CN113848551B - Landslide depth inversion method using InSAR lifting rail deformation data - Google Patents
Landslide depth inversion method using InSAR lifting rail deformation data Download PDFInfo
- Publication number
- CN113848551B CN113848551B CN202111123816.XA CN202111123816A CN113848551B CN 113848551 B CN113848551 B CN 113848551B CN 202111123816 A CN202111123816 A CN 202111123816A CN 113848551 B CN113848551 B CN 113848551B
- Authority
- CN
- China
- Prior art keywords
- landslide
- deformation
- slope
- data
- depth
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 238000000034 method Methods 0.000 title claims abstract description 48
- 238000012545 processing Methods 0.000 claims abstract description 20
- 238000004364 calculation method Methods 0.000 claims abstract description 14
- 238000005516 engineering process Methods 0.000 claims abstract description 13
- 238000005070 sampling Methods 0.000 claims description 12
- 230000001174 ascending effect Effects 0.000 claims description 9
- 230000008859 change Effects 0.000 claims description 7
- 239000011159 matrix material Substances 0.000 claims description 7
- 150000001875 compounds Chemical class 0.000 claims description 6
- 238000001914 filtration Methods 0.000 claims description 5
- 238000000354 decomposition reaction Methods 0.000 claims description 4
- 238000003384 imaging method Methods 0.000 claims description 4
- 230000003044 adaptive effect Effects 0.000 claims description 3
- 235000006040 Prunus persica var persica Nutrition 0.000 description 14
- 238000001514 detection method Methods 0.000 description 13
- 238000012544 monitoring process Methods 0.000 description 10
- 244000144730 Amygdalus persica Species 0.000 description 8
- 240000005809 Prunus persica Species 0.000 description 6
- 230000008569 process Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000000605 extraction Methods 0.000 description 4
- 239000000126 substance Substances 0.000 description 4
- 238000006073 displacement reaction Methods 0.000 description 3
- 239000000463 material Substances 0.000 description 3
- 239000002689 soil Substances 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 238000013459 approach Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 239000004927 clay Substances 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 230000005484 gravity Effects 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000011835 investigation Methods 0.000 description 2
- 230000000149 penetrating effect Effects 0.000 description 2
- 230000002829 reductive effect Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000012502 risk assessment Methods 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000002238 attenuated effect Effects 0.000 description 1
- 210000004556 brain Anatomy 0.000 description 1
- 230000002490 cerebral effect Effects 0.000 description 1
- 230000009194 climbing Effects 0.000 description 1
- 230000019771 cognition Effects 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000005553 drilling Methods 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 238000010191 image analysis Methods 0.000 description 1
- 230000002401 inhibitory effect Effects 0.000 description 1
- 238000005305 interferometry Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 239000000178 monomer Substances 0.000 description 1
- 230000036961 partial effect Effects 0.000 description 1
- 238000010587 phase diagram Methods 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000002441 reversible effect Effects 0.000 description 1
- 238000000518 rheometry Methods 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 239000011435 rock Substances 0.000 description 1
- 239000004575 stone Substances 0.000 description 1
- 238000012876 topography Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
- G01S13/9021—SAR image post-processing techniques
- G01S13/9023—SAR image post-processing techniques combined with interferometric techniques
Landscapes
- Engineering & Computer Science (AREA)
- Remote Sensing (AREA)
- Radar, Positioning & Navigation (AREA)
- Physics & Mathematics (AREA)
- Electromagnetism (AREA)
- Computer Networks & Wireless Communication (AREA)
- General Physics & Mathematics (AREA)
- Pit Excavations, Shoring, Fill Or Stabilisation Of Slopes (AREA)
- Testing Or Calibration Of Command Recording Devices (AREA)
Abstract
The invention discloses a landslide depth inversion method by utilizing InSAR lifting rail deformation data, which comprises the following steps of: s1, processing a lifting rail radar image in a landslide area by using 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 the landslide depth. The method is suitable for successfully extracting the landslide mass of the satellite lifting orbit InSAR deformation field, and in addition, the rheological parameters of the slope mass can obviously image inversion landslide depth, and the accuracy of the parameters obviously improves the reliability and the applicability of the method.
Description
Technical Field
The invention belongs to the technical field of landslide depth detection, and particularly relates to a landslide depth inversion method utilizing InSAR lifting rail deformation data.
