CN109541592A - Loess Landslide type and sliding-modes analysis method based on InSAR multidimensional deformation data - Google Patents

Abstract

The invention discloses a loess landslide type and sliding mode analysis method based on InSAR multi-dimensional deformation information. Phase unwrapping, geocoding and sampling to the same coordinate grid and spatial resolution; 3. For the unwrapped interferogram in the geographic coordinate system, calculate the multi-dimensional surface deformation rate and obtain the multi-dimensional surface deformation time series; 4. Based on the multi-dimensional surface The deformation rate and multi-dimensional deformation time series are combined with remote sensing images and topographic maps to analyze the deformation mechanism of the landslide to determine the type and sliding mode of the landslide. The present invention adopts InSAR technology to analyze the loess landslide type and sliding mode, and only uses the SAR image, remote sensing image and topographic map of the research area. Regional landslide research provides important technical support for disaster prevention and mitigation of loess landslides.

Description

Analysis of Loess Landslide Types and Sliding Modes Based on InSAR Multidimensional Deformation Information method

technical field

The invention relates to the field of loess landslide type identification and sliding mode analysis, in particular to a loess landslide type and sliding mode analysis method based on InSAR multi-dimensional deformation information.

Background technique

As one of the birthplaces of ancient Chinese civilization, the Loess Plateau is the most important energy and chemical base in China. With economic development and population expansion in recent years, it has become one of the areas with the most serious soil erosion and the most fragile ecological environment in the world. Affected by human activities and engineering construction, geological disasters such as ground subsidence, ground fissures, collapses, landslides, mud flows, etc. occur frequently, forming a complex disaster chain, seriously endangering the safety of traffic arteries, major projects and people's lives and property. Among the frequent geological disasters, loess landslides are widely distributed and concentrated, with strong suddenness and the most serious damage. Therefore, the prevention and mitigation of loess landslide disasters is urgent. Among them, the analysis of the types and sliding modes of loess landslides is particularly critical to the engineering management of loess landslide disasters and disaster prevention and mitigation.

As a new type of space-to-Earth observation technology, interferometric synthetic aperture radar (InSAR) has the advantages of all-weather, all-day, high spatial resolution, and is basically unaffected by meteorological conditions. It is widely used in the identification and monitoring of landslides. For the analysis of the types and sliding modes of loess landslides, the existing technologies are mainly obtained through field surveys by geological staff or conventional engineering survey methods. These methods require rich geological work experience, are not only time-consuming and labor-intensive, have limited coverage, but also require Large investment in economy, manpower, and material resources; in the prior art, only a single SAR data set is used to obtain the one-dimensional LOS-directed deformation rate and deformation time series of the landslide, which cannot reveal the spatial deformation characteristics of the landslide, and due to the time resolution Due to the low rate, it is difficult to capture the instantaneous mutation signal of the landslide body, and the research on the type and sliding mode of the landslide has great limitations, and it is difficult to deeply analyze the shortcomings of the deformation mechanism of the landslide body.

SUMMARY OF THE INVENTION

The purpose of the present invention is to address the time-consuming and labor-intensive analysis of loess landslide types and sliding modes, the limited coverage, and the limitations of landslide types and sliding modes research. A single SAR data cannot reveal the spatial deformation characteristics of the landslide, and it is difficult to deeply analyze the deformation mechanism of the landslide body. This paper provides an analysis method for loess landslide types and sliding modes based on InSAR multi-dimensional deformation information.

In order to achieve the above goals, the present invention adopts the following technical solutions:

A loess landslide type and sliding mode analysis method based on InSAR multi-dimensional deformation information, the method comprises the following steps:

Step 1: Obtain a filtered InSAR differential interferogram through the SAR data collected by T SAR sensors covering the research area;

Step 2, perform phase unwrapping on the filtered InSAR differential interferogram, encode the unwrapped filtered InSAR differential interferogram into a geographic coordinate system, and sample to the same coordinate grid and spatial resolution to obtain a solution in the geographic coordinate system. interferogram;

Step 3, for the unwrapped interferogram in the geographic coordinate system, calculate the multi-dimensional surface deformation rate of the study area, and calculate the multi-dimensional cumulative surface deformation of the study area;

For the study area, multi-dimensional cumulative surface deformation is selected in chronological order to form a multi-dimensional surface deformation time series;

Step 4: Based on the obtained multi-dimensional surface deformation rate and multi-dimensional surface deformation time series of the study area, combined with the remote sensing images and topographic maps of the study area, the landslide deformation mechanism is analyzed, and the type and sliding mode of the loess landslide are determined.

