CN115343317A - Loess landslide disaster comprehensive monitoring method and system - Google Patents
Loess landslide disaster comprehensive monitoring method and system Download PDFInfo
- Publication number
- CN115343317A CN115343317A CN202210974875.6A CN202210974875A CN115343317A CN 115343317 A CN115343317 A CN 115343317A CN 202210974875 A CN202210974875 A CN 202210974875A CN 115343317 A CN115343317 A CN 115343317A
- Authority
- CN
- China
- Prior art keywords
- monitored
- area
- data
- time sequence
- gnss
- 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
- 238000012544 monitoring process Methods 0.000 title claims abstract description 80
- 238000000034 method Methods 0.000 title claims abstract description 32
- 239000002689 soil Substances 0.000 claims abstract description 119
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Chemical compound O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 118
- 238000005516 engineering process Methods 0.000 claims abstract description 36
- 230000004044 response Effects 0.000 claims abstract description 34
- 230000008859 change Effects 0.000 claims abstract description 17
- 238000006073 displacement reaction Methods 0.000 claims description 36
- 241001061260 Emmelichthys struhsakeri Species 0.000 claims description 18
- 238000012937 correction Methods 0.000 claims description 9
- 238000001914 filtration Methods 0.000 claims description 6
- 230000010355 oscillation Effects 0.000 claims description 6
- 238000010219 correlation analysis Methods 0.000 claims description 5
- 238000012952 Resampling Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000001514 detection method Methods 0.000 claims description 3
- 238000010183 spectrum analysis Methods 0.000 claims description 3
- 239000005436 troposphere Substances 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 claims description 2
- 230000007613 environmental effect Effects 0.000 abstract 2
- 238000010586 diagram Methods 0.000 description 4
- 230000003247 decreasing effect Effects 0.000 description 3
- 230000004927 fusion Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- KIEDNEWSYUYDSN-UHFFFAOYSA-N clomazone Chemical compound O=C1C(C)(C)CON1CC1=CC=CC=C1Cl KIEDNEWSYUYDSN-UHFFFAOYSA-N 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000001965 increasing effect Effects 0.000 description 2
- 230000001939 inductive effect Effects 0.000 description 2
- 238000001556 precipitation Methods 0.000 description 2
- 208000028257 Joubert syndrome with oculorenal defect Diseases 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 230000000875 corresponding effect Effects 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 238000011065 in-situ storage Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N22/00—Investigating or analysing materials by the use of microwaves or radio waves, i.e. electromagnetic waves with a wavelength of one millimetre or more
- G01N22/04—Investigating moisture content
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B15/00—Measuring arrangements characterised by the use of electromagnetic waves or particle radiation, e.g. by the use of microwaves, X-rays, gamma rays or electrons
- G01B15/06—Measuring arrangements characterised by the use of electromagnetic waves or particle radiation, e.g. by the use of microwaves, X-rays, gamma rays or electrons for measuring the deformation in a solid
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/24—Earth materials
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/24—Earth materials
- G01N33/246—Earth materials for water content
-
- 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
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
- G01S19/35—Constructional details or hardware or software details of the signal processing chain
- G01S19/37—Hardware or software details of the signal processing chain
-
- 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
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Remote Sensing (AREA)
- Analytical Chemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Biochemistry (AREA)
- Geology (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Environmental & Geological Engineering (AREA)
- Electromagnetism (AREA)
- Food Science & Technology (AREA)
- Medicinal Chemistry (AREA)
- Signal Processing (AREA)
- Radar, Positioning & Navigation (AREA)
- Computer Networks & Wireless Communication (AREA)
- Pit Excavations, Shoring, Fill Or Stabilisation Of Slopes (AREA)
- Testing Or Calibration Of Command Recording Devices (AREA)
Abstract
The invention discloses a loess landslide disaster comprehensive monitoring method and a system, wherein the loess landslide disaster comprehensive monitoring method comprises the following steps of: acquiring various monitoring data of an area to be monitored; acquiring a three-dimensional deformation time sequence of the earth surface, an atmospheric water vapor content time sequence and an earth surface soil water content time sequence of a region to be monitored by utilizing a GNSS relative positioning technology and a GNSS remote sensing technology; analyzing the correlation between the atmospheric water vapor content time sequence and rainfall data of the area to be monitored; analyzing the correlation between the three-dimensional surface deformation time sequence and the surface soil moisture content time sequence; carrying out rainfall early warning on the area to be monitored, and carrying out landslide comprehensive monitoring on the area to be monitored. According to the method, the GNSS technology is utilized to obtain the deformation information of the area to be monitored, the atmospheric water vapor content, the change of the soil water content and other environmental information, the response relation among the environmental information and the atmospheric water vapor content is analyzed, and the GNSS technology is better served for the comprehensive monitoring of landslide disasters.
Description
Technical Field
The invention relates to the technical field of geological disaster monitoring, in particular to a loess landslide disaster comprehensive monitoring method and system.
Background
The loess plateau is one of the most serious regions of three geological disasters in China, the water sensitivity is the most remarkable characteristic of the loess, rainwater infiltrates into the loess under the action of strong continuity and rainfall capacity to quickly increase the saturation of the soil body, and the change of the moisture in the soil influences the shear strength of the soil body, so that the loess shallow landslide is induced. The deformation and incentive monitoring work of the landslide is effectively carried out, and the method has important significance for the disaster prevention and reduction work of China.
At present, a global navigation satellite system GNSS is widely applied to the field of landslide disaster monitoring as a technical means capable of directly acquiring real-time earth surface three-dimensional vector deformation. Meanwhile, the GNSS can provide continuous L-band microwave signals to obtain surface environment information such as atmospheric water vapor content, soil water content and the like in the survey station area.
