CN114812496B - A regional land subsidence early warning method based on multi-source heterogeneous data - Google Patents
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Abstract
The invention relates to the technical field of engineering geology/geological disaster monitoring, in particular to a regional ground subsidence early warning method based on multi-source heterogeneous data, which comprises a data acquisition module, a data transmission module, a resolving module, an analysis module and an early warning module, wherein the data acquisition module is used for identifying and quantifying ground subsidence main control factors and screening and evaluating a multi-source monitoring technology, the resolving module comprises monitoring data resolving and early warning index system construction, the information acquisition module is based on index remote sensing building ground index IBI extraction, firstly, three index wave bands are overlapped to generate a new image, spectral characteristics of each wave band of the generated new image are further analyzed, the maximum value of building ground, vegetation and water is obtained under NDBI, SAVI and MNDWI conditions, and the index-based building ground index IBI is constructed by using the three index wave bands. The system is stable, low in cost and high in real-time performance.
Description
Technical Field
The invention relates to the technical field of engineering geology/geological disaster monitoring, in particular to a regional ground subsidence early warning method based on multi-source heterogeneous data.
Background
The ground subsidence is used as a 'slowly-changing geological disaster', has the characteristics of long duration, slow generation, wide influence range, complex causative mechanism, large prevention and control difficulty and the like, and becomes a global environmental geological problem.
Along with the aggravation of ground subsidence, the influence of the ground subsidence on urban planning development and economic construction is gradually highlighted, such as induced ground cracks, damage to building (construction) and loss of land resource use value in a certain range are caused, the ground elevation is reduced, the defensive capability of urban flood prevention facilities is reduced, the underground tunnel is cracked and damaged, the safe operation of subways is influenced, and the maintenance cost is increased.
The existing ground subsidence early warning only considers the ground deformation information such as regional accumulated subsidence quantity, subsidence rate and the like, cannot embody the layered deformation characteristics and distribution of ground subsidence, and cannot accurately judge the main subsidence layer. The related specifications of the ground subsidence in China lack a clear ground subsidence early warning index system, so that accurate early warning of disaster-stricken bodies built at different layers of the underground space is difficult to realize, and the implementation difficulty of ground subsidence prevention and control measures is increased. Meanwhile, each monitoring and early warning facility is unevenly distributed, and the limitation of effective units is lacked, so that the early warning precision is reduced. Therefore, it is needed to establish a ground settlement layering and zonal early warning index system to develop high-precision ground settlement monitoring and early warning.
Disclosure of Invention
In view of the above, the present invention aims to provide a regional ground subsidence early warning method based on multi-source heterogeneous data to solve the problems mentioned in the background art.
The regional ground subsidence early warning method based on the multi-source heterogeneous data comprises the steps of dividing ground subsidence units based on index factors in a ground subsidence early warning index system, setting multi-type monitoring means in each ground subsidence unit, classifying and optimizing the multi-type monitoring means, establishing a ground subsidence information resolving model, obtaining data to be monitored, calculating to obtain layered subsidence values in each ground subsidence unit through the ground subsidence information resolving model, determining ground subsidence risk levels according to the calculated layered subsidence values, and carrying out early warning. The evaluation system of the method comprises ground subsidence main control factor identification and quantification, multisource monitoring technology screening and evaluation, monitoring data resolving and early warning index system construction. The specific modules comprise a data acquisition module, a data transmission module, a resolving module, an analysis module and an early warning module
Further, the ground subsidence early warning index system comprises a target layer, a standard and standard layer and an index layer.
The index layer comprises a ground subsidence partition index and a ground subsidence layering index. The ground subsidence layering indexes comprise the vertical development depth of ground subsidence, a main contribution layer and a sensitive layer of ground subsidence, the ground subsidence index reflecting the deformation characteristics of the ground surface, and the development and utilization degree, depth and scale of underground space. The ground subsidence information resolving model comprises a leveling point correcting model, a deformation projection conversion model and a ground water-ground subsidence coupling model.
Before dividing the ground subsidence units based on each index factor in the ground subsidence early warning index system, the ground subsidence early warning index system is established, and each index weight is obtained through PCA. And designing a ground subsidence early warning index system framework structure, and carrying out index system screening based on regional ground subsidence research results. Before obtaining data to be monitored and obtaining layered settlement values in each ground settlement unit through calculation of a ground settlement information resolving model, classifying and optimizing the multi-type monitoring means, and after establishing the ground settlement information resolving model, designing a ground settlement early warning index system framework structure, and screening an index system based on regional ground settlement research results. And after the ground settlement risk level is determined by combining with the ground settlement early warning grading standard and early warning is carried out, cloud computing, big data and WebGIS technology are comprehensively applied, so that early warning result display is realized.
Further, the ground subsidence main control factors are identified and quantified, and are selected from the aspects of natural factors and human factors based on the ground subsidence influence factors. Natural factors mainly comprise substrate construction movement, stratum lithology and structural characteristics, and human factors mainly comprise groundwater level amplitude, static load of tall buildings and dense building groups, dynamic load of linear work such as rail traffic and the like. Based on a statistical analysis means, the response relationship between different factors and ground settlement is obtained.
The method comprises the steps of acquiring underground water level amplitude data through a water level measuring instrument placed in an underground water monitoring well, and acquiring the space-time evolution information of a building load by selecting remote sensing images covering a research area based on a remote sensing building land index method and further adopting an Erdas model tool on the basis of NDBI, MNDWI, SAVI index inversion. The remote sensing image preprocessing process comprises geometric correction, radiometric calibration, atmospheric correction and image masking. Then, the vegetation index SAVI extraction formula is shown as formula (1):
SAVI= [ (NIR-Red) (1+L) ] (NIR+Red+L) formula (1)
Wherein NIR is the pixel brightness value of the near infrared band, red is the pixel brightness value of the Red band, L is the soil adjusting factor, and the value range is 0-1. When l=0, it indicates zero vegetation coverage, and when l=1, it indicates zero influence of soil background, i.e., vegetation coverage is highest, and zero influence of soil background, which occurs only at places covered by tall trees with dense crowns. In urban areas, huete recommends that the effect of soil background can be well weakened by taking l=0.5, and the paper takes the value of L as 0.5 to calculate the soil conditioning vegetation index SAVI in beijing areas.