Further, the described acquisition filtering InSAR differential interferogram, comprising:

Through T SAR sensors covering the research area, T SAR image datasets covering the research area are obtained, and external DEM data is obtained by using UAV photogrammetry. The images of the image data set are respectively differenced in pairs to form a differential interferogram, and then each differential interferogram is subtracted from the external DEM data to obtain an InSAR differential interferogram, and the InSAR differential interferogram is filtered to obtain a filtered InSAR differential interferogram.

Further, the calculation of the multi-dimensional surface deformation rate of the research area includes:

The following Equation 3 is used to calculate the multidimensional surface deformation rate of the study area:

In the above formula, A ¹ to A ^T represent the coefficient matrices of 1 to T ascending and descending orbit image datasets, respectively, and the multi-dimensional surface deformation rates include V _N , V _E , and V _U , where V _N represents the north-south surface deformation velocity, V _E represents the east-west surface deformation rate, and V _U represents the vertical surface deformation rate; arrive represent the observed phase values of 1 to T datasets, respectively.

Further, the multi-dimensional cumulative surface deformation of the study area is calculated, including:

The multi-dimensional cumulative surface deformation of the study area is calculated using the following equation 4:

In the above formula, represents the total number of SAR images in the T SAR image datasets, are the accumulated surface deformation in the north-south, east-west and vertical directions, respectively, represents the north-south deformation rate of the earth's surface at time t _i ; represents the east-west deformation rate of the earth's surface at time t _i ; represents the vertical deformation rate of the surface at time t _i ; Δt _i is the time interval between time t _i-1 and time t _i .

Compared with the prior art, the present invention has the following technical effects:

1. The present invention does not require field investigation, requires less geological expertise for operators, and can also conduct research on landslides that are difficult for operators to reach, and is suitable for loess landslide types and sliding modes in large areas, difficult areas and dangerous areas. High-efficiency analysis has no limitations compared with field geological surveys for traditional loess landslide types and sliding mode analysis.

2. The present invention only needs to obtain the lifting track SAR image data set covering the research area, obtain the multi-dimensional surface deformation information of the research area through data processing, and analyze the types and sliding modes of loess landslides in combination with the remote sensing images and topographic maps of the research area. In-depth analysis of the deformation mechanism of the landslide body; simple operation, high degree of automation, high reliability and efficiency.

Description of drawings

Fig. 1 is the loess landslide type and sliding mode analysis flow chart provided by the present invention;

Fig. 2 is a one-dimensional and two-dimensional deformation diagram of a landslide of loess-bedrock contact surface type obtained by using InSAR technology provided by the present invention; wherein, (a) is the LOS direction deformation rate of the landslide obtained from the orbit-raising SAR data, and (b) is The LOS deformation rate map of the landslide obtained from the down-orbit SAR data, (c) is the vertical deformation rate of the landslide, and (d) is the east-west deformation rate of the landslide;

Fig. 3 is the remote sensing image and sliding mode of the loess-bedrock interface type landslide in the experimental area provided by the present invention; wherein, (a) is the remote sensing image of the experimental area, and (b) is the loess- The sliding process of the bedrock contact surface type landslide, (c) is the two-dimensional deformation time series of the test area obtained by InSAR technology;

Figure 4 is the field photo of the landslide in the test area obtained from the field geological survey; among them, (a) is the whole picture of the landslide, (b) is an enlarged view of local cracks on the surface of the landslide, and (c) is the crack at the trailing edge of the landslide;

Figure 5 is a one-dimensional and two-dimensional deformation map of a shallow collapse-type landslide obtained by using InSAR technology provided by the present invention; wherein, (a) is the LOS direction deformation rate obtained from the orbit-raising data, (b) is the orbit-descending data obtained. The obtained LOS deformation rate, (c) is the vertical deformation rate, (d) is the east-west deformation rate;

