A method, device and equipment for estimating longitudinal section ground stress parameter values of a deep-buried long tunnel and a storage medium

Abstract

This invention discloses a method, apparatus, equipment, and storage medium for estimating geostress parameters in the longitudinal profile of a deep-buried, long tunnel, relating to the field of geological surveying technology. The method treats the tunnel geostress field as a random field and the measured geostress as several realizations of the random field. After obtaining the measured values ​​of geostress parameters from multiple points on the tunnel's longitudinal profile, the method first uses these measured data to fit multiple experimental variogram models to obtain model coefficients. Then, through error analysis, the optimal model is selected. Finally, using this optimal model, spatial interpolation calculations of geostress are performed to obtain the geostress results along the tunnel axis. This method eliminates the need for detailed and accurate geological data and soil parameters, and can quickly and efficiently obtain the geostress distribution results along the tunnel axis that meet the requirements and accuracy of tunnel engineering demonstration and design stages, using only a small amount of measured data from a few points. This shortens the required time and has significant theoretical and practical value.

Description

Technical Field

[0001] This invention belongs to the field of geological exploration technology, specifically relating to a method, device, equipment, and storage medium for estimating geostress parameters in the longitudinal profile of a deep-buried long tunnel. Background Technology

[0002] In-situ stress is a key factor in the feasibility study, design, and construction stages of deep-buried long tunnel projects. The most direct and effective way to obtain the distribution of in-situ stress in tunnels is through on-site in-situ in-situ measurement, but this method is difficult, time-consuming, and costly. Furthermore, relying on only a small number of measurement points cannot meet the actual needs of in-situ stress surveying for deep-buried long tunnels. With the rise of artificial intelligence and computer technology, research on intelligent inversion of in-situ stress is increasing, and various inversion and analysis methods for in-situ stress have been well developed and applied. For example, through geological modeling, numerical inversion can be used to obtain the distribution of in-situ stress in tunnels.

[0003] However, numerical inversion technology for in-situ stress requires detailed and accurate geological data and soil parameters. In the tunnel engineering investigation and design stages, relevant data is often lacking or inaccurate. Furthermore, frequent modifications to tunnel routes and design schemes necessitate the reconstruction of tunnel geological models and subsequent calculations, consuming significant time. Therefore, providing a technical solution that meets the usage and accuracy requirements of tunnel engineering demonstration and design stages, and can quickly and efficiently obtain the in-situ stress distribution results along the tunnel axis, is a crucial research topic for those skilled in the art. Summary of the Invention

[0004] The purpose of this invention is to provide a method, apparatus, computer equipment, and computer-readable storage medium for estimating the geostress parameters of the longitudinal profile of a deep-buried long tunnel, in order to solve the problem that existing geostress numerical inversion techniques require a lot of time due to the need for relatively detailed and accurate geological data and soil parameters.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] Firstly, a method for estimating the geostress parameters of the longitudinal profile of a deep-buried, long tunnel is provided, including:

[0007] Obtain measured values ​​of geostress parameters at multiple measured points on the longitudinal profile of a long, deeply buried tunnel;

[0008] Based on the measured values ​​of the geostress parameters at the multiple measured points, the semivariance of the geostress parameters for each pair of measured points is calculated to obtain the semivariance values ​​of the geostress parameters for multiple pairs of measured points. In addition, based on the known coordinates of the multiple measured points, the distance values ​​of the multiple pairs of measured points are calculated.

[0009] Determine the lag distance h1 and the longest distance H among the distance values ​​of the multiple pairs of measured points. max Where h1 represents a positive number, H max This represents a positive number greater than h1;

[0010] Based on the hysteresis h1, the first interval (0, H) max The interval is divided into the following first subintervals: (0,h1],(h1,2*h1],…,((k-1)*h1,k*h1],…,((K-1)*h1,H max ], where K = Ceiling(H max / h1), Ceiling() represents the floor function, and k represents a positive integer less than k;

[0011] Based on the distance values ​​of the multiple pairs of measured points and the attribution relationship of the multiple first sub-intervals, the multiple pairs of measured points are divided into multiple first groups that correspond one-to-one with the multiple first sub-intervals;

[0012] Based on the semivariance values ​​and distance values ​​of the geostress parameters of the multiple pairs of measured points, the average semivariance values ​​and average distance values ​​of the geostress parameters of each of the multiple first groups are calculated.

[0013] Based on the average semivariogram and average distance of the geostress parameters of each first group, the model coefficients of multiple experimental variation function models are fitted. The average semivariogram of the geostress parameters is used as the experimental variation function value during the fitting process, and the average distance is used as the distance to the point to be estimated as a regional variable during the fitting process.

[0014] Based on the measured values ​​of geostress parameters at the multiple measured points, error analysis is performed using the model coefficients of the multiple experimental variogram models to obtain the model quality evaluation index value of each experimental variogram model in the multiple experimental variogram models.

[0015] Based on the model quality evaluation index values ​​of each experimental variation function model, the optimal experimental variation function model that best meets the preset conditions for model optimization is determined from the multiple experimental variation function models.

[0016] Based on the known coordinates of the plurality of measured points and the known coordinates of the target measuring point on the longitudinal profile of the deep-buried long tunnel, m measured points located around the target measuring point are determined from the plurality of measured points, where m represents a positive integer greater than 2;

[0017] Based on the known coordinates of the target measurement point and the known coordinates of the m measured points, the distance from the target measurement point to each of the m measured points is calculated. This distance is then used as a regional variable to represent the distance to the point to be estimated and substituted into the optimal experimental variation function model. Finally, the model coefficients of the optimal experimental variation function model are applied to calculate the experimental variation function values ​​between the target measurement point and each of the m measured points.

[0018] Based on the semivariance values ​​of the geostress parameters of each pair of measured points in the m measured points and the experimental variation function values ​​of the target measuring point and each of the m measured points, the following ordinary Kriging equations are established:

[0019]

[0020] In the formula, i and j represent positive integers, and λ i γ(x) represents the weight coefficient to be solved for the i-th measured point among the m measured points. i ,x j ) represents the semivariance value of the geostress parameter corresponding to the i-th measured point and the j-th measured point among the m measured points, u represents the Lagrange multiplier factor to be solved, and γ(x) represents the semivariance value of the geostress parameter corresponding to the i-th measured point and the j-th measured point among the m measured points. i (x0) represents the experimental variation function value between the target measurement point and the i-th measured point;

[0021] Solving the ordinary Kriging equations yields m weighting coefficients that correspond one-to-one with the m measured points.

