Multi-linear fitting deep well crustal stress inversion method and system based on multi-working-condition characteristics

Abstract

The invention provides a multivariate linear fitting deep well crustal stress inversion method and system with multi-working-condition characteristics, and the method comprises the steps: carrying out the simulation modeling of a deep well region through modeling software FLAC3D, endowing a model with related parameters, and obtaining the general situation of the model of a mine region; respectively applying normal stresses in x, y and z directions and shear stresses on x y, y z and z x surfaces to the mine area model in different forms by using FLAC3D; and stress solving is carried out on different working conditions. A multiple linear fitting regression method is adopted for the obtained stress solving result in MATLAB, and a linear fitting regression equation is obtained; and carrying out FLAC3D inversion on the data to obtain a required whole model crustal stress field. According to the invention, the technical problem of low accuracy of inversion operation is solved.

Description

Technical Field

[0001] The invention relates to the field of address and mining survey data processing, and in particular to a multivariate linear fitting deep well geostress inversion method and system with multiple working condition characteristics. Background Art

[0002] In the existing tunnel inversion technology, for example, the existing invention patent application document "A method and device for inversion detection and positioning of a strip-shaped underground tunnel in a mountain" with publication number CN114330074A, the existing method includes: using a thermal radiation model between a mountain and an air layer, combining DEM data, calculating solar radiation energy, and iteratively filtering out the background thermal flow field energy of the mountain; combining hyperspectral data, calculating the background thermal propagation energy of the mountain; using an underground target inversion model, filtering out the background thermal flow field energy of each layer of the mountain in the infrared remote sensing image, obtaining the optimal altitude of the strip-shaped underground tunnel in the mountain and the disturbance signal distribution image constructed by the thermal flow field energy of the strip-shaped underground tunnel in each layer of the mountain; using the Hough transform detection method to detect straight lines in the disturbance signal distribution image, and according to the correlation principle of the tunnel engineering design, fitting to obtain the detection position of the tunnel; realizing the inversion detection and positioning of the strip-shaped underground tunnel in the mountain environment. And the existing invention patent application document "A tunnel seismic wave velocity inversion method and system based on deep learning" with publication number CN114035228A adds environmental information of the tunnel construction site, geological prior information and tunnel detection observation method information encoding to the deep learning seismic inversion method to help the algorithm obtain corresponding prior knowledge, and uses the existing database to perform preliminary network pre-training on the tunnel seismic wave velocity inversion network to assist the algorithm in matching the current tunnel engineering detection task; at the same time, an inversion optimization method for network update synchronous tunnel excavation is established. While the tunnel is being dug and explored, the database is expanded and the algorithm is optimized as the detection progresses, and the training method gradually becomes unsupervised.

[0003] And the existing invention patent application document with publication number "CN103206206A Method for calculating ultra-deep well formation fracture pressure"

[0004] The existing method comprises: a) obtaining static rock mechanical parameters of the ultra-deep well formation through indoor experiments, which at least include static elastic modulus and static Poisson's ratio; b) obtaining dynamic rock mechanical parameters of the ultra-deep well formation through well logging interpretation, which at least include dynamic elastic modulus and dynamic Poisson's ratio; c) performing correlation analysis on the static rock mechanical parameters and the dynamic rock mechanical parameters to determine the correlation relationship between the elastic modulus and Poisson's ratio of the ultra-deep well formation; d) establishing a geostress inversion model of the ultra-deep well formation to obtain the maximum horizontal principal stress and the minimum horizontal principal stress; e) calculating the temperature difference ΔT between the temperature when the fluid enters the ultra-deep well formation and the temperature of the ultra-deep well formation; f) calculating the fracture pressure Pf at the ultra-deep well formation according to the following formula: Pf = 3σh-σH+C1St-αPp-δ[α(1-2μ)1-μ-φ]Pp-EBΔT1-μ1-δ[α(1- 2μ)1-μ-φ]]]>Wherein: C1 indicates whether there are cracks in the wellbore wall. When there are cracks in the wellbore wall, C1=0, and when there are no cracks in the wellbore wall, C1=1; α=1-Cs / Cv, where Cs is the particle compression coefficient of the solid, the unit is MPa-1, and Cv is the volume compression coefficient of the solid, the unit is MPa-1; σH is the horizontal maximum principal stress, the unit is MPa; σh is the horizontal minimum principal stress, the unit is MPa; St is the tensile strength of rock, the unit is MPa; μ is Poisson's ratio; E is the elastic modulus of rock, the unit is MPa; Pp is the formation pressure, Pp=pgh, the unit is MPa, where p is the fluid density, the unit is g / mm3, g is the acceleration of gravity, the unit is N / Kg, and h is the formation depth, the unit is m; δ is the comprehensive seepage coefficient, where K is the seepage coefficient, is the porosity, B is the thermal expansion coefficient, the unit is m / ℃, and ΔT is the temperature difference, the unit is ℃. However, the aforementioned existing technologies do not perform necessary decomposition of the complex geostresses to which underground mines are subjected, making it difficult to substantially reduce the inversion error and unable to effectively approach a more realistic stress condition.

