CN113253270A - Method and system for inverting underground mining parameters based on InSAR and Okada models - Google Patents
Method and system for inverting underground mining parameters based on InSAR and Okada models Download PDFInfo
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Abstract
The application provides a method and a system for inverting underground mining parameters based on InSAR and Okada models, which can comprise the following steps: and acquiring the surface deformation quantity of the mining area by utilizing an InSAR technology. And constructing a functional relation between the surface deformation quantity and the model parameters. And obtaining actual mining parameters by adopting a Bayesian inversion algorithm according to the surface deformation variables and the functional relation. According to the method, the earth surface deformation caused by underground mining is obtained through a time sequence synthetic aperture radar interferometry technology, and denser original data are provided for inversion. In addition, aiming at the working face of the mining area, under the condition that the mining parameters are unknown or less mining parameters are known, an Okada model of underground mining surface deformation is established, the reliable actual mining parameters are obtained by utilizing the surface deformation observation data acquired by the InSAR and through a Bayesian inversion algorithm, and the method is favorable for scientifically making an underground mining plan, monitoring the border-crossing mining and the like.
Description
Technical Field
The application relates to the technical field of mining, in particular to a method and a system for inverting underground mining parameters based on InSAR and Okada models.
Background
Mining refers to the technology and science of taking nature as a working object and obtaining natural resources by means of mining and the like. Mining in its broadest sense also includes the production of coal and oil. The surface deformation caused by mining is the visual representation of parameters such as mining length, width, depth and the like of an underground working surface on the surface, and a mining deformation model is usually adopted to predict the mining deformation. The model is a probability integral model, mining parameters such as mining length, width and depth of an underground working face need to be known, the mining parameters are often difficult to obtain, and the model has certain limitation on the condition that some unknown underground mining parameters or known few mining parameters.
The Interferometric Synthetic Aperture Radar (Interferometric Synthetic Aperture Radar) technology has become a research hotspot in recent years due to its advantages of all-time, all-weather, non-contact, large area, high precision, high spatial and temporal resolution, and the like. The InSAR technology is to use two or more images to do differential interference processing, and the earth surface deformation with millimeter to centimeter level precision can be obtained through differential phase. Furthermore, after the surface deformation information is obtained, the real parameters of mining can be inverted by using a proper mining area deformation model, so that the problems of scientifically making an underground mining plan, supervising the underground mining progress and the like are solved.
Disclosure of Invention
The application provides a method for inverting underground mining parameters based on InSAR and Okada models, which can comprise the following steps: and acquiring the surface deformation quantity of the mining area by utilizing an InSAR technology. And constructing a functional relation between the surface deformation quantity and the model parameters. And obtaining actual mining parameters by adopting a Bayesian inversion algorithm according to the surface deformation variables and the functional relation.
In some embodiments, obtaining the amount of surface deformation of the mine area using InSAR technology may include: acquiring a plurality of single-view complex images with coherence including a mine area through repeated orbits by a synthetic aperture radar antenna. Reading a single-view complex image. And cutting out the single-view complex image of the mining area from the single-view complex image. And carrying out data processing on the single-vision complex image of the mining area to obtain the visual line direction surface deformation quantity of the mining area, wherein the data processing comprises track encryption, image registration, baseline analysis, generation of a differential interference graph and interference phase time sequence analysis.
In some embodiments, constructing a functional relationship between the amount of surface deformation and the model parameters may include: and constructing an Okada model about the deformation of the subsurface mining surface, wherein the Okada model comprises model parameters. And constructing a ground surface three-dimensional deformation formula by using the model parameters, wherein the ground surface three-dimensional deformation formula represents the east-west deformation quantity, the south-north deformation quantity and the vertical deformation quantity of the ground surface point of the mining area under the condition of the model parameters.
In some embodiments, the surface three-dimensional deformation formula is:
wherein,
a vector of parameters of the model is represented,
the east-west deformation quantity of the ground surface point of the mining area under the condition of the model parameter vector is represented,
representing the north and south deformation quantity of the ground surface point of the mine area under the condition of the model parameter vector,
represents the vertical deformation quantity of the ground surface point of the mining area under the condition of the model parameter vector,
is the position coordinate of the surface point along the fault run in the fault coordinate system of the Okada model,
is the position coordinate of the surface point along the perpendicular to the fault strike in the fault coordinate system of the Okada model,
is the inclination angle of the working face of the mine area, d is the depth of the working face of the mine area, W is the width of the working face of the mine area,l is the length of the mine face and (x, y, z) is the position coordinates of the surface point in the local cartesian rectangular coordinate system.
