CN116205028A - Three-dimensional initial ground stress field inversion method and related equipment - Google Patents

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

A three-dimensional initial geostress field inversion method and related equipment

Technical Field

The present invention relates to the field of numerical back analysis of stress fields, and more specifically, to a three-dimensional initial geostress field inversion method and related equipment.

Background Art

Geostress is a key parameter in underground engineering excavation construction, stability analysis and surrounding rock support design, because its size and direction directly determine the scale, spatial distribution and risk level of deformation and damage of surrounding rock in underground engineering. Therefore, before excavation of underground engineering (especially deep large-scale complex underground engineering), it is very important to accurately obtain the three-dimensional initial geostress field of rock mass in the engineering area for support control and safety assessment of deep underground rock engineering.

The current technology relies on a combination of numerical simulation and field measurement to determine the initial geostress field of the engineering area. However, the current method regards each influencing factor as an independent variable to establish a geostress field inversion mathematical model containing all variables. The existence of redundant variables makes the mathematical model complicated, the variable weight prediction is distorted, the calculation workload is heavy, and the formation mechanism of the stress field inferred from this is also questionable.

Summary of the invention

A series of simplified concepts are introduced in the Summary of the Invention, which will be further described in detail in the Detailed Description of the Invention. The Summary of the Invention does not mean to attempt to define the key features and essential technical features of the claimed technical solution, nor does it mean to attempt to determine the scope of protection of the claimed technical solution.

In order to improve the accuracy and computational efficiency of the numerical back analysis of the initial stress field, in a first aspect, the present invention proposes a three-dimensional initial geostress field inversion method, the method comprising:

Establish a three-dimensional geological model of the target area based on geological exploration data;

The three-dimensional geological model is simulated and analyzed by numerical simulation software to obtain inversion independent variables of the geostress field, and a geostress field inversion mathematical model is constructed by a stepwise regression method according to the correlation between the inversion independent variables of the geostress field, wherein the inversion mathematical model of the geostress field includes independent variables significantly correlated with the geostress field, constants and weight coefficients corresponding to the significantly correlated independent variables, and the inversion independent variables are stress components at the stress measuring point positions;

Eliminate the constants in the above-mentioned geostress field inversion mathematical model, and modify the weight coefficients of the above-mentioned significant variables based on sensitivity analysis to obtain the optimal geostress field mathematical model and the inversion initial geostress field;

The correctness of the inverted geostress field is evaluated based on measured geological data, including stress tensor components, principal stress magnitudes and orientations, and cavern damage conditions.

Optionally, the above geological exploration data includes surface information, stratum information and fault information.

Optionally, the three-dimensional geological model is simulated and analyzed using numerical simulation software to obtain independent variables for geostress field inversion, and a geostress field inversion mathematical model is constructed through stepwise regression according to the correlation between the independent variables for geostress field inversion, including:

The three-dimensional geological model is simulated and analyzed using numerical simulation software to obtain independent variables of the geostress field;

Based on the stepwise regression method, the independent variables and constants significantly correlated with the geostress field are screened, and the weight coefficients corresponding to the above-mentioned significantly correlated independent variables are obtained;

The above-mentioned inversion mathematical model of the geostress field is constructed based on the above-mentioned independent variables, the above-mentioned constants and the above-mentioned weight coefficients.

Optionally, the above method further includes:

Based on the Pearson correlation coefficients between the different inversion independent variables and between the inversion independent variables and the dependent variables, the correlations between the independent variables and the dependent variables are obtained, wherein the dependent variable is the stress field formed by the measured data;

Based on the stepwise regression method, the independent variables significantly correlated with the dependent variable were determined as the above-mentioned independent variables significantly correlated with the geostress field.

Optionally, the above method further includes:

The significance of each independent variable was determined by a stepwise regression method based on the following formula:

是上述仿真应力源，

是上述实测应力源的平均值，x_i是上述实测应力源，m是上述显著应力源的数量，n是上述实测应力源对应的实测样本数量。In the formula, ESS is the regression sum of squares, RSS is the residual sum of squares,

is the above simulation stress source,

is the average value of the above measured stress source, _xi is the above measured stress source, m is the number of the above significant stress sources, and n is the number of measured samples corresponding to the above measured stress source.

Optionally, the above-mentioned constants in the inversion mathematical model of the geostress field are eliminated, and the weight coefficients of the above-mentioned significant variables are corrected based on sensitivity analysis to obtain the optimal mathematical model of the geostress field and the inversion initial geostress field, including:

Eliminate the constants in the above mathematical model of geostress field;

Based on the 95% confidence interval of the independent variable coefficient obtained by stepwise regression, the weight coefficients of the above significant variables were modified to obtain the inverted initial geostress field.

Optionally, the correctness of the inverted geostress field is evaluated based on measured geological data, including:

Verify the correctness of the inversion of stress tensors between measured and predicted stress tensors based on stress tensor components, principal stress magnitudes and orientations;

Numerical simulation of cavern excavation is carried out based on the inverted geostress field and compared with the on-site cavern damage to verify the correctness of the inverted stress field.

In a second aspect, the present application also provides a three-dimensional initial geostress field inversion device, including:

A geological model building unit, used to build a three-dimensional geological model of the target area based on geological exploration data;

A mathematical model construction unit is used to simulate and analyze the above-mentioned three-dimensional geological model using numerical simulation software to obtain inversion independent variables of the geostress field, and to construct an inversion mathematical model of the geostress field by a stepwise regression method according to the correlation between the inversion independent variables of the geostress field, wherein the inversion mathematical model of the geostress field includes independent variables significantly correlated with the geostress field, constants and weight coefficients corresponding to the above-mentioned independent variables significantly correlated with the geostress field, and the above-mentioned inversion independent variables are stress components at the positions of stress measuring points;

A correction unit is used to eliminate the constants in the above-mentioned geostress field inversion mathematical model, and to correct the weight coefficients of the above-mentioned significant variables based on sensitivity analysis to obtain the optimal geostress field mathematical model and the inversion initial geostress field;

The verification unit is used to evaluate the correctness of the inverted geostress field based on measured geological data, wherein the measured geological data include stress tensor components, principal stress magnitude and orientation, and cavern damage conditions.

In a third aspect, an electronic device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is used to implement the steps of the three-dimensional initial geostress field inversion method of any one of the above-mentioned first aspects when executing the computer program stored in the memory.

In a fourth aspect, the present invention further proposes a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the three-dimensional initial geostress field inversion method of any one of the above items in the first aspect is implemented.

In summary, a three-dimensional initial geostress field inversion method proposed in an embodiment of the present application includes: establishing a three-dimensional geological model of the target area based on geological exploration data; using numerical simulation software to simulate and analyze the three-dimensional geological model to obtain geostress field inversion independent variables, and constructing a geostress field inversion mathematical model through a stepwise regression method based on the correlation between the geostress field inversion independent variables; eliminating constants in the geostress field inversion mathematical model, and correcting the weight coefficients of significant variables based on sensitivity analysis to obtain the optimal mathematical model of the geostress field and the inverted initial geostress field; and evaluating the correctness of the inverted geostress field based on measured geological data. The three-dimensional initial geostress field inversion method proposed in this application solves the correlation problem between geostress inversion independent variables caused by complex geological environments. After eliminating insignificant variables through stepwise regression, the physical meaning of the regression model is clearer, the prediction is more accurate, and the calculation workload is greatly reduced.

The stress field inversion method of the present invention, other advantages, objectives and features of the present invention will be reflected in part through the following description, and in part will be understood by those skilled in the art through research and practice of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present specification. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

FIG1 is a schematic diagram of a stress field inversion method provided in an embodiment of the present application;

FIG2 is a schematic diagram of a stress source provided in an embodiment of the present application;

FIG3 is a schematic diagram of a three-dimensional geological model for geostress inversion provided in an embodiment of the present application;

FIG4 is a schematic diagram of a curve showing a change in the optimal variable coefficient and the residual sum of squares in a sensitivity analysis provided in an embodiment of the present application;

FIG5 is a schematic diagram of a Pearson correlation coefficient matrix of independent variables of geostress inversion provided in an embodiment of the present application;

FIG6 is a schematic diagram of stress components measured and predicted by inversion provided in an embodiment of the present application;

FIG7 is a schematic diagram of a maximum principal stress orientation measured and inversely predicted according to an embodiment of the present application;

FIG8 is a schematic diagram of a maximum principal stress inclination angle measured and inverted predicted in an embodiment of the present application;

FIG9 is a schematic diagram of the actual location of cavern spalling and damage in a target area provided by an embodiment of the present application;

FIG10 is a schematic diagram of a simulation result of a cavern spalling failure position in a certain target area provided in an embodiment of the present application;

FIG11 is a schematic structural diagram of a stress field inversion device provided in an embodiment of the present application;

FIG12 is a schematic diagram of the structure of an electronic device for a stress field inversion method provided in an embodiment of the present application.

DETAILED DESCRIPTION

Compared with the traditional geostress field inversion method that ignores the correlation problem between independent variables and establishes a full variable model, the stress field inversion method proposed in the embodiment of the present application solves the correlation problem between independent variables of geostress inversion caused by complex geological environment. After eliminating insignificant variables through stepwise regression, the physical meaning of the regression model is clearer, the prediction is more accurate, and the calculation workload is greatly reduced.

The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

Due to the complexity and variability of geostress, in-situ geostress measurement technology is still the most direct method to understand the original rock stress distribution of engineering rock mass. However, the in-situ geostress measurement practice of many deep buried underground projects in recent years has shown that in large and complex deep underground projects, such as high side walls, large-span hydropower station caverns and long-distance, large-diameter transportation/hydraulic tunnels, it is difficult to implement all-round measurements due to complex geological conditions, high testing costs, and limited on-site measurement conditions, resulting in limited in-situ stress measurement data, which is difficult to reflect the macroscopic distribution law of the initial geostress field in the engineering area.

In order to solve the practical difficulty that limited in-situ stress measurement data are not representative enough, researchers have combined in-situ stress measurement technology and three-dimensional numerical simulation to propose a variety of inversion analysis methods based on incomplete in-situ stress data to estimate the complete three-dimensional in-situ stress field in the study area, including regression analysis methods, neural networks and genetic algorithms, displacement back analysis methods, grey theory methods, etc. Among them, since the regression coefficient solution of the regression inversion analysis method is unique, it is widely used to estimate the direction and size of the in-situ stress field in deep rock mass.

However, the current research on geostress inversion mainly focuses on how to determine the weights of each inversion independent variable, but ignores the fact that variables are collinear. Although considering all influencing factors as independent variables to establish a geostress field mathematical model containing all variables can basically meet the prediction function of the geostress field, the existence of redundant variables makes the mathematical model redundant, the variable weight prediction is distorted, the calculation workload is heavy, and the formation mechanism of the stress field inferred from this is also questionable and inaccurate. Therefore, how to avoid this correlation (collinearity) in the inversion analysis of the in-situ stress field of deep rock mass and the series of problems it causes is still a scientific problem that needs to be solved urgently.

In order to propose a more accurate stress field analysis method, please refer to FIG. 1, which is a schematic diagram of a stress field inversion method provided in an embodiment of the present application, which may specifically include:

S110, establishing a three-dimensional geological model of the target area based on geological exploration data;

For example, the target area may be a mountain where the target area is located, or a horizontal stratum, etc. The initial geological model is a simulation model constructed according to the geological characteristics of the target area.

S120, using numerical simulation software to simulate and analyze the three-dimensional geological model to obtain inversion independent variables of the geostress field, and constructing a geostress field inversion mathematical model through a stepwise regression method according to the correlation between the inversion independent variables of the geostress field, wherein the geostress field inversion mathematical model includes independent variables significantly correlated with the geostress field, constants, and weight coefficients corresponding to the significantly correlated independent variables, and the inversion independent variables are stress components at the stress measuring point positions;

Exemplarily, based on the three-dimensional geological model, numerical simulation software is used to calculate six initial basic working conditions to obtain the calculated stress component values of the stress measuring point positions under each working condition, that is, the inversion independent variables of the geostress field, and the correlations between the inversion independent variables of the geostress field and between the independent variables and the dependent variables are analyzed, and the insignificant variables are screened out by the stepwise regression method to construct a mathematical model of the geostress field, wherein the mathematical model of the stress field includes independent variables, independent variable weight coefficients and constants that are significantly correlated with the geostress field, and the dependent variable is the measured stress component value of the stress measuring point position. The six initial basic working conditions are, as shown in Figure 2, ① self-weight stress state; ② horizontal uniform extrusion tectonic movement in the X direction; ③ horizontal uniform extrusion tectonic movement in the Y direction; ④ uniform shear tectonic movement in the horizontal plane XY; ⑤ vertical uniform shear tectonic movement in the vertical plane of the X direction; ⑥ vertical uniform shear tectonic movement in the vertical plane of the Y direction.

S130, eliminating the constants in the above-mentioned geostress field inversion mathematical model, and modifying the weight coefficients of the above-mentioned significant variables based on sensitivity analysis to obtain the optimal geostress field mathematical model and the inversion initial geostress field;

Exemplarily, constants in the mathematical model of the geostress field are eliminated, and the weight coefficients of significant variables are modified based on sensitivity analysis to obtain the optimal mathematical model of the geostress field. Based on the optimal mathematical model of the geostress field, six initial basic working conditions are superimposed and calculated to obtain the inverted initial geostress field, wherein the weight coefficient is modified based on its 95% confidence interval, which can be determined during stepwise regression.

S140. Evaluate the correctness of the inverted geostress field based on measured geological data, wherein the measured geological data include stress tensor components, principal stress magnitudes and orientations, and cavern damage conditions.

Exemplarily, the correctness of the inverted geostress field is verified by comprehensive comparison based on the actual measurement of the stress measuring point locations, the inverted stress data, and the on-site cavern damage conditions, wherein the stress data include stress tensor components, principal stress magnitudes and orientations, and the on-site cavern damage conditions include surrounding rock deformation, spalling, etc.

In summary, the stress field inversion method proposed in the embodiment of the present application solves the correlation problem between independent variables of geostress inversion caused by complex geological environment, compared with the traditional geostress field inversion method that ignores the correlation problem between independent variables and establishes a full variable model. After eliminating insignificant variables through stepwise regression, the physical meaning of the regression model is clearer, the prediction is more accurate, and the calculation workload is greatly reduced.

In some examples, the geological exploration data include surface information, stratum information, and fault information.

Optionally, the geological exploration data is an initial geological model constructed based on the geological information obtained from actual measurements, that is, the surface, stratum and fault information according to the geological characteristics of the target area. As shown in Figure 3, the constructed initial geological model includes multiple fault types, B1, B2, B3 and B4, the shape and contour of the surface, the geological characteristics of the stratum, etc. The model height is 895m, the length is 781m, and the width is 692m.

In some examples, the three-dimensional geological model is simulated and analyzed using numerical simulation software to obtain independent variables for geostress field inversion, and a geostress field inversion mathematical model is constructed through stepwise regression according to the correlation between the independent variables for geostress field inversion, including:

The three-dimensional geological model is simulated and analyzed using numerical simulation software to obtain independent variables of the geostress field;

Based on the stepwise regression method, the independent variables and constants significantly correlated with the geostress field are screened, and the weight coefficients corresponding to the above-mentioned significantly correlated independent variables are obtained;

The above-mentioned inversion mathematical model of the geostress field is constructed based on the above-mentioned independent variables, the above-mentioned constants and the above-mentioned weight coefficients.

Exemplarily, significant variables are screened based on the measured stress data and the simulated stress data based on the stepwise regression method, and the influence of non-significant variables on the simulation results can be eliminated by using significant variables and their weight coefficients. Based on the three-dimensional geological model, six initial basic working conditions are calculated using numerical simulation software to obtain the calculated stress component values of the stress measuring point positions under each working condition, so as to obtain the inversion independent variables of the geostress field; based on the stepwise regression method, independent variables significantly correlated with the dependent variables are screened, and the weight coefficients corresponding to the significantly correlated independent variables are obtained, and the mathematical model of the geostress field is constructed based on the independent variables and the weight coefficients, and the mathematical model also contains constants.

In some examples, the method further includes:

Based on the Pearson correlation coefficients between the different inversion independent variables and between the inversion independent variables and the dependent variables, the correlations between the independent variables and the dependent variables are obtained, wherein the dependent variable is the stress field formed by the measured data;

Based on the stepwise regression method, the independent variables significantly correlated with the dependent variable were determined as the above-mentioned independent variables significantly correlated with the geostress field.

Optionally, based on the Pearson correlation coefficients between different inversion independent variables and between the inversion independent variables and dependent variables, the correlations between the independent variables and the correlations between the independent variables and the dependent variables are obtained, wherein the dependent variable is the measured stress component value at the stress measuring point; based on the stepwise regression method, the independent variables significantly correlated with the dependent variables are determined as the independent variables significantly correlated with the ground stress field. There may be 6 independent variables in total, namely gravity Ug, x-direction extrusion movement Ux, y-direction extrusion movement Uy, horizontal shear movement Uxy, y-z plane shear movement Uyz and x-z plane shear movement Uxz. The Pearson Correlation Coefficient is used to measure whether two data sets are on the same line. It is used to measure the linear relationship between fixed-distance variables. The regression model is introduced in order from high to low according to the Pearson correlation coefficient.

The significant independent variables were screened by stepwise regression method to obtain the optimal regression model. The specific steps are as follows:

A: Sort the Pearson correlation coefficients of the independent variables and the dependent variables from large to small as the order of introducing the independent variables in the stepwise regression;

B: Introduce the six independent variables into the regression model separately in the order described in A;

C: Use the F value to test the significance of the introduced independent variable. If the variable is significant, it will be retained in the regression model; if it is not significant, it will be eliminated;

D: After introducing a new independent variable, use the F value to test the significance of the existing independent variables in the regression model. If the variable is still significant, keep it; if not, remove it;

E: Repeat C and D until there are no significant independent variables to introduce into the regression model, at which point the optimal regression model containing only significant independent variables is obtained;

The stepwise regression method is a method for selecting independent variables in the linear regression model. Its essence is to consider whether the existing variables in the model can be eliminated when introducing each variable, and to obtain an optimized mathematical model of the geostress field when no significant independent variables can be introduced.

In summary, the stress field inversion method provided in the embodiment of the present application can eliminate independent variables with no significant influence based on the stepwise regression method, reduce the number of independent variables, improve calculation efficiency, reduce the influence of irrelevant independent variables, and improve calculation accuracy.

In some examples, the method further includes:

The significance of each independent variable was determined by a stepwise regression method based on the following formula:

是上述仿真应力源，

是上述实测应力源的平均值，x_i是上述实测应力源，m是上述显著应力源的数量，n是上述实测应力源对应的实测样本数量。In the formula, ESS is the regression sum of squares, RSS is the residual sum of squares,

is the above simulation stress source,

is the average value of the above measured stress source, _xi is the above measured stress source, m is the number of the above significant stress sources, and n is the number of measured samples corresponding to the above measured stress source.

Optionally, the significance discriminant value F of each independent variable can be obtained through formula (1), and the ratio of the regression sum of squares to the residual sum of squares (variance ratio) obeys the F distribution, and its degrees of freedom are m and n-m-1. For a specific confidence level α, when the F value of the variable satisfies formula (2), the variable is significant, that is, it has a significant impact on the dependent variable (actual stress field).

F＞F _1-α (m,nm-1) (2)

In summary, the stress field inversion method proposed in the embodiment of the present application can effectively distinguish the independent variables, that is, the significance of the influence of the stress source on the target area, by determining the significance discrimination value through statistical methods, and can effectively eliminate the influence of non-significant stress sources, thereby improving the calculation speed and improving the accuracy of the calculation.

In some examples, the above-mentioned constants in the above-mentioned geostress field inversion mathematical model are eliminated, and the weight coefficients of the above-mentioned significant variables are corrected based on sensitivity analysis to obtain the optimal mathematical model of the geostress field and the inversion initial geostress field, including:

Eliminate the constants in the above mathematical model of geostress field;

Based on the 95% confidence interval of the independent variable coefficient obtained by stepwise regression, the weight coefficients of the above significant variables were modified to obtain the inverted initial geostress field.

Exemplarily, the constant in the regression model is eliminated, and a sensitivity analysis is performed on the 95% confidence interval of the regression coefficient of the significant variable. As shown in Figure 4, when the residual sum of squares (RSS) is minimized, the optimal variable coefficient of the independent variable will be determined. This step is repeated until the optimal variable coefficients of all significant variables are determined. It should be noted that for the numerical inversion of the geostress field, the presence of constants in the regression model will lead to a series of problems such as distortion of the calculation results and non-convergence of the calculation. Therefore, in the actual inversion process, by eliminating constants, the calculation convergence time can usually be shortened and the calculation efficiency can be improved.

In summary, the stress field inversion method proposed in the embodiment of the present application proposes the influence of constants on the calculation results based on sensitivity analysis, which can shorten the convergence time and improve the calculation efficiency.

In some examples, the correctness of the inverted geostress field is evaluated based on measured geological data, including:

Verify the correctness of the inversion of stress tensors between measured and predicted stress tensors based on stress tensor components, principal stress magnitudes and orientations;

Numerical simulation of cavern excavation is carried out based on the inverted geostress field and compared with the on-site cavern damage to verify the correctness of the inverted stress field.

Exemplarily, the stress field inversion method of the present invention is used to determine the stress field of the target area, as shown in FIG5 , which is a scatter plot of the correlation coefficient matrix of the inverse analysis variables of a certain area; the diagonal line in the figure is a distribution histogram between two variables, where σ is the dependent variable, and U _x , U _y , U _g , U _xy , U _yz , U _xz are independent variables; the lower part of the diagonal line is a scatter plot of two variables and a linear fit is performed; the upper part of the diagonal line is the Person correlation coefficient between two variables (represented by the symbol r) and a significant mark is performed, where **. represents a significance level of 0.01, *. represents a significance level of 0.05, and the significance level (represented by the symbol α) refers to the probability or risk of being rejected when the original hypothesis is correct. Note: Due to the complex geological conditions, the distribution histogram of the two variables does not show a good normal distribution.

As shown in Figure 6, it is a schematic diagram of the comparison of the component values of the measured stress source and the simulated stress source at the measuring point, where the horizontal axis is the measuring point number, the vertical axis is the stress value, the unit is megapascal (MPa), the measured values are the measured stress source, and the predicted values are the simulated stress source. The values of the measured stress source and the simulated stress source are basically consistent, but the complex geological environment of the target area makes the error of the shear stress of the measuring point relatively large, but in general, the comparison shows that the stress field predicted by the numerical back analysis reflects the distribution characteristics of the stress field value described by the measured value.