[0005] In summary, the prior art has the technical problem of low inversion operation accuracy. Summary of the invention

[0006] The technical problem to be solved by the present invention is: how to solve the technical problem of low accuracy of inversion operation.

[0007] The present invention adopts the following technical solutions to solve the above technical problems: a multivariate linear fitting deep well ground stress inversion method with multiple working condition characteristics includes:

[0008] S1. Use a preset modeling tool to simulate and model the deep well area, assign values ​​to the preset three-dimensional model of the deep well area, and obtain general information of the preset three-dimensional model of the deep well area;

[0009] S2. Using the preset modeling tool, apply the normal stress in the x, y, and z directions and the shear stress on the xy, yz, and zx planes to the mine area model in a differential stress application manner, and then perform stress solutions for no less than two different working conditions to obtain stress field superposition data as the stress solution result;

[0010] S3. In the preset modeling tool, a multivariate linear fitting regression method is used to process the stress solution results to obtain a linear fitting regression equation;

[0011] S4. Substitute the components obtained under the different working conditions into the linear fitting regression equation to obtain the inversion result.

[0012] The deep well geostress inversion method adopted in the present invention is suitable for application scenarios where the geostress of deep wells is complex and the underground space is almost completely closed. At the same time, it takes into account different differential geostress field characteristics and working conditions, reduces the inversion error in the inversion operation, improves the accuracy, and has a positive impact on deep well area exploration and deep well operations.

[0013] In a more specific technical solution, in step S1, a multi-condition feature method is used to perform modeling operations on the deep well area.

[0014] In a more specific technical solution, step S1 includes:

[0015] S11, collecting information on mechanical properties of rock formations and information on the regularity of lithologic interlayers in the deep well area, and performing generalized zoning operations based on the information, so as to sequentially divide the rock formations in the mining area into simplified mining layers;

[0016] S12. According to the information of rock mechanical properties and the regularity of lithologic interlayers, the coverage range and rock formation conditions of the deep well area are determined, and the assignment operation is performed accordingly to complete the FLAC3D modeling operation.

[0017] In a more specific technical solution, in the FLAC3D modeling operation of step S12, the displacement boundary condition definition operation is performed by setting the velocity boundary condition.

[0018] In a more specific technical solution, in step S2, the differential stress application method includes: a uniform stress application method, a triangular stress application method and a trapezoidal stress application method.

[0019] The main advantage of the inversion method used in the present invention is that the inversion error is substantially reduced, and the stress condition is closer to the real one. The reason is that the complex ground stress of the underground mine is mainly decomposed into no less than two different boundary stress forms by applying uniform load, triangular load and trapezoidal load.

[0020] The present invention adopts a multi-condition characteristic method in the inversion operation process and adopts three stress application forms including uniform load, triangular load and trapezoidal load to more realistically present the diversity and complexity of ground stress.

[0021] In a more specific technical solution, step S2 includes:

[0022] S21. During the in-situ stress inversion process, normal stress is applied to the deep well area along the x, y, and z directions in a differential stress application manner;

[0023] S22, applying shear stress on each side of the shear tectonic movement in the xy, xz, and yz planes according to the velocity boundary conditions corresponding to the differential stress application method;

[0024] S23. Solve unit stress for no less than two different working conditions respectively to obtain the index stress field data of the three-dimensional model of the deep well area, and obtain stress field superposition data based on the superposition processing.

[0025] In a more specific technical solution, in step S23, the finger stress field data includes: finger stress fields of no less than 2 different working conditions and finger stress fields of no less than 2 preset measured points.

[0026] According to the existing mining conditions and geological conditions of the mining area in the specific application scenario, the present invention sets the ground stress measuring points as far as possible to avoid the areas with complex geological structures and the areas affected by mining, thereby further improving the accuracy of the inversion operation.

