CN115343317B - Loess landslide hazard comprehensive monitoring method and system - Google Patents
Loess landslide hazard comprehensive monitoring method and system Download PDFInfo
- Publication number
- CN115343317B CN115343317B CN202210974875.6A CN202210974875A CN115343317B CN 115343317 B CN115343317 B CN 115343317B CN 202210974875 A CN202210974875 A CN 202210974875A CN 115343317 B CN115343317 B CN 115343317B
- Authority
- CN
- China
- Prior art keywords
- monitored
- area
- time sequence
- data
- earth surface
- 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.)
- Active
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 85
- 238000000034 method Methods 0.000 title claims abstract description 33
- 239000002689 soil Substances 0.000 claims abstract description 119
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Chemical compound O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 113
- 238000005516 engineering process Methods 0.000 claims abstract description 37
- 230000004044 response Effects 0.000 claims abstract description 34
- 230000008859 change Effects 0.000 claims abstract description 18
- 238000006073 displacement reaction Methods 0.000 claims description 36
- 238000012937 correction Methods 0.000 claims description 9
- 238000010219 correlation analysis Methods 0.000 claims description 7
- 238000001914 filtration Methods 0.000 claims description 6
- 230000010355 oscillation Effects 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 4
- 238000012952 Resampling Methods 0.000 claims description 3
- 238000001514 detection method Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 238000010183 spectrum analysis Methods 0.000 claims description 3
- 239000005436 troposphere Substances 0.000 claims description 3
- 230000007613 environmental effect Effects 0.000 abstract description 3
- 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
- 238000010586 diagram Methods 0.000 description 2
- 230000001965 increasing effect Effects 0.000 description 2
- 241001061260 Emmelichthys struhsakeri Species 0.000 description 1
- 208000028257 Joubert syndrome with oculorenal defect Diseases 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 230000003321 amplification Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000000969 carrier Substances 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000011065 in-situ storage Methods 0.000 description 1
- 230000006698 induction Effects 0.000 description 1
- 230000001939 inductive effect Effects 0.000 description 1
- 239000005433 ionosphere Substances 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 238000001556 precipitation Methods 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
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 hazard comprehensive monitoring method and a loess landslide hazard comprehensive monitoring system, wherein the loess landslide hazard comprehensive monitoring method comprises the following steps: acquiring various monitoring data of an area to be monitored; acquiring a ground surface three-dimensional deformation time sequence, an atmospheric water vapor content time sequence and a ground surface soil water content time sequence of a region to be monitored by using a GNSS relative positioning technology and a GNSS remote sensing technology; analyzing the relevance between the atmospheric water vapor content time sequence and rainfall data of the area to be monitored; the association between the three-dimensional earth surface deformation time sequence and the earth surface soil water content time sequence is analyzed; and 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 invention, the GNSS technology is utilized to acquire environmental information such as deformation information of a region to be monitored, the atmospheric water vapor content, the change of the soil water content and the like, and the response relationship among the environmental information is analyzed, so that the GNSS technology is better used for comprehensively monitoring 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
Loess plateau is one of areas with most serious three geological disasters in China, water sensitivity is the most remarkable characteristic of loess, rainwater infiltrates into loess under the action of strong rainfall to quickly increase the saturation of soil, and the change of water in soil influences the shear strength of the soil, so that loess shallow landslide is induced. The deformation and incentive monitoring work of the landslide is effectively developed, and the landslide monitoring method has important significance for disaster prevention and reduction work in China.
At present, a Global Navigation Satellite System (GNSS) is widely applied to the landslide hazard monitoring field as a technical means capable of directly acquiring real-time three-dimensional vector deformation of the earth surface. Meanwhile, the global navigation satellite system GNSS can also provide continuous L-band microwave signals to acquire surface environment information such as the atmospheric water vapor content, the soil water content and the like of the station measuring area.
For the past landslide hazard monitoring research, the global navigation satellite system GNSS is only used for providing three-dimensional deformation information, and the capability of providing environmental information around a monitoring station is almost completely ignored. In addition, the research of applying the surrounding environment information of the monitoring station provided by the navigation satellite system GNSS to the comprehensive monitoring of landslide disasters in the prior art is not yet involved.