In some embodiments, obtaining the actual mining parameters according to the surface deformation variables and the functional relationship and by using a bayesian inversion algorithm may include: and determining the mathematical relation between the deformation quantity of the earth surface and the model parameters on the basis of the position coordinates of the earth surface points. And acquiring the model parameter with the maximum likelihood function corresponding to the posterior probability density as the actual mining parameter by combining the function relation and the mathematical relation between the surface deformation quantity and the model parameter and adopting a Bayesian inversion algorithm.
The present application further provides a system for inverting underground mining parameters based on InSAR and Okada models, which may include: the system comprises a synthetic aperture radar, a function relation establishing module and an inversion module. Synthetic aperture radar is used for acquiring the surface deformation quantity of a mining area by utilizing InSAR technology. The functional relation establishing module is used for establishing a functional relation between the surface deformation quantity and the model parameters. And the inversion module is used for obtaining actual mining parameters by adopting a Bayesian inversion algorithm according to the surface deformation variables and the functional relation.
In some embodiments, the performing of the synthetic aperture radar may comprise: acquiring a plurality of single-view complex images with coherence including a mine area through repeated orbits by a synthetic aperture radar antenna. Reading a single-view complex image. And cutting out the single-view complex image of the mining area from the single-view complex image. And carrying out data processing on the single-vision complex image of the mining area to obtain the visual line direction surface deformation quantity of the mining area, wherein the data processing comprises track encryption, image registration, baseline analysis, generation of a differential interference graph and interference phase time sequence analysis.
In some embodiments, the step of executing the functional relationship establishing module may include: and constructing an Okada model about the deformation of the subsurface mining surface, wherein the Okada model comprises model parameters. And constructing a ground surface three-dimensional deformation formula by using the model parameters, wherein the ground surface three-dimensional deformation formula represents the east-west deformation quantity, the south-north deformation quantity and the vertical deformation quantity of the ground surface point of the mining area under the condition of the model parameters.
In some embodiments, the performing step of the inversion module may comprise: a mathematical relationship between the amount of surface deformation and the model parameters is determined. And calculating the prior probability density of the likelihood function and the model parameter by combining the function relation between the surface deformation quantity and the model parameter. And obtaining the posterior probability density of the model parameters according to Bayes' theorem, wherein a group of model parameters with the maximum likelihood function corresponding to the posterior probability density are obtained as the inverted optimal model parameters of the mining area, namely the actual mining parameters based on the surface deformation.
The present application also provides a computer device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor when executing the program implementing the steps of the method for inverting subsurface mining parameters based on the InSAR and Okada models.
The present application also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of a method for inverting subsurface mining parameters based on InSAR and Okada models.
According to the technical scheme of the embodiment, at least one of the following advantages can be obtained.
According to the method and the system for inverting the underground mining parameters based on the InSAR and the Okada model, the surface deformation caused by underground mining is obtained through a time sequence synthetic aperture radar interferometry technology, and denser raw data are provided for inversion. In addition, aiming at the working face of the mining area, under the condition of unknown mining parameters or known few mining parameters, an Okada model of underground mining surface deformation is established, and the original observation data acquired by the synthetic aperture radar is utilized to obtain reliable actual mining parameters through a Bayesian inversion algorithm, thereby being beneficial to scientifically appointing an underground mining plan, monitoring cross-border mining and the like.
