CN114812496A - Regional ground settlement early warning method based on multi-source heterogeneous data - Google Patents
Regional ground settlement early warning method based on multi-source heterogeneous data Download PDFInfo
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Abstract
本发明涉及工程地质/地质灾害监测技术领域，尤其涉及一种基于多源异构数据的区域地面沉降预警方法，包括包括数据采集模块、数据传输模块、解算模块、分析模块和预警模块，所述数据采集模块用于地面沉降主控因素识别及量化、多源监测技术筛选及评价，所述解算模块包括监测数据解算和预警指标体系构建，信息采集模块基于指数的遥感建筑用地指数IBI提取，首先将三个指数波段叠加生成新影像，进一步分析生成的新影像各波段的波谱特征，发现建筑用地、植被和水体分别在NDBI、SAVI和MNDWI条件下获得最大值，利用这三个指数波段构建基于指数的建筑用地指数IBI。本发明系统稳定，成本低，实时性强。The invention relates to the technical field of engineering geology/geological disaster monitoring, in particular to a regional land subsidence early warning method based on multi-source heterogeneous data, comprising a data acquisition module, a data transmission module, a solution module, an analysis module and an early warning module. The data acquisition module is used for identification and quantification of the main controlling factors of land subsidence, screening and evaluation of multi-source monitoring technology, the calculation module includes monitoring data calculation and early warning index system construction, and the information acquisition module is based on the index of remote sensing building land index IBI. To extract, firstly superimpose the three index bands to generate a new image, and further analyze the spectral characteristics of each band in the generated new image, and find that the building land, vegetation and water body get the maximum values under the conditions of NDBI, SAVI and MNDWI, respectively. Band constructs the index-based Built Land Index IBI. The system of the invention is stable, the cost is low, and the real-time performance is strong.
Description
Technical Field
The invention relates to the technical field of engineering geology/geological disaster monitoring, in particular to a regional ground settlement early warning method based on multi-source heterogeneous data.
Background
The ground settlement is used as a slowly-varying geological disaster and has the characteristics of long duration, slow generation, wide influence range, complex cause mechanism, high prevention and control difficulty and the like, and becomes a global environmental geological problem.
With the aggravation of ground settlement, the influence of the ground settlement on urban planning development and economic construction is gradually highlighted, such as ground cracks are induced, the damage of buildings (structures) is caused, and the loss of the use value of land resources is caused in a certain range; the ground elevation is reduced, so that the defense capability of the urban flood prevention facilities is reduced, the underground tunnel is cracked and damaged, the safe operation of the subway is affected, and the maintenance cost is increased.
The existing ground settlement early warning only considers the ground surface deformation information such as the accumulated settlement amount of an area, the settlement rate and the like, can not reflect the layered deformation characteristics and distribution of the ground settlement, and can not accurately judge the main settlement layer. The relevant specifications of the ground subsidence in China lack a clear ground subsidence early warning index system, so that accurate early warning of disaster-affected bodies built at different levels of an underground space is difficult to realize, and the difficulty in implementing ground subsidence prevention and control measures is increased. Meanwhile, each monitoring and early warning facility is unevenly distributed, effective unit limitation is lacked, and early warning precision is reduced. Therefore, a ground settlement layering and partition early warning index system needs to be established to carry out high-precision ground settlement monitoring and early warning.
Disclosure of Invention
In view of the above, the present invention provides a regional ground settlement early warning method based on multi-source heterogeneous data to solve the problems mentioned in the background art.
The invention relates to a regional ground settlement early warning method based on multi-source heterogeneous data, which is characterized in that ground settlement units are divided based on each index factor in a ground settlement early warning index system; in every ground subsides the unit, set up the polymorphic type monitoring means, the polymorphic type monitoring means includes: precision leveling, GNSS and InSAR; classifying and optimizing various types of monitoring means, and establishing a ground settlement information resolving model; obtaining data to be monitored, calculating to obtain a layered settlement value in each ground settlement unit through a ground settlement information resolving model; and determining the ground settlement risk level according to the calculated layered settlement value, and performing early warning. The evaluation system of the method comprises the steps of identifying and quantifying the main control factor of the ground settlement, screening and evaluating the multi-source monitoring technology, resolving the monitoring data and constructing the early warning index system. The specific modules comprise a data acquisition module, a data transmission module, a resolving module, an analysis module and an early warning module
Further, ground subsidence early warning index system includes: target layer, standard and criterion layer, and index layer.
The index layer includes: ground settlement partition index and ground settlement layering index. The ground settlement layering indexes are embodied as follows: the vertical development depth of the ground subsidence, the main contribution layer and the sensitive layer of the ground subsidence, the ground subsidence index reflecting the ground surface deformation characteristics and the development and utilization degree, depth and scale of the underground space. The ground settlement information resolving model comprises: a leveling point correction model, a deformation projection conversion model and a ground water-ground settlement coupling model.
Before ground settlement units are divided based on each index factor in a ground settlement early warning index system, the ground settlement early warning index system is established, and each index weight is obtained through PCA. And designing a ground settlement early warning index system frame structure, and developing index system screening based on regional ground settlement research results. Before data to be monitored are obtained, a ground settlement information resolving model is used, and layered settlement values in all ground settlement units are obtained through calculation, after various types of monitoring means are classified and optimized and the ground settlement information resolving model is built, a ground settlement early warning index system frame structure is designed, and index system screening is carried out based on regional ground settlement research results. And determining the grade of the ground settlement risk by combining with the ground settlement early warning grading standard, and after early warning, comprehensively applying cloud computing, big data and WebGIS technology to realize the display of early warning results.
