A regional land subsidence early warning method based on multi-source heterogeneous data

Abstract

The present invention relates to the field of engineering geology / geological disaster monitoring technology, and in particular to a regional ground subsidence early warning method based on multi-source heterogeneous data, including a data acquisition module, a data transmission module, a solution module, an analysis module and an early warning module, wherein the data acquisition module is used for identifying and quantifying the main controlling factors of ground subsidence, screening and evaluating multi-source monitoring technologies, and the solution module includes monitoring data solution and early warning indicator system construction, and the information acquisition module extracts the remote sensing building land index IBI based on the index, firstly superimposes three index bands to generate a new image, further analyzes the spectral characteristics of each band of the generated new image, and finds that building land, vegetation and water bodies obtain maximum values ​​under the conditions of NDBI, SAVI and MNDWI respectively, and uses these three index bands to construct the building land index IBI based on the index. The system of the present invention is stable, low-cost and highly real-time.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering geology / geological disaster monitoring, and in particular to a regional ground subsidence early warning method based on multi-source heterogeneous data. Background Art

[0002] As a "slow-changing geological disaster", ground subsidence has the characteristics of long duration, slow generation, wide impact range, complex causal mechanism and great difficulty in prevention and control. It has become a global environmental geological problem.

[0003] As ground subsidence intensifies, its impact on urban planning and economic construction has become increasingly prominent, such as inducing ground fissures, causing damage to buildings and structures, and resulting in the loss of land resource use value within a certain range; lowering the ground elevation, causing the defense capability of urban flood control facilities to decline, leading to cracks and damage in underground tunnels, affecting the safe operation of the subway and increasing maintenance costs.

[0004] The current ground subsidence warning only considers surface deformation information such as regional cumulative subsidence and subsidence rate, which cannot reflect the stratified deformation characteristics and distribution of ground subsidence, and cannot accurately identify the main subsidence layer. my country's ground subsidence-related regulations lack a clear ground subsidence warning indicator system, making it difficult to achieve accurate warnings for disaster-stricken bodies built at different levels of underground space, increasing the difficulty of implementing ground subsidence prevention and control measures. At the same time, the uneven layout of various monitoring and early warning facilities and the lack of effective unit limitations reduce the accuracy of early warnings. Therefore, it is urgent to establish a ground subsidence stratification and zoning early warning indicator system to carry out high-precision ground subsidence monitoring and early warning. Summary of the invention

[0005] In view of this, an object of the present invention is to provide a regional land subsidence early warning method based on multi-source heterogeneous data to solve the problems mentioned in the background technology.

[0006] The present invention provides a regional ground subsidence early warning method based on multi-source heterogeneous data. Based on each indicator factor in the ground subsidence early warning index system, the ground subsidence unit is divided; in each ground subsidence unit, multiple types of monitoring means are set, and the multiple types of monitoring means include: precision leveling, GNSS and InSAR; the multiple types of monitoring means are classified and optimized, and a ground subsidence information solution model is established; the data to be monitored is obtained, and the layered settlement value in each ground subsidence unit is calculated through the ground subsidence information solution model; according to the calculated layered settlement value, the ground subsidence risk level is determined, and an early warning is issued. This method consists of an evaluation system and modules. The evaluation system of this method includes the identification and quantification of the main controlling factors of ground subsidence, the screening and evaluation of multi-source monitoring technologies, the solution of monitoring data, and the construction of an early warning index system. The specific modules include a data acquisition module, a data transmission module, a solution module, an analysis module, and an early warning module

[0007] Furthermore, the ground subsidence early warning indicator system includes: target layer, standard and criterion layer, and indicator layer.

[0008] The indicator layer includes: ground subsidence zoning indicators and ground subsidence stratification indicators. The ground subsidence stratification indicators reflect: the vertical development depth of ground subsidence, the main contribution layer and sensitive layer of ground subsidence, the ground subsidence indicators reflecting the characteristics of surface deformation, and the degree, depth and scale of underground space development and utilization. The ground subsidence information solution model includes: level point correction model, deformation projection conversion model, and groundwater-ground subsidence coupling model.

[0009] Before dividing the ground subsidence units based on the indicator factors in the ground subsidence early warning indicator system, a ground subsidence early warning indicator system is established, and the weights of each indicator are obtained through PCA. The framework structure of the ground subsidence early warning indicator system is designed, and the indicator system is screened based on the research results of regional ground subsidence. Before obtaining the data to be monitored and calculating the layered settlement values ​​in each ground subsidence unit through the ground subsidence information solution model, after classifying and optimizing multiple types of monitoring methods and establishing a ground subsidence information solution model, the framework structure of the ground subsidence early warning indicator system is designed, and the indicator system is screened based on the research results of regional ground subsidence. After combining the ground subsidence early warning classification standard, determining the ground subsidence risk level, and conducting an early warning, cloud computing, big data, and WebGIS technologies are comprehensively applied to display the early warning results.

[0010] Furthermore, the main controlling factors of ground subsidence are identified and quantified. Starting from the factors affecting ground subsidence, the main controlling factors of ground subsidence are selected from the perspectives of natural factors and human factors. Natural factors mainly include: basement tectonic movement, stratum lithology and structural characteristics; human factors mainly include: groundwater level fluctuation, static loads of tall buildings and dense building complexes, and dynamic loads of linear operations such as rail transit. Based on statistical analysis methods, the relationship between different factors and ground subsidence response is obtained.

[0011] Furthermore, groundwater level fluctuation data are obtained through water level measuring instruments placed in groundwater monitoring wells; static loads of tall buildings and dense building complexes: Based on the remote sensing building land index method, remote sensing images covering the study area are selected. On the basis of NDBI, MNDWI, and SAVI index inversion, the Erdas Modeler tool is further used to obtain the spatiotemporal evolution information of building loads. The remote sensing image preprocessing process includes: geometric correction, radiation calibration, atmospheric correction, and image masking. Then the vegetation index SAVI extraction formula is as follows:

[0012] SAVI=[(NIR-Red)(1+L)](NIR+Red+L) Formula (1)

[0013] In the formula, NIR is the pixel brightness value of the near infrared band, Red is the pixel brightness value of the red light band, and L is the soil adjustment factor, which ranges from 0 to 1. When L = 0, it means that the vegetation coverage is zero; when L = 1, it means that 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. This situation only occurs in places covered by tall trees with dense canopies. In urban areas, Huete recommends taking L = 0.5, which can better reduce the influence of the soil background. This paper takes the L value as 0.5 to calculate the soil adjustment vegetation index SAVI in Beijing.