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Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, with reference to the accompanying drawings in which:
FIG. 1 is a flow chart of a method of inverting subsurface mining parameters based on InSAR and Okada models according to an exemplary embodiment of the present application;
FIG. 2 is a schematic illustration of SAR image data coverage and study area for an InSAR-based method of inverting subsurface mining parameters according to an exemplary embodiment of the present application;
FIG. 3 is a schematic Okada model of subsurface mining surface deformation in elastic media based on a method of inverting subsurface mining parameters based on InSAR and the Okada model according to an exemplary embodiment of the present application;
FIG. 4 is an inversion flow chart of a method of inverting subsurface mining parameters based on InSAR and Okada models in accordance with an exemplary embodiment of the present application;
FIG. 5 is a surface deformation quantity schematic diagram of InSAR acquisition for a method of inverting subsurface mining parameters based on InSAR and Okada models, according to an exemplary embodiment of the present application;
FIG. 6 is a schematic representation of a deformation of a mine region forward from optimal model parameters for a method for inverting subsurface mining parameters based on InSAR and Okada models, according to an exemplary embodiment of the present application;
FIG. 7 is a schematic representation of the residual between the surface deformation quantities of InSAR acquisitions and the deformation of the mine field forward by the optimal model parameters for a method of inverting subsurface mining parameters based on InSAR and Okada models according to an exemplary embodiment of the present application;
FIG. 8 is a residual histogram between InSAR acquired surface deformation quantities and the mine deformation forward of the optimal model parameters for a method of inverting subsurface mining parameters based on InSAR and Okada models according to an exemplary embodiment of the present application;
FIG. 9 is a line along the line in FIG. 5 of a method for inverting subsurface mining parameters based on InSAR and Okada models, according to an exemplary embodiment of the present application
Drawing a section schematic diagram of the surface deformation quantity, the forward modeling result and the residual error collected by the InSAR; and
fig. 10 is a system architecture diagram for inverting subsurface mining parameters based on InSAR and Okada models in accordance with exemplary embodiments of the present application.
Detailed Description
For a better understanding of the present application, various aspects of the present application will be described in more detail with reference to the accompanying drawings. It should be understood that the detailed description is merely illustrative of exemplary embodiments of the present application and does not limit the scope of the present application in any way. Like reference numerals refer to like elements throughout the specification. The expression "and/or" includes any and all combinations of one or more of the associated listed items.
In the drawings, the size, dimension, and shape of elements have been slightly adjusted for convenience of explanation. The figures are purely diagrammatic and not drawn to scale. As used herein, the terms "approximately", "about" and the like are used as table-approximating terms and not as table-degree terms, and are intended to account for inherent deviations in measured or calculated values that would be recognized by one of ordinary skill in the art. In addition, in the present application, the order in which the processes of the respective steps are described does not necessarily indicate an order in which the processes occur in actual operation, unless explicitly defined otherwise or can be inferred from the context.
It will be further understood that terms such as "comprising," "including," "having," "including," and/or "containing," when used in this specification, are open-ended and not closed-ended, and specify the presence of stated features, elements, and/or components, but do not preclude the presence or addition of one or more other features, elements, components, and/or groups thereof. Furthermore, when a statement such as "at least one of" appears after a list of listed features, it modifies that entire list of features rather than just individual elements in the list. Furthermore, when describing embodiments of the present application, the use of "may" mean "one or more embodiments of the present application. Also, the term "exemplary" is intended to refer to an example or illustration.
Unless otherwise defined, all terms (including engineering and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
In addition, the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 is a flow chart of a method of inverting subsurface mining parameters based on InSAR and Okada models according to an exemplary embodiment of the present application.
As shown in fig. 1, the present application provides a method for inverting subsurface mining parameters based on InSAR and Okada models, which may include: and step S1, acquiring the surface deformation quantity of the mining area by utilizing InSAR technology. Step S2, a functional relationship between the surface deformation quantity and the model parameters is constructed. And step S3, obtaining actual mining parameters by adopting a Bayesian inversion algorithm according to the surface deformation variables and the functional relation.
And step S1, acquiring the surface deformation quantity of the mining area by utilizing InSAR technology. In particular, coal has a thousands of years of mining history in China, and has an extremely important position in energy structures in China. Large-scale coal mining promotes economic development of China, but a series of surface deformation problems caused by the coal mining are increasingly serious. Therefore, the method is particularly important for monitoring and analyzing the deformation of the ground surface of the mining area. The method adopts an Interferometric Synthetic Aperture Radar (InSAR) technology to observe the surface deformation of the mining area. The technology has the advantages of all-time, all-weather, large-range, high precision, continuous monitoring, easy repeated observation and the like.
Fig. 2 is a schematic view of SAR image data coverage and study area for a method for inverting subsurface mining parameters based on InSAR and Okada models according to an exemplary embodiment of the present application.