[0027] In a more specific technical solution, step S3 includes:

[0028] S31, perform finite element calculation on the deep well area to obtain the self-weight stress field value, and obtain the stress component of the stress field of the difference working condition in the structural stress field;

[0029] S32, obtaining measured values ​​of ground stress components at preset measured points from stress solution results;

[0030] S33, subtracting the self-weight stress field value from the measured value of the ground stress component at the preset measured point to obtain the dependent variable, and taking the stress component of the stress field under the difference working condition as the independent variable;

[0031] S34, importing the independent variable and the dependent variable into the preset data software, performing regression analysis using the least squares method, and calculating the regression coefficient;

[0032] S35. Perform a linear fitting operation according to the regression coefficient to obtain a linear fitting regression equation.

[0033] In a more specific technical solution, in step S35, the linear fitting regression equation is expressed using the following logic:

[0034] σ=-304.5084+-0.023566*x1+0.0070381*x2+0.010844*x3+-0.00048163*x4+0.018981*x5+-0.015356*x6+-0.00 97379*x7+-0.006483*x8+0.012934*x9+-0.0011913*x10+-0.0018466*x11+0.0031507*x12+-0.0025996*x13+-0 .0038987*x14+0.0065462*x15+-0.03545*x16+-0.078195*x17+0.089273*x18+-0.083762*x19+-0.14413*x20+0 .21834*x21+-0.0039707*x22+-0.0072038*x23+0.010623*x24+-0.0088549*x25+-0.034648*x26+0.048791*x27 (1)

[0036] Where, σ is the inverse ground stress value, unit: MPa; x n —It is the stress component obtained by applying different working conditions to the model, in Pa.

[0037] In a more specific technical solution, the multivariate linear fitting deep well geostress inversion system with multiple working condition characteristics includes:

[0038] A deep well area modeling module is used to use a preset modeling tool to simulate and model the deep well area, assign values ​​to the preset deep well area three-dimensional model, and obtain the overview information of the preset deep well area three-dimensional model;

[0039] The stress solving module is used to use the preset modeling tool to respectively apply the normal stress in the x, y, and z directions and the shear stress on the xy, yz, and zx planes to the mine area model in a differential stress application manner, and then respectively solve the stress for no less than two different working conditions to obtain the stress field superposition data as the stress solution result. The stress solving module is connected to the deep well area modeling module;

[0040] A linear fitting regression processing module is used to process the stress solution result by using a multivariate linear fitting regression method in a preset modeling tool to obtain a linear fitting regression equation. The linear fitting regression processing module is connected to the stress solution module;

[0041] The model geostress field inversion module is used to substitute the components obtained under different working conditions into the linear fitting regression equation to obtain the inversion result. The model geostress field inversion module is connected with the linear fitting regression processing module and the stress solution module.

[0042] Compared with the prior art, the present invention has the following advantages:

[0043] The deep well geostress inversion method adopted in the present invention is suitable for application scenarios where the geostress of deep wells is complex and the underground space is almost completely closed. At the same time, it takes into account different differential geostress field characteristics and working conditions, reduces the inversion error in the inversion operation, improves the accuracy, and has a positive impact on deep well area exploration and deep well operations.

[0044] The main advantage of the inversion method used in the present invention is that the inversion error is substantially reduced, and the stress condition is closer to the real one. The reason is that the complex ground stress of the underground mine is mainly decomposed into no less than two different boundary stress forms by applying uniform load, triangular load and trapezoidal load.

[0045] The present invention adopts a multi-condition characteristic method in the inversion operation process and adopts three stress application forms including uniform load, triangular load and trapezoidal load to more realistically present the diversity and complexity of ground stress.

[0046] According to the existing mining conditions and geological conditions of the mining area in the specific application scenario, the present invention sets the ground stress measuring points as far as possible to avoid the areas with complex geological structures and the areas affected by mining, thereby further improving the accuracy of the inversion operation.