[0014] Modified Normalized Difference Water Index (MNDWI) extraction:

[0015] MNDWI=(Green-MIR) / (Green+MIR) Formula (2)

[0016] Where Green represents the pixel brightness value of the green light band, and MIR represents the pixel brightness value of the mid-infrared band.

[0017] Normalized building land index NDBI extraction:

[0018] NDBI=(MIR-NIR) / (MIR+NIR) Formula (3)

[0019] In the formula, MIR is the pixel grayscale value of the mid-infrared band, NIR is the pixel brightness value of the near-infrared band, and NDBI ranges from -1 to 1. The spectral characteristics of building land and dry land are that the mean value of the mid-infrared band is greater than that of the near-infrared band, while water bodies, woodlands and farmlands show the opposite characteristics. Places where the NDBI value is greater than 0 are urban land, and places where the NDBI value is less than 0 are non-building land.

[0020] Furthermore, the remote sensing building land index IBI is extracted based on the index. First, the three index bands are superimposed to generate a new image. The spectral characteristics of each band of the generated new image are further analyzed. It is found that building land, vegetation and water bodies obtain the maximum value under the conditions of NDBI, SAVI and MNDWI respectively. Therefore, the index-based building land index IBI is constructed using these three index bands, and the formula is as follows:

[0021] IBI=[NDBI-(SAVI+MNDWI)2] / [NDBI+(SAVI+MNDWI) / 2] Formula (4)

[0022] Dynamic load of linear work such as rail transit: Use the average static load of rail transit engineering, and the calculation formula is as follows:

[0023]

[0024] Where P E: static load of rail transit; V E Vehicle running speed. ES : average static load of vehicles; n: number of vehicles; Mi: contribution rate; Pi average static load of vehicles PVS linear engineering average static load.

[0025] The statistical analysis methods mainly include: SRCC, random forest, and the above data are calculated by the following formula, as shown in formula (6):

[0026]

[0027] Where: Pk represents the proportion of the k-th class samples in the current sample set D.

[0028] Furthermore, multi-source monitoring technology screening and evaluation, starting from the perspective of space measurement technology, selected the "sky-ground" ground subsidence three-dimensional monitoring technology, the main technical means include: InSAR, GPS, leveling, layered markers, distributed optical fiber. From the perspectives of spatial resolution, temporal resolution and monitoring accuracy, each source monitoring technology is evaluated, and the monitoring means suitable for ground subsidence warning frequency and accuracy are selected to provide technical support for the subsequent ground subsidence monitoring and early warning.

[0029] Furthermore, monitoring data solution and information collection, establish the solution architecture of each source data after screening, build monitoring data - ground subsidence intuitive response data solution platform, so as to obtain high-precision solution data in real time. InSAR, GNSS, and distributed optical fiber are selected as the advantageous monitoring means for ground subsidence monitoring and early warning. InSAR data has high spatial resolution and can obtain regional ground subsidence deformation information and control the trend of ground subsidence deformation. GNSS can obtain ground deformation information in real time and can be used to carry out long-term monitoring of deformation characteristics of ground subsidence center and early warning of ground subsidence deformation in subsidence center. Distributed optical fiber has the advantages of combining transmission and measurement, and can obtain soil strain information in real time within the full monitoring depth, which can be used for layered settlement monitoring in the settlement center and settlement edge area, and realize layered monitoring and early warning of ground subsidence.

[0030] Furthermore, InSAR solution mainly adopts: small baseline interferometry (SBAS-InSAR) technology, the formula can be expressed as follows:

[0031]

[0032] Among them, ν(·) and β(·) are the average velocity and nonlinear components in the residual deformation of the high-resolution single-view differential interferogram, respectively, △Z(x, r) is the terrain component in the high-resolution single-view differential interferogram, and △n(x, r) is the noise error. For the estimation of ν(·) and △Z(x, r), they must satisfy the maximum phase coherence factor, which is expressed as formula (8):

[0033]

[0034] Among them, δφ mo is the simulated phase, expressed as follows, as shown in equation (9):

[0035]

[0036] Subtracting formula (8) from formula (9) yields a new phase, including β(·) and △m(x, r). Using the singular value decomposition method, the nonlinear deformation rate β(·) is removed. The overall deformation can be expressed as formula (10): d(t n , x, r) = d L (tn,x,r)+(t n -t0)v(x,r)+β(t n , x, r), n = 0, 1, 2…N Formula (10)

[0037] Furthermore, the GNSS solution uses the GAMIT baseline solution:

[0038] GAMIT uses the double difference method to process the original observations. The double difference observations can completely eliminate the influence of satellite clock errors and receiver clock errors, and can also significantly weaken the influence of system errors such as orbit errors and atmospheric refraction. Assuming that satellite p is observed at station i at time t, the linearized dual-frequency carrier phase observation equation is: In formulas (11) and (12), f1 is the carrier frequency of L1; f2 is the carrier frequency of L2; is the geometric distance between the satellite and the receiver; is the ionospheric delay; δT i p is the tropospheric delay; δt i is the receiver clock error; δt p is the satellite clock error; is the initial integer ambiguity; is the residual.

[0039] 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:

[0040]

[0041] In formula (13), the process delay can be weakened by parameter estimation or model correction. The ionospheric refraction is affected by various factors and it is difficult to deal with it by a specific method. At present, the dual-frequency phase observation value ionospheric elimination combination LC is often used to weaken the first-order ionospheric refraction effect, as shown in formula (14).