In some embodiments, the amount of surface deformation of the mine is obtained using InSAR technology. First, a plurality of single-view complex images including a mine area are acquired through a repetitive orbit by a Synthetic Aperture Radar (SAR) antenna. Note that the single-view complex image has coherence. Further, the single-view complex image is analyzed and processed by utilizing a time sequence InSAR technology. Specifically, since the single-view complex image is a single-view complex image corresponding to the SAR coverage, after the single-view complex image is read, the single-view complex image corresponding to the research mining area of the present application needs to be cut out from the single-view complex image. And then utilize the time sequence synthetic aperture radar interferometry technique to carry out analysis processing to the single vision complex number image of the working mining area, including: track encryption, image registration, baseline analysis, generation of differential interference diagrams, interference phase time sequence analysis and the like to obtain the visual line direction surface deformation quantity of the mining area. As shown in fig. 2, the irregular frame region is a single-view complex image corresponding to the SAR coverage, and may be an image of an inner Mongolia region under any weather condition provided by the SAR sensor mounted on Sentinel # 1 (Sentinel-1). The rectangular frame in fig. 2 is a cut mining area in the deldos city, the coverage area of the rectangular frame is 6.9 × 7.4 square kilometers, and the rectangular frame is an accumulated deformation amount of the SAR visual line in the monitoring period obtained by performing data processing on 20-scene images covering the mining area between 2016, 9, 25 and 2017, 5, 11.
Step S2, a functional relationship between the surface deformation quantity and the model parameters is constructed.
The underground mining in China usually adopts the longwall method for mining, in the process of advancing a working face, a goaf collapses to cause the deformation of the ground surface, and the process can be generalized to the deformation caused by the influence of rectangular tensile faults in an elastic medium. FIG. 3 is a schematic Okada model of subsurface mining surface deformation in elastic media based on a method of inverting subsurface mining parameters using InSAR and Okada models according to an exemplary embodiment of the present application. As shown in fig. 3, o-xyz is a local cartesian rectangular coordinate system with the x-axis pointing in the north direction, the y-axis pointing in the east direction, and the z-axis pointing in the direction perpendicular to the ground.
Is a tomographic coordinate system, which
Points to the trend of the fault,
points to the direction vertical to the trend of the fault,
pointing in a direction perpendicular to the ground, i.e. in the same direction as the z-axis. The fault coordinate system conforms to the right-hand rule. Origin of a tomographic coordinate system
The coordinates in a local Cartesian rectangular coordinate system are
。
The included angle between the axis and the north direction is phi.
Is a fault plane coordinate system which
Pointing to the direction of the fault, p pointing to the direction vertical to the fault, q pointing to the direction vertical to the fault plane, and the origin of the fault plane coordinate system
Located in the lower left corner of the fault plane. The position coordinate of the surface point P in the local cartesian rectangular coordinate system is (x, y, 0), and the local cartesian rectangular coordinate of the surface point P is converted into the fault coordinate system, which is:
suppose a fault plane is seatedOrigin of the system
And (3) the method is positioned at the underground depth d, and the local Cartesian rectangular coordinate of the earth surface point P is converted into a fault plane coordinate system, so that the method is as follows:
r is the distance from the surface point P to the origin of the fault plane coordinate system under the fault plane coordinate system, namely:
if the surface point P has the coordinate of
And then:
wherein,
wherein, in the formula (4) to the formula (8), U represents the tensile score of the tensile faultAn amount;
elastic modulus and shear modulus, respectively; v is a Poisson's ratio, which is an elastic constant representing the transverse deformation of the elastic material; p and q are respectively the surface points P, i.e. points
Coordinates in a fault plane coordinate system; r is the distance from the earth surface point P to the origin of the fault plane coordinate system;
the dip angle of the fault plane of the mining area; d is the depth of the fault plane of the mine area. It should be noted that the fault plane is the working plane of the mining area in the present application.
In fig. 3 it can also be seen that the width of the fault plane is W and the length of the fault plane is L. Thus, the model parameter vector is known
。
Further, a surface three-dimensional deformation formula is constructed by utilizing the model parameters, wherein the surface three-dimensional deformation formula represents the east-west deformation quantity, the south-north deformation quantity and the vertical deformation quantity of the surface point P (x, y, z) of the mining area under the condition of the model parameters and the Poisson ratio is v.