Disclosure of Invention
Aiming at the problems in the background technology, the invention provides a loess landslide hazard comprehensive monitoring method and a loess landslide hazard comprehensive monitoring system.
The invention provides a loess landslide hazard comprehensive monitoring method, which comprises the following steps:
acquiring various monitoring data of an area to be monitored;
According to each item of monitoring data, calculating the earth surface three-dimensional deformation time sequence of the area to be monitored by using a GNSS relative positioning technology; calculating the time sequence of the atmospheric water vapor content of the area to be monitored by using 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 using a GNSS reflection remote sensing technology;
Analyzing the response relation between the rainfall data of the area to be monitored and the time sequence of the atmospheric water vapor content to obtain the relevance between the atmospheric water vapor content and the rainfall;
analyzing a response relation between the three-dimensional earth surface deformation time sequence and the earth surface soil moisture content time sequence to obtain the association between landslide deformation of the area to be monitored and the earth surface soil moisture content;
According to the correlation between the atmospheric water vapor content of the area to be monitored and the rainfall and the calculated atmospheric water vapor content of the area to be monitored, rainfall early warning is carried out on the area to be monitored; and according to the correlation between the 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, comprehensively monitoring the landslide of the area to be monitored.
Further, the acquiring each item of monitoring data of the area to be monitored includes:
Setting a GNSS monitoring reference station and a mobile station in the area to be monitored, and acquiring GNSS 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.
Further, according to each item of monitoring data, calculating a surface three-dimensional deformation time sequence of the area to be monitored by using a GNSS relative positioning technology, and the method comprises the following steps:
Calculating the position, speed and clock difference of the satellite according to the GNSS station observation data;
According to the GNSS station observation data, calculating the outline coordinates of the mobile station and the reference station by using pseudo-range single-point positioning;
calculating a non-difference residual term of the reference station and the mobile station according to the GNSS station observation data;
Carrying out tide correction, troposphere correction and antenna phase center correction on the GNSS station observation data;
Performing Kalman filtering on non-difference residual terms of the reference station and the mobile station;
determining the whole cycle ambiguity by using an M-W combined observation algorithm in cycle slip detection according to the rough coordinates of a reference station and a mobile station and a non-difference residual term after Kalman filtering processing;
extracting station center coordinates NEU of the mobile station relative to the reference station;
And obtaining the earth 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, according to each item of monitoring data, calculating the time sequence of the atmospheric water vapor content of the area to be monitored by using the GNSS refraction remote sensing technology comprises the following steps:
calculating total zenith-direction delay ZTD in the GNSS station observation data;
Acquiring the air pressure and the temperature of an area to be monitored from the meteorological data, and calculating the air weighted average temperature;
Calculating zenith dry delay ZHD in the GNSS station observation data;
the wet delay ZWD is calculated according to the following formula:
ZWD＝ZTD-ZHD (1)
And according to the wet delay ZWD and the atmospheric weighted average temperature, calculating the atmospheric water vapor content PWV at different moments to obtain an 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 water content of the earth surface soil around the area to be monitored by using a GNSS reflection remote sensing technology, including:
Extracting signal-to-noise ratio (SNR) data in the GNSS station observation data;
Selecting an altitude angle of an observation satellite, setting an azimuth angle, and intercepting signal-to-noise ratio (SNR) data of high-frequency oscillation in the SNR data;
separating the straight and reflected signals in the SNR data of the high-frequency oscillation by using a low-order polynomial;
resampling the reflected signal separated from the signal-to-noise ratio (SNR) data;
LS spectrum analysis is carried out on the resampled reflected signal, and the reflection height h from the GNSS station antenna phase center to the ground is obtained;
Substituting the reflection height h, the wavelength lambda of the reflected signal, the 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 according to the acquired phase parameter psi, establishing a linear inversion model to acquire a time sequence of the water content of the earth surface soil around the area to be monitored.
Further, the 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 comprises the following steps:
And drawing a double-axis relation chart 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 chart to obtain the relevance between the atmospheric water vapor content and the rainfall.
Further, the analyzing the response relation between the three-dimensional earth surface deformation time sequence and the earth surface soil moisture content time sequence to obtain the association between the landslide deformation of the area to be monitored and the earth surface soil moisture content comprises the following steps:
According to the three-dimensional earth surface deformation time sequence, landslide accumulated displacement is obtained;
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 correlation between the landslide accumulated displacement and the water content of the earth surface soil.