[0022] Based on the measured values ​​of the geostress parameters at the m measured points, the estimated value of the geostress parameter Z(x0) at the target measuring point is calculated according to the following formula:

[0023]

[0024] In the formula, Z(x) i ) represents the measured value of the geostress parameter at the i-th measured point;

[0025] Based on the estimated values ​​of the geostress parameters of all target measuring points in the longitudinal profile of the deep-buried long tunnel, a geostress parameter estimation contour map is drawn on the longitudinal profile of the deep-buried long tunnel, and the geostress parameter estimation contour map is output and displayed.

[0026] Based on the above-mentioned invention, a spatial interpolation calculation scheme for ground stress in deep-buried long tunnels based on geostatistical kriging is provided. This scheme treats the tunnel ground stress field as a random field and the measured ground stress as several realizations of the random field. After obtaining the measured values ​​of ground stress parameters at multiple measured points on the longitudinal profile of the deep-buried long tunnel, the model coefficients of multiple experimental variogram models are first obtained by fitting these measured data. Then, the optimal model is selected through error analysis. Finally, the optimal model is used to perform spatial interpolation calculation of ground stress to obtain the ground stress result along the tunnel axis. This method eliminates the need for detailed and accurate geological data and soil parameters, and can quickly and efficiently obtain the ground stress distribution result along the tunnel axis that meets the requirements and accuracy of tunnel engineering demonstration and design stages, relying only on measured data from a small number of measured points. This significantly shortens the required time, thus possessing certain theoretical significance and high engineering practical value, facilitating practical application and promotion.

[0027] In a possible design, determining the hysteresis h1 includes the following steps S31 to S34:

[0028] S31. In the interval (0, H) max Select a value from the [value] as the current value of the hysteresis distance h1, and then execute step S32, where H max This represents the longest distance value among the distance values ​​of the multiple pairs of measured points;

[0029] S32. For each experimental variogram model in multiple experimental variogram models, based on the current value of the lag distance h1 and the measured values ​​of the geostress parameters at the multiple measured points, cross-validation is used to obtain the corresponding estimated values ​​of the geostress parameters at the multiple measured points, and then step S33 is executed.

[0030] S33. For each experimental variogram model, calculate the corresponding model quality evaluation index value based on the measured values ​​of the geostress parameters at the multiple measured points and the corresponding estimated values ​​of the geostress parameters at the multiple measured points, and then execute step S34.

[0031] S34. Determine whether the model quality assessment index values ​​of each experimental variation function model meet the preset iteration stopping condition. If yes, determine the current value of the lag distance h1 as the final value; otherwise, in the interval (0, H... max Select a new value as the current value of the hysteresis distance h1, and then execute step S32.

[0032] In one possible design, for each experimental variogram model among multiple experimental variogram models, based on the current value of the hysteresis distance h1 and the measured values ​​of the geostress parameters at the multiple measured points, cross-validation is used to obtain the corresponding estimated values ​​of the geostress parameters at the multiple measured points, including:

[0033] For each target measured point among the plurality of measured points, all other measured points among the plurality of measured points are determined as corresponding plurality of reference measured points;

[0034] For a specific experimental variation function model among multiple experimental variation function models and a specific target measured point among multiple measured points, based on the current value of the lag distance h1, the model parameters of the specific experimental variation function model, and the measured values ​​of the geostress parameters at multiple reference measured points of the specific target measured point, the corresponding geostress parameter estimates are calculated according to the following steps:

[0035] Determine the longest distance value among multiple pairs of reference measured points. Among them, the multiple pairs of reference measured points refer to a point pair among the multiple reference measured points of a certain target measured point;

[0036] Based on the current value of the lag distance h1, the second interval It is divided into the following multiple second sub-intervals: in, Ceiling() is a function that rounds up the value. Indicates less than Positive integers;

[0037] Based on the distance values ​​of the multiple pairs of reference measured points and the attribution relationship of the multiple second sub-intervals, the multiple pairs of reference measured points are divided into multiple second groups that correspond one-to-one with the multiple second sub-intervals;

[0038] Based on the semivariance values ​​and distance values ​​of the geostress parameters of the multiple pairs of reference measured points, the average semivariance values ​​and average distance values ​​of the geostress parameters of each of the multiple second groups are calculated.

[0039] Based on the average semivariogram and average distance of the geostress parameters of each second group, the model coefficients of a certain experimental variation function model are fitted, wherein the average semivariogram of the geostress parameters is used as the experimental variation function value during the fitting process, and the average distance is used as the distance to the point to be estimated as a regional variable during the fitting process.

[0040] Based on the known coordinates of multiple reference measured points of a certain target measured point and the known coordinates of the target measured point, the distance value from the target measured point to each of the multiple reference measured points is calculated. This distance value is then used as a regional variable to the distance to the point to be estimated and substituted into the experimental variation function model. Then, the model parameters of the experimental variation function model are applied to calculate the experimental variation function value between the target measured point and each of the multiple reference measured points of the target measured point.

[0041] Based on the semivariance values ​​of geostress parameters of each pair of reference measured points among multiple reference measured points of a certain target measured point and the experimental variability function values ​​of each reference measured point among multiple reference measured points of the certain target measured point, the ordinary Kriging equation system is established and solved to obtain multiple reference weight coefficients that correspond one-to-one with the multiple reference measured points of the certain target measured point.

[0042] Based on the measured values ​​of geostress parameters of multiple reference measured points of a certain target measured point and the multiple reference weight coefficients, the estimated value of geostress parameters of the certain target measured point is calculated.

[0043] Secondly, a device for estimating geostress parameters in the longitudinal profile of a deep-buried long tunnel is provided, comprising a measured data acquisition module, an intermediate data calculation module, an intermediate data determination module, a numerical interval division module, a measured point pair grouping module, a mean data calculation module, a model coefficient fitting module, a model error analysis module, an optimal model selection module, a surrounding measuring point determination module, a variation function calculation module, an equation system construction module, an equation system solution module, an estimated data calculation module, and a contour map drawing module, all connected in sequence.

[0044] The measured data acquisition module is used to acquire the measured values ​​of geostress parameters at multiple measured points on the longitudinal profile of a deep-buried long tunnel.

[0045] The intermediate data calculation module is used to calculate the semivariance of the geostress parameters of each pair of measured points according to the measured values ​​of the geostress parameters of the multiple measured points, to obtain the semivariance values ​​of the geostress parameters of multiple pairs of measured points, and also to calculate the distance values ​​of the multiple pairs of measured points according to the known coordinates of the multiple measured points.

