CN119986652B - Method, device, equipment, medium and product for estimating three-dimensional deformation of mining subsidence surface - Google Patents
Method, device, equipment, medium and product for estimating three-dimensional deformation of mining subsidence surfaceInfo
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Abstract
The application discloses a method, a device, equipment, a medium and a product for estimating three-dimensional deformation of a mining subsidence surface, and relates to the field of three-dimensional deformation estimation of the surface; the method comprises the steps of calculating different observables including lifting and lowering tracks by InSAR, simulating vertical, coal seam trend and coal seam trend by PIM, constructing an observables matrix according to observables errors and observables types, constructing a real model according to three-dimensional deformation to be solved and a design matrix product error by combining a design matrix based on the observables matrix, determining a variance matrix of the observables errors based on the real model, constructing an observation equation according to the variance matrix, updating a weight matrix for the observables matrix based on the observation matrix by adopting an optimal estimation criterion of total least squares, and determining a three-dimensional deformation estimation result of the three-dimensional deformation to be solved.
Description
Technical Field
The application relates to the field of three-dimensional deformation estimation of earth surface, in particular to a three-dimensional deformation estimation method, device, equipment, medium and product of mining subsidence earth surface.
Background
Different from the one-dimensional deformation obtained by the synthetic aperture radar interferometry (Interferometric SyntheticAperture Radar, inSAR) method, the three-dimensional deformation can more intuitively reflect the real deformation condition of the earth surface, the traditional method needs at least three synthetic aperture radar (SYNTHETIC APERTURE RADAR, SAR) data with different angles to calculate the three-dimensional deformation, and the InSAR data has smaller projected components in the north-south direction due to the limitation of a satellite platform (north-south direction flight, east-west direction shooting) and cannot accurately estimate the displacement in the north-south direction.
Disclosure of Invention
The application aims to provide a three-dimensional deformation estimation method, device, equipment, medium and product for mining subsidence surface, which are used for solving the problem that the displacement in the north-south direction cannot be accurately estimated.
In order to achieve the above object, the present application provides the following solutions:
In a first aspect, the application provides a method for estimating three-dimensional deformation of a mining subsidence surface, comprising the steps of:
Determining observation types according to sources and characteristics of different observation quantities in a subsidence monitoring mining area, wherein the different observation quantities comprise lifting rails and lowering rails calculated by InSAR, and PIM simulated vertical, coal seam trend and coal seam trend, the observation types comprise InSAR observation parameters and PIM simulated observation parameters, the InSAR observation parameters comprise lifting rail deformation L as and lowering rail deformation L de, and the PIM simulated observation parameters comprise trend deformation L str, trend deformation L ran and vertical deformation L up;
Constructing an observed quantity matrix according to the observed quantity error and the observed quantity type;
based on the observed quantity matrix, combining a design matrix, and constructing a real model according to the three-dimensional deformation to be solved and the product error of the design matrix, wherein the three-dimensional deformation to be solved comprises a north-south deformation, an east-west deformation and a vertical deformation;
determining a variance matrix of the observed quantity error based on the real model;
constructing an observation equation according to the variance matrix;
based on the observation matrix, updating a weight matrix for the observation matrix by adopting an optimal estimation criterion of total least square, and determining a three-dimensional deformation estimation result of the three-dimensional deformation to be solved.
In a second aspect, the present application provides a device for estimating three-dimensional deformation of a mined subsidence surface, comprising:
The observation quantity type determining module is used for determining observation quantity types according to sources and characteristics of different observation quantities in a subsidence monitoring mining area, wherein the different observation quantities comprise lifting rails and lowering rails calculated by InSAR, PIM simulated vertical, coal seam trend and coal seam trend, the observation quantity types comprise InSAR observation parameters and PIM simulated observation parameters, the InSAR observation parameters comprise lifting rail deformation L as and lowering rail deformation L de, and the PIM simulated observation parameters comprise trend deformation L str, inclined deformation L ran and vertical deformation L up;
The observed quantity matrix construction module is used for constructing an observed quantity matrix according to the observed quantity errors and the observed quantity types;
The real model construction module is used for constructing a real model according to the three-dimensional deformation to be solved and the product error of the design matrix based on the observed quantity matrix and combining with a design matrix, wherein the three-dimensional deformation to be solved comprises a north-south deformation, an east-west deformation and a vertical deformation;
The variance matrix determining module is used for determining a variance matrix of the observed quantity error based on the real model;
the observation equation construction module is used for constructing an observation equation according to the variance matrix;
And the three-dimensional deformation estimation result determining module is used for updating the weight matrix for the observed quantity matrix by adopting an optimal estimation criterion of total least square based on the observed matrix and determining the three-dimensional deformation estimation result of the three-dimensional deformation to be solved.