Further, when a double-coordinate axis relation graph between the landslide accumulated displacement and the water content of the earth surface soil is drawn, quantitatively analyzing the lag time of the landslide displacement rate on the change response of the water content of the earth surface soil by a time-lapse cross-correlation analysis method;
And drawing a double-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 on the change response 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 hazard 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 the earth surface three-dimensional deformation time sequence of the area to be monitored by using a GNSS relative positioning technology according to each item of monitoring data; calculating the time sequence of the atmospheric water vapor content of the area to be monitored by using 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 using 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 time sequence of the atmospheric water vapor content, and obtaining the relevance 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 earth surface deformation time sequence and the earth surface soil moisture content time sequence to obtain the relevance between landslide deformation of the area to be monitored and the earth 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 of the area to be monitored and the rainfall and the calculated atmospheric water vapor content of the area to be monitored; and according to the correlation between the 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, comprehensively monitoring the landslide of the area to be monitored.
Compared with the prior art, the invention has the beneficial effects that:
The invention provides a comprehensive loess landslide disaster monitoring method based on foundation GNSS remote sensing, which has the advantages that based on a data set acquired by one GNSS, three-dimensional deformation, atmosphere water vapor content and soil moisture content information of a landslide area can be acquired by adopting GNSS carrier phase difference, GNSS refraction and GNSS reflection remote sensing technologies respectively, and carriers used by GNSS satellites are positioned in an L wave band of microwaves, are less influenced by severe weather such as heavy fog, rain and snow, and can penetrate through cloud layers, so that long-term stable and all-weather observation on landslide deformation and surrounding environment can be realized. Along with the continuous improvement of the GNSS technology, the continuous perfection of the global CORS station and the continuous increase of the total number of satellites of a future global satellite navigation system, the method can provide rich data for researching the environment of a landslide area interpreted by the GNSS and monitoring the deformation of the earth surface, further promote the research and application of the technology in the aspect of monitoring the environment of a landslide body area, enable the GNSS to better play a role in the aspects of landslide disaster monitoring, early warning and the like, and has important significance for the comprehensive monitoring of landslide.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
Fig. 1 is a comprehensive monitoring system diagram in an embodiment of a loess landslide hazard comprehensive monitoring method provided by the invention;
Fig. 2 is a three-dimensional deformation time sequence of an area to be monitored according to an embodiment of the present invention.
FIG. 3 illustrates the GNSS-resolved atmospheric moisture content PWV and the E-direction deformation of the mobile station of the area to be monitored relative to the reference station according to the embodiment of the present invention;
FIG. 4 is a partial satellite Fresnel reflection region within a 180-360 azimuth angle of an rover station of an area to be monitored relative to a reference station provided by an embodiment of the present invention;
FIG. 5 is a graph showing the comparison result and correlation analysis of the interpreted soil humidity and the measured soil humidity of each satellite according to the embodiment of the present invention;
FIG. 6 is a graph showing the comparison and correlation analysis of the soil humidity results and measured soil humidity obtained by combining multi-satellite interpretation according to the embodiment of the present invention;
FIG. 7 is a graph showing the relationship between soil moisture and displacement according to an embodiment of the present invention;
FIG. 8 is a graph showing the relationship between displacement rate and soil humidity according to an embodiment of the present invention;
FIG. 9 is a sequence of displacement rate versus soil moisture lag correlations provided by an embodiment of the present invention;
FIG. 10 is a graph showing the relationship between the displacement rate and the soil humidity change rate according to the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, but it should be understood that the protection scope of the present invention is not limited by the specific embodiments.
As shown in fig. 1, the invention provides a loess landslide hazard comprehensive monitoring method, which comprises the following steps:
step 1: and acquiring various monitoring data of the area to be monitored.