Corrected normalized water index MNDWI extraction:
MNDWI = (Green-MIR)/(Green+MIR) formula (2)
Wherein Green represents the pixel brightness value of the Green light wave band, and MIR is the pixel brightness value of the middle infrared wave band.
Normalized building site index NDBI extraction:
Ndbi= (MIR-NIR)/(mir+nir) formula (3)
Wherein MIR is the gray value of the pixel in the middle infrared band, NIR is the brightness value of the pixel in the near infrared band, and NDBI is-1. The spectral characteristics of the building land and the dry land are shown that the average value of the mid-infrared band is larger than the near-infrared band, and the water body, the forest land, the farmland and the like are shown to have opposite characteristics. The places with the NDBI value greater than 0 are urban land, and the places with the NDBI value less than 0 are non-building land.
Further, the index-based remote sensing building land index IBI is extracted, first, three index wave bands are overlapped to generate a new image, the spectrum characteristics of each wave band of the generated new image are further analyzed, and the maximum values of the building land, vegetation and water are obtained under the conditions of NDBI, SAVI and MNDWI respectively. Thus, constructing an index-based construction land index IBI using the three index bands, formula (4);
IBI= [ NDBI- (SAVI+ MNDWI) 2]/[ NDBI+ (SAVI+ MNDWI)/2 ] formula (4)
The dynamic load of linear operation such as rail traffic, namely selecting the average static load of the rail traffic engineering, and the calculation formula is as follows:
Wherein P E is the static load of the rail transit, and V E is the running speed of the vehicle. P ES vehicle average static load, n vehicle number, mi contribution rate, pi vehicle average static load PVS linear engineering average static load.
The statistical analysis means mainly comprises SRCC and random forest, and is calculated by the following formula (6)
Wherein, pk represents the proportion of the kth sample in the current sample set D as Pk.
Further, the multi-source monitoring technology is selected and evaluated, and from the perspective of the space measurement technology, the sky-ground subsidence three-dimensional monitoring technology is selected, wherein the main technical means comprise InSAR, GPS, leveling measurement, layering mark and distributed optical fiber. And evaluating the monitoring technology of each source from the angles of spatial resolution, time resolution, monitoring precision and the like, screening out the monitoring means suitable for the ground settlement early warning frequency and precision, and providing technical support for the follow-up development of ground settlement monitoring early warning.
Further, the monitoring data is resolved and information is acquired, a filtered source data resolving framework is established, a monitoring data-ground subsidence visual response data resolving platform is established, and high-precision resolving data is obtained in real time. InSAR, GNSS and distributed optical fibers are selected as ground subsidence monitoring and early warning dominant monitoring means, inSAR data has high spatial resolution, regional ground subsidence deformation information can be obtained, and ground subsidence deformation trend is controlled. The GNSS can acquire the ground deformation information in real time, and can be used for carrying out long-term monitoring on the deformation characteristics of the ground settlement center and carrying out early warning on the ground settlement deformation of the settlement center. The distributed optical fiber has the advantage of combining transmission and measurement, can acquire soil strain information in the full monitoring depth in real time, is used for settlement central area and settlement edge area, and realizes ground settlement layered monitoring and early warning.
Further, inSAR solution mainly adopts a small baseline interferometry (SBAS-InSAR) technology, and a formula can be expressed as formula (7):
Wherein v (·) and β (·) are the average velocity of the high resolution single parallax component interferogram and the nonlinear component in the residual deformation, respectively, Δz (x, r) is the topography component in the high resolution single parallax component interferogram, Δn (x, r) is the noise error, and for the estimates of v (·) and Δz (x, r) to satisfy the maximized phase coherence factor, the expression as shown in formula (8):
Wherein δΦ mo is an analog phase, expressed as follows, as in equation (9):
Subtracting equation (8) from equation (9) to obtain a new phase, including β (·) and Δm (x, r), using a singular value decomposition method to remove the nonlinear deformation rate β (·), the total body shape variable can be represented as equation (10)： d(tn,x,r)＝dL(tn,x,r)+(tn-t0)v(x,r)+β(tn,x,r),n＝0,1,2…N (10)
Further, the GNSS solution uses GAMIT baseline solutions:
GAMIT adopts a double difference method to process the original observed value, and the double difference observed value can completely eliminate the influence of satellite clock difference and receiver clock difference, and can obviously weaken the influence of systematic errors such as orbit error, atmospheric refraction and the like. Assuming that satellite p is observed at station i at time t, the linearized dual-frequency carrier phase observation equation is: F 1 in the formulas (11) and (12) is the carrier frequency of L 1, f 2 is the carrier frequency of L 2; is the geometric distance between the satellite and the receiver; Is ionospheric delay, δT i p is tropospheric delay, δt i is receiver clock bias, δt p is satellite clock bias; Is the initial integer ambiguity; Is the residual.
Assuming that satellites p and q are observed at stations i and j at time t, the linearized double-difference carrier phase observation equation is equation (13):
In the formula (13), the flow delay can be weakened by adopting a parameter estimation method or a model correction method, the ionospheric refraction is influenced by various factors and is difficult to be treated by a specific method, and the influence of the first-order ionospheric refraction is weakened by adopting a dual-frequency phase observation value and ionospheric combination LC, as shown in the formula (14).
In the formula (14), the LC observations are combined by double differences to eliminate the ionospheric influence, but the ambiguity of the LC observations no longer has integer characteristics, and in order to accurately fix the integer ambiguity of the LC observations, the LC ambiguity can be resolved by means of the combined observations of the wide lane WL and the narrow lane NL.
The strain amount and brillouin shift of an optical fiber can be expressed by the following formula:
Wherein v B (ε) is the amount of shift in the Brillouin frequency when the strain is ε, and v B (0) is the amount of shift in the Brillouin frequency when the strain is 0; is a proportionality coefficient, about 493MHz (/% strain), and ε is the strain capacity of the fiber.