In a third aspect, the application provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the computer program to implement the mining subsidence surface three-dimensional deformation estimation method of any of the above.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method of estimating three-dimensional deformation of a mined subsidence surface as defined in any of the preceding claims.
In a fifth aspect, the application provides a computer program product comprising a computer program which when executed by a processor implements a method of estimating three-dimensional deformation of a mined subsidence surface as defined in any of the preceding claims.
According to the specific embodiment provided by the application, the application discloses the following technical effects:
According to the application, the probability integration model (Probability Integration Method, PIM) is fused to simulate the observation parameters and the InSAR observation parameters, the coal seam trend and the coal seam trend are mutually perpendicular on a plane, and can be projected to any direction in the plane according to the projection relation, so that the problem of poor North-south observation capability of the InSAR is solved, and the estimation precision and the accuracy of North-south direction are improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for estimating three-dimensional deformation of a mining subsidence surface.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The foregoing objects, features, and advantages of the application will be more readily apparent from the following detailed description of the application when taken in conjunction with the accompanying drawings and detailed description.
According to the method, different observed quantity types are considered, variance component estimation weights are introduced, and EIV is introduced to improve estimation accuracy to optimize three-dimensional deformation estimation in consideration of errors of coefficient matrixes due to angles.
The embodiment of the application provides a three-dimensional deformation estimation method of a mining subsidence surface, which is executed by computer equipment, in particular to the method which can be singly executed by computer equipment such as a terminal or a server or can be jointly executed by the terminal and the server.
S1, determining observation types according to sources and characteristics of different observation quantities in a subsidence monitoring mining area, wherein the different observation quantities comprise lifting rails and lowering rails calculated by InSAR, PIM simulated vertical, coal seam trend and coal seam trend, the observation types comprise InSAR observation parameters and PIM simulated observation parameters, the InSAR observation parameters comprise lifting rail deformation L as and lowering rail deformation L de, and the PIM simulated observation parameters comprise trend deformation L str, trend deformation L ran and vertical deformation L up.
S2, constructing an observed quantity matrix according to the observed quantity error and the observed quantity type.
And S3, based on the observed quantity matrix, combining a design matrix, and constructing a real model according to the three-dimensional deformation to be solved and the product error of the design matrix, wherein the three-dimensional deformation to be solved comprises a north-south deformation, a east-west deformation and a vertical deformation, and the design matrix is obtained by calculating angles of a mobile phone satellite and a mining subsidence working face.
S4, determining a variance matrix of the observed quantity error based on the real model.
S5, constructing an observation equation according to the variance matrix.
And S6, based on the observation matrix, updating a weight matrix for the observation matrix by adopting an optimal estimation criterion of total least square, and determining a three-dimensional deformation estimation result of the three-dimensional deformation to be solved.
In one exemplary embodiment, 2 observations of lifting and lowering of the InSAR calculation and 3 observations of vertical, (coal) strike and (coal) dip of the PIM simulation are obtained in S1, and are divided into independent 2 types of observations according to different observational sources and characteristics, namely an InSAR observation parameter L 1 and a PIM simulation observation parameter L 2.
The application only adopts 2 observables of track lifting and track lowering, improves the defect of smaller estimation of the south-north deformation quantity of the traditional InSAR observation, reduces the InSAR deformation estimation which originally needs to depend on at least three different observation directions to two, and reduces the dependence on SAR data observation in different directions.
