide body that can successfully extract the InSAR deformation field of the satellite ascending and descending orbit. In addition, the rheological parameters of the slope body material can significantly invert the depth of the landslide, and the accuracy of this parameter significantly improves the reliability and applicability of the method. , specifically in the following points:

(1) The landslide depth inversion method proposed by the present invention can realize the planar observation of the depth of the landslide body based on the InSAR observation data, which improves the accuracy of the calculation results of the depth of the landslide;

(2) SAR satellite image data among the present invention is the amount that obtains periodically, provides low-cost means for the change monitoring of landslide depth;

(3) The high spatial density landslide depth data obtained by the method of the present invention can be further used to calculate characteristic parameters such as landslide volume, and provide a reliable data basis for landslide disaster image analysis;

Description of drawings

Fig. 1 is a flow chart of the landslide depth inversion method using the deformation data of the InSAR lifting rail provided by the present invention.

Fig. 2 is a schematic diagram of a landslide motion coordinate system provided by the present invention.

Fig. 3 shows the LOS direction deformation field and the cumulative deformation of the maximum deformation point of the lifting rail of the ancient landslide in Taoping Township provided by the present invention.

Fig. 4 is a schematic diagram of the two-dimensional deformation field of the ancient landslide in Taoping Township provided by the present invention.

Fig. 5 is a schematic diagram of the depth distribution of the ancient landslide in Taoping Township provided by the present invention.

Fig. 6 is a sectional view of landslide depth provided by the present invention.

Detailed ways

The specific embodiments of the present invention are described below so that those skilled in the art can understand the present invention, but it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes Within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are included in the protection list.

Example 1:

As shown in Figure 1, a landslide depth inversion method using InSAR lift rail deformation data includes the following steps:

S1. Use the time-series InSAR technology to process the radar images of the ascending and descending rails in the landslide area, and obtain the time-series deformation data of the landslide area;

S2. According to the time-series deformation data, the spatial geometric relationship of the landslide area and the DEM data, obtain the two-dimensional deformation data of the landslide surface along the slope direction and the normal direction;

S3. Construct a landslide depth inversion model based on two-dimensional deformation under the principle of mass conservation, and calculate the landslide depth inversion model according to the two-dimensional deformation data, and obtain the inversion calculation result of landslide depth.

This embodiment considers that the small baseline interferometry (SBAS-InSAR) technology can better overcome the space-time decoherence problem faced by conventional DInSAR, and effectively suppress terrain and atmospheric errors, and has been widely used in time-series deformation monitoring of geological disasters such as earthquakes and landslides . In step S1 of the embodiment, SBAS-InSAR technology is used to process the radar image of the lifting rail in the landslide area, and the processing process is specifically as follows:

S11. Obtain the SAR image of the lifting rail in the landslide area, perform clipping and image registration processing on it, and obtain the registered SAR image;

Among them, the acquisition of registered SAR images is to prepare for subsequent interference, and the main purpose of image registration is to match the same location in the two images together;

S12. Perform interference processing on the registered SAR image, and then obtain a corresponding differential interferogram according to the DEM data of the landslide area, and perform adaptive filtering processing on it to obtain a high-quality differential interferogram;

S13. Perform phase unwrapping on the obtained differential interferogram, obtain the unwrapped image, and use a filter to remove the atmospheric phase;

S14. Using the singular value decomposition algorithm to solve the time-series deformation of the target point on the unwrapped graph with the atmospheric phase removed;

S15. Geocoding the calculated time-series deformation data of the target point, the high-quality differential interferogram, and the unwrapped image without the atmospheric phase to obtain the time-series deformation data of the LOS direction of the ascending and descending rails in the landslide area.

Specifically, the above steps S12-S14 are as follows: set the baseline threshold in the registered SAR image to generate an interferogram; use the DEM data of the landslide area to remove the topographic phase in the interferogram to obtain a differential interferogram; for the differential interferogram Perform adaptive filtering to obtain a high-quality differential interferogram; in this embodiment, use the Goldstein method to perform adaptive filtering on the differential interferogram; use the minimum cost flow method to perform phase unwrapping on the interferometric phase image, and then select EDFP highly coherent The unwrapped graph composed of points; the singular value decomposition algorithm is used to calculate the time-series deformation of the target point on the unwrapped graph with the atmospheric phase removed, and obtain the time-series deformation data of the target point.

