CN113281742B - SAR landslide early warning method based on landslide deformation information and meteorological data - Google Patents
SAR landslide early warning method based on landslide deformation information and meteorological data Download PDFInfo
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Abstract
The invention discloses an SAR landslide early warning method based on landslide deformation information and meteorological data, which comprises the following steps: s1, determining a rainfall threshold value of a landslide caused by effective rainfall; s2, utilizing the rainfall threshold value and the regional rainfall information to perform landslide early warning; s3, acquiring time sequence deformation data of the early warning area; s4, calculating a critical deformation threshold value of landslide occurrence; s5, checking the landslide early warning area obtained in the step S2 and the time sequence deformation data obtained in the step S3 by utilizing a critical deformation threshold value, and realizing the secondary early warning of the early warning area. Aiming at the complex characteristics of special sudden property, concealment, uncertainty and the like of landslide, the invention realizes the generalization of the relation among landslide, rainfall and deformation by researching the relativity between the time sequence rainfall and the time sequence deformation of the landslide, and can accurately determine whether the target is a landslide early warning area.
Description
Technical Field
The invention relates to the technical field of synthetic aperture radar monitoring of geological disasters, in particular to an SAR landslide early warning method based on landslide deformation information and meteorological data.
Background
The country is a country with multiple mountains, the mountain area occupies 69% of the total area of the country, and the common geological disasters in the mountain area cause great threat to road construction and operation. Landslide refers to the phenomenon that a rock-soil body on a side slope loses stability under the influence of natural or human factors and slides down along the whole through damaged surface, and is used as a common geological disaster, and has wide distribution and large harm. According to incomplete statistics, in addition to casualties caused by landslide disasters, landslide disasters have suffered from varying degrees of damage to thousands of kilometers of roads during the past 10 years, resulting in hundreds of millions of economic losses. With the development of the economy of China, the national highway construction is continuously increased and extends to mountainous areas, which means that the road disasters are increasingly serious. Through experience and training of a series of landslide disaster events, early warning of landslide disasters can be seen, and further effective disaster analysis is a main way for changing 'passive disaster avoidance and relief' into 'active disaster prevention and treatment', and reducing losses caused by the disasters.
The conventional earth surface deformation monitoring method is mostly focused on the ground monitoring technology and the underground monitoring technology, such as a geodetic method, a GPS method, drilling point position monitoring, geophysical detection technology and the like. The traditional landslide monitoring method has no continuity in time and space, and the ground and underground monitoring technology faces the problems of difficult communication, cruising and arrangement and installation in complex difficult mountain areas which are rarely to be reached by people and difficult to reach. In recent years, with rapid development of communication technology and sensor technology, an aerospace remote sensing monitoring technology has developed, and optical remote sensing, a satellite-borne InSAR technology and an aerial photogrammetry and airborne LiDAR technology change landslide hazard monitoring from a point location-based monitoring mode to a dynamic monitoring mode based on a plane. However, landslide disasters occur accidentally and often with severe weather conditions, even at night, optical remote sensing, aerial photogrammetry and laser LiDAR technologies are difficult to acquire image data with high resolution and high timeliness. All-day and all-day radars can penetrate through cloud and forests to acquire images, so that the advantages incomparable with optical images are achieved.
Synthetic aperture radar interferometry (InSAR) is a quantitative microwave remote sensing technology developed in the last half century. The method has the advantages that the three-dimensional information of the ground surface can be extracted by utilizing an interference phase diagram and platform attitude data carrying a radar sensor, the change information of the ground surface coverage can be extracted by utilizing interference coherence analysis, and the influence of topography and other factors can be removed from the interference phase diagram by utilizing a secondary differential technology in terms of ground surface deformation detection, so that the deformation information, namely a synthetic aperture radar differential interference technology (D-InSAR), is extracted, and meanwhile, a small baseline set technology and a permanent scatterer technology developed in the D-InSAR technology have the advantages of acquiring micro deformation and long-time sequence slow ground surface deformation, so that the InSAR technology has wider application prospects in the landslide disaster early warning and monitoring fields. Meanwhile, according to the statistics discovery of sudden landslide disasters in China in recent years, the continuous rainfall-induced landslide occupies 65% of the total occurrence amount of the landslide, wherein the local rainfall-induced landslide occupies 43% of the total occurrence amount and occupies 66% of the continuous rainfall-induced landslide. That is, about two-thirds of sudden landslide disasters are due to the close correlation of atmospheric rainfall or meteorological factors. Therefore, the InSAR technology and landslide induction factors (rainfall) are combined to be an effective way for landslide disaster early warning.