The measured orientation of the maximum principal stress at each measuring point in a target area is shown in Figure 7, and the predicted orientation is shown in Figure 8. The maximum principal stress orientation at each measuring point in the predicted stress field is NW80° to EW, with an overall inclination toward the river valley and an inclination of about 30°, which is consistent with the measured results. The comparison shows that the stress field predicted by the numerical inverse analysis reflects the stress field orientation distribution characteristics described by the measured values.

The location of the spalling failure of the cavern in a certain target area is shown in Figure 9, and the location of the failure predicted by numerical simulation is shown in Figure 10. The predicted location and degree of failure of the cavern are characterized by the RFD (Rock Failure Degree) index. RFD is obtained by calculating the excavation process of the cavern through numerical simulation. When RFD is greater than 1, it indicates that the surrounding rock has been damaged; the larger the value, the more serious the damage. The surrounding rock damage revealed by the numerical simulation in Figure 10 is most obvious at the location of the spandrel on the right side of the cavern, which is consistent with the location of the spalling failure of the cavern on site. The comparison shows that the cavern damage obtained by the simulation calculation of the predicted stress field reflects the actual cavern damage characteristics on site.

In summary, the stress field inversion method proposed in the embodiment of the present application helps to better understand the in-situ geostress field formation mechanism of large-scale and complex deep underground projects, thereby providing a useful reference for optimizing excavation plans and support designs.

As shown in FIG11 , the embodiment of the present application further proposes a stress field inversion device, comprising:

A geological model building unit 21 is used to build a three-dimensional geological model of the target area based on geological exploration data;

The mathematical model construction unit 22 is used to simulate and analyze the above-mentioned three-dimensional geological model using numerical simulation software to obtain inversion independent variables of the geostress field, and to construct the geostress field inversion mathematical model through a stepwise regression method according to the correlation between the above-mentioned geostress field inversion independent variables, wherein the above-mentioned geostress field inversion mathematical model includes independent variables significantly correlated with the geostress field, constants and weight coefficients corresponding to the above-mentioned independent variables significantly correlated with the geostress field, and the above-mentioned inversion independent variables are stress components at the positions of stress measuring points;

A correction unit 23 is used to remove the constants in the above-mentioned geostress field inversion mathematical model, and to correct the weight coefficients of the above-mentioned significant variables based on sensitivity analysis to obtain the optimal geostress field mathematical model and the inversion initial geostress field;

The verification unit 24 is used to evaluate the correctness of the inverted geostress field based on the measured geological data, wherein the measured geological data includes stress tensor components, principal stress magnitude and orientation, and cavern damage conditions.

As shown in Figure 12, an embodiment of the present application also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 320 and executable on the processor. When the processor 320 executes the computer program 311, the steps of any method of the above-mentioned stress field inversion method are implemented.

Since the electronic device introduced in this embodiment is a device used to implement a stress field inversion device in the embodiment of the present application, based on the method introduced in the embodiment of the present application, the technical personnel in this field can understand the specific implementation mode of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present application is not introduced in detail here. As long as the technical personnel in this field implement the method in the embodiment of the present application, the equipment adopted by the technical personnel in this field belongs to the scope of protection of this application.

In a specific implementation process, when the computer program 311 is executed by a processor, any implementation method in the embodiment corresponding to FIG. 1 can be implemented.

It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

The present application is described with reference to the flowchart and/or block diagram of the method, device (system) and computer program product according to the embodiment of the present application. It should be understood that each process and/or box in the flowchart and/or block diagram, and the combination of the process and/or box in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for realizing the function specified in one process or multiple processes in the flowchart and/or one box or multiple boxes in the block diagram.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

An embodiment of the present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device executes the process of the stress field inversion method in the embodiment corresponding to FIG. 1 .

The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can 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 can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integration. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state drive (solid state disk, SSD)), etc.

Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple 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 into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

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.

Images