Figure 6 is the remote sensing image and sliding mode of the shallow collapse landslide in the test area provided by the present invention; wherein, (a) is the remote sensing image of the test area, and (b) is the shallow collapse landslide obtained based on traditional field geological survey methods (c) is the two-dimensional deformation time series of the landslide in the test area obtained by InSAR technology;

Figure 7 is a field photo of the landslide in the test area obtained from the field geological survey;

Figure 8 is a one-dimensional and two-dimensional deformation diagram of a loess landslide with progressive and backward sliding mode obtained by using InSAR technology provided by the present invention; wherein, (a) is the LOS direction deformation rate obtained from the orbit-raising data, (b) is the descending The LOS deformation rate obtained from the orbital data, (c) is the vertical deformation rate, (d) is the east-west deformation rate;

Fig. 9 is the remote sensing image of the gradual and backward sliding of the loess landslide in the test area provided by the present invention;

Figure 10 shows the progressive and receding sliding process of the loess landslide obtained based on the field geological survey; (a) is the schematic diagram of the local instability of the loess landslide, (b) is the schematic diagram of the first global instability of the loess landslide, and (c) is the schematic diagram of the second global instability of the loess landslide, and (d) is the schematic diagram of the third global instability of the loess landslide;

Figure 11 is the 2D deformation time series of the landslide in the test area obtained by using InSAR technology, in which (a) represents the 2D deformation time series of point P2 in Figure 9, (b) represents the 2D deformation time of point P3 in Figure 9 sequence, (c) represents the two-dimensional deformation time series of point P4 in Figure 9, (d) represents the two-dimensional deformation time series of point P5 in Figure 9;

Figure 12 is the field photo of the landslide in the test area obtained from the field geological survey; in which, (a) is the enlarged view of the III area in the figure, (b) is the field photo of the landslide, and (c) is the crack of the white rectangle in the figure. Enlarged image, (d) is an enlarged image of area I in the image, and (e) is an enlarged image of the ground hole indicated by the white rectangle in the image.

Detailed ways

As shown in Fig. 1 to Fig. 12 , the present invention discloses a method for analyzing loess landslide types and sliding modes based on InSAR multi-dimensional deformation information. The detailed steps are as follows:

Step 1, through T SAR (Synthetic Aperture Radar, SAR for short) sensors covering the research area, obtain T lift-orbit SAR image datasets covering the research area, and use UAV photogrammetry to obtain an external DEM (Digital Elevation Model, Digital Elevation Model (DEM for short) data, the images in each SAR image data set in the acquired T ascending and descending orbit SAR image data sets are respectively made difference in pairs to form a differential interferogram, and then the external DEM is subtracted from each differential interferogram. data, obtain InSAR (Synthetic Aperture Radar Interferometry, InSAR for short) differential interferogram, filter the InSAR differential interferogram to obtain the filtered InSAR differential interferogram;

In this scheme, the SAR sensor is used to obtain the ascending and descending orbit SAR image data set covering the research area, and the external DEM data is obtained by means of UAV photogrammetry. There are T SAR sensors in this scheme, and the ascending orbit or The down-orbit image constitutes a data set, and the SAR data sets obtained by T SAR sensors together constitute the SAR data set obtained by the SAR sensor; the InSAR technology is used to process the SAR data set obtained by the SAR sensor. The images of each SAR data set in the acquired SAR data set are compared in pairs to form a differential interferogram, and other SAR data sets are processed according to the same processing method, and the formed differential interferogram is a series of images; secondly, use Each differential interferogram formed subtracts the external DEM data to obtain the InSAR differential interferogram; finally, the InSAR differential interferogram is filtered to obtain the filtered InSAR differential interferogram; the image is filtered to make the image more clear. Well, the signal-to-noise ratio can be improved.