Disclosure of Invention
The invention aims to provide an SAR landslide early warning method based on landslide deformation information and meteorological data, so as to overcome the defects existing in the prior art.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
a SAR landslide early warning method based on landslide deformation information and meteorological data comprises the following steps:
s1, calculating a correlation coefficient between the times of occurrence of a historical landslide and the time sequence rainfall before occurrence of the landslide, and determining a rainfall threshold value of the landslide caused by the effective rainfall;
s2, correcting the rainfall threshold obtained in the step S1 according to a correlation coefficient between the time sequence effective rainfall and the time sequence deformation data;
s3, utilizing the rainfall threshold value and the regional rainfall information to perform landslide early warning;
s4, acquiring time sequence deformation data of the early warning area;
s5, determining effective deformation quantity for inducing landslide according to the relation between the historical landslide occurrence times and the time sequence accumulated deformation data, and calculating critical deformation threshold value of landslide occurrence according to the effective deformation quantity;
s6, checking the landslide early warning area obtained in the step S2 and the time sequence deformation data obtained in the step S3 by utilizing a critical deformation threshold value, and realizing the secondary early warning of the early warning area.
Further, the effective rainfall in the step S1 is calculated by the following method:
γ n ＝γ 0 +Tγ 1 +T 2 γ 2 +T 3 γ 3 +...+T n γ n ；
in gamma n Representing effective rainfall; gamma ray 0 Represents the rainfall on the same day; gamma ray 1 、γ 2 、γ 3 、...、γ n Represents the rainfall before the current day; n is the number of days elapsed; t is rainfall coefficient, and is taken as 0.9, 0.8, 0.7, 0.6, 0.5 and 0.4.
Further, the step S3 specifically includes: and judging the area where the rainfall threshold is larger than the set value as a landslide is easy to occur, and judging the area where the rainfall threshold is smaller than or equal to the set value as a stable area.
Further, the step S4 is to perform time sequence InSAR processing on the landslide prone area to obtain time sequence deformation data of the area.
Further, the step of obtaining the time sequence deformation data of the landslide prone area by performing time sequence InSAR processing on the landslide prone area specifically comprises the following steps:
the j-th interference phase map is set to be expressed in the azimuth-distance pixel coordinate system (x, r) as:
wherein lambda is the radar wavelength; d (t) B X, r) and d (t) A X, r) are each t B And t A The moment corresponds to the reference moment t 0 The deformation amount accumulated by the line of sight defense line is d (t) 0 X, r) ≡0; with d (t) i X, r), (i=1, 2,3 … N) to represent the deformation time series, the corresponding phase isThe method comprises the following steps: />
M phase values corresponding to the deformation of each pixel point of the research area are expressed by vectors
Will be calculated from the differential interferogramsThe individual values are expressed as vectors:
wherein, the main image ie= [ IE 1 ，IE 2 ，IE 3 ，...，IE M ]Side image is= [ IS ] 1 ，IS 2 ，IS 3 ，...，IS M ]；
The vector phase of the interferogram is expressed in the form of a matrix on the basis of the above formula:
in the matrix, each row corresponds to each auxiliary differential interference phase diagram, each column corresponds to SAR images at different time, the columns of the main image and the auxiliary image in the matrix are + -1, the rest columns are 0,
such asI.e. can be used to make [ M.times.N ]]The rank matrix G is expressed as
When a series of interference pairs are generated and are in the same small base line subset, the rank of M is more than or equal to N and N is G can be obtained through a least square methodPhase matrix:
for the phenomenon that interference pairs of all images are unlikely to be in the same baseline subset, the existing SAR is combined into a subset with a certain quantity according to a threshold value, and then G T G is a matrix with reduced rank, if the matrix is a non-full rank matrix, singular value decomposition is adopted to decompose the singular matrix into: g=usv T Wherein V is T For the rate of average phase, U is the quadrature matrix, S is the diagonal matrix, and then the phase can be converted to average phase velocity:
and then obtaining the minimum norm solution of the velocity vector V, and integrating to obtain the estimated value of the phase, namely the accurate deformation phase vector delta.