[0042]

[0043] In formula (14), the LC observation value eliminates the ionospheric effect after double difference combination, but the ambiguity of the LC observation value no longer has an integer characteristic. In order to accurately fix the integer ambiguity of the LC observation value, the LC ambiguity can be decomposed with the help of the wide lane WL and narrow lane NL combined observation values.

[0044] The strain and Brillouin frequency shift of the optical fiber can be expressed by the following formula:

[0045]

[0046] Among them, v B (ε) is the drift of the Brillouin frequency when the strain is ε; v B (0) is the drift of the Brillouin frequency when the strain is 0; is the proportionality coefficient, which is approximately 493MHz ( / % strain); ε is the strain of the optical fiber.

[0047] Furthermore, the early warning indicator system is constructed and the thresholds are determined. Based on the identification and quantification results of the main controlling factors of ground subsidence, the various evaluation factors and their weights required for the construction of the ground subsidence early warning indicator system are determined. Cluster analysis and fuzzy mathematics methods are used to conduct coupled superposition analysis on various single-factor evaluation indicator maps to establish a ground subsidence early warning evaluation indicator system.

[0048] Furthermore, a ground subsidence monitoring and early warning device includes one or more processors; a storage device 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 a regional ground subsidence early warning method based on multi-source heterogeneous data according to any one of the above-mentioned items.

[0049] The early warning standard is established based on the comprehensive unit of land subsidence. The early warning standard integrates the "National Land Subsidence Prevention and Control Plan", the "Regional Land Subsidence Prevention and Control Plan", the threshold of deformation of each building / linear project caused by land subsidence and the "Regional Land Subsidence Disaster Risk Assessment Factor Level", and conducts a comprehensive comparison of various thresholds to divide the early warning levels from small to large. Regional early warning indicators

[0050] 1) Groundwater level

[0051] The annual ground subsidence prevention and control indicators are decomposed into quarters, months and days according to the development laws of the year. The compression of the key settlement strata and the groundwater drawdown in the adjacent aquifer causing the subsidence are used to form a clear substitution model on the basis of continuous iteration. The settlement values ​​are decomposed in detail in time and vertical space, and the groundwater level is used as the main warning threshold and indicator, and real-time monitoring and early warning are carried out based on the dynamic monitoring data results of the groundwater level.

[0052] 2) Surface deformation

[0053] The annual control indicators of ground subsidence are decomposed into quarters, months and days according to the development patterns within the year. The monitoring results are implemented by applying GPS continuous station monitoring in typical areas. By constructing grids and interpolating using GIS, it is possible to monitor the surface deformation, compare the monitoring values ​​with the control indicators, and carry out real-time monitoring and early warning.

[0054] (3) Warning level classification

[0055] Early warning and forecasting are achieved by building a dual early warning indicator system that combines the settlement indicator represented by the dynamic cumulative settlement value of the year and the groundwater level indicator. The early warning risk control level is divided into three levels according to the risk size, and each level corresponds to the corresponding settlement and groundwater level indicators. The first level is the highest, displayed in red, the second level is the second, displayed in orange, and the third level is the lowest, displayed in yellow.

[0056] The present invention will propose a ground subsidence monitoring and early warning system based on an evaluation system and a module composition, which can obtain high-precision surface and underground three-dimensional monitoring data in real time, and realize monitoring and early warning within the scale range of the entire ground subsidence. It solves the defects of low precision and low spatial resolution of traditional ground subsidence model early warning, which can only carry out regional (small-scale) ground subsidence early warning, and cannot realize layered early warning of ground subsidence at a small regional scale. The system integrates multiple processes such as multi-source monitoring technology screening and real-time processing of information data, identification and quantification of main control factors, early warning indicator system, information system, achievement expression form and real-time release. Through computer programming, the organic integration of various processes is realized, and real-time, one-stop early warning of ground subsidence disasters can be carried out, which simplifies the intermediate process and is more conducive to promotion and use. In view of the defect that traditional early warning methods can only give early warning based on surface deformation information but cannot realize layered and regional early warning, this design is based on the identification of the main influencing factors of ground subsidence, constructs a multi-source ground subsidence monitoring technology framework system, obtains real-time settlement information, overcomes the limitations of existing monitoring technology, combines regional and key project ground subsidence evaluation standards, establishes a ground subsidence early warning evaluation index system, and comprehensively applies cloud computing, big data, and WebGIS technologies to realize ground subsidence monitoring and early warning. DETAILED DESCRIPTION

[0057] The following is a specific embodiment to describe the present invention in detail. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0058] It consists of an evaluation system and modules. The evaluation system includes: identification and quantification of the main controlling factors of land subsidence, screening and evaluation of multi-source monitoring technologies, monitoring data solution, and early warning indicator system construction. The modules include: data acquisition module, data transmission module, solution module, analysis module, and early warning module

[0059] In this embodiment, the main controlling factors of ground subsidence are identified and quantified, starting from the factors affecting ground subsidence, and selecting the main controlling factors of ground subsidence from the perspectives of natural factors and human factors. Natural factors mainly include: basement tectonic movement, stratum lithology and structural characteristics; human factors mainly include: groundwater level fluctuation, tall buildings, static loads of dense building complexes, dynamic loads of linear work such as rail transit. Based on statistical analysis methods, the relationship between different factors and ground subsidence response is obtained.

[0060] In this embodiment, the groundwater level amplitude data is obtained by a water level measuring instrument placed in a groundwater monitoring well; static loads of tall buildings and dense building complexes: refer to the remote sensing building land index method, select remote sensing images covering the study area, and further use the Erdas Modeler tool on the basis of NDBI, MNDWI, and SAVI index inversion to obtain the spatiotemporal evolution information of building loads. The remote sensing image preprocessing process includes: geometric correction, radiation calibration, atmospheric correction, and image masking. Then the vegetation index SAVI extraction formula is as follows (1):

[0061] SAVI=[(NIR-Red)(1+L)] / (NIR+Red+L) Formula (1)

[0062] In the formula, NIR is the pixel brightness value of the near infrared band, Red is the pixel brightness value of the red light band, and L is the soil adjustment factor, which ranges from 0 to 1. When L = 0, it means that the vegetation coverage is zero; when L = 1, it means that 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. This situation only occurs in places covered by tall trees with dense canopies. In urban areas, Huete recommends taking L = 0.5, which can better reduce the influence of the soil background. This paper takes the L value as 0.5 to calculate the soil adjustment vegetation index SAVI in Beijing.