[0046] The intermediate data determination module is used to determine the lag distance h1 and the longest distance value H among the distance values ​​of the multiple pairs of measured points. max Where h1 represents a positive number, H max This represents a positive number greater than h1;

[0047] The numerical interval division module is used to divide the first interval (0, H) according to the lag distance h1. max The interval is divided into the following first subintervals: (0,h1],(h1,2*h1],…,((k-1)*h1,k*h1],…,((K-1)*h1,H max ], where K = Ceiling(H max / h1), Ceiling() represents the floor function, and k represents a positive integer less than k;

[0048] The measured point pair grouping module is used to divide the multiple pairs of measured points into multiple first groups corresponding one-to-one with the multiple first sub-intervals based on the distance values ​​of the multiple pairs of measured points and the attribution relationship of the multiple first sub-intervals;

[0049] The mean data calculation module is used to calculate the average value of the half variance of the ground stress parameters and the average value of the distance in each of the multiple first groups based on the half variance values ​​and distance values ​​of the ground stress parameters of the multiple pairs of measured points.

[0050] The model coefficient fitting module is used to fit the model coefficients of multiple experimental variation function models based on the average semivariogram and average distance of the geostress parameters of each first group. The average semivariogram of the geostress parameters is used as the experimental variation function value during the fitting process, and the average distance is used as the distance to the point to be estimated as a regional variable during the fitting process.

[0051] The model error analysis module is used to perform error analysis based on the measured values ​​of the geostress parameters at the multiple measured points, applying the model coefficients of the multiple experimental variogram models, and obtaining the model quality evaluation index value of each experimental variogram model in the multiple experimental variogram models.

[0052] The optimal model selection module is used to determine the optimal experimental variation function model that best meets the preset conditions for model optimization from the multiple experimental variation function models based on the model quality evaluation index value of each experimental variation function model.

[0053] The surrounding measuring point determination module is used to determine m measuring points located around the target measuring point from the plurality of measuring points based on the known coordinates of the plurality of measuring points and the known coordinates of the target measuring point on the longitudinal profile of the deep-buried long tunnel, where m represents a positive integer greater than 2;

[0054] The variation function calculation module is used to calculate the distance value from the target measurement point to each of the m measured points based on the known coordinates of the target measurement point and the known coordinates of the m measured points, and to substitute this distance value as the distance to the point to be estimated into the optimal experimental variation function model. Then, the model coefficients of the optimal experimental variation function model are applied to calculate the experimental variation function value between the target measurement point and each of the m measured points.

[0055] The equation system construction module is used to establish the following ordinary Kriging equation system based on the semivariance values ​​of the geostress parameters of each pair of measured points in the m measured points and the experimental variation function values ​​of the target measuring point and each of the m measured points:

[0056]

[0057] In the formula, i and j represent positive integers, and λ i γ(x) represents the weight coefficient to be solved for the i-th measured point among the m measured points. i ,x j ) represents the semivariance value of the geostress parameter corresponding to the i-th measured point and the j-th measured point among the m measured points, u represents the Lagrange multiplier factor to be solved, and γ(x) represents the semivariance value of the geostress parameter corresponding to the i-th measured point and the j-th measured point among the m measured points. i (x0) represents the experimental variation function value between the target measurement point and the i-th measured point;

[0058] The equation solving module is used to solve the ordinary Kriging equations to obtain m weight coefficients that correspond one-to-one with the m measured points.

[0059] The estimation data calculation module is used to calculate the estimated value Z(x0) of the geostress parameter of the target measuring point according to the measured values ​​of the geostress parameters of the m measured points, using the following formula:

[0060]

[0061] In the formula, Z(x) i ) represents the measured value of the geostress parameter at the i-th measured point;

[0062] The contour map drawing module is used to draw a contour map of estimated ground stress parameters on the longitudinal profile of the deep-buried long tunnel based on the estimated ground stress parameters of all target measuring points in the entire region on the longitudinal profile of the deep-buried long tunnel, and output and display the contour map of estimated ground stress parameters.

[0063] Thirdly, the present invention provides a computer device comprising a memory, a processor, and a transceiver connected in sequence, wherein the memory is used to store a computer program, the transceiver is used to send and receive data, and the processor is used to read the computer program and execute the method for estimating the geostress parameter values ​​of the longitudinal profile of a deep-buried long tunnel as described in the first aspect or any possible design in the first aspect.

[0064] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, perform the method for estimating the geostress parameter values ​​of the longitudinal profile of a deep-buried long tunnel as described in the first aspect or any possible design in the first aspect.

[0065] Fifthly, the present invention provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform the method for estimating the geostress parameter values ​​of the longitudinal profile of a deep-buried long tunnel as described in the first aspect or any possible design in the first aspect.

[0066] The beneficial effects of the above scheme are:

[0067] (1) This invention creatively provides a spatial interpolation calculation scheme for ground stress in deep-buried long tunnels based on geostatistical kriging. The tunnel ground stress field is treated as a random field and the measured ground stress is treated as several realizations of the random field. After obtaining the measured values ​​of ground stress parameters at multiple measured points on the longitudinal section of the deep-buried long tunnel, the model coefficients of multiple experimental variogram models are first obtained by fitting these measured data. Then, the optimal model is selected through error analysis. Finally, the optimal model is used to perform spatial interpolation calculation of ground stress to obtain the ground stress result of the tunnel axis. In this way, it is not necessary to have detailed and accurate geological data and soil parameters. The measured data of a small number of measured points can be used to quickly and efficiently obtain the ground stress distribution result of the tunnel axis that meets the needs and accuracy requirements of the tunnel engineering demonstration and design stages, which greatly shortens the required time. Therefore, it has certain theoretical significance and high engineering practical value.

[0068] (2) The appropriate lag distance h1 that can be obtained by cross-validation and iteration can be automatically determined so as to facilitate the quick and efficient acquisition of the final tunnel axis stress distribution results.

[0069] (3) By using average error, root mean square error, standardized root mean square error and average standard error to evaluate the quality of various calculation models, and by using cross-validation + iteration to calculate each error index, it is possible to select the optimal function model from various variogram models for geostress interpolation calculation.

[0070] (4) Through engineering examples, it was found that the final obtained ground stress value had a high degree of fit with the measured ground stress value. The fitting error of the maximum horizontal principal stress was within ±15%, and the fitting degree was about 85%. This result shows that the spatial interpolation calculation scheme of ground stress for deep buried long tunnels based on geostatistical kriging is feasible and the results obtained are reasonable and can meet the accuracy and practical application requirements of tunnel engineering for ground stress. Attached Figure Description

[0071] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0072] Figure 1 This is a flowchart illustrating the method for estimating geostress parameters in the longitudinal profile of a long, deeply buried tunnel, as provided in this application embodiment.

