CN113281742A - 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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- 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
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- 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
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- 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
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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 the landslide caused by the effective rainfall; s2, performing landslide initial early warning by using the rainfall threshold and the regional rainfall information; s3, acquiring time sequence deformation data of the primary early warning area; s4, calculating a critical deformation threshold value of the landslide; and S5, checking the landslide primary early warning area obtained in the step S2 and the time sequence deformation data obtained in the step S3 by using a critical deformation threshold value, and realizing secondary early warning of the primary early warning area. Aiming at the special complex characteristics of the landslide, such as burstiness, concealment, uncertainty and the like, the method realizes the induction of the relation among the landslide, the rainfall and the deformation quantity by researching the correlation between the landslide time sequence rainfall and the time sequence deformation quantity, 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
China is a mountainous country, the area of mountainous regions accounts for 69% of the total area of the national soil, and common geological disasters in mountainous regions pose great threats to road construction and operation. Landslide refers to the phenomenon that rock and soil mass on a side slope loses stability under the influence of natural or human factors and integrally slides down along a through damaged surface, and the landslide is used as a common geological disaster and is wide in distribution and great in harm. According to incomplete statistics, in addition to casualties caused by landslide disasters, landslide disasters have caused thousands of kilometers of roads to suffer different degrees of damage during the past 10 years, causing hundreds of millions of economic losses. With the development of economy in China, the road construction in China will be increased continuously and will be extended to mountainous areas continuously, which means that the road disasters will be increasingly serious. Through experience and training of a series of landslide disaster events, early warning of landslide disasters and effective disaster analysis are the main ways of changing 'passive disaster prevention and relief' into 'active disaster prevention and control' and reducing loss caused by disasters.
The traditional earth surface deformation monitoring method mostly focuses on ground monitoring technology and underground monitoring technology, such as geodetic surveying method, GPS method, drilling point location monitoring and geophysical exploration technology. The traditional landslide monitoring method has no continuity in time and space, and in the face of complex and difficult mountainous areas which are rare and difficult to reach, the ground and underground monitoring technology faces the problems of difficult communication, difficult cruising and difficult arrangement and installation. In recent years, with the rapid development of communication technology and sensor technology, the aerospace remote sensing monitoring technology comes along, and optical remote sensing, satellite-borne InSAR technology, aerial photogrammetry and airborne LiDAR technology change landslide disaster monitoring from the past point location-based monitoring mode to a surface-based dynamic monitoring means. However, when a landslide disaster occurs accidentally and is often accompanied by severe weather conditions, even at night, the optical remote sensing, aerial photogrammetry and laser LiDAR technologies have difficulty in acquiring image data with high resolution and high timeliness. The radar in all weather and all time can penetrate through cloud and fog and forests to obtain images, and further has incomparable advantages of optical images.
Synthetic aperture radar interferometry (InSAR) is a quantitative microwave remote sensing technology developed in nearly half a century. The method has the advantages of obtaining micro deformation and long-time sequence slow earth surface deformation, and enables the InSAR technology to have wider application prospect in the fields of landslide early warning and monitoring. Meanwhile, according to the statistics of sudden landslide disasters in China in recent years, the continuous rainfall induced landslide accounts for 65% of the total landslide, wherein the local rainfall induced landslide accounts for 43% of the total landslide and accounts for 66% of the continuous rainfall induced landslide. That is, about two-thirds of sudden landslide disasters are closely related due to atmospheric rainfall or meteorological factors. Therefore, the combination of InSAR technology and landslide induction factors (rainfall) is 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, and aims to overcome the defects in the prior art.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
an SAR landslide early warning method based on landslide deformation information and meteorological data comprises the following steps:
s1, calculating a correlation coefficient between the occurrence frequency of the historical landslide and the time sequence rainfall before the landslide occurs, and determining a rainfall threshold value of the landslide caused by the effective rainfall;
s2, correcting the rainfall threshold value obtained in the step S1 according to the correlation coefficient between the time sequence effective rainfall and the time sequence deformation data;
s3, performing landslide initial early warning by using the rainfall threshold and the regional rainfall information;
s4, acquiring time sequence deformation data of the primary early warning area;
s5, determining the effective deformation quantity of the induced landslide according to the relation between the historical landslide occurrence times and the time sequence accumulated deformation data, and calculating the critical deformation threshold value of the landslide occurrence according to the effective deformation quantity;
and S6, checking the landslide primary early warning area obtained in the step S2 and the time sequence deformation data obtained in the step S3 by using a critical deformation threshold value, and realizing secondary early warning of the primary early warning area.