In an exemplary embodiment, S2 specifically includes:
By means of Constructing an observed quantity matrix, wherein alpha str is a running azimuth angle of a working surface, b=tan alpha str-cotαstr,θas is an incident angle in an ascending track mode, theta de is an incident angle in a descending track mode, alpha as is an azimuth angle in the ascending track mode, alpha de is an azimuth angle in the descending track mode, N is a north-south three-dimensional deformation quantity, E is a east-west three-dimensional deformation quantity, U is a vertical three-dimensional deformation quantity, V as is an InSAR ascending track observation error, V de is an InSAR descending track observation error, V str is a PIM trend simulation observation error, V ran is a PIM trend simulation observation error, and V up is a PIM vertical simulation observation error.
In practical application, the InSAR observation parameter L 1 is subjected to InSAR data processing to obtain the line-of-sight deformation, the PIM simulation observation parameter L 2 is subjected to InSAR data and mining subsidence parameters are subjected to probability integration model inversion to obtain the angle alpha str, the angle alpha str can be obtained through collecting data, the angle theta as,θde,αas,αde can be obtained from satellite data parameters, the V asVde Vstr Vran Vup is the error of five observables, the unknown quantity is not needed to be solved, and the subsequent calculation is to calculate the parameter to be solved on the premise that a certain function of the error is minimum.
In an exemplary embodiment, the model only considers the observed errors V asVde Vstr Vran Vup, and does not consider the errors (e.g., angle measurement errors) of the related variables in the design matrix, so a more realistic model considers the case where all the variables have errors. Therefore, the real model constructed in consideration of the case where the variables have errors is shown as S3, and S3 specifically includes:
And constructing a real model by using the L-delta L＝AX+ΔAX, wherein, L is an observed quantity matrix; Δ L is the observed quantity error matrix; a is a design matrix, and the design matrix is a design matrix, X is the three-dimensional deformation quantity to be solved, delta AX is the error of AX product, delta AX can be expressed as the sum of the products of column vectors obtained by line straightening of the error matrix E A of the design matrix A in a left-to-right sequence and corresponding parameters X to be solved in the actual operation process, E A is the error matrix of A, i is the column vector serial number, t is the number of column vectors, X i is the three-dimensional deformation estimated value corresponding to the ith observation component, and E Ai is the design matrix error matrix corresponding to the ith observation component.
In one exemplary embodiment, L is obtained by InSAR data processing and PIM (probabilistic integral model) inversion, Δ L is an observed error, belongs to an unknown quantity, A is obtained by collecting the angles of satellites and mining subsidence work surfaces and calculating, X and Δ AX belong to the unknown quantity, E A is also the unknown quantity, and the subsequent calculation principle is to minimize an error function formed by E A and Δ L, wherein the error function is the optimal estimation criterion of total least squares.
Then the variance of E A X and L can be expressed as:
wherein D L is the variance matrix of the observed quantity matrix; Sigma L is the variance of the column vector corresponding to the observed quantity matrix L, L is L 1 or L 2,L1 is an InSAR observed parameter matrix, and L 2 is a PIM simulated observed parameter matrix; a variance component of L 1; the variance component of L 2, and the unit diagonal matrix of I.
Since observables belong to different sources, statistical characteristics of observables have certain differences, and equal-weight parameter estimation can cause deviation of results. Therefore, the observed quantity variances obtained by different observed sources (InSAR data processing and PIM inversion) are distinguished, and then the observed quantity is weighted again through variance component estimation, and three-dimensional deformation is estimated again.
The observation equation at this time can be written as:
L=AX+ΔAX+ΔL=AX+Δ
where Δ represents the overall error, i.e., the sum of the observed error and the independent error, Δ AX+△L, the variance of which can be expressed as:
the above equation can be written as when ignoring the variance of the error component of a:
in the EIV model, the application estimates the variance components of L1 and L2 according to the observed quantity error V The variance of the error components of matrix a is ignored (or not estimated), so the two variance components to be estimated are estimated.