In step S2 of this embodiment, in the process of calculating the two-dimensional deformation field of the landslide, the slope and aspect of the landslide body are the basic elements for establishing the slope surface coordinate system. The slope indicates the steepness of the surface unit and directly affects the slope. surface material flow. Slope aspect is defined as the projection direction of the slope normal on the horizontal plane. For a single landslide with complex undulating terrain, the landslide surface is often a changing surface, and the slope surface slope has certain micro-geomorphological characteristics. Considering that the direction of motion of most continuous moving landslides is often controlled by the overall orientation and slope of the slope, in this embodiment, the two-dimensional deformation field of the landslide is calculated based on the EDM data of the landslide area, and the step S2 based on this embodiment is specifically:

S21. According to the spatial geometric relationship of the landslide area, determine the imaging parameters of the SAR image of the lifting rail, and then construct a two-dimensional deformation calculation model;

S22, process the DEM data of the landslide area, and obtain the average slope and slope aspect of the landslide area;

Specifically, import the DEM data of the landslide area into arcgis, use the grid surface tool in the 3D analysis tool to export the slope and aspect data, and then use the grid conversion tool to obtain the relevant data of the slope and aspect points, and then obtain the average Slope and aspect data;

S23. Based on the time series deformation data, determine the LOS direction deformation data of the ascending rail and the LOS direction deformation data of the descending rail, and perform deformation sampling of the ascending and descending rails;

S24. According to the sampling data of the lifting rail deformation, the average slope and slope direction of the landslide area, solve the constructed two-dimensional deformation calculation model, and obtain the two-dimensional deformation field of the landslide surface along the slope direction and the normal direction.

In the step S22 of the present embodiment, as shown in Figure 2, the three-dimensional Cartesian coordinate system of the landslide body movement space is shown, wherein the slope direction axis (OK) points to the slope body motion method, and the normal axis (OI) points to the normal direction of the slope surface, The vertical aspect (OT) direction and them form a right-handed spiral coordinate system; at the same time, it is defined that the downward movement along the aspect axis is positive, and the sliding in-plane movement along the normal axis is positive. Considering that the joint satellite lifting rail can only solve the two-dimensional deformation data, and the landslide slope usually moves downward along the sliding surface under the action of gravity, resulting in the deformation of the aspect of the slope is often greater than the deformation of the vertical slope. Therefore, in this embodiment, it is assumed that The slope deformation is mainly composed of deformation along the slope direction and normal direction, and the two-dimensional deformation calculation model is:

分别为雷达卫星的入射方向角和飞行方向角。In the formula, a and b are the projection coefficients of the deformation along the slope direction and along the normal direction, respectively, D _K and D _I are the deformation values along the OK direction and the OI direction, respectively, and α and β are the average slope and aspect of the landslide mass, respectively angle, θ and

are the incident direction angle and flight direction angle of the radar satellite, respectively.

In step S3 of this embodiment, in the process of landslide depth inversion calculation, it is considered that the landslide material density remains constant during the observation period; then the landslide depth can be determined according to the mass conservation method; accordingly, the proposed The vertical integral mass conservation equation between the landslide sliding base and the slope surface:

为坡体沿深度方向平均运动速度，且满足

为坡体表面运动速度，f为流变参数，取值范围为0～1，根据密绿滑坡流变学，f＝2/3时代表物质为牛顿黏性流体，即表示坡体从表面滑动面的整个深度区域已经屈服并且活塞流区域消失；2/3＜f＜1表示活塞流，即屈服区相对较薄；f＝1表示整个坡体为一个没有屈服区的刚性滑块；Among them, h is the landslide depth, t is the time,

is the average moving velocity of the slope along the depth direction, and satisfies

is the surface velocity of the slope, f is the rheological parameter, and its value ranges from 0 to 1. According to the rheology of Milu landslide, f=2/3 means that the substance is a Newtonian viscous fluid, which means that the slope slides from the surface The entire depth region of the surface has yielded and the plug flow region disappears; 2/3<f<1 means plug flow, that is, the yield zone is relatively thin; f=1 means that the entire slope body is a rigid slider without yield zone;