Specifically, the surface three-dimensional deformation formula is as follows:
wherein,
a vector of parameters of the model is represented,
the east-west deformation quantity of the ground surface point of the mining area under the condition of the model parameter vector is represented,
representing the north and south deformation quantity of the ground surface point of the mine area under the condition of the model parameter vector,
represents the vertical deformation quantity of the ground surface point of the mining area under the condition of the model parameter vector,
is the position coordinate of the surface point along the fault run in the fault coordinate system of the Okada model,
is the position coordinate of the surface point along the perpendicular to the fault strike in the fault coordinate system of the Okada model,
the dip angle of the working surface of the mine area, d the depth of the working surface of the mine area, W the width of the working surface of the mine area, L the length of the working surface of the mine area, and (x, y, z) the position coordinates of the surface point in the local Cartesian rectangular coordinate system.
And step S3, obtaining actual mining parameters by adopting a Bayesian inversion algorithm according to the surface deformation variables and the functional relation.
Specifically, fig. 4 is an inversion flow chart of a method of inverting subsurface mining parameters based on InSAR and Okada models according to an exemplary embodiment of the present application. As shown in fig. 4, the mining parameter inversion problem is a process of researching the evolution characteristics and rules of surface deformation by combining an Okada model and existing data on the basis of determining the surface deformation quantity of a mining area by utilizing the InSAR technology, and calculating the material characteristics inside the earth and the optimal model parameters, namely an optimization problem of the mining parameters.
Specifically, according to a mathematical model, on the basis of the known coordinates of the surface point P and the Poisson ratio v, establishing a mathematical relation between the surface deformation quantity and the model parameters of the mining area determined by the InSAR technology:
in the formula (10), the first and second groups,
determining the surface deformation quantity of a mining area by utilizing InSAR technology;
representing a model parameter vector;
is the incident angle of radar waves of InSAR, alpha is the heading angle of a satellite carrying the InSAR,
to observe errors.
In equation (10), the unknowns are model parameter vectors
. According to the formula (9) and the formula (10), a complex nonlinear relationship exists between the model parameters and the surface deformation quantity. Because the linear relation between the two can not be obtained, the difficulty of solving the model parameters by using the linear algorithm is higher, so the Bayesian inversion algorithm is adopted in the application.
In some embodiments, a Bayesian algorithm is used for inverse extrapolation of model parameters. Specifically, the bayesian algorithm can know that:
in the formula (11), the reaction mixture,
is the posterior probability density of the model parameters;
forward modeling a likelihood function of a result and an observation data residual by using a model parameter;
is the prior probability density of the model parameter vector;
is a prior probability distribution of a geological condition, typically a constant.
In the inversion process, likelihood functions
Reflecting the degree of match between the surface deformation quantity and the model parameters. Since the surface deformation quantity has noise in determination and a certain error in the inversion process, when the mean value of the noise and the error is set to be 0 and the Gaussian distribution is followed, the likelihood function
Can be expressed as:
in the formula (12), S is an adjustment factor,
for the Okada forward gaze distortion value,
is the variance and covariance matrix of the surface deformation.
Further, the rationality of the model parameters was analyzed. Specifically, the interval and step size of the model parameters are given
Selecting model parameter initial value from prior probability density of model parameter by using simulated annealing method
And combining with Okada model to make forward calculation, and calculating likelihood function according to formula (12)
By perturbation functions
Obtaining a new set of model parameters
Calculating a new likelihood function
Wherein
Is between-1, 1 corresponding to model parameters]A random number in between. If it is not
Then the step size is reserved
And model parameters; if it is not
And is and
wherein 0 is<b<1, taking b as a new step length to carry out disturbance processing, and calculating a likelihood function; otherwise, the original step length is utilized
And (5) carrying out disturbance and calculating a likelihood function. Setting a termination condition by setting an iteration time threshold, and acquiring the posterior probability density of the model parameters by using Bayes' theorem, wherein a group with the maximum likelihood function corresponding to the posterior probability densityThe model parameters are the optimal solutions of the model parameters of the Okada model, i.e. the actual mining parameters based on the amount of surface deformation.
Fig. 5 is a surface deformation quantity schematic diagram of an InSAR acquisition of a method for inverting subsurface mining parameters based on InSAR and Okada models, according to an exemplary embodiment of the present application. FIG. 6 is a schematic representation of a deformation of a mine region forward from optimal model parameters for a method for inverting subsurface mining parameters based on InSAR and Okada models, according to an exemplary embodiment of the present application; fig. 7 is a schematic representation of the residual between the surface deformation quantities of the InSAR acquisition and the mine deformation forward of the optimal model parameters for a method of inverting subsurface mining parameters based on InSAR and Okada models according to an exemplary embodiment of the present application.