[0073] Figure 2 An example diagram showing the longitudinal profile of a long, deep-buried tunnel and the distribution of geostress boreholes, provided in an embodiment of this application.

[0074] Figure 3 Example figure showing the comparison results between interpolated data and measured data of the maximum horizontal principal stress at 29 measured points provided in the embodiments of this application.

[0075] Figure 4 Example figure showing the comparison results between interpolated data and measured data of minimum horizontal principal stress at 29 measured points provided in the embodiments of this application.

[0076] Figure 5 Example figure showing the comparison results of interpolated data and measured data of vertical principal stress at 29 measured points provided in the embodiments of this application.

[0077] Figure 6 Example contour plot of the maximum horizontal principal stress provided for embodiments of this application.

[0078] Figure 7 Example contour plot of minimum horizontal principal stress provided for embodiments of this application.

[0079] Figure 8 Example contour plot of vertical principal stress provided for embodiments of this application.

[0080] Figure 9 This is an example diagram showing the distribution of the three principal stresses on the axis of a long, deeply buried tunnel, as provided in an embodiment of this application.

[0081] Figure 10This is a schematic diagram of the device for estimating the geostress parameters of a long, deep-buried tunnel longitudinal profile, as provided in an embodiment of this application.

[0082] Figure 11 A schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0083] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in conjunction with the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following description of the structure of the accompanying drawings is only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0084] It should be understood that although the terms "first" and "second", etc., may be used herein to describe various objects, these objects should not be limited by these terms. These terms are only used to distinguish one object from another. For example, the first object may be referred to as the second object, and similarly, the second object may be referred to as the first object, without departing from the scope of the exemplary embodiments of the invention.

[0085] It should be understood that the term "and / or" that may appear in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, or A and B exist simultaneously. Another example is A, B and / or C, which can mean that any one of A, B, and C or any combination thereof exists. The term " / and" that may appear in this document describes another relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone or A and B exist simultaneously. In addition, the character " / " that may appear in this document generally indicates that the related objects before and after it are in an "or" relationship.

[0086] Example:

[0087] like Figure 1As shown, the method for estimating the geostress parameters of the longitudinal profile of a long, deeply buried tunnel provided in the first aspect of this embodiment can be executed, but is not limited to, by a computer device with certain computing resources, such as a platform server, a personal computer (PC, referring to a multi-purpose computer of a size, price, and performance suitable for personal use; desktop computers, laptops, mini-laptops, tablets, and ultrabooks all belong to personal computers), a smartphone, a personal digital assistant (PDA), or a wearable device. Figure 1 As shown, the method for estimating the geostress parameter values ​​of the longitudinal profile of a deep-buried long tunnel may include, but is not limited to, the following steps S1 to S15.

[0088] S1. Obtain the measured values ​​of geostress parameters at multiple measured points on the longitudinal profile of a long, deeply buried tunnel.

[0089] In step S1, the measured values ​​of the geostress parameters at the multiple measured points are the geostress measurement data obtained by surveying a small number of field geostress measurement points, such as... Figure 2 The selected example is a long, deeply buried tunnel project: the tunnel axis is approximately 10km long, with a maximum burial depth of approximately 1760m. There are 12 geostress monitoring boreholes within the tunnel axis area. By actually measuring the geostress at different elevations in these 12 boreholes, the measured values ​​of the geostress parameters at these multiple measurement points can be obtained. The specific method for obtaining the measured values ​​of the geostress parameters at these multiple measurement points can be, but is not limited to, conventional external data import methods. Furthermore, the measured values ​​of the geostress parameters include, but are not limited to, the measured values ​​of the maximum horizontal principal stress, the minimum horizontal principal stress, and / or the vertical principal stress.

[0090] S2. Based on the measured values ​​of the geostress parameters at the multiple measured points, calculate the semivariance of the geostress parameters for each pair of measured points to obtain the semivariance values ​​of the geostress parameters for multiple pairs of measured points. Also, based on the known coordinates of the multiple measured points, calculate the distance values ​​between the multiple pairs of measured points.

[0091] In step S2, the semivariance of the geostress parameter represents half the square of the difference between the measured values ​​of the geostress parameter at two measuring points, i.e., the semivariance of the geostress parameter between measuring point A and measuring point B. Among them, Z A Z represents the measured value of the geostress parameter at measuring point A. B This represents the measured value of the geostress parameter at measuring point B. Since the semivariance of the geostress parameter between any two points is related to the distance between those two points, it is also necessary to calculate the distance values ​​between the multiple pairs of measured points.

[0092] Before step S2, considering that this embodiment is based on the ordinary kriging method for estimation, which requires the data to follow a normal distribution, preferably, before calculating the semivariance of the geostress parameters for each pair of measured points based on the measured values ​​of the geostress parameters at the multiple measured points, the method further includes, but is not limited to: determining whether the measured values ​​of the geostress parameters at the multiple measured points follow a normal distribution; if not, performing a power transformation or logarithmic transformation on the measured values ​​of the geostress parameters at the multiple measured points to obtain new measured values ​​of the geostress parameters at the multiple measured points that follow a normal distribution. The specific method of the aforementioned determination is a conventional method, such as based on the condition of following a normal distribution (i.e., if the random variable X follows a mathematical expectation of μ and a variance of σ). 2 The normal distribution is denoted as N(μ,σ). 2 The judgment is made by performing a power transformation. The specific formula for the aforementioned power transformation process is: The specific formula for the aforementioned logarithmic transformation is as follows: Where Z(x) represents the measured value of the geostress parameter before processing. This represents the newly measured value of the geostress parameter obtained after processing, and η represents the preset positive coefficient.

[0093] S3. Determine the hysteresis distance h1 and the longest distance value H among the distance values ​​of the multiple pairs of measured points. max Where h1 represents a positive number, H max This represents a positive number greater than h1.

[0094] In step S3, the hysteresis distance is an academic term in the Kriging method, and it can be determined manually, randomly, or automatically.

[0095] S4. Based on the hysteresis distance h1, divide the first interval (0, H) max The interval is divided into the following first subintervals: (0,h1],(h1,2*h1],…,((k-1)*h1,k*h1],…,((K-1)*h1,H max ], where K = Ceiling(H max / h1), Ceiling() represents the floor function, and k represents a positive integer less than k.

[0096] In step S4, for example, if the lag distance h1 is 10m, the longest distance value H max If the length is 1000 meters, then the first interval can be divided into 100 (i.e., K=100) first sub-intervals.

[0097] S5. Based on the distance values ​​of the multiple pairs of measured points and the attribution relationship of the multiple first sub-intervals, divide the multiple pairs of measured points into multiple first groups that correspond one-to-one with the multiple first sub-intervals.