Step 2: according to each item of monitoring data, calculating the earth surface three-dimensional deformation time sequence of the area to be monitored by using a GNSS relative positioning technology; calculating the time sequence of the atmospheric water vapor content of the area to be monitored by using 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 using a GNSS reflection remote sensing technology;
step 3: analyzing the response relation between the rainfall data of the area to be monitored and the time sequence of the atmospheric water vapor content to obtain the relevance between the atmospheric water vapor content and the rainfall; ;
Step 4: analyzing a response relation between the three-dimensional earth surface deformation time sequence and the earth surface soil moisture content time sequence to obtain the association between landslide deformation of the area to be monitored and the earth surface soil moisture content;
Step 5: according to the correlation between the atmospheric water vapor content of the area to be monitored and the rainfall and the calculated atmospheric water vapor content of the area to be monitored, rainfall early warning is carried out on the area to be monitored; and according to the correlation between the 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, comprehensively monitoring the landslide of the area to be monitored.
In step 1, acquiring each item of monitoring data of an area to be monitored, specifically including:
setting a GNSS monitoring reference station and a mobile station in a region to be monitored, and acquiring GNSS station observation data of the region to be monitored;
Acquiring broadcast ephemeris data of an area to be monitored;
Acquiring precise ephemeris data of a region to be monitored;
And acquiring meteorological data of the area to be monitored.
And analyzing the data such as the utilization rate, the multipath effect, the cycle slip, the signal-to-noise ratio, the ionosphere delay rate, the precision factor and the like in the GNSS station observation data by using software such as TEQC, RTKLIB and the like, thereby realizing the evaluation of the quality of the GNSS station observation data acquired in real time.
The step 2 specifically comprises the following steps:
step 2.1: according to each monitoring data, calculating a surface three-dimensional deformation time sequence of the region to be monitored by using a GNSS relative positioning technology, wherein the method comprises the following steps:
calculating the position, speed and clock difference of the satellite according to the GNSS station observation data;
According to the GNSS station observation data, calculating the rough coordinates of the mobile station and the reference station by using pseudo-range single-point positioning;
according to the GNSS station observation data, calculating a non-difference residual error item of the reference station and the mobile station;
carrying out tide correction, troposphere correction and antenna phase center correction on GNSS observation data;
Performing Kalman filtering on non-difference residual terms of the reference station and the mobile station;
determining the whole cycle ambiguity by using an M-W combined observation algorithm in cycle slip detection according to the rough coordinates of a reference station and a mobile station and a non-difference residual term after Kalman filtering processing; ;
extracting station center coordinates NEU of the mobile station relative to the reference station;
And obtaining the earth 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 each item of monitoring data, calculating an atmospheric water vapor content time sequence of the area to be monitored by using a GNSS refraction remote sensing technology, wherein the method comprises the following steps:
calculating total zenith-direction delay ZTD in the GNSS station observation data;
Specifically, GAMIT/GLOBK data processing software is utilized to select a global projection function model and GAMITA default 10-degree height cut-off angle when calculation is performed for total zenith-direction delay ZTD in GNSS station observation data.
Acquiring the air pressure and the temperature of an area to be monitored from meteorological data, and calculating the air weighted average temperature;
zenith dry delay ZHD in the GNSS station observations was calculated, specifically using the most commonly used saestamonen (saastamonen) model to calculate zenith dry delay ZHD.
The wet delay ZWD is calculated according to the following formula:
ZWD＝ZTD-ZHD (1)
And according to the wet delay ZWD and the atmospheric weighted average temperature, calculating the atmospheric water vapor content PWV at different moments to obtain an atmospheric water vapor content time sequence of the area to be monitored.
Step 2.3: according to each item of monitoring data, calculating a time sequence of the water content of the earth surface soil around the area to be monitored by using a GNSS reflection remote sensing technology, wherein the time sequence comprises the following steps:
extracting signal-to-noise ratio (SNR) data in GNSS station observation data;
Selecting a low altitude angle of an observation satellite, setting an azimuth angle, and intercepting signal-to-noise ratio (SNR) data of which the high frequency oscillation occurs in the SNR data;
separating the straight and reflected signals in the SNR data of the high-frequency oscillation by using a low-order polynomial;
resampling the reflected signal separated from the signal-to-noise ratio (SNR) data;
LS spectrum analysis is carried out on the resampled reflected signal to obtain the distance from the phase center of the GNSS 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 altitude angle theta into a formula (2), and obtaining a phase parameter psi and an amplitude A by using a nonlinear least square fitting method;
And according to the obvious linear relation between the acquired phase parameter psi and the soil humidity, establishing a linear inversion model to acquire a time sequence of the surface soil moisture content around the area to be monitored.
