A three-dimensional initial ground stress field inversion method and related device

Abstract

This application discloses a three-dimensional initial geostress field inversion method. The method includes: establishing a three-dimensional geological model of the target area based on geological exploration data; performing simulation analysis on the three-dimensional geological model using numerical simulation software to obtain geostress field inversion independent variables; constructing a geostress field inversion mathematical model based on the correlation between the geostress field inversion independent variables using a stepwise regression method; removing constants from the geostress field inversion mathematical model; correcting the weight coefficients of significant variables based on sensitivity analysis to obtain the optimal geostress field mathematical model and the inverted initial geostress field; and evaluating the accuracy of the inverted geostress field based on measured geological data. The three-dimensional initial geostress field inversion method proposed in this application solves the problem of correlation between geostress inversion independent variables caused by complex geological environments. After removing insignificant variables through stepwise regression, the regression model has a clearer physical meaning, more accurate predictions, and a significantly reduced computational workload.

Description

Technical Field

[0001] This specification relates to the field of numerical inverse analysis of stress fields, and more specifically, to a three-dimensional initial geostress field inversion method and related equipment. Background Technology

[0002] In-situ stress is a key parameter for underground engineering excavation, stability analysis, and surrounding rock support design, as its magnitude and direction directly determine the scale, spatial distribution, and risk level of deformation and failure of the surrounding rock. Therefore, accurately obtaining the three-dimensional initial in-situ stress field of the rock mass in the engineering area before excavation (especially for deep, large, and complex underground engineering projects) is crucial for support control and safety assessment of deep underground rock mass engineering.

[0003] Current technology relies on a combination of numerical simulation and field measurement to determine the initial geostress field of an engineering area. However, the current method treats all influencing factors as independent variables to establish a geostress field inversion mathematical model that includes all variables. The presence of redundant variables makes the mathematical model cumbersome, the variable weights distorted in the prediction, and the computational workload heavy. Furthermore, the formation mechanism of the stress field inferred from this is also questionable. Summary of the Invention

[0004] The summary section introduces a series of simplified concepts, which will be further explained in detail in the detailed description section. The summary section of this invention is not intended to limit the key features and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0005] To improve the accuracy and computational efficiency of numerical inverse analysis of the initial stress field, in a first aspect, this invention proposes a three-dimensional initial geostress field inversion method, the method comprising:

[0006] Establish a three-dimensional geological model of the target area based on geological exploration data;

[0007] The above three-dimensional geological model is simulated and analyzed using numerical simulation software to obtain the independent variables of the geostress field inversion. Based on the correlation between the independent variables of the geostress field inversion, a geostress field inversion mathematical model is constructed using the stepwise regression method. The geostress field inversion mathematical model includes independent variables, constants, and weight coefficients corresponding to the independent variables that are significantly related to the geostress field. The inversion independent variables are the stress components at the stress measurement point locations.

[0008] By removing the constants from the above geostress field inversion mathematical model, and correcting the weight coefficients of the above significant variables based on sensitivity analysis, the optimal geostress field mathematical model and the inverted initial geostress field are obtained.

[0009] The accuracy of the inverted geostress field is evaluated based on measured geological data, which includes stress tensor components, principal stress values ​​and orientations, and cavern damage conditions.

[0010] Optionally, the above geological exploration data includes surface information, stratigraphic information, and fault information.

[0011] Optionally, the above-mentioned three-dimensional geological model is simulated and analyzed using numerical simulation software to obtain the independent variables for inversion of the geostress field. Based on the correlation between the independent variables for inversion of the geostress field, a stepwise regression is used to construct a mathematical model for inversion of the geostress field, including:

[0012] The above three-dimensional geological model was simulated and analyzed using numerical simulation software to obtain the independent variables of the geostress field.

[0013] The stepwise regression method was used to screen the independent variables and constants that are significantly related to the geostress field, and the weight coefficients corresponding to the above-mentioned significantly related independent variables were obtained.

[0014] The above-mentioned mathematical model for inverting the geostress field is constructed based on the above-mentioned independent variables, constants, and weighting coefficients.