And further, identifying and quantifying the ground settlement main control factors, and selecting the ground settlement main control factors from the aspects of the ground settlement influence factors and natural factors and human factors. The natural factors mainly include: substrate tectonic movement, formation lithology and structural characteristics; the human factors mainly include: the water level of underground water becomes amplitude, the static load of high buildings and dense building groups, and the dynamic load of linear work such as rail transit. And acquiring the response relation between different factors and ground settlement based on a statistical analysis means.
Further, groundwater level amplitude variation data is obtained through a water level measuring instrument placed in a groundwater monitoring well; static load of high buildings and dense building groups: by using a remote sensing building land index method, selecting a remote sensing image covering a research area, and further adopting an Erdas Modler tool to acquire building load spatial-temporal evolution information on the basis of NDBI, MNDWI and SAVI index inversion. The remote sensing image preprocessing process comprises the following steps: geometric correction, radiometric calibration, atmospheric correction, image masking. Then extracting vegetation index SAVI according to the formula (1):
SAVI ═ [ (NIR-Red) (1+ L) ] (NIR + Red + L) formula (1)
In the formula, NIR is a pixel brightness value of a near infrared band, Red is a pixel brightness value of a Red light band, and L is a soil conditioning factor, and the value range is 0-1. When L is 0, it means that vegetation coverage is zero; when L is 1, the influence of the soil background is zero, that is, the vegetation coverage is the highest, and the influence of the soil background is zero, which only occurs in a place covered by tall trees with dense tree crowns. In urban areas, Huete recommends that L is 0.5, which can reduce the influence of soil background relatively well, and the paper calculates the soil regulation vegetation index SAVI in Beijing areas by setting the L value to 0.5.
And (3) extracting the corrected normalized water body index MNDWI:
MNDWI ═ Green-MIR)/(Green + MIR) formula (2)
In the formula, Green represents the pixel brightness value of a Green light wave band, and MIR is the pixel brightness value of a middle infrared wave band.
Extracting the normalized building land index NDBI:
NDBI ═ (MIR-NIR)/(MIR + NIR) formula (3)
In the formula, MIR is a pixel gray value of a middle infrared band, NIR is a pixel brightness value of a near infrared band, and NDBI takes a value of-1. The spectral characteristics of the land for building and the dry land are represented by that the mean value of the mid-infrared wave band is larger than the near-infrared wave band, and the water body, the forest land, the farmland and the like represent opposite characteristics. The place with the NDBI value larger than 0 is the town land, and the place with the NDBI value smaller than 0 is the non-construction land.
And further, extracting the remote sensing construction land index IBI based on the index, firstly superposing three index wave bands to generate a new image, further analyzing the wave spectrum characteristics of each wave band of the generated new image, and finding that the construction land, the vegetation and the water body respectively obtain the maximum values under the NDBI, SAVI and MNDWI conditions. Therefore, the three index wave bands are used for constructing an index-based construction land index IBI, and the formula is shown as formula (4);
IBI ═ NDBI- (SAVI + MNDWI)2]/[ NDBI + (SAVI + MNDWI)/2] formula (4)
Dynamic load of linear operation such as rail transit: the average static load of the rail traffic engineering is selected, and the calculation formula is as follows:
in the formula P E : static load of rail transit; v E The vehicle operating speed. P ES : average static load of the vehicle; n: the number of vehicles; mi: a contribution rate; and (3) carrying out linear engineering on the average static load of the Pi vehicle and the PVS.
The statistical analysis means mainly comprises: SRCC, random forest, by calculating the above data by the following formula, formula (6)
Wherein: pk represents the proportion of the kth type sample in the current sample set D as Pk.
Furthermore, the screening and evaluation of the multi-source monitoring technology selects a 'space ground' ground settlement three-dimensional monitoring technology from the perspective of a space measurement technology, and the main technical means comprises the following steps: InSAR, GPS, leveling, hierarchical marking and distributed optical fiber. And evaluating each source monitoring technology from the angles of spatial resolution, temporal resolution, monitoring precision and the like, screening out monitoring means suitable for ground settlement early warning frequency and precision, and providing technical support for subsequently developing ground settlement monitoring early warning.
Further, monitoring data resolving and information acquisition are carried out, a data resolving framework of each source after screening is established, a monitoring data-ground settlement visual response data resolving platform is established, and high-precision resolving data can be obtained in real time. The InSAR, the GNSS and the distributed optical fiber are selected as ground settlement monitoring and early warning advantage monitoring means, InSAR data have high spatial resolution, regional ground settlement deformation information can be obtained, and ground settlement deformation trend is controlled. The GNSS can acquire ground deformation information in real time, can be used for carrying out long-term monitoring on ground settlement center deformation characteristics, and can be used for carrying out early warning on settlement center ground settlement deformation. The distributed optical fiber has the advantages of combining transmission and measurement, can acquire soil body strain information in the full monitoring depth in real time, is used for monitoring settlement in a settlement center area and a settlement edge area in a layering mode, and achieves ground settlement layering monitoring and early warning.