[0063] Modified Normalized Difference Water Index (MNDWI) extraction:

[0064] MNDWI=(Green-MIR) / (Green+MIR) Formula (2)

[0065] Where Green represents the pixel brightness value of the green light band, and MIR represents the pixel brightness value of the mid-infrared band.

[0066] Normalized building land index NDBI extraction:

[0067] NDBI=(MIR-NIR) / (MIR+NIR) Formula (3)

[0068] In the formula, MIR is the pixel grayscale value of the mid-infrared band, NIR is the pixel brightness value of the near-infrared band, and NDBI ranges from -1 to 1. The spectral characteristics of building land and dry land are that the mean value of the mid-infrared band is greater than that of the near-infrared band, while water bodies, woodlands and farmlands show the opposite characteristics. Places where the NDBI value is greater than 0 are urban land, and places where the NDBI value is less than 0 are non-building land.

[0069] In this embodiment, the remote sensing building land index IBI is extracted based on the index. First, the three index bands are superimposed to generate a new image. The spectral characteristics of each band of the generated new image are further analyzed. It is found that the building land, vegetation and water body obtain the maximum value under the conditions of NDBI, SAVI and MNDWI respectively. Therefore, the index-based building land index IBI is constructed using these three index bands, and the formula is as follows:

[0070] IBI=[NDBI-(SAVI+MNDWI) / 2] / [NDBI+(SAVI+MNDWI) / 2] Formula (4)

[0071] Dynamic load of linear work such as rail transit: Use the average static load of rail transit engineering, and the calculation formula is as follows:

[0072]

[0073] Where P E : static load of rail transit; V E Vehicle running speed. ES : average static load of vehicles; n: number of vehicles; Mi: contribution rate; Pi average static load of vehicles PVS linear engineering average static load.

[0074] The statistical analysis methods mainly include: SRCC, random forest, and the above data are calculated by the following formula, as shown in formula (6):

[0075]

[0076] Where: Pk represents the proportion of the k-th class samples in the current sample set D.

[0077] In this embodiment, multi-source monitoring technology screening and evaluation, starting from the perspective of space measurement technology, selected the "sky-ground" ground subsidence three-dimensional monitoring technology, the main technical means include: InSAR, GPS, leveling, layered markers, distributed optical fiber. From the perspectives of spatial resolution, temporal resolution and monitoring accuracy, each source monitoring technology is evaluated, and the monitoring means suitable for ground subsidence warning frequency and accuracy are selected to provide technical support for the subsequent ground subsidence monitoring and early warning.

[0078] In this embodiment, monitoring data solution and information collection are performed, a solution architecture for each source data after screening is established, and a monitoring data-ground subsidence intuitive response data solution platform is built to achieve real-time acquisition of high-precision solution data. InSAR, GNSS, and distributed optical fiber are selected as advantageous monitoring means for ground subsidence monitoring and early warning. InSAR data has high spatial resolution and can obtain regional ground subsidence deformation information and control ground subsidence deformation trends. GNSS can obtain ground deformation information in real time and can be used to carry out long-term monitoring of deformation characteristics of ground subsidence centers and to conduct early warning of ground subsidence deformation in subsidence centers. Distributed optical fiber has the advantage of combining transmission and measurement, and can obtain soil strain information in the full monitoring depth in real time, which can be used for layered settlement monitoring in the settlement center and settlement edge areas, to achieve layered monitoring and early warning of ground subsidence.

[0079] InSAR solution mainly adopts: small baseline interferometry (SBAS-InSAR) technology, the formula can be expressed as follows:

[0080]

[0081] Among them, ν(·) and β(·) are the average velocity and nonlinear components in the residual deformation of the high-resolution single-view differential interferogram, respectively, △Z(x, r) is the terrain component in the high-resolution single-view differential interferogram, and △n(x, r) is the noise error. For the estimation of ν(·) and △Z(x, r), they must satisfy the maximum phase coherence factor, which is expressed as formula (8):

[0082]

[0083] Among them, δφ mo is the simulated phase, expressed as follows, as shown in equation (9):

[0084]

[0085] Subtracting formula (8) from formula (9) yields a new phase, including β(·) and △m(x, r). Using the singular value decomposition method, the nonlinear deformation rate β(·) is removed. The overall deformation can be expressed as formula (10): d(t n, x, r) = d L (t n ,x,r)+(t n -t0)ν(x,r)+β(t n , x, r), n = 0, 1, 2 ... N Formula (10)

[0086] In this embodiment, GNSS solution uses GAMIT baseline solution:

[0087] GAMIT uses the double difference method to process the original observations. The double difference observations can completely eliminate the influence of satellite clock errors and receiver clock errors, and can also significantly weaken the influence of system errors such as orbit errors and atmospheric refraction. Assuming that satellite p is observed at station i at time t, the linearized dual-frequency carrier phase observation equation is: In formulas (11) and (12), f1 is the carrier frequency of L1; f2 is the carrier frequency of L2; is the geometric distance between the satellite and the receiver; is the ionospheric delay; δT i p is the tropospheric delay; δt i is the receiver clock error; δt p is the satellite clock error; is the initial integer ambiguity; is the residual.

[0088] 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:

[0089]

[0090] In formula (13), the process delay can be weakened by parameter estimation or model correction. The ionospheric refraction is affected by various factors and it is difficult to deal with it by a specific method. At present, the dual-frequency phase observation value ionospheric elimination combination LC is often used to weaken the first-order ionospheric refraction effect, as shown in formula (14).