Further, the effective rainfall in step S1 is calculated as:
γn＝γ0+Tγ1+T2γ2+T3γ3+...+Tnγn；
in the formula of gammanRepresents an effective rainfall; gamma ray0Representing the amount of rainfall that day; gamma ray1、γ2、γ3、...、γnRepresents the amount of rainfall before the day; n is the number of days elapsed; t is rainfall coefficient, and is selected from 0.9, 0.8, 0.7, 0.6, 0.5 and 0.4.
Further, the step S3 is specifically: and judging the area with the rainfall threshold value larger than the set value as a landslide and judging the area with the rainfall threshold value smaller than or equal to the set value as a stable area.
Further, the step S4 is to perform time series InSAR processing on the landslide prone area to obtain time series deformation data of the area.
Further, the obtaining of the time series deformation data of the region by performing the time series InSAR processing on the landslide incident region specifically includes:
setting the j interference phase diagram to be expressed in an azimuth-distance pixel coordinate system (x, r) as:
in the formula, lambda is radar wavelength; d (t)BX, r) and d (t)AX, r) are each tBAnd tAThe time corresponding to the reference time t0The amount of distortion of the line-of-sight defensive line of (1) is d (t)0X, r) ≡ 0; with d (t)iX, r), (i ═ 1, 2, 3 … N) and the corresponding phase is
Comprises the following steps:
the M phase values corresponding to the deformation quantity of each pixel point of the research area are expressed by vectors
To be calculated from differential interferograms
The values are represented as vectors:
The vector phase of the interferogram is expressed in matrix form on the basis of the above formula:
in the matrix, each column corresponds to each sub-differential interference phase diagram, each row corresponds to SAR images of different time, the column of the main image and the sub-image in the matrix is + -1, the rest columns are 0,
such as
That is, can be [ M × N]The order matrix G is represented as
When a series of interference pairs are generated and are positioned in the same small base line subset, M is larger than or equal to N, N is the rank of G, and a phase matrix can be obtained through a least square method:
for the phenomenon that interference pairs of all images cannot be in the same baseline subset, combining the existing SAR into a subset with a certain number according to a threshold value, and then GTG is a matrix after rank reduction, and if the matrix is a phenomenon of a matrix with a less than full rank, a singular value decomposition method is adopted to decompose the singular matrix into: G-USVTIn the formula, VTFor the rate of average phase, U is an orthogonal matrix and S is a diagonal matrix, then the phase can be converted to average phase velocity:
and then solving the minimum norm solution of the velocity vector V, and carrying out integration to obtain an estimated value of the phase, namely the accurate deformation phase vector delta.
Further, the step S5 is specifically: and obtaining a deformation threshold value by calculating a correlation coefficient between the time sequence accumulated deformation quantity when the landslide occurs and the landslide occurrence frequency, obtaining the deformation quantity corresponding to the landslide occurrence frequency or the landslide occurrence frequency when the landslide occurs, and taking the deformation value corresponding to the first jump as an early warning level deformation threshold value and the deformation value corresponding to the second jump as an alarm level deformation threshold value.
Further, a landslide region deformation criterion model is trained according to deformation data of a historical landslide region, and an accurate early warning level deformation threshold value and an accurate alarm level deformation threshold value are obtained according to the model.