[0047] The present invention solves the technical problem of low inversion operation accuracy in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of a multivariate linear fitting deep well geostress inversion method with multiple working condition characteristics of Example 1 of the present invention;

[0049] Figure 2 This is a schematic diagram of different working conditions of Example 1 of the present invention;

[0050] Figure 3 This is a schematic diagram of the generalized modeling of the Pansan mining area in Example 1 of the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] Example 1

[0053] like Figure 1 As shown, the multivariate linear fitting deep well geostress inversion method with multiple working condition characteristics provided by the present invention includes the following basic steps:

[0054] S1. Use the modeling software FLAC3D to simulate the deep well area, assign relevant parameters to the model to obtain the model overview of the mining area;

[0055] In this embodiment, a multi-condition characteristic method is used for FLAC3D modeling; in this embodiment, generalized zoning is performed according to the mechanical properties of rock formations, the laws of lithological interlayers, etc., and the rock formations in the mining area are simplified and layered from bottom to top; modeling is performed in FLAC3D by clarifying the coverage of the mining area and the rock formation conditions;

[0056] In this embodiment, the definition of displacement boundary conditions in FLAC3D is different from that in other finite element software such as ANSYS, which cannot apply displacement values ​​on the boundary surface, and thus controls it by setting velocity boundary conditions;

[0057] S2. Using FLAC3D to apply normal stress in x, y, and z directions and shear stress on xy, yz, and zx planes to the mine area model in different forms, and then solving stress for different working conditions. In this embodiment, the number of different working conditions can be, for example, 27;

[0058] like Figure 2 As shown, in the inversion process of this embodiment, the multi-condition characteristics adopt a differential stress application method. In this embodiment, the differential stress application method includes but is not limited to: three stress application forms of uniform load, triangular load, and trapezoidal load, which truly presents the diversity and complexity of ground stress;

[0059] like Figure 3As shown, in this embodiment, the inversion of ground stress applies normal stress in three forms of uniform, triangular and trapezoidal loads along the x, y and z directions in each direction; shear stress is applied in the form of 1000mm shear boundary displacement on each side of the xy, xz and yz plane shear tectonic movement; the displacement load applied is controlled according to the velocity boundary conditions of each shear stress in three forms of uniform, triangular and trapezoidal, with a boundary velocity of 5e-4 and an operating step of 2000 steps. Thus, different working conditions are obtained;

[0060] In this embodiment, the unit stress is solved to obtain at least two index stress fields of the mining area model, that is, at least two index stress fields of five measuring points are obtained, and stress field superposition data is obtained;

[0061] S3, using the obtained stress solution results in MATLAB using a multivariate linear fitting regression method to obtain an equation suitable for the deep ground stress field;

[0062] S4. Substitute the components obtained under different working conditions into the fitting regression equation to obtain the inversion result.

[0063] Example 2

[0064] In this embodiment, Pansan Mine is taken as an example for field application. The survey area of ​​Pansan Mine includes most of the tunnels and mining areas. The mining area of ​​Pansan Mine is 11,500 meters long and 9,500 meters wide, as shown in Table 1 below.

[0065] Table 1 Position coordinates and stress component values ​​of measured points in Pansan Mine

[0066]

[0067] In this embodiment, according to the existing mining conditions and geological conditions of the West Three Mining Area and the East Wing Mining Area of ​​Pansan Coal Mine, the geostress measuring points are avoided as much as possible in areas with complex geological structures and areas affected by mining. Two geostress measuring points are arranged in the 1672 (1) Yunshun gas control lane, and three geostress measuring points are arranged in the 2121 (1) Yunshun gas control lane to carry out geostress measurement work. A total of five measuring points are selected as follows: Figure 1 The measuring points in the East Wing Mining Area and the West Three Mining Area have a large horizontal span.

[0068] In this embodiment, generalized zoning is performed according to the mechanical properties of rock formations, the laws of lithological interlayers, etc., and the rock formations in the mining area are simplified and layered from bottom to top. The thickness of each rock layer and the necessary related parameters are shown in Table 2 below:

[0069] Table 2 Simplified rock strata division and related parameters of Pansan Mine

[0070] Layer Number Layer thickness / m Elastic modulus / GPa Poisson's ratio Density (kg·m-3) Lithology description layer 40 0.04 0.32 2000 Quaternary loose layer and wind oxidation layer Second floor 100 14 0.3 2600 Interbedded sandstone and mudstone Three-layer 300 16 0.28 2600 Interbedded siltstone and mudstone Four layers 100 15 0.3 2600 Medium sandstone, fine sandstone and argillaceous sandstone Five layers 40 18 0.28 2600 Medium sandstone, fine sandstone, mudstone and mudstone