Background
Landslide disasters have the characteristics of strong emergencies, great harm and difficulty in early identification, monitoring and early warning, since the fifties of the last century, at least 22 provinces, cities and autonomous regions in China suffer from landslide disasters with different degrees, huge casualties and property loss are caused, and the safety of major human engineering facilities such as highways, railways, water conservancy and hydropower, mines and the like is directly threatened. Therefore, early identification, monitoring and early warning, risk assessment and the like of landslide disasters become important work contents in the field of disaster prevention and reduction in China. The landslide surface deformation speed, the landslide depth, the landslide volume and the like are important factors for landslide risk assessment, and accurate acquisition of the landslide characteristic parameters directly influences accurate assessment of the landslide hazard degree, the landslide hazard range and the instability risk.
With the rapid development of modern geodetic surveying technology, GNSS technology and the like are beginning to be widely applied to landslide deformation monitoring, high-precision GNSS equipment can provide reliable slope surface single-point three-dimensional deformation data for scientific research workers and engineers, but is limited by high layout and monitoring cost and the like, the GNSS technology often only obtains deformation conditions of a plurality of sparse discrete points on the surface of a slope, and is not beneficial to cognition on landslide deformation spatial distribution characteristics. The InSAR technology rapidly developed in recent years well makes up the problems of conventional GNSS single-point measurement and observation data sparsity, and particularly, with the obvious improvement of the space-time resolution of modern SAR satellites, the InSAR technology is becoming an important means for landslide deformation monitoring and obviously promotes the early identification of landslide disasters. However, with the continuous improvement of landslide monitoring requirements, researchers find that geodetic deformation data only reflect the motion state of a landslide surface, and cannot effectively reveal the depth distribution condition of a landslide body, so that the landslide amount, the influence range and the influence degree of landslide instability and the like are difficult to accurately estimate.
The landslide depth detection method mainly comprises a contact type landslide depth detection method and a non-contact type landslide depth detection method, wherein the contact type landslide depth detection method comprises deep displacement detection, ground penetrating radar detection and the like, drilling a hole in a landslide body and placing a deep displacement detector, the landslide depth can be accurately obtained by the method, the detection cost is high, the consumed time is long, the method is often only used for detecting the depth of a single point, and the depth partial situation of the whole landslide body is not reflected favorably. The ground penetrating radar equipment is often used for detecting the boundary range and the depth of landslide, can completely obtain the landslide depth in the motion range of a landslide body, is extremely low in efficiency, is difficult to apply to large-scale and huge landslides, is extremely low in efficiency, and is difficult to apply to large-scale and huge landslides; in addition, the contact detection method has poor practicability on a landslide body with high and steep terrain, difficult climbing and high risk.
The non-contact landslide depth detection method mainly comprises the following steps: a balanced section method, an elastic dislocation method and a mass conservation method, wherein the balanced section method considers the landslide material as an incompressible rigid material, and does not consider the rheological property of the landslide substance, so that the reliability of the estimated landslide depth is not high; the elastic dislocation law remarkably simplifies the landslide body from a geometric and physical model, so that the method can only be generally applied to the initial stage of landslide development, or the landslide body has no obvious inelastic deformation, and the application range of the elastic dislocation method is remarkably limited. The mass conservation method is a new method for calculating the depth of deformation bodies such as glaciers, landslides and the like in a non-contact manner, which is developed in recent years, fully considers the deformation condition of the surface of a slope body and the rheological coefficient of a soil body, and is expected to provide a reliable technical approach for the inversion of the thickness of the non-contact landslide. However, when the mass conservation method is used for inverting the landslide depth, a three-dimensional deformation field of the landslide surface is needed, in the existing deformation observation means, only the GNSS technology can effectively provide the three-dimensional deformation field of the landslide surface, but the observation density with sparse GNSS obviously cannot meet the requirement of integral depth detection of the landslide. The InSAR technology of surface monitoring is limited by the reason that deformation in the north-south direction is insensitive, and three-dimensional deformation of a slope body is difficult to extract even if combined multi-track data are combined.
Therefore, the traditional landslide depth detection method can only determine the depth of a sparse single point of a slope body, and cannot accurately reflect the depth distribution condition of the whole landslide body.
Disclosure of Invention
Aiming at the defects in the prior art, the landslide depth inversion method utilizing the InSAR lifting rail deformation data solves the problem that the depth distribution condition of the whole landslide body cannot be accurately reflected by the traditional landslide depth detection method.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: a landslide depth inversion method utilizing InSAR lifting rail deformation data comprises 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 the landslide depth.