Because the phase with high reflection has a remarkable 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 surface soil moisture content around the area to be monitored.
In step 3, analyzing a response relationship between the atmospheric moisture content time sequence and the rainfall data to obtain a correlation between the atmospheric moisture content and the rainfall, including:
And drawing a double-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.
In step 4, analyzing a response relation between the three-dimensional earth surface deformation time sequence and the earth surface soil moisture content time sequence to obtain a correlation between landslide deformation of the area to be monitored and the earth surface soil moisture content, including:
according to the three-dimensional earth surface deformation time sequence, obtaining landslide accumulated displacement;
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 correlation between the landslide accumulated displacement and the water content of the earth surface soil.
Step 4: the method also comprises the step of quantitatively analyzing the lag time of the landslide displacement rate on the change response of the surface soil moisture content by a time-lapse cross-correlation analysis method when a double-coordinate axis relation diagram between the landslide accumulated displacement and the surface soil moisture content is drawn;
And drawing a double-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 on the change response 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 hazard 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 the earth surface three-dimensional deformation time sequence of the area to be monitored by using a GNSS relative positioning technology according to each item of monitoring data; calculating the time sequence of the atmospheric water vapor content of the area to be monitored by using 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 using 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 time sequence of the atmospheric water vapor content, and obtaining the relevance 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 earth surface deformation time sequence and the earth surface soil moisture content time sequence to obtain the relevance between landslide deformation of the area to be monitored and the earth 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 of the area to be monitored and the rainfall and the calculated atmospheric water vapor content of the area to be monitored; and according to the correlation between the 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, comprehensively monitoring the landslide of the area to be monitored.
The technical scheme in the invention is further described in detail below 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 chart of three directions of a measuring station on a typical characteristic point of an example area calculated by the technical scheme of the invention.
Fig. 2 (a) is a GAMIT calculated annual deformation time sequence of the station in 2020, from which it can be seen that the station has no obvious deformation in the north N, east E and high U directions in the period of 1 st to 245 th of 2020, the deformation mainly occurs in the 245 th to 313 th days, the eastern deformation is most obvious, and after that the station has no obvious deformation, and all three directions tend to be in a stable state.
In order to analyze landslide deformation more carefully, the technology uses a TRACK single epoch to calculate data from the 245 th day to the 335 th day, and as a result, as shown in fig. 2 (b), analysis shows that the direction of the station N is changed by about 2cm, the direction of E is changed by about 78cm, and the direction of U is changed by about 12cm in the period. 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 slower and smoother, but at the beginning of the 273 th day, the deformation of the measuring station is rapidly increased, the landslide deformation tends to be stable at the 287 th to 302 th days, the smaller deformation appears again at the 302 th to 313 th days, and the deformation of the measuring station becomes smooth again in the last period.
2. And acquiring a time sequence of the atmospheric water vapor content of the area to be monitored.
Fig. 3 shows the E-direction deformation (the main direction of sliding), the atmospheric moisture content PWV, and the rainfall collected by the sensor when the example area resolved by the solution of the present invention is deformed significantly three times from the 245 th to 335 th of 2020. The coarsest linear columns represent the rainfall, the narrowest linear lines represent the deformation sequence, and the sub-thin linear lines represent the atmospheric water vapor content PWV. As can be seen from the graph, there are more rainfall events and greater rainfall before and after 275 days, and the deformation rate at this time reaches the maximum, which indicates that rainfall is an important factor for inducing this landslide. And it has been found that there is always a cumulative process of the atmospheric moisture content PWV before the occurrence of a rainfall event, and that the atmospheric moisture content PWV drops rapidly after the rainfall event. Landslide caused by rainfall events usually has certain hysteresis, so that monitoring of the rainfall events has important significance for landslide disaster early warning, and the atmospheric water vapor content PWV and the rainfall events have strong correlation.
3. And acquiring a time sequence of the water content of the surface soil around the area to be monitored, and analyzing a response relation between the time sequence of the water content of the atmosphere and the rainfall data.
Fig. 4 shows a first fresnel reflection area of each satellite at different low altitude angles in the azimuth angle range of 180 ° to 360 ° for the example area station calculated in the present technical solution, i.e. the area of the selected inversion soil moisture content. Different colors represent different satellites, and the figure is drawn in combination with Google earth.