Further, the secondary early warning of the primary early warning area specifically includes that the activity degree of the primary early warning area is judged 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 special complex characteristics of the landslide, such as burstiness, concealment, uncertainty and the like, the method realizes the induction of the relation among the landslide, the rainfall and the deformation quantity by researching the correlation between the landslide time sequence rainfall and the time sequence deformation quantity, and can accurately determine whether the target is a landslide early warning area.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of an SAR landslide early warning method based on landslide deformation information and meteorological data.
Fig. 2 is a rainfall scatter plot of a landslide area.
FIG. 3 is a graph showing the relationship between the effective rainfall and the number of landslides
FIG. 4 is a diagram of preliminary warning criteria.
FIG. 5 is a flow chart of acquiring time-series deformation data for a target region.
Fig. 6 is a graph of deformation data for a study area.
Fig. 7 is a schematic diagram of the two-shot 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, and the scope of the present invention will be more clearly and clearly defined.
Referring to fig. 1, a typical SAR landslide early warning method based on landslide deformation information and meteorological data disclosed in this embodiment includes the following steps:
and step S1, calculating a correlation coefficient between the occurrence frequency of the historical landslide and the time sequence rainfall before the landslide occurs, and determining the rainfall threshold value of the landslide caused by the effective rainfall. The method specifically comprises the following steps:
when the number of times of rainfall is positively correlated with the amount of rainfall, the possibility of occurrence of landslide is increased. The embodiment collects rainfall data of a rainfall observation station near the existing landslide area, and the daily rainfall data is multiplied by the daily effective rainfall coefficient respectively in a plurality of time before landslide to obtain the effective rainfall, wherein the effective rainfall coefficient uses a calculation mode in the form of power exponent:
γn＝γ0+Tγ1+T2γ2+T3γ3+...+Tnγn (1)；
in the formula of gammanRepresents an effective rainfall; gamma ray0Representing the amount of rainfall that day; gamma ray1、γ2、γ3、...、γnRepresents the amount of rainfall before the day; n is the number of days elapsed; t is a rainfall coefficient which is 0.9, 0.8, 0.7, 0.6, 0.5 and 0.4, and the correlation between the rainfall and the occurrence frequency of landslides is utilized to determine T in the invention.
In the embodiment, 500 landslide points in the Sichuan province are obtained, time sequence rainfall data of the rainfall monitoring station closest to each landslide point is collected, the relation between the occurrence frequency of landslide and the rainfall is subjected to statistical analysis, a relation scatter diagram between the occurrence frequency of landslide and different effective rainfall is formulated, and the rainfall scatter diagram in a selected part of landslide area is shown in fig. 2. According to the relation curve graph (as shown in fig. 3) between the effective rainfall and the occurrence frequency of the landslide, when the effective rainfall reaches 60mm, the curve becomes steep, the slope increases, the occurrence frequency of the landslide is a jumping phenomenon, the correlation coefficient at the moment is more than half of the coefficient, and when the effective rainfall reaches 120mm and 200mm, similar phenomena also occur, so that the effective rainfall threshold value D is respectively set to be 60mm, 120mm and 200mm, and the early warning level of the first landslide corresponds to the early warning level of the first landslide: an attention level, an early warning level and an alarm level, and then obtaining a landslide disaster preliminary early warning criterion model, as shown in fig. 4, the landslide disaster preliminary early warning criterion model is divided into a lowest critical line, a middle critical line and A, B, C areas, wherein the lowest critical line lower than rainfall is a non-early warning area, namely an area a; the early warning area to be selected is higher than the lowest critical line and lower than the middle critical line (namely the area B); and the area higher than the middle critical line (namely the area C) is a key early warning area.
And step S2, calculating the correlation between the time sequence rainfall and the time sequence deformation quantity according to the formula (2), correcting the effective rainfall threshold value obtained in the step S1, and obtaining the contribution amount of the effective rainfall to the deformation quantity of the landslide area according to the height of the correlation coefficient between the time sequence rainfall and the time sequence deformation quantity so as to verify the effectiveness of the effective rainfall threshold value. In the present embodiment, the correlation coefficient obtained according to the formula (2) has consistency with the effective rainfall threshold obtained at S1.