Further, the step S1 specifically includes:
s11, acquiring a lifting rail SAR image in a landslide area, and performing cutting and image registration processing on the lifting rail SAR image to obtain a registered SAR image;
s12, carrying out interference processing on the registered SAR image, then obtaining a corresponding differential interferogram according to DEM data of a landslide area, and carrying out 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 difference interference image and the unwrapping image without the atmospheric phase, and obtaining time sequence deformation data of the lifting rail in the landslide area in the LOS direction.
Further, the step S2 specifically includes:
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.
Further, the two-dimensional deformation calculation model in 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.
Further, the method for constructing the landslide depth inversion calculation model in the step S3 specifically includes:
if the landslide thickness change rate of the landslide sliding basal plane is equal to the landslide normal deformation speed during the deformation observation period, the vertical integral mass conservation equation between the landslide sliding basal plane 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.
The invention has the beneficial effects that:
the method is suitable for successfully extracting the landslide mass of the satellite lifting orbit InSAR deformation field, in addition, the rheological parameters of the slope mass can obviously image inversion landslide depth, the accuracy of the parameters obviously improves the reliability and the applicability of the method, and the method is specifically embodied in the following points:
(1) The landslide depth inversion method provided by the invention can realize surface observation of the depth of the landslide body based on InSAR observation data, but not a few data obtained by a conventional method, so that the accuracy of the landslide depth calculation result is improved;
(2) The SAR satellite image data is periodically acquired, and a low-cost means is provided for monitoring the change of the landslide depth;
(3) The high-space-density landslide depth data obtained by the method can be further used for calculating characteristic parameters such as landslide mass and the like, and providing a reliable data base for landslide disaster image analysis;
drawings
Fig. 1 is a flowchart of a landslide depth inversion method using InSAR lifting rail deformation data according to 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 deformation field and the accumulated deformation of the maximum deformation point of the mountain area ancient landslide lifting rail provided by the invention.
Fig. 4 is a schematic diagram of a two-dimensional deformation field of the local landslide of the peaches provided by the invention.
Fig. 5 is a schematic diagram of depth distribution of township ancient landslide of peaches provided by the present invention.
FIG. 6 is a sectional view of the depth of a landslide provided in accordance with the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined by the appended claims, and all changes that can be made by the invention using the inventive concept are intended to be protected.
Example 1:
as shown in fig. 1, a landslide depth inversion method using InSAR lifting rail deformation data includes 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 the landslide depth.
In the embodiment, the small baseline interference (SBAS-InSAR) technology is considered to be capable of better overcoming the problem of space-time loss coherence faced by the conventional DInSAR, effectively inhibiting terrain and atmospheric errors, and is widely applied to time sequence deformation monitoring of geological disasters such as earthquakes, landslides and the like at present. In step S1 of the embodiment, an SBAS-InSAR technology is used to process a lifting rail radar image in a landslide area, and the processing process specifically includes:
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;
the method comprises the steps that a registration SAR image is obtained to prepare for subsequent interference, and the main purpose of image registration is to match the same position in two images together;
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.
Specifically, the steps S12 to S14 are specifically: setting a baseline threshold value in the registered SAR image to generate an interferogram; removing a terrain phase in the interference pattern by using DEM data of a landslide area to obtain a differential interference pattern; carrying out self-adaptive filtering on the differential interference pattern to obtain a high-quality differential interference pattern; in the embodiment, a Goldstein method is used for carrying out self-adaptive filtering on the differential interference pattern; carrying out phase unwrapping processing on the interference phase diagram by adopting a minimum cost stream method, and further selecting an unwrapping diagram consisting of EDFP high-coherence points; and performing target point time sequence deformation resolving on the unwrapping graph without the atmospheric phase by using a singular value decomposition algorithm to obtain target point time sequence deformation data.
In step S2 of this embodiment, in the process of calculating the two-dimensional deformation field of the landslide, the slope and the slope direction of the landslide body are basic elements for establishing a slope coordinate system, and the slope represents the degree of steepness of the surface unit and directly affects the flow of slope substances. The slope direction is defined as the projection direction of the normal of the slope surface on the horizontal plane, and for a landslide monomer with complex actual terrain relief, the landslide surface is often a changed curved surface, and the slope of the surface of the slope body has certain micro-landform characteristics. Considering that the motion direction of most continuously moving landslide mass is often controlled by the overall orientation and gradient of the landslide mass, the method of calculating the landslide two-dimensional deformation field based on the landslide region EDM data in the embodiment specifically includes the following steps:
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 gradient and the slope direction of the landslide area;
specifically, DEM data of a landslide region are imported into arcgis, gradient and slope data are exported by using a grid surface tool in a 3D analysis tool, and relevant data of a gradient point and a slope point are obtained by using a grid point turning tool, so that average gradient and slope data are obtained;
s23, determining ascending rail LOS deformation data and descending rail LOS deformation data based on the time sequence deformation data, and sampling ascending rail 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.