[0091]

[0092] In formula (14), the LC observation value eliminates the ionospheric effect after double difference combination, but the ambiguity of the LC observation value no longer has an integer characteristic. In order to accurately fix the integer ambiguity of the LC observation value, the LC ambiguity can be decomposed with the help of the wide lane WL and narrow lane NL combined observation values.

[0093] The strain and Brillouin frequency shift of the optical fiber can be expressed by the following formula:

[0094]

[0095] Among them, v B (ε) is the drift of the Brillouin frequency when the strain is ε;

[0096] v B (0) is the drift of the Brillouin frequency when the strain is 0; is the proportionality coefficient, which is approximately 493MHz ( / % strain); ε is the strain of the optical fiber.

[0097] In this embodiment, the early warning indicator system is constructed and the threshold is determined. Based on the identification and quantification results of the main controlling factors of ground subsidence, various evaluation factors and their weights required for the construction of the ground subsidence early warning indicator system are determined. Cluster analysis and fuzzy mathematics methods are used to couple and overlay analysis of various single-factor evaluation indicator maps to establish a ground subsidence early warning evaluation indicator system.

[0098] In this embodiment, a ground subsidence monitoring and early warning device includes one or more processors; a storage device 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 a regional ground subsidence early warning method based on multi-source heterogeneous data according to any one of the above-mentioned items.

[0099] A second embodiment of the present invention is a regional land subsidence early warning method based on multi-source heterogeneous data, comprising:

[0100] Design the framework structure of the ground subsidence early warning indicator system, and carry out the indicator system screening based on the regional ground subsidence research results. The framework structure includes three parts: target layer, standard and criterion layer, and indicator layer.

[0101] The target layer is to realize the monitoring and early warning of ground subsidence, and the standard and criterion layer is to follow the current relevant specifications and criteria for ground subsidence monitoring and evaluation. Each indicator reflects the slow-changing geological disaster caused by the consolidation and compression of underground loose rock layers caused by natural factors or human engineering activities, which leads to the reduction of ground elevation within a certain area. In the process of indicator screening, (1) the screening indicators are expressed in numerical form (quantitative); (2) the deformation characteristics of ground subsidence are highlighted; (3) each indicator is irreplaceable and unique. The indicator layer reflects the temporal and spatial variation characteristics of ground subsidence and supports various indicators for ground subsidence early warning.

[0102] The index layer includes two parts: ground subsidence zoning and ground subsidence stratification. The screening of ground subsidence zoning indicators should highlight the following four parts: (1) Natural factors affecting the development of ground subsidence mainly include: geological structure, compressible layer thickness, and aquifer characteristics; (2) Human factors affecting the development of ground subsidence, mainly including underground resource exploitation layer, underground resource exploitation volume, building static load and vehicle dynamic load; (3) Urban planning and major projects, lifeline projects, etc.; (4) Areas with existing ground subsidence disasters. The screening of ground subsidence stratification indicators should highlight the following four parts: (1) Vertical development depth of ground subsidence; (2) Main contributing layers and sensitive layers of ground subsidence; (3) Ground subsidence indicators reflecting surface deformation characteristics; (4) The degree, depth and scale of underground space development and utilization. Establish a ground subsidence early warning indicator system and obtain the weight of each indicator.

[0103] The early warning indicator system includes ground subsidence zoning indicators and ground subsidence stratification indicators. This step mainly uses statistical analysis to comprehensively determine the early warning indicator system. The specific determination steps are as follows:

[0104] Based on the results of urban planning and land subsidence mechanism research, indicators that can fully reflect the characteristics of regional land subsidence zoning are selected and distributed to experts in relevant fields. Different indicators are scored from multiple angles such as geological background and structural damage. The scores are divided into 4 levels: very important (10-8 points), relatively important (8-6 points), generally important (6-4 points), and unimportant (4-2 points). After removing the unimportant parts, the indicators selected in the remaining 3 levels are quantified. Based on statistical principles, the relationship between each indicator and land subsidence (including the impact on the development of land subsidence and the degree of impact of land subsidence) is analyzed respectively. The analysis method is mainly through correlation analysis combined with principal component analysis (PCA) to conduct another comprehensive screening of each indicator. Finally, it is selected as the land subsidence monitoring and early warning indicator, and a land subsidence monitoring and early warning indicator system is established.

[0105] Based on various index factors, the ground subsidence units are divided.

[0106] Based on the screening results and weights of the zoning indicators, the indicator factors that represent natural factors are selected, and the linear weighted comprehensive method (equation 1) is used to superimpose and analyze the factors to obtain the ground foundation settlement unit, which mainly reflects the basic pattern of ground settlement. The result is superimposed with the human factors and ground settlement disaster indicator factors to obtain the ground settlement dynamic unit, which represents the severity and development range of ground settlement. Finally, the indicator factors reflecting the deformation characteristics of major projects are superimposed to obtain the ground settlement comprehensive unit, which represents the impact of ground settlement on urban construction, as shown in formula (16);

[0107]

[0108] In the formula: y——comprehensive evaluation value; x j ——Evaluation index; w j ——Evaluation index; x j The corresponding weight coefficient; n – the number of evaluation indicators.

[0109] In this embodiment, multiple types of monitoring means are provided in each ground subsidence unit, and each monitoring means can obtain surface deformation information or vertical deformation characteristics of strata.

[0110] Based on the results of the comprehensive unit of land subsidence and according to the internal main control influencing indicators / receptors, select monitoring methods or means that can effectively reflect the characteristics of each indicator. The main means include distributed optical fiber, CMT multi-level monitoring wells, GNSS, InSAR and other monitoring technologies that can obtain data information in real time.

[0111] In this embodiment, various types of monitoring means are classified, integrated and optimized, and a ground subsidence information solution model is established.