Step 2, perform phase unwrapping on the filtered InSAR differential interferogram, encode the unwrapped filtered InSAR differential interferogram into a geographic coordinate system, and sample to the same coordinate grid and spatial resolution to obtain a solution in the geographic coordinate system. interferogram;

In this scheme, phase unwrapping is performed on each filtered InSAR differential interferogram. The purpose of phase unwrapping is to restore the phase of each filtered InSAR differential interferogram from the main value or phase difference value to the true value, and the unwrapped The filtered InSAR differential interferogram is encoded to the geographic coordinate system. Usually, the geographic coordinate system selects the WGS84 coordinate system, or an appropriate coordinate system can be selected according to the actual situation, such as the Xi'an 80 coordinate system. Encoding the image into the geographic coordinate system can establish a connection between the data containing the location in the geographic coordinate system and the image, and assign the image to the data record containing the corresponding location in the geographic coordinate. After the image is geocoded, it can be displayed in space. The position of each image is displayed, which facilitates further analysis of the information; since different SAR sensors have different spatial resolutions, the size of the identified ground targets is inconsistent, so it is difficult to combine the differential interferograms formed by SAR sensors with different resolutions deal with. In addition, the SAR data obtained by different SAR sensors still have the problem of inconsistency in geographic reference. Even if they are encoded in the same geographic coordinate system, there is a deviation between the target positions of the same objects. Therefore, in order to eliminate the inconsistency of spatial resolution and geographic bias of interferograms obtained by different SAR sensors, it is necessary to resample the unwrapped interferograms obtained from different SAR sensor datasets after geocoding so that they have the same coordinate grid and space. resolution.

Step 3, for the unwrapped interferogram in the geographic coordinate system, calculate the multi-dimensional surface deformation rate of the study area, and calculate the multi-dimensional cumulative surface deformation of the study area;

For the study area, multi-dimensional cumulative surface deformation is selected in chronological order to form a multi-dimensional surface deformation time series;

Specifically, in this embodiment, the method for calculating the multi-dimensional deformation rate of the research area is:

For the data set from a single SAR platform, there is only one incident angle θ and one satellite flight azimuth α. According to the principle of Small Baseline Set (SBAS) InSAR technology, the one-dimensional surface deformation time series can be calculated according to Equation 1:

Among them, A is an M×N coefficient matrix, M is the number of filtered InSAR differential interferograms, N+1 is the number of up-and-down orbit images in the dataset of T SAR sensors, and V _los is the line-of-sight (LOS) of the satellite to be determined. ) to the deformation rate, is the observed phase value, A ⁺ is the pseudo-inverse of matrix A, is the cumulative surface deformation in the LOS direction at time t ⁱ , is the cumulative surface deformation in the LOS direction at time t ⁱ⁺¹ , is the surface deformation rate in the LOS direction at time t ⁱ⁺¹ , and Δt ⁱ⁺¹ is the time interval between time t ⁱ and time t ⁱ⁺¹ .

For T orbit image datasets from different SAR sensors, they have different incidence angles and satellite flight azimuths, assuming:

V _los =SV=S _N V _N +S _E V _E +S _U V _U ,

S={S _N , S _E , S _U }={sinαsinθ,-cosαsinθ,cosθ};

According to the projection relationship between the satellite LOS deformation and the three-dimensional deformation of the ground, for any one of the SAR data sets t (t=1,2,...T), Equation 1 can be written in the form shown in Equation 2:

Among them, S is a unit vector in the LOS direction, which is composed of components S _N , S _E , and S _U in the north-south, east-west, and vertical directions; V is a surface deformation rate vector to be determined, including north, east, and vertical The deformation components V _N , V _E , and V _U in three directions, represents the phase observations of the SAR dataset t, represents the north-south component of the LOS direction unit vector of the SAR dataset t; represents the east-west component of the LOS oriented unit vector of the SAR dataset t; Represents the vertical component of the LOS to the unit vector of the SAR dataset t; for T ascending and descending orbit image datasets from different SAR sensors, Equation 2 can be written in the form of a matrix shown in Equation 3:

or abbreviated as

in,

In the above formula, A ¹ to A ^T represent the coefficient matrices of 1 to T orbital image datasets, respectively, V _N represents the north-south surface deformation rate, _VE represents the east-west surface deformation rate, and V _U represents the vertical direction. Surface deformation rate; arrive represent the observed phase values of 1 to T data sets, respectively; represents the coefficient matrix of the equation, Represents the unknown parameter vector to be found, that is, the three-dimensional deformation rate of the surface; represents the observed phase value.