[0098] In step S5, for example, if the distance between a pair of measured points is 78, then the pair of measured points can be assigned to a first group corresponding to a first sub-interval (60, 70), and so on. Furthermore, for non-last groups among the multiple first groups, there may be cases where no pair of measured points is assigned.

[0099] S6. Based on the semivariance values ​​and distance values ​​of the geostress parameters of the multiple pairs of measured points, calculate the average semivariance value and average distance value of the geostress parameters of each of the multiple first groups.

[0100] S7. Based on the average semivariogram and average distance of the geostress parameters of each first group, the model coefficients of multiple experimental variation function models are fitted, wherein the average semivariogram of the geostress parameters is used as the experimental variation function value during the fitting process, and the average distance is used as the distance to the point to be estimated as a regional variable during the fitting process.

[0101] In step S7, the experimental variation function model is an important research tool of the Kriging method. Specifically, the multiple experimental variation function models include, but are not limited to, any combination of the following models (A) to (C):

[0102] (A) Spherical model, expressed as:

[0103] (B) Exponential model, expressed as:

[0104] (C) Gaussian model, expressed as:

[0105] In the above expression, γ(h) represents the experimental variogram value, h represents the distance from the regionalized variable to the point to be estimated, C0 represents the sill value as the first model coefficient, C represents the arch height as the second model coefficient (i.e., the maximum value of the spatial variation of the regionalized variable), and a represents the range as the third model coefficient (i.e., the range within which the regionalized variable has correlation). Therefore, the model coefficients to be fitted include the sill value C0, the arch height C, and the range a. The specific fitting method can be, but is not limited to, the least squares method. Furthermore, for the non-last group among the multiple first groups that has no measured point pair to which it belongs, considering that the mean semivariogram and mean distance of its corresponding geostress parameters are both zero, it has no fitting value and needs to be skipped in the fitting process.

[0106] S8. Based on the measured values ​​of the geostress parameters at the multiple measured points, error analysis is performed using the model coefficients of the multiple experimental variogram models to obtain the model quality evaluation index value of each experimental variogram model in the multiple experimental variogram models.

[0107] In step S8, the model quality evaluation index values ​​include, but are not limited to, the mean error value, the root mean square error value, the standardized root mean square error value, and / or the mean standard error value. The calculation formulas for the aforementioned index values ​​are as follows:

[0108] Average error value

[0109] Root mean square error

[0110] Standardized root mean square error

[0111] Mean standard error

[0112] In the above formula, N represents the total number of measured points, n represents a positive integer, and Z(x) n () represents the measured value of the geostress parameter at the nth measurement point. δ represents the estimated value of the geostress parameter at the nth measured point. 2 This represents the square root of the variance. Since obtaining the model quality assessment index value requires estimated values ​​of the geostress parameters, specifically, based on the measured values ​​of the geostress parameters at the multiple measured points, error analysis is performed using the model coefficients of the multiple experimental variogram models to obtain the model quality assessment index value for each experimental variogram model, including but not limited to the following steps S81-S82.

[0113] S81. For each experimental variogram model in the plurality of experimental variogram models, based on the measured values ​​of the geostress parameters at the plurality of measured points and the corresponding model coefficients, the estimated values ​​of the geostress parameters at the plurality of measured points are obtained by cross-validation.

[0114] In step S81, the specific idea of ​​the cross-validation method is as follows: First, remove one of the multiple measured points, and then use the measured values ​​of the geostress parameters of the remaining measured points to calculate the estimated value of the geostress parameters of the removed point. Repeat this operation until the estimated values ​​of the geostress parameters of all measured points are obtained. Specifically, for each experimental variogram model in the multiple experimental variogram models, based on the measured values ​​of the geostress parameters of the multiple measured points and the corresponding model coefficients, the cross-validation method is used to obtain the corresponding estimated values ​​of the geostress parameters of the multiple measured points, including but not limited to the following steps S811 to S812.

[0115] S811. For each target measured point (i.e., a removed measured point) among the plurality of measured points, all other measured points (i.e., the remaining measured points) among the plurality of measured points are determined as the corresponding plurality of reference measured points.

[0116] S812. For a certain experimental variation function model among multiple experimental variation function models and a certain target measured point among multiple measured points, based on the model parameters of the certain experimental variation function model and the measured values ​​of the geostress parameters of multiple reference measured points of the target measured point, the corresponding geostress parameter estimates are calculated according to the following steps S8121 to S8123.

[0117] S8121. Based on the known coordinates of multiple reference measured points of the target measured point and the known coordinates of the target measured point, calculate the distance value from the target measured point to each of the multiple reference measured points of the target measured point, and substitute this distance value as the distance to the point to be estimated into the experimental variation function model. Then, apply the model parameters of the experimental variation function model to calculate the experimental variation function value between the target measured point and each of the multiple reference measured points of the target measured point.

[0118] S8122. Based on the semivariogram values ​​of the geostress parameters of each pair of reference measured points among the multiple reference measured points of the target measured point and the experimental variogram values ​​of each reference measured point among the multiple reference measured points of the target measured point, establish and solve the ordinary Kriging equation system to obtain multiple reference weight coefficients that correspond one-to-one with the multiple reference measured points of the target measured point.

[0119] In step S8122, the specific establishment and solution process of the ordinary Kriging equations can be derived by referring to the subsequent steps S12 to S13, and will not be repeated here.

[0120] S8123. Based on the measured values ​​of geostress parameters of multiple reference measured points of the target measured point and the multiple reference weight coefficients, the estimated value of geostress parameters of the target measured point is calculated.

[0121] In step S8123, the specific calculation formula can be derived by referring to the subsequent step S14, and will not be repeated here.

[0122] S82. For each experimental variation function model, the corresponding model quality evaluation index value is calculated based on the measured values ​​of the geostress parameters at the multiple measured points and the corresponding estimated values ​​of the geostress parameters at the multiple measured points.

[0123] S9. Based on the model quality evaluation index values ​​of each experimental variation function model, determine the optimal experimental variation function model that best meets the preset conditions for model optimization from among the multiple experimental variation function models.

[0124] In step S9, specifically, the preferred preset conditions for the model include, but are not limited to, an average error value close to 0, a standardized root mean square error value close to 1, and / or a root mean square error value close to the average standard error value. The more the aforementioned preferred preset conditions for the model are met, the better the quality of the corresponding model. Based on the aforementioned example of a deep-buried long tunnel project, the spherical model, the exponential model, and the Gaussian model are used to calculate the maximum horizontal principal stress S. H Minimum horizontal principal stress S h and vertical principal stress S v The error analysis results of the isotropic stress parameters are shown in Table 1 below:

[0125] Table 1. Error analysis results of the spherical model, exponential model, and Gaussian model

[0126]

[0127] As shown in Table 1 above, the average error values ​​of the three principal stresses are close to 0 in all three models. Among the three models, the normalized root mean square error value is closest to 1 in the Gaussian model, and the root mean square error value is closest to the average normalized error value. Therefore, the Gaussian model is the optimal experimental variation function model in this case.