[0015] Optionally, the above methods also include:

[0016] Based on the Pearson correlation coefficients between different inverted independent variables and between the inverted independent variables and the dependent variable, the correlation between independent variables and the correlation between independent variables and the dependent variable are obtained, wherein the dependent variable is the stress field formed by measured data.

[0017] Based on the stepwise regression method, the independent variables that are significantly correlated with the dependent variable are identified as the above-mentioned independent variables that are significantly correlated with the geostress field.

[0018] Optionally, the above methods also include:

[0019] The significance of each independent variable is determined using the stepwise regression method based on the following formula:

[0020]

[0021]

[0022]

[0023] In the formula, ESS is the regression sum of squares, and RSS is the residual sum of squares. The above-mentioned simulated stress source, It is the average value of the above measured stress sources, x i is the measured stress source mentioned above, m is the number of the significant stress sources mentioned above, and n is the number of measured samples corresponding to the measured stress sources mentioned above.

[0024] Optionally, the constants in the above-mentioned geostress field inversion mathematical model are removed, 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 initial geostress field inversion, including:

[0025] Remove the constants from the above mathematical model of the geostress field;

[0026] Based on the 95% confidence interval of the independent variable coefficients obtained by stepwise regression, the weight coefficients of the above significant variables are adjusted to obtain the inverted initial geostress field.

[0027] Optionally, the above assessment of the correctness of the inverted geostress field based on measured geological data includes:

[0028] The correctness of the stress tensor inversion between the measured and predicted values ​​is verified based on the stress tensor components, principal stress values, and orientation.

[0029] Numerical simulations of tunnel excavation were performed based on the inverted geostress field, and the results were compared with the actual tunnel failure on site to verify the accuracy of the inverted stress field.

[0030] Secondly, embodiments of this application also propose a three-dimensional initial geostress field inversion device, comprising:

[0031] Geological model building unit, used to build a three-dimensional geological model of the target area based on geological exploration data;

[0032] The mathematical model construction unit is used to simulate and analyze the above-mentioned three-dimensional geological model using numerical simulation software to obtain the independent variables of the geostress field inversion. Based on the correlation between the independent variables of the geostress field inversion, the mathematical model of geostress field inversion is constructed by stepwise regression. The mathematical model of geostress field inversion includes independent variables, constants, and weight coefficients corresponding to the independent variables that are significantly related to the geostress field. The inversion independent variables are the stress components at the stress measurement point locations.

[0033] The correction unit is used to remove constants from the above geostress field inversion mathematical model and correct the weight coefficients of the above significant variables based on sensitivity analysis to obtain the optimal geostress field mathematical model and the inverted initial geostress field.

[0034] The verification unit is used to evaluate the correctness of the inverted geostress field based on measured geological data, which includes stress tensor components, principal stress values ​​and orientations, and cavern damage conditions.

[0035] Thirdly, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program stored in the memory to implement the steps of the three-dimensional initial geostress field inversion method as described in any of the first aspects above.

[0036] Fourthly, the present invention also proposes a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the three-dimensional initial geostress field inversion method of any of the preceding claims of the first aspect.

[0037] In summary, the three-dimensional initial geostress field inversion method proposed in this application includes: establishing a three-dimensional geological model of the target area based on geological exploration data; performing simulation analysis on the three-dimensional geological model using numerical simulation software to obtain geostress field inversion independent variables; constructing a geostress field inversion mathematical model based on the correlation between the geostress field inversion independent variables using a stepwise regression method; removing constants from the geostress field inversion mathematical model; correcting the weight coefficients of significant variables based on sensitivity analysis to obtain the optimal geostress field mathematical model 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 problem of correlation between geostress inversion independent variables caused by complex geological environments. After removing insignificant variables through stepwise regression, the regression model has a clearer physical meaning, more accurate predictions, and a significantly reduced computational workload.

