CN109709550A - A kind of reservoir stability deformation monitoring processing method based on InSAR image data - Google Patents
A kind of reservoir stability deformation monitoring processing method based on InSAR image data Download PDFInfo
- Publication number
- CN109709550A CN109709550A CN201910043057.2A CN201910043057A CN109709550A CN 109709550 A CN109709550 A CN 109709550A CN 201910043057 A CN201910043057 A CN 201910043057A CN 109709550 A CN109709550 A CN 109709550A
- Authority
- CN
- China
- Prior art keywords
- deformation
- monitoring
- point
- data
- reservoir
- 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.)
- Granted
Links
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
Abstract
The reservoir stability deformation monitoring processing method based on InSAR image data that the invention discloses a kind of, this method is related on the basis of acquiring the InSAR data of basin reservoir stability, the deformation data in monitoring region is extracted first with the means such as SqueeSAR technology and differential interferometry processing, and carries out outlier identification for the deformation data obtained to define the interior unstable region occurred of monitoring range；Secondly consider two factors of reservoir level and rainfall, in conjunction with related geological conditions, by correlation analysis, the reason of Deformation Anomalies occur in judgement and explanation side slope；Monitoring data and correlation analysis are embodied in monitoring report finally by forms such as figure, tables.Compared with prior art, this method may be implemented that basin reservoir stability is a wide range of, high-precision continuous, semi-automatic monitoring, and can more reasonably carry out the early-warning and predicting of deformation of slope exception.
Description
Technical field
The invention belongs to slope stability monitorings and early warning technology field, in particular to a kind of to be based on InSAR image data
Reservoir stability deformation monitoring processing method.
Background technique
As numerous hydraulic and hydroelectric engineerings build up water storage and operation, basin reservoir stability is monitored by more and more extensive pass
Note.In every monitoring quantity, deformation quantity is the current library bank slope texture of reflection and the most direct physical quantity of motion state.To library
Bank slope body carries out deformation monitoring, can with objective reality note down the evolution of library bank slope body deformation, grasps library to understanding
The status and prediction deformation development trend of bank slope body are of great significance.
Traditional reservoir stability deformation monitoring method mainly carries out Ground Deformation monitoring and peace by precise leveling
It fills clinograph and carries out internal distortions monitoring, but wherein precise leveling is spent human and material resources greatly, data are acquired by environment
Weather and orographic condition influence greatly, and there are the deficiencies of at high cost, maintenance is difficult for inclinometer.At the same time, it is contemplated that reservoir stability
In the presence of having inconvenient traffic, being difficult to situations such as climbing, carrying out data acquisition just with novel measurement means becomes deformation of slope monitoring
Developing direction.
The development of synthetic aperture radar technique (SAR technology) provides in recent years for water conservancy and hydropower reservoir stability deformation monitoring
New technical support, wherein InSAR technology has good due to having the advantages that monitoring accuracy height, time and space high resolution
Application prospect.Currently, existing part engineering is by InSAR Technology application in the acquisition of deformation of slope information and mainly with average shape
The static images form such as variable Rate figure transmit monitoring information, these ways achieved in terms of detecting side slope unstability much into
Step, but there is also many deficiencies in terms of side slope early warning and risk management.It can be to a wide range of side slope it can be seen that inventing one kind
Deformation continuously monitors and the method for automatic early-warning is particularly important.
Summary of the invention
The purpose of the invention is to overcome the shortcoming of background technique, and provide a kind of company based on InSAR data
Continuous, semi-automatic reservoir stability deformation monitoring processing method.