Further, the step S5 is specifically: and acquiring a deformation threshold value by calculating a correlation coefficient between a time sequence accumulated deformation amount and the landslide occurrence frequency when the landslide occurs, acquiring or obtaining the deformation amount corresponding to the landslide occurrence frequency according to a relation diagram between the time sequence accumulated deformation amount and the landslide occurrence frequency, taking the deformation value corresponding to the first jump as an early warning level deformation threshold value, and taking the deformation value corresponding to the second jump as an alarm level deformation threshold value.
Further, training a landslide region deformation criterion model according to deformation data of a historical landslide region, and obtaining an accurate early-warning level deformation threshold value and an accurate alarm level deformation threshold value according to the model.
Further, the secondary early warning of the primary early warning area is specifically to judge the activity degree of the primary early warning area according to the relation between the time sequence deformation data of the primary early warning area and the early warning level deformation threshold value and the alarm level deformation threshold value.
Compared with the prior art, the invention has the advantages that: aiming at the complex characteristics of special sudden property, concealment, uncertainty and the like of landslide, the invention realizes the generalization of the relation among landslide, rainfall and deformation by researching the relativity between the time sequence rainfall and the time sequence deformation of the landslide, and can accurately determine whether the target is a landslide early warning area.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of the SAR landslide warning method based on landslide deformation information and meteorological data.
Fig. 2 is a map of a landslide area rainfall scatter.
FIG. 3 is a graph showing the relationship between effective rainfall and the number of landslide occurrences
Fig. 4 is a preliminary pre-warning criteria graph.
FIG. 5 is a flow chart of acquiring time-series deformation data of a target area.
Fig. 6 is a graph of deformation data for the study area.
Fig. 7 is a schematic diagram of two early warning.
Detailed Description
The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making clear and defining the scope of the present invention.
Referring to fig. 1, a typical SAR landslide early warning method based on landslide deformation information and meteorological data is disclosed in this embodiment, which includes the following steps:
and S1, calculating a correlation coefficient between the times of occurrence of the historical landslide and the time sequence rainfall before occurrence of the landslide, and determining a rainfall threshold value of the landslide caused by the effective rainfall. The method comprises the following steps:
in the case where the number of rainfall is positively correlated with the rainfall, the possibility of occurrence of landslide increases. According to the embodiment, rainfall data of a rainfall observation station near an existing landslide area are collected, and the rainfall data of the same day in a plurality of time periods before landslide are multiplied by the effective rainfall coefficients of the same day to obtain the effective rainfall, wherein the effective rainfall coefficients use a power exponent form calculation mode:
γ n ＝γ 0 +Tγ 1 +T 2 γ 2 +T 3 γ 3 +...+T n γ n (1)；
in gamma n Representing effective rainfall; gamma ray 0 Represents the rainfall on the same day; gamma ray 1 、γ 2 、γ 3 、...、γ n Represents the rainfall before the current day; n is the number of days elapsed; t is a rainfall coefficient, and is taken as 0.9, 0.8, 0.7, 0.6, 0.5 and 0.4, and the relevance of the rainfall and landslide occurrence frequency is utilized to determine T.
In this embodiment, the landslide points 500 in the Sichuan province are obtained, the time sequence rainfall data of the rainfall monitoring station closest to each landslide point are collected, the relation between the landslide occurrence times and the rainfall is statistically analyzed, a relation scatter diagram between the landslide occurrence times and different effective rainfall is formulated, and the rainfall scatter diagram of the selected part of landslide areas is shown in fig. 2. According to the graph (as shown in fig. 3) of the relation between the effective rainfall and the occurrence times of landslide, when the effective rainfall reaches 60mm, the curve becomes steep, the slope increases, the occurrence of the jumping phenomenon of the landslide occurs, at the moment, the correlation coefficient is more than half, and when the effective rainfall reaches 120mm and 200mm, the similar phenomenon also occurs, so that the effective rainfall threshold D is respectively set to 60mm, 120mm and 200mm and corresponds to the early warning grade of the primary landslide: the attention level, the early warning level and the alarm level are used for acquiring a landslide hazard preliminary early warning criterion model, and as shown in fig. 4, the landslide hazard preliminary early warning criterion model is divided into a lowest critical line, a middle critical line and a A, B, C region, wherein the lowest critical line lower than the rainfall is a non-early warning region, namely a region A; the pre-warning area which is higher than the lowest critical line and lower than the middle critical line (namely the area B) is the pre-warning area to be selected; and the important early warning area is higher than the middle critical line (namely the C area).