In step S22 of this embodiment, a three-dimensional rectangular coordinate system of the motion space of the sliding mass is shown as shown in fig. 2, wherein the slope direction axis (OK) points to the motion method of the sliding mass, the normal direction axis (OI) points to the normal direction of the slope surface, and the vertical slope direction (OT) direction and them form a right-handed spiral coordinate system; and meanwhile, the downward movement along the slope axis is defined as positive, and the movement in the sliding surface along the normal axis is defined as positive. Considering that the combined satellite lifting rail can only solve two-dimensional deformation data, and usually, a landslide slope body moves downwards along a sliding surface under the action of gravity, so that the magnitude of slope deformation is usually greater than that of a vertical slope deformation, therefore, in the embodiment, assuming that slope deformation mainly consists of slope deformation and normal deformation together, a two-dimensional deformation solving model is as follows:
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.
In step S3 of this embodiment, in the process of performing landslide depth inversion calculation, it is considered that the landslide material density is kept constant within an observation time period; the landslide depth can be determined according to a mass conservation method; accordingly, the vertical integral mass conservation equation between the sliding basal surface of the landslide and the surface of the slope body is provided:
wherein h is the landslide depth, t is the time,
the average moving speed of the slope body along the depth direction is satisfied
The surface movement speed of the slope body is adopted, f is a rheological parameter, the value range is 0-1, according to the dense-green landslide rheology, when f =2/3, the substance is a Newtonian viscous fluid, namely the slope body is already yielded from the whole depth area of the surface sliding surface and the piston flow area disappears; 2/3 < f < 1 indicates plug flow, i.e. the yield zone is relatively thin; f =1 indicates that the entire slope is a rigid slide without a yielding zone;
based on this, the method for constructing the landslide depth inversion calculation model in the step S3 specifically includes:
if the landslide thickness change rate of the landslide sliding basal plane is equal to the landslide normal deformation speed during the deformation observation period, the vertical integral mass conservation equation between the landslide sliding basal plane 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 the data of only one point;
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 landslide depth at location (i, j);
further considering that the deformation of the slope body is mainly controlled by gravity, and mainly deforms along the slope direction and the normal direction, the deformation of the surface of the slope body along the vertical slope direction can be ignored, and the mass conservation equation is as follows:
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, namely is a matrix containing all normal deformations,
is a diagonal dominant parameter matrix comprising sampling intervals, rheological parameters and surface deformation rates,
the integral slope body landslide depth is obtained.
Therefore, under the condition of obtaining the landslide two-dimensional deformation data and the normal two-dimensional deformation data, the inversion calculation of the landslide depth can be carried out.
Example 2:
in this embodiment, the method in embodiment 1 is used for landslide depth inversion in the peachback rural landslide area, where the peachback rural landslide is located in the peachback rural area in administrative county of the four-chuan province, and belongs to the mountainous and alpine regions in northwest of the west of the chuan, and is located in the mountainous and monsoon climate areas, and the altitude difference in the regions is great, and the terrain is complex. Under the influence of climate, the rainfall in the area is more in 7-9 months per year, the rainfall in winter is relatively less, and the annual rainfall is between 650mm and 1000 mm. The whole ancient landslide in the township of the peaches is in a round-backed shape, the terrain is south, west, high, north and east and low, the slope body is narrow at the top and wide at the bottom, the average slope is about 27-53 degrees, the ancient landslide is a steeply and slowly combined landslide body, the elevation of the front edge of the slope body is about 1480m, the elevation of the rear edge of the slope body is 2710m, the relative height difference is 1230m, the longitudinal length is 2700m, and the transverse width is 2000m, and the ancient landslide belongs to a large landslide body.
In this embodiment, the lifting orbit SAR images observed in the Sentinel-1 satellite interferometry wide-width mode (IW) in the region are collected, the monitoring period is about 1 month in 2017 to 5 months in 2019, the total number of the lifting orbit SAR images in the period is 69, the total number of the lowering orbit SAR images in the period is 66, and the basic parameters of the lifting orbit SAR images are shown in table 1.
Table 1: sentine-1 lifting rail SAR image parameter
Considering that InSAR processing is susceptible to orbit errors, POD precision orbit determination ephemeris data provided by the European Bureau is adopted in the experiment to refine the orbit of SAR data, and AlOSWorld-3D digital elevation model data is utilized to carry out InSAR differential interference terrain phase removal processing.