Further, the construction of the early warning index system and the determination of the threshold value are carried out, various evaluation factors and weights thereof required by the construction of the early warning index system for the ground subsidence are determined according to the identification and quantification results of the main control factors for the ground subsidence, and the coupling superposition analysis is carried out on various single factor evaluation index graphs by adopting a clustering analysis and fuzzy mathematic method, so as to establish the early warning evaluation index system for the ground subsidence.
Further, the ground subsidence monitoring and early warning device comprises one or more processors and a storage device, wherein 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 realize the regional ground subsidence early warning method based on the multi-source heterogeneous data.
And establishing an early warning standard by taking the ground subsidence comprehensive unit as a reference. The early warning standard is integrated with 'national ground subsidence prevention and control plan', 'regional ground subsidence prevention and control plan', thresholds of each building/linear engineering deformation caused by ground subsidence and 'regional ground subsidence disaster risk assessment factor grade', various thresholds are comprehensively compared, and early warning grades are divided from small to large. Regional early warning index
1) Groundwater level
The method is characterized in that the ground subsidence annual prevention and control index is decomposed into quarterly, monthly and daily according to the annual development rule, the compression amount of a key subsidence stratum and the underwater descent amount of an adjacent water-bearing layer causing subsidence form a clear substitution model on the basis of continuous iteration, the subsidence value is decomposed in detail in time and vertical space, and the subsidence value and the groundwater level are used as main early warning threshold and index, and real-time monitoring early warning is carried out according to the groundwater level dynamic monitoring data result.
2) Deformation of the earth's surface
The earth subsidence annual prevention and control index is decomposed into quarterly, monthly and daily according to the annual development rule, the monitoring result is implemented by applying the GPS continuous station monitoring in the typical region, the earth surface deformation is monitored by constructing a grid and interpolating by utilizing a GIS (geographic information system) means, the monitoring value is compared with the prevention and control index, and the real-time monitoring and early warning is carried out.
(3) Early warning ranking
The early warning and forecasting are realized by constructing a double early warning index system which combines a sedimentation index represented by dynamic accumulated sedimentation values in the year with an underground water level index. The early warning risk prevention and control grades are divided into three grades according to the risk, each grade corresponds to corresponding sedimentation and groundwater level indexes, wherein the first grade is highest, the display color is red, the second grade is second grade, the display color is orange, the third grade is lowest, and the display color is yellow.
The invention provides a ground subsidence monitoring and early warning system based on an evaluation system and a module, which can acquire high-precision ground surface and underground three-dimensional monitoring data in real time and realize monitoring and early warning within the whole ground subsidence scale range. The method solves the defects that the traditional ground subsidence model has low precision and low spatial resolution, can only perform regional (small scale) ground subsidence early warning, and cannot realize small-regional scale ground subsidence layering early warning. The system integrates a plurality of processes such as multi-source monitoring technology screening, information data real-time processing, main control factor identification and quantification, early warning index system, information system, result expression form, real-time release and the like, realizes the organic fusion of the processes through computer programming, can perform real-time and one-stop early warning on ground settlement disasters, simplifies the intermediate process, and is more beneficial to popularization and use. Aiming at the defect that the traditional early warning method can only perform early warning according to the ground surface deformation information and cannot realize layering and partition early warning, the method is designed to construct a multisource ground settlement monitoring technology framework system on the basis of identifying the ground settlement main control influence factors, acquire and real-time settlement information, overcome the limitations of the existing monitoring technology, combine the ground settlement evaluation standards of areas and key engineering, establish a ground settlement early warning evaluation index system, comprehensively apply cloud computing, big data and WebGIS technology and realize ground settlement monitoring early warning.
Detailed Description
The following detailed description of the application is of specific embodiments, it being evident that the embodiments described are only some, but not all, of the embodiments of the application, and that all other embodiments, based on the embodiments of the application, as would be apparent to one of ordinary skill in the art without inventive effort, are intended to be within the scope of the application.
Consists of an evaluation system and a module. The evaluation system comprises ground subsidence main control factor identification and quantification, multisource monitoring technology screening and evaluation, monitoring data resolving and early warning index system construction. The module comprises a data acquisition module, a data transmission module, a resolving module, an analysis module and an early warning module
In this embodiment, the ground settlement master control factor is identified and quantified, and is selected from the two angles of natural factors and human factors based on the ground settlement influence factor. Natural factors mainly comprise substrate construction movement, stratum lithology and structural characteristics, and human factors mainly comprise groundwater level amplitude, static load of tall buildings and dense building groups, dynamic load of linear work such as rail traffic and the like. Based on a statistical analysis means, the response relationship between different factors and ground settlement is obtained.
In the embodiment, the variable amplitude data of the groundwater level is obtained through a water level measuring instrument placed in a groundwater monitoring well, and the static load of a tall building and a dense building group is obtained by selecting remote sensing images covering a research area based on a remote sensing building land index method, and further adopting an Erdas model tool on the basis of NDBI, MNDWI, SAVI index inversion. The remote sensing image preprocessing process comprises geometric correction, radiometric calibration, atmospheric correction and image masking. Then, the vegetation index SAVI extraction formula is shown as formula (1):
SAVI= [ (NIR-Red) (1+L) ]/(NIR+Red+L) of formula (1)
Wherein NIR is the pixel brightness value of the near infrared band, red is the pixel brightness value of the Red band, L is the soil adjusting factor, and the value range is 0-1. When l=0, it indicates zero vegetation coverage, and when l=1, it indicates zero influence of soil background, i.e., vegetation coverage is highest, and zero influence of soil background, which occurs only at places covered by tall trees with dense crowns. In urban areas, huete recommends that the effect of soil background can be well weakened by taking l=0.5, and the paper takes the value of L as 0.5 to calculate the soil conditioning vegetation index SAVI in beijing areas.
Corrected normalized water index MNDWI extraction:
MNDWI = (Green-MIR)/(Green+MIR) formula (2)
Wherein Green represents the pixel brightness value of the Green light wave band, and MIR is the pixel brightness value of the middle infrared wave band.