And S2, calculating the correlation between the time sequence rainfall and the time sequence deformation according to a formula (2), correcting the effective rainfall threshold value obtained in the step S1, and obtaining the contribution quantity of the effective rainfall to the landslide area deformation according to the correlation coefficient between the time sequence rainfall and the time sequence deformation, so as to verify the effectiveness of the effective rainfall threshold value. In this embodiment, the correlation coefficient obtained according to the formula (2) has consistency with the effective rainfall threshold obtained in S1.
S3, carrying out landslide early warning by utilizing rainfall threshold value and regional rainfall information, wherein the method specifically comprises the following steps: and carrying out preliminary early warning on the target area according to the effective rainfall threshold. Firstly, time sequence rainfall data of a target area are acquired, the effective rainfall of each time point is calculated according to the formula (1), preliminary early warning is carried out on the target area according to the formula (3), and whether the target is a landslide easily-generated area is judged.
Step S4, acquiring time sequence deformation data of an early warning area, wherein the time sequence deformation data specifically comprises the following steps: and 3, judging a landslide easily-developed area, performing time sequence InSAR processing on SAR data of the area, and acquiring time sequence deformation data of the area. In the embodiment, an SBAS-InSAR technology is adopted, and firstly, a space-time base line threshold value is set to obtain an interference pair; then, performing image registration, wherein all images are registered to the super main image; then, carrying out coherence computation and interference pattern filtering to generate a coherence coefficient pattern and a filtered secondary differential interference pattern; the unwrapping of the filtered secondary differential interferogram is realized in a mode of minimizing the difference between the unwrapped phase gradient and the true phase gradient; obtaining a high coherence point according to the size of the coherence coefficient, performing track refining and re-flattening, and removing the residual track phase and the horizon phase to the maximum extent; on the basis, carrying out statistical analysis on the phases of the high coherence points obtained by a coherence coefficient threshold method, modeling and resolving each coherence point, and obtaining the linear deformation phases and the elevation error phases of the high coherence region by utilizing a singular value decomposition mode; and finally, separating out a residual phase, carrying out secondary unwrapping on the residual phase, obtaining an atmospheric phase by using a spatial domain filtering mode, solving all components in the interference phase, and carrying out difference between an original phase time sequence and each solved interference phase to obtain a linear deformation phase and a nonlinear deformation phase of a research area.
The main process flow of the SBAS-InSAR technology is shown in FIG. 5.
Step S5, determining an effective deformation quantity for inducing landslide according to a correlation coefficient between the effective rainfall of the time sequence and the time sequence deformation data, and calculating a critical deformation threshold value for landslide occurrence according to the effective deformation quantity, wherein the critical deformation threshold value specifically comprises the following steps:
in the embodiment, the time sequence SAR data of the existing landslide area is subjected to interference processing, and the time sequence deformation data of the landslide at all places is obtained by using an SBAS-InSAR technology, wherein the specific operation process is as follows:
after removing the interference phase, it can be assumed that the obtained interference pattern does not contain residual topographic phase, atmospheric phase and noise phase, and at this time, the jth interference phase pattern can be expressed as:
wherein lambda is the radar wavelength; d (t) B X, r) and d (t) A X, r) are each t B And t A The moment corresponds to the reference moment t 0 The cumulative deformation of the line of sight defense line of (c) is d (t) 0 X, r) ≡0; with d (t) i X, r), (i=1, 2,3 … N (number of images)) to represent a deformation time series, the corresponding phase isThe method comprises the following steps:
m phase values corresponding to the deformation of each pixel point of the research area are represented by vectors:
will be calculated from the differential interferogramsThe individual values are expressed as vectors:
wherein, the main image ie= [ IE 1 ，IE 2 ，IE 2 ，...，IE M ]Side image is= [ IS ] 1 ，IS 2 ，IS 3 ，...，IS M ]。
The vector phases of the interferograms can be expressed in the form of a matrix from the above formula:
in the matrix, each row corresponds to each auxiliary differential interference phase diagram, each column corresponds to SAR images at different time, the columns of the main image and the auxiliary image in the matrix are + -1, and the other columns are 0, such asI.e. can be used to make [ M.times.N ]]The rank matrix G is expressed as:
when a series of interference pairs are generated and are in the same small baseline subset, the rank M is more than or equal to N, N is G, and a phase matrix can be obtained through a least square method:
for the phenomenon that interference pairs of all images are unlikely to be in the same baseline subset, the existing SAR is combined into a subset with a certain quantity according to a threshold value, and then G T G is a reduced rank matrix,
so that the influence of noise and coherence on the interference pair can be reduced. A method of singular value decomposition (core algorithm of SBAS-InSAR) is adopted for the phenomenon that the matrix is a non-full order matrix, namely singular matrix is decomposed into:
G＝USV T (12)
wherein V is T The rate of the average phase, U, is the orthogonal matrix and S is the diagonal matrix. The phase may then be converted to an average phase velocity:
after the minimum norm solution of the velocity vector V is obtained, the phase estimation value is obtained by integration. The more accurate deformation phase vector delta can be obtained. A partial time sequence deformation rate diagram of the embodiment is shown in fig. 6, and a landslide region deformation criterion model is trained according to deformation data of a landslide region.