Since the number of unknown parameters to be calculated in formula (3) is larger than the number of linear equations, the coefficient matrix is rank deficient, and the solution of the equation can be obtained by using singular value decomposition (SVD) or Tikhonov regularization method, that is, the three-dimensional surface deformation rate. The multi-dimensional cumulative surface deformation can be obtained by further using Equation 4:

In the formula, represents the total number of SAR images in the T SAR image datasets, are the accumulated surface deformation in the north-south, east-west and vertical directions, respectively, represents the north-south deformation rate of the earth's surface at time t _i ; represents the east-west deformation rate of the earth's surface at time t _i ; represents the vertical deformation rate of the surface at time t _i ; Δt _i is the time interval between time t _i-1 and time t _i .

In this embodiment, for the research area, in one year, multiple multi-dimensional cumulative surface deformations are selected in chronological order to form a multi-dimensional surface deformation time series for research; the abscissa represents the date, and the ordinate represents the multi-dimensional cumulative surface deformation, as shown in Fig. 3 (c).

Since SAR satellites are less sensitive to the north-south deformation, if only two different SAR sensors are obtained from the ascending and descending orbit data sets, the north-south deformation can be ignored in equations 3 and 4, so as to calculate the horizontal east-west and vertical directions of the landslide 2D surface deformation.

Step 4: Based on the obtained multi-dimensional surface deformation rate and multi-dimensional surface deformation time series of the study area, combined with the remote sensing images and topographic maps of the study area, the landslide deformation mechanism is analyzed, and the type and sliding mode of the loess landslide are determined.

In this scheme, the multi-dimensional deformation rate and time series of the landslide refer to two-dimensional or three-dimensional. The embodiment of this scheme provides one-dimensional and two-dimensional deformation maps, only when two different SAR sensors are obtained. When the deformation in the north-south direction can be ignored, the two-dimensional surface deformation in the horizontal east-west and vertical directions of the landslide can be calculated; In which direction the deformation rate is larger, and the coverage in the landslide boundary is wide, the deformation of the landslide can be obtained in which direction. For example, the deformation rate of the landslide in the vertical direction of the landslide boundary can be obtained from the figure. Compared with the deformation rate in the east-west direction and the coverage is wide, it can be concluded that the landslide has deformation in the vertical direction; for the deformation time series, the selected date is used as the abscissa, and the vertical and east-west direction obtained according to the corresponding date are used. The cumulative surface deformation is used as the ordinate to form the coordinate system, and the deformation time series can be obtained. The date selected for the abscissa of this scheme is from January 2016 to December 2016. According to the coordinate system, it is judged which direction the accumulated surface deformation data is larger. It is based on the deformation in which direction, and then compares the existing landslide types to determine which landslide type belongs to the same type of landslide as the deformation of which landslide type in the loess landslide type. In order to verify the reliability of the judged landslide type and sliding mode, remote sensing images and topographic maps obtained by DEM are used to further verify the judged landslide type, which can be compared with the landslide type judged by the two-dimensional deformation rate and deformation time series of the study area. Comparisons are made to determine the correctness of the type of analysis.

The experimental data of the present invention adopts the real TerraSAR-X data of the ascending orbit, with 23 scenes of ascending orbit data and 19 scenes of descending orbit data. meters, the total number of cells covered is 5600×4200. The coverage area is Heifangtai loess landslide area in Gansu Province.

The invention firstly processes the TerraSAR-X data of the lifting rails in the experimental area separately, obtains the deformation rate map of the lifting data of the Heifangtai Xinyuan landslide group respectively, and identifies the potential landslides in the experimental area. A total of three potential loess landslides are identified and The results obtained from the ascending and descending orbit data are consistent. Then, the types and sliding modes of these three potential loess landslides are analyzed using InSAR multi-dimensional deformation information. The high-quality lifting orbit unwrapped interferogram is geocoded into the WGS84 coordinate system and sampled to a latitude and longitude grid with a spatial resolution of 3 meters. The two-dimensional deformation rate and time series of the study area are obtained by using Equation 3 and Equation 4. The Google earth image and topographic map of the test area were used to analyze landslide types and sliding patterns.

Figure 2 shows the one-dimensional and two-dimensional deformation maps of the loess-bedrock interface type landslide obtained by InSAR technology; (a) is the LOS-direction deformation rate obtained from the orbit-raising data; (b) is the orbit-descending data obtained. (c) is the vertical deformation rate; (d) is the east-west deformation rate.

