CN109541592A - Loess Landslide type and sliding-modes analysis method based on InSAR multidimensional deformation data - Google Patents
Loess Landslide type and sliding-modes analysis method based on InSAR multidimensional deformation data Download PDFInfo
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Abstract
The invention discloses a kind of Loess Landslide type and sliding-modes analysis method based on InSAR multidimensional deformation data, comprising the following steps: 1. filter InSAR differential interferometry figure by the data acquisition of T SAR sensor acquisition；2. pair filtering InSAR differential interferometry figure phase unwrapping, geocoding are simultaneously sampled to identical coordinate axiom and spatial resolution；3. the solution under pair geographic coordinate system twines interference pattern, calculates multidimensional Ground Deformation rate and obtain multidimensional Ground Deformation time series；4. based on multidimensional Ground Deformation rate and multidimensional deformation data and remote sensing image, topographic map is combined to carry out landslide Deformation Mechanisms analysis, the type and sliding-modes on landslide are determined.The present invention carries out Loess Landslide type and sliding-modes analysis using InSAR technology, it can be carried out merely with SAR image, remote sensing image and the topographic map of survey region, efficiency and accuracy rate are higher, significantly reduce field process amount, and it is suitable for the research on landslide of danger zone, it prevents and reduces natural disasters for Loess Landslide and important technology support is provided.
Description
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
The loess plateau is one of auspicious places of ancient civilizations of the famous Chinese nationality, and is the most important energy and chemical industry base in China. With the development of economy and the expansion of population in recent years, the water loss and soil erosion are the most serious and the ecological environment is the most vulnerable region in the world. The method is influenced by human activities and engineering construction, and geological disasters such as ground settlement, ground cracks, collapse, landslide, mud flow and the like frequently occur to form a complex disaster chain, so that the safety of a traffic trunk line, major projects and lives and properties of people is seriously threatened. In frequently occurring geological disasters, loess landslides are widely and intensively distributed, the outbreak is strong, and the destructive power is the most serious. Therefore, the work of disaster prevention and reduction for loess landslide disasters is not slow. The analysis of the type and the sliding mode of the loess landslide is particularly critical to the engineering management of the loess landslide disaster and the work of preventing and reducing the disaster.
Synthetic aperture radar (InSAR) as a novel space-to-ground observation technology has the advantages of all weather, all time, high spatial resolution, no influence of meteorological conditions and the like, and is widely applied to landslide identification and monitoring in recent years. The loess landslide type and sliding mode are analyzed, the prior art is mainly obtained by field investigation of geological workers or conventional engineering investigation methods, and the methods need abundant geological working experience, are time-consuming and labor-consuming, have limited coverage and also need great investment on economy, manpower and material resources; in the prior art, only a single SAR data set is used for obtaining the one-dimensional LOS direction deformation rate and the deformation time sequence of a landslide body, the spatial deformation characteristic of the landslide cannot be disclosed, and due to the fact that the time resolution is low and the transient sudden change signal of the landslide body is difficult to capture, the research on the landslide type and the sliding mode has great limitation, and the defect that the deformation mechanism of the landslide body is difficult to deeply analyze is overcome.
Disclosure of Invention
The invention aims to provide an InSAR multi-dimensional deformation information-based loess landslide type and sliding mode analysis method aiming at the problems that the loess landslide type and sliding mode analysis is time-consuming and labor-consuming, the coverage area is limited, the landslide type and sliding mode research is limited, the spatial deformation characteristics of landslides cannot be revealed by single SAR data, and the landslide body deformation mechanism is difficult to deeply analyze.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a loess landslide type and sliding mode analysis method based on InSAR multi-dimensional deformation information comprises the following steps:
step 1, acquiring a filtering InSAR differential interferogram through SAR data acquired by T SAR sensors covering a research area;
step 2, phase unwrapping is carried out on the filtering InSAR differential interferogram, the unwrapped filtering InSAR differential interferogram is encoded to a geographic coordinate system, and the same coordinate grid and the same spatial resolution are sampled to obtain the unwrapped interferogram under the geographic coordinate system;
step 3, calculating the multidimensional earth surface deformation rate of the research area and the multidimensional accumulated earth surface deformation of the research area for the unwrapped interferogram under the geographic coordinate system;
aiming at a research area, selecting multidimensional accumulated surface deformation according to a time sequence to form a multidimensional surface deformation time sequence;
and 4, carrying out landslide deformation mechanism analysis on the obtained multidimensional ground surface deformation rate and multidimensional ground surface deformation time sequence of the research area by combining the remote sensing image and the topographic map of the research area, and determining the type and the sliding mode of the loess landslide.