Normalized building site index NDBI extraction:
Ndbi= (MIR-NIR)/(mir+nir) formula (3)
Wherein MIR is the gray value of the pixel in the middle infrared band, NIR is the brightness value of the pixel in the near infrared band, and NDBI is-1. The spectral characteristics of the building land and the dry land are shown that the average value of the mid-infrared band is larger than the near-infrared band, and the water body, the forest land, the farmland and the like are shown to have opposite characteristics. The places with the NDBI value greater than 0 are urban land, and the places with the NDBI value less than 0 are non-building land.
In this embodiment, index-based remote sensing building land index IBI is extracted, first, three index bands are superimposed to generate a new image, and spectral features of each band of the generated new image are further analyzed, so that the maximum values of the building land, vegetation and water are obtained under NDBI, SAVI and MNDWI conditions respectively. Thus, an index-based construction land index IBI is constructed using these three index bands, as shown in equation (4):
IBI= [ NDBI- (SAVI+ MNDWI)/2 ]/[ NDBI+ (SAVI+ MNDWI)/2 ] formula (4)
The dynamic load of linear operation such as rail traffic, namely selecting the average static load of the rail traffic engineering, and the calculation formula is as follows:
Wherein P E is the static load of the rail transit, and V E is the running speed of the vehicle. P ES vehicle average static load, n vehicle number, mi contribution rate, pi vehicle average static load PVS linear engineering average static load.
The statistical analysis means mainly comprises SRCC and random forest, and is calculated by the following formula (6)
Wherein, pk represents the proportion of the kth sample in the current sample set D as Pk.
In the embodiment, the multi-source monitoring technology is selected and evaluated, and the sky-ground subsidence three-dimensional monitoring technology is selected from the perspective of the space measurement technology, wherein the main technical means comprise InSAR, GPS, leveling measurement, layering mark and distributed optical fiber. And evaluating the monitoring technology of each source from the angles of spatial resolution, time resolution, monitoring precision and the like, screening out the monitoring means suitable for the ground settlement early warning frequency and precision, and providing technical support for the follow-up development of ground settlement monitoring early warning.
In the embodiment, the monitoring data is resolved and the information is acquired, a screened source data resolving framework is established, a monitoring data-ground subsidence visual response data resolving platform is established, and high-precision resolving data is acquired in real time. InSAR, GNSS and distributed optical fibers are selected as ground subsidence monitoring and early warning dominant monitoring means, inSAR data has high spatial resolution, regional ground subsidence deformation information can be obtained, and ground subsidence deformation trend is controlled. The GNSS can acquire the ground deformation information in real time, and can be used for carrying out long-term monitoring on the deformation characteristics of the ground settlement center and carrying out early warning on the ground settlement deformation of the settlement center. The distributed optical fiber has the advantage of combining transmission and measurement, can acquire soil strain information in the full monitoring depth in real time, is used for settlement central area and settlement edge area, and realizes ground settlement layered monitoring and early warning.
InSAR solution mainly adopts a small baseline interferometry (SBAS-InSAR) technology, and a formula can be expressed as formula (7):
Wherein v (·) and β (·) are the average velocity of the high resolution single parallax component interferogram and the nonlinear component in the residual deformation, respectively, Δz (x, r) is the topography component in the high resolution single parallax component interferogram, Δn (x, r) is the noise error, and for the estimates of v (·) and Δz (x, r) to satisfy the maximized phase coherence factor, the expression as shown in formula (8):
Wherein δΦ mo is an analog phase, expressed as follows, as in equation (9):
Subtracting equation (8) from equation (9) to obtain a new phase, including β (·) and Δm (x, r), using a singular value decomposition method to remove the nonlinear deformation rate β (·), the total body shape variable can be represented as equation (10)： d(tn,x,r)＝dL(tn,x,r)+(tn-t0)ν(x,r)+β(tn,x,r),n＝0,1,2…N (10)
In this embodiment, the GNSS solution uses GAMIT baseline solutions:
GAMIT adopts a double difference method to process the original observed value, and the double difference observed value can completely eliminate the influence of satellite clock difference and receiver clock difference, and can obviously weaken the influence of systematic errors such as orbit error, atmospheric refraction and the like. Assuming that satellite p is observed at station i at time t, the linearized dual-frequency carrier phase observation equation is: F 1 in the formulas (11) and (12) is the carrier frequency of L 1, f 2 is the carrier frequency of L 2; is the geometric distance between the satellite and the receiver; Is ionospheric delay, δT i p is tropospheric delay, δt i is receiver clock bias, δt p is satellite clock bias; Is the initial integer ambiguity; Is the residual.
Assuming that satellites p and q are observed at stations i and j at time t, the linearized double-difference carrier phase observation equation is equation (13):
In the formula (13), the flow delay can be weakened by adopting a parameter estimation method or a model correction method, the ionospheric refraction is influenced by various factors and is difficult to be treated by a specific method, and the influence of the first-order ionospheric refraction is weakened by adopting a dual-frequency phase observation value and ionospheric combination LC, as shown in the formula (14).
In the formula (14), the LC observations are combined by double differences to eliminate the ionospheric influence, but the ambiguity of the LC observations no longer has integer characteristics, and in order to accurately fix the integer ambiguity of the LC observations, the LC ambiguity can be resolved by means of the combined observations of the wide lane WL and the narrow lane NL.
The strain amount and brillouin shift of an optical fiber can be expressed by the following formula:
wherein v B (ε) is the amount of shift in the brillouin frequency when the strain is ε;
v B (0) is the shift amount of the brillouin frequency when the strain is 0; Is a proportionality coefficient, about 493MHz (/% strain), and ε is the strain capacity of the fiber.
In the embodiment, the construction of the early warning index system and the determination of the threshold value are carried out, various evaluation factors and weights thereof required by the construction of the early warning index system for the ground subsidence are determined according to the identification and quantification results of the main control factors for the ground subsidence, and the coupling superposition analysis is carried out on various single factor evaluation index graphs by adopting the clustering analysis and fuzzy mathematic method, so as to establish the early warning evaluation index system for the ground subsidence.
In this embodiment, the ground subsidence monitoring and early warning device comprises one or more processors and a storage device, wherein 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 realize the regional ground subsidence early warning method based on the multi-source heterogeneous data.