Figure 3 shows the remote sensing image and sliding mode of the landslide at the loess-bedrock contact surface in the test area; (a) is the remote sensing image of the test area; (b) is the loess-bedrock contact surface obtained based on traditional field geological survey methods (c) is the two-dimensional deformation time series of the landslide in the test area obtained by InSAR technology.

It can be seen from (c) and (d) of Figure 2 that the landslide is dominated by east-west deformation, and slides westward, with smaller-scale deformation in the vertical direction. This deformation feature is consistent with the height of the loess-bedrock interface type loess landslide, so we can determine that this landslide is a typical loess-bedrock interface type landslide, and its sliding process is shown in Figure 3(b). Under the action of groundwater, the fracture surface developed in the loess layer and propagated to the lower bedrock layer. Over time, it causes the landslide to become unstable and slide along the bedrock face. In order to verify the reliability of the landslide type and sliding mode obtained from the multi-dimensional deformation information of InSAR, the field geological survey is used to verify the reliability. The field photo of this landslide is shown in Figure 4. It can be seen from the field map that the real deformation characteristics of the landslide are highly consistent with the two-dimensional deformation characteristics obtained by InSAR technology, and it belongs to a typical loess-bedrock interface type landslide, which proves that the loess landslide analyzed by InSAR multi-dimensional deformation information type of reliability.

Figure 5 shows the one-dimensional and two-dimensional deformation maps of the shallow collapse-type loess landslide obtained by using InSAR multi-dimensional deformation information; (a) is the LOS-direction deformation rate obtained from the orbit-raising data; (b) is the orbit-descending data obtained. The obtained LOS deformation rate; (c) is the vertical deformation rate; (d) is the east-west deformation rate.

Figure 6 shows the remote sensing image and sliding mode of the shallow collapsing loess landslide; (a) is the remote sensing image of the landslide in the test area; (b) is the sliding process of the shallow collapsing loess landslide obtained based on traditional field geological survey methods ; (c) is the two-dimensional deformation time series of the landslide in the test area.

It can be seen from (c) and (d) of Figure 5 that the landslide is mainly dominated by vertical deformation, and there is a smaller amount of deformation in the east-west direction. This deformation feature is consistent with the height of the shallow collapsing loess landslide, so it can be determined that this landslide is a typical shallow collapsing loess landslide, and its sliding process is shown in (b) of Figure 6. The rupture surface develops in the whole loess layer. With the development of time, the rupture surface becomes unstable and slips. This type of landslide usually has a small size. In order to verify the reliability of the landslide type and sliding mode obtained by the InSAR technology, the field geological survey was used to verify. The field photo of this landslide is shown in Figure 7. It can be seen from the field map that the real deformation characteristics of the landslide are highly consistent with the two-dimensional deformation characteristics obtained by InSAR technology, and it belongs to a typical shallow collapse type loess landslide, which proves the reliability of the loess landslide type analyzed by InSAR multi-dimensional deformation information. sex.

Figure 8 shows the one-dimensional and two-dimensional deformation diagrams of the loess landslide in progressive and backward sliding mode obtained by InSAR technology; (a) is the LOS direction deformation rate obtained from the orbit-up data; (b) is obtained from the orbit-descend data. LOS deformation rate; (c) vertical deformation rate; (d) east-west deformation rate.

Fig. 9 shows the remote sensing image of the landslide in the test area; Fig. 10 shows the sliding process of the progressively receding loess landslide obtained based on the traditional field geological survey method; Fig. 11 is the two-dimensional deformation time series of the landslide obtained by InSAR technology.