[0038] The stress field inversion method of the present invention, other advantages, objectives and features of the present invention will be apparent in part from the following description, and in part will be understood by those skilled in the art through study and practice of the present invention. Attached Figure Description

[0039] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit this specification. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0040] Figure 1 A schematic diagram of a stress field inversion method provided in this application embodiment;

[0041] Figure 2 A schematic diagram of a stress source provided in an embodiment of this application;

[0042] Figure 3 A schematic diagram of a three-dimensional geological model for geostress inversion provided in this application embodiment;

[0043] Figure 4This application provides a schematic diagram of the variation curves of the optimal variable coefficients and the sum of squared residuals in a sensitivity analysis.

[0044] Figure 5 A schematic diagram of the Pearson correlation coefficient matrix for geostress inversion provided in this application embodiment;

[0045] Figure 6 A schematic diagram of measured and inverted predicted stress components provided for an embodiment of this application;

[0046] Figure 7 A schematic diagram of the orientation of the maximum principal stress provided in this application embodiment;

[0047] Figure 8 A schematic diagram of the measured and inverted predicted maximum principal stress tilt angle provided for an embodiment of this application;

[0048] Figure 9 This application provides a schematic diagram illustrating the actual location of cavern spalling and damage in a target area, as part of an embodiment of the present application.

[0049] Figure 10 This application provides a schematic diagram of the simulation results of the location of cavern spalling and damage in a target area, as shown in the embodiments of this application.

[0050] Figure 11 A schematic diagram of a stress field inversion device provided in an embodiment of this application;

[0051] Figure 12 This is a schematic diagram of an electronic device structure provided by an embodiment of the stress field inversion method in this application. Detailed Implementation

[0052] Compared with the traditional geostress field inversion method that ignores the correlation between independent variables and establishes a full-variable model, the stress field inversion method proposed in this application solves the correlation problem between independent variables in geostress inversion caused by complex geological environment. After eliminating insignificant variables through stepwise regression, the regression model has a clearer physical meaning, more accurate prediction, and a significant reduction in computational workload.

[0053] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The technical solutions of the embodiments of this application will now be clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0054] Due to the complexity and variability of geostress, in-situ geostress measurement technology remains the most direct method for understanding the original stress distribution in engineering rock masses. However, recent in-situ geostress measurement practices in numerous deep underground engineering projects have shown that in large-scale, complex, deep underground projects, such as high sidewalls, large-span hydropower station cavern complexes, and long-distance, large-diameter transportation / hydraulic tunnels, it is difficult to implement comprehensive measurements due to complex geological conditions, high testing costs, and limited on-site measurement conditions. This results in limited in-situ stress measurement data, making it difficult to reflect the macroscopic distribution law of the initial geostress field in the engineering area.

[0055] To address the practical difficulty of insufficient representativeness of finite in-situ stress measurement data, researchers have combined in-situ stress measurement technology with three-dimensional numerical simulation to propose various inversion analysis methods for estimating the complete three-dimensional in-situ stress field of the study area based on incomplete in-situ stress data. These methods include regression analysis, neural networks and genetic algorithms, displacement inversion analysis, and grey theory. Among these, the regression inversion analysis method is widely used to estimate the direction and magnitude of the in-situ stress field in deep rock masses because the regression coefficients are unique.

[0056] However, current research on geostress inversion mainly focuses on determining the weights of each inversion variable, neglecting the fact that variables can exhibit collinearity. Although treating all influencing factors as independent variables to establish a geostress field mathematical model that includes all variables can basically satisfy the predictive function of the geostress field, the presence of redundant variables makes the mathematical model cumbersome, distorts the predicted variable weights, increases the computational workload, and makes the inferred stress field formation mechanism questionable and inaccurate. Therefore, how to avoid this correlation (collinearity) and the series of problems it causes in the in-situ stress field inversion analysis of deep rock masses remains an urgent scientific problem to be solved.

[0057] To propose a more accurate method for stress field analysis, please refer to [link to relevant documentation]. Figure 1 This is a schematic diagram of a stress field inversion method provided in an embodiment of this application, which may specifically include:

[0058] S110. Establish a three-dimensional geological model of the target area based on geological exploration data;

[0059] For example, the target area can be a mountain range or a horizontal stratum. The initial geological model is a simulation model constructed based on the geological characteristics of the target area.