In a second embodiment of the present invention, a regional ground subsidence early warning method based on multi-source heterogeneous data includes:
and designing a ground subsidence early warning index system framework structure, and carrying out index system screening based on regional ground subsidence research results. The framework structure comprises three parts, a target layer, a standard layer and a criterion layer, and an index layer.
The target layer is used for realizing ground subsidence monitoring and early warning, and the standard and rule layer is used for following the current related standards and rules related to ground subsidence monitoring and evaluation, wherein each index reflects the consolidation and compression of underground loose rock stratum caused by natural factors or human engineering activities and leads to the slow-change geological disaster with reduced ground elevation in a certain area range. The index screening process is followed by (1) the screening index is expressed (quantified) in a numerical form, (2) the characteristic of ground sedimentation deformation is reflected in a prominent manner, and (3) each index has irreplaceability and uniqueness. The index layer is used for reflecting the characteristics of the ground sedimentation and the air change and supporting various indexes of the ground sedimentation early warning.
The index layer comprises two parts, namely a ground subsidence partition and a ground subsidence layering. The ground subsidence partition index screening is to highlight four parts including (1) natural factors influencing ground subsidence development mainly including geological structure, compressible layer thickness and aquifer characteristics, (2) artificial factors influencing ground subsidence development mainly including underground resource exploitation layers, underground resource exploitation amount, building static load and vehicle dynamic load, (3) city planning, major engineering, life line engineering and the like, and (4) existing ground subsidence disaster areas. The ground subsidence layering index screening is characterized by comprising four parts of (1) vertical development depth of ground subsidence, (2) a main contribution layer and a sensitive layer of ground subsidence, (3) ground subsidence indexes reflecting the ground surface deformation characteristics, and (4) development and utilization degree, depth and scale of underground space. And establishing a ground subsidence early warning index system and acquiring each index weight.
The early warning index system comprises a ground subsidence partition index and a ground subsidence layering index. The step mainly adopts a statistical analysis means to comprehensively determine an early warning index system. The specific determination steps are as follows:
Based on urban planning and ground subsidence mechanism research results, indexes capable of fully reflecting regional ground subsidence partition characteristics are selected and distributed to relevant field experts, and different indexes are scored from multiple angles such as geological background, structural damage and the like, wherein the scores are classified into 4 grades, very important (10-8 points), relatively important (8-6 points), generally important (6-4 points) and unimportant (4-2 points). And removing unimportant parts, quantifying each index selected from the remaining 3 grades, respectively analyzing the relationship between each index and the ground subsidence (including the influence on the ground subsidence development and the degree of influence on the ground subsidence) based on a statistical principle, and comprehensively screening each index again by an analysis means mainly through a correlation analysis method combined with a Principal Component Analysis (PCA). And finally, selecting the ground settlement monitoring and early warning index as a ground settlement monitoring and early warning index system.
And dividing the ground subsidence units based on each index factor.
Based on the partition index screening result and the weight, index factors representing natural factors are selected, the factors are subjected to superposition analysis by a linear weighted synthesis method (equation 1) to obtain a ground foundation settlement unit, the unit mainly reflects the ground foundation settlement pattern, the result is superimposed with human factors and ground settlement disaster index factors to obtain a ground settlement dynamic unit, and the unit represents the severity degree and development range of ground settlement. Finally, overlapping index factors reflecting the deformation characteristics of the major engineering to obtain a ground subsidence comprehensive unit, wherein the ground subsidence comprehensive unit characterizes the influence of ground subsidence on urban construction as shown in a formula (16);
wherein, y is a comprehensive evaluation value, x j is an evaluation index, w j is an evaluation index, x j is a corresponding weight coefficient, and n is the number of the evaluation indexes.
In this embodiment, multiple types of monitoring means are disposed in each ground subsidence unit, and each monitoring means may obtain ground deformation information or vertical deformation characteristics of the stratum.
Based on the ground subsidence comprehensive unit result, according to the internal main control influence index/receptor, a monitoring method or means capable of effectively reflecting the characteristics of each index is selected. The main means comprise distributed optical fibers, CMT multi-stage monitoring wells, GNSS, inSAR and other monitoring technologies capable of acquiring data information in real time.
In the embodiment, various monitoring means are classified, integrated optimization is performed, and a ground subsidence information resolving model is established.
The system is classified according to different monitoring scales and is generally divided into a ground surface monitoring system and a vertical monitoring system, wherein the ground surface monitoring system comprises a regional monitoring means and a key engineering small-range monitoring means, and the monitoring means are slightly different for different engineering and are referred to different engineering monitoring standard specifications. The current area monitoring means mainly comprise precise leveling, GNSS and InSAR, wherein the precise leveling obtains the ground deformation information through laying a multi-level leveling net, adjustment calculation and space interpolation, the method cannot meet the requirement of real-time dynamic monitoring of ground subsidence, and the precise leveling is generally used for verifying the precision of the ground subsidence monitoring technology by the advantage of high precision. The GNSS measurement technology has the advantages of short period, high positioning accuracy, rapid network deployment, full weather and the like. However, the deformation information of the ground monitoring points distributed in a punctiform manner is obtained by GNSS measurement, so that the method is mainly used for monitoring the ground subsidence information in the subsidence center area or the area with serious differential subsidence. The synthetic aperture radar differential interferometry technology can be used for real-time rapid, large-scale and high-precision acquisition of vertical deformation information, the monitoring precision can reach the mm level, and the method can be used for real-time monitoring of regional settlement. But has limited detectability in terms of horizontal deformation monitoring and is insensitive to horizontal deformation. And is severely affected by atmospheric delays and space-time mismatch in terms of phase unwrapping, the effects of these errors need to be removed during the unwrapping. Based on the analysis, an InSAR data comprehensive multi-interpretation means is selected as a main method for real-time dynamic monitoring of regional ground subsidence, and results are corrected by using GNSS and precision leveling results.
Level point correction model
And correcting the precise level point and the PS point to the same coordinate system by using a space projection conversion model, taking the level point as a circle center, averaging the accumulated sedimentation quantity of the PS point within the range of 100m, comparing with the measured value of the precise level point, and calculating the correlation coefficient and the root mean square error. The method comprises the following specific steps:
the PS-point visual line direction deformation is converted into a vertical direction deformation through a formula a.