Further, InSAR resolving mainly employs: a small baseline interferometry (SBAS-InSAR) technique, where the formula can be expressed as formula (7):
wherein ν (-) and β (-) are nonlinear components in the average velocity and residual distortion of the high-resolution single-view differential interferogram, respectively, Δ Z (x, r) is a topographic component in the high-resolution single-view differential interferogram, Δ n (x, r) is a noise error, and ν (-) and Δ Z (x, r) are estimated such that they satisfy a maximum temporal coherence factor, expressed as formula (8):
wherein, delta phi mo Is the simulated phase, expressed as follows, as in equation (9):
subtracting the formula (8) and the formula (9) to obtain a new phase comprising beta (·) and delta m (x, r), and removing the nonlinear deformation rate beta (·) by using a singular value decomposition method, wherein the total deformation quantity can be changedRepresented by formula (10): d (t) n ，x，r)＝d L (tn，x，r)+(t n -t 0 )v(x，r)+β(t n X, r), N ═ 0, 1, 2 … N formula (10)
Further, the GNSS solution uses a GAMIT baseline solution:
GAMIT adopts a double-difference method to process an original observation value, the double-difference observation quantity can completely eliminate the influence of satellite clock difference and receiver clock difference, and meanwhile, the influence of system errors such as orbit errors, atmospheric refraction and the like can be obviously weakened. Assuming that the satellite p is observed at the observation station i at the time t, the linearized dual-frequency carrier phase observation equation is as follows:
in the formulae (11), (12) f 1 Is L 1 The carrier frequency of (a); f. of 2 Is L 2 The carrier frequency of (a);
the geometric distance between the satellite and the receiver;
is the ionospheric delay; delta T i p Is tropospheric delay; δ t i Is the receiver clock error; δ t p Is the satellite clock error;
is the initial integer ambiguity;
is the residual error.
Assuming that the satellites p and q are observed at the stations i and j at the time t, the linearized double-difference carrier phase observation equation is as shown in formula (13):
in equation (13), the flow delay can be attenuated by using a parameter estimation or model correction method; ionospheric refraction is affected by various factors and is difficult to handle by a specific method, and a dual-frequency phase observation value is usually adopted to eliminate ionospheric combination LC to weaken the influence of first-order ionospheric refraction at present, as shown in formula (14).
In the formula (14), the LC observed value is subjected to double-difference combination to eliminate the influence of an ionized layer, but the ambiguity of the LC observed value no longer has integer characteristics, and in order to accurately fix the ambiguity of the LC observed value in the whole cycle, the LC ambiguity can be decomposed by means of a wide lane WL and a narrow lane NL combined observed value.
The strain amount and brillouin frequency shift of the optical fiber can be expressed by the following formula:
wherein v is B (epsilon) is the amount of drift of the brillouin frequency when strain is epsilon; v. of B (0) Is the amount of drift of the brillouin frequency when strain is 0;
is a scaling factor of about 493MHz (/% strain); ε is the strain of the fiber.
Further, an early warning index system is established and a threshold value is determined, various evaluation factors and weights thereof required by the construction of the ground settlement early warning index system are determined according to the identification and quantification results of the main control factors of the ground settlement, and the ground settlement early warning index system is established by performing coupling superposition analysis on various single-factor evaluation index graphs by adopting a cluster analysis and fuzzy mathematic method.
Further, the ground settlement monitoring and early warning device comprises one or more processors; the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the area ground settlement early warning method based on the multi-source heterogeneous data according to any one of the above items.
And establishing an early warning standard by taking the ground settlement comprehensive unit as a reference. The early warning standard integrates 'national ground settlement prevention and control plan', 'regional ground settlement prevention and control plan', thresholds of deformation of buildings/linear engineering caused by ground settlement and 'regional ground settlement disaster risk assessment factor grades', various thresholds are comprehensively compared, and early warning grades are classified from small to large. Regional early warning index
1) Ground water level
The method comprises the steps of decomposing a ground settlement annual prevention and control index into seasons, months and days according to an annual development rule, forming a clear substitution model by emphasizing settlement stratum compression amount and adjacent aquifer underground water descent amount causing settlement on the basis of continuous iteration, decomposing settlement amount values in detail in time and vertical space, taking the settlement amount values and underground water level as main early warning threshold values and indexes, and carrying out real-time monitoring and early warning on the dynamic underground water level monitoring data result.
2) Deformation of earth's surface
The method comprises the steps of decomposing a ground settlement annual prevention and control index into seasons, months and days according to an annual development rule, monitoring and implementing monitoring results by applying GPS continuous station monitoring in a typical area, monitoring surface deformation by constructing a grid and utilizing GIS means for interpolation, comparing a monitoring value with the prevention and control index, and carrying out real-time monitoring and early warning.
(3) Early warning ranking
The early warning and forecasting are realized by constructing a double early warning index system combining a settlement index represented by a dynamic accumulative settlement value in the year and an underground water level index. The early warning risk prevention and control grade is divided into three grades according to the risk size, each grade corresponds to corresponding sedimentation and underground water level indexes, the first grade is the highest grade, the display color is red, the second grade is orange, the third grade is the lowest grade, and the display color is yellow.
The invention provides a ground settlement monitoring and early warning system based on an evaluation system and modules, which can acquire high-precision ground surface and underground three-dimensional monitoring data in real time and realize monitoring and early warning in the full ground settlement scale range. The method and the device solve the defects that the traditional ground settlement model has low precision and low spatial resolution, can only perform regional (small scale) ground settlement early warning, and cannot realize small regional scale ground settlement layering early warning. The system integrates multiple processes of multi-source monitoring technology screening and information data real-time processing, main control factor identification and quantification, early warning index system, information system, achievement expression form and real-time release and the like, organic integration of the processes is realized through computer programming, real-time and one-stop early warning can be carried out on ground settlement disasters, the middle process is simplified, and popularization and use are facilitated. Aiming at the defect that the traditional early warning method can only carry out early warning according to surface deformation information and cannot realize layering and zoning early warning, the method is designed to construct a multi-source ground settlement monitoring technology framework system on the basis of identifying the main control influence factor of ground settlement, acquire and real-time settlement information, overcome the limitation of the existing monitoring technology, combine regional and key engineering ground settlement evaluation standards, establish a ground settlement early warning evaluation index system, comprehensively apply cloud computing, big data and WebGIS technology and realize ground settlement monitoring and early warning.