In the past landslide hazard monitoring research, the global navigation satellite system GNSS is only used for providing three-dimensional deformation information, but the capability of providing the surrounding environment information of the monitoring station is almost completely ignored. In addition, research on applying the surrounding environment information of the monitoring station provided by the GNSS to landslide disaster comprehensive monitoring in the prior art is not involved.
Disclosure of Invention
Aiming at the problems in the background art, the invention provides a loess landslide disaster comprehensive monitoring method and a loess landslide disaster comprehensive monitoring system.
The invention provides a loess landslide disaster comprehensive monitoring method, which comprises the following steps:
acquiring various monitoring data of an area to be monitored;
according to various monitoring data, calculating a three-dimensional deformation time sequence of the earth surface of the area to be monitored by utilizing a GNSS relative positioning technology; calculating an atmospheric water vapor content time sequence of a region to be monitored by utilizing a GNSS refraction remote sensing technology; calculating a time sequence of the water content of the earth surface soil around the area to be monitored by utilizing a GNSS reflection remote sensing technology;
the rainfall data of the area to be monitored is obtained, and the response relation between the rainfall data and the atmospheric water vapor content time sequence is analyzed to obtain the correlation between the atmospheric water vapor content and the rainfall;
analyzing the response relation between the three-dimensional surface deformation time sequence and the surface soil moisture content time sequence to obtain the correlation between the landslide deformation of the area to be monitored and the surface soil moisture content;
carrying out rainfall early warning on the area to be monitored according to the correlation between the atmospheric water vapor content and the rainfall of the area to be monitored and the calculated atmospheric water vapor content of the area to be monitored; and carrying out landslide comprehensive monitoring on the area to be monitored according to the relevance between landslide deformation of the area to be monitored and the water content of the earth surface soil and the interpreted water content of the earth surface soil of the area to be monitored.
Further, the acquiring various items of monitoring data of the area to be monitored includes:
setting a GNSS monitoring reference station and a rover station in the area to be monitored, and acquiring observation data of the GNSS survey station in the area to be monitored;
acquiring broadcast ephemeris data of the area to be monitored;
acquiring precise ephemeris data of the area to be monitored;
and acquiring meteorological data of the area to be monitored.
Further, the method for calculating the time sequence of the three-dimensional deformation of the earth surface of the area to be monitored by utilizing the GNSS relative positioning technology according to the various monitoring data comprises the following steps:
calculating the position, the speed and the clock error of a satellite according to the observation data of the GNSS observation station;
according to the observation data of the GNSS observation station, calculating the approximate coordinates of the rover station and the reference station by utilizing pseudo-range point positioning;
calculating a non-difference residual error item between the reference station and the rover station according to the GNSS observation data;
carrying out tide correction, troposphere correction and antenna phase center correction on the observation data of the GNSS observation station;
performing Kalman filtering on the non-difference residual error items of the reference station and the rover station;
determining integer ambiguity by using an M-W combined observation algorithm in cycle slip detection according to the approximate coordinates of a reference station and a rover and a non-difference residual error item subjected to Kalman filtering;
extracting the station center coordinates NEU of the rover station relative to the reference station;
and obtaining a ground surface three-dimensional deformation time sequence of the area to be monitored according to the station center coordinates NEU of the mobile station relative to the reference station.
Further, the calculating the atmospheric water vapor content time sequence of the region to be monitored by utilizing the GNSS refraction remote sensing technology according to each item of monitoring data includes:
calculating the total delay ZTD in the zenith direction in the observation data of the GNSS observation station;
acquiring the air pressure and the temperature of an area to be monitored from the meteorological data, and calculating the weighted average temperature of the atmosphere;
calculating the zenith trunk delay ZHD in the observation data of the GNSS observation station;
the wet retardation ZWD is calculated by the formula:
ZWD＝ZTD-ZHD (1)
and calculating the atmospheric water vapor content PWV at different moments according to the wet delay ZWD and the atmospheric weighted average temperature to obtain the atmospheric water vapor content time sequence of the area to be monitored.
Further, according to each item of monitoring data, calculating a time sequence of the moisture content of the earth surface soil around the region to be monitored by utilizing a GNSS reflection remote sensing technology, the method comprises the following steps:
extracting signal-to-noise ratio (SNR) data in the GNSS observation station observation data;
selecting an altitude angle of an observation satellite, setting an azimuth angle, and intercepting SNR (signal-to-noise ratio) data of high-frequency oscillation in the SNR data;
separating a direct signal and a reflected signal in SNR (signal to noise ratio) data with high-frequency oscillation by using a low-order polynomial;
resampling a reflection signal separated from SNR data;
performing LS spectrum analysis on the re-sampled reflection signal to obtain the reflection height h from the phase center of the GNSS observation station antenna to the ground;
substituting the reflection height h, the wavelength lambda of the reflection signal, signal-to-noise ratio SNR data and the satellite height angle theta into a formula (2), and fitting by using a nonlinear least square method to obtain a phase parameter psi and an amplitude A;
and establishing a linear inversion model according to the obtained phase parameter psi to obtain a time sequence of the water content of the earth surface soil around the region to be monitored.
Further, the analyzing the response relationship between the atmospheric water vapor content time series and the rainfall data to obtain the correlation between the atmospheric water vapor content and the rainfall includes:
and drawing a dual-coordinate axis relation graph between the atmospheric water vapor content time sequence and the rainfall data, and analyzing the relation between the atmospheric water vapor content time sequence and the rainfall data from the graph to obtain the correlation between the atmospheric water vapor content and the rainfall.