A level point and converted PS deformation error value (E vari) is calculated, which contains the orbit error, the atmospheric delay and the random error.
Atmospheric delays and terrain induced errors are calculated using a systematic error model (E s), and regional error estimates are calculated in combination with the variation function (E a).
PS point correction is performed using equation b.
V v＝Vlos/cos θ type (17)
E vair＝Vv-Vb (18)
E vair＝Es+Ea (19)
V verif＝Vv-Evair type (20)
In the above expression, V v is a PS value vertical direction deformation amount, V Los is a PS value LOS direction deformation amount, Δh is a displacement amount of the ground surface in the vertical direction, and θ is an imaging side view angle of the SAR satellite.
In this embodiment, the deformation projection conversion model of GNSS and InSAR
(1) GNSS data projective transformation model
Let the actual deformation vector of the ground surface be V, and the horizontal deformation components in the east and north directions of the ground surface be (Δe GNSS,ΔNGNSS) and V GPS＝(ΔEGNSS,ΔNGNSS by GNSS observation, where the deformation components in the east, north and vertical directions are (Δe, Δn, Δh). Assuming that the included angle between the SAR satellite motion track direction (SAR image Azimuth direction) and the north direction is phi, converting the GNSS horizontal displacement quantity (delta E GNSS,ΔNGNSS) into deformation projection components (delta R, delta A) of the GNSS horizontal displacement quantity in the distance direction (Range) and the Azimuth direction (Azimuth) of the SAR image by means of the phi angle, and enabling V SAR = (delta R, delta A) to be as follows:
Since the azimuth direction is perpendicular to both the distance direction and the LOS direction, the deformation amount Δa of the azimuth direction is zero in the LOS direction deformation projection, and the angle between the deformation amount Δr of the distance direction (Range) and the LOS direction is θ, so that the deformation projection component of Δr in the LOS direction is (Δe GNSScosΦ+ΔNGNSS sin Φ) ·sin θ, the deformation component of the deformation amount Δh of the vertical direction in the LOS direction is Δh·cos θ, and the sum of both is the deformation amount Δr of the ground surface three-dimensional deformation amount projected to the radar line-of-sight direction (the deformation Δr of LOS, therefore:
Deltar=DeltaH, cos θ+ (DeltaE GNSScosΦ+ΔNGNSS sin Φ), sin θ (22)
In the above formula, Δr is the deformation amount in the LOS direction measured by InSAR, Δh is the displacement amount of the earth surface in the vertical direction, and θ is the imaging side view angle of the SAR satellite. The above is rewritten as follows:
the formula establishes the conversion relation between the radar LOS deformation and the deformation in the vertical direction of the ground surface. From this equation, if the horizontal displacement amount (Δe GNSS,ΔNGNSS) of the earth's surface and the angles Φ, θ calculated based on the satellite orbit parameters have been measured, the deformation amount Δr of InSAR in the radar line of sight (LOS) can be converted into the deformation amount Δh of the earth's surface in the vertical direction using (3).
In this embodiment, the atmospheric delay mean model
① And calculating high-precision total zenith delay (ZTD) of the troposphere in the passing time of the SAR satellite by using the GNSS calculation result. Zenith statics delay (ZHD) is calculated using ground meteorological data, and Zenith Wet Delay (ZWD) is derived by ZTD-ZHD. And performing regression fitting on zenith wet delay GNSS (ZWD) calculated by combining GNSS with ground meteorological data and zenith wet delay MODIS (ZWD) obtained by inversion of MODIS data to realize correction of MODIS (ZWD) by using GPS (ZWD).
Wherein P s is the ground atmospheric pressure value,For the GPS site latitude, H is the GPS site elevation value.
② And calculating differential atmospheric delay phases of the SAR main and auxiliary image acquisition moments. The formula is:
where θ inc is the radar incident angle. To mitigate the effects of noise and operating errors, it is necessary to provide a pair of Low pass filtering is performed.
Atmospheric delay phase separated from PS-InSAR residual phaseAtmospheric delay phase obtained by joint inversion with GPS/MODIS dataAnd (3) carrying out mean value fusion processing, and establishing 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 picture element, it is also necessary toRegistering the phase map and the initial differential interference phase map to unify pixel dimensions, and calculating grids to obtain a high-precision differential interference phase map for eliminating the influence of atmospheric delay.
In the formula,For the differential interference phase map after the atmospheric delay correction,Is an initial differential interference phase map.
The vertical monitoring system mainly comprises a bedrock-layering mark, a distributed optical fiber and a groundwater monitoring well at present. The method for monitoring the foundation rock mark and the layered mark realizes the acquisition of high-precision vertical layered ground settlement deformation information by putting the mark post into different settlement layers, and the precision reaches 0.01-0.1 mm. However, the method is only used for verifying a ground subsidence layering monitoring model and researching a ground subsidence mechanism due to complex operation, large occupied area, higher construction process, high cost and the like. The distributed optical fiber technology refers to a technical method for laying a sensing optical cable in a measured object to realize multi-physical parameter continuity test in one-dimensional direction. The method has the advantages that the characteristics of optical fiber strain can be reflected by utilizing Brillouin scattering, the construction process is relatively simple through formula calculation, multi-layer continuous sedimentation information can be obtained, the method is widely applied, but the construction cost is relatively high, and the method can be used for sedimentation center/sub-center layered monitoring.
Wherein, v B (epsilon, T) is the drift amount of the Brillouin frequency of the optical fiber when the ambient temperature is T and the strain is epsilon, and v B(0,T0 is the drift amount of the Brillouin frequency of the optical fiber when the temperature is T 0 and the strain is 0; The proportionality coefficients of strain and temperature, respectively, are expressed in relation to the type of fiber.
FBGs reflect light of a specific wavelength that satisfies the following condition:
lambda B＝2neff lambda type (30)
Where lambda B is the center wavelength of the reflected light, n eff is the effective refractive index of the fiber core, and lambda is the spatial period of the refractive index modulation of the fiber grating.