Detailed Description
The present invention is described in detail with reference to the following specific embodiments, and it is apparent that the described embodiments are only a part of the embodiments of the present application, and not all embodiments, and all other embodiments obtained by those skilled in the art without any inventive work based on the embodiments in the present application belong to the protection scope of the present application.
The system consists of an evaluation system and a module. The evaluation system comprises: the method comprises the steps of identifying and quantifying main control factors of ground settlement, screening and evaluating multi-source monitoring technology, resolving monitoring data and constructing an early warning index system. The module includes: data acquisition module, data transmission module, resolving module, analysis module and early warning module
In this embodiment, the ground settlement master control factors are identified and quantified, and the ground settlement master control factors are selected from the ground settlement influence factors and from the two aspects of natural factors and human factors. The natural factors mainly include: substrate tectonic movement, formation lithology and structural features; the human factors mainly include: the water level of underground water becomes amplitude, the static load of high buildings and dense building groups, and the dynamic load of linear work such as rail transit. And acquiring the response relation between different factors and ground settlement based on a statistical analysis means.
In the embodiment, the underground water level amplitude variation data is obtained by a water level measuring instrument placed in an underground water monitoring well; static load of high buildings and dense building groups: by using a remote sensing building land index method, selecting a remote sensing image covering a research area, and further adopting an Erdas Modler tool to acquire building load spatial-temporal evolution information on the basis of NDBI, MNDWI and SAVI index inversion. The remote sensing image preprocessing process comprises the following steps: geometric correction, radiometric calibration, atmospheric correction, image masking. Then extracting vegetation index SAVI according to the formula (1):
SAVI ═ [ (NIR-Red) (1+ L) ]/(NIR + Red + L) formula (1)
In the formula, NIR is a pixel brightness value of a near infrared band, Red is a pixel brightness value of a Red light band, and L is a soil conditioning factor, and the value range is 0-1. When L is 0, it means that vegetation coverage is zero; when L is 1, the influence of the soil background is zero, that is, the vegetation coverage is the highest, and the influence of the soil background is zero, which only occurs in a place covered by tall trees with dense tree crowns. In urban areas, Huete recommends that L is 0.5, which can reduce the influence of soil background relatively well, and the paper calculates the soil regulation vegetation index SAVI in Beijing areas by setting the L value to 0.5.
And (3) extracting the corrected normalized water body index MNDWI:
MNDWI ═ Green-MIR)/(Green + MIR) formula (2)
In the formula, Green represents the pixel brightness value of a Green light wave band, and MIR is the pixel brightness value of a middle infrared wave band.
Extracting the normalized building land index NDBI:
NDBI ═ (MIR-NIR)/(MIR + NIR) formula (3)
In the formula, MIR is a pixel gray value of a middle infrared band, NIR is a pixel brightness value of a near infrared band, and NDBI takes a value of-1. The spectral characteristics of the land for building and the dry land are represented by that the mean value of the mid-infrared wave band is larger than the near-infrared wave band, and the water body, the forest land, the farmland and the like represent opposite characteristics. The place with the NDBI value larger than 0 is the town land, and the place with the NDBI value smaller than 0 is the non-construction land.
In the embodiment, the remote sensing construction land index IBI based on the index is extracted, the three index wave bands are overlapped to generate a new image, the spectral characteristics of each wave band of the generated new image are further analyzed, and the fact that the construction land, the vegetation and the water body respectively obtain the maximum values under the NDBI, SAVI and MNDWI conditions is found. Therefore, the index-based construction land index IBI is constructed by using the three index bands, and the formula is as shown in formula (4):
IBI ═ NDBI- (SAVI + MNDWI)/2]/[ NDBI + (SAVI + MNDWI)/2] formula (4)
Dynamic load of linear operation such as rail transit: the average static load of the rail traffic engineering is selected, and the calculation formula is as follows:
in the formula P E : static load of rail transit; v E The vehicle operating speed. P ES : average static load of the vehicle; n: the number of vehicles; mi: a contribution rate; and (3) carrying out linear engineering on the average static load of the Pi vehicle and the PVS.
The statistical analysis means mainly comprises: SRCC, random forest, by calculating the above data by the following formula, formula (6)
Wherein: pk represents the proportion of the kth type sample in the current sample set D as Pk.
In this embodiment, the screening and evaluation of the multi-source monitoring technology selects a "sky-space-ground" ground settlement stereoscopic monitoring technology from the perspective of the spatial measurement technology, and the main technical means includes: InSAR, GPS, leveling, hierarchical marking and distributed optical fiber. And evaluating each source monitoring technology from the angles of spatial resolution, temporal resolution, monitoring precision and the like, screening out monitoring means suitable for ground settlement early warning frequency and precision, and providing technical support for subsequently developing ground settlement monitoring early warning.
In the embodiment, the monitoring data is resolved and information is acquired, a screened data resolving framework of each source is established, a monitoring data-ground settlement visual response data resolving platform is established, and high-precision resolving data can be obtained in real time. The InSAR, the GNSS and the distributed optical fiber are selected as ground settlement monitoring and early warning advantage monitoring means, InSAR data have high spatial resolution, regional ground settlement deformation information can be obtained, and ground settlement deformation trend is controlled. The GNSS can acquire ground deformation information in real time, can be used for carrying out long-term monitoring on ground settlement center deformation characteristics, and can be used for carrying out early warning on settlement center ground settlement deformation. The distributed optical fiber has the advantages of combining transmission and measurement, can acquire soil body strain information in the full monitoring depth in real time, is used for monitoring settlement in a settlement center area and a settlement edge area in a layering mode, and achieves ground settlement layering monitoring and early warning.