Further, the acquiring the filtered InSAR differential interferogram includes:
the method comprises the steps of obtaining T lifting rail SAR image data sets covering a research area through T SAR sensors covering the research area, obtaining external DEM data through an unmanned aerial vehicle photogrammetry mode, carrying out pairwise difference on images of each SAR image data set in the obtained T lifting rail SAR image data sets to form differential interferograms, then subtracting the external DEM data from each differential interferogram to obtain an InSAR differential interferogram, and filtering the InSAR differential interferogram to obtain a filtered InSAR differential interferogram.
Further, the calculating the multidimensional surface deformation rate of the research area comprises:
the multidimensional surface deformation rate of the study area is calculated using the following formula 3:
in the above formula, A1To ATCoefficient matrixes respectively representing 1 to T lifting rail image data sets, and the multidimensional surface deformation rate comprises VN、VE、VUIn which V isNThe surface deformation rate in the north-south direction, VERepresenting the rate of surface deformation in the east-west direction, VURepresenting the deformation rate of the earth surface in the vertical direction;toRepresenting observed phase values for 1 to T data sets, respectively.
Further, calculating a multi-dimensional cumulative surface deformation of the study area, comprising:
the multi-dimensional cumulative surface deformation of the study area is calculated using the following equation 4:
in the above formula, the first and second carbon atoms are,representing the number of SAR images in total for the T SAR image data sets,respectively north-south, east-west and vertical cumulative surface deformations,denotes at the t-thiThe north-south deformation rate of the earth's surface at any moment;is shown at tiThe east-west direction deformation rate of the earth's surface at that moment;is shown at tiThe vertical deformation rate of the earth's surface at the moment; Δ tiIs ti-1Time and tiThe time interval of the moment.
Compared with the prior art, the invention has the following technical effects:
1. the method does not need field investigation, has lower requirement on the geological professional knowledge of operators, can be used for researching landslides which are difficult to reach by the operators, is suitable for quickly and efficiently analyzing the loess landslide type and the sliding mode in large-area and high-difficulty areas and dangerous areas, and has no limitation compared with the field geological investigation performed during the analysis of the traditional loess landslide type and sliding mode.
2. According to the method, only the lifting rail SAR image data set covering the research area needs to be acquired, the multidimensional earth surface deformation information of the research area is acquired through data processing, the loess landslide type and the sliding mode analysis are carried out by combining the remote sensing image and the topographic map of the research area, and the landslide body deformation mechanism can be deeply analyzed; the operation is simple, the automation degree is high, and the reliability and the efficiency are high.
Drawings
Fig. 1 is a flow chart for analyzing loess landslide types and sliding patterns according to the present invention;
fig. 2 is a one-dimensional and two-dimensional deformation diagram of the loess-bedrock contact surface type landslide obtained by utilizing the InSAR technology provided by the invention; the system comprises a slope LOS (LoS) direction deformation rate chart, a slope LOS direction deformation rate chart, a slope vertical direction deformation rate chart and a slope east-west direction deformation rate chart, wherein the slope LOS direction deformation rate chart is obtained by track ascending SAR data;
FIG. 3 is a remote sensing image and a sliding mode of a loess-bedrock contact surface type landslide in a test area provided by the invention; the method comprises the following steps of (a) obtaining a remote sensing image of a test area, (b) obtaining a sliding process of a loess-bedrock contact surface type landslide based on a traditional field geological survey mode, and (c) obtaining a two-dimensional deformation time sequence of the test area by an InSAR technology;
FIG. 4 is a field photograph of a test area landslide taken from a field geological survey; wherein, (a) is the full view of the landslide, (b) is the enlarged view of the local crack on the surface of the landslide, and (c) is the crack on the rear edge of the landslide;