Step S3, carrying out landslide initial early warning by using the rainfall threshold and the regional rainfall information, specifically: and carrying out preliminary early warning on the target area according to the effective rainfall threshold. The method comprises the steps of firstly obtaining time sequence rainfall data of a target area, calculating effective rainfall of each time point according to the formula (1), carrying out preliminary early warning on the target area according to the formula (3), and judging whether the target is a landslide incidence area.
Step S4, acquiring time sequence deformation data of the initial warning area, specifically: the landslide prone region is judged through the step S3, and the time sequence InSAR processing is carried out on the SAR data of the region to obtain the time sequence deformation data of the region. In the embodiment, an SBAS-InSAR technology is adopted, and firstly, a space-time baseline threshold value is set to obtain an interference image pair; then, carrying out image registration, wherein all images are registered to the super main image; then, performing coherence calculation and interference pattern filtering to generate a coherence coefficient pattern and a filtered quadratic differential interference pattern; realizing the unwrapping of the filtered quadratic differential interferogram by adopting a mode of minimizing the difference between an unwrapping phase gradient and a real phase gradient; obtaining high coherence points according to the coherence coefficient, refining the track and re-leveling the track, and removing residual track phase and horizon phase to the maximum extent; on the basis, the phase of the high coherence point obtained by a coherence coefficient threshold method is subjected to statistical analysis, each coherence point is modeled and solved, and the linear deformation phase and the elevation error phase of the high coherence area are obtained by using a singular value decomposition mode; and finally, separating out a residual phase, performing secondary unwrapping on the residual phase, acquiring an atmospheric phase by using a spatial domain filtering mode, obtaining each component in the interference phase, and subtracting the original phase time sequence and each obtained interference phase to obtain a linear deformation phase and a nonlinear deformation phase of the research area.
The main process flow of the SBAS-InSAR technique is shown in fig. 5.
Step S5, determining the effective deformation quantity of the induced landslide according to the correlation coefficient between the time sequence effective rainfall and the time sequence deformation data, and calculating the critical deformation threshold value of the landslide according to the effective deformation quantity, specifically:
in this embodiment, interference processing is performed on the time sequence SAR data of the existing landslide area, and the SBAS-InSAR technology is used to obtain the time sequence deformation data of each landslide, and the specific operation process is as follows:
after removing the interference phase, it can be assumed that the obtained interference pattern does not include the residual topographic phase, the atmospheric phase and the noise phase, and then the jth interference phase pattern can be represented in the azimuth-distance pixel coordinate system (x, r) as:
in the formula, lambda is radar wavelength; d (t)BX, r) and d (t)AX, r) are each tBAnd tAThe time corresponding to the reference time t0The line-of-sight defensive line of (1) is accumulated, and thus d (t)0X, r) ≡ 0; with d (t)iX, r), (i ═ 1, 2, 3 … N (number of images)) represents a deformation time series, and the corresponding phase is
Namely, the method comprises the following steps:
the M phase values corresponding to the deformation quantity of each pixel point of the research area are expressed by vectors:
to be calculated from differential interferograms
The values are represented as vectors:
wherein, the main image IE [ IE ]1，IE2，IE2，...，IEM]The secondary image IS ═ IS1，IS2，IS3，...，ISM]。
The vector phase of the interferogram can be expressed in matrix form by the above formula:
in the matrix, each column corresponds to each sub-differential interference phase diagram, each row corresponds to SAR images at different time, the column of the main image and the sub-image in the matrix is +/-1, and the rest columns are 0, for example
That is, can be [ M × N]The order matrix G is represented as:
when a series of interference pairs are generated and are positioned in the same small base line subset, M is larger than or equal to N, N is the rank of G, and a phase matrix can be obtained through a least square method:
for the phenomenon that interference pairs of all images cannot be in the same baseline subset, combining the existing SAR into a subset with a certain number according to a threshold value, and then GTG is a matrix after the rank reduction,
the effect of noise and coherence on the interference pair can be reduced. For the phenomenon that the matrix is not full rank matrix, a singular value decomposition (SBAS-InSAR core algorithm) method is adopted, namely the singular matrix is decomposed into:
G＝USVT (12)
in the formula, VTFor the rate of average phase, U is the orthogonal matrix and S is the diagonal matrix. The phase can then be converted to an average phase velocity:
the minimum norm solution of the velocity vector V is obtained, and an estimated value of the phase is obtained by integration. And a relatively accurate deformation phase vector delta can be obtained. A partial time-series deformation rate graph of the present 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 graph is basically similar to the method for acquiring the preliminary early warning criterion graph, a deformation threshold value is acquired by calculating a correlation coefficient between a time sequence accumulated deformation quantity when landslide occurs and landslide occurrence times, a deformation quantity corresponding to the landslide occurrence times or the landslide occurrence times is acquired according to a relation graph between the time sequence accumulated deformation quantity and the landslide occurrence times, a deformation value corresponding to the first jump is used as an early warning level deformation threshold value, and a deformation value corresponding to the second jump is used as an alarm level deformation threshold value. The secondary warning criterion graph of the embodiment is shown in fig. 7.
And S6, checking the landslide primary early warning area obtained in the step S3 and the time sequence deformation data obtained in the step S4 by using a critical deformation threshold value, and realizing secondary early warning of the primary early warning area. The method specifically comprises the following steps: and (3) comprehensively utilizing inverted deformation data before and after the landslide disaster occurs to realize accurate inversion of the time sequence deformation quantity and the deformation rate of the landslide area, and on the basis, step S5 establishes a critical deformation criterion map of landslide occurrence. Checking the landslide initial early warning area and the time sequence deformation data thereof obtained in the steps S3 and S4 by using the critical landslide deformation threshold 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 map, 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, various changes or modifications may be made by the patentees within the scope of the appended claims, and within the scope of the invention, as long as they do not exceed the scope of the invention described in the claims.
Claims (8)
1. A SAR landslide early warning method based on landslide deformation information and meteorological data is characterized by comprising the following steps:
s1, calculating a correlation coefficient between the occurrence frequency of the historical landslide and the time sequence rainfall before the landslide occurs, and determining a rainfall threshold value of the landslide caused by the effective rainfall;
s2, correcting the rainfall threshold value obtained in the step S1 according to the correlation coefficient between the time sequence effective rainfall and the time sequence deformation data;
s3, performing landslide initial early warning by using the rainfall threshold and the regional rainfall information;
s4, acquiring time sequence deformation data of the primary early warning area;
s5, determining the effective deformation quantity of the induced landslide according to the relation between the occurrence frequency of the landslide and the accumulated time sequence deformation data, and calculating the critical deformation threshold value of the landslide according to the effective deformation quantity;
and S6, checking the landslide primary early warning area obtained in the step S2 and the time sequence deformation data obtained in the step S3 by using a critical deformation threshold value, and realizing secondary early warning of the primary early warning area.
2. The SAR landslide warning method based on landslide deformation information and weather data as claimed in claim 1, wherein the effective rainfall in step S1 is calculated by:
γn＝γ0+Tγ1+T2γ2+T3γ3+...+Tnγn；
in the formula of gammanRepresents an effective rainfall; gamma ray0Representing the amount of rainfall that day; gamma ray1、γ2、γ3、...、γnRepresents the amount of rainfall before the day; n is the number of days elapsed; t is rainfall coefficient, and is selected from 0.9, 0.8, 0.7, 0.6, 0.5 and 0.4.