The time sequence deformation rate criterion diagram is basically similar to the acquisition method of the preliminary early warning criterion diagram, the deformation threshold is acquired by calculating the correlation coefficient between the time sequence accumulated deformation quantity when landslide occurs and the landslide occurrence frequency, the deformation quantity corresponding to the occurrence of jumping of the landslide or the landslide frequency is acquired according to the relation diagram between the time sequence accumulated deformation quantity and the landslide occurrence frequency, the deformation value corresponding to the first jump is taken as the early warning level deformation threshold, and the deformation value corresponding to the second jump is taken as the alarm level deformation threshold. The secondary pre-warning criteria diagram of this embodiment is shown in fig. 7.
And S6, checking the landslide early warning area obtained in the step S3 and the time sequence deformation data obtained in the step S4 by utilizing a critical deformation threshold value, so as to realize the secondary early warning of the early warning area. The method comprises the following steps: and on the basis of comprehensively utilizing inversion deformation data before and after landslide hazard occurrence, realizing accurate inversion of time sequence deformation quantity and deformation rate of a landslide region, and establishing a critical deformation criterion diagram of landslide occurrence in step S5. Checking the landslide initial early warning area and time sequence deformation data thereof obtained in the step S3 and the step S4 by utilizing the landslide critical deformation threshold value obtained in the step S5, further judging the activity degree of the initial early warning area according to the relation between the time sequence deformation data of the initial early warning area and the deformation criterion diagram, and finally judging the type of the target area: attention level, early warning level, alarm level.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, the patentees may make various modifications or alterations within the scope of the appended claims, and are intended to be within the scope of the invention as described in the claims.
Claims (8)
1. The SAR landslide early warning method based on landslide deformation information and meteorological data is characterized by comprising the following steps of:
s1, calculating a correlation coefficient between the occurrence times of historical landslide and effective rainfall before occurrence of the landslide, and determining a rainfall threshold value of the landslide caused by the effective rainfall;
s2, correcting the rainfall threshold obtained in the step S1 according to a correlation coefficient between the time sequence rainfall and the time sequence deformation data;
s3, carrying out initial landslide early warning by utilizing the rainfall threshold value and the regional rainfall information to obtain an initial landslide early warning region;
s4, acquiring time sequence deformation data of a landslide early warning area;
s5, determining effective deformation quantity for inducing landslide according to the relation between landslide occurrence times and time sequence accumulated deformation data, and calculating critical deformation threshold value of landslide occurrence according to the effective deformation quantity;
s6, checking the landslide initial early warning area obtained in the step S3 and the time sequence deformation data obtained in the step S4 by utilizing a critical deformation threshold value, and realizing secondary early warning of the landslide initial early warning area.
2. The SAR landslide warning method based on landslide deformation information and meteorological data according to claim 1, wherein the effective rainfall in step S1 is calculated by:
γ n ＝γ 0 +Tγ 1 +T 2 γ 2 +T 3 γ 3 +...+T n γ n ；
in gamma n Representing effective rainfall; gamma ray 0 Represents the rainfall on the same day; gamma ray 1 、γ 2 、γ 3 、...、γ n Represents the rainfall before the current day; n is the number of days elapsed; t is rainfall coefficient, and is taken as 0.9, 0.8, 0.7, 0.6, 0.5 and 0.4.