Fig. 5 shows the comparison result and correlation analysis of the soil humidity and the measured soil humidity interpreted by four satellites in the example area calculated by the technical scheme, and as can be found from fig. 5, each satellite result curve calculated by the technology has better consistency with the soil humidity curve, all shows increasing and decreasing trend along with the measured soil humidity, accords with the condition of the change of the soil humidity, and has obvious correlation coefficients which respectively reach 0.78, 0.85, 0.77 and 0.88. The reliability of the GNSS-IR interpretation results was verified.
Fig. 6 shows the soil humidity, rainfall and soil humidity of a measuring station with multiple satellite estimation based on the example area fusion calculated by the technical scheme. The boldful columns represent rainfall, the finest lines represent GNSS estimated soil moisture, and the next finest lines represent in-situ soil moisture. As can be seen from the graph, the soil humidity curve increases significantly with two precipitation events at 274 and 302, and the soil weight water content reaches a peak at 276 and 303, respectively, and is 33.34% and 32.85% due to the rainfall weather still occurring at the later stage. In the time periods of 276-301 days and 303-328 days, the rainfall has obvious decreasing trend, the soil humidity slowly falls back until a new rainfall event occurs, and the rising fluctuation occurs again. The consistency of the fusion results of the satellites is larger than that of the single satellite, the correlation analysis (fig. 6 (right)) is carried out on the fusion results and the measured soil weight water content, the correlation coefficients reach 0.89 respectively, and the correlation of the single satellite is improved to a certain extent.
4. And analyzing the response relation between the landslide accumulated displacement and the water content of the earth surface soil.
Fig. 7 is a graph of the relationship between the accumulated displacement and the soil moisture content of the monitoring points in the example area calculated by the technical scheme, and the result is shown in fig. 7, whether the monitoring result of the soil moisture or the soil moisture value interpreted by the GNSS is the result, the soil moisture value is obviously improved when three deformations occur, the measured soil weight moisture content is respectively improved from 30.48%, 30% and 26.87% to 31.92%, 31.12% and 28.43% on days 257, 274 and 302, and the amplification is sequentially 4%, 4% and 6%. The interpreted results, although decreasing on day 274 due to inversion errors, were improved in soil weight water content on the following 275 days, with increases of 4%, 5% and 6% on days 257, 275 and 302 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 most main induction factors of loess landslide deformation.
5. Analysis of correlation between landslide displacement rate and surface soil moisture content
Fig. 8 is a graph of displacement rate and soil humidity of monitoring stations in an example area calculated by the technical scheme. As can be seen from visual interpretation of fig. 8, the displacement rate and the soil humidity have the same obvious change trend in the monitoring time, and it can be determined that there is a certain correlation between the two.
Fig. 9 is a cross-correlation sequence chart of displacement rate and soil humidity time-lapse of monitoring stations in an example area calculated by the technical scheme. As can be seen from fig. 9: when the hysteresis period is 0d, the monitoring result of the soil humidity and the GNSS-interpreted soil humidity value reach the peak value of the influence of the monitoring result on the displacement change, the correlation extremum of the actually measured soil humidity is 0.7, and the correlation extremum of the GNSS-interpreted soil humidity is 0.65, and the monitoring result and the GNSS-interpreted soil humidity value are highly correlated.
Fig. 10 is a graph of the displacement rate of the monitoring station in the example area calculated by the technical scheme and the change rate of the soil moisture content. As can be seen from fig. 10, the deformation rates of 258, 276 and 304 days after the change rate of the soil humidity reaches the peak value also reach respective peak values in three time periods, and the deformation rates change along with the actual measurement and interpretation of the change rate of the soil moisture content, and the deformation rates are consistent in overall transformation trend, which means that the sudden increase of the change rate of the soil moisture content is also one of factors influencing the loess landslide deformation, and the deformation rates have good response relationship.
The last explanation is: the above disclosure is only one specific embodiment of the present invention, but the embodiment of the present invention is not limited thereto, and any changes that can be thought by those skilled in the art should fall within the protection scope of the present invention.