To achieve the goals above, the technical solution of the present invention is as follows:
A kind of reservoir stability deformation monitoring processing method based on InSAR image data, which is characterized in that mainly include with
Lower step:
Step 1, the reservoir stability deformation data based on InSAR image are obtained and are updated
Several SAR for choosing covering monitoring region first answer image interference data sequence, by SqueeSAR technology from shadow
The monitoring point of Permanent scatterers PS point and distributed diffusion body DS point as deformation of slope is identified as in；Then joint utilizes prison
Phase signal on measuring point, successively isolates deformation data from phase signal；The deformation data of all monitoring points is finally obtained,
And the deformation data graph of each monitoring point is drawn with the sequence of SAR image collection time；
Step 2, the analysis of monitoring point strain mode and exceptional value automatic identification
The deformation data of all monitoring points obtained using step 1 is as input, by the monitoring week of entire time series
Phase (T0-Tn) it is divided into two subintervals, i.e. historical time sequence phase (T0-Tn-k) and monitoring time sequence phase (Tn-k-Tn), k is indicated
Time window, n indicate time serial number namely satellite image serial number, wherein exceptional value to be detected is included in monitoring time sequence
In phase；Compare (T0-Tn-k) strain mode in section, point analysis is in (T one by onen-k-Tn) deformation data is in monitoring cycle
No to deviate, i.e., whether displacement time series show non-linear；When displacement time series show non-linear, calculate disconnected
The rate of deformation of point front and back, and by the absolute value of the two difference compared with given threshold, if rate of deformation difference before and after breakpoint
Absolute value is greater than given threshold, and breakpoint is labeled as exceptional value；
Step 3, reservoir stability abnormal deformation driving factor analysis
Consider two key factors of Space Consistency and time duration, phase will be shown according to what is identified in step 2
As nonlinear displacement time series, and be marked as one group of abnormal monitoring point in continuous at least twice update and be defined as
Side slope unstable region；Consider the influence of reservoir level and rainfall factor pair reservoir stability abnormal deformation, and combines local geology
There are the driving factors of abnormal deformation in condition, judgement and explanation side slope；
Step 4, the management of monitoring data, transmitting and side slope early warning
Monitoring report is drawn, accumulative deflection and rate of deformation etc. is considered, respectively indicates monitoring regional edge in different colors
Slope there is no it is abnormal, there are new abnormal, lasting sexual abnormality and in dangerous range, and by deformation of slope and reservoir level with
And the correlation analysis of rain factor graphically embodies；It will be necessary the zone of ignorance further analyzed, Yi Ji
There are the preliminary analysis of Deformation Anomalies to be embodied in monitoring report in terms of Space Consistency and time persistence.
Preferably, in step 1, PS point is identified from time series SAR image using SqueeSAR technology, it is specific to use
Amplitude index of dispersion chooses PS point as evaluation number；Identify that DS point, specially selection KS are examined from time series SAR image
Spatially adaptive filtering, which is carried out, as Statistical Identifying Method is critical to vegetarian refreshments as DS candidate point to identify.
Preferably, in step 1, once collecting SAR image new in monitoring region, same method will be immediately obtained
The new deformation data in all monitoring points, and the time series databases that timely update.
Preferably, in step 1, the method for drafting of graph is carried out to the Deformation Monitoring Data of monitoring point are as follows: will measure
Time as horizontal axis, using accumulative deformation quantity as vertical pivot, using line symbol, point symbol to accumulative deformation quantity and time of measuring
Relationship drawn.
Preferably, in step 2, to judge whether deformation data shows nonlinear method particularly includes: to deformation
Time series carries out linear fit, should if straight slope changes after section linear fitting of time series before and after certain monitoring point
Monitoring point deformation data shows non-linear.
Preferably, in step 2, the specific obtaining value method of time window and deformation given threshold are as follows: according to different works
Journey, different detection requirements, after testing different time window and threshold speed, the demand based on different engineerings and monitoring
(such as: 1 month to date deformation is more than 5 centimetres, i.e. 5 centimetres/month), selects the optimal time to combine with rate-valve value.
Preferably, in step 3, the impact analysis method of reservoir level and rainfall factor pair reservoir stability abnormal deformation are as follows:
Using grey Relational Analysis Method, the degree of association between deformation values and reservoir level and the rainfall factor is calculated, wherein the degree of association takes
Being worth range is -1~1, and the influence of reservoir level and rainfall factor pair reservoir stability abnormal deformation is judged according to relating value.
Preferably, the deformation data of the measurement point includes orbit error, DEM error and atmospheric perturbation.
Compared with prior art, the present invention its advantages are as follows:
1, the InSAR technology that the present invention uses has very high spatial resolution and measurement accuracy, and monitoring accuracy can reach
To grade, the high-resolution imaging to monitoring region may be implemented.Further, since using non-contact measurement mode and radar
Image coverage area is very big, effectively overcomes placement sensor difficulty in traditional basin reservoir stability deformation monitoring, obtains monitoring
The deficiencies of information is few.