The InSAR ascending and descending track time-series deformation field of the township landslide obtained by the method in the step S1 in the embodiment 1 is shown in fig. 3, in which fig. 3a is an ascending track LOS radial deformation rate field, fig. 3b is a descending track LOS radial deformation rate field, and fig. 3c and 3d are time-series deformation curves of the maximum deformation point of the ascending track respectively. Observing the graph 3, the InSAR lifting rail deformation field of the ancient landslide in the plateau area has obvious difference in spatial distribution and magnitude, the lifting rail deformation rate is changed between-120 mm/a and 20mm/a, the maximum change difference is 140mm/a, the lowering rail deformation speed is changed between-86 mm/a and 20mm/a, the maximum change difference is 106mm/a, certain deviation also exists between the position and magnitude of the maximum deformation point of the two rails (figure 3), the accumulated deformation of the maximum deformation point of the lifting rail is 250mm, and the accumulated deformation of the maximum deformation point of the lowering rail is 190mm. The main reasons for the deformation difference of the InSAR lifting rail include the following three aspects: firstly, due to the difference of the observation direction, the incident angle and the course angle of the lifting orbit radar satellite, the projection of the three-dimensional deformation of the same point of the earth surface on the LOS of different satellite attitudes to the sight line direction is different inevitably; secondly, in a mountain area with large topographic relief, the SAR image has geometric distortion of different degrees and directly influences SAR observation data; thirdly, due to the change of the slope and the slope direction, the deformation direction of the deformation of the same point (area 4 in fig. 3) on the ground surface in the direction of the LOS is possibly opposite to that of the deformation of the lifting rail, and as can be seen from fig. 3a-3b, the large-magnitude deformation speed is located in the area 3, and the deformation speed is gradually reduced and diffused outwards by taking the area as the deformation center, and the area 3 is highly overlapped with the dislocation area. The deformation rate of region 2 is weaker than that of region 3, but they are both in a deformation active state. Reliable deformation data are not detected and found in the area 1 by the lifting orbit InSAR, which is mainly caused by that C-band radar loaded by a Sentinel-1 satellite cannot penetrate through relatively dense vegetation coverage of the area, so that the lifting orbit InSAR in the area has obvious interference and loss correlation phenomena, and deformation extraction fails. Further, compared with the extraction of InSAR deformation of the Tanshink rural landslide time sequence such as Dan Gulin, the results of the extraction of InSAR time sequence deformation and Dan Gulin and the like in the embodiment have extremely high consistency in the aspects of deformation distribution, deformation magnitude and the like, the two research results show that the ground surface deformation near a staggered platform area is most obvious, the maximum deformation rate of a lifting rail is 120mm/a to 90mm/a, and the consistency in the deformation distribution and magnitude better verifies the reliability of the extraction of the InSAR deformation result.
In the two-dimensional forming field resolving process, the two-dimensional field deformation resolving model is used, based on InSAR deformation data of the rural ancient landslide lifting rail of the peach plateau, satellite imaging parameters and average gradient and slope direction data of the rural ancient landslide body of the peach plateau are combined, the two-dimensional deformation fields of the rural ancient landslide of the peach plateau along the slope direction and the normal direction are obtained through inversion, wherein a graph 4a represents a deformation rate field along the slope direction, a graph 4b represents a deformation rate field along the normal direction, color marks in the graph indicate the deformation rate, and black arrows indicate the movement direction of the slope body along the slope. The landslide two-dimensional deformation field (figure 4) clearly shows the motion intensity and direction of different areas of the local ancient landslide of the peach plateau, the deformation body of the ancient landslide is dominated by slope-direction deformation, the magnitude of the slope-direction deformation is relatively large and changes between 0 and 350mm/a, and the deformation is downward along the slope body. The deformation magnitude along the normal direction of the slope body is relatively small, the deformation magnitude is changed between 0mm/a and 80mm/a, and the movement directions are vertical to the slope body downwards. According to the magnitude and the displacement direction of the deformation magnitude, the region with significant deformation of the landslide is divided into two regions A, B, fig. 4a shows that the region a is a region with a small deformation rate in a deformation field along the slope direction, the deformation rate of most regions is 0-130 mm/a, the region B (dislocation region) is a region with the maximum deformation of the landslide along the slope direction, the deformation rate is 130-350 mm/a, and the maximum deformation along the slope direction is positioned on the lower left part of the dislocation region. FIG. 4B shows that the region A is a region with a smaller deformation rate in the normal deformation field, the deformation rate is between 0 and 20mm/a, and the region B is also a region with a largest landslide normal deformation, the deformation rate is between 20 and 80mm/a, wherein the largest normal deformation is located in the right toe region of the dislocation region. The two-dimensional deformation field distribution of the landslide is analyzed, the ancient landslide in the plateau countryside is mainly concentrated in the staggered platform area along the slope direction and the normal direction, the area also has the possibility of secondary instability on the landform and the landform, meanwhile, historical deposits on the front edge of the staggered platform area are continuously washed by miscellaneous valley cerebral river water, the slope of the front edge of the landslide becomes steep, the stability of a slope body is weakened due to exposed rocks and the like, and the risk of instability of the staggered platform area is further increased.