Thus, the observation equation l=ax+Δ AX+ΔL =ax+Δ can be written as
Wherein L 1 is an InSAR observation parameter matrix, L 2 is a PIM simulation observation parameter matrix, A is a design matrix, X is a three-dimensional deformation amount to be solved, delta L is an observation error matrix, A 1 is a design matrix corresponding to the 1 st group of observation components, A 2 is a design matrix corresponding to the 2 nd group of observation components, E A1 is an error matrix of A 1, E A2 is an error matrix of A 2, V 1 is a residual error corresponding to the 1 st group of observation components, and V 2 is a residual error corresponding to the 2 nd group of observation components; The predicted value of the observed quantity matrix L; delta is total error, E A is error matrix of A; The method comprises the steps of designing a matrix A for straightening, obtaining an estimated value of a vector obtained by the matrix A, obtaining a vector obtained by the matrix A for straightening, and obtaining a vector obtained by an error matrix E A for straightening (E A).
In an exemplary embodiment, the overall least squares optimal estimation criteria are:
min=ΔTPΔ+vec(EA)Tvec(EA)
wherein P is a weight matrix, and T is a transpose.
In order to meet the optimal solution of the conditions, the weight matrix is updated for the observed quantity at the same time, and an iterative calculation method is adopted, which comprises the following specific steps:
(1) And carrying out variance component estimation by adopting an iteration method.
Order theWherein N aa is normal equation matrix, N aai is normal equation matrix of ith observed component, A i is design matrix of ith observed component, W i is weight matrix of ith observed component, weight matrix P i of first iteration is unit diagonal matrix, variance component at this timeThe relationship between the observation residual W σ is:
Wherein the method comprises the steps of ,Wσ＝[V1 TP1V1 V2 TP2V2]T,
N i is the number of observed values in each group, i=1 or 2, and tr represents the tracing operation. The weight matrix is then updated and the weight matrix is updated,The above operation is iterated repeatedly until the errors in the unit weights are approximately equal, namely:
at this time, the weight matrix P is updated:
(2) Based on the total least square criterion, an iterative method is adopted to estimate the parameter X
Taking the least squares solution of X as an initial value The first estimate is:
The iterative calculation process is as follows:
The iteration termination conditions are as follows:
wherein epsilon 0 is a given normal number in advance, and can be given according to the accuracy of the observed data.
Based on the same inventive concept, the embodiment of the application also provides a mining subsidence surface three-dimensional deformation estimation device for realizing the mining subsidence surface three-dimensional deformation estimation method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the device for estimating three-dimensional deformation of the subsidence surface provided below may be referred to the limitation of the method for estimating three-dimensional deformation of the subsidence surface, which is not repeated herein.
In one exemplary embodiment, there is provided a mining subsidence surface three-dimensional deformation estimation device comprising:
The observation quantity type determining module is used for determining observation quantity types according to sources and characteristics of different observation quantities in a subsidence monitoring mining area, wherein the different observation quantities comprise lifting rails and lowering rails calculated by InSAR, PIM simulated vertical, coal seam trend and coal seam trend, the observation quantity types comprise InSAR observation parameters and PIM simulated observation parameters, the InSAR observation parameters comprise lifting rail deformation L as and lowering rail deformation L de, and the PIM simulated observation parameters comprise trend deformation L str, inclined deformation L ran and vertical deformation L up.
And the observed quantity matrix construction module is used for constructing an observed quantity matrix according to the observed quantity error and the observed quantity type.
The real model construction module is used for constructing a real model according to the three-dimensional deformation to be solved and the product error of the design matrix based on the observed quantity matrix and the design matrix, wherein the three-dimensional deformation to be solved comprises north-south deformation, east-west deformation and vertical deformation, and the design matrix is obtained through calculation of angles of mobile phone satellites and mining subsidence working faces.
And the variance matrix determining module is used for determining a variance matrix of the observed quantity error based on the real model.
And the observation equation construction module is used for constructing an observation equation according to the variance matrix.
And the three-dimensional deformation estimation result determining module is used for updating the weight matrix for the observed quantity matrix by adopting an optimal estimation criterion of total least square based on the observed matrix and determining the three-dimensional deformation estimation result of the three-dimensional deformation to be solved.