[0112] According to different monitoring scales, it is generally divided into surface monitoring system and vertical monitoring system. The surface monitoring system includes regional monitoring means and small-scale monitoring means for key projects. The monitoring means for different projects are slightly different. Refer to the monitoring standards and specifications of different project means. At present, regional monitoring means mainly include precision leveling, GNSS and InSAR. Among them, precision leveling obtains surface deformation information by laying out multi-level leveling networks, adjusting calculations and spatial interpolation. This method cannot meet the requirements of real-time dynamic monitoring of ground subsidence. Precision leveling is usually used to verify the accuracy of ground subsidence monitoring technology with its high precision. GNSS measurement technology has the advantages of short cycle, high positioning accuracy, rapid network deployment and all-weather. However, GNSS measurement obtains deformation information of ground monitoring points with point distribution. Therefore, this method is mainly used for ground subsidence information monitoring in subsidence center areas or areas with serious differential subsidence. Synthetic aperture radar differential interferometry measurement technology can be fast, large-scale and high-precision in real time, and the monitoring accuracy of vertical deformation information can reach mm level, which can be used for real-time monitoring of regional subsidence. However, its detection capability is limited in horizontal deformation monitoring and it is insensitive to horizontal deformation. In terms of phase unwrapping, it is seriously affected by atmospheric delay and time-space decorrelation, so the influence of these errors needs to be eliminated during the solution. Based on the above analysis, the comprehensive multi-interpretation method of InSAR data is selected as the main method for real-time dynamic monitoring of regional ground subsidence, and the results are corrected using GNSS and precision leveling measurement results.

[0113] Benchmark Correction Model

[0114] Using the spatial projection conversion model, the precise leveling points and PS points are corrected to the same coordinate system. With the leveling point as the center of the circle, the accumulated settlement of the PS points within 100m is averaged and compared with the measured value of the precise leveling point to calculate the correlation coefficient and root mean square error. The specific steps are as follows:

[0115] The line-of-sight deformation of point PS is converted into the vertical deformation using formula a.

[0116] Calculate the error value between the level point and the transformed PS deformation (E vari ), which includes orbit error, atmospheric delay and random error.

[0117] The systematic error model is used to calculate the error caused by atmospheric delay and terrain (E s ), combined with the variogram to calculate the regional error estimate (E a ).

[0118] PS point correction is performed using equation b.

[0119] V v =V los / cosθ Formula (17)

[0120] E vair =V v -V b Formula (18)

[0121] E vair =E s +E a Formula (19)

[0122] V verif =V v -E vair Formula (20)

[0123] In the above formula, V v is the deformation in the vertical direction of PS value, V Los is the deformation of the PS value in the LOS direction, ΔH is the displacement of the ground surface in the vertical direction, and θ is the imaging side viewing angle of the SAR satellite.

[0124] In this embodiment, the deformation projection conversion model of GNSS and InSAR

[0125] (1) GNSS data projection transformation model

[0126] Assume that the actual deformation vector of the surface is V, and the deformation components in the east, north, and vertical directions are (ΔE, ΔN, ΔH). The horizontal deformation components in the east and north directions obtained by GNSS observation are (ΔE GNSS , ΔN GNSS ), let VGPS =(ΔE GNSS , ΔN GNSS ). Assuming that the angle between the SAR satellite trajectory direction (SAR image azimuth) and the north direction is Φ, the GNSS horizontal displacement (ΔE GNSS , ΔN GNSS ) is converted into its deformation projection components (Δ R, Δ A) in the range direction (Range) and azimuth direction (Azimuth) of the SAR image, let V SAR =(ΔR, ΔA), the conversion relationship between the two is:

[0127]

[0128] The azimuth direction is perpendicular to both the range direction and the LOS direction. Therefore, the deformation projection of the azimuth direction deformation variable ΔA in the LOS direction is zero, and the angle between the range direction deformation variable ΔR and the LOS direction is θ. Therefore, the deformation projection component of ΔR in the LOS direction is (ΔE GNSS cosΦ+ΔN GNSS sinΦ)·sinθ, the deformation component of the vertical deformation variable ΔH in the LOS direction is ΔH·cosθ, and the sum of the two is the deformation Δr of the three-dimensional deformation of the surface projected to the radar line of sight direction (LOS), so:

[0129] Δr=ΔH·cosθ+(ΔE GNSS cosΦ+ΔN GNSS sinΦ)·sinθ Formula (22)

[0130] In the above formula, Δr is the deformation in the LOS direction measured by InSAR, ΔH is the displacement of the ground surface in the vertical direction, and θ is the imaging side viewing angle of the SAR satellite. Rewrite the above formula as follows:

[0131]

[0132] The above formula establishes the conversion relationship between the radar LOS deformation and the vertical deformation of the surface. It can be seen from this formula that if the horizontal displacement of the surface (ΔE GNSS , ΔN GNSS ) and the angles Φ and θ calculated based on the satellite orbit parameters, the InSAR deformation variable Δr in the radar line of sight (LOS) direction can be converted into the surface deformation variable ΔH in the vertical direction using (3).

[0133] In this embodiment, the atmospheric delay mean model

[0134] ①Use GNSS solution results to calculate the high-precision tropospheric zenith total delay (ZTD) during the SAR satellite transit time. Use ground meteorological data to calculate the zenith static delay (ZHD), and then use ZTD-ZHD to get the zenith wet delay (ZWD). Perform regression fitting on the zenith wet delay GNSS (ZWD) calculated by GNSS combined with ground meteorological data and the zenith wet delay MODIS (ZWD) obtained by inversion of MODIS data, and use GPS (ZWD) to correct MODIS (ZWD).

[0135]

[0136] Where, P s is the atmospheric pressure on the ground, is the latitude of the GPS site, and H is the elevation of the GPS site.

[0137] ② Calculate the differential atmospheric delay phase at the time of acquisition of the SAR main and auxiliary images. The formula is:

[0138]

[0139]

[0140] In the formula, θ inc is the radar incident angle. In order to weaken the influence of noise and operation errors, it is necessary to Perform low-pass filtering.

[0141] The atmospheric delay phase separated from the PS-InSAR residual phase Atmospheric delay phase obtained by joint inversion with GPS / MODIS data Perform mean fusion processing to establish a high-precision, high-temporal and high-spatial resolution atmospheric delay mean model.