In order to verify the reliability of the landslide depth inversion method, in this embodiment, the two-dimensional deformation data of the rural ancient landslide of the peach plateau shown in fig. 5 is used, and the sliding depth of the rural ancient landslide of the peach plateau is obtained through inversion under different rheological parameter values (see table 2) according to the developed landslide depth inversion method based on the two-dimensional deformation of the slope (see fig. 5). In the following table 2, the slope depth centralized distribution interval and the slope volume characteristic parameter obtained by inversion under the condition of different rheological parameters are listed, and it can be found that the slope depth obtained by inversion can be effectively reduced by using larger rheological parameters (table 2):
table 2: downslope depth inversion result with different rheological parameters
It should be noted that the rheological parameter f represents the compression property of a slope body substance, and when the rheological parameter is equal to 0.3 or 0.5, the compressibility of the soil of the slip slope body is larger, at this time, the overall stability of the slip slope body is lower, and the probability of instability is relatively larger; when the rheological parameter is equal to 0.9, the soil compressibility of the landslide mass is small and basically approaches to a rigid body, and the ancient landslide mass of the peach plateau mainly comprises broken stone-containing silty clay and breccia-containing silty clay, and the bulk looseness of the mass is large; and it can be known from the deformation time sequence curves shown in fig. 3c-3d that the whole local and ancient landslide of the current peach plateau should be in a stable deformation period rather than a temporary slip state with a high probability. By combining the factors, the experiment considers that the rheological parameter is 0.7 which is most approximate to the actual situation of the local ancient landslide of the peaches, at the moment, the depth of the inversion local ancient landslide of the peaches is mainly and intensively distributed at 9-33 m, and the landslide volume is 3.49 multiplied by 10 7 m 3 . Meanwhile, wang Dong rising and the like judge that the thickness of the landslide body is 9.20-30.5 m according to data such as topography, landform and lithology of the rural landslide of the peaches through field investigation, the result has higher consistency with an inversion result when the rheological parameter takes a value of 0.7, and the reliability of the inversion result is verified. Fig. 5c shows that when the rheological parameter is 0.7, the sliding depth of the region I on the upper part of the mountain township ancient landslide obtained by inversion is relatively shallow (0 m-13 m), the region II adjacent to the region I with the remarkably deformed platform has the remarkably deepened sliding depth (5 m-45 m), the maximum sliding depth is located in the region of the mountain township ancient landslide historic site on the west side of the platform-staggering region, and the region has a large amount of landslide deposits and is rich in water resources, so that a foundation is provided for deep movement of the landslide. Further downwards along the staggered platform area, the depth of the reverse landslide is quickly attenuated to zero, field investigation and remote sensing images are displayed, a dotted line of the lower boundary of an area II in the graph 5c is just a relatively flat and steep dividing boundary of the terrain of the area, the terrain of the brain and the river bank along the miscellaneous valleys below the dotted line is flat and mostly hardened ground and houses, inSAR deformation data also shows that the area has no bright appearanceShows signs of deformation and has high relative stability.