FIG. 5 is a one-dimensional and two-dimensional deformation diagram of a shallow collapse type landslide obtained by InSAR technology provided by the present invention; the LOS deformation rate obtained by the rail ascending data, (b) the LOS deformation rate obtained by the rail descending data, (c) the vertical deformation rate, and (d) the east-west deformation rate;
FIG. 6 shows a remote sensing image and a sliding mode of a shallow collapse type landslide in a test area according to the present invention; the method comprises the following steps of (a) obtaining a remote sensing image of a test area, (b) obtaining a sliding process of a shallow collapse type landslide based on a traditional field geological survey mode, and (c) obtaining a two-dimensional deformation time sequence of the test area landslide by an InSAR technology;
FIG. 7 is a field photograph of a test area landslide taken from a field geological survey;
fig. 8 is a one-dimensional and two-dimensional deformation diagram of a loess landslide gradual-advancing backward sliding mode obtained by utilizing an InSAR technology provided by the present invention; the LOS deformation rate obtained by the rail ascending data, (b) the LOS deformation rate obtained by the rail descending data, (c) the vertical deformation rate, and (d) the east-west deformation rate;
FIG. 9 is a remote sensing image of gradual backward sliding loess landslide in a test area according to the present invention;
fig. 10 is a loess landslide gradual backward sliding process obtained based on a field geological survey mode; wherein, (a) is a schematic diagram of local instability of the loess landslide, (b) is a schematic diagram of first global instability of the loess landslide, (c) is a schematic diagram of second global instability of the loess landslide, and (d) is a schematic diagram of third global instability of the loess landslide;
fig. 11 is a two-dimensional deformation time series of a test area landslide obtained using the InSAR technique, in which (a) shows a two-dimensional deformation time series of a point P2 in fig. 9, (b) shows a two-dimensional deformation time series of a point P3 in fig. 9, (c) shows a two-dimensional deformation time series of a point P4 in fig. 9, and (d) shows a two-dimensional deformation time series of a point P5 in fig. 9;
FIG. 12 is a field photograph of a test area landslide taken from a field geological survey; wherein, (a) is the enlarged view of the area III in the figure, (b) is the scene photograph of the landslide, (c) is the enlarged view of the crack shown by the white rectangle in the figure, (d) is the enlarged view of the area I in the figure, and (e) is the enlarged view of the hole shown by the white rectangle in the figure.
Detailed Description
As shown in fig. 1 to 12, the invention discloses a loess landslide type and sliding mode analysis method based on InSAR multi-dimensional deformation information, which comprises the following detailed steps:
step 1, acquiring T lifting rail SAR image data sets covering a research area through T SAR (Synthetic Aperture radar, SAR for short) sensors covering the research area, acquiring external DEM (Digital Elevation Model, DEM for short) data by using an unmanned aerial vehicle photogrammetry mode, performing pairwise subtraction on images in each SAR image data set in the acquired T lifting rail SAR image data sets to form differential interferograms, subtracting the external DEM data from each differential interferogram to obtain InSAR (Synthetic Aperture radar interferometry, Synthetic Aperture radar radiometer for short) differential interferograms, and filtering the InSAR differential interferograms to obtain filtered InSAR differential interferograms;
in the scheme, a lifting rail SAR image data set covering a research area is obtained through SAR sensors, external DEM data are obtained in an unmanned aerial vehicle photogrammetry mode, T SAR sensors are arranged in the scheme, lifting rail or falling rail images obtained by each SAR sensor form a data set, and SAR data sets obtained by the T SAR sensors form the SAR data set obtained by the SAR sensors together; processing SAR data sets acquired by an SAR sensor by adopting an InSAR technology, wherein the specific process comprises the steps of firstly, carrying out pairwise subtraction on images of each SAR data set in the SAR data sets acquired by the SAR sensor to form a differential interference graph, and processing other SAR data sets according to the same processing method, wherein the formed differential interference graph is a series of images; secondly, subtracting external DEM data from each formed differential interference image to obtain an InSAR differential interference image; finally, filtering the InSAR differential interference pattern to obtain a filtered InSAR differential interference pattern; the image is filtered to make the image have better definition and improve the signal-to-noise ratio.