Based on this, the method for constructing the landslide depth inversion calculation model in the above step S3 is specifically as follows:

Assuming that the rate of change of landslide thickness during the deformation observation period of the landslide sliding base is equal to the normal deformation velocity of the landslide, the vertical integral mass conservation equation between the landslide sliding base and the slope surface is:

为坡体表面运动速度，f为流变参数，v_z为滑坡法向形变速度，仅表示一个点的数据；In the formula,

is the velocity of the surface of the slope, f is the rheological parameter, and _vz is the normal deformation velocity of the landslide, which only represents the data of one point;

The finite difference form of the vertical integration mass conservation equation is:

In the formula, Δx and Δy are the data sampling intervals along the aspect and vertical aspect, respectively, and v _x (i,j) and v _y (i,j) are the aspect deformation rate and Vertical aspect deformation, h _{i, j} is the landslide depth of the slope body at position (i, j);

Further considering that the deformation of the slope body is mainly controlled by gravity, mainly along the slope direction and the normal direction, the deformation of the slope surface along the vertical slope direction can be ignored, and the above mass conservation equation is:

Based on v _z (i, j), the landslide depth inversion model in matrix form is obtained as follows:

为坡体法向速度，即为包含所有法向形变的矩阵，

为对角占优的参数矩阵，包括采样间距、流变参数和表面形变速率，

为整体坡体滑坡深度。In the formula,

is the normal velocity of the slope, that is, the matrix containing all normal deformations,

is a diagonally dominant parameter matrix, including sampling spacing, rheological parameters, and surface deformation rates,

is the landslide depth of the overall slope.

Accordingly, the inversion calculation of the landslide depth can be carried out when the two-dimensional deformation data of the slope direction and the normal direction of the landslide are obtained.

Example 2:

In this example, the landslide depth inversion is carried out in the ancient landslide area of Taoping Township through the method in Example 1 above. The mountainous monsoon climate zone has a wide range of elevation differences and complex terrain. Affected by the climate, there is more rainfall in this region from July to September every year, and relatively less precipitation in winter, with annual rainfall between 650mm and 1000mm. The ancient landslide in Taoping Township is generally in the shape of a chair. The terrain is high in the south-west and low in the north-east. The slope is narrow at the top and wide at the bottom. The average slope is about 27°-53°. The edge elevation is about 2710m, the relative height difference is ~1230m, the longitudinal length is about 2700m, and the lateral width is ~2000m, which belongs to a large landslide.

In this example, the SAR images of the ascending and descending orbits observed under the interferometric wide mode (IW) of the Sentinel-1 satellite in this area were collected. There are 66 descending orbit images in total, and the basic parameters of ascending and descending orbit images are shown in Table 1.

Table 1: SAR image parameters of Sentine-1 lifting rail

Considering that InSAR processing is easily affected by orbit errors, the experiment uses POD precise orbit determination ephemeris data provided by ESA to refine the orbit of SAR data, and uses ALOSWorld-3D digital elevation model data to carry out InSAR differential interferometric terrain phase removal processing .