Fig. 6 shows the data of the landslide depth along different sections (see fig. 5c for the section position), wherein section 1 is located at the middle upper part of the area ii, fig. 6a shows that the terrain is low in east and west, the area with larger landslide depth is mainly concentrated at the middle gully and the west side slope of the staggered platform area, the maximum landslide depth is 16m, and the section line landslide depth is basically similar under different rheological parameters. Section 2 is located below section ii and fig. 6b shows that the section line passes through the region of greatest landslide depth, the section east side landslide depth being essentially zero, the region of greater landslide depth still being concentrated primarily in the west part of the dislocation, the greatest sliding depths along the section being 45m, 35m and 32m when the rheological parameter is 0.3, 0.5 and 0.7 respectively, and the greatest landslide depth decaying rapidly to 25m when the rheological parameter is 0.9. The profile 3 is a longitudinal profile along the slope and passes through the maximum landslide depth region, fig. 6c shows that the landslide depth gradually increases from the upper edge of the slope to zero along the slope, and the inversion landslide depth rapidly decreases from the maximum to zero in the range from the error station region to the flat region below the slope, and meanwhile, the local enlargement of the profile 3 (fig. 6 d) shows that when the rheological parameters take different values, the landslide depth profile lines have basically similar shapes, and the overlap ratio of the profile lines is higher when the rheological parameters are 0.5 and 0.7, and only a few slight differences exist in the maximum landslide depth (fig. 6d, in the black dotted line oval region), but when the rheological parameters also need to take values of 0.5 and 0.7, the inversion depth has relatively obvious differences in the west ancient landslide accumulation region of the slope (fig. 5b-6 c).
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.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111123816.XA CN113848551B (en) | 2021-09-24 | 2021-09-24 | Landslide depth inversion method using InSAR lifting rail deformation data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111123816.XA CN113848551B (en) | 2021-09-24 | 2021-09-24 | Landslide depth inversion method using InSAR lifting rail deformation data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113848551A CN113848551A (en) | 2021-12-28 |
| CN113848551B true CN113848551B (en) | 2023-03-21 |
Family
ID=78979431
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111123816.XA Expired - Fee Related CN113848551B (en) | 2021-09-24 | 2021-09-24 | Landslide depth inversion method using InSAR lifting rail deformation data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113848551B (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114185048B (en) * | 2022-02-15 | 2022-05-31 | 湖南吉赫信息科技有限公司 | Method, system and storage medium for extracting landslide displacement vector by foundation InSAR |
| CN114791273B (en) * | 2022-06-24 | 2022-09-13 | 中国铁道科学研究院集团有限公司铁道建筑研究所 | InSAR deformation monitoring result interpretation method for landslide |
| CN115236667B (en) * | 2022-07-08 | 2024-05-03 | 长安大学 | Potential landslide volume estimation method fusing multisource SAR data |
| CN115963462B (en) * | 2023-03-09 | 2023-05-16 | 成都理工大学 | InSAR maximum deformation gradient detection method considering gradient and slope direction |
| CN117036222B (en) * | 2023-08-18 | 2024-04-19 | 广东省水利水电科学研究院 | Water body detection method, device and medium for fusing multi-scale polarized SAR images |
| CN116912068B (en) * | 2023-09-12 | 2023-11-21 | 成都理工大学 | Landslide early warning method based on area deformation observation |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101685159A (en) * | 2009-08-17 | 2010-03-31 | 北京航空航天大学 | Method for constructing spaceborne SAR signal high precision phase-keeping imaging processing platform |
| CN111474544A (en) * | 2020-03-04 | 2020-07-31 | 广东明源勘测设计有限公司 | Landslide deformation monitoring and early warning method based on SAR data |
| CN112395789A (en) * | 2020-10-23 | 2021-02-23 | 马培峰 | Method for analyzing urban landslide deformation by coupling InSAR and numerical simulation |
| CN112540369A (en) * | 2020-11-27 | 2021-03-23 | 武汉大学 | Landslide three-dimensional deformation resolving method and system integrating GNSS and lifting rail time sequence InSAR |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB0316858D0 (en) * | 2003-07-18 | 2003-08-20 | Univ Nottingham | A process for radar measurements of the position and movement of indivual targets and of areas of land and ice |
| GB201709525D0 (en) * | 2017-06-15 | 2017-08-02 | Univ Nottingham | Land deformation measurement |
| CN107476275B (en) * | 2017-08-29 | 2020-01-10 | 成都理工大学 | Early warning method for rock landslide in red zone and application thereof |
| CN108983232B (en) * | 2018-06-07 | 2020-06-09 | 中南大学 | InSAR two-dimensional surface deformation monitoring method based on adjacent rail data |