[0128] S10. Based on the known coordinates of the plurality of measured points and the known coordinates of the target measuring point on the longitudinal profile of the deep-buried long tunnel, determine m measured points located around the target measuring point from the plurality of measured points, where m represents a positive integer greater than 2.

[0129] In step S10, the target measurement point can be a real measurement point or a non-real measurement point (i.e., a measurement point that needs to be valued). The area surrounding the target measurement point can specifically refer to a circular region centered on the target measurement point with a radius of a specific value. This specific value can be adjusted appropriately based on the search results of the real measurement point. For example, the radius value can be increased when m is too small, and decreased when m is too large.

[0130] S11. Based on the known coordinates of the target measuring point and the known coordinates of the m measured points, calculate the distance from the target measuring point to each of the m measured points, and substitute this distance as the distance to the point to be estimated into the optimal experimental variation function model. Then, apply the model coefficients of the optimal experimental variation function model to calculate the experimental variation function values ​​between the target measuring point and each of the m measured points.

[0131] S12. Based on the semivariance values ​​of the geostress parameters of each pair of measured points among the m measured points and the experimental variation function values ​​of the target measuring point and each of the m measured points, the following ordinary Kriging equations are established:

[0132]

[0133] In the formula, i and j represent positive integers, and λ i γ(x) represents the weight coefficient to be solved for the i-th measured point among the m measured points. i ,x j ) represents the semivariance value of the geostress parameter corresponding to the i-th measured point and the j-th measured point among the m measured points, u represents the Lagrange multiplier factor to be solved, and γ(x) represents the semivariance value of the geostress parameter corresponding to the i-th measured point and the j-th measured point among the m measured points. i x0) represents the experimental variation function value between the target measurement point and the i-th measured point.

[0134] In step S12, the ordinary Kriging equations are a combination of m+1 equations based on Kriging interpolation (also known as the Kriging method, a spatial interpolation method proposed by South African engineer Krige DG; its basic assumption is that the attribute value of a point is related to the attribute values ​​of the points surrounding that point, and can be derived from the attribute values ​​of the points surrounding that point; it is an optimal, unbiased estimation method with strong spatial correlation, using variograms / variance functions as computational tools and combined with structural analysis). This is the unbiased estimation condition for the Kriging interpolation method.

[0135] S13. Solve the ordinary Kriging equations to obtain m weighting coefficients that correspond one-to-one with the m measured points.

[0136] In step S13, since there are only m+1 unknowns in the ordinary Kriging equations, the m weight coefficients corresponding to the m measured points can be obtained by conventional equation-solving methods.

[0137] S14. Based on the measured values ​​of the geostress parameters at the m measured points, the estimated value of the geostress parameter Z(x0) at the target measuring point is calculated according to the following formula:

[0138]

[0139] In the formula, Z(x) i ) represents the measured value of the geostress parameter at the i-th measured point.

[0140] In step S14, since the measured ground stress value can be used as the attribute value of a known point in tunnel engineering, and the ground stress on the longitudinal section of the tunnel axis can be regarded as multiple realizations of a random field, the principal stress result on the longitudinal section of the tunnel axis can be estimated / interpolated using the above formula.

[0141] S15. Based on the estimated values ​​of the geostress parameters of all the target measuring points in the longitudinal profile of the deep-buried long tunnel, draw the geostress parameter estimation contour map on the longitudinal profile of the deep-buried long tunnel, and output and display the geostress parameter estimation contour map.

[0142] In step S15, since the target measuring point can be a specific measuring point, the measured values ​​of geostress parameters, estimated values ​​of geostress parameters, and corresponding fitting results of 29 measuring points from three boreholes (②, ③, and ⑧) at different locations and with greater burial depths can be obtained as shown in Table 2 below:

[0143] Table 2. Measured values ​​of geostress parameters, estimated values ​​of geostress parameters, and corresponding fit results

[0144]

[0145]

[0146] The maximum horizontal principal stress S at the above 29 measured points H Minimum horizontal principal stress S h and vertical principal stress S v The interpolated data (i.e., the estimated data) was compared with the measured data, and the results are as follows: Figures 3-5 As shown: the goodness of fit for the maximum principal stress values ​​at most measured points is between 0.85 and 1.15, with the error controlled within ±15%, and the fitting accuracy is approximately 85%. Only a few measured points in the shallow part of the borehole have a goodness of fit of 0.76, which is preliminarily analyzed as being due to the influence of slope topography and the inherent error of the test results on the ground stress in the shallow part of the borehole. The goodness of fit for the minimum principal stress values ​​at most measured points is between 0.8 and 1.15, with a few measured points in the shallow part of the borehole showing poor fit. The goodness of fit for the vertical principal stress values ​​at most measured points is between 0.8 and 1.1, which proves that the estimated results obtained based on the optimal experimental variogram model have a good fit with the measured data and can meet the needs of practical engineering applications. Therefore, contour maps of the maximum horizontal principal stress, minimum horizontal principal stress, and vertical principal stress of the longitudinal profile of the deep-buried long tunnel can be drawn respectively, as shown in the figure. Figures 6-8 As shown.

[0147] Furthermore, after drawing the contour maps of each principal stress, the stress distribution characteristics along the tunnel axis can be analyzed based on these contour maps to achieve engineering application objectives. For example, based on the actual location of the tunnel axis, from... Figures 6-8 The values ​​of the three principal stresses along the tunnel axis are extracted from the principal stress contour map shown, as follows: Figure 9 As shown: at approximately 2200m along the tunnel axis, the maximum burial depth is approximately 1760m, and the maximum horizontal principal stress is 51.13MPa; the results of segmented statistical analysis of the maximum horizontal principal stress along the tunnel axis are shown in Table 3 below:

[0148] Table 3. Division of Maximum Horizontal Principal Stress

[0149] Tunnel section / m <![CDATA[Maximum principal stress S H / MPa]]> Length / m Percentage / % 0～200 <![CDATA[S H <10]]> 200 2.13 200～750 <![CDATA[10<S H <20]]> 550 5.85 750～1150 <![CDATA[20<S H <30]]> 400 4.26 1150～1600 <![CDATA[30<S H <40]]> 450 4.79 1600～2000 <![CDATA[40<S H <50]]> 400 4.26 2000～2400 <![CDATA[S H >50]]> 400 4.26 2400～3100 <![CDATA[40<S H <50]]> 700 7.45 3100～4000 <![CDATA[30<S H <40]]> 900 9.57 4000～4500 <![CDATA[20<S H <30]]> 500 5.32 4500～6300 <![CDATA[10<S H <20]]> 1800 19.15 6300～6800 <![CDATA[20<S H <30]]> 500 5.32 6800～7100 <![CDATA[30<S H <40]]> 300 3.19 7100～7500 <![CDATA[20<S H <30]]> 400 4.26 7500～8700 <![CDATA[10<S H <20]]> 1200 12.77 8700～9100 <![CDATA[20<S H <30]]> 400 4.26 9100～9400 <![CDATA[10<S H <20]]> 300 3.19

[0150] Based on Table 3 above, the length of sections with a maximum horizontal principal stress greater than 50 MPa is approximately 400 m, accounting for 4.26%; the length with a maximum horizontal principal stress greater than 40 MPa but less than 50 MPa is approximately 1100 m, accounting for 11.70%; the length with a maximum horizontal principal stress greater than 30 MPa but less than 40 MPa is approximately 1650 m, accounting for 17.55%; the length with a maximum horizontal principal stress greater than 20 MPa but less than 30 MPa is approximately 2200 m, accounting for 23.40%; the length with a maximum horizontal principal stress greater than 10 MPa but less than 20 MPa is approximately 3850 m, accounting for 40.96%; and the length with a maximum horizontal principal stress less than 10 MPa is approximately 200 m, accounting for 2.13%.

[0151] Therefore, based on the method for estimating the geostress parameters of the longitudinal profile of a deep-buried long tunnel described in steps S1 to S15 above, a spatial interpolation calculation scheme for geostress in deep-buried long tunnels based on geostatistical kriging is provided. This scheme treats the tunnel geostress field as a random field and the measured geostress as several realizations of the random field. After obtaining the measured values ​​of geostress parameters at multiple measured points on the longitudinal profile of the deep-buried long tunnel, the model coefficients of multiple experimental variogram models are first obtained by fitting these measured data. Then, the optimal model is selected through error analysis. Finally, spatial interpolation calculation of geostress is performed using this optimal model to obtain the geostress results along the tunnel axis. This method eliminates the need for detailed and accurate geological data and soil parameters, and can quickly and efficiently obtain the geostress distribution results along the tunnel axis that meet the requirements and accuracy of tunnel engineering demonstration and design stages, relying only on measured data from a small number of measured points. This significantly shortens the required time and has certain theoretical significance and high engineering practical value, facilitating practical application and promotion.

[0152] Based on the technical solution of the first aspect mentioned above, this embodiment also provides a possible design for automatically determining a suitable hysteresis distance h1, that is, determining the hysteresis distance h1, including but not limited to the following steps S31 to S34.

[0153] S31. In the interval (0, H) max Select a value from the [value] as the current value of the hysteresis distance h1, and then execute step S32, where H max This represents the longest distance value among the distance values ​​of the multiple pairs of measured points.

[0154] In step S31, the specific selection method may be, but is not limited to, random selection.

[0155] S32. For each experimental variogram model in multiple experimental variogram models, based on the current value of the lag distance h1 and the measured values ​​of the geostress parameters at the multiple measured points, cross-validation is used to obtain the corresponding estimated values ​​of the geostress parameters at the multiple measured points, and then step S33 is executed.

[0156] In step S32, the specific approach of the cross-validation method is similar to that in step S81. Specifically, for each experimental variogram model among multiple experimental variogram models, based on the current value of the lag distance h1 and the measured values ​​of the geostress parameters at the multiple measured points, cross-validation is used to obtain the corresponding estimated values ​​of the geostress parameters at the multiple measured points, including but not limited to the following steps S321 to S322:

[0157] S321. For each target measured point among the plurality of measured points, all other measured points among the plurality of measured points are determined as corresponding plurality of reference measured points.

[0158] S322. For a certain experimental variation function model among multiple experimental variation function models and a certain target measured point among multiple measured points, based on the current value of the lag distance h1, the model parameters of the certain experimental variation function model, and the measured values ​​of the geostress parameters of multiple reference measured points of the target measured point, the corresponding geostress parameter estimates are calculated according to the following steps S3221 to S3228.

[0159] S3221. Determine the longest distance value among multiple pairs of reference measured points. The multiple pairs of reference measured points refer to a point pair among the multiple reference measured points of a certain target measured point.

[0160] S3222. Based on the current value of the hysteresis distance h1, the second interval... It is divided into the following multiple second sub-intervals: in, Ceiling() is a function that rounds up the value. Indicates less than Positive integers.

[0161] S3223. Based on the distance values ​​of the multiple pairs of reference measured points and the attribution relationship of the multiple second sub-intervals, divide the multiple pairs of reference measured points into multiple second groups that correspond one-to-one with the multiple second sub-intervals.

[0162] S3224. Based on the semivariance values ​​and distance values ​​of the geostress parameters of the multiple pairs of reference measured points, calculate the average semivariance values ​​and average distance values ​​of the geostress parameters of each of the multiple second groups.

[0163] S3225. Based on the average semivariogram and average distance of the geostress parameters of each second group, the model coefficients of a certain experimental variation function model are fitted, wherein the average semivariogram of the geostress parameters is used as the experimental variation function value during the fitting process, and the average distance is used as the distance to the point to be estimated as a regional variable during the fitting process.

[0164] S3226. Based on the known coordinates of multiple reference measured points of the target measured point and the known coordinates of the target measured point, calculate the distance value from the target measured point to each of the multiple reference measured points of the target measured point, and substitute this distance value as the distance to the point to be estimated into the experimental variation function model. Then, apply the model parameters of the experimental variation function model to calculate the experimental variation function value between the target measured point and each of the multiple reference measured points of the target measured point.

[0165] S3227. Based on the semivariance values ​​of the geostress parameters of each pair of reference measured points among the multiple reference measured points of the target measured point and the experimental variation function values ​​of each reference measured point among the multiple reference measured points of the target measured point, establish and solve the ordinary Kriging equation system to obtain multiple reference weight coefficients that correspond one-to-one with the multiple reference measured points of the target measured point.

[0166] S3228. Based on the measured values ​​of geostress parameters of multiple reference measured points of the target measured point and the multiple reference weight coefficients, the estimated value of geostress parameters of the target measured point is calculated.

[0167] The specific details of steps S3221 to S3228 above can be derived by referring to similar steps in the first aspect, and will not be repeated here.