In one exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing three-dimensional deformation estimation data of the mining subsidence surface. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program when executed by a processor implements a method for estimating three-dimensional deformation of a production subsidence surface.
In one exemplary embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the above method when executing the computer program.
In one exemplary embodiment, a computer readable storage medium is provided, storing a computer program which, when executed by a processor, implements the above method.
In an exemplary embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the above method.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magneto-resistive Memory (Magnetoresistive RandomAccess Memory, MRAM), ferroelectric Memory (Ferroelectric RandomAccess Memory, FRAM), phase change Memory (PHASE CHANGE Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (RandomAccess Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static RandomAccess Memory, SRAM) or dynamic random access memory (Dynamic RandomAccess Memory, DRAM), etc.
In the present application, all the actions of obtaining signals, information or data are performed under the premise of conforming to the corresponding data protection regulation policy of the country of the location and obtaining the authorization given by the owner of the corresponding device.
The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The principles and embodiments of the present application have been described herein with reference to specific examples, which are intended to facilitate an understanding of the principles and concepts of the application and are to be varied in scope and detail by persons of ordinary skill in the art based on the teachings herein. In view of the foregoing, this description should not be construed as limiting the application.
Claims (10)
1. The method for estimating the three-dimensional deformation of the mining subsidence surface is characterized by comprising the following steps of:
Determining observation types according to sources and characteristics of different observation quantities in a subsidence monitoring mining area, wherein the different observation quantities comprise lifting rails and lowering rails calculated by InSAR, and PIM simulated vertical, coal seam trend and coal seam trend, the observation types comprise InSAR observation parameters and PIM simulated observation parameters, the InSAR observation parameters comprise lifting rail deformation L as and lowering rail deformation L de, and the PIM simulated observation parameters comprise trend deformation L str, trend deformation L ran and vertical deformation L up;
Constructing an observed quantity matrix according to the observed quantity error and the observed quantity type;
based on the observed quantity matrix, combining a design matrix, and constructing a real model according to the three-dimensional deformation to be solved and the product error of the design matrix, wherein the three-dimensional deformation to be solved comprises a north-south deformation, an east-west deformation and a vertical deformation;
determining a variance matrix of the observed quantity error based on the real model;
constructing an observation equation according to the variance matrix;
Based on the observation equation, updating a weight matrix for the observation matrix by adopting an optimal estimation criterion of total least square, and determining a three-dimensional deformation estimation result of the three-dimensional deformation to be solved.
2. The method for estimating three-dimensional deformation of a subsidence surface according to claim 1, wherein constructing an observed quantity matrix according to observed quantity errors and observed quantity types comprises:
By means of Constructing an observed quantity matrix, wherein alpha str is a running azimuth angle of a working surface, b=tan alpha str-cotαstr,θas is an incident angle in an ascending track mode, theta de is an incident angle in a descending track mode, alpha as is an azimuth angle in the ascending track mode, alpha de is an azimuth angle in the descending track mode, N is a north-south three-dimensional deformation quantity, E is a east-west three-dimensional deformation quantity, U is a vertical three-dimensional deformation quantity, V as is an InSAR ascending track observation error, V de is an InSAR descending track observation error, V str is a PIM trend simulation observation error, V ran is a PIM trend simulation observation error, and V up is a PIM vertical simulation observation error.
3. The method for estimating three-dimensional deformation of a subsidence surface according to claim 1, wherein the constructing a real model based on the observed quantity matrix, in combination with a design matrix, according to the three-dimensional deformation to be solved and the design matrix product error, specifically comprises:
And constructing a real model by using the L-delta L＝AX+ΔAX, wherein, L is an observed quantity matrix; Δ L is the observed quantity error matrix; a is a design matrix, and the design matrix is a design matrix, X is the three-dimensional deformation quantity to be calculated, delta AX is the product error of AX, E A is the error matrix of A, i is the serial number of the observed component, t is the number of column vectors, and X i is the three-dimensional deformation estimated value corresponding to the i-th observed component; The error matrix of the design matrix a i corresponding to the i-th observed component.
4. The mining subsidence surface three-dimensional deformation estimation method according to claim 1, wherein determining the variance matrix of the observed quantity error based on the real model comprises:
By means of Determining a variance matrix of the observed quantity error, wherein D L is the variance matrix of the observed quantity matrix; Sigma L is the variance of the column vector corresponding to the observed quantity matrix L, L is L 1 or L 2,L1 is an InSAR observed parameter matrix, and L 2 is a PIM simulated observed parameter matrix; a variance component of L 1; the variance component of L 2, and the unit diagonal matrix of I.
5. The method of three-dimensional deformation estimation of a mining subsidence surface according to claim 1, wherein the observation equation is:
Wherein L 1 is an InSAR observation parameter matrix, L 2 is a PIM simulation observation parameter matrix, A is a design matrix, X is a three-dimensional deformation to be solved, delta L is an observed quantity error matrix, A 1 is a design matrix corresponding to the 1 st group of observation components, and A 2 is a design matrix corresponding to the 2 nd group of observation components; an error matrix of A 1; The error matrix is A 2, V 1 is the residual error corresponding to the 1 st group of observed components, and V 2 is the residual error corresponding to the 2 nd group of observed components; The predicted value of the observed quantity matrix L; delta is total error, E A is error matrix of A; The method comprises the steps of designing a matrix A for straightening, obtaining an estimated value of a vector obtained by the matrix A, obtaining a vector obtained by the matrix A for straightening, and obtaining a vector obtained by an error matrix E A for straightening (E A).
6. The method of three-dimensional deformation estimation of a mining subsidence surface according to claim 5, wherein the overall least squares optimal estimation criteria is:
min=ΔTPΔ+vec(EA)Tvec(EA)
Wherein P is a weight matrix, T is a transpose, and delta is an overall error.
7. A mining subsidence surface three-dimensional deformation estimation device, comprising:
The observation quantity type determining module is used for determining observation quantity types according to sources and characteristics of different observation quantities in a subsidence monitoring mining area, wherein the different observation quantities comprise lifting rails and lowering rails calculated by InSAR, PIM simulated vertical, coal seam trend and coal seam trend, the observation quantity types comprise InSAR observation parameters and PIM simulated observation parameters, the InSAR observation parameters comprise lifting rail deformation L as and lowering rail deformation L de, and the PIM simulated observation parameters comprise trend deformation L str, inclined deformation L ran and vertical deformation L up;
The observed quantity matrix construction module is used for constructing an observed quantity matrix according to the observed quantity errors and the observed quantity types;
The real model construction module is used for constructing a real model according to the three-dimensional deformation to be solved and the product error of the design matrix based on the observed quantity matrix and combining with a design matrix, wherein the three-dimensional deformation to be solved comprises a north-south deformation, an east-west deformation and a vertical deformation;
The variance matrix determining module is used for determining a variance matrix of the observed quantity error based on the real model;
the observation equation construction module is used for constructing an observation equation according to the variance matrix;
And the three-dimensional deformation estimation result determining module is used for updating the weight matrix for the observed quantity matrix by adopting an optimal estimation criterion of total least square based on the observed equation and determining the three-dimensional deformation estimation result of the three-dimensional deformation to be solved.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the mining subsidence surface three-dimensional deformation estimation method of any one of claims 1-6.
9. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the mining subsidence surface three-dimensional deformation estimation method of any one of claims 1-6.
10. A computer program product comprising a computer program which, when executed by a processor, implements the method of three-dimensional deformation estimation of a mining subsidence surface of any one of claims 1-6.
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| CN111780660A (en) * | 2020-07-13 | 2020-10-16 | 内蒙古工业大学 | Three-dimensional multi-order deformation optimization method and optimization device in mining area |
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| CN110058236A (en) * | 2019-05-21 | 2019-07-26 | 中南大学 | It is a kind of towards three-dimensional Ground Deformation estimation InSAR and GNSS determine Quan Fangfa |
| CN111780660A (en) * | 2020-07-13 | 2020-10-16 | 内蒙古工业大学 | Three-dimensional multi-order deformation optimization method and optimization device in mining area |
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