According to the method in step S1 in Example 1, the InSAR time-series deformation field of the ascending and descending rails of the ancient landslide in Taoping Township is shown in Figure 3, where Figure 3a is the deformation rate field in the ascending rail LOS direction, and Figure 3b is the deformation rate in the descending rail LOS direction Fig. 3c and 3d are the time-series deformation curves of the maximum deformation point of the lifting rail respectively. Observing Figure 3, it can be found that there are obvious differences in the spatial distribution and magnitude of the deformation field of the InSAR ascending rail of the ancient landslide in Taoping Township. The deformation speed of the descending rail varies between -86 and 20 mm/a, and the maximum change difference is 106 mm/a, and there is a certain deviation in the position and magnitude of the maximum deformation point of the two rails (Figure 3), and the maximum deformation point of the ascending rail The cumulative deformation is 250mm, and the cumulative deformation of the maximum deformation point of the descending rail is 190mm. The main reasons for the above-mentioned differences in the deformation of the InSAR ascending and descending orbits include the following three aspects: First, due to the differences in the observation direction, incident angle, and heading angle of the ascending and descending orbit radar satellites, the three-dimensional deformation of the same point on the ground is in the direction of the line of sight of the LOS of different satellite attitudes. Second, in mountainous areas with large terrain fluctuations, SAR images will have different degrees of geometric distortion, which will directly affect the SAR observation data; third, due to changes in slope and aspect, the same point on the surface (Fig. 3 The deformation direction of the deformation in area 4) in the LOS direction of the lifting rail may be opposite. Observing Figures 3a-3b, it can be found that a large number of deformation velocities are located in area 3, and the area is used as the deformation center to diffuse outwards, and area 3 and dislocation The platform area is highly overlapped. The deformation rate of region 2 is weaker than that of region 3, but they are both in the active state of deformation. No reliable deformation data was detected in area 1 by the InSAR of the ascending and descending orbits. This was mainly because the C-band radar carried by the Sentinel-1 satellite could not penetrate the relatively dense vegetation coverage in this area, resulting in obvious distortions in the InSAR of the ascending and descending orbits in this area. Interference is a de-correlation phenomenon that causes deformation extraction to fail. Further comparison with the time series InSAR deformation of the landslide in Taoping Township extracted by Shi Gulin and others found that the InSAR time series deformation extracted in this example and the results of Shi Gulin and others have a very high consistency in terms of deformation distribution and deformation level. All the research results have found that the surface deformation is most significant near the staggered platform area, and the maximum deformation rate of the lifting rail is also ~120mm/a and ~90mm/a. The above-mentioned deformation distribution and magnitude consistency have well verified The invention extracts the reliability of InSAR deformation results.

In the process of calculating the two-dimensional formation field, the above-mentioned two-dimensional field deformation calculation model is used, based on the InSAR deformation data of the ancient landslide in Taoping Township, combined with satellite imaging parameters and the average slope and aspect data of the ancient landslide body in Taoping Township , the inversion obtained the two-dimensional deformation field of the ancient landslide in Taoping Township along the slope direction and the normal direction. Figure 4a represents the deformation rate field along the slope direction, and Figure 4b represents the deformation rate field along the normal direction. The color marks the deformation rate in the figure, and the black Arrows indicate the direction of movement of the slope along the slope. The two-dimensional deformation field of the landslide (Fig. 4) clearly shows the movement intensity and direction of the different areas of the ancient landslide in Taoping Township. 350mm/a, and all of them are deformed downward along the slope. The deformation level along the normal direction of the slope is relatively small, and the deformation level varies between 0 and 80mm/a, and the movement direction is perpendicular to the slope downward. According to the deformation magnitude and displacement direction, this paper divides the significant deformation area of the landslide into two areas, A and B. Figure 4a shows that area A is the area with a small deformation rate in the deformation field along the slope direction, and the deformation rate of most of the areas is Between 0 and 130 mm/a, area B (staggered platform area) is the area with the largest deformation along the slope direction of the landslide, and the deformation rate is between 130 and 350 mm/a. Figure 4b shows that area A is the area with a small deformation rate in the normal deformation field, and the deformation rate is between 0 and 20 mm/a, while area B is also the area with the largest normal deformation of the landslide, and the deformation rate is between 20 and 80 mm /a, where the maximum normal deformation is located in the right slope toe area of the staggered platform area. Analyzing the distribution of the two-dimensional deformation field of the landslide above, it can be seen that the deformation of the ancient landslide in Taoping Township along the slope direction and normal direction is mainly concentrated in the staggered platform area, and this area also has the possibility of secondary instability in terms of topography. The historical deposits at the front edge are continuously scoured by the water of the Zagunao River, which will cause the slope of the landslide front to become steeper, and the exposed rocks will also weaken the stability of the slope body, further increasing the risk of instability in the misplaced platform area.