In some embodiments, the inclination of the Okada model is set to 0 by using the near-horizontal coal seam as a working layer, and the optimal model parameters of the model are solved by using a bayesian inversion algorithm. As shown in fig. 5, the actual mine field line of sight monitored for InSAR is the deformation, i.e. the amount of surface deformation of the actual mine field. As shown in fig. 6, the results of the deformation of the mine area forward using the Okada model of subsurface mining surface deformation from the inverted optimal model parameters. As shown in fig. 7, is the residual of the surface deformation quantity of the actual mine and the mine deformation result forward modeled by the Okada model.
In this embodiment, the specific values of the optimal model parameters for Okada for inverting subsurface mining surface deformation by the bayesian algorithm are as follows:
TABLE 1
As can be seen from Table 1, the inverted Okada model is 1335.8 meters in length, which is close to the actual mining progress; the depth of the inverted Okada model is similar to the face extraction depth; the mining direction of the working face of the mining area is from south to north, the trend angle of the reverse performance is 0.2 degrees, and the mining direction has high consistency with the real mining direction. The Okada model parameters of the underground mining surface deformation can be well corresponding to the actual parameters of the mining area through inversion of a Bayesian inversion algorithm, and the feasibility of the model in the mining under the bottom is proved.
In some embodiments, referring to fig. 5 and 6, it can be seen that the deformation of the mine area forward modeled by the optimal model parameters and the surface deformation monitored by InSAR have similarity in the shape of the deformation field, the gradient of the deformation, and the residual distribution is between [ -40mm,40mm ]. Fig. 8 is a histogram of residuals, which follow a gaussian distribution with a root mean square error of 13.2 mm. In order to better illustrate the accuracy of the forward deformation of the mining area, a section is drawn along a line pp' in fig. 5, as shown in fig. 9, the simulation result is consistent with the monitoring result, the residual error is concentrated between [ -17mm and 20mm ], the root mean square error is 6.4mm, the forward calculation result of the Okada model by utilizing the underground mining surface deformation through the optimal model parameters has higher consistency with the surface deformation quantity monitored by InSAR, and the feasibility of applying the method to the surface deformation caused by underground mining is proved. The amount of change in the terrain elevation is the amount of surface deformation. The InSAR monitoring value is the surface deformation quantity monitored by the InSAR.
According to the method for inverting the underground mining parameters based on the InSAR and the Okada models, which is disclosed by the embodiment of the application, the surface deformation caused by underground mining is acquired through a time sequence synthetic aperture radar interferometry technology, and denser original data are provided for inversion. In addition, aiming at the working face of the mining area, under the condition of unknown mining parameters or known few mining parameters, an Okada model of underground mining surface deformation is established, and the original observation data acquired by the synthetic aperture radar is utilized to obtain reliable actual mining parameters through a Bayesian inversion algorithm, thereby being beneficial to scientifically appointing an underground mining plan, monitoring cross-border mining and the like.
Fig. 10 is a system architecture diagram for inverting subsurface mining parameters based on InSAR and Okada models in accordance with exemplary embodiments of the present application.
As shown in fig. 10, the present application further proposes a system for inverting subsurface mining parameters based on InSAR and Okada models, which may include: the system comprises a synthetic aperture radar 1, a function relation establishing module 2 and an inversion module 3. The synthetic aperture radar 1 is used for acquiring the surface deformation quantity of a mining area by utilizing an InSAR technology. And the functional relation establishing 2 module is used for establishing a functional relation between the surface deformation quantity and the model parameters. And the inversion module 3 is used for obtaining actual mining parameters according to the surface deformation quantity and the functional relation and by adopting a Bayesian inversion algorithm.
In some embodiments, the step of performing the synthetic aperture radar 1 may comprise: acquiring a plurality of single-view complex images with coherence including a mine area through repeated orbits by a synthetic aperture radar antenna. Reading a single-view complex image. And cutting out the single-view complex image of the mining area from the single-view complex image. And carrying out data processing on the single-vision complex image of the mining area to obtain the visual line direction surface deformation quantity of the mining area, wherein the data processing comprises track encryption, image registration, baseline analysis, generation of a differential interference graph and interference phase time sequence analysis.