[0142]

[0143] (3) Atmospheric delay correction model

[0144] Since the atmospheric delay phase is removed for pixels, it is also necessary to The phase map is aligned with the initial differential interferometry phase map to achieve the unity of pixel scale, so as to perform grid calculation and obtain a high-precision differential interferometry phase map that eliminates the influence of atmospheric delay.

[0145]

[0146] In the formula, is the differential interferometry phase diagram after atmospheric delay correction, is the initial differential interference phase diagram.

[0147] The vertical monitoring system currently mainly includes bedrock-layered markers, distributed optical fibers and groundwater monitoring wells. The bedrock-layered marker monitoring method achieves high-precision vertical layered ground settlement deformation information acquisition by placing the markers in different settlement layers, with an accuracy of 0.01-0.1mm. However, due to the complex operation, large footprint, high construction technology, and high cost, this method is only used to verify the ground settlement layered monitoring model and study the mechanism of ground settlement. Distributed optical fiber technology refers to the technical method of laying sensor optical cables in the object to be measured to achieve continuous testing of multiple physical parameters in one-dimensional direction. Brillouin scattering can reflect the characteristics of optical fiber strain. The method is deduced through the formula. The construction process is relatively simple and can obtain continuous settlement information in multiple layers. It is widely used, but its construction cost is relatively high. It can be used for layered monitoring of settlement center / sub-center.

[0148]

[0149] In the formula, υ B (ε,T) is the drift of the fiber Brillouin frequency when the ambient temperature is T and the strain is ε; υ B (0, T0) represents the drift of the fiber Brillouin frequency when the temperature is T0 and the strain is 0; represent the proportionality coefficients of strain and temperature related to the fiber type, respectively.

[0150] FBG reflects light of a specific wavelength that satisfies the following conditions:

[0151] λ B =2n eff Λ Formula (30)

[0152] In the formula, λ B is the central wavelength of the reflected light; n eff is the effective refractive index of the fiber core; Λ is the spatial period of the fiber grating refractive index modulation.

[0153] External stress and temperature changes can cause changes in the refractive index and grating pitch, resulting in changes in the FBG wavelength λ B The shift satisfies the linear relationship:

[0154]

[0155] Where Δλ is the wavelength change of FBG, ε is the axial strain of the optical fiber, ΔT is the temperature change, and P s is the optical fiber photoelastic coefficient, α is the optical fiber thermal expansion coefficient, and ζ is the optical fiber thermo-optic coefficient.

[0156] In areas of my country where land subsidence is severely developed, groundwater overexploitation is the main influencing factor. The drop in groundwater level causes the dissipation of pore water pressure, and the increase in effective stress leads to settlement of compressed soil layers. Therefore, the fluctuation of groundwater level can indicate the amount of settlement of the aquifer system.

[0157] The groundwater-land subsidence coupling model or empirical model is used to realize the layered monitoring of land subsidence. The formula is as follows:

[0158]

[0159] Where: Ω—seepage area; h—groundwater level; K—permeability coefficient; w—source and sink term of aquifer; h0—initial water level; S s —water storage rate; Γ1—first-class boundary; Γ2—second-class boundary; K n —permeability coefficient in the normal direction of the second-class boundary interface, n—normal direction of the second-class boundary interface; Δb—deformation; b—soil layer thickness; S sk —Skeleton water storage rate (when the water level is lower than the previous minimum water level, this parameter is S skv —Inelastic skeleton water storage rate, when the water level is higher than the previous minimum water level, this parameter is S skv —Elastic skeleton water storage rate).

[0160] Where: S sk and S skv They are elastic water storage rate and inelastic water storage rate:

[0161]

[0162]

[0163] Where: C c Compression index; C r Resilience index; γ w Bulk density of water; σ effective stress; e0 initial porosity.

[0164] In this embodiment, based on the multi-objective ground subsidence evaluation standard, ground subsidence early warning classification standards are established in each ground subsidence unit.

[0165] Based on the comprehensive unit of land subsidence, establish the internal early warning standards of each unit. The early warning standards integrate the "National Land Subsidence Prevention and Control Plan", "Regional Land Subsidence Prevention and Control Plan", the thresholds of deformation of each building / linear project caused by land subsidence in the unit, and the "Regional Land Subsidence Disaster Risk Assessment Factor Level", and conduct a comprehensive comparison of various thresholds. According to the thresholds of receptors in different settlement layers, the early warning levels are divided from small to large. Obtain the data to be monitored, and calculate the layered settlement values ​​in each unit by solving the model. Combined with the classification standards, determine the land subsidence risk level and issue an early warning. Comprehensively apply cloud computing, big data, and WebGIS technologies to realize the display of early warning results.

[0166] For example, the ground subsidence monitoring and early warning device can be used to act as a computing device for the downward extension process. As described herein, the ground subsidence monitoring and early warning device can be used to implement the function of downward extension calculation of gravity or magnetic field data in a computing device. The ground subsidence monitoring and early warning device can be implemented in a single node, or the function of the ground subsidence monitoring and early warning device can be implemented in multiple nodes in a network. Those skilled in the art should be aware that the term ground subsidence monitoring and early warning device includes devices in a broad sense, and the ground subsidence monitoring and early warning device is only one example. Including the ground subsidence monitoring and early warning device is for clarity of expression, and is not intended to limit the application of the present invention to a specific ground subsidence monitoring and early warning device embodiment or a certain type of ground subsidence monitoring and early warning device embodiment. At least some of the features / methods described in the present invention can be implemented in a network device or component, such as a ground subsidence monitoring and early warning device. For example, the features / methods in the present invention can be implemented in hardware, firmware and / or software installed and run on hardware. The ground subsidence monitoring and early warning device can be any device that processes, stores and / or forwards data frames through a network, such as a server, a client, a data source, etc. The ground subsidence monitoring and early warning device may include a transceiver (Tx / Rx) 210, which may be a transmitter, a receiver, or a combination thereof. Tx / Rx210 may be coupled to a plurality of ports 250 (e.g., an uplink interface and / or a downlink interface) for sending and / or receiving frames from other nodes. A processor 230 may be coupled to Tx / Rx 210 to process frames and / or determine which nodes to send frames to. The processor 230 may include one or more multi-core processors and / or a memory device 232, which may be used as a data memory, a buffer, etc. The 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).