| CN109541592A (en) * | 2018-10-30 | 2019-03-29 | 长安大学 | Loess Landslide type and sliding-modes analysis method based on InSAR multidimensional deformation data |
| CN111458709B (en) * | 2020-06-08 | 2023-12-22 | 河南大学 | Method and device for monitoring wide-area earth surface two-dimensional deformation field of spaceborne radar |
| CN112834432A (en) * | 2021-01-08 | 2021-05-25 | 兰州大学 | Landslide thickness inversion method based on remote sensing technology and kinematics principle |
| CN113064170A (en) * | 2021-03-29 | 2021-07-02 | 长安大学 | Expansive soil area surface deformation monitoring method based on time sequence InSAR technology |
| CN112904337B (en) * | 2021-05-07 | 2021-11-05 | 北京东方至远科技股份有限公司 | Side slope deformation time sequence monitoring method based on Offset Tracking technology |
-
2021
- 2021-09-24 CN CN202111123816.XA patent/CN113848551B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101685159A (en) * | 2009-08-17 | 2010-03-31 | 北京航空航天大学 | Method for constructing spaceborne SAR signal high precision phase-keeping imaging processing platform |
| CN111474544A (en) * | 2020-03-04 | 2020-07-31 | 广东明源勘测设计有限公司 | Landslide deformation monitoring and early warning method based on SAR data |
| CN112395789A (en) * | 2020-10-23 | 2021-02-23 | 马培峰 | Method for analyzing urban landslide deformation by coupling InSAR and numerical simulation |
| CN112540369A (en) * | 2020-11-27 | 2021-03-23 | 武汉大学 | Landslide three-dimensional deformation resolving method and system integrating GNSS and lifting rail time sequence InSAR |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113848551A (en) | 2021-12-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113848551B (en) | Landslide depth inversion method using InSAR lifting rail deformation data | |
| Li et al. | Monitoring active motion of the Guobu landslide near the Laxiwa Hydropower Station in China by time-series point-like targets offset tracking | |
| Ventura et al. | Tracking and evolution of complex active landslides by multi-temporal airborne LiDAR data: The Montaguto landslide (Southern Italy) | |
| Necsoiu et al. | Rock glacier dynamics in Southern Carpathian Mountains from high-resolution optical and multi-temporal SAR satellite imagery | |
| Kääb et al. | Analysing the creep of mountain permafrost using high precision aerial photogrammetry: 25 years of monitoring Gruben rock glacier, Swiss Alps | |
| Baldi et al. | Kinematics of a landslide derived from archival photogrammetry and GPS data | |
| Guerriero et al. | Influence of slip-surface geometry on earth-flow deformation, Montaguto earth flow, southern Italy | |
| Liu et al. | Surface displacement and topographic change analysis of the Changhe landslide on September 14, 2019, China | |
| Ikuemonisan et al. | Characterisation and mapping of land subsidence based on geodetic observations in Lagos, Nigeria | |
| Jiang et al. | A monitoring method integrating terrestrial laser scanning and unmanned aerial vehicles for different landslide deformation patterns | |
| Fukuda et al. | Recent variations in the terminus position, ice velocity and surface elevation of Langhovde Glacier, East Antarctica | |
| Ma et al. | Investigation on mining subsidence based on sentinel-1A Data by SBAS-InSAR technology-Case Study of Ningdong Coalfield, China | |
| Huang et al. | Ancient landslide in Wanzhou District analysis from 2015 to 2018 based on ALOS-2 data by QPS-InSAR | |
| Hu et al. | Time-series InSAR technology for ascending and descending orbital images to monitor surface deformation of the metro network in Chengdu | |
| Zhu et al. | Rainfall and water level fluctuations dominated the landslide deformation at Baihetan Reservoir, China | |
| CN114199189A (en) | Mining subsidence monitoring method combining unmanned aerial vehicle and DInSAR technology | |
| Jiao et al. | Identification of paleoearthquakes based on geomorphological evidence and their tectonic implications for the southern part of the active Anqiu–Juxian fault, eastern China | |
| Fatholahi et al. | Monitoring surface deformation over oilfield using MT-Insar and production well data | |
| Yuwono et al. | Land Subsidence monitoring 2016-2018 analysis using GNSS CORS UDIP and DinSAR in Semarang | |
| Székely et al. | Geomorphic expression of neotectonic activity in a low relief area in an Airborne Laser Scanning DTM: A case study of the Little Hungarian Plain (Pannonian Basin) | |
| Shi et al. | Investigating the Intra-Annual Dynamics of Kunlun Glacier in the West Kunlun Mountains, China, From Ascending and Descending Sentinel-1 SAR Observations | |
| Lu et al. | Geostatistical Data Fusion of Multiple Type Observations to Improve Land Subsidence Monitoring Resolution in the Choushui River Fluvial Plain, Taiwan. | |
| Nolan et al. | Which are the highest peaks in the US Arctic? Fodar settles the debate | |
| Delacourt et al. | Potential and limitation of ERS-Differential SAR Interferometry for landslide studies in the French Alps and Pyrenees | |
| Jackson et al. | Uplift in the Nepal Himalaya revealed by spirit leveling |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20230321 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |