CN116205028A - Three-dimensional initial ground stress field inversion method and related equipment - Google Patents
Three-dimensional initial ground stress field inversion method and related equipment Download PDFInfo
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Abstract
The application discloses a three-dimensional initial ground stress field inversion method. The method comprises the following steps: establishing a three-dimensional geological model of the target area based on geological exploration data; performing simulation analysis on the three-dimensional geological model by using numerical simulation software to obtain ground stress field inversion independent variables, and constructing a ground stress field inversion mathematical model by a stepwise regression method according to the correlation between the ground stress field inversion independent variables; removing constants in the ground stress field inversion mathematical model, and carrying out weight coefficient correction on the significant variable based on sensitivity analysis to obtain a ground stress field optimal mathematical model and an inversion initial ground stress field; and evaluating the correctness of the inversion ground stress field based on the measured geological data. The three-dimensional initial ground stress field inversion method solves the problem of correlation among ground stress inversion independent variables caused by a complex geological environment, and after insignificant variables are removed through stepwise regression, the regression model is more definite in physical meaning, more accurate in prediction and greatly reduces the calculation workload.
Description
Technical Field
The present disclosure relates to the field of stress field numerical inverse analysis, and more particularly, to a three-dimensional initial ground stress field inversion method and related apparatus.
Background
The ground stress is a key parameter of underground engineering excavation construction, stability analysis and surrounding rock support design, because the size and direction of the ground stress directly determine the appearance scale, spatial distribution and risk degree of deformation and damage of surrounding rock of the underground engineering. Therefore, before the underground engineering (especially the deep large complex underground engineering) is excavated, the three-dimensional initial ground stress field of the rock mass in the engineering area is accurately obtained, and the method is very important for the support control and the safety evaluation of the underground deep rock mass engineering.
In the prior art, an initial ground stress field of an engineering area is determined by means of a method combining numerical simulation and field measurement, but in the currently adopted method, all influence factors are regarded as independent variables so as to establish a ground stress field inversion mathematical model containing all variables, redundant variables cause redundancy of the mathematical model, weight prediction distortion of the variables and heavy calculation workload, and a formation mechanism of the deduced stress field is doubtful.
Disclosure of Invention
In the summary, a series of concepts in a simplified form are introduced, which will be further described in detail in the detailed description. The summary of the invention is not intended to define the key features and essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
In order to improve accuracy and calculation efficiency of initial stress field numerical inverse analysis, in a first aspect, the invention provides a three-dimensional initial ground stress field inversion method, which comprises the following steps:
establishing a three-dimensional geological model of the target area based on geological exploration data;
performing simulation analysis on the three-dimensional geological model by using numerical simulation software to obtain a ground stress field inversion independent variable, and constructing a ground stress field inversion mathematical model by a stepwise regression method according to the correlation between the ground stress field inversion independent variables, wherein the ground stress field inversion mathematical model comprises an independent variable obviously related to a ground stress field, a constant and a weight coefficient corresponding to the independent variable obviously related to the ground stress field, and the inversion independent variable is a stress component at a stress measuring point position;
removing constants in the ground stress field inversion mathematical model, and carrying out weight coefficient correction on the significant variables based on sensitivity analysis to obtain a ground stress field optimal mathematical model and an inversion initial ground stress field;
and evaluating the correctness of the inversion ground stress field based on measured geological data, wherein the measured geological data comprises a stress tensor component, a main stress magnitude, an azimuth and a cavity damage condition.
Optionally, the geological exploration data includes surface information, stratum information and fault information.
Optionally, the simulating analysis of the three-dimensional geological model by using numerical simulation software to obtain the inversion independent variables of the ground stress field, and constructing the inversion mathematical model of the ground stress field by stepwise regression according to the correlation between the inversion independent variables of the ground stress field, including:
performing simulation analysis on the three-dimensional geological model by using numerical simulation software to obtain a ground stress field independent variable;
screening independent variables and constants obviously related to a ground stress field based on a stepwise regression method, and acquiring weight coefficients corresponding to the independent variables obviously related to the ground stress field;
and constructing the ground stress field inversion mathematical model based on the independent variable, the constant and the weight coefficient.
Optionally, the method further comprises:
based on the Pearson correlation coefficients of the inversion independent variables and the dependent variables, obtaining the correlation between the independent variables and the dependent variables, wherein the dependent variables are stress fields formed by measured data;
the independent variable significantly related to the dependent variable is determined as the independent variable significantly related to the ground stress field based on the stepwise regression method.
Optionally, the method further comprises:
the significance of each independent variable is determined by a stepwise regression method based on the following equation:
where ESS is the sum of squares of the regression, RSS is the sum of squares of the residuals,
is the simulation stress source, is->
Is the average value, x of the measured stress sources i The measured stress source is m is the number of the remarkable stress sources, and n is the number of measured samples corresponding to the measured stress source.
Optionally, the removing constants in the ground stress field inversion mathematical model, and performing weight coefficient correction on the significant variable based on sensitivity analysis to obtain a ground stress field optimal mathematical model and inversion initial ground stress field, including:
removing constants in the mathematical model of the ground stress field;
and (3) carrying out weight coefficient correction on the significant variable based on the 95% confidence interval of the independent variable coefficient obtained by stepwise regression so as to obtain an inversion initial ground stress field.
Optionally, the evaluating correctness of the inversion ground stress field based on the measured geological data includes:
verifying inversion correctness of the stress tensor between actual measurement and prediction based on the stress tensor component, the principal stress magnitude and the azimuth;
and carrying out the numerical simulation of the excavation of the cavern based on the inversion ground stress field, and comparing with the damage condition of the on-site cavern so as to verify the correctness of the inversion stress field.
The second aspect, the embodiment of the application also provides a three-dimensional initial ground stress field inversion device, which comprises:
the geological model construction unit is used for establishing a three-dimensional geological model of the target area based on geological exploration data;
a mathematical model construction unit, configured to perform simulation analysis on the three-dimensional geological model by using numerical simulation software to obtain a ground stress field inversion independent variable, and construct a ground stress field inversion mathematical model according to a correlation between the ground stress field inversion independent variables by a stepwise regression method, where the ground stress field inversion mathematical model includes an independent variable significantly related to a ground stress field, a constant, and a weight coefficient corresponding to the significantly related independent variable, and the inversion independent variable is a stress component at a stress measurement point position;
the correction unit is used for eliminating constants in the ground stress field inversion mathematical model, and carrying out weight coefficient correction on the significant variable based on sensitivity analysis to obtain a ground stress field optimal mathematical model and an inversion initial ground stress field;
and the verification unit is used for evaluating the correctness of the inversion ground stress field based on the actually measured geological data, wherein the actually measured geological data comprises a stress tensor component, a principal stress magnitude, an azimuth and a cavity damage condition.
In a third aspect, an electronic device, comprising: a memory, a processor and a computer program stored in and executable on the processor for performing the steps of the three-dimensional initial ground stress field inversion method according to any one of the first aspects described above when the computer program stored in the memory is executed.
In a fourth aspect, the invention also proposes a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the three-dimensional initial ground stress field inversion method of any of the first aspects.
In summary, the three-dimensional initial ground stress field inversion method provided by the embodiment of the application comprises the following steps: establishing a three-dimensional geological model of the target area based on geological exploration data; performing simulation analysis on the three-dimensional geological model by using numerical simulation software to obtain ground stress field inversion independent variables, and constructing a ground stress field inversion mathematical model by a stepwise regression method according to the correlation between the ground stress field inversion independent variables; removing constants in the ground stress field inversion mathematical model, and carrying out weight coefficient correction on the significant variable based on sensitivity analysis to obtain a ground stress field optimal mathematical model and an inversion initial ground stress field; and evaluating the correctness of the inversion ground stress field based on the measured geological data. The three-dimensional initial ground stress field inversion method solves the problem of correlation among ground stress inversion independent variables caused by a complex geological environment, and after insignificant variables are removed through stepwise regression, the regression model is more definite in physical meaning, more accurate in prediction and greatly reduces the calculation workload.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the specification. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
FIG. 1 is a schematic flow chart of a stress field inversion method according to an embodiment of the present application;
FIG. 2 is a schematic view of a stressor according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a three-dimensional geologic model for inversion of ground stress according to an embodiment of the present application;
fig. 4 is a schematic diagram of a variation curve of the sum of squares of the optimal variable coefficient and the residual error in the sensitivity analysis according to the embodiment of the present application;
FIG. 5 is a schematic diagram of a matrix of the inversion independent variable Pelson correlation coefficients of the ground stress according to the embodiment of the present application;
FIG. 6 is a schematic diagram of stress components of an actual measurement and inversion prediction provided in an embodiment of the present application;
FIG. 7 is a schematic diagram of a maximum principal stress azimuth predicted by actual measurement and inversion according to an embodiment of the present application;
FIG. 8 is a schematic diagram of a maximum principal stress dip angle predicted by actual measurement and inversion according to an embodiment of the present application;
FIG. 9 is a schematic diagram of an actual location of spalling damage in a target area in situ, according to an embodiment of the present disclosure;
FIG. 10 is a schematic diagram of simulation results of a spalling failure location of a cavity in a target area according to an embodiment of the present disclosure;
FIG. 11 is a schematic structural diagram of a stress field inversion apparatus according to an embodiment of the present disclosure;
fig. 12 is a schematic structural diagram of an electronic device for stress field inversion method according to an embodiment of the present application.
Detailed Description
Compared with the traditional ground stress field inversion method for establishing a full-variable model by neglecting the correlation problem among independent variables, the stress field inversion method provided by the embodiment of the application solves the correlation problem among ground stress inversion independent variables caused by a complex geological environment, and after the insignificant variable is removed through stepwise regression, the physical meaning of the regression model is more definite, the prediction is more accurate, and the calculation workload is greatly reduced.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims of this application and in the above-described figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application.
Due to the complexity and variability of the ground stress, in-situ ground stress measurement technology is still the most direct method for mastering the stress distribution of the original rock of the engineering rock mass. However, in-situ stress measurement practices of numerous deep underground projects in recent years show that in large-scale complex deep underground projects, such as high side walls, large-span hydropower station cavern groups and long-distance and large-hole-diameter traffic/hydraulic tunnels, the in-situ stress measurement data are limited due to complex geological conditions, high test cost and limited on-site measurement conditions, and the macroscopic distribution rule of an initial stress field of a project area is difficult to reflect because of the limited in-situ stress measurement data.
In order to solve the practical difficulty that limited in-situ stress measurement data are not representative enough, researchers combine in-situ stress measurement technology and three-dimensional numerical simulation, and put forward a plurality of inversion analysis methods for estimating a complete three-dimensional in-situ stress field of a research area based on incomplete in-situ stress data, including a regression analysis method, a neural network and genetic algorithm, a displacement inverse analysis method, a gray theory method and the like. The regression coefficient solution of the regression inversion analysis method has uniqueness, so that the regression inversion analysis method is widely applied to estimating the direction and the size of an in-situ stress field in a deep rock mass.
However, current research on ground stress inversion is focused mainly on how to determine the weights of the individual inversion arguments, ignoring the fact that the variables are co-linear. Although establishing a mathematical model of the ground stress field containing all the variables by considering each influencing factor as an independent variable can basically meet the prediction effect of the ground stress field, the existence of redundant variables makes the mathematical model redundant, the prediction of the weights of the variables is distorted, the calculation workload is heavy, and the formation mechanism of the presumed stress field is suspected to be inaccurate. Therefore, how to avoid this correlation (collinearity) and its resulting series of problems in the in situ stress field inversion analysis of deep rock masses remains a scientific problem that is still in need of solution.
In order to provide a more accurate stress field analysis method, please refer to fig. 1, which is a schematic flow chart of a stress field inversion method provided in an embodiment of the present application, the method may specifically include:
s110, establishing a three-dimensional geological model of a target area based on geological exploration data;
the target area may be a mountain where the target area is located, or may be an area such as a horizontal stratum. The initial geologic model is a simulation model constructed from geologic features of the target region.
S120, performing simulation analysis on the three-dimensional geological model by using numerical simulation software to obtain a ground stress field inversion independent variable, and constructing a ground stress field inversion mathematical model by a stepwise regression method according to the correlation between the ground stress field inversion independent variable, wherein the ground stress field inversion mathematical model comprises an independent variable obviously related to a ground stress field, a constant and a weight coefficient corresponding to the obviously related independent variable, and the inversion independent variable is a stress component at a stress measuring point position;
the method comprises the steps of performing calculation of 6 initial basic working conditions by using numerical value simulation software on the basis of a three-dimensional geological model to obtain calculated stress component values of stress measuring point positions under each working condition, namely ground stress field inversion independent variables, analyzing correlations between the ground stress field inversion independent variables and dependent variables, and screening insignificant variables in the ground stress field inversion independent variables by a stepwise regression method to construct a ground stress field mathematical model, wherein the stress field mathematical model comprises the independent variables, independent variable weight coefficients and constants which are significantly correlated with the ground stress field, the dependent variables are actual measurement stress component values of the stress measuring point positions, and the 6 initial basic working conditions are as shown in fig. 2, (1) dead weight stress states; (2) the X-direction horizontally and uniformly extrudes the structure movement; (3) the Y-direction horizontal uniform extrusion structure moves; (4) uniform shear configuration movement in the horizontal plane XY; (5) vertical uniform shear configuration motion in the X-direction vertical plane; (6) vertical uniform shear configuration motion in the Y-direction vertical plane.
S130, eliminating constants in the ground stress field inversion mathematical model, and carrying out weight coefficient correction on the significant variable based on sensitivity analysis to obtain a ground stress field optimal mathematical model and an inversion initial ground stress field;
illustratively, constants in the mathematical model of the ground stress field are eliminated, a significant variable is corrected based on sensitivity analysis to obtain an optimal mathematical model of the ground stress field, 6 kinds of initial basic working conditions are calculated in a superposition mode based on the optimal mathematical model of the ground stress field, and an inversion initial ground stress field is obtained, wherein the correction of the weight coefficient is performed based on a 95 confidence interval thereof, and the 95 confidence interval can be determined during stepwise regression.
S140, evaluating the correctness of the inversion ground stress field based on the actually measured geological data, wherein the actually measured geological data comprises a stress tensor component, a principal stress magnitude, an azimuth and a cavity damage condition.
The accuracy of the inversion ground stress field is verified based on actual measurement of the stress measuring point position, inversion stress data and on-site cavity damage conditions comprehensively, wherein the stress data comprises a stress tensor component, a main stress magnitude and an azimuth, and the on-site cavity damage conditions comprise surrounding rock deformation, spalling and the like.
In summary, compared with the traditional ground stress field inversion method for establishing a full-variable model by neglecting the correlation problem between independent variables, the stress field inversion method provided by the embodiment of the application solves the correlation problem between ground stress inversion independent variables caused by a complex geological environment, and after the insignificant variable is removed through stepwise regression, the physical meaning of the regression model is more definite, the prediction is more accurate, and the calculation workload is greatly reduced.
In some examples, the geological exploration data includes surface information, formation information, and fault information.
Optionally, the geological exploration data is an initial geological model constructed according to geological information obtained through actual measurement, that is, the earth surface, stratum and fault information according to geological features of a target area, and the initial geological model constructed is shown in fig. 3 and comprises various fault types, B1, B2, B3 and B4, shape contours of the earth surface, geological features of the stratum and the like, wherein the model height is 895m, the length 781m and the width 692m.
In some examples, the simulating analysis of the three-dimensional geologic model by numerical simulation software to obtain the ground stress field inversion independent variables includes:
performing simulation analysis on the three-dimensional geological model by using numerical simulation software to obtain a ground stress field independent variable;
screening independent variables and constants obviously related to a ground stress field based on a stepwise regression method, and acquiring weight coefficients corresponding to the independent variables obviously related to the ground stress field;
and constructing the ground stress field inversion mathematical model based on the independent variable, the constant and the weight coefficient.
By way of example, the significant variable is screened based on the stepwise regression method according to the measured stress data and the simulated stress data, and the effect of the non-significant variable on the simulation result can be eliminated by using the significant variable and the weight coefficient thereof. Calculating 6 initial basic working conditions by utilizing numerical simulation software based on a three-dimensional geological model to obtain calculated stress component values of stress measuring point positions under each working condition so as to obtain ground stress field inversion independent variables; and screening independent variables obviously related to the dependent variables based on a stepwise regression method, acquiring weight coefficients corresponding to the independent variables obviously related to the dependent variables, and constructing a mathematical model of the ground stress field based on the independent variables and the weight coefficients, wherein the mathematical model simultaneously comprises constants.
In some examples, the above method further comprises:
based on the Pearson correlation coefficients of the inversion independent variables and the dependent variables, obtaining the correlation between the independent variables and the dependent variables, wherein the dependent variables are stress fields formed by measured data;
the independent variable significantly related to the dependent variable is determined as the independent variable significantly related to the ground stress field based on the stepwise regression method.
Optionally, based on Pearson correlation coefficients between the different inversion independent variables and between the inversion independent variables and the dependent variables, obtaining correlation between the independent variables and the dependent variables, wherein the dependent variables are measured stress component values at stress measurement point positions; an independent variable that is significantly related to the dependent variable is determined as the independent variable that is significantly related to the ground stress field based on a stepwise regression method. The total of 6 independent variables is possible, namely gravity Ug, x-direction extrusion motion Ux, y-direction extrusion motion Uy, horizontal shearing motion Uxy, y-z plane shearing motion Uyz and x-z plane shearing motion Uxz. The Pearson correlation coefficient (Pearson Correlation Coefficient) is used to measure whether two data sets are above a line, and it is used to measure the linear relationship between distance variables. Sequentially introducing regression models according to the sequence of the Pearson correlation coefficient from high to low
The method comprises the following specific steps of:
a: sequencing the Pearson correlation coefficients of the independent variables and the dependent variables from big to small as the introduction sequence of the independent variables in stepwise regression;
b: introducing the 6 independent variables into the regression model according to the sequence A;
and C, checking the significance of the introduced independent variable by using an F value. If the variable is significant, then the variable is retained in the regression model; if not, rejecting;
d: after introducing a new argument, the F-value is used to check the significance of the existing argument in the regression model. If the variable is still significant, then the variable is retained; if not, rejecting the variable;
e: repeating C and D until no significant independent variable can be introduced in the regression model, and obtaining an optimal regression model only containing significant independent variable;
the stepwise regression method is an independent variable selection method of a linear regression model, and is essentially to consider whether or not an existing variable in the model can be eliminated when each variable is introduced, and to obtain an optimized ground stress field mathematical model when no significant independent variable can be introduced.
In summary, the stress field inversion method provided by the embodiment of the application can eliminate independent variables without obvious influence based on a stepwise regression method, reduce the number of the independent variables and improve the calculation efficiency; the influence of irrelevant independent variables is reduced, and the calculation accuracy is improved.
In some examples, the above method further comprises:
the significance of each independent variable is determined by a stepwise regression method based on the following equation:
where ESS is the sum of squares of the regression, RSS is the sum of squares of the residuals,
is the simulation stress source, is->
Is the average value, x of the measured stress sources i The measured stress source is m is the number of the remarkable stress sources, and n is the number of measured samples corresponding to the measured stress source. />
Alternatively, the significance discrimination value F for each independent variable can be found by the formula (1), the ratio of the sum of squares of the regression to the sum of squares of the residual (variance ratio) obeys the F assignment, and its degrees of freedom are m and n-m-1. For a certain confidence level α, when the F value of the variable satisfies equation (2), then the variable is significant, i.e., has a significant effect on the dependent variable (actual stress field).
F＞F 1-α (m,n-m-1) (2)
In summary, the stress field inversion method provided by the embodiment of the application determines the significance discrimination value through a statistical method, can effectively distinguish the independent variable, namely the significance of the influence of the stress source on the target area, and can effectively eliminate the influence of the non-significant stress source, so that the calculation speed is improved, and the calculation accuracy is improved.
In some examples, the rejecting constants in the ground stress field inversion mathematical model, and performing weight coefficient correction on the significant variable based on sensitivity analysis to obtain a ground stress field optimal mathematical model and inverting an initial ground stress field, includes:
removing constants in the mathematical model of the ground stress field;
and (3) carrying out weight coefficient correction on the significant variable based on the 95% confidence interval of the independent variable coefficient obtained by stepwise regression so as to obtain an inversion initial ground stress field.
Illustratively, constants in the regression model are removed and a sensitivity analysis is performed on the 95% confidence interval for the significant variable regression coefficients, as shown in FIG. 4, when the sum of squares Residual (RSS) is minimal, the optimal variable coefficients for the independent variables are determined, and by repeating this step until the optimal variable coefficients for all significant variables are determined. It should be noted that, for numerical inversion of the ground stress field, the existence of constants in the regression model can cause a series of problems such as distortion of calculation results and non-convergence of calculation, so in the actual inversion process, the calculation convergence time can be generally shortened by removing the constants, and the calculation efficiency is improved.
In summary, the stress field inversion method provided by the embodiment of the application provides the influence of the constant on the calculation result based on the sensitivity analysis, so that the convergence time can be shortened, and the calculation efficiency can be improved.
In some examples, assessing correctness of the inverted ground stress field based on the measured geological data includes:
verifying inversion correctness of the stress tensor between actual measurement and prediction based on the stress tensor component, the principal stress magnitude and the azimuth;
and carrying out the numerical simulation of the excavation of the cavern based on the inversion ground stress field, and comparing with the damage condition of the on-site cavern so as to verify the correctness of the inversion stress field.
Exemplary, the stress field of the target region is determined by the present stress field inversion method, as shown in FIG. 5, which is the inverse of the regionAnalyzing a variable correlation coefficient matrix scatter diagram; the diagonal line in the figure is the distribution histogram between the bivariates, where σ is the dependent variable, U x ,U y ,U g ,U xy ,U yz ,U xz Is an independent variable; the lower part of the diagonal is a bivariate scatter diagram and is subjected to linear fitting; the upper part of the diagonal is the Person correlation coefficient (denoted by symbol r) between the bivariates and marked with significance, where the representative significance level is 0.01, the representative significance level is 0.05, and the significance level (denoted by symbol α) refers to the probability or risk that the original assumption was correct. And (3) injection: the bivariate distribution histogram does not exhibit a good normal distribution due to the complexity of the geological conditions.
Fig. 6 is a schematic diagram showing the comparison of component values of a Measured stress source and a simulated stress source, wherein the abscissa is a Measured point number, the ordinate is a stress value, the unit is megapascals (MPa), measured values are Measured stress sources, and Predicted values are simulated stress sources. The measured stress source and the simulation stress source are basically consistent in numerical value, however, the complex geological environment of the target area leads to relatively large error of the measuring point shear stress, but in general, the stress field predicted by numerical inverse analysis reflects the stress field value distribution characteristics described by the measured value.
The measured azimuth of the maximum principal stress of each measuring point in a certain target area is shown in fig. 7, and the predicted azimuth is shown in fig. 8. The maximum principal stress azimuth of each measuring point of the predicted stress field is NW 80-EW direction, the overall trend is about 30 degrees, the predicted stress field is well matched with the actual measurement result, and comparison shows that the predicted stress field of numerical inverse analysis reflects the azimuth distribution characteristic of the stress field described by the actual measurement value.
The spalling damage position of the on-site cavern in a certain target area is shown in fig. 9, and the numerical simulation predicted damage position is shown in fig. 10. Predicting the damage position and damage degree of the cavity, representing the damage degree by using an RFD (Rock Failure Degree, rock damage degree) index, wherein the RFD is obtained by calculating a numerical simulation cavity excavation process, and when the RFD is greater than 1, the damage of surrounding rock is indicated; the larger the number, the more severe the damage. The numerical simulation in fig. 10 reveals that the surrounding rock damage is most pronounced in the right shoulder of the cavern, consistent with the spalling damage location of the on-site cavern. The comparison shows that the failure condition of the cavern obtained by simulation calculation according to the predicted stress field reflects the actual failure characteristics of the cavern on site.
In conclusion, the stress field inversion method provided by the embodiment of the application is beneficial to better understand the in-situ stress field formation mechanism of large-scale complex deep underground engineering, so that a beneficial reference is provided for optimizing an excavation scheme and a support design.
As shown in fig. 11, an embodiment of the present application further provides a stress field inversion apparatus, including:
a geologic model construction unit 21 for constructing a three-dimensional geologic model of the target area based on the geologic exploration data;
a mathematical model construction unit 22, configured to perform simulation analysis on the three-dimensional geological model by using numerical simulation software to obtain a ground stress field inversion independent variable, and construct a ground stress field inversion mathematical model according to a correlation between the ground stress field inversion independent variables by a stepwise regression method, where the ground stress field inversion mathematical model includes an independent variable significantly related to a ground stress field, a constant, and a weight coefficient corresponding to the significantly related independent variable, and the inversion independent variable is a stress component at a stress measurement point position;
a correction unit 23, configured to reject constants in the ground stress field inversion mathematical model, and perform weight coefficient correction on the significant variable based on sensitivity analysis to obtain a ground stress field optimal mathematical model and an inversion initial ground stress field;
and a verification unit 24 for evaluating the correctness of the inversion ground stress field based on measured geological data, wherein the measured geological data comprises a stress tensor component, a principal stress magnitude and azimuth and a cavity destruction condition.
As shown in fig. 12, the embodiment of the present application further provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored on the memory 320 and executable on the processor, where the processor 320 implements the steps of any of the stress field inversion methods described above when the computer program 311 is executed.
Since the electronic device described in this embodiment is a device for implementing a stress field inversion apparatus in this embodiment, based on the method described in this embodiment, those skilled in the art can understand the specific implementation of the electronic device in this embodiment and various modifications thereof, so how the electronic device implements the method in this embodiment will not be described in detail herein, and those skilled in the art should only implement the device for implementing the method in this embodiment, which is within the scope of protection of this application.
In a specific implementation, the computer program 311 may implement any of the embodiments corresponding to fig. 1 when executed by a processor.
In the foregoing embodiments, the descriptions of the embodiments are focused on, and for those portions of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Embodiments of the present application also provide a computer program product comprising computer software instructions which, when run on a processing device, cause the processing device to perform the flow of the stress field inversion method as in the corresponding embodiment of fig. 1.
The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). Computer readable storage media can be any available media that can be stored by a computer or data storage devices such as servers, data centers, etc. that contain an integration of one or more available media. Usable media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid State Disks (SSDs)), among others.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting thereof; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (10)
1. A method of three-dimensional initial ground stress field inversion comprising:
establishing a three-dimensional geological model of the target area based on geological exploration data;
performing simulation analysis on the three-dimensional geological model by using numerical simulation software to obtain a ground stress field inversion independent variable, and constructing a ground stress field inversion mathematical model by a stepwise regression method according to the correlation between the ground stress field inversion independent variable, wherein the ground stress field inversion mathematical model comprises an independent variable obviously related to a ground stress field, a constant and a weight coefficient corresponding to the obviously related independent variable, and the inversion independent variable is a stress component at a stress measuring point position;
removing constants in the ground stress field inversion mathematical model, and carrying out weight coefficient correction on the significant variable based on sensitivity analysis to obtain a ground stress field optimal mathematical model and an inversion initial ground stress field;
and evaluating the correctness of the inversion ground stress field based on measured geological data, wherein the measured geological data comprises a stress tensor component, a main stress magnitude, an azimuth and a cavity damage condition.