[0167] The early warning standard is established based on the comprehensive unit of land subsidence. The early warning standard integrates the "National Land Subsidence Prevention and Control Plan", the "Regional Land Subsidence Prevention and Control Plan", the threshold of deformation of each building / linear project caused by land subsidence and the "Regional Land Subsidence Disaster Risk Assessment Factor Level", and conducts a comprehensive comparison of various thresholds to divide the early warning levels from small to large. Regional early warning indicators

[0168] 1) Groundwater level

[0169] The annual ground subsidence prevention and control indicators are decomposed into quarters, months and days according to the development laws of the year. The compression of the key settlement strata and the groundwater drawdown in the adjacent aquifer causing the subsidence are used to form a clear substitution model on the basis of continuous iteration. The settlement values ​​are decomposed in detail in time and vertical space, and the groundwater level is used as the main warning threshold and indicator, and real-time monitoring and early warning are carried out based on the dynamic monitoring data results of the groundwater level.

[0170] 2) Surface deformation

[0171] The annual control indicators of ground subsidence are decomposed into quarters, months and days according to the development patterns within the year. The monitoring results are implemented by applying GPS continuous station monitoring in typical areas. By constructing grids and interpolating using GIS, it is possible to monitor the surface deformation, compare the monitoring values ​​with the control indicators, and carry out real-time monitoring and early warning.

[0172] (3) Warning level classification

[0173] Early warning and forecasting are achieved by building a dual early warning indicator system that combines the settlement indicator represented by the dynamic cumulative settlement value of the year and the groundwater level indicator. The early warning risk control level is divided into three levels according to the risk size, and each level corresponds to the corresponding settlement and groundwater level indicators. The first level is the highest, displayed in red, the second level is the second, displayed in orange, and the third level is the lowest, displayed in yellow.

[0174] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions of the present invention, which should be included in the scope of the claims of the present invention. The techniques, shapes, and structural parts not described in detail in the present invention are all known technologies.

Claims

1. A regional land subsidence early warning method based on multi-source heterogeneous data, It is characterized in that The method includes 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 identifying and quantifying the main controlling factors of land subsidence, screening and evaluating multi-source monitoring technologies, and the solution module includes monitoring data solution and early warning indicator system construction. The identification and quantification of the main controlling factors of ground subsidence are obtained through water level measuring instruments placed in groundwater monitoring wells; the static loads of tall buildings and dense building complexes are obtained by using the remote sensing building land index method, selecting remote sensing images covering the study area, and using the Erdas Modeler tool based on the inversion of NDBI, MNDWI, and SAVI indices to obtain the spatiotemporal evolution information of building loads. The remote sensing image preprocessing process includes: geometric correction, radiation calibration, atmospheric correction, image masking, and then the vegetation index SAVI extraction formula is as follows (1): Formula (1) Among them, NIR is the pixel brightness value of the near infrared band, Red is the pixel brightness value of the red light band, and L is the soil adjustment factor, which ranges from 0 to 1; The modified normalized water index MNDWI is extracted as follows: Formula (2) In the formula, Green represents the pixel brightness value of the green light band, MIR represents the pixel brightness value of the mid-infrared band, Normalized building land index NDBI is extracted as follows: Formula (3) In the formula, MIR is the pixel grayscale value of the mid-infrared band, NIR is the pixel brightness value of the near-infrared band, and NDBI is -1~1; The information acquisition module extracts the remote sensing building land index IBI based on the index. First, the three index bands are superimposed to generate a new image. The spectral characteristics of each band of the generated new image are further analyzed. It is found that the building land, vegetation and water body obtain the maximum value under the conditions of NDBI, SAVI and MNDWI respectively. The three index bands are used to construct the index-based building land index IBI. The formula is as follows: Formula (4) Dynamic load of rail transit: Use the average static load of rail transit engineering, and the calculation formula is as follows (5) P ES =0.26 P E (1+0.004V E ) ; Formula (5) Where P E : static load of rail transit; V E Vehicle running speed; P ES： Average static load of vehicles; n: number of vehicles; Mi: contribution rate; Pi Average static load of vehicles PVS Average static load of rail transit; The analysis module includes SRCC and random forest methods, and the above acquired data is calculated by the following formula as shown in Formula (6); Formula (6) Among them: Pk means: the proportion of the k-th class samples in the current sample set D is Pk; The multi-source monitoring technology screening and evaluation includes InSAR, GPS, leveling, layered markers, and distributed optical fiber methods. InSAR, GNSS, and distributed optical fiber are used as advantageous monitoring means for ground subsidence monitoring and early warning. InSAR solution uses small baseline interferometry (SBAS-InSAR) technology, and the formula is as follows: Formula (7) Where ν (·) and β (·) are the average velocity and nonlinear components in the residual deformation of the high-resolution single-view differential interferogram, respectively. △Z (x, r) is the terrain component in the high-resolution single-view differential interferogram. △n (x, r) is the noise error. For the estimation of ν (·) and △Z (x, r), they must satisfy the maximum phase coherence factor, which is expressed as follows: Formula (8) in, is the analog phase and is expressed as follows: Formula (9) Subtracting formula (8) from (9) yields a new phase, including β (·) and △m (x, r). Using the singular value decomposition method, the nonlinear deformation rate β (·) is removed. The overall deformation can be expressed as formula (10): Formula (10); The construction of the early warning indicator system and the determination of thresholds are based on the identification and quantification results of the main controlling factors of ground subsidence, and the various evaluation factors and their weights required for the construction of the ground subsidence early warning indicator system are determined. Cluster analysis and fuzzy mathematics methods are used to conduct coupled superposition analysis on various single-factor evaluation indicator maps to establish a ground subsidence early warning evaluation indicator system.

2. A ground subsidence monitoring and early warning device, It is characterized in that It includes one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement a regional ground subsidence early warning method based on multi-source heterogeneous data according to claim 1.

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