In some embodiments, the step of executing the functional relationship establishing module 2 may include: an Okada model is constructed relating to the deformation of the subsurface mining surface, wherein the Okada model includes model parameters of a face of the mine area. And constructing a ground surface three-dimensional deformation formula by using the model parameters, wherein the ground surface three-dimensional deformation formula represents the east-west deformation quantity, the south-north deformation quantity and the vertical deformation quantity of the ground surface point of the mining area under the condition of the model parameters.
In some embodiments, the performing step of the inversion module 3 may include: a mathematical relationship between the amount of surface deformation and the model parameters is determined. Calculating the prior probability density of the likelihood function and the model parameter by combining the function relation between the surface deformation quantity and the model parameter; and obtaining posterior probability density of the model parameters according to Bayes' theorem, wherein a group of model parameters with the maximum likelihood function corresponding to the posterior probability density are inverted optimal model parameters of the mining area, namely actual mining parameters based on the surface deformation.
The above system of the present application is a corresponding step for implementing the method for inverting underground mining parameters based on the InSAR and Okada models of the present application, and therefore, specific implementation principles and implementation steps of each module may refer to the above, and are not described herein again.
According to the system for inverting the underground mining parameters based on the InSAR and the Okada models, which is disclosed by the embodiment of the application, the surface deformation caused by underground mining is acquired through a time sequence synthetic aperture radar interferometry technology, so that denser raw data are provided for inversion. In addition, aiming at the working face of the mining area, under the condition of unknown mining parameters or known few mining parameters, an Okada model of underground mining surface deformation is established, and the original observation data acquired by the synthetic aperture radar is utilized to obtain reliable actual mining parameters through a Bayesian inversion algorithm, thereby being beneficial to scientifically appointing an underground mining plan, monitoring cross-border mining and the like.
The present application also provides a computer device, such as a smart phone, a tablet computer, a notebook computer, a desktop computer, a rack server, a blade server, a tower server or a rack server (including an independent server or a server cluster composed of a plurality of servers) capable of executing programs, and the like. The computer devices of the present application include at least, but are not limited to: a memory, a processor communicatively coupled to each other via a system bus.
In this embodiment, the memory (i.e., the readable storage medium) includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the memory may be an internal storage unit of the computer device, such as a hard disk or a memory of the computer device. In other embodiments, the memory may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), etc. provided on the computer device. Of course, the memory may also include both internal and external storage devices for the computer device. In the present embodiment, the memory is generally used for storing an operating system and various types of application software installed in the computer device. In addition, the memory may also be used to temporarily store various types of data that have been output or are to be output.
The processor may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor is typically used to control the overall operation of the computer device.
Specifically, in this embodiment, the processor is configured to execute the program of the method for updating configuration stored in the processor, and when the program of the method for updating configuration is executed, the following steps are implemented: and acquiring the surface deformation quantity of the mining area by utilizing an InSAR technology. And constructing a functional relation between the surface deformation quantity and the model parameters. And obtaining actual mining parameters by adopting a Bayesian inversion algorithm according to the surface deformation variables and the functional relation.
The specific implementation process of the above method steps can be referred to a method for inverting underground mining parameters based on InSAR and Okada models, and repeated description is omitted here.
The present application also provides a computer readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App, etc., having stored thereon a computer program that when executed by a processor implements the method steps of:
and acquiring the surface deformation quantity of the mining area by utilizing an InSAR technology. And constructing a functional relation between the surface deformation quantity and the model parameters. And obtaining actual mining parameters by adopting a Bayesian inversion algorithm according to the surface deformation variables and the functional relation.
The specific implementation process of the above method steps can be referred to a method for inverting underground mining parameters based on InSAR and Okada models, and repeated description is omitted here.
The objects, technical solutions and advantageous effects of the present invention are further described in detail with reference to the above-described embodiments. It should be understood that the above description is only a specific embodiment of the present invention, and is not intended to limit the present invention. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.
Claims (10)
1. A method for inverting underground mining parameters based on InSAR and Okada models, comprising:
acquiring the surface deformation quantity of a mining area by utilizing an InSAR technology;
constructing a functional relation between the surface deformation quantity and the model parameter; and
and obtaining actual mining parameters according to the surface deformation quantity and the function relation by adopting a Bayesian inversion algorithm.