InSAR solution mainly adopts: a small baseline interferometry (SBAS-InSAR) technique, where the formula can be expressed as formula (7):
wherein ν (-) and β (-) are nonlinear components in the average velocity and residual distortion of the high-resolution single-view differential interferogram, respectively, Δ Z (x, r) is a topographic component in the high-resolution single-view differential interferogram, Δ n (x, r) is a noise error, and ν (-) and Δ Z (x, r) are estimated such that they satisfy a maximum temporal coherence factor, expressed as formula (8):
wherein，δφ mo Is the simulated phase, expressed as follows, as in equation (9):
subtracting the formula (8) and the formula (9) to obtain a new phase, including β (·) and Δ m (x, r), removing the nonlinear deformation rate β (·) by using singular value decomposition, and the total deformation quantity can be expressed as formula (10): d (t) n ，x，r)＝d L (t n ，x，r)+(t n -t 0 )ν(x，r)+β(t n X, r), N ═ 0, 1, 2 … N formula (10)
In this embodiment, the GNSS solution uses a GAMIT baseline solution:
GAMIT adopts a double-difference method to process an original observed value, the double-difference observed value can completely eliminate the influence of satellite clock difference and receiver clock difference, and meanwhile, the influence of system errors such as orbit errors and atmospheric refraction can be obviously weakened. Assuming that the satellite p is observed at the observation station i at the time t, the linearized dual-frequency carrier phase observation equation is as follows:
in formulae (11), (12) f 1 Is L 1 The carrier frequency of (a); f. of 2 Is L 2 The carrier frequency of (a);
the geometric distance between the satellite and the receiver;
is the ionospheric delay; delta T i p Is the tropospheric delay; δ t i Is the receiver clock error; δ t p Is the satellite clock error;
for the initial whole circle of the mouldPasting degree;
is the residual error.
Assuming that satellites p and q are observed at observation stations i and j at time t, the linearized double-difference carrier phase observation equation is given by equation (13):
in equation (13), the flow delay can be attenuated by using a parameter estimation or model correction method; ionospheric refraction is affected by various factors and is difficult to handle by a specific method, and a dual-frequency phase observation value is usually adopted to eliminate ionospheric combination LC to weaken the influence of first-order ionospheric refraction at present, as shown in formula (14).
In the formula (14), the LC observed value is subjected to double-difference combination to eliminate the influence of an ionized layer, but the ambiguity of the LC observed value no longer has integer characteristics, and in order to accurately fix the ambiguity of the LC observed value in the whole cycle, the LC ambiguity can be decomposed by means of a wide lane WL and a narrow lane NL combined observed value.
The strain and Brillouin frequency shift of the optical fiber can be expressed by the following formula:
wherein v is B (epsilon) is the amount of drift of the brillouin frequency when strain is epsilon;
v B (0) is the amount of drift of the brillouin frequency when strain is 0;
is a scaling factor of about 493MHz (/% strain); ε is the strain of the fiber.
In the embodiment, an early warning index system is established and a threshold value is determined, various evaluation factors and weights thereof required by the construction of the ground settlement early warning index system are determined according to the recognition and quantization results of the main control factors of the ground settlement, and the ground settlement early warning index system is established by performing coupling superposition analysis on various single-factor evaluation index graphs by adopting a cluster analysis and fuzzy mathematic method.
In this embodiment, a ground settlement monitoring and early warning device includes one or more processors; the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the area ground settlement early warning method based on the multi-source heterogeneous data according to any one of the above items.
In a second embodiment of the present invention, a regional ground settlement early warning method based on multi-source heterogeneous data includes:
and designing a ground settlement early warning index system frame structure, and developing index system screening based on regional ground settlement research results. The frame structure comprises three parts, a target layer, a standard and criterion layer and an index layer.
The target layer is a layer for realizing ground settlement monitoring and early warning, and the standard layer is a layer conforming to various relevant specifications and criterions related to the existing ground settlement monitoring and evaluation, and each index reflects the slowly-changed geological disasters of the consolidation compression of the underground unconsolidated rock stratum caused by natural factors or human engineering activities and the ground height reduction in a certain area range. The screening indexes are expressed in a numerical form (quantification) in the screening process according to (1); (2) the characteristic of ground settlement deformation is reflected; (3) each index has irreplaceability and uniqueness. The index layer reflects the characteristics of ground settlement and air change and supports various indexes of ground settlement early warning.
The indicator layer comprises two parts: ground settlement zoning and ground settlement layering. The ground settlement partition index screening highlights the following four parts: (1) the natural factors influencing the settlement development of the ground mainly include: geological structure, compressible layer thickness, aquifer properties; (2) human factors influencing ground settlement development mainly comprise underground resource exploitation positions, underground resource exploitation amount, building static load and vehicle dynamic load; (3) city planning and major projects, lifeline projects, etc.; (4) areas with ground settlement disasters exist. The ground settlement layering index screening highlights the following four parts: (1) vertical development depth of ground subsidence; (2) a ground settlement main contribution layer and a sensitive layer; (3) reflecting the ground settlement index of the ground surface deformation characteristic; (4) underground space development and utilization degree, depth and scale. And establishing a ground settlement early warning index system and acquiring each index weight.