3. The SAR landslide early warning method based on landslide deformation information and meteorological data according to claim 1, wherein the step S3 specifically comprises: and judging the area with the rainfall threshold value larger than the set value as a landslide and judging the area with the rainfall threshold value smaller than or equal to the set value as a stable area.
4. The SAR landslide early warning method based on landslide deformation information and weather data as claimed in claim 1, wherein step S4 is to perform time sequence InSAR processing on a landslide incidence area to obtain time sequence deformation data of the area.
5. The SAR landslide early warning method based on landslide deformation information and weather data as claimed in claim 4, wherein the obtaining of the time series deformation data of the landslide prone area by performing time series InSAR processing on the landslide prone area specifically comprises:
setting the j interference phase diagram to be expressed in an azimuth-distance pixel coordinate system (x, r) as:
in the formula, lambda is radar wavelength; d (t)BX, r) and d (t)AX, r) are each tBAnd tAThe time corresponding to the reference time t0The amount of distortion of the line-of-sight defensive line of (1) is d (t)0X, r) ≡ 0; with d (t)iX, r), (i ═ 1, 2, 3 … N) and the corresponding phase is
Comprises the following steps:
the M phase values corresponding to the deformation quantity of each pixel point of the research area are expressed by vectors
To be calculated from differential interferograms
The values are represented as vectors:
The vector phase of the interferogram can be expressed in matrix form by the above formula:
in the matrix, each column corresponds to each sub-differential interference phase diagram, each row corresponds to SAR images of different time, the column of the main image and the sub-image in the matrix is + -1, the rest columns are 0,
such as
That is, can be [ M × N]The order matrix G is represented as
When a series of interference pairs are generated and are positioned in the same small base line subset, M is larger than or equal to N, N is the rank of G, and a phase matrix can be obtained through a least square method:
for the phenomenon that interference pairs of all images cannot be in the same baseline subset, combining the existing SAR into a subset with a certain number according to a threshold value, and then GTG is a matrix after the rank reduction, and if the matrix is a matrix with a less than full rankThe singular matrix is decomposed into the following components by a singular value decomposition method: G-USVTIn the formula, VTFor the rate of average phase, U is an orthogonal matrix and S is a diagonal matrix, then the phase can be converted to average phase velocity:
and then solving the minimum norm solution of the velocity vector V, and carrying out integration to obtain an estimated value of the phase, namely the accurate deformation phase vector S.
6. The SAR landslide early warning method based on landslide deformation information and meteorological data according to claim 1, wherein the step S5 is specifically: and obtaining a deformation threshold value by calculating a correlation coefficient between the time sequence accumulated deformation quantity when the landslide occurs and the landslide occurrence frequency, obtaining the deformation quantity corresponding to the landslide occurrence frequency when the landslide occurs jumping according to a relation graph between the deformation quantity and the landslide occurrence frequency, and taking the deformation value corresponding to the first jumping as an early warning level deformation threshold value and the deformation value corresponding to the second jumping as an alarm level deformation threshold value.
7. The SAR landslide early warning method based on landslide deformation information and weather data as claimed in claim 6, wherein a landslide region deformation criterion model is obtained according to deformation data of a historical landslide region, and a quasi-early 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 weather data as claimed in claim 1, wherein the secondary early warning of the primary early warning region is specifically that the activity degree of the primary early warning region is judged according to the relation between the time sequence deformation data of the primary 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 |
| CN114333249A (en) * | 2022-01-14 | 2022-04-12 | 四川九洲北斗导航与位置服务有限公司 | Landslide early warning method and device |
| CN114440758A (en) * | 2022-01-09 | 2022-05-06 | 西北大学 | Analysis method for response of landslide to rainfall on regional scale |
| WO2023047441A1 (en) * | 2021-09-21 | 2023-03-30 | 日本電気株式会社 | Information providing device, information providing method, recording medium, and observation system |
| CN116299446A (en) * | 2022-12-27 | 2023-06-23 | 速度时空信息科技股份有限公司 | Surface deformation monitoring method and system based on INSAR technology |
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