2. The method for inverting underground mining parameters based on InSAR and Okada models according to claim 1, the InSAR technique being used to obtain the amount of surface deformation of the mine, comprising:
acquiring, by a synthetic aperture radar antenna, a plurality of single-view complex images with coherence including the mine area through a repetitive orbit;
reading the single-view complex image;
cropping out a single-view complex image of the mining area from the single-view complex image; and
and carrying out data processing on the single-vision complex image of the mining area to obtain the visual line direction surface deformation quantity of the mining area, wherein the data processing comprises track encryption, image registration, baseline analysis, generation of a differential interference graph and interference phase time sequence analysis.
3. The method for inverting underground mining parameters based on InSAR and Okada models according to claim 1, said constructing a functional relationship between said surface deformation quantities and model parameters, comprising:
constructing an Okada model for subsurface mining surface deformation, wherein the Okada model comprises model parameters; and
and constructing a surface three-dimensional deformation formula by using the model parameters, wherein the surface three-dimensional deformation formula represents the east-west deformation quantity, the south-north deformation quantity and the vertical deformation quantity of the surface point of the mining area under the condition of the model parameters.
4. The method for inverting underground mining parameters based on InSAR and Okada models according to claim 3, characterized in that the surface three-dimensional deformation formula is:
wherein,
a vector of parameters of the model is represented,
an east-west deformation quantity representing the earth surface point of the mining area under the condition of the model parameter vector,
representing north and south deformation quantities of the mine surface points under the condition of the model parameter vector,
representing the amount of vertical deformation of the mine surface points under the condition of the model parameter vector,
is the position coordinate of the surface point along the fault strike in the fault coordinate system of the Okada model,
is the position coordinate of the surface point along the direction vertical to the fault in the fault coordinate system of the Okada model,
the dip angle of the mine working face, d the depth of the mine working face, W the width of the mine working face, L the length of the mine working face, and (x, y, z) the position coordinates of the earth surface point in a local cartesian rectangular coordinate system.
5. The method for inverting underground mining parameters based on InSAR and Okada models according to claim 4, wherein actual mining parameters are obtained according to the surface deformation quantity and the functional relation by adopting a Bayesian inversion algorithm, and the method is characterized by comprising the following steps:
determining a mathematical relationship between the surface deformation quantity and the model parameters on the basis of the position coordinates of the surface points; and
and obtaining the model parameter with the maximum likelihood function corresponding to the posterior probability density as the actual mining parameter by adopting a Bayesian inversion algorithm according to the mathematical relationship between the surface deformation quantity and the model parameter and combining the function relationship.
6. A system for inverting underground mining parameters based on InSAR and Okada models, comprising:
the synthetic aperture radar is used for acquiring the surface deformation quantity of the mining area by utilizing an InSAR technology;
the functional relationship establishing module is used for establishing a functional relationship between the surface deformation quantity and the model parameters; and
and the inversion module is used for obtaining actual mining parameters according to the surface deformation quantity and the functional relation and by adopting a Bayesian inversion algorithm.
7. The InSAR and Okada model based system for inverting underground mining parameters of claim 6, wherein the synthetic aperture radar performing step comprises:
acquiring, by a synthetic aperture radar antenna, a plurality of single-view complex images with coherence including the mine area through a repetitive orbit;
reading the single-view complex image;
cropping out a single-view complex image of the mining area from the single-view complex image; and
and carrying out data processing on the single-vision complex image of the mining area to obtain the visual line direction surface deformation quantity of the mining area, wherein the data processing comprises track encryption, image registration, baseline analysis, generation of a differential interference graph and interference phase time sequence analysis.
8. The InSAR and Okada model based system for inverting underground mining parameters of claim 6, wherein the functional relationship building module is executed by the steps of:
constructing an Okada model for subsurface mining surface deformation, wherein the Okada model comprises model parameters; and
and constructing a surface three-dimensional deformation formula by using the model parameters, wherein the surface three-dimensional deformation formula represents the east-west deformation quantity, the south-north deformation quantity and the vertical deformation quantity of the surface point of the mining area under the condition of the model parameters.
9. A computer device, the computer device comprising: memory, processor and computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1 to 5 are implemented when the processor executes the program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 5.
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