The early warning index system comprises a ground settlement partition index and a ground settlement layering index. The step is to comprehensively determine an early warning index system by mainly adopting a statistical analysis means. The specific determination steps are as follows:
based on urban planning and ground settlement mechanism research results, indexes capable of fully reflecting regional ground settlement zone characteristics are selected and distributed to experts in related fields, and scoring is carried out on different indexes from multiple angles such as geological background, structural damage and the like, wherein the scoring is divided into 4 grades, namely, the scoring is very important (10-8 grades), more important (8-6 grades), generally important (6-4 grades) and unimportant (4-2 grades). Removing unimportant parts, carrying out quantitative processing on each index selected from the rest 3 grades, respectively analyzing the relation between each index and ground settlement (including the influence on the ground settlement development and the degree of influence of the ground settlement) based on a statistical principle, and carrying out comprehensive screening on each index by combining a correlation analysis method with a Principal Component Analysis (PCA) through an analysis means. And finally, selecting the ground settlement monitoring early warning index and establishing a ground settlement monitoring early warning index system.
And dividing the ground settlement unit based on each index factor.
Selecting index factors representing natural factors based on the partition index screening results and the weights, performing superposition analysis on the factors by utilizing a linear weighting synthesis method (equalization 1) to obtain a ground foundation settlement unit, wherein the unit mainly reflects the ground settlement foundation pattern, and superposing the results with human factors and ground settlement disaster index factors to obtain a ground settlement dynamic unit representing the ground settlement severity and the development range. Finally, overlaying index factors reflecting deformation characteristics of major projects to obtain a ground settlement comprehensive unit, wherein the unit represents the influence of ground settlement on urban construction, and is shown as a formula (16);
in the formula: y is a comprehensive evaluation value; x is the number of j -an evaluation index; w is a j -an evaluation index; x is the number of j The corresponding weight coefficient; n is the number of evaluation indexes.
In this embodiment, multiple types of monitoring means are provided in each ground settlement unit, and each monitoring means can acquire ground surface deformation information or stratum vertical deformation characteristics.
And selecting a monitoring method or means capable of effectively reflecting each index characteristic according to the internal main control influence index/receptor based on the result of the ground settlement comprehensive unit. The main means comprises monitoring technologies capable of acquiring data information in real time, such as distributed optical fibers, CMT (China Mobile technology for railway) multistage monitoring wells, GNSS (global navigation satellite system), InSAR (interferometric synthetic aperture radar) and the like.
In the embodiment, various types of monitoring means are classified, integrated optimization is performed, and a ground settlement information calculation model is established.
The method is classified according to different monitoring scales and generally comprises a ground surface monitoring system and a vertical monitoring system, wherein the ground surface monitoring system comprises a region monitoring means and a key engineering small-range monitoring means, the different engineering monitoring means are slightly different, and the monitoring standard specifications are monitored by referring to the different engineering means. The current regional monitoring means mainly comprise precision leveling, GNSS and InSAR, wherein the precision leveling obtains the earth surface deformation information through arranging a multi-level leveling network and carrying out adjustment calculation and spatial interpolation. The GNSS measurement technology has the advantages of short period, high positioning precision, rapid network distribution, all-day and the like. However, the GNSS measurement obtains the deformation information of the ground monitoring points distributed in a point shape, so the method is mainly used for monitoring the ground settlement information of the settlement center area or the area with serious differential settlement. The synthetic aperture radar differential interferometry can be used for real-time rapid, large-scale and high-precision measurement, the monitoring precision of the obtained vertical deformation information can reach mm level, and the method can be used for real-time monitoring of regional settlement. But has limited detection capability in the aspect of horizontal deformation monitoring and is not sensitive to horizontal deformation. And the influence of atmospheric delay and time-space loss correlation is serious in phase unwrapping, so that the influence of errors needs to be eliminated in resolving. Based on the analysis, an InSAR data comprehensive multi-interpretation method is selected as a main method for real-time dynamic monitoring of regional ground settlement, and results are corrected by utilizing a GNSS and precision leveling results.
Leveling point correction model
And correcting the precise leveling point and the PS point to the same coordinate system by using a space projection conversion model, taking the leveling point as the center of a circle, averaging the accumulated settling amount of the PS point within the range of 100m, comparing the average settling amount with the measured value of the precise leveling point, and calculating a correlation coefficient and a root mean square error. The method comprises the following specific steps:
and converting the PS point sight line deformation quantity into a vertical direction deformation quantity through a formula a.
Calculating the error value (E) of the level point and the converted PS deformation quantity vari ) The values include orbit error, atmospheric delay, and random error.
Calculating atmospheric delay and terrain-induced errors using a systematic error model (E) s ) Calculating a regional error estimate (E) in combination with the variogram a )。
And (5) performing PS point correction by using the formula b.
V v ＝V los [ cos ] (17)
E vair ＝V v -V b Formula (18)
E vair ＝E s +E a Formula (19)
V verif ＝V v -E vair Formula (20)
In the above formula, V v Is the PS value vertical squareDirection of change of shape, V Los The deformation quantity of the PS value in the LOS direction, the delta H is the displacement quantity of the earth surface in the vertical direction, and the theta is the imaging side view angle of the SAR satellite.