Further, the analyzing the response relationship between the three-dimensional surface deformation time sequence and the surface soil moisture content time sequence to obtain the correlation between the landslide deformation of the region to be monitored and the surface soil moisture content includes:
obtaining the landslide accumulated displacement according to the three-dimensional surface deformation time sequence;
and drawing a double coordinate axis relation graph between the landslide accumulated displacement and the water content of the earth surface soil to obtain the relevance between the landslide accumulated displacement and the water content of the earth surface soil.
Further, when the bi-coordinate axis relation graph between the landslide accumulated displacement and the water content of the surface soil is drawn, the lag time of response of the landslide displacement rate to the change of the water content of the surface soil is quantitatively analyzed through a time-lag cross-correlation analysis method;
and drawing a bi-coordinate axis relation graph between the landslide displacement rate and the water content of the earth surface soil according to the lag time of the landslide displacement rate in response to the change of the water content of the earth surface soil, and analyzing the correlation between the landslide displacement rate and the water content of the earth surface soil from the graph.
The invention provides a loess landslide disaster comprehensive monitoring system, which comprises:
the data acquisition module is used for acquiring various monitoring data of the area to be monitored;
the data resolving module is used for calculating a ground surface three-dimensional deformation time sequence of the area to be monitored by utilizing a GNSS relative positioning technology according to each monitoring data; calculating an atmospheric water vapor content time sequence of a region to be monitored by utilizing a GNSS refraction remote sensing technology; calculating a time sequence of the water content of the earth surface soil around the area to be monitored by utilizing a GNSS reflection remote sensing technology;
the first response relation acquisition module is used for acquiring rainfall data of the area to be monitored, analyzing the response relation between the rainfall data and the atmospheric water vapor content time sequence and obtaining the correlation between the atmospheric water vapor content and the rainfall;
the second response relation acquisition module is used for analyzing the response relation between the three-dimensional surface deformation time sequence and the surface soil moisture content time sequence to obtain the correlation between the landslide deformation of the area to be monitored and the surface soil moisture content;
the comprehensive monitoring module is used for carrying out rainfall early warning on the area to be monitored according to the correlation between the atmospheric water vapor content and the rainfall of the area to be monitored and the calculated atmospheric water vapor content of the area to be monitored; and carrying out landslide comprehensive monitoring on the area to be monitored according to the relevance between landslide deformation of the area to be monitored and the water content of the earth surface soil and the interpreted water content of the earth surface soil of the area to be monitored.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a method for comprehensively monitoring loess landslide disasters based on ground-based GNSS remote sensing, which has the advantages that three-dimensional deformation, atmospheric water vapor content and soil moisture content information of a landslide measuring area can be obtained by respectively adopting GNSS carrier phase difference, GNSS refraction and GNSS reflection remote sensing technologies based on a data set obtained by a GNSS, and a carrier used by a GNSS satellite is positioned in an L wave band of microwave, is less influenced by severe weather such as heavy fog, rain, snow and the like, can penetrate through a cloud layer, and is favorable for realizing long-term stable and all-weather observation of landslide deformation and surrounding environment. With the continuous improvement of the GNSS technology, the continuous improvement of the global CORS station and the continuous increase of the total number of satellites of the future global satellite navigation system, abundant data are provided for researching the GNSS for interpreting landslide area environment and monitoring surface deformation, the research and application of the technology in landslide area environment monitoring are further promoted, the GNSS can play a better role in landslide disaster monitoring, early warning and the like, and the work has important significance for the comprehensive landslide monitoring.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a comprehensive monitoring system diagram in an embodiment of a loess landslide disaster comprehensive monitoring method provided by the present invention;
fig. 2 is a three-dimensional deformation time sequence of a region to be monitored according to an embodiment of the present invention.
Fig. 3 shows calculated atmospheric water vapor content PWV of the GNSS and E-direction deformation of the rover of the area to be monitored relative to the reference station according to the embodiment of the present invention;
FIG. 4 is a diagram of a Fresnel reflection area of a satellite in a 180-360 azimuth part of a rover of an area to be monitored relative to a reference station according to an embodiment of the invention;
FIG. 5 is a graph illustrating the comparison between the interpreted soil humidity and the measured soil humidity for each satellite according to an embodiment of the present invention;
FIG. 6 is a graph illustrating the comparison and correlation between the soil humidity results and the measured soil humidity according to the embodiment of the present invention;
FIG. 7 is a graph of soil moisture versus displacement provided by an embodiment of the present invention;
FIG. 8 is a graph of displacement rate versus soil moisture provided by an embodiment of the present invention;
FIG. 9 is a displacement rate-soil moisture time lag cross-correlation sequence provided by an embodiment of the present invention;
FIG. 10 is a graph of displacement rate versus soil moisture rate of change according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, but it should be understood that the scope of the present invention is not limited by the specific embodiments.
As shown in fig. 1, the present invention provides a loess landslide disaster comprehensive monitoring method, which comprises the following steps:
step 1: and acquiring various monitoring data of the area to be monitored.
And 2, step: according to various monitoring data, calculating a three-dimensional deformation time sequence of the earth surface of the area to be monitored by utilizing a GNSS relative positioning technology; calculating an atmospheric water vapor content time sequence of a region to be monitored by utilizing a GNSS refraction remote sensing technology; calculating a time sequence of the water content of the earth surface soil around the area to be monitored by utilizing a GNSS reflection remote sensing technology;
and step 3: the rainfall data of the area to be monitored is obtained, and the response relation between the rainfall data and the atmospheric water vapor content time sequence is analyzed to obtain the correlation between the atmospheric water vapor content and the rainfall; (ii) a
And 4, step 4: analyzing the response relation between the three-dimensional surface deformation time sequence and the surface soil moisture content time sequence to obtain the correlation between the landslide deformation of the area to be monitored and the surface soil moisture content;
and 5: carrying out rainfall early warning on the area to be monitored according to the correlation between the atmospheric water vapor content and the rainfall of the area to be monitored and the calculated atmospheric water vapor content of the area to be monitored; and carrying out landslide comprehensive monitoring on the area to be monitored according to the relevance between landslide deformation of the area to be monitored and the water content of the earth surface soil and the interpreted water content of the earth surface soil of the area to be monitored.