Claims (9)
1. The loess landslide hazard comprehensive monitoring method is characterized by comprising the following steps of:
acquiring various monitoring data of an area to be monitored;
According to each item of monitoring data, calculating the earth surface three-dimensional deformation time sequence of the area to be monitored by using a GNSS relative positioning technology; calculating the time sequence of the atmospheric water vapor content of the area to be monitored by using 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 using a GNSS reflection remote sensing technology;
Analyzing the response relation between the rainfall data of the area to be monitored and the time sequence of the atmospheric water vapor content to obtain the relevance between the atmospheric water vapor content and the rainfall;
analyzing a response relation between the three-dimensional earth surface deformation time sequence and the earth surface soil moisture content time sequence to obtain the association between landslide deformation of the area to be monitored and the earth surface soil moisture content;
According to the correlation between the atmospheric water vapor content of the area to be monitored and the rainfall and the calculated atmospheric water vapor content of the area to be monitored, rainfall early warning is carried out on the area to be monitored; and according to the correlation between the 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, comprehensively monitoring the landslide of the area to be monitored.
2. The loess landslide hazard comprehensive monitoring method as set forth in claim 1, wherein: the acquiring each item of 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 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 hazard comprehensive monitoring method as set forth in claim 2, wherein: according to each item of monitoring data, calculating a surface three-dimensional deformation time sequence of the area to be monitored by using a GNSS relative positioning technology, wherein the method comprises the following steps of:
Calculating the position, speed and clock difference of the satellite according to the GNSS station observation data;
According to the GNSS station observation data, calculating the outline coordinates of the mobile station and the reference station by using pseudo-range single-point positioning;
calculating a non-difference residual term of the reference station and the mobile station according to the GNSS station observation data;
Carrying out tide correction, troposphere correction and antenna phase center correction on the GNSS station observation data;
Performing Kalman filtering on non-difference residual terms of the reference station and the mobile station;
determining the whole cycle ambiguity by using an M-W combined observation algorithm in cycle slip detection according to the rough coordinates of a reference station and a mobile station and a non-difference residual term after Kalman filtering processing;
extracting station center coordinates NEU of the mobile station relative to the reference station;
And obtaining the earth 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 hazard comprehensive monitoring method as set forth in claim 3, wherein: according to each item of monitoring data, calculating an atmospheric water vapor content time sequence of the area to be monitored by using a GNSS refraction remote sensing technology, wherein the method comprises the following steps:
calculating total zenith-direction delay ZTD in the GNSS station observation data;
Acquiring the air pressure and the temperature of an area to be monitored from the meteorological data, and calculating the air weighted average temperature;
Calculating zenith dry delay ZHD in the GNSS station observation data;
the wet delay ZWD is calculated according to the following formula:
ZWD＝ZTD-ZHD (1)
And according to the wet delay ZWD and the atmospheric weighted average temperature, calculating the atmospheric water vapor content PWV at different moments to obtain an atmospheric water vapor content time sequence of the area to be monitored.
5. The loess landslide hazard comprehensive monitoring method as set forth in claim 1, wherein: according to each item of monitoring data, calculating a time sequence of the water content of the earth surface soil around the area to be monitored by using a GNSS reflection remote sensing technology, wherein the time sequence comprises the following steps:
Extracting signal-to-noise ratio (SNR) data in the GNSS station observation data;
Selecting an altitude angle of an observation satellite, setting an azimuth angle, and intercepting signal-to-noise ratio (SNR) data of high-frequency oscillation in the SNR data;
separating the straight and reflected signals in the SNR data of the high-frequency oscillation by using a low-order polynomial;
resampling the reflected signal separated from the signal-to-noise ratio (SNR) data;
LS spectrum analysis is carried out on the resampled reflected signal, and the reflection height h from the GNSS station antenna phase center to the ground is obtained;
Substituting the reflection height h, the wavelength lambda of the reflected signal, the 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 according to the acquired phase parameter psi, establishing a linear inversion model to acquire a time sequence of the water content of the earth surface soil around the area to be monitored.
6. The loess landslide hazard comprehensive monitoring method as set forth in claim 1, wherein: the analysis of the response relationship between the atmospheric water vapor content time sequence and the rainfall data to obtain the association between the atmospheric water vapor content and the rainfall comprises the following steps:
And drawing a double-axis relation chart 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 chart to obtain the relevance between the atmospheric water vapor content and the rainfall.