In this embodiment, the deformation projection transformation model of GNSS and InSAR
(1) GNSS data projection transformation model
Setting the actual deformation vector of the earth surface as V, the deformation components in the east, north and vertical directions as (delta E, delta N, delta H), and adopting GNSS observation to obtain the horizontal deformation components in the east and north directions as (delta E) GNSS ，ΔN GNSS ) Let V GPS ＝(ΔE GNSS ，ΔN GNSS ). Assuming that the included angle between the SAR satellite motion track direction (SAR image azimuth direction) and the north direction is phi, the GNSS horizontal displacement (delta E) is displaced by the phi angle GNSS ，ΔN GNSS ) Converting into deformation projection components (delta R, delta A) of the SAR image in the Range direction (Range) and Azimuth direction (Azimuth), and making V SAR (Δ R, Δ a), the conversion relationship between the two is:
the direction of the direction is perpendicular to the direction of the distance and the direction of the LOS, so that the deformation projection of the deformation quantity delta A of the direction in the direction of the LOS is zero, and the included angle between the deformation quantity delta R of the distance direction (Range) and the direction of the LOS is theta, so that the deformation projection component of the delta R in the direction of the LOS is (delta E) GNSS cosΦ+ΔN GNSS sin Φ) · sin θ, the deformation component of the deformation amount Δ H in the vertical direction in the LOS direction is Δ H · cos θ, and the sum of both is the projection of the three-dimensional deformation amount of the ground surface to the radar line-of-sight direction (the deformation Δ r of LOS, and therefore:
Δr＝ΔH·cosθ+(ΔE GNSS cosΦ+ΔN GNSS sin phi) sin theta formula (22)
In the above formula, Δ r is a deformation amount in the LOS direction obtained by InSAR measurement, Δ H is a displacement amount of the ground surface in the vertical direction, and θ is an imaging side view angle of the SAR satellite. The above formula is rewritten as follows:
the above formula establishes the conversion relation between the radar LOS deformation and the deformation in the vertical direction of the earth surface. From this equation, it can be seen that if the horizontal displacement (Δ E) of the earth's surface has been measured GNSS ，ΔN GNSS ) And (3) converting the deformation quantity delta r of the InSAR in the radar line of sight (LOS) into the deformation quantity delta H of the earth surface in the vertical direction based on the included angles phi and theta calculated by the satellite orbit parameters.
In this embodiment, the atmosphere delayed mean value model
And solving the total troposphere zenith delay (ZTD) with high precision in the SAR satellite transit time by utilizing a GNSS solving result. And calculating zenith statics delay (ZHD) by using the ground meteorological data, and further obtaining Zenith Wet Delay (ZWD) through ZTD-ZHD. And performing regression fitting on the zenith wet delay GNSS (ZWD) calculated by combining the GNSS and the ground meteorological data and the zenith wet delay MODIS (ZWD) acquired by the inversion of the MODIS data to realize the correction of the MODIS (ZWD) by utilizing the GPS (ZWD).
In the formula, P s Is the value of the atmospheric pressure on the ground,
the latitude of the GPS station and the elevation of the GPS station are H.
And calculating the differential atmospheric delay phase of the SAR main image acquisition time and the SAR auxiliary image acquisition time. The formula is as follows:
in the formula, theta inc For radar purposesAnd (4) the angle of incidence. To attenuate noise and operational error effects, a pair of
Low pass filtering is performed.
Atmospheric delay phase separated from PS-InSAR residual phase
Atmospheric delay phase obtained by joint inversion with GPS/MODIS data
And performing mean value fusion processing to establish an atmospheric delay mean value model with high precision and high space-time resolution.
(3) Atmospheric delay correction model
Since the removal of the atmospheric delay phase is performed for the pixel, the atmospheric delay phase is removed
And registering the phase diagram and the initial differential interference phase diagram to achieve the unification of pixel dimensions, and then performing grid calculation to obtain the high-precision differential interference phase diagram for eliminating the influence of atmospheric delay.
In the formula (I), the compound is shown in the specification,
for the differential interference phase diagram after atmospheric delay correction,
is an initial differential interference phase map.
The vertical monitoring system mainly comprises a bedrock-layered standard, a distributed optical fiber and a groundwater monitoring well at present. According to the bedrock mark-layered mark monitoring method, the mark post is placed at different settlement positions, so that high-precision vertical layered ground settlement deformation information is obtained, and the precision reaches 0.01-0.1 mm. However, the method is only used for verifying a ground settlement layered monitoring model and researching a ground settlement mechanism due to complex operation, large occupied area, higher construction process, high cost and the like. The distributed optical fiber technology is a technical method for distributing a sensing optical cable in a measured object to realize the continuity test of multiple physical parameters in one-dimensional direction. The characteristic of optical fiber strain can be reflected by using Brillouin scattering, the construction process of the method is relatively simple by calculation through a formula, multi-level continuous settlement information can be obtained, the method is widely applied, but the construction cost is relatively high, and the method can be used for layered monitoring of settlement centers/sub-centers.
In the formula, u B (epsilon, T) is the drift amount of the Brillouin frequency of the optical fiber when the environmental temperature is T and the strain is epsilon; upsilon is B (0,T 0 ) Denotes a temperature of T 0 The drift amount of the Brillouin frequency of the optical fiber when the strain is 0;
respectively representing the proportionality coefficients of strain and temperature with respect to the type of fiber.
FBGs reflect light of a specific wavelength that satisfies the following condition:
λ B ＝2n eff lambda type (30)
In the formula, λ B Is the center wavelength of the reflected light; n is eff Is the effective index of the core; and Λ is the spatial period of the fiber grating refractive index modulation.
External stress and temperature changes can cause changes in refractive index and grating distance, resulting in FBG wavelength λ B Satisfies the linear relation:
where Δ λ is FBG wavelength variation, ε is fiber axial strain, Δ T is temperature variation, P s The photoelastic coefficient of the optical fiber, α, and ζ are the thermal expansion coefficient of the optical fiber.
In the serious development area of ground settlement in China, underground water is over-mined as a main influence factor, the pressure of pore water is dissipated due to the reduction of underground water level, and the settlement of a compressed soil layer is caused due to the increase of effective stress, so that the amplitude of underground water level can indicate the settlement amount of a water-bearing stratum system.