2, it is very short to carry out a data acquisition time for current satellite-borne synthetic aperture radar, therefore is carried out based on InSAR data
Basin reservoir stability monitoring cycle is shorter compared to for other monitoring means, and the continuous monitoring to region may be implemented.Meanwhile
The regularity of data acquisition, which ensure that update immediately after obtaining each new SAR image, can be used for system input cunning
The displacement time series of slope Failure prediction models to identify that is moved before landslide ramps up, and gradually refine the out-of-service time
Prediction.
3, SAR image data can be with Free Acquisition, and the funds of Primary Stage Data acquisition and monitoring later maintenance are greatly reduced in this
Expense, therefore have huge economic and social benefit.
4, the Time series analysis method that the present invention uses has the remarkable advantage for the conventional analysis for being based only upon rate of deformation,
And since currently available computing capability increases, it can be executed in seconds in millions of a time serieses.
5, the present invention can allow manager that can rapidly and accurately grasp the deformation data of entire side reservoir slope, realize quick
Decision, to achieve the purpose that contain disaster, mitigate casualty loss, stable society order.
Detailed description of the invention
Fig. 1 is reservoir stability deformation monitoring processing method overall flow figure of the present invention.
Fig. 2 is that deformation data of the present invention obtains and update functional block diagram.
Fig. 3 is monitoring point Deformation Anomalies value automatic identification function structural block diagram of the present invention.
Fig. 4 is reservoir stability deformation anomaly analysis functional block diagram of the present invention.
Specific embodiment
The present invention is illustrated with reference to the accompanying drawing.
As shown in Figure 1, a kind of reservoir stability deformation monitoring processing method based on InSAR image data, mainly include with
Lower step:
Step 1: the reservoir stability deformation data based on InSAR image is obtained and is updated
Several SAR for choosing covering monitoring region first answer image interference data sequence, by SqueeSAR technology from shadow
The monitoring point of Permanent scatterers (PS point) and distributed diffusion body (DS point) as deformation of slope is identified as in；Then joint benefit
With the phase signal on PS point and DS point, orbit error, DEM error, atmospheric perturbation etc. are successively isolated from phase signal；Most
The deformation data of all monitoring points is obtained eventually, and the deformation time sequence of each monitoring point is drawn with the sequence of SAR image collection time
Column graph.
In order to realize the continuous monitoring of side slope, once collect SAR image new in monitoring region, same method
The new deformation data in all monitoring points, and the time series databases that timely update will be immediately obtained.
Step 2: the analysis of monitoring point strain mode and exceptional value automatic identification
The deformation data of all monitoring points obtained using step 1 is as input, by the monitoring week of entire time series
Phase (T0-Tn) it is divided into two subintervals: historical time sequence phase (T0-Tn-k) and monitoring time sequence phase (Tn-k-Tn) (when k is indicated
Between window n indicate time serial number namely satellite image serial number), wherein exceptional value to be detected is included in the monitoring time sequence phase
It is interior；Compare (T0-Tn-k) strain mode in section, point analysis is in (T one by onen-k-Tn) in monitoring cycle deformation data whether
Deviate, i.e., whether displacement time series show non-linear；When displacement time series show non-linear, breakpoint is calculated
The rate of deformation of front and back, and by the absolute value of the two difference compared with given threshold, if rate of deformation difference is exhausted before and after breakpoint
Given threshold is greater than to value, breakpoint is labeled as exceptional value.
Step 3: reservoir stability abnormal deformation driving factor analysis
Consider two key factors of Space Consistency and time duration, table will be shown according to what is identified in step 2
Reveal similar nonlinear displacement time series and is marked as one group of abnormal monitoring point in continuous at least twice update
Define side slope unstable region；Consider the influence of reservoir level and rainfall factor pair reservoir stability abnormal deformation, and combines locality
There are the driving factors of abnormal deformation in geological conditions, judgement and explanation side slope.
Step 4: the management of monitoring data, transmitting and side slope early warning
Monitoring report is drawn, considers accumulative deflection and rate of deformation etc., with green, yellow, orange and red table respectively
Show monitoring regional slope there is no it is abnormal, there are new abnormal, lasting sexual abnormality and in dangerous range, and by side slope shape
Become and is graphically embodied with the correlation analysis of the factors such as reservoir level and rainfall；It will be necessary further to analyze not
Know region, and there are the preliminary analysis of Deformation Anomalies to be embodied in monitoring report in terms of Space Consistency and time persistence
In.