[0168] S33. For each experimental variation function model, calculate the corresponding model quality evaluation index value based on the measured values ​​of the geostress parameters at the multiple measured points and the corresponding estimated values ​​of the geostress parameters at the multiple measured points, and then execute step S34.

[0169] S34. Determine whether the model quality assessment index values ​​of each experimental variation function model meet the preset iteration stopping condition. If yes, determine the current value of the lag distance h1 as the final value; otherwise, in the interval (0, H... max Select a new value as the current value of the hysteresis distance h1, and then execute step S32.

[0170] In step S34, the iteration stopping condition can be set based on, but is not limited to, some index thresholds. For example, it may include an average error value less than a preset average error threshold and / or a standardized root mean square error value greater than a preset standardized root mean square error threshold, etc., to measure whether a relatively ideal experimental variation function model has been obtained. Furthermore, the specific method of reselection can also be, but is not limited to, random selection.

[0171] Therefore, based on the aforementioned possible design one, the appropriate lag distance h1 that can obtain a more ideal model can be automatically determined through cross-validation and iteration, so as to further facilitate the quick and efficient acquisition of the final tunnel axis ground stress distribution results.

[0172] like Figure 10 As shown, the second aspect of this embodiment provides a virtual device for implementing the method for estimating the geostress parameter values ​​of the longitudinal profile of a deep-buried long tunnel as described in the first aspect or a possible design. The device includes a measured data acquisition module, an intermediate data calculation module, an intermediate data determination module, a numerical interval division module, a measured point pair grouping module, a mean data calculation module, a model coefficient fitting module, a model error analysis module, an optimal model selection module, a surrounding measuring point determination module, a variation function calculation module, an equation system construction module, an equation system solution module, an estimated data calculation module, and a contour map drawing module, all connected in sequence.

[0173] The measured data acquisition module is used to acquire the measured values ​​of geostress parameters at multiple measured points on the longitudinal profile of a deep-buried long tunnel.

[0174] The intermediate data calculation module is used to calculate the semivariance of the geostress parameters of each pair of measured points according to the measured values ​​of the geostress parameters of the multiple measured points, to obtain the semivariance values ​​of the geostress parameters of multiple pairs of measured points, and also to calculate the distance values ​​of the multiple pairs of measured points according to the known coordinates of the multiple measured points.

[0175] The intermediate data determination module is used to determine the lag distance h1 and the longest distance value H among the distance values ​​of the multiple pairs of measured points. max Where h1 represents a positive number, H max This represents a positive number greater than h1;

[0176] The numerical interval division module is used to divide the first interval (0, H) according to the lag distance h1. max The interval is divided into the following first subintervals: (0,h1],(h1,2*h1],…,((k-1)*h1,k*h1],…,((K-1)*h1,H max ], where K = Ceiling(H max / h1), Ceiling() represents the floor function, and k represents a positive integer less than k;

[0177] The measured point pair grouping module is used to divide the multiple pairs of measured points into multiple first groups corresponding one-to-one with the multiple first sub-intervals based on the distance values ​​of the multiple pairs of measured points and the attribution relationship of the multiple first sub-intervals;

[0178] The mean data calculation module is used to calculate the average value of the half variance of the ground stress parameters and the average value of the distance in each of the multiple first groups based on the half variance values ​​and distance values ​​of the ground stress parameters of the multiple pairs of measured points.

[0179] The model coefficient fitting module is used to fit the model coefficients of multiple experimental variation function models based on the average semivariogram and average distance of the geostress parameters of each first group. The average semivariogram of the geostress parameters is used as the experimental variation function value during the fitting process, and the average distance is used as the distance to the point to be estimated as a regional variable during the fitting process.

[0180] The model error analysis module is used to perform error analysis based on the measured values ​​of the geostress parameters at the multiple measured points, applying the model coefficients of the multiple experimental variogram models, and obtaining the model quality evaluation index value of each experimental variogram model in the multiple experimental variogram models.

[0181] The optimal model selection module is used to determine the optimal experimental variation function model that best meets the preset conditions for model optimization from the multiple experimental variation function models based on the model quality evaluation index value of each experimental variation function model.

[0182] The surrounding measuring point determination module is used to determine m measuring points located around the target measuring point from the plurality of measuring points based on the known coordinates of the plurality of measuring points and the known coordinates of the target measuring point on the longitudinal profile of the deep-buried long tunnel, where m represents a positive integer greater than 2;

[0183] The variation function calculation module is used to calculate the distance value from the target measurement point to each of the m measured points based on the known coordinates of the target measurement point and the known coordinates of the m measured points, and to substitute this distance value as the distance to the point to be estimated into the optimal experimental variation function model. Then, the model coefficients of the optimal experimental variation function model are applied to calculate the experimental variation function value between the target measurement point and each of the m measured points.

[0184] The equation system construction module is used to establish the following ordinary Kriging equation system based on the semivariance values ​​of the geostress parameters of each pair of measured points in the m measured points and the experimental variation function values ​​of the target measuring point and each of the m measured points:

[0185]

[0186] In the formula, i and j represent positive integers, and λ i γ(x) represents the weight coefficient to be solved for the i-th measured point among the m measured points. i ,x j ) represents the relationship between the i-th measured point and the... m The semivariance of the geostress parameter corresponding to the j-th measured point in the measured points, u represents the Lagrange multiplier factor to be solved, γ(x i (x0) represents the experimental variation function value between the target measurement point and the i-th measured point;

[0187] The equation solving module is used to solve the ordinary Kriging equations to obtain m weight coefficients that correspond one-to-one with the m measured points.

[0188] The estimation data calculation module is used to calculate the estimated value Z(x0) of the geostress parameter of the target measuring point according to the measured values ​​of the geostress parameters of the m measured points, using the following formula:

[0189]

[0190] In the formula, Z(x) i ) represents the measured value of the geostress parameter at the i-th measured point;

[0191] The contour map drawing module is used to draw a contour map of estimated ground stress parameters on the longitudinal profile of the deep-buried long tunnel based on the estimated ground stress parameters of all target measuring points in the entire region on the longitudinal profile of the deep-buried long tunnel, and output and display the contour map of estimated ground stress parameters.

[0192] The working process, working details and technical effects of the aforementioned device provided in the second aspect of this embodiment can be found in the method for estimating the geostress parameter values ​​of the longitudinal profile of a deep-buried long tunnel as described in the first aspect or possible design, and will not be repeated here.

[0193] like Figure 11 As shown, the third aspect of this embodiment provides a computer device for executing the method for estimating the longitudinal profile in-situ stress parameters of a deep-buried long tunnel as described in the first aspect or a possible design. The device includes a memory, a processor, and a transceiver connected