In step 1, acquiring various monitoring data of an area to be monitored specifically includes:
setting a GNSS monitoring reference station and a mobile station in an area to be monitored, and acquiring GNSS observation station observation data of the area to be monitored;
acquiring broadcast ephemeris data of an area to be monitored;
acquiring precise ephemeris data of an area to be monitored;
and acquiring meteorological data of an area to be monitored.
And analyzing data such as utilization rate, multipath effect, cycle slip, signal to noise ratio, ionospheric delay rate, precision factor and the like in the observation data of the GNSS observation station by using software such as TEQC, RTKLIB and the like, thereby realizing the evaluation of the quality of the observation data of the GNSS observation station acquired in real time.
The step 2 specifically comprises the following steps:
step 2.1: according to various monitoring data, calculating a ground surface three-dimensional deformation time sequence of a region to be monitored by utilizing a GNSS relative positioning technology, and comprising the following steps of:
calculating the position, the speed and the clock error of a satellite according to the observation data of the GNSS observation station;
according to the observation data of the GNSS observation station, calculating the approximate coordinates of the rover station and the reference station by using pseudo-range single-point positioning;
calculating a non-difference residual error item between a reference station and a rover station according to observation data of the GNSS observation station;
carrying out tide correction, troposphere correction and antenna phase center correction on observation data of the GNSS observation station;
performing Kalman filtering on the non-difference residual error item of the reference station and the rover station;
determining integer ambiguity by using an M-W combined observation algorithm in cycle slip detection according to the approximate coordinates of a reference station and a rover and a non-difference residual error item subjected to Kalman filtering; (ii) a
Extracting the station center coordinates NEU of the rover station relative to the reference station;
and obtaining a ground surface three-dimensional deformation time sequence of the area to be monitored according to the station center coordinates NEU of the mobile station relative to the reference station.
Step 2.2: according to various monitoring data, calculating the atmospheric water vapor content time sequence of the region to be monitored by utilizing a GNSS refraction remote sensing technology, wherein the time sequence comprises the following steps:
calculating the total zenith direction delay ZTD in the observation data of the GNSS observation station;
specifically, GAMIT/GLOBK data processing software is used for calculating the total zenith direction delay ZTD in the observation data of the GNSS observation station, and a global projection function model and a GAMIT default 10-degree height cut-off angle are selected during calculation.
Acquiring the air pressure and temperature of an area to be monitored from meteorological data, and calculating the weighted average temperature of the atmosphere;
and calculating the zenith stem delay ZHD in the observation data of the GNSS observation station, specifically calculating the zenith stem delay ZHD by using the most common Sastamonine (Saastamoinen) model.
The wet retardation ZWD is calculated by the formula:
ZWD＝ZTD-ZHD (1)
and calculating the air water vapor content PWV at different moments according to the wet delay ZWD and the air weighted average temperature to obtain an air water vapor content time sequence of the area to be monitored.
Step 2.3: according to each item of monitoring data, the GNSS reflection remote sensing technology is utilized to calculate the time sequence of the earth surface soil moisture content around the region to be monitored, and the time sequence comprises the following steps:
extracting signal-to-noise ratio (SNR) data in observation data of the GNSS observation station;
selecting a low altitude angle of an observation satellite, setting an azimuth angle, and intercepting SNR (signal-to-noise ratio) data of high-frequency oscillation in SNR data;
separating a direct signal and a reflected signal in SNR (signal to noise ratio) data with high-frequency oscillation by using a low-order polynomial;
resampling a reflection signal separated from SNR data;
performing LS spectrum analysis on the re-sampled reflection signal to obtain the distance from the phase center of the GNSS observation station antenna to the ground, which is also called reflection height h;
substituting the reflection height h, the signal wavelength lambda, the signal-to-noise ratio SNR and the satellite height angle theta into a formula (2), and fitting by using a nonlinear least square method to obtain a phase parameter psi and an amplitude A;
and establishing a linear inversion model to obtain a time sequence of the water content of the earth surface soil around the area to be monitored according to the obvious linear relation between the obtained phase parameter psi and the soil humidity.
Because the phase with high reflection has an obvious linear relation with the soil humidity, a linear inversion model is established according to the phase parameter psi with high reflection to obtain the time sequence of the water content of the earth surface soil around the region to be monitored.
In step 3, analyzing the response relationship between the atmospheric water vapor content time series and the rainfall data to obtain the correlation between the atmospheric water vapor content and the rainfall, comprising:
and drawing a dual-coordinate axis relation graph between the atmospheric water vapor content time series and the rainfall data, and analyzing the relation between the atmospheric water vapor content time series and the rainfall data from the graph.
In step 4, analyzing the response relation between the three-dimensional surface deformation time sequence and the surface soil moisture content time sequence to obtain the correlation between the landslide deformation of the area to be monitored and the surface soil moisture content, and the method comprises the following steps:
obtaining the landslide accumulated displacement according to the three-dimensional surface deformation time sequence;
and drawing a bi-coordinate axis relation graph between the landslide accumulated displacement and the surface soil water content to obtain the relevance between the landslide accumulated displacement and the surface soil water content.
And 4, step 4: when a dual-coordinate axis relation graph between the landslide accumulated displacement and the water content of the surface soil is drawn, the lag time of response of the landslide displacement rate to the change of the water content of the surface soil is quantitatively analyzed through a time-lag cross-correlation analysis method;
and drawing a bi-coordinate axis relation graph between the landslide displacement rate and the water content of the soil on the ground according to the lag time of the landslide displacement rate in response to the change of the water content of the soil on the ground, and analyzing the correlation between the landslide displacement rate and the water content of the soil on the ground from the graph.
The invention provides a loess landslide disaster comprehensive monitoring system, which comprises:
the data acquisition module is used for acquiring various monitoring data of the area to be monitored;
the data resolving module is used for calculating a ground surface three-dimensional deformation time sequence of the area to be monitored by utilizing a GNSS relative positioning technology according to each monitoring data; calculating an atmospheric water vapor content time sequence of a region to be monitored by utilizing a GNSS refraction remote sensing technology; calculating a time sequence of the water content of the earth surface soil around the area to be monitored by utilizing a GNSS reflection remote sensing technology;
the first response relation acquisition module is used for acquiring rainfall data of the area to be monitored and analyzing the response relation between the rainfall data and the atmospheric water vapor content time sequence to obtain the correlation between the atmospheric water vapor content and the rainfall;
the second response relation acquisition module is used for analyzing the response relation between the three-dimensional surface deformation time sequence and the surface soil moisture content time sequence to obtain the relevance between the landslide deformation of the area to be monitored and the surface soil moisture content;
the comprehensive monitoring module is used for carrying out rainfall early warning on the area to be monitored according to the correlation between the atmospheric water vapor content and the rainfall of the area to be monitored and the calculated atmospheric water vapor content of the area to be monitored; and carrying out landslide comprehensive monitoring on the area to be monitored according to the relevance between landslide deformation of the area to be monitored and the water content of the earth surface soil and the interpreted water content of the earth surface soil of the area to be monitored.
The technical solution of the present invention will be further described in detail with reference to specific examples.
1. And acquiring a surface three-dimensional deformation time sequence of the area to be monitored.
Fig. 2 is a time-series deformation diagram of three directions of a measuring station on a typical characteristic point of an example area solved by the technical scheme of the invention.
Fig. 2 (a) is a time sequence of deformation of a 2020 observation station of gamma calculation all year round, and it can be seen from this that, in the period from 1 st day to 245 th day of 2020, the observation station has no obvious deformation in the north N, east E and high U directions, the deformation mainly occurs in the 245 th to 313 th days, and the eastern deformation is most obvious, and thereafter, the observation station has no obvious deformation, and all three directions tend to a stable state.
In order to analyze the landslide deformation more finely, the technology uses TRACK single epoch to solve the data from the 245 th to the 335 th days, and the result is shown in fig. 2 (b), and the analysis shows that the N direction, the E direction and the U direction of the test station in the time period are changed by about 2cm, about 78cm and about 12cm respectively. It is known that the main sliding direction of the landslide is the east direction. In addition, in the beginning period of the deformation time sequence, the deformation rate of the measuring station is slow and stable, but the deformation of the measuring station is rapidly increased from the 273 st day, the landslide deformation tends to be stable from the 287 th day to the 302 th day, a small deformation occurs from the 302 th day to the 313 rd day again, and the deformation of the measuring station becomes stable again in the last period.
2. And acquiring an atmospheric water vapor content time sequence of the region to be monitored.
Fig. 3 shows the E-direction deformation (main direction of sliding), the atmospheric water vapor content PWV and the rainfall collected by the sensor when the solution of the present invention is solved for an example region with three distinct deformations from 245 to 335 of 2020. The thickest line of the line indicates the rainfall, the thinnest line of the line indicates the deformation sequence, and the second thinnest line of the line indicates the atmospheric water vapor content PWV. It can be seen from the figure that, before and after 275 th day, there are more rainfall events and the rainfall is greater, and the deformation rate reaches the maximum, which indicates that the rainfall is an important factor for inducing the occurrence of the landslide. And it can be seen that there is always an accumulation process of the atmospheric water vapor content PWV before the occurrence of a rainfall event, and that the atmospheric water vapor content PWV rapidly decreases after rainfall. Landslide caused by a rainfall event generally has certain hysteresis, so that monitoring of the rainfall event has important significance for landslide disaster early warning, and the PWV (atmospheric water vapor content) and the rainfall event have strong correlation.
3. And acquiring a ground surface soil water content time sequence around the area to be monitored, and analyzing a response relation between the atmospheric water vapor content time sequence and rainfall data.
Fig. 4 is a first fresnel reflection area of each satellite at different low altitude angles within an azimuth range of 180 ° to 360 ° in the example regional survey station solved by the present technical solution, that is, a selected region for inverting the water content of soil. Different colors represent different satellites and the figure is drawn in conjunction with Google earth.
Fig. 5 is a comparison result and correlation analysis of soil humidity and actually measured soil humidity interpreted by four satellites in an example area calculated by the technical scheme, and it can be found from fig. 5 that the result curves of the satellites calculated by the technology have better consistency with the soil humidity curve, and show a trend of increasing and decreasing along with the actually measured soil humidity, so as to meet the condition of soil humidity change, and the correlation coefficients are all significant and respectively reach 0.78, 0.85, 0.77 and 0.88. The reliability of the GNSS-IR interpretation result is verified.
Fig. 6 shows soil humidity of a survey station estimated by regional fusion of multiple satellites, rainfall and soil humidity acquired by a sensor in an example solved by the technical scheme. The thickest line of the bars represents rainfall, the thinnest line of the lines represents the soil humidity estimated by GNSS, and the second thinnest line of the lines represents the in situ soil humidity. As can be seen, the soil moisture curve rises significantly with the two precipitation events on days 274 and 302, and the soil moisture content peaks on subsequent days 276 and 303 because of the late rainfall, which is 33.34% and 32.85%, respectively. During the periods of 276-301 days and 303-328 days, the rainfall shows a remarkable decreasing trend, the soil humidity slowly falls back, and the floating fluctuation occurs again until a new precipitation event occurs. The goodness of fit of the multi-satellite fusion result shows greater consistency than that of a single satellite, correlation analysis is carried out on the fused result and actually-measured soil weight moisture content (figure 6 (right)), correlation coefficients respectively reach 0.89, and the correlation of the multi-satellite fusion result is improved to a certain extent compared with that of a single satellite.
4. And analyzing the response relation between the accumulated landslide displacement and the water content of the earth surface soil.
Fig. 7 is a graph of a relationship between accumulated displacement and soil moisture content in a corresponding time period of a monitoring point in an example area calculated by the technical scheme, and the result is shown in fig. 7, and no matter the soil moisture monitoring result is obtained, or the soil moisture value is obtained through GNSS interpretation, the soil moisture value is obviously improved when three times of deformation occurs, and the actually measured soil weight moisture content is respectively improved from 30.48%, 30% and 26.87% to 31.92%, 31.12% and 28.43% in 257 days, 274 days, and the increase is 4%, 4% and 6% in sequence. The interpretation result, although the soil weight water content was rather decreased at the 274 th day due to inversion error, at the 275 th day thereafter, the soil weight water content was improved, and the increases at the 257 th, 275 th and 302 th days were 4%, 5% and 6% in this order. The result shows that the change of the moisture in the soil influences the shear strength of the soil body and is one of the main inducing factors of the deformation of the loess landslide.
5. Analyzing the correlation between landslide displacement rate and the water content of the earth surface soil
FIG. 8 is a graph of displacement rate versus soil moisture for monitored sites of an example area that is solved by the present technique. It can be seen from fig. 8 through visual interpretation that the displacement rate and the soil humidity have the same obvious variation trend within the monitoring time, and it can be judged that there is a certain correlation between the two.
Fig. 9 is a cross-correlation sequence diagram of displacement rate and soil humidity time lag of monitoring stations in an example area calculated by the technical scheme. As can be seen in fig. 9: when the lag period is 0d, the influence of the monitoring result of the soil humidity and the soil humidity value interpreted by the GNSS on the displacement change reaches a peak value, the correlation extreme value of the actually measured soil humidity is 0.7, and the correlation extreme value of the soil humidity interpreted by the GNSS is 0.65, which are both highly correlated.
Fig. 10 is a graph of the relationship between the displacement rate of the monitoring station in the example area and the change rate of the soil moisture content, which is calculated by the technical scheme. It can be seen from fig. 10 that the deformation rates at 258, 276 and 304 days after the soil moisture content change rate reaches the peak value reach respective peak values in three time periods, and the deformation rates change with the measured and interpreted soil moisture content change rate, which are consistent in the overall transformation trend, which shows that the sudden increase of the soil moisture content change rate is also one of the factors influencing the deformation of the loess landslide, and the two have good response relation.
Finally, the description is as follows: the above disclosure is only one specific embodiment of the present invention, however, the present invention is not limited thereto, and any modifications that can be made by those skilled in the art should fall within the protection scope of the present invention.
Claims (9)
1. A loess landslide disaster comprehensive monitoring method is characterized by comprising the following steps:
acquiring various monitoring data of an area to be monitored;
according to various monitoring data, calculating a three-dimensional deformation time sequence of the earth surface of the area to be monitored by utilizing a GNSS relative positioning technology; calculating an atmospheric water vapor content time sequence of a region to be monitored by utilizing a GNSS refraction remote sensing technology; calculating a time sequence of the water content of the earth surface soil around the area to be monitored by utilizing a GNSS reflection remote sensing technology;
the rainfall data of the area to be monitored is obtained, and the response relation between the rainfall data and the atmospheric water vapor content time sequence is analyzed to obtain the correlation between the atmospheric water vapor content and the rainfall;
analyzing the response relation between the three-dimensional surface deformation time sequence and the surface soil moisture content time sequence to obtain the correlation between the landslide deformation of the area to be monitored and the surface soil moisture content;
according to the correlation between the atmospheric water vapor content and the rainfall capacity of the area to be monitored and the calculated atmospheric water vapor content of the area to be monitored, carrying out rainfall early warning on the area to be monitored; and carrying out landslide comprehensive monitoring on the area to be monitored according to the relevance between landslide deformation of the area to be monitored and the water content of the earth surface soil and the interpreted water content of the earth surface soil of the area to be monitored.
2. The loess landslide disaster comprehensive monitoring method according to claim 1, wherein: the acquiring of various monitoring data of the area to be monitored comprises:
setting a GNSS monitoring reference station and a mobile station in the area to be monitored, and acquiring GNSS observation station observation data of the area to be monitored;
acquiring broadcast ephemeris data of the area to be monitored;
acquiring precise ephemeris data of the area to be monitored;
and acquiring meteorological data of the area to be monitored.
3. The loess landslide disaster comprehensive monitoring method according to claim 2, wherein: the method for calculating the three-dimensional deformation time sequence of the earth surface of the area to be monitored by utilizing the GNSS relative positioning technology according to the monitoring data comprises the following steps:
calculating the position, the speed and the clock error of a satellite according to the observation data of the GNSS observation station;
according to the observation data of the GNSS observation station, calculating the approximate coordinates of the rover station and the reference station by utilizing pseudo-range point positioning;
calculating a non-difference residual error item between the reference station and the rover station according to the observation data of the GNSS observation station;
carrying out tide correction, troposphere correction and antenna phase center correction on the observation data of the GNSS observation station;
performing Kalman filtering on the non-difference residual error items of the reference station and the rover station;
determining integer ambiguity by using an M-W combined observation algorithm in cycle slip detection according to the approximate coordinates of a reference station and a rover and a non-difference residual error item subjected to Kalman filtering;
extracting the station center coordinates NEU of the rover station relative to the reference station;
and obtaining a ground surface three-dimensional deformation time sequence of the area to be monitored according to the station center coordinates NEU of the mobile station relative to the reference station.
4. The loess landslide disaster comprehensive monitoring method according to claim 3, wherein: according to various monitoring data, calculating the atmospheric water vapor content time sequence of the area to be monitored by utilizing a GNSS refraction remote sensing technology, wherein the calculation comprises the following steps:
calculating the total zenith direction delay ZTD in the observation data of the GNSS observation station;
acquiring the air pressure and the temperature of an area to be monitored from the meteorological data, and calculating the weighted average temperature of the atmosphere;
calculating the zenith trunk delay ZHD in the observation data of the GNSS observation station;
the wet retardation ZWD is calculated by the formula:
ZWD＝ZTD-ZHD (1)
and calculating the air water vapor content PWV at different moments according to the wet delay ZWD and the air weighted average temperature to obtain an air water vapor content time sequence of the area to be monitored.
5. The loess landslide disaster comprehensive monitoring method according to claim 1, wherein: according to each item of monitoring data, utilize GNSS reflection remote sensing technology to calculate the earth's surface soil moisture content time series around the region of waiting to monitor, include:
extracting signal-to-noise ratio (SNR) data in the GNSS observation station observation data;
selecting an altitude angle of an observation satellite, setting an azimuth angle, and intercepting SNR (signal-to-noise ratio) data of high-frequency oscillation in the SNR data;
separating a direct signal and a reflected signal in SNR (signal to noise ratio) data with high-frequency oscillation by using a low-order polynomial;
resampling the reflected signals separated from the SNR data;
performing LS spectrum analysis on the re-sampled reflection signal to obtain the reflection height h from the phase center of the GNSS observation station antenna to the ground;
substituting the reflection height h, the wavelength lambda of the reflection signal, signal-to-noise ratio (SNR) data and the satellite height angle theta into a formula (2), and fitting by utilizing a nonlinear least square method to obtain a phase parameter psi and an amplitude A;
and establishing a linear inversion model according to the obtained phase parameter psi to obtain a time sequence of the water content of the earth surface soil around the region to be monitored.
6. The loess landslide disaster comprehensive monitoring method according to claim 1, wherein: analyzing the response relation between the atmospheric water vapor content time sequence and the rainfall data to obtain the correlation between the atmospheric water vapor content and the rainfall, wherein the correlation comprises the following steps:
and drawing a dual-coordinate axis relation graph between the atmospheric water vapor content time series and the rainfall data, and analyzing the relation between the atmospheric water vapor content time series and the rainfall data from the graph to obtain the correlation between the atmospheric water vapor content and the rainfall.
7. The loess landslide disaster comprehensive monitoring method according to claim 1, wherein: the analysis of the response relationship between the three-dimensional surface deformation time sequence and the surface soil moisture content time sequence to obtain the correlation between the landslide deformation of the area to be monitored and the surface soil moisture content comprises the following steps:
obtaining the landslide accumulated displacement according to the three-dimensional earth surface deformation time sequence;
and drawing a double coordinate axis relation graph between the landslide accumulated displacement and the water content of the earth surface soil to obtain the relevance between the landslide accumulated displacement and the water content of the earth surface soil.
8. The loess landslide disaster comprehensive monitoring method according to claim 7, wherein: when the bi-coordinate axis relation graph between the landslide accumulated displacement and the surface soil moisture content is drawn, the lag time of response of the landslide displacement rate to the surface soil moisture content change is quantitatively analyzed through a time-lag cross-correlation analysis method;
and drawing a bi-coordinate axis relation graph between the landslide displacement rate and the water content of the earth surface soil according to the lag time of the landslide displacement rate in response to the change of the water content of the earth surface soil, and analyzing the correlation between the landslide displacement rate and the water content of the earth surface soil from the graph.
9. The utility model provides a loess landslide disaster integrated monitoring system which characterized in that: the method comprises the following steps:
the data acquisition module is used for acquiring various monitoring data of the area to be monitored;
the data resolving module is used for calculating a ground surface three-dimensional deformation time sequence of the area to be monitored by utilizing a GNSS relative positioning technology according to each monitoring data; calculating an atmospheric water vapor content time sequence of a region to be monitored by utilizing a GNSS refraction remote sensing technology; calculating a time sequence of the water content of the earth surface soil around the area to be monitored by utilizing a GNSS reflection remote sensing technology;
the first response relation acquisition module is used for acquiring rainfall data of the area to be monitored and analyzing the response relation between the rainfall data and the atmospheric water vapor content time sequence to obtain the correlation between the atmospheric water vapor content and the rainfall;
the second response relation acquisition module is used for analyzing the response relation between the three-dimensional surface deformation time sequence and the surface soil moisture content time sequence to obtain the relevance between the landslide deformation of the area to be monitored and the surface soil moisture content;
the comprehensive monitoring module is used for carrying out rainfall early warning on the area to be monitored according to the correlation between the atmospheric water vapor content and the rainfall of the area to be monitored and the calculated atmospheric water vapor content of the area to be monitored; and carrying out landslide comprehensive monitoring on the area to be monitored according to the relevance between landslide deformation of the area to be monitored and the water content of the earth surface soil and the interpreted water content of the earth surface soil of the area to be monitored.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210974875.6A CN115343317B (en) | 2022-08-15 | 2022-08-15 | Loess landslide hazard comprehensive monitoring method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210974875.6A CN115343317B (en) | 2022-08-15 | 2022-08-15 | Loess landslide hazard comprehensive monitoring method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115343317A true CN115343317A (en) | 2022-11-15 |
| CN115343317B CN115343317B (en) | 2024-04-26 |
Family
ID=83951807
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210974875.6A Active CN115343317B (en) | 2022-08-15 | 2022-08-15 | Loess landslide hazard comprehensive monitoring method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115343317B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105976569A (en) * | 2016-07-26 | 2016-09-28 | 长安大学 | Landslide hazard monitoring system and method |
| KR102124546B1 (en) * | 2019-12-24 | 2020-06-18 | 주식회사 지오코리아이엔지 | Management and warning system of falling rock and soil and stone measure facilities to be communicated with the slope warning device |
| CN111504392A (en) * | 2020-06-10 | 2020-08-07 | 中国地质调查局水文地质环境地质调查中心 | Landslide multi-element three-dimensional space monitoring system and method |
| AU2020103449A4 (en) * | 2020-11-16 | 2021-01-28 | China University Of Mining And Technology | Method for monitoring the water level of reservoir by using GNSS triple-frequency phase combination data |
| CN114440758A (en) * | 2022-01-09 | 2022-05-06 | 西北大学 | Analysis method for response of landslide to rainfall on regional scale |
-
2022
- 2022-08-15 CN CN202210974875.6A patent/CN115343317B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105976569A (en) * | 2016-07-26 | 2016-09-28 | 长安大学 | Landslide hazard monitoring system and method |
| KR102124546B1 (en) * | 2019-12-24 | 2020-06-18 | 주식회사 지오코리아이엔지 | Management and warning system of falling rock and soil and stone measure facilities to be communicated with the slope warning device |
| CN111504392A (en) * | 2020-06-10 | 2020-08-07 | 中国地质调查局水文地质环境地质调查中心 | Landslide multi-element three-dimensional space monitoring system and method |
| AU2020103449A4 (en) * | 2020-11-16 | 2021-01-28 | China University Of Mining And Technology | Method for monitoring the water level of reservoir by using GNSS triple-frequency phase combination data |
| CN114440758A (en) * | 2022-01-09 | 2022-05-06 | 西北大学 | Analysis method for response of landslide to rainfall on regional scale |
Non-Patent Citations (2)
| Title |
|---|
| 石晓春;李成钢;徐峰;魏平新;: "浅层堆积降雨诱发滑坡监测试验研究", 测绘, no. 01, 15 February 2013 (2013-02-15) * |
| 陈波;: "GNSS技术在滑坡应急变形监测中的应用分析", 世界有色金属, no. 21, 31 December 2016 (2016-12-31) * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115343317B (en) | 2024-04-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Fujiwara et al. | Crustal deformation measurements using repeat‐pass JERS 1 synthetic aperture radar interferometry near the Izu Peninsula, Japan | |
| Garrison et al. | Detection of ocean reflected GPS signals: Theory and experiment | |
| CN112505068B (en) | GNSS-IR-based earth surface soil humidity multi-star combination inversion method | |
| Marom et al. | Energy density directional spectra of a nearshore wave field measured by interferometric synthetic aperture radar | |
| Yang et al. | Land surface characterization using BeiDou signal-to-noise ratio observations | |
| WO2023197714A1 (en) | Gnss multi-path error reducing method suitable for dynamic carrier platform | |
| CN114397425B (en) | GNSS-IR soil humidity inversion method based on generalized extension approximation | |
| CN113805208A (en) | GNSS-IR height measurement method suitable for navigation receiver | |
| Akilan et al. | GPS derived Zenith Total Delay (ZTD) observed at tropical locations in South India during atmospheric storms and depressions | |
| Lowe et al. | An ocean-altimetry measurement using reflected GPS signals observed from a low-altitude aircraft | |
| CN113419232B (en) | River boundary and width detection method of land-based GNSS (global navigation satellite system) reflectometer | |
| CN115980317B (en) | Foundation GNSS-R data soil moisture estimation method based on corrected phase | |
| CN115343317B (en) | Loess landslide hazard comprehensive monitoring method and system | |
| Durand et al. | GPS reflectometry study detecting snow height changes in the Southern Patagonia Icefield | |
| Braun et al. | Development of a water vapor tomography system using low cost L1 GPS receivers | |
| CN116029162A (en) | Flood disaster inundation range monitoring method and system by using satellite-borne GNSS-R data | |
| CN113589350B (en) | Sea surface wind speed measuring method based on measuring GNSS receiver | |
| CN116087997A (en) | Water vapor real-time inversion method and system based on Beidou/GNSS-supporting electronic equipment | |
| May | Spaced antenna versus Doppler radars: A comparison of techniques revisited | |
| CN113625307A (en) | Landslide monitoring system and method based on GNSS | |
| Jia et al. | Polarimetric GNSS-R measurements for soil moisture and vegetation sensing | |
| Wang et al. | Deformation monitoring and evaluation of mountain slope stability combined with ground-based radar and spaceborne InSAR methods | |
| Skone et al. | Atmospheric moisture estimation using GPS on a moving platform | |
| CN115453577B (en) | Method and device for extracting river boundary by GNSS-IR | |
| Gravalon et al. | The Impact of Barometric Variations on the Sea Level in Coastal Areas Using GNSS Reflectometry |
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 |