2. The method of claim 1, wherein the geological exploration data includes surface information, formation information, and fault information.
3. The method of claim 1, wherein simulating the three-dimensional geologic model using numerical simulation software to obtain ground stress field inversion arguments, constructing a ground stress field inversion mathematical model by stepwise regression from correlations between the ground stress field inversion arguments, comprises:
performing simulation analysis on the three-dimensional geological model by using numerical simulation software to obtain a ground stress field independent variable;
screening independent variables and constants obviously related to a ground stress field based on a stepwise regression method, and acquiring a weight coefficient corresponding to the independent variables obviously related to the ground stress field;
and constructing the ground stress field inversion mathematical model based on the independent variable, the constant and the weight coefficient.
4. The method as recited in claim 1, further comprising:
based on the Pearson correlation coefficients of the inversion independent variables and the dependent variables, obtaining the correlation between the independent variables and the dependent variables, wherein the dependent variables are stress fields formed by measured data;
an independent variable that is significantly related to the dependent variable is determined as the independent variable that is significantly related to the ground stress field based on a stepwise regression method.
5. The method as recited in claim 4, further comprising:
the significance of each independent variable is determined by a stepwise regression method based on the following equation:
where ESS is the sum of squares of the regression, RSS is the sum of squares of the residuals,
is the simulation stress source,/->
Is the average value of the measured stress sources, x i And the measured stress sources are, m is the number of the remarkable stress sources, and n is the number of measured samples corresponding to the measured stress sources.
6. The method of claim 1, wherein the rejecting constants in the ground stress field inversion mathematical model, performing weight coefficient correction on the salient variables based on sensitivity analysis to obtain a ground stress field optimal mathematical model and inverting an initial ground stress field, comprises:
removing constants in the mathematical model of the ground stress field;
and carrying out weight coefficient correction on the significant variable based on the 95% confidence interval of the independent variable coefficient obtained by stepwise regression so as to obtain an inversion initial ground stress field.
7. The method of claim 1, wherein evaluating the correctness of the inverted ground stress field based on measured geological data comprises:
verifying inversion correctness of the stress tensor between actual measurement and prediction based on the stress tensor component, the principal stress magnitude and the azimuth;
and carrying out the numerical simulation of the excavation of the cavern based on the inversion ground stress field, and comparing with the damage condition of the on-site cavern so as to verify the correctness of the inversion stress field.
8. A three-dimensional initial ground stress field inversion apparatus, comprising:
the geological model construction unit is used for establishing a three-dimensional geological model of the target area based on geological exploration data;
a mathematical model construction unit, configured to perform simulation analysis on the three-dimensional geological model by using numerical simulation software to obtain a ground stress field inversion independent variable, and construct a ground stress field inversion mathematical model according to a correlation between the ground stress field inversion independent variables by a stepwise regression method, where the ground stress field inversion mathematical model includes an independent variable significantly related to a ground stress field, a constant, and a weight coefficient corresponding to the significantly related independent variable, and the inversion independent variable is a stress component at a stress measurement point position;
the correction unit is used for eliminating constants in the ground stress field inversion mathematical model, and carrying out weight coefficient correction on the significant variable based on sensitivity analysis to obtain a ground stress field optimal mathematical model and an inversion initial ground stress field;
and the verification unit is used for evaluating the correctness of the inversion ground stress field based on the actually measured geological data, wherein the actually measured geological data comprises a stress tensor component, a principal stress magnitude, an azimuth and a cavity damage condition.
9. An electronic device, comprising: memory and processor, characterized in that the processor is adapted to carry out the steps of the three-dimensional initial ground stress field inversion method according to any of claims 1-7 when executing a computer program stored in the memory.
10. A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program, when executed by a processor, implements the three-dimensional initial ground stress field inversion method according to any of claims 1-7.
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