[0060] S120. The above three-dimensional geological model is simulated and analyzed using numerical simulation software to obtain the independent variables of the geostress field inversion. Based on the correlation between the independent variables of the geostress field inversion, a geostress field inversion mathematical model is constructed using the stepwise regression method. The geostress field inversion mathematical model includes independent variables, constants, and weight coefficients corresponding to the independent variables that are significantly related to the geostress field. The inversion independent variables are the stress components at the stress measurement point locations.

[0061] For example, based on a three-dimensional geological model, numerical simulation software is used to calculate six initial basic working conditions to obtain the calculated stress component values ​​at the stress measurement points under each working condition, i.e., the independent variables of the geostress field inversion. The correlation between the independent variables of the geostress field inversion and between them and the dependent variable is analyzed, and insignificant variables are screened out using a stepwise regression method to construct a geostress field mathematical model. The stress field mathematical model includes independent variables significantly correlated with the geostress field, independent variable weight coefficients, and constants. The dependent variable is the measured stress component value at the stress measurement point location. The six initial basic working conditions are as follows: Figure 2 As shown, 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 X direction vertical plane; ⑥ vertical uniform shear tectonic movement in the Y direction vertical plane.

[0062] S130. Remove the constants from the above geostress field inversion mathematical model, and correct the weight coefficients of the above significant variables based on sensitivity analysis to obtain the optimal geostress field mathematical model and the inverted initial geostress field.

[0063] For example, constants in the geostress field mathematical model are removed, and the weight coefficients of significant variables are corrected based on sensitivity analysis to obtain the optimal geostress field mathematical model. Based on the optimal geostress field mathematical model, the superposition calculation of six initial basic working conditions is performed to obtain the inverted initial geostress field. The correction of the weight coefficients is based on their 95 confidence interval, which can be determined during stepwise regression.

[0064] S140. Evaluate the accuracy of the inverted geostress field based on measured geological data, including stress tensor components, principal stress values ​​and orientations, and cavern damage conditions.

[0065] For example, the correctness of the inverted geostress field is verified by comprehensively comparing the measured stress points with the inverted stress data and the on-site cavern damage conditions. The stress data includes stress tensor components, principal stress values ​​and orientations, and the on-site cavern damage conditions include surrounding rock deformation, spalling, etc.

[0066] In summary, the stress field inversion method proposed in this application, compared with the traditional geostress field inversion method that ignores the correlation problem between independent variables and establishes a full variable model, solves the correlation problem between independent variables in geostress inversion caused by complex geological environment. After stepwise regression to remove insignificant variables, the regression model has a clearer physical meaning, more accurate prediction, and a significantly reduced computational workload.

[0067] In some examples, the aforementioned geological exploration data includes surface information, stratigraphic information, and fault information.

[0068] Optionally, geological exploration data is an initial geological model constructed based on measured geological information, namely surface, strata, and fault information, according to the geological characteristics of the target area, such as... Figure 3 The aforementioned initial geological model includes various fault types, B1, B2, B3, and B4, the shape and outline of the surface, and the geological characteristics of the strata. The model is 895m high, 781m long, and 692m wide.

[0069] In some examples, the aforementioned three-dimensional geological model is simulated and analyzed using numerical simulation software to obtain the independent variables for inversion of the geostress field. Based on the correlation between the independent variables for inversion of the geostress field, a stepwise regression is used to construct a mathematical model for inversion of the geostress field, including:

[0070] The above three-dimensional geological model was simulated and analyzed using numerical simulation software to obtain the independent variables of the geostress field.

[0071] The stepwise regression method was used to screen the independent variables and constants that are significantly related to the geostress field, and the weight coefficients corresponding to the above-mentioned significantly related independent variables were obtained.

[0072] The above-mentioned mathematical model for inverting the geostress field is constructed based on the above-mentioned independent variables, constants, and weighting coefficients.

[0073] For example, significant variables are screened using stepwise regression based on measured and simulated stress data. By utilizing these significant variables and their weighting coefficients, the influence of insignificant variables on the simulation results can be eliminated. Based on a three-dimensional geological model, numerical simulation software is used to calculate six initial basic working conditions to obtain the calculated stress component values ​​at stress measurement points under each condition, thus obtaining the independent variables for inverting the geostress field. Independent variables significantly correlated with the dependent variable are screened using stepwise regression, and the weighting coefficients corresponding to these significantly correlated independent variables are obtained. Based on these independent variables and the weighting coefficients, a mathematical model of the geostress field is constructed, which also includes constants.