External stress and temperature change can cause the change of refractive index and grid distance, and the shift of FBG wavelength lambda B is caused, so that the linear relation is satisfied:
Wherein Deltalambda is the wavelength variation of FBG, epsilon is the axial strain of the optical fiber, deltaT is the temperature variation, P s is the photoelastic coefficient of the optical fiber, alpha is the thermal expansion coefficient of the optical fiber, and zeta is the thermal optical coefficient of the optical fiber.
In the ground subsidence severe development area of China, the underground water overdrainage is taken as a main influencing factor, the underground water level is lowered to cause the dissipation of pore water pressure, the effective stress is increased to cause the settlement of a compressed soil layer, and therefore the amplitude of the underground water level can indicate the settlement of the aquifer system.
And the ground subsidence layering monitoring is realized through a groundwater-ground subsidence coupling model or an empirical model. The formula is as follows:
the method comprises the steps of omega-seepage area, h-water level elevation of underground water, K-seepage coefficient, w-source-sink item of an aquifer, h 0 -initial water level, S s -water storage rate, gamma 1 -first-class boundary, gamma 2 -second-class boundary, K n -seepage coefficient in the normal direction of the second-class boundary interface, n-normal direction of the second-class boundary interface, delta b-deformation amount, b-soil layer thickness, S sk -skeleton water storage rate (when the water level is lower than the lowest water level in the earlier stage, the parameter is S skv -inelastic skeleton water storage rate, and when the water level is higher than the lowest water level in the earlier stage, the parameter is S skv -elastic skeleton water storage rate).
Wherein S sk and S skv are respectively elastic water storage rate and inelastic water storage rate:
wherein, the formula is C c compression index, C r rebound index, gamma w water volume weight, sigma effective stress and e 0 initial pore ratio.
In this embodiment, based on the multi-objective ground settlement evaluation criteria, ground settlement pre-warning classification criteria are respectively established in each ground settlement unit.
And establishing an internal early warning standard of each unit by taking the ground subsidence comprehensive unit as a reference. The early warning standard is synthesized with 'national ground subsidence prevention and control plan', 'regional ground subsidence prevention and control plan', thresholds of each building/linear engineering deformation caused by the ground subsidence in a unit 'regional ground subsidence disaster risk assessment factor grade', various thresholds are comprehensively compared, and early warning grades are divided from small to large according to the thresholds of receptors in different subsidence layers. And obtaining data to be monitored, and calculating to obtain the sedimentation value in each unit through a calculation model. And combining the grading standards, determining the ground settlement risk level, and carrying out early warning. And comprehensively applying cloud computing, big data and WebGIS technology to realize early warning result display.
For example, the ground settlement monitoring and early warning device can be used as a computing device for a downward continuation process. As described herein, the ground settlement monitoring and early warning device may be used to implement the functionality of downward continuation calculation of gravity or magnetic field data in a computing device. The ground settlement monitoring and early warning device can be realized in a single node, or the functions of the ground settlement monitoring and early warning device can be realized in a plurality of nodes in a network. Those skilled in the art will appreciate that the term ground settlement monitoring and warning device includes devices in a broad sense, with ground settlement monitoring and warning devices being just one example. The ground settlement monitoring and early warning device is included for clarity of description and is not intended to limit the application of the present invention to a specific ground settlement monitoring and early warning device embodiment or a certain class of ground settlement monitoring and early 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 settlement monitoring and early warning device. For example, the features/methods of the present invention may be implemented in hardware, firmware, and/or software running on hardware. The ground settlement monitoring and early warning device can be any device which 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 early warning device may include a transceiver (Tx/Rx) 210, which may be a transmitter, a receiver, or a combination thereof. The Tx/Rx 210 may be coupled to a plurality of ports 250 (e.g., uplink and/or downlink interfaces) for transmitting and/or receiving frames from other nodes. Processor 230 may be coupled to Tx/Rx 210 to process the frame and/or to determine to which nodes to send the frame. 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 (ASIC) and/or digital signal processor (DIGITAL SIGNAL processor DSP).
And establishing an early warning standard by taking the ground subsidence comprehensive unit as a reference. The early warning standard is integrated with 'national ground subsidence prevention and control plan', 'regional ground subsidence prevention and control plan', thresholds of each building/linear engineering deformation caused by ground subsidence and 'regional ground subsidence disaster risk assessment factor grade', various thresholds are comprehensively compared, and early warning grades are divided from small to large. Regional early warning index
1) Groundwater level
The method is characterized in that the ground subsidence annual prevention and control index is decomposed into quarterly, monthly and daily according to the annual development rule, the compression amount of a key subsidence stratum and the underwater descent amount of an adjacent water-bearing layer causing subsidence form a clear substitution model on the basis of continuous iteration, the subsidence value is decomposed in detail in time and vertical space, and the subsidence value and the groundwater level are used as main early warning threshold and index, and real-time monitoring early warning is carried out according to the groundwater level dynamic monitoring data result.
2) Deformation of the earth's surface
The earth subsidence annual prevention and control index is decomposed into quarterly, monthly and daily according to the annual development rule, the monitoring result is implemented by applying the GPS continuous station monitoring in the typical region, the earth surface deformation is monitored by constructing a grid and interpolating by utilizing a GIS (geographic information system) means, the monitoring value is compared with the prevention and control index, and the real-time monitoring and early warning is carried out.
(3) Early warning ranking
The early warning and forecasting are realized by constructing a double early warning index system which combines a sedimentation index represented by dynamic accumulated sedimentation values in the year with an underground water level index. The early warning risk prevention and control grades are divided into three grades according to the risk, each grade corresponds to corresponding sedimentation and groundwater level indexes, wherein the first grade is highest, the display color is red, the second grade is second grade, the display color is orange, the third grade is lowest, and the display color is yellow.
The above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered by the scope of the claims of the present invention. The technology, shape, and construction parts of the present invention, which are not described in detail, are known in the art.