7. The loess landslide hazard comprehensive monitoring method as set forth in claim 1, wherein: the analysis of the response relation between the three-dimensional earth surface deformation time sequence and the earth surface soil moisture content time sequence to obtain the relevance between landslide deformation of the area to be monitored and the earth surface soil moisture content comprises the following steps:
According to the three-dimensional earth surface deformation time sequence, landslide accumulated displacement is obtained;
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 correlation between the landslide accumulated displacement and the water content of the earth surface soil.
8. The loess landslide hazard comprehensive monitoring method as set forth in claim 7, wherein: when the double-coordinate axis relation graph between the landslide accumulated displacement and the water content of the earth surface soil is drawn, quantitatively analyzing the lag time of the landslide displacement rate on the change response of the water content of the earth surface soil by a time-lapse cross-correlation analysis method;
And drawing a double-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 on the change response 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. Loess landslide disaster integrated monitoring system, its characterized in that: comprising 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 the earth surface three-dimensional deformation time sequence of the area to be monitored by using a GNSS relative positioning technology according to each item of monitoring data; calculating the time sequence of the atmospheric water vapor content of the area to be monitored by using 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 using 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 time sequence of the atmospheric water vapor content, and obtaining the relevance 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 earth surface deformation time sequence and the earth surface soil moisture content time sequence to obtain the relevance between landslide deformation of the area to be monitored and the earth 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 of the area to be monitored and the rainfall and the calculated atmospheric water vapor content of the area to be monitored; and according to the correlation between the 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, comprehensively monitoring the landslide 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 CN115343317A (en) | 2022-11-15 |
| CN115343317B true 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 |
|---|
| GNSS技术在滑坡应急变形监测中的应用分析;陈波;;世界有色金属;20161231(第21期);全文 * |
| 浅层堆积降雨诱发滑坡监测试验研究;石晓春;李成钢;徐峰;魏平新;;测绘;20130215(第01期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115343317A (en) | 2022-11-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106646538B (en) | A kind of deformation monitoring GNSS signal multipath correcting method based on single poor filtering | |
| Frappart et al. | Radar altimetry backscattering signatures at Ka, Ku, C, and S bands over West Africa | |
| Zhang et al. | Avoiding errors attributable to topography in GPS-IR snow depth retrievals | |
| CN113805208B (en) | GNSS-IR height measurement method suitable for navigation receiver | |
| Yang et al. | Land surface characterization using BeiDou signal-to-noise ratio observations | |
| Goad et al. | A new approach to precision airborne GPS positioning for photogrammetry | |
| CN114397425B (en) | GNSS-IR soil humidity inversion method based on generalized extension approximation | |
| Akilan et al. | GPS derived Zenith Total Delay (ZTD) observed at tropical locations in South India during atmospheric storms and depressions | |
| CN113419232B (en) | River boundary and width detection method of land-based GNSS (global navigation satellite system) reflectometer | |
| CH712623A2 (en) | Method for the determination of snow parameters. | |
| 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 | |
| Ware et al. | A Global Positioning System baseline determination including bias fixing and water vapor radiometer corrections | |
| CN116087997A (en) | Water vapor real-time inversion method and system based on Beidou/GNSS-supporting electronic equipment | |
| CN113589350B (en) | Sea surface wind speed measuring method based on measuring GNSS receiver | |
| CN113625307A (en) | Landslide monitoring system and method based on GNSS | |
| Stoew et al. | Temporal correlations of atmospheric mapping function errors in GPS estimation | |
| Mao et al. | Ionospheric phase delay correction for time series multiple-aperture InSAR constrained by polynomial deformation model | |
| Vinande et al. | GPS bistatic radar measurements of aircraft altitude and ground objects with a software receiver | |
| Skone et al. | Atmospheric moisture estimation using GPS on a moving platform | |
| Saleem et al. | Analysis and Mitigation of Tropospheric Error Effect on GPS Positioning Using Real GPS Data | |
| CN115453577B (en) | Method and device for extracting river boundary by GNSS-IR | |
| CN113899302B (en) | Non-contact slope deformation measurement method and system based on navigation reflection signals | |
| CN113009525B (en) | Method for establishing real-time troposphere grid product |
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 |