[0074] In some examples, the above method also includes:

[0075] Based on the Pearson correlation coefficients between different inverted independent variables and between the inverted independent variables and the dependent variable, the correlation between independent variables and the correlation between independent variables and the dependent variable are obtained, wherein the dependent variable is the stress field formed by measured data.

[0076] Based on the stepwise regression method, the independent variables that are significantly correlated with the dependent variable are identified as the above-mentioned independent variables that are significantly correlated with the geostress field.

[0077] Optionally, based on the Pearson correlation coefficients between different inverted independent variables and between the inverted independent variables and the dependent variable, the correlation between the independent variables and the dependent variable is obtained, wherein the dependent variable is the measured stress component value at the stress measurement point location; based on the stepwise regression method, the independent variables significantly correlated with the dependent variable are determined as the independent variables significantly correlated with the geostress field. There may be a total of six independent variables: gravity Ug, x-axis compressive motion Ux, y-axis compressive motion Uy, horizontal shear motion Uxy, yz-plane shear motion Uyz, and xz-plane shear motion Uxz. The Pearson correlation coefficient is used to measure whether two data sets are aligned; it measures the linear relationship between interval variables. The regression model is introduced sequentially according to the Pearson correlation coefficients from high to low.

[0078] The stepwise regression method is used to screen for significant independent variables and obtain the optimal regression model. The specific steps are as follows:

[0079] A: Sort the Pearson correlation coefficients of the independent and dependent variables from largest to smallest as the order in which the independent variables are introduced in stepwise regression;

[0080] B: Introduce the six independent variables into the regression model individually, following the order described in A;

[0081] C: Use the F-value to test the significance of the introduced independent variable. If the variable is significant, retain it in the regression model; otherwise, remove it.

[0082] 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 remains significant, retain it; otherwise, remove it.

[0083] E: Repeat C and D until there are no significant independent variables to introduce into the regression model. At this point, the optimal regression model containing only significant independent variables is obtained.

[0084] The stepwise regression method is a method for selecting independent variables in a linear regression model. Its essence is to consider whether existing variables in the model can be eliminated when introducing each variable, and to obtain an optimized mathematical model of the geostress field when there are no significant independent variables to introduce.

[0085] In summary, the stress field inversion method provided in this application can eliminate independent variables with no significant impact based on the stepwise regression method, reduce the number of independent variables, and improve computational efficiency; it can also reduce the influence of irrelevant independent variables and improve computational accuracy.

[0086] In some examples, the above method also includes:

[0087] The significance of each independent variable is determined using the stepwise regression method based on the following formula:

[0088]

[0089] In the formula, ESS is the regression sum of squares, and RSS is the residual sum of squares. The above-mentioned simulated stress source, It is the average value of the above measured stress sources, x i is the measured stress source mentioned above, m is the number of the significant stress sources mentioned above, and n is the number of measured samples corresponding to the measured stress sources mentioned above.

[0090] Optionally, the significance discriminant value F of each independent variable can be obtained through equation (1). The ratio of the regression sum of squares to the residual sum of squares (variance ratio) follows the F distribution, and its degrees of freedom are m and nm-1. For a specific confidence level α, when the F value of the variable satisfies equation (2), the variable is significant, that is, it has a significant impact on the dependent variable (actual stress field).

[0091] F > F 1-α (m,nm-1) (2)

[0092] In summary, the stress field inversion method proposed in this application uses statistical methods to determine the significance discrimination value, which can effectively distinguish the independent variable, i.e., the significance of the stress source's influence on the target area, and can effectively eliminate the influence of non-significant stress sources, thereby improving the calculation speed and accuracy.

[0093] In some examples, the constants in the above-mentioned geostress field inversion mathematical model are removed, 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 initial geostress field inversion, including:

[0094] Remove the constants from the above mathematical model of the geostress field;

[0095] Based on the 95% confidence interval of the independent variable coefficients obtained by stepwise regression, the weight coefficients of the above significant variables are adjusted to obtain the inverted initial geostress field.

[0096] For example, by removing constants from the regression model, a sensitivity analysis is performed on the 95% confidence intervals of the regression coefficients of significant variables, such as... Figure 4 As shown, the optimal coefficient of the independent variable is determined when the residual sum of squares (RSS) is minimized. This step is repeated until the optimal coefficients of all significant variables are determined. It should be noted that for numerical inversion of the geostress field, the presence of constants in the regression model can lead to a series of problems such as distorted calculation results and non-convergence. Therefore, in actual inversion processes, removing constants can usually shorten the convergence time and improve computational efficiency.

[0097] In summary, the stress field inversion method proposed in this application, based on sensitivity analysis, identifies the influence of constants on the calculation results, which can shorten the convergence time and improve computational efficiency.

[0098] In some examples, the above-mentioned assessment of the correctness of the inverted geostress field based on measured geological data includes:

[0099] The correctness of the stress tensor inversion between the measured and predicted values ​​is verified based on the stress tensor components, principal stress values, and orientation.

[0100] Numerical simulations of tunnel excavation were performed based on the inverted geostress field, and the results were compared with the actual tunnel failure on site to verify the accuracy of the inverted stress field.

[0101] For example, the stress field inversion method described herein is used to determine the stress field of the target region, such as... Figure 5 The figure shows a scatter plot of the correlation coefficient matrix of inverse analysis variables for a certain region; the diagonal lines in the figure are the distribution histograms between the two variables, where σ is the dependent variable and U... x U y U g U xyU yz U xz The independent variable is represented by ; the lower diagonal section shows a bivariate scatter plot with linear fitting; the upper diagonal section shows the Person correlation coefficient (denoted by r) between the two variables, marked with significance, where **. represents a significance level of 0.01, *. represents a significance level of 0.05, and the significance level (denoted by α) refers to the probability or risk of rejection when the null hypothesis is true. Note: Due to complex geological conditions, the bivariate distribution histogram does not exhibit a good normal distribution.

[0102] like Figure 6 The diagram shows a comparison of the component values ​​of the measured stress source and the simulated stress source at the measuring point. The horizontal axis represents the measuring point number, and the vertical axis represents the stress value in megapascals (MPa). Measured values ​​represent the measured stress source, and predicted values ​​represent the simulated stress source. The measured and simulated stress source values ​​are basically consistent. However, the complex geological environment of the target area leads to a relatively large error in the shear stress at the measuring point. Overall, the comparison shows that the stress field predicted by the numerical back analysis reflects the stress field distribution characteristics described by the measured values.

[0103] The measured orientations of the maximum principal stress at each measuring point in a certain target area are as follows: Figure 7 As shown, the predicted direction is as follows: Figure 8 As shown, the predicted stress field has maximum principal stress orientations at each measuring point from NW80° to EW, generally dipping towards the valley at an angle of approximately 30°. These results are in good agreement with the measured values, indicating that the stress field predicted by the numerical back analysis reflects the orientational distribution characteristics of the stress field described by the measured values.

[0104] Location of cavern spalling and damage in a certain target area, as shown in the figure Figure 9 As shown, the numerical simulation predicts the location of the damage. Figure 10 As shown. The location and extent of cavern failure are predicted using the RFD (Rock Failure Degree) index. RFD is obtained through numerical simulation of the cavern excavation process. When RFD is greater than 1, it indicates that the surrounding rock has failed; the larger the value, the more severe the failure. Figure 10 The numerical simulation revealed that the surrounding rock failure was most pronounced at the right abutment of the cavern, consistent with the location of the spalling failure in the actual cavern. The comparison shows that the cavern failure calculated based on the predicted stress field reflects the actual cavern failure characteristics on site.

[0105] In summary, the stress field inversion method proposed in this application helps to better understand the formation mechanism of in-situ stress field in large, complex, deep underground engineering projects, thereby providing a useful reference for optimizing excavation schemes and support designs.

[0106] like Figure 11 As shown in the embodiments of this application, a stress field inversion device is also proposed, comprising:

[0107] Geological model building unit 21 is used to build a three-dimensional geological model of the target area based on geological exploration data;

[0108] The mathematical model construction unit 22 is used to perform simulation analysis on the above three-dimensional geological model using numerical simulation software to obtain the independent variables of the geostress field inversion. Based on the correlation between the independent variables of the geostress field inversion, a geostress field inversion mathematical model is constructed using the stepwise regression method. The geostress field inversion mathematical model includes independent variables that are significantly related to the geostress field, constants, and weight coefficients corresponding to the independent variables that are significantly related to the geostress field. The inversion independent variables are the stress components at the stress measurement point locations.

[0109] Correction unit 23 is used to remove constants in the above-mentioned geostress field inversion mathematical model and correct the weight coefficients of the above-mentioned significant variables based on sensitivity analysis to obtain the optimal mathematical model of geostress field and the inverted initial geostress field.

[0110] Verification unit 24 is used to evaluate the correctness of the inverted geostress field based on measured geological data, which includes stress tensor components, principal stress values ​​and orientations, and cavern damage conditions.

[0111] like Figure 12 As shown, this application embodiment 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, it implements the steps of any of the above-described stress field inversion methods.

[0112] Since the electronic device described in this embodiment is the device used to implement a stress field inversion device in the embodiments of this application, those skilled in the art can understand the specific implementation method and various variations of the electronic device in this embodiment based on the method described in the embodiments of this application. Therefore, how the electronic device implements the method in the embodiments of this application will not be described in detail here. Any device used by those skilled in the art to implement the method in the embodiments of this application is within the scope of protection of this application.

[0113] In practical implementation, when the computer program 311 is executed by the processor, it can achieve the following: Figure 1 Any of the corresponding implementation methods in the embodiments.

[0114] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0115] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can 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, etc.) containing computer-usable program code.

[0116] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can 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 a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0117] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0118] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0119] This application also provides a computer program product, which includes computer software instructions that, when executed on a processing device, cause the processing device to perform actions such as... Figure 1 The flowchart of the stress field inversion method in the corresponding embodiment.

[0120] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0121] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0122] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0123] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0124] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0125] If the integrated unit is implemented as 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 this application, in essence, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0126] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method of three-dimensional initial geostress field inversion, characterized in that, include: Establish a three-dimensional geological model of the target area based on geological exploration data; The three-dimensional geological model is simulated and analyzed using numerical simulation software to obtain the independent variables for inversion of the geostress field. Based on the correlation between the independent variables for inversion of the geostress field, a mathematical model for inversion of the geostress field is constructed using a stepwise regression method. The mathematical model for inversion of the geostress field includes independent variables that are significantly related to the geostress field, constants, and weight coefficients corresponding to the significantly related independent variables. The inversion independent variables are the stress components at the stress measurement point locations. By removing constants from the inversion mathematical model of the geostress field, the weight coefficients of significant variables are corrected based on sensitivity analysis to obtain the optimal mathematical model of the geostress field and the initial inversion geostress field. The accuracy of the inverted geostress field is evaluated based on measured geological data, which includes stress tensor components, principal stress values ​​and orientations, and cavern damage conditions. The process of removing constants from the geostress field inversion mathematical model and adjusting the weight coefficients of significant variables based on sensitivity analysis to obtain the optimal geostress field mathematical model and the initial geostress field inversion includes: Remove the constants from the inversion mathematical model of the geostress field; Based on the 95% confidence intervals of the independent variable coefficients obtained by stepwise regression, the weight coefficients of the significant variables are corrected to obtain the inverted initial geostress field. To obtain the inverted initial geostress field, the weighting coefficients of the significant variables are adjusted, including: When the sum of squared residuals is minimized, the optimal coefficient of the independent variable will be determined. This step is repeated until the optimal coefficients of all significant variables are determined. The measured geological data includes stress tensor components, principal stress values ​​and azimuths, and cavern damage conditions. The assessment of the accuracy of the inverted geostress field based on the measured geological data includes: The correctness of the stress tensor inversion between measured and predicted values ​​is verified based on stress tensor components, principal stress values, and orientation. Numerical simulations of tunnel excavation were performed based on the inverted geostress field, and the results were compared with the actual tunnel failure on site to verify the accuracy of the inverted stress field.

2. The method of claim 1, wherein, The geological exploration data includes surface information, stratigraphic information, and fault information.

3. The method of claim 1, wherein, The three-dimensional geological model is simulated and analyzed using numerical simulation software to obtain the independent variables for inversion of the geostress field. Based on the correlation between the independent variables for inversion of the geostress field, a stepwise regression is used to construct a mathematical model for inversion of the geostress field, including: The three-dimensional geological model was simulated and analyzed using numerical simulation software to obtain the independent variables of the geostress field. The stepwise regression method was used to screen the independent variables and constants that are significantly related to the geostress field, and the weight coefficients corresponding to the significantly related independent variables were obtained. The geostress field inversion mathematical model is constructed based on the independent variables, the constants, and the weighting coefficients.

4. The method of claim 1, wherein, Also includes: Based on the Pearson correlation coefficients between different inverted independent variables and between the inverted independent variables and the dependent variable, the correlation between the independent variables and the dependent variable is obtained, wherein the dependent variable is the stress field formed by measured data. Based on the stepwise regression method, the independent variables that are significantly correlated with the dependent variable are identified as the independent variables that are significantly correlated with the geostress field.

5. The method of claim 4, wherein, Also includes: The significance of each independent variable is determined using the stepwise regression method based on the following formula: where ESS is the regression sum of squares, RSS is the residual sum of squares, is the simulated stressor, is the average of the measured stressor, is the measured stressor, m is the number of significant stressors, and n is the number of measured samples corresponding to the measured stressor.

6. A three-dimensional initial geostress field inversion device, characterized in that, include: Geological model building unit, used to build a three-dimensional geological model of the target area based on geological exploration data; The mathematical model construction unit is used to simulate and analyze the three-dimensional geological model using numerical simulation software to obtain the independent variables of the geostress field inversion, and to construct the geostress field inversion mathematical model by stepwise regression based on the correlation between the independent variables of the geostress field inversion. The geostress field inversion mathematical model includes independent variables that are significantly related to the geostress field, constants, and weight coefficients corresponding to the significantly related independent variables. The inversion independent variables are the stress components at the stress measurement point locations. The correction unit is used to remove constants from the geostress field inversion mathematical model and correct the weight coefficients of significant variables based on sensitivity analysis to obtain the optimal geostress field mathematical model and the inverted initial geostress field. The verification unit is used to evaluate the correctness of the inverted geostress field based on measured geological data, which includes stress tensor components, principal stress values ​​and orientations, and cavern damage conditions. The process of removing constants from the geostress field inversion mathematical model and adjusting the weight coefficients of significant variables based on sensitivity analysis to obtain the optimal geostress field mathematical model and the initial geostress field inversion includes: Remove the constants from the inversion mathematical model of the geostress field; Based on the 95% confidence intervals of the independent variable coefficients obtained by stepwise regression, the weight coefficients of the significant variables are corrected to obtain the inverted initial geostress field. To obtain the inverted initial geostress field, the weighting coefficients of the significant variables are adjusted, including: When the sum of squared residuals is minimized, the optimal coefficient of the independent variable will be determined. This step is repeated until the optimal coefficients of all significant variables are determined. The measured geological data includes stress tensor components, principal stress values ​​and azimuths, and cavern damage conditions. The assessment of the accuracy of the inverted geostress field based on the measured geological data includes: The correctness of the stress tensor inversion between measured and predicted values ​​is verified based on stress tensor components, principal stress values, and orientation. Numerical simulations of tunnel excavation were performed based on the inverted geostress field, and the results were compared with the actual tunnel failure on site to verify the accuracy of the inverted stress field.

7. An electronic device, comprising: The memory and processor are characterized in that the processor, when executing a computer program stored in the memory, implements the steps of the three-dimensional initial geostress field inversion method as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the three-dimensional initial geostress field inversion method as described in any one of claims 1-5.

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