It can be seen from (c) and (d) of Figure 8 that the landslide slides eastward as a whole, and the vertical deformation only occurs at the edge of the landslide. This deformation feature is highly consistent with the progressive and receding sliding mode of the loess landslide, so it can be determined that the landslide belongs to a typical progressive and receding sliding mode, and its sliding process is shown in Figure 10. Under the action of groundwater, the saturated loess undergoes static liquefaction, so that the upper loess takes the weak base at the bottom as the bottom slip surface, and the top tensile cracks serve as the back boundary, resulting in local instability, and the excess pore water pressure is stimulated to cause the loess to disintegrate and form flow Statistical accumulation. After the local instability occurs, arc-shaped grooves are formed on the edge of the platform, forming a new local void surface. The stress on the trailing edge of the landslide continues to change, and new near-upright tensile cracks are generated, resulting in the first global instability, and the reciprocation continues to produce the second and third global instability, forming a progressive retreat at the same location. landslide, so the vertical deformation only occurs at the edge of the landslide body. In order to verify the reliability of the landslide type and sliding mode obtained by the InSAR technology, the field geological survey was also used to verify the reliability. The field photo of this landslide is shown in Figure 12. It can be seen from the field map that the real deformation characteristics of the landslide are highly consistent with the two-dimensional deformation characteristics obtained by the InSAR technology, which belongs to a typical progressive and receding sliding mode, which proves the reliability of the loess landslide type analyzed by the multi-dimensional deformation information of InSAR. sex.

The above disclosures are only a few specific embodiments of the present invention, however, the embodiments of the present invention are not limited thereto, and any changes that can be conceived by those skilled in the art should fall within the protection scope of the present invention.

Claims (4)

1. a kind of loess landslide type and sliding mode analysis method based on InSAR multidimensional deformation information, it is characterised in that described method comprises the following steps: Step 1: Obtain a filtered InSAR differential interferogram through data collected by T SAR sensors covering the research area; Step 2, perform phase unwrapping on the filtered InSAR differential interferogram, encode the unwrapped filtered InSAR differential interferogram into a geographic coordinate system, and sample to the same coordinate grid and spatial resolution to obtain a solution in the geographic coordinate system. interferogram; Step 3, for the unwrapped interferogram in the geographic coordinate system, calculate the multi-dimensional surface deformation rate of the study area, and calculate the multi-dimensional cumulative surface deformation of the study area; For the study area, multi-dimensional cumulative surface deformation is selected in chronological order to form a multi-dimensional surface deformation time series; Step 4: Based on the obtained multi-dimensional surface deformation rate and multi-dimensional deformation time series of the study area, combined with the remote sensing images and topographic maps of the study area, the landslide deformation mechanism is analyzed, and the type and sliding mode of the loess landslide are determined. 2. loess landslide type and sliding mode analysis method based on InSAR multi-dimensional deformation information as claimed in claim 1, is characterized in that, described acquisition filtering InSAR differential interferogram, comprises: Through T SAR sensors covering the research area, T SAR image datasets covering the research area are obtained, and external DEM data is obtained by using UAV photogrammetry. The images of the image data set are respectively differenced in pairs to form a differential interferogram, and then each differential interferogram is subtracted from the external DEM data to obtain an InSAR differential interferogram, and the InSAR differential interferogram is filtered to obtain a filtered InSAR differential interferogram. 3. loess landslide type and sliding mode analysis method based on InSAR multi-dimensional deformation information as claimed in claim 1, it is characterized in that, the multi-dimensional surface deformation rate of described calculation research area, comprises: The following Equation 3 is used to calculate the multidimensional surface deformation rate of the study area: In the above formula, A ¹ to A ^T represent the coefficient matrices of 1 to T ascending and descending orbit image datasets, respectively, and the multi-dimensional surface deformation rates include V _N , V _E , and V _U , where V _N represents the north-south surface deformation velocity, V _E represents the east-west surface deformation rate, and V _U represents the vertical surface deformation rate; arrive represent the observed phase values of 1 to T datasets, respectively. 4. the loess landslide type and sliding mode analysis method based on InSAR multi-dimensional deformation information as claimed in claim 1, is characterized in that, the multi-dimensional cumulative surface deformation of described calculation research area, comprises: The multi-dimensional cumulative surface deformation of the study area is calculated using the following equation 4: In the above formula, represents the total number of SAR images in the T SAR image datasets, are the accumulated surface deformation in the north-south, east-west and vertical directions, respectively, represents the north-south deformation rate of the earth's surface at time t _i ; represents the east-west deformation rate of the earth's surface at time t _i ; represents the vertical deformation rate of the surface at time t _i ; Δt _i is the time interval between time t _i-1 and time t _i .