In order to verify the reliability of the above-mentioned landslide depth inversion method, in this example, the two-dimensional deformation data of the ancient landslide in Taoping Township shown in Fig. Under the conditions of rheological parameters (see Table 2), the inversion obtained the sliding depth of the ancient landslide in Taoping Township (Fig. 5). Table 2 below lists the concentrated distribution interval of landslide depth obtained by inversion and the characteristic parameters of landslide volume under different rheological parameters. It can be found that larger rheological parameters can effectively reduce the landslide depth obtained by inversion ( Table 2):

Table 2: Inversion results of landslide depth with different rheological parameters

It should be pointed out that the rheological parameter f represents the compressive properties of the slope mass. When the rheological parameter is equal to 0.3 or 0.5, it means that the soil quality of the landslide mass is highly compressible. At this time, the overall stability of the landslide mass is low and instability occurs. The probability is relatively large; when the rheological parameter is equal to 0.9, it means that the soil compressibility of the landslide is relatively small, and it is basically close to a rigid body. It is composed of silty clay, and the overall looseness of the slope material is relatively large; and from the deformation time-series curves shown in Figure 3c-3d, it can be seen that the ancient landslide in Taoping Township should be in a stable deformation period rather than a sliding state . Based on the above factors, this experiment believes that the rheological parameter of 0.7 is the closest to the actual situation of the ancient landslide in Taoping Township. At this time, the inversion depth of the ancient landslide in Taoping Township is mainly distributed between 9 and 33 m, and the volume of the landslide is 3.49×10 ⁷ m ³ . At the same time, Wang Dongsheng and others judged the thickness of the landslide to be 9.20-30.5m through field surveys based on the topography, landform and lithology data of the landslide in Taoping Township. The high consistency verifies the reliability of the inversion results. Figure 5c shows that when the rheological parameter is 0.7, the sliding depth of the upper area I of the Taoping Township ancient landslide obtained by inversion is relatively shallow (0m-13m), while the adjacent area of staggered significant deformation II, the landslide depth Significantly deeper (5m-45m), and the maximum sliding depth is located in the ancient landslide site area of the original Taoping Township on the west side of the staggered platform area. There are a large number of landslide deposits in this area, and the water resources are abundant, which provides the foundation for the deep movement of the landslide . Further down along the staggered area, the inversion depth of the landslide quickly decays to zero. Field surveys and remote sensing images show that the dotted line at the lower boundary of area II in Figure 5c is the boundary between relatively flat and steep topography in this area. The banks of the Gunao River are flat, and most of them are hardened ground and houses. InSAR deformation data also show that there is no obvious sign of deformation in this area, and the relative stability is relatively high.

Figure 6 shows the landslide depth data along different sections (see Figure 5c for the section location), among which Section 1 is located in the middle and upper part of Area II. Figure 6a shows that the terrain of this area is low in the east and high in the west, and the areas with larger landslide depths are mainly concentrated in the staggered platforms The maximum landslide depth is 16m at the gully in the middle of the area and at the western slope, and the landslide depth of the profile line is basically similar under different rheological parameters. Section 2 is located in the lower part of area II. Figure 6b shows that the section line passes through the area with the largest landslide depth, and the landslide depth on the east side of the section is basically zero, and the area with larger landslide depth is still mainly concentrated in the west side of the staggered platform. When the rheological parameters When the rheological parameters are 0.3, 0.5 and 0.7, the maximum sliding depth along the section is 45m, 35m and 32m, respectively, and when the rheological parameter is 0.9, the maximum landslide depth quickly decays to 25m. Section 3 is a longitudinal section along the slope and passes through the area of maximum landslide depth. Figure 6c shows that the depth of the landslide gradually increases from zero to the maximum along the slope direction from the upper edge of the slope, and from the fault platform area to the flat area below the slope. In the regional scope, the inversion landslide depth rapidly decreased from the maximum value to zero, and at the same time, the local enlargement of section 3 (Fig. 6d) showed that when the rheological parameters were different values, the shapes of the landslide depth profiles were basically similar, and the rheological parameters were 0.5 and 0.5. At 0.7, the coincidence of profile lines is relatively high, and there are only some slight differences in the maximum landslide depth (in the black dotted ellipse area in Figure 6d), but it should also be pointed out that when the rheological parameters are 0.5 and 0.7, the inversion depth is on the west side of the slope There are relatively obvious differences in the accumulation areas of ancient landslides (Fig. 5b-6c).