And realizing the layered monitoring of the ground settlement through a ground water-ground settlement coupling model or an empirical model. The formula is as follows:
in the formula: Ω -percolation region; h, water level elevation of underground water; k-permeability coefficient; w-source and sink terms of the aquifer; h is 0 -an initial water level; s s -water storage rate; gamma-shaped 1 -a type of boundary; gamma-shaped 2 -a class two boundary; k n -permeability coefficient in the direction of the normal to the boundary surface of class two, n-the direction of the normal to the boundary surface of class two; Δ b — amount of deformation; b, soil layer thickness; s sk Skeleton water storage rate (when the water level is lower than the lowest earlier stage water level, the parameter is S skv Water storage rate of inelastic skeleton, when water level is higher than lowest water level in earlier stage, the parameter is S skv Water storage rate of the elastic skeleton).
Wherein: s sk And S skv Elastic and inelastic water storage rates, respectively:
in the formula: c c A compression index; c r A rebound index; gamma ray w The volume weight of water; sigma effective stress; e.g. of the type 0 The initial void fraction.
In this embodiment, ground settlement early warning classification standards are respectively established in each ground settlement unit based on a multi-target ground settlement evaluation standard.
And establishing an internal early warning standard of each unit by taking the ground settlement comprehensive unit as a reference. The early warning standard integrates 'national ground settlement prevention and control plan', 'regional ground settlement prevention and control plan', thresholds of deformation of buildings/linear engineering caused by ground settlement in units and 'regional ground settlement disaster risk assessment factor grades', various thresholds are comprehensively compared, and early warning grades are classified from small to large according to the thresholds of receptors in different settlement layers. And obtaining data to be monitored, and calculating to obtain a layered settlement value in each unit through a resolving model. And determining the ground settlement risk level by combining with the classification standard, and performing early warning. And the early warning result display is realized by comprehensively applying cloud computing, big data and WebGIS technology.
For example, the ground settlement monitoring and forewarning device may be used to act as a computing device for a downward continuation process. As described herein, the ground settlement monitoring and forewarning device may be used to implement the functionality of downwardly extending calculations of gravity or magnetic field data in a computing device. The ground settlement monitoring and early warning device can be implemented in a single node, or the function of the ground settlement monitoring and early warning device can be implemented in a plurality of nodes in the network. Those skilled in the art will appreciate that the term ground settlement monitoring and forewarning device includes equipment in a broad sense, of which ground settlement monitoring and forewarning device is only one example. The ground settlement monitoring and pre-warning device is included for clarity and is not intended to limit the application of the present invention to a particular ground settlement monitoring and pre-warning device embodiment or to a certain class of ground settlement monitoring and pre-warning device embodiments. At least some of the features/methods described herein may be implemented in a network device or component, such as a ground subsidence monitoring and warning device. For example, the features/methods of the present invention may be implemented in hardware, firmware, and/or software running installed on hardware. The ground settlement monitoring and early warning device may be any device that processes, stores and/or forwards data frames through a network, such as a server, a client, a data source, and the like. The ground settlement monitoring and forewarning device may include a transceiver (Tx/Rx)210, which may be a transmitter, a receiver, or a combination thereof. Tx/Rx 210 may be coupled to a plurality of ports 250 (e.g., an uplink interface and/or a downlink interface) for transmitting and/or receiving frames from other nodes. Processor 230 may be coupled to Tx/Rx 210 to process frames and/or determine to which nodes to send frames. Processor 230 may include one or more multi-core processors and/or memory devices 232, which may serve as data stores, buffers, and the like. Processor 230 may be implemented as a general-purpose processor or may be part of one or more Application Specific Integrated Circuits (ASICs) and/or Digital Signal Processors (DSPs).
And establishing an early warning standard by taking the ground settlement comprehensive unit as a reference. The early warning standard integrates 'national ground settlement prevention and control plan', 'regional ground settlement prevention and control plan', thresholds of deformation of buildings/linear engineering caused by ground settlement and 'regional ground settlement disaster risk assessment factor grades', various thresholds are comprehensively compared, and early warning grades are classified from small to large. Regional early warning index
1) Ground water level
The method comprises the steps of decomposing a ground settlement annual prevention and control index into seasons, months and days according to an annual development rule, forming a clear substitution model by emphasizing settlement stratum compression amount and adjacent aquifer underground water descent amount causing settlement on the basis of continuous iteration, decomposing settlement amount values in detail in time and vertical space, taking the settlement amount values and underground water level as main early warning threshold values and indexes, and carrying out real-time monitoring and early warning on the dynamic underground water level monitoring data result.
2) Deformation of earth's surface
The method comprises the steps of decomposing a ground settlement annual prevention and control index into seasons, months and days according to an annual development rule, monitoring and implementing monitoring results by applying GPS continuous station monitoring in a typical area, monitoring surface deformation by constructing a grid and utilizing GIS means for interpolation, comparing a monitoring value with the prevention and control index, and carrying out real-time monitoring and early warning.
(3) Early warning ranking
The early warning and forecasting are realized by constructing a double early warning index system combining a settlement index represented by a dynamic accumulative settlement value in the year and an underground water level index. The early warning risk prevention and control grade is divided into three grades according to the risk size, each grade corresponds to corresponding sedimentation and underground water level indexes, the first grade is the highest grade, the display color is red, the second grade is orange, the third grade is the lowest grade, and the display color is yellow.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the invention as defined in the appended claims. The techniques, shapes, and configurations not described in detail in the present invention are all known techniques.
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| CN118644091B (en) * | 2024-08-16 | 2024-10-29 | 山东省地质矿产勘查开发局第七地质大队（山东省第七地质矿产勘查院） | Geological hazard risk assessment platform and method based on big data |
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| CN121302296A (en) * | 2025-12-11 | 2026-01-09 | 四川电力设计咨询有限责任公司 | Ground subsidence risk evaluation method and system based on integration of InSAR observation and machine learning |
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