Step 2, phase unwrapping is carried out on the filtering InSAR differential interferogram, the unwrapped filtering InSAR differential interferogram is encoded to a geographic coordinate system, and the same coordinate grid and the same spatial resolution are sampled to obtain the unwrapped interferogram under the geographic coordinate system;
in the scheme, each filtering InSAR differential interference map is subjected to phase unwrapping, the purpose of the phase unwrapping is to restore the phase of each filtering InSAR differential interference map from a main value or a phase difference value to a true value, and the unwrapped filtering InSAR differential interference map is encoded into a geographic coordinate system, wherein a WGS84 coordinate system is usually selected as the geographic coordinate system, and a suitable coordinate system, such as a Xian 80 coordinate system, can also be selected according to actual conditions. Encoding the images to a geographic coordinate system, establishing a relation between data containing positions of the geographic coordinate system and the images, distributing the images to data records containing corresponding positions of the geographic coordinate system, and displaying the positions of the images in a space after the images are geocoded, so that further analysis can be performed on information; because different SAR sensors have different spatial resolutions and the sizes of recognized ground targets are different, it is difficult to process differential interferograms formed by the SAR sensors with different resolutions together. In addition, the SAR data obtained by different SAR sensors also has the problem of inconsistent geographic references, and even if the SAR data are coded in the same geographic coordinate system, the target positions of the same ground objects have deviation. Therefore, in order to solve the problems of inconsistency of spatial resolutions of interferograms obtained by different SAR sensors and geographic deviation, it is necessary to resample the unwrapped interferograms obtained by different SAR sensor data sets after geocoding so that the unwrapped interferograms have the same coordinate grid and spatial resolutions.
Step 3, calculating the multidimensional earth surface deformation rate of the research area and the multidimensional accumulated earth surface deformation of the research area for the unwrapped interferogram under the geographic coordinate system;
aiming at a research area, selecting multidimensional accumulated surface deformation according to a time sequence to form a multidimensional surface deformation time sequence;
specifically, in this embodiment, the method for calculating the multidimensional deformation rate of the study area includes:
for a data set from a single SAR platform, with only one angle of incidence θ and one satellite azimuth of flight α, a one-dimensional time series of surface deformations can be calculated according to equation 1, based on the small baseline set (SBAS) InSAR technique principle:
wherein A is a coefficient matrix of M multiplied by N, M is the number of filtered InSAR differential interferograms, N +1 is the number of lifting rail images in the data set of the T SAR sensors, VlosFor the desired satellite line of sight (LOS) velocity,for observed phase values, A+Is the pseudo-inverse of the matrix a,is at tiThe cumulative surface deformation of the LOS towards the time,is at ti+1The cumulative surface deformation of the LOS towards the time,is at ti+1Rate of surface deformation, Δ t, in LOS direction at timei+1Is tiTime and ti+1The time interval of the moment.
For T orbit image datasets from different SAR sensors, then they have different angles of incidence and satellite attitude, assuming:
Vlos＝SV＝SNVN+SEVE+SUVU，
S＝{SN,SE,SU}＝{sinαsinθ,-cosαsinθ,cosθ}；
according to the projection relationship of the satellite LOS to deformation and ground three-dimensional deformation, for any SAR data set T (T is 1,2, … T), equation 1 can be written into the form shown in equation 2:
wherein S is a LOS unit vector consisting of north-south, east-west and vertical components SN、SE、SUForming; v is a ground surface deformation rate vector to be solved and comprises deformation components V in the north direction, the east direction and the vertical directionN、VE、VU，A phase observation representing the SAR data set t,a north-south component representing the LOS to unit vector of the SAR data set t;east-west components of LOS to unit vectors representing SAR dataset t;a vertical component representing the LOS to unit vector of the SAR data set t; for T lifting rail image datasets from different SAR sensors, equation 2 can be written in the form of a matrix as shown in equation 3:
or simply as
Wherein,
in the above formula, A1To ATCoefficient matrices, V, representing 1 to T lifting rail image datasets, respectivelyNThe surface deformation rate in the north-south direction, VERepresenting the rate of surface deformation in the east-west direction, VURepresenting the deformation rate of the earth surface in the vertical direction;toRepresenting observed phase values for 1 to T data sets, respectively;a matrix of coefficients representing the equation is formed,expressing an unknown parameter vector to be solved, namely the three-dimensional deformation rate of the earth surface;representing the observed phase values.