Claims (4)

1. A landslide depth inversion method utilizing InSAR lifting rail deformation data is characterized by comprising the following steps:

s1, processing a lifting rail radar image in a landslide area by utilizing a time sequence InSAR technology to obtain time sequence deformation data of the landslide area;

s2, resolving and obtaining two-dimensional deformation data of the landslide surface along the slope direction and the normal direction according to the time sequence deformation data, the space geometric relation of the landslide region and the DEM data;

s3, constructing a landslide depth inversion model based on two-dimensional deformation under a mass conservation criterion, and calculating the landslide depth inversion model according to two-dimensional deformation data to obtain an inversion calculation result of landslide depth;

the method for constructing the landslide depth inversion calculation model in the step S3 specifically comprises the following steps:

if the landslide thickness change rate of the landslide sliding base surface is equal to the landslide normal deformation speed during the deformation observation period, the vertical integral mass conservation equation between the landslide sliding base surface and the surface of the slope body is as follows:

in the formula (I), the compound is shown in the specification,

is the surface movement speed of the slope body, f is a rheological parameter, v _z The normal deformation speed of the landslide is adopted;

the finite difference form of the vertical integral mass conservation equation is:

where Δ x and Δ y are data sampling intervals in the slope direction and the vertical slope direction, respectively, v _x (i, j) and v _y (i, j) are the rate of slope deformation and the vertical slope deformation at position (i, j), respectively, h _i,j Is the slope body landslide depth at position (i, j);

based on v _z (i, j) obtaining a matrix form landslide depth inversion model as follows:

in the formula (I), the compound is shown in the specification,

is the normal speed of the slope body,

is a diagonal dominant parameter matrix comprising sampling intervals, rheological parameters and surface deformation rates,

the integral slope body landslide depth is obtained.

2. The landslide depth inversion method using InSAR lifting rail deformation data according to claim 1, wherein the step S1 specifically comprises:

s11, acquiring a lifting rail SAR image of a landslide area, and performing cutting and image registration processing on the lifting rail SAR image to obtain a registered SAR image;

s12, performing interference processing on the registered SAR image, then obtaining a corresponding differential interferogram according to DEM data of a landslide area, and performing adaptive filtering processing on the differential interferogram to obtain a high-quality differential interferogram;

s13, performing phase unwrapping on the obtained differential interference pattern to obtain an unwrapped pattern, and removing an atmospheric phase by using a filter;

s14, performing target point time sequence deformation calculation on the unwrapping graph without the atmospheric phase by using a singular value decomposition algorithm;

s15, geocoding the calculated target point time sequence deformation data, the high-quality differential interference image and the unwrapping image without the atmospheric phase, and obtaining the time sequence deformation data of the lifting rail in the landslide area in the LOS direction.

3. The landslide depth inversion method using InSAR lifting rail deformation data according to claim 2, wherein the step S2 specifically comprises:

s21, determining imaging parameters of the lifting rail SAR image according to the space geometric relation of the landslide area, and further constructing a two-dimensional deformation resolving model;

s22, processing DEM data of the landslide area to obtain the average slope and the slope direction of the landslide area;

s23, determining deformation data in an ascending LOS direction and deformation data in a descending LOS direction based on the time sequence deformation data, and sampling the ascending LOS direction deformation;

and S24, resolving the constructed two-dimensional deformation resolving model according to the lifting rail deformation sampling data, the average gradient and the slope direction of the landslide area to obtain a two-dimensional deformation field of the landslide surface along the slope direction and the normal direction.

4. The landslide depth inversion method using InSAR lifting rail deformation data according to claim 3, wherein the two-dimensional deformation solving model in the step S21 is:

wherein a and b are projection coefficients of the deformation along the slope direction and the normal direction, respectively, D _K And D _I The deformation amounts in the OK direction and OI direction, alpha and beta are the average slope and slope angle of the slip mass, theta and

the incident direction angle and the flight direction angle of the radar satellite are respectively.

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