[0071] In this embodiment, in the MATLAB multivariate linear fitting regression process, the measured values ​​of 30 groups of 6 stress components at five measured points of ground stress minus the value of the self-weight stress field calculated by finite element calculation is used as the dependent variable, and the stress components of the tectonic stress field calculated by finite element calculation corresponding to at least 2 stress fields of the measured values ​​are used as independent variables. The least squares regression analysis is used by importing MATLAB mathematical software, and the regression coefficient is calculated and solved. The linear fitting operation is performed to obtain the final regression equation:

[0072] σ=-304.5084+-0.023566*x1+0.0070381*x2+0.010844*x3+-0.00048163*x4+0.018 981*x5+-0.015356*x6+-0.0097379*x7+-0.006483*x8+0.012934*x9+-0.0011913*x 10 +-0.0018466*x 11 +0.0031507*x 12 +-0.0025996*x 13 +-0.0038987*x 14 +0.0065462*x 15 +-0.03545*x 16 +-0.078195*x 17 +0.089273*x 18 +-0.083762*x 19 +-0.14413*x 20 +0.21834*x 21 +-0.0039707*x 22 +-0.0072038*x 23 +0.010623*x 24 +-0.0088549*x 25 +-0.034648*x 26 +0.048791*x 27 (1)

[0073] In this example, the correlation coefficients of the regression equation of the Pansan Mine mining area are R = 0.9903, F = 76.2413, and P = 0.013; it can be seen that the fitting effect of the regression equation of the measured point stress in this measurement area is significant. Substituting the independent variable values ​​corresponding to the measured values ​​into equation (1), 30 regression values ​​can be obtained as shown in Table 3, the corresponding residuals are shown in Table 4, and the stress components are converted into principal stresses as shown in Table 5.

[0074] Table 3 Comparison between true value and regression value of stress components at Pansan Mine measuring points

[0075]

[0076] Table 4 Stress component residuals at the Pansan Mine measuring points

[0077]

[0078] Table 5 Principal stress errors at the Pansan Mine measurement points

[0079]

[0080] In summary, the deep well geostress inversion method adopted in the present invention is suitable for application scenarios where the geostress of deep wells is complex and the underground space is almost completely closed. At the same time, it takes into account different differential geostress field characteristics and working conditions, reduces the inversion error in the inversion operation, and improves the accuracy, which has a positive impact on deep well area exploration and deep well operations.

[0081] The main advantage of the inversion method used in the present invention is that the inversion error is substantially reduced, and the stress condition is closer to the real one. The reason is that the complex ground stress of the underground mine is mainly decomposed into no less than two different boundary stress forms by applying uniform load, triangular load and trapezoidal load.

[0082] The present invention adopts a multi-condition characteristic method in the inversion operation process and adopts three stress application forms including uniform load, triangular load and trapezoidal load to more realistically present the diversity and complexity of ground stress.

[0083] According to the existing mining conditions and geological conditions of the mining area in the specific application scenario, the present invention sets the ground stress measuring points as far as possible to avoid the areas with complex geological structures and the areas affected by mining, thereby further improving the accuracy of the inversion operation.

[0084] The present invention solves the technical problem of low inversion operation accuracy in the prior art.

[0085] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those 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 invention.

Claims

1. A multivariate linear fitting deep well ground stress inversion method with multiple working condition characteristics, characterized by: The method comprises: S1. Use a preset modeling tool to simulate and model the deep well area, assign values ​​to the preset three-dimensional model of the deep well area, and obtain general information of the preset three-dimensional model of the deep well area; S2. Using the preset modeling tool, respectively apply normal stress in the x, y, and z directions and shear stress on the xy, yz, and zx planes to the mine area model in a differential stress application manner, and then perform stress solutions for no less than two different working conditions to obtain stress field superposition data as a stress solution result; S3. In the preset modeling tool, a multivariate linear fitting regression method is used to process the stress solution result to obtain a linear fitting regression equation; S4. Substitute the components obtained under the different working conditions into the linear fitting regression equation to obtain the inversion result.

2. The multivariate linear fitting deep well geostress inversion method with multiple working condition characteristics according to claim 1 is characterized in that: In the step S1, a multi-operating condition feature method is used to perform modeling operations on the deep well area.

3. The multivariate linear fitting deep well geostress inversion method with multiple working condition characteristics according to claim 1 is characterized in that: The step S1 comprises: S11, collecting information on mechanical properties of rock formations and information on the regularity of lithologic interlayers in the deep well area, and performing a generalized zoning operation based on the information, so as to divide the rock formations in the mining area into simplified mining layers in sequence; S12. Determine the coverage range and rock formation conditions of the deep well area according to the rock formation mechanical property information and the lithology interlayer regularity information, perform value assignment operation accordingly, and complete the FLAC3D modeling operation.

4. The multivariate linear fitting deep well geostress inversion method with multiple working condition characteristics according to claim 3 is characterized in that: In the FLAC3D modeling operation of step S12, the displacement boundary condition definition operation is performed by setting the velocity boundary condition.

5. The multivariate linear fitting deep well geostress inversion method with multiple working condition characteristics according to claim 1 is characterized in that: In the step S2, the differential stress applying method includes: a uniform stress applying method, a triangular stress applying method and a trapezoidal stress applying method.

6. The multivariate linear fitting deep well geostress inversion method with multiple working condition characteristics according to claim 1 is characterized in that: The step S2 comprises: S21. During the in-situ stress inversion process, applying normal stress to the deep well area along the x, y, and z directions in the differential stress application manner; S22, applying shear stress on each side of the shear tectonic movement in the xy, xz, and yz planes according to the velocity boundary conditions corresponding to the differential stress application method; S23, respectively solving unit stress for at least two of the different working conditions to obtain the index stress field data of the three-dimensional model of the deep well area, and performing superposition processing to obtain the stress field superposition data.

7. The multivariate linear fitting deep well geostress inversion method with multiple working condition characteristics according to claim 6 is characterized in that: In the step S23, the finger stress field data includes: finger stress fields of at least two different working conditions and finger stress fields of at least two preset measured points.

8. The multivariate linear fitting deep well geostress inversion method with multiple working condition characteristics according to claim 1 is characterized in that: The step S3 comprises: S31, performing finite element calculation on the deep well area to obtain the self-weight stress field value, and obtaining the stress component of the stress field of the difference working condition in the tectonic stress field; S32, obtaining measured values ​​of ground stress components at preset measured points from the stress solution results; S33, subtracting the self-weight stress field value from the measured value of the ground stress component at the preset measured point to obtain a dependent variable, and taking the stress component of the stress field of the difference working condition as an independent variable; S34, importing the independent variable and the dependent variable into a preset data software to perform regression analysis using the least squares method and calculate the regression coefficient; S35. Perform a linear fitting operation according to the regression coefficient to obtain the linear fitting regression equation.

9. The multivariate linear fitting deep well geostress inversion method with multiple working condition characteristics according to claim 8 is characterized in that: In step S35, the applicable equation of the deep geostress field is expressed using the following logic: σ=-304.5084+-0.023566*x1+0.0070381*x2+0.010844*x3+-0.00048163*x4+0.018981*x5+-0.015356*x6+-0.00 97379*x7+-0.006483*x8+0.012934*x9+-0.0011913*x10+-0.0018466*x11+0.0031507*x12+-0.0025996*x13+-0 .0038987*x14+0.0065462*x15+-0.03545*x16+-0.078195*x17+0.089273*x18+-0.083762*x19+-0.14413*x20+0 .21834*x21+-0.0039707*x22+-0.0072038*x23+0.010623*x24+-0.0088549*x25+-0.034648*x26+0.048791*x27 (1) Where, σ is the in-situ stress value obtained by inversion; x n —It is the stress component obtained by applying different working conditions to the model, in Pa.

10. A multivariate linear fitting deep well ground stress inversion system with multiple working condition characteristics, characterized by: The system comprises: A deep well area modeling module is used to use a preset modeling tool to simulate and model the deep well area, assign values ​​to the preset deep well area three-dimensional model, and obtain the overview information of the preset deep well area three-dimensional model; A stress solving module, used for using the preset modeling tool to respectively apply normal stress in the x, y, and z directions and shear stress on the xy, yz, and zx planes to the mine area model in a differential stress application manner, and then respectively performing stress solving for no less than two different working conditions to obtain stress field superposition data as a stress solving result, wherein the stress solving module is connected to the deep well area modeling module; A linear fitting regression processing module, used to process the stress solution result by a multivariate linear fitting regression method in the preset modeling tool to obtain a linear fitting regression equation, wherein the linear fitting regression processing module is connected to the stress solution module; The model geostress field inversion module is used to substitute the components obtained under the difference working conditions into the linear fitting regression equation to obtain the inversion result. The model geostress field inversion module is connected with the linear fitting regression processing module and the stress solving module.

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