Claims (2)
1. The regional ground subsidence early warning method based on the multi-source heterogeneous data is characterized by comprising a data acquisition module, a data transmission module, a resolving module, an analysis module and an early warning module, wherein the data acquisition module is used for ground subsidence main control factor identification and quantification and multi-source monitoring technology screening and evaluation, and the resolving module comprises monitoring data resolving and early warning index system construction;
the ground subsidence main control factor identification and quantification are obtained by a water level measuring instrument placed in a ground water monitoring well, the static load of tall buildings and dense building groups is obtained by using a remote sensing image covering a research area based on a remote sensing building ground index method, an Erdas model tool is adopted on the basis of NDBI, MNDWI, SAVI index inversion to obtain the space-time evolution information of the building load,
The remote sensing image preprocessing process comprises geometric correction, radiation calibration, atmosphere correction and image masking, and then vegetation index SAVI extraction formula is shown as formula (1): (1)
Wherein NIR is the pixel brightness value of the near infrared band, red is the pixel brightness value of the Red band, L is the soil adjusting factor, and the value range is 0-1;
corrected normalized water index MNDWI extraction, as in (2) (2)
Wherein Green represents the pixel brightness value of the Green light wave band, MIR is the pixel brightness value of the middle infrared wave band,
Normalized building floor index NDBI extraction, as in formula (3)(3)
Wherein MIR is the gray value of the pixel in the middle infrared band, NIR is the brightness value of the pixel in the near infrared band, and NDBI is-1;
The information acquisition module extracts the index-based remote sensing building land index IBI, firstly, three index wave bands are overlapped to generate a new image, the spectrum characteristics of each wave band of the generated new image are further analyzed, the maximum values of the building land, vegetation and water are obtained under the conditions of NDBI, SAVI and MNDWI, the index-based building land index IBI is constructed by utilizing the three index wave bands, and the formula is shown as formula (4): (4)
Dynamic load of track traffic, namely, the average static load of track traffic engineering is selected, and a calculation formula is shown as (5)
PES=0.26 PE(1+0.004VE) ;(5)
Wherein P E is the track traffic static load, the running speed of the V E vehicle, the average static load of the P ES： vehicle, n is the number of vehicles, mi is the contribution rate, pi is the average static load of the PVS track traffic average static load;
the analysis module comprises a SRCC method and a random forest method, and the data obtained through the method is calculated according to the following formula (6); (6)
Wherein Pk represents that the proportion of the kth sample in the current sample set D is Pk;
The multi-source monitoring technology screening and evaluating method comprises InSAR, GPS, leveling measurement, layering mark and distributed optical fiber, the InSAR, GNSS and the distributed optical fiber are used as ground subsidence monitoring and early warning dominant monitoring means, the InSAR is calculated by adopting a small baseline interferometry (SBAS-InSAR) technology, and the formula is shown as (7)
(7)
Wherein v (·) and β (·) are the average velocity of the high resolution single parallax component interferogram and the nonlinear component in the residual deformation, respectively, Δz (x, r) is the topography component in the high resolution single parallax component interferogram, Δn (x, r) is the noise error, and the estimates of v (·) and Δz (x, r) are expressed as formula (8) provided they satisfy the maximized phase coherence factor(8)
Wherein, Is an analog phase, expressed as follows: (9)
Subtracting equations (8) and (9) to obtain a new phase, including β (·) and Δm (x, r), using a singular value decomposition method to remove the nonlinear deformation rate β (·), the total body shape variable can be represented as equation (10)
Formula (10);
and (3) constructing an early warning index system, determining various evaluation factors and weights thereof required by the construction of the ground settlement early warning index system according to the identification and quantification results of the ground settlement main control factors, and performing coupling superposition analysis on various single factor evaluation index graphs by adopting a clustering analysis and fuzzy mathematic method to establish the ground settlement early warning evaluation index system.
2. The ground subsidence monitoring and early warning device is characterized by comprising one or more processors and a storage device, wherein 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 realize the regional ground subsidence early warning method based on the multi-source heterogeneous data.
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| CN117213443B (en) * | 2023-11-07 | 2024-03-19 | 江苏省地质调查研究院 | Construction and updating method of ground settlement monitoring network with integration of heaves, earth and depth |
| CN118194024B (en) * | 2024-05-14 | 2024-07-23 | 中电建路桥集团西部投资发展有限公司 | Highway soft soil foundation settlement prediction method and system |
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| CN119879842B (en) * | 2025-03-28 | 2025-07-22 | 中国地质调查局水文地质环境地质调查中心 | A method and system for monitoring ground subsidence based on weak grating array monitoring |
| CN120084277B (en) * | 2025-04-30 | 2025-09-12 | 天津市地质环境监测总站 | A method for monitoring ground subsidence layering based on force sensors |
| CN120338292A (en) * | 2025-06-19 | 2025-07-18 | 中化地质矿山总局山东地质勘查院 | A method and system for urban land subsidence analysis based on multi-source data fusion |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105488253A (en) * | 2015-11-24 | 2016-04-13 | 首都师范大学 | Method for determining correlation between ground subsidence and regional static load |
| CN107665570A (en) * | 2017-11-20 | 2018-02-06 | 水禾测绘信息技术有限公司 | A kind of expressway disasters monitoring and warning system |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090093959A1 (en) * | 2007-10-04 | 2009-04-09 | Trimble Navigation Limited | Real-time high accuracy position and orientation system |
| CN110390267B (en) * | 2019-06-25 | 2021-06-01 | 东南大学 | Mountain landscape building extraction method and device based on high-resolution remote sensing image |
| CN111426300B (en) * | 2020-05-19 | 2022-04-22 | 北京市水文地质工程地质大队(北京市地质环境监测总站) | Method and device for monitoring and early warning of land subsidence by zone and layer |
| CN112184902B (en) * | 2020-09-21 | 2022-09-02 | 东华理工大学 | Underground mining face inversion method for boundary crossing mining identification |
| CN112883646B (en) * | 2021-02-20 | 2023-11-24 | 首都师范大学 | Method, system and device for extracting building settlement amount by combining machine learning and soil mechanics model |
-
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105488253A (en) * | 2015-11-24 | 2016-04-13 | 首都师范大学 | Method for determining correlation between ground subsidence and regional static load |
| CN107665570A (en) * | 2017-11-20 | 2018-02-06 | 水禾测绘信息技术有限公司 | A kind of expressway disasters monitoring and warning system |
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