3. The SAR landslide warning method based on landslide deformation information and meteorological data according to claim 1, wherein the step S3 specifically comprises: and judging that the effective rainfall is larger than the rainfall threshold value as a landslide early warning area, and judging that the effective rainfall is smaller than or equal to the effective rainfall threshold value as a stable area.
4. The method for SAR landslide warning based on landslide deformation information and meteorological data according to claim 3, wherein step S4 is to obtain the time series deformation data of the landslide early warning region by performing time series InSAR processing on the region.
5. The method for SAR landslide warning based on landslide deformation information and meteorological data according to claim 4, wherein the step of obtaining the time series deformation data of the landslide early warning area by performing time series InSAR processing on the area specifically comprises the following steps:
the j-th interference phase map is set to be expressed in the azimuth-distance pixel coordinate system (x, r) as:
wherein lambda is the radar wavelength; d (t) B X, r) and d (t) A X, r) are each t B And t A The moment corresponds to the reference moment t 0 The deformation amount accumulated by the line of sight defense line is d (t) 0 X, r) ≡0; with d (t) i X, r), i=1, 2,3 … N to represent the deformation time series, the corresponding phase isThe method comprises the following steps: />
The N phase value corresponding to the deformation of each pixel point of the research area is expressed by a vector
Representing M values calculated from the differential interferogram as vectors, wherein
Wherein, the main image ie= [ IE 1 ，IE 2 ，IE 3 ，...，IE M ]Side image is= [ IS ] 1 ，IS 2 ，IS 3 ，...，IS M ]；
The vector phases of the interferograms can be expressed in the form of a matrix from the above formula:
in the matrix, each row corresponds to each auxiliary differential interference phase diagram, each column corresponds to SAR images at different time, the columns of the main image and the auxiliary image in the matrix are + -1, the rest columns are 0,
such asI.e. can be used to make [ M.times.N ]]The rank matrix G is expressed as
When a series of interference pairs are generated and are in the same small baseline subset, the rank M is more than or equal to N, N is G, and a phase matrix can be obtained through a least square method:
for the phenomenon that interference pairs of all images are unlikely to be in the same baseline subset, the existing SAR is combined into a subset with a certain quantity according to a threshold value, and then G T G is a matrix with reduced rank, if the matrix is a non-full rank matrix, singular value decomposition is adopted to decompose the singular matrix into: g=usv T Wherein V is T For the rate of average phase, U is the quadrature matrix, S is the diagonal matrix, and then the phase can be converted to average phase velocity:
and then obtaining the minimum norm solution of the velocity vector V, and integrating to obtain the estimated value of the phase, namely the accurate deformation phase vector delta.
6. The SAR landslide warning method based on landslide deformation information and meteorological data according to claim 1, wherein the step S5 is specifically: and obtaining a critical deformation threshold value by calculating a correlation coefficient between a time sequence accumulated deformation quantity and landslide occurrence times when landslide occurs, obtaining a deformation quantity corresponding to the occurrence of jumping of the landslide times according to a relation diagram between the time sequence accumulated deformation quantity and the landslide occurrence times, and taking a deformation value corresponding to the first jumping as an early warning level deformation threshold value and a deformation value corresponding to the second jumping as an alarm level deformation threshold value.
7. The SAR landslide warning method based on landslide deformation information and meteorological data according to claim 6, wherein a landslide region deformation criterion model is obtained according to deformation data of a historical landslide region, and a warning level deformation threshold value and an alarm level deformation threshold value are obtained according to the model.
8. The SAR landslide early warning method based on landslide deformation information and meteorological data according to claim 1, wherein the secondary early warning of the landslide early warning region is specifically to determine the activity level of the landslide early warning region according to the relation between the time sequence deformation data of the landslide early warning region and the early warning level deformation threshold value and the alarm level deformation threshold value.
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| CN114093133A (en) * | 2021-11-24 | 2022-02-25 | 合肥工业大学 | Regional geological disaster weather forecast early warning method |
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| CN116299446B (en) * | 2022-12-27 | 2023-11-10 | 速度科技股份有限公司 | Surface deformation monitoring method and system based on INSAR technology |
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