Because the number of the unknown parameters to be solved in the formula (3) is greater than that of the linear equation, the coefficient matrix is rank deficient, and the solution of the equation, namely the three-dimensional surface deformation rate, can be obtained by adopting a Singular Value Decomposition (SVD) or Tikhonov regularization method. Further multi-dimensional cumulative surface deformation can be obtained using equation 4:
in the formula,representing the number of SAR images in total for the T SAR image data sets,respectively north-south, east-west and vertical cumulative surface deformations,denotes at the t-thiThe north-south deformation rate of the earth's surface at any moment;is shown at tiThe east-west direction deformation rate of the earth's surface at that moment;is shown at tiThe vertical deformation rate of the earth's surface at the moment; Δ tiIs ti-1Time and tiThe time interval of the moment.
In the embodiment, for a research area, in one year, a plurality of multidimensional accumulated surface deformations are selected according to a time sequence to form a multidimensional surface deformation time sequence for research; the date is represented in abscissa and the multi-dimensional cumulative surface deformation is represented in ordinate, as shown in fig. 3 (c).
Because the SAR satellite has low deformation sensitivity to the north-south direction, if only when the lifting track data sets of two different SAR sensors are obtained, the deformation of the north-south direction can be ignored in the formulas 3 and 4, and therefore the two-dimensional surface deformation of the landslide horizontal east-west direction and the vertical direction is calculated.
And 4, carrying out landslide deformation mechanism analysis on the obtained multidimensional ground surface deformation rate and multidimensional ground surface deformation time sequence of the research area by combining the remote sensing image and the topographic map of the research area, and determining the type and the sliding mode of the loess landslide.
In the scheme, the multidimensional deformation rate and the time sequence of the landslide are two-dimensional or three-dimensional, the embodiment of the scheme provides one-dimensional and two-dimensional deformation graphs, and the deformation in the north-south direction can be ignored only when the lifting rail data sets of two different SAR sensors are obtained, so that the two-dimensional surface deformation of the landslide in the horizontal east-west direction and the vertical direction is calculated; analyzing according to the obtained two-dimensional deformation rate, the vertical deformation rate and the east-west deformation rate of the research area, judging which direction of the deformation rate is larger in the boundary of the landslide, and obtaining which direction of the deformation of the landslide is the main when the coverage range in the boundary of the landslide is wide; for the deformation time sequence, the selected date is used as an abscissa, accumulated surface deformation in the vertical direction and the east-west direction obtained according to the corresponding date is used as an ordinate to form a coordinate system, the deformation time sequence can be obtained, the date of the abscissa in the scheme is 2016 (1) month to 2016 (12) month, the accumulated surface deformation data in which direction is larger is judged according to the coordinate system, namely the deformation in which direction is taken as a main part, and the existing landslide type is used as a comparison to judge which landslide type belongs to which landslide type as the deformation in the loess landslide type. In order to verify the reliability of the judged landslide type and the sliding mode, the remote sensing image and a topographic map obtained by the DEM are adopted to further verify the judged landslide type, and the landslide type can be compared with the landslide type judged by a two-dimensional deformation rate and a deformation time sequence of a research area to determine the correctness of the analyzed type.
The experimental data of the invention adopts real terraSAR-X data of the lifting track, 23 scenes of the lifting track data and 19 scenes of the lowering track data, the incidence angles are 41.2 degrees and 41.8 degrees respectively, the pixel resolution distance is 0.91 meter, the azimuth is 1.26 meter, and the total covered pixels are 5600 multiplied by 4200. The coverage area is a yellow soil landslide area of a black square platform in Gansu province.
The method comprises the steps of firstly, independently processing terraSAR-X data of a lifting rail in an experimental area, respectively obtaining a deformation rate diagram of lifting data of a landslide group of a new highland of a black square platform, identifying potential landslides in the experimental area, identifying three potential loess landslides in total, and enabling results obtained by the lifting rail data to be consistent. And then analyzing the types and sliding modes of the three potential loess landslides by adopting InSAR multi-dimensional deformation information. Geocoding the high-quality lifting rail unwrapping interference pattern to a WGS84 coordinate system, sampling to a longitude and latitude grid with 3 m spatial resolution, calculating by adopting a formula 3 and a formula 4 to obtain a two-dimensional deformation rate and a time sequence of a research area, and analyzing the landslide type and the sliding mode by combining a Google earth image and a topographic map of a test area.
FIG. 2 is a one-dimensional and two-dimensional deformation diagram of a loess-bedrock contact surface type landslide obtained by InSAR technology; (a) LOS strain rate obtained for up-track data; (b) LOS mean strain rate obtained for the down-track data; (c) is the vertical deformation rate; (d) is the east-west direction change rate.