Preferably, PS point is identified from time series SAR image using SqueeSAR technology in step 1, specifically use width
Index of dispersion, which is spent, as evaluation number chooses PS point；DS point is identified from time series SAR image, specially selection KS, which is examined, makees
Spatially adaptive filtering, which is carried out, for Statistical Identifying Method is critical to vegetarian refreshments as DS candidate point to identify.
Preferably, the method for drafting of graph is carried out in step 1 to the Deformation Monitoring Data of monitoring point are as follows: when by measuring
Between be used as horizontal axis, using accumulative deformation quantity as vertical pivot, using line symbol, point symbol to accumulative deformation quantity and time of measuring
Relationship is drawn.
Preferably, to judge whether deformation data shows in step 2 nonlinear method particularly includes: when to deformation
Between sequence carry out linear fit, if straight slope changes after section linear fitting of time series before and after certain monitoring point, the prison
Measuring point deformation data shows non-linear.
Preferably, in step 2 time window and deformation given threshold specific obtaining value method are as follows: according to different engineerings,
Different detection requirements, after testing different time window and threshold speed, the demand based on different engineerings and monitoring
(such as: 1 month to date deformation is more than 5 centimetres, i.e. 5 centimetres/month), selects the optimal time to combine with rate-valve value.
Preferably, in step 3 reservoir level and rainfall factor pair reservoir stability abnormal deformation impact analysis method are as follows: answer
With grey Relational Analysis Method, the degree of association between deformation values and reservoir level and the rainfall factor is calculated, wherein degree of association value
Range is -1~1, and the influence of reservoir level and rainfall factor pair reservoir stability abnormal deformation is judged according to relating value.
Claims (8)
1. a kind of reservoir stability deformation monitoring processing method based on InSAR image data, which is characterized in that mainly include following
Step:
Step 1, the reservoir stability deformation data based on InSAR image are obtained and are updated
Several SAR for choosing covering monitoring region first answer image interference data sequence, through SqueeSAR technology from image
Identify the monitoring point of Permanent scatterers PS point and distributed diffusion body DS point as deformation of slope；Then joint utilizes monitoring point
On phase signal, deformation data is successively isolated from phase signal；Finally obtain the deformation data of all monitoring points, and with
The sequence of SAR image collection time draws the deformation data graph of each monitoring point；
Step 2, the analysis of monitoring point strain mode and exceptional value automatic identification
The deformation data of all monitoring points obtained using step 1 is as input, by the monitoring cycle (T of entire time series0-
Tn) it is divided into two subintervals, i.e. historical time sequence phase (T0-Tn-k) and monitoring time sequence phase (Tn-k-Tn), k indicates time window
Mouthful, n indicates time serial number namely satellite image serial number, wherein exceptional value to be detected was included in the monitoring time sequence phase；It is right
Than (T0-Tn-k) strain mode in section, point analysis is in (T one by onen-k-Tn) whether deformation data occurs in monitoring cycle
Deviate, i.e., whether displacement time series show non-linear；When displacement time series show non-linear, breakpoint front and back is calculated
Rate of deformation, and by the absolute value of the two difference compared with given threshold, if before and after breakpoint rate of deformation difference absolute value
Greater than given threshold, breakpoint is labeled as exceptional value；
Step 3, reservoir stability abnormal deformation driving factor analysis
Consider two key factors of Space Consistency and time duration, it will be non-linear according to showing of identifying in step 2
Displacement time series, and be marked as one group of abnormal monitoring point in continuous at least twice update and be defined as side slope shakiness
Determine region；Consider the influence of reservoir level and rainfall factor pair reservoir stability abnormal deformation, and combines local geological conditions, judgement
There are the driving factors of abnormal deformation with explanation side slope；
Step 4, the management of monitoring data, transmitting and side slope early warning
Monitoring report is drawn, considers accumulative deflection and rate of deformation etc., respectively indicates monitoring regional slope in different colors not
There are exception, there are new abnormal, lasting sexual abnormality and in dangerous range, and by deformation of slope and reservoir level and drop
The correlation analysis of rain factor graphically embodies；It will be necessary the zone of ignorance further analyzed, and in space
There are the preliminary analysis of Deformation Anomalies to be embodied in monitoring report in terms of consistency and time persistence.
2. reservoir stability deformation monitoring processing method as described in claim 1, it is characterised in that: in step 1, utilize
SqueeSAR technology identifies PS point from time series SAR image, is specifically chosen using amplitude index of dispersion as evaluation number
PS point；Identify that DS point, specially selection KS are examined adaptive as Statistical Identifying Method progress space from time series SAR image
It should filter to identify and be critical to vegetarian refreshments as DS candidate point.
3. reservoir stability deformation monitoring processing method as described in claim 1, it is characterised in that: in step 1, once acquisition
New SAR image in monitoring region, same method will immediately obtain the new deformation data in all monitoring points, and timely update
Time series databases.
4. reservoir stability deformation monitoring processing method as described in claim 1, it is characterised in that: in step 1, to monitoring point
Deformation Monitoring Data carry out the method for drafting of graph are as follows: using time of measuring as horizontal axis, using accumulative deformation quantity as vertical pivot,
The relationship of accumulative deformation quantity and time of measuring is drawn using line symbol, point symbol.
5. reservoir stability deformation monitoring processing method as described in claim 1, it is characterised in that: in step 2, judge deformation
It is nonlinear whether time series shows method particularly includes: linear fit is carried out to deformation data, if before certain monitoring point
Straight slope changes after back segment linear fitting of time series, then the monitoring point deformation data is shown non-linear.
6. reservoir stability deformation monitoring processing method as described in claim 1, it is characterised in that: in step 2, time window
With the specific obtaining value method of deformation given threshold are as follows: according to different engineerings, different detection requirements is testing the different time
After window and threshold speed, the demand based on different engineerings and monitoring selects the optimal time to combine with rate-valve value.
7. reservoir stability deformation monitoring processing method as described in claim 1, it is characterised in that: in step 3, reservoir level and
The impact analysis method of rainfall factor pair reservoir stability abnormal deformation are as follows: apply grey Relational Analysis Method, calculate deformation values with
The degree of association between reservoir level and the rainfall factor, wherein degree of association value range is -1~1, judges reservoir level according to relating value
With the influence of rainfall factor pair reservoir stability abnormal deformation.
8. reservoir stability deformation monitoring processing method as claimed in any one of claims 1 to 7, it is characterised in that: the survey
The deformation data of amount point includes orbit error, DEM error and atmospheric perturbation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910043057.2A CN109709550B (en) | 2019-01-17 | 2019-01-17 | InSAR image data-based bank slope deformation monitoring processing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910043057.2A CN109709550B (en) | 2019-01-17 | 2019-01-17 | InSAR image data-based bank slope deformation monitoring processing method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109709550A true CN109709550A (en) | 2019-05-03 |
| CN109709550B CN109709550B (en) | 2020-10-30 |
Family
ID=66262127
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910043057.2A Active CN109709550B (en) | 2019-01-17 | 2019-01-17 | InSAR image data-based bank slope deformation monitoring processing method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109709550B (en) |
Cited By (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109212528A (en) * | 2018-10-30 | 2019-01-15 | 中国科学院遥感与数字地球研究所 | A kind of multidimensional chromatography historic site SAR ruins deformation monitoring method |
| CN110058237A (en) * | 2019-05-22 | 2019-07-26 | 中南大学 | InSAR point Yun Ronghe and three-dimensional deformation monitoring method towards High-resolution SAR Images |
| CN110333554A (en) * | 2019-07-10 | 2019-10-15 | 南京恩瑞特实业有限公司 | NRIET heavy rain intelligence similarity analysis method |
| CN110568440A (en) * | 2019-09-10 | 2019-12-13 | 四川省地质工程勘察院集团有限公司 | method for monitoring deformation of complex mountain area based on DS-InSAR technology |
| CN110888132A (en) * | 2019-11-22 | 2020-03-17 | 深圳市城市公共安全技术研究院有限公司 | Bridge deformation analysis method and system based on InSAR monitoring |
| CN111308468A (en) * | 2019-11-27 | 2020-06-19 | 北京东方至远科技股份有限公司 | Method for automatically identifying deformation risk area based on In SAR technology |
| CN111322969A (en) * | 2020-04-07 | 2020-06-23 | 中国水利水电科学研究院 | Rock slope stability testing system and method |
| CN111524323A (en) * | 2020-04-29 | 2020-08-11 | 中国水利水电科学研究院 | Slope fission early warning method and system |
| CN111580098A (en) * | 2020-04-29 | 2020-08-25 | 深圳大学 | Bridge deformation monitoring method, terminal and storage medium |
| CN111854596A (en) * | 2020-08-26 | 2020-10-30 | 中国地质大学（北京） | Ground surface deformation real-time monitoring method and system based on ground-based interferometric radar data |
| CN112213722A (en) * | 2019-10-14 | 2021-01-12 | 兰州交通大学 | Side slope monitoring method combining InSAR and deep learning |
| CN112288308A (en) * | 2020-11-11 | 2021-01-29 | 华能澜沧江水电股份有限公司 | DS evidence theory landslide evaluation method based on multiple coefficient of variation weighting |
| CN112381309A (en) * | 2020-11-23 | 2021-02-19 | 珠江水利委员会珠江水利科学研究院 | Reservoir dam safety monitoring and early warning method, device and system and storage medium |
| CN112444188A (en) * | 2021-02-01 | 2021-03-05 | 深圳大学 | Multi-view InSAR sea wall high-precision three-dimensional deformation measurement method |
| CN112505700A (en) * | 2020-11-27 | 2021-03-16 | 华能澜沧江水电股份有限公司 | Landslide identification method and system based on satellite-borne lifting rail SAR and time sequence InSAR |
| CN113175949A (en) * | 2021-04-23 | 2021-07-27 | 首都师范大学 | Method and system for inverting water release coefficient by combining surface deformation and water level information |
| CN113790667A (en) * | 2021-11-18 | 2021-12-14 | 中大检测(湖南)股份有限公司 | Dam deformation detection method based on radar |
| CN114859351A (en) * | 2022-06-10 | 2022-08-05 | 重庆地质矿产研究院 | Method for detecting surface deformation field abnormity based on neural network |
| CN115205345A (en) * | 2022-02-23 | 2022-10-18 | 西南交通大学 | Land surface non-uniform deformation information extraction and management method for airport in sea reclamation area |
| CN117233762A (en) * | 2023-11-14 | 2023-12-15 | 广东广宇科技发展有限公司 | Reservoir monitoring method based on GB-SAR |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090237297A1 (en) * | 2008-02-06 | 2009-09-24 | Halliburton Energy Services, Inc. | Geodesy Via GPS and INSAR Integration |
| CN103970932A (en) * | 2014-02-28 | 2014-08-06 | 杭州师范大学 | High-resolution permanent scatterer modeling method for separation of building and background |
| CN104062660A (en) * | 2014-07-14 | 2014-09-24 | 中南大学 | Mining area earth surface time sequence deformation monitoring method based on time domain discrete InSAR interference pair |
| CN104111457A (en) * | 2014-07-23 | 2014-10-22 | 中国国土资源航空物探遥感中心 | Mutual-inspection and temporal fusion method for surface subsidence monitoring result of PSInSAR for lifting track |
| CN104111456A (en) * | 2014-07-23 | 2014-10-22 | 中国国土资源航空物探遥感中心 | Ground surface deformation high-resolution interferometric synthetic aperture radar (InSAR) monitoring method along high speed railway |
| WO2015008310A1 (en) * | 2013-07-19 | 2015-01-22 | Consiglio Nazionale Delle Ricerche | Method for filtering of interferometric data acquired by synthetic aperture radar (sar) |
| CN105866776A (en) * | 2016-03-28 | 2016-08-17 | 北京理工大学 | Method for selecting high quality dynamic PS point of ground based SAR |
| CN106950556A (en) * | 2017-05-03 | 2017-07-14 | 三亚中科遥感研究所 | Heritage area deformation monitoring method based on distributed diffusion body sequential interference SAR technology |
-
2019
- 2019-01-17 CN CN201910043057.2A patent/CN109709550B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090237297A1 (en) * | 2008-02-06 | 2009-09-24 | Halliburton Energy Services, Inc. | Geodesy Via GPS and INSAR Integration |
| WO2015008310A1 (en) * | 2013-07-19 | 2015-01-22 | Consiglio Nazionale Delle Ricerche | Method for filtering of interferometric data acquired by synthetic aperture radar (sar) |
| CN103970932A (en) * | 2014-02-28 | 2014-08-06 | 杭州师范大学 | High-resolution permanent scatterer modeling method for separation of building and background |
| CN104062660A (en) * | 2014-07-14 | 2014-09-24 | 中南大学 | Mining area earth surface time sequence deformation monitoring method based on time domain discrete InSAR interference pair |
| CN104111457A (en) * | 2014-07-23 | 2014-10-22 | 中国国土资源航空物探遥感中心 | Mutual-inspection and temporal fusion method for surface subsidence monitoring result of PSInSAR for lifting track |
| CN104111456A (en) * | 2014-07-23 | 2014-10-22 | 中国国土资源航空物探遥感中心 | Ground surface deformation high-resolution interferometric synthetic aperture radar (InSAR) monitoring method along high speed railway |
| CN105866776A (en) * | 2016-03-28 | 2016-08-17 | 北京理工大学 | Method for selecting high quality dynamic PS point of ground based SAR |
| CN106950556A (en) * | 2017-05-03 | 2017-07-14 | 三亚中科遥感研究所 | Heritage area deformation monitoring method based on distributed diffusion body sequential interference SAR technology |
Non-Patent Citations (4)
| Title |
|---|
| ALESSANDRO FERRETTI等: "A New Algorithm for Processing Interferometric Data-Stacks: SqueeSAR", 《IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING》 * |
| TOMMASO CARLA等: "A simple method to help determine landslide susceptibility from spaceborne InSAR data: the Montescaglioso case study", 《ENVIRON EARTH SCI》 * |
| 朱建军等: "InSAR变形监测方法与研究进展", 《测绘学报》 * |
| 詹艳艳: "基于斜率提取边缘点的时间序列分段线性表示方法", 《计算机科学》 * |
Cited By (27)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109212528B (en) * | 2018-10-30 | 2020-08-04 | 中国科学院遥感与数字地球研究所 | Multidimensional chromatography SAR historic site deformation monitoring method |
| CN109212528A (en) * | 2018-10-30 | 2019-01-15 | 中国科学院遥感与数字地球研究所 | A kind of multidimensional chromatography historic site SAR ruins deformation monitoring method |
| CN110058237A (en) * | 2019-05-22 | 2019-07-26 | 中南大学 | InSAR point Yun Ronghe and three-dimensional deformation monitoring method towards High-resolution SAR Images |
| CN110333554A (en) * | 2019-07-10 | 2019-10-15 | 南京恩瑞特实业有限公司 | NRIET heavy rain intelligence similarity analysis method |
| CN110333554B (en) * | 2019-07-10 | 2021-10-22 | 南京恩瑞特实业有限公司 | NRIET rainstorm intelligent similarity analysis method |
| CN110568440A (en) * | 2019-09-10 | 2019-12-13 | 四川省地质工程勘察院集团有限公司 | method for monitoring deformation of complex mountain area based on DS-InSAR technology |
| CN112213722A (en) * | 2019-10-14 | 2021-01-12 | 兰州交通大学 | Side slope monitoring method combining InSAR and deep learning |
| CN110888132A (en) * | 2019-11-22 | 2020-03-17 | 深圳市城市公共安全技术研究院有限公司 | Bridge deformation analysis method and system based on InSAR monitoring |
| CN110888132B (en) * | 2019-11-22 | 2022-05-20 | 深圳市城市公共安全技术研究院有限公司 | Bridge deformation analysis method and system based on InSAR monitoring |
| CN111308468A (en) * | 2019-11-27 | 2020-06-19 | 北京东方至远科技股份有限公司 | Method for automatically identifying deformation risk area based on In SAR technology |
| CN111322969A (en) * | 2020-04-07 | 2020-06-23 | 中国水利水电科学研究院 | Rock slope stability testing system and method |
| CN111322969B (en) * | 2020-04-07 | 2021-02-19 | 中国水利水电科学研究院 | Rock slope stability testing system and method |
| CN111580098A (en) * | 2020-04-29 | 2020-08-25 | 深圳大学 | Bridge deformation monitoring method, terminal and storage medium |
| CN111524323A (en) * | 2020-04-29 | 2020-08-11 | 中国水利水电科学研究院 | Slope fission early warning method and system |
| CN111854596A (en) * | 2020-08-26 | 2020-10-30 | 中国地质大学（北京） | Ground surface deformation real-time monitoring method and system based on ground-based interferometric radar data |
| CN112288308A (en) * | 2020-11-11 | 2021-01-29 | 华能澜沧江水电股份有限公司 | DS evidence theory landslide evaluation method based on multiple coefficient of variation weighting |
| CN112381309A (en) * | 2020-11-23 | 2021-02-19 | 珠江水利委员会珠江水利科学研究院 | Reservoir dam safety monitoring and early warning method, device and system and storage medium |
| CN112505700A (en) * | 2020-11-27 | 2021-03-16 | 华能澜沧江水电股份有限公司 | Landslide identification method and system based on satellite-borne lifting rail SAR and time sequence InSAR |
| CN112444188B (en) * | 2021-02-01 | 2021-05-11 | 深圳大学 | Multi-view InSAR sea wall high-precision three-dimensional deformation measurement method |
| CN112444188A (en) * | 2021-02-01 | 2021-03-05 | 深圳大学 | Multi-view InSAR sea wall high-precision three-dimensional deformation measurement method |
| CN113175949A (en) * | 2021-04-23 | 2021-07-27 | 首都师范大学 | Method and system for inverting water release coefficient by combining surface deformation and water level information |
| CN113175949B (en) * | 2021-04-23 | 2022-08-02 | 首都师范大学 | Method and system for inverting water release coefficient by combining surface deformation and water level information |
| CN113790667A (en) * | 2021-11-18 | 2021-12-14 | 中大检测(湖南)股份有限公司 | Dam deformation detection method based on radar |
| CN115205345A (en) * | 2022-02-23 | 2022-10-18 | 西南交通大学 | Land surface non-uniform deformation information extraction and management method for airport in sea reclamation area |
| CN114859351A (en) * | 2022-06-10 | 2022-08-05 | 重庆地质矿产研究院 | Method for detecting surface deformation field abnormity based on neural network |
| CN117233762A (en) * | 2023-11-14 | 2023-12-15 | 广东广宇科技发展有限公司 | Reservoir monitoring method based on GB-SAR |
| CN117233762B (en) * | 2023-11-14 | 2024-02-09 | 广东广宇科技发展有限公司 | Reservoir monitoring method based on GB-SAR |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109709550B (en) | 2020-10-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109709550A (en) | A kind of reservoir stability deformation monitoring processing method based on InSAR image data | |
| CN108663017A (en) | A kind of method of ground settlement along monitoring city underground | |
| Barla et al. | Monitoring of the Beauregard landslide (Aosta Valley, Italy) using advanced and conventional techniques | |
| Wang et al. | Radar–raingauge data combination techniques: a revision and analysis of their suitability for urban hydrology | |
| Jiang et al. | Comparative evaluation of geological disaster susceptibility using multi-regression methods and spatial accuracy validation | |
| CN105761436B (en) | A kind of Based on Red Bed landslide method for early warning and its application | |
| Jia et al. | Improved offset tracking for predisaster deformation monitoring of the 2018 Jinsha River landslide (Tibet, China) | |
| CN104123470A (en) | Method for optimizing land subsidence monitoring net | |
| CN104133996A (en) | Ground settlement risk grade evaluation method based on cloud model and data field | |
| Nie et al. | A new prediction model for rock slope failure time: a case study in West Open-Pit mine, Fushun, China | |
| CN105701975A (en) | Shallow soil landslide early-warning method in red-bed area | |
| CN109238227A (en) | A method of characterization surface subsidence Spatio-temporal Evolution | |
| CN103778480A (en) | Fissure-zone height prediction method based on sensitivity analysis | |
| Infante et al. | Differential SAR interferometry technique for control of linear infrastructures affected by ground instability phenomena | |
| CN113064170A (en) | Expansive soil area surface deformation monitoring method based on time sequence InSAR technology | |
| CN108332696A (en) | landslide monitoring method selection method | |
| CN104459692B (en) | Quick data processing method for improving GEOSAR difference interference deformation measuring accuracy | |
| CN116658246A (en) | Coal mine water damage monitoring and early warning system and method | |
| Jovančević et al. | Deterministic landslide susceptibility analyses using LS-Rapid software | |
| Pu et al. | Refined mapping and kinematic trend assessment of potential landslides associated with large-scale land creation projects with multitemporal InSAR | |
| CN112233381B (en) | Debris flow early warning method and system based on mechanism and machine learning coupling | |
| Yao et al. | Average-DInSAR method for unstable escarpments detection induced by underground coal mining | |
| Farina et al. | Development of an early warning system for shallow landslide hazard in the Grasberg area, Indonesia | |
| Matsoukas et al. | Fusion of radar and rain gage measurements for an accurate estimation of rainfall | |
| Wang et al. | Research on GBSAR deformation extraction method based on three-threshold |
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 |