FIG. 3 shows a remote sensing image and a sliding mode of a loess-bedrock contact surface type landslide in a test area; (a) remote sensing images of the test area; (b) the sliding process of the loess-bedrock contact surface type landslide obtained based on the traditional field geological survey mode; (c) the method is a two-dimensional deformation time sequence of the landslide in a test area obtained by InSAR technology.
As can be seen from fig. 2 (c) and (d), the landslide becomes dominant primarily in east-west deformation and slides westward, with a smaller magnitude of deformation in the vertical direction. This deformation characteristic is highly consistent with the loess-bedrock contact surface type loess landslide, and thus we can determine that the landslide belongs to a typical loess-bedrock contact surface type landslide, the sliding process of which is shown in fig. 3 (b). Under the action of groundwater, the fracture develops in the loess layer and propagates towards the underlying basement rock. Over time, the resulting landslide becomes unstable and slides along the bedrock face. In order to verify the landslide type and the reliability of the sliding mode obtained by InSAR multi-dimensional deformation information, a field geological survey mode is adopted for verification, and a scene photo of the landslide is shown in FIG. 4. As can be seen from a field diagram, the field real deformation characteristic of the landslide is highly consistent with the two-dimensional deformation characteristic obtained by the InSAR technology, and the landslide belongs to a typical loess-bedrock contact surface type landslide, and the reliability of the loess landslide type analyzed by InSAR multi-dimensional deformation information is proved.
FIG. 5 is a one-dimensional and two-dimensional deformation diagram of a shallow collapsed loess landslide obtained using InSAR multi-dimensional deformation information; (a) LOS strain rate obtained for up-track data; (b) LOS mean strain rate obtained for the down-track data; (c) is the vertical deformation rate; (d) is the east-west direction change rate.
FIG. 6 shows a remote sensing image and a sliding mode of a shallow collapsed loess landslide; (a) remote sensing images of landslides in a test area; (b) the method is a sliding process of shallow collapsed loess landslide obtained based on a traditional field geological survey mode; (c) is a two-dimensional deformation time sequence of the landslide in the test area.
As can be seen from fig. 5 (c) and (d), the landslide becomes dominant mainly in the vertical direction, with a smaller magnitude of deformation in the east-west direction. This deformation characteristic is highly consistent with the shallow collapsed loess landslide, and thus it can be determined that the landslide belongs to a typical shallow collapsed loess landslide, the sliding process of which is shown in fig. 6 (b). The fracture develops throughout the loess layer and as time progresses the fracture destabilizes and slides, this type of landslide generally having a smaller size. In order to verify the landslide type and the reliability of the sliding mode obtained by the InSAR technology, a field geological survey mode is adopted for verification, and a field photograph of the landslide is shown in FIG. 7. As can be seen from a field diagram, the field real deformation characteristic of the landslide is highly consistent with the two-dimensional deformation characteristic obtained by the InSAR technology, and the landslide belongs to a typical shallow collapse type loess landslide, so that the reliability of the loess landslide type analyzed by InSAR multi-dimensional deformation information is proved.
FIG. 8 is a one-dimensional and two-dimensional deformation diagram of loess landslide progressive retreat type sliding mode obtained by InSAR technology; (a) LOS strain rate obtained for up-track data; (b) LOS mean strain rate obtained for the down-track data; (c) is the vertical deformation rate; (d) is the east-west direction change rate.
FIG. 9 shows a remote sensing image of a test area landslide; fig. 10 shows a sliding process of a gradual receding loess landslide obtained based on a conventional field geological survey; fig. 11 is a two-dimensional deformation time series of landslides obtained by InSAR technology.
As can be seen from fig. 8 (c) and (d), the landslide as a whole slides eastward, and the vertical deformation occurs only at the edge of the landslide. This deformation characteristic is highly consistent with the progressive backward sliding mode of the loess landslide, and thus it can be determined that the loess landslide belongs to a typical progressive backward sliding mode, the sliding process of which is shown in fig. 10. Under the action of underground water, saturated loess is statically liquefied, so that the upper loess uses a bottom weak base as a bottom sliding surface and a top tension crack as a rear side boundary to generate local instability, and the super-pore water pressure is stimulated to cause the loess to disintegrate to form fluidized accumulation. After local unstability takes place, the tableland limit forms the arc recess, forms new local face of empting. The stress of the rear edge of the landslide is continuously changed, a new nearly vertical tensile crack is generated, the first global instability is generated, the second and third global instability is continuously generated in a reciprocating way, and the gradual retreating type landslide is formed at the same position, so that the vertical deformation only appears at the edge of the landslide body. In order to verify the landslide type and the reliability of the sliding mode obtained by the InSAR technology, the field photograph of the landslide is shown in FIG. 12, and the field photograph is also verified by adopting a field geological survey mode. As can be seen from a field diagram, the field real deformation characteristic of the landslide is highly consistent with the two-dimensional deformation characteristic obtained by the InSAR technology, belongs to a typical progressive backward sliding mode, and proves the reliability of the loess landslide type analyzed by the InSAR multi-dimensional deformation information.
The above disclosure is only for a few specific embodiments of the present invention, however, the present invention is not limited to the above embodiments, and any variations that can be made by those skilled in the art are intended to fall within the scope of the present invention.
Claims (4)
1. A loess landslide type and sliding mode analysis method based on InSAR multi-dimensional deformation information is characterized by comprising the following steps:
step 1, acquiring a filtering InSAR differential interferogram through data acquired by T SAR sensors covering a research area;
step 2, phase unwrapping is carried out on the filtering InSAR differential interferogram, the unwrapped filtering InSAR differential interferogram is encoded to a geographic coordinate system, and the same coordinate grid and the same spatial resolution are sampled to obtain the unwrapped interferogram under the geographic coordinate system;
step 3, calculating the multidimensional earth surface deformation rate of the research area and the multidimensional accumulated earth surface deformation of the research area for the unwrapped interferogram under the geographic coordinate system;
aiming at a research area, selecting multidimensional accumulated surface deformation according to a time sequence to form a multidimensional surface deformation time sequence;
and 4, carrying out landslide deformation mechanism analysis on the obtained multidimensional earth surface deformation rate and multidimensional deformation time sequence of the research area by combining the remote sensing image and the topographic map of the research area, and determining the type and the sliding mode of the loess landslide.
2. The method for analyzing loess landslide type and sliding mode based on InSAR multi-dimensional deformation information as claimed in claim 1, wherein said obtaining a filtered InSAR differential interferogram comprises:
the method comprises the steps of obtaining T lifting rail SAR image data sets covering a research area through T SAR sensors covering the research area, obtaining external DEM data through an unmanned aerial vehicle photogrammetry mode, carrying out pairwise difference on images of each SAR image data set in the obtained T lifting rail SAR image data sets to form differential interferograms, then subtracting the external DEM data from each differential interferogram to obtain an InSAR differential interferogram, and filtering the InSAR differential interferogram to obtain a filtered InSAR differential interferogram.
3. The method for analyzing loess landslide type and sliding mode based on InSAR multi-dimensional deformation information as claimed in claim 1, wherein the calculating the multi-dimensional earth surface deformation rate of the research area comprises:
the multidimensional surface deformation rate of the study area is calculated using the following formula 3:
in the above formula, A1To ATCoefficient matrixes respectively representing 1 to T lifting rail image data sets, and the multi-dimensional earth surfaceThe rate of deformation includes VN、VE、VUIn which V isNThe surface deformation rate in the north-south direction, VERepresenting the rate of surface deformation in the east-west direction, VURepresenting the deformation rate of the earth surface in the vertical direction;toRepresenting observed phase values for 1 to T data sets, respectively.
4. The method for analyzing loess landslide type and sliding mode based on InSAR multi-dimensional deformation information as claimed in claim 1, wherein the calculating the multi-dimensional accumulated surface deformation of the research area comprises:
the multi-dimensional cumulative surface deformation of the study area is calculated using the following equation 4:
in the above formula, the first and second carbon atoms are, representing the number of SAR images in total for the T SAR image data sets,respectively north-south, east-west and vertical cumulative surface deformations,denotes at the t-thiThe north-south deformation rate of the earth's surface at any moment;is shown at tiThe east-west direction deformation rate of the earth's surface at that moment;is shown at tiThe vertical deformation rate of the earth's surface at the moment; Δ tiIs ti-1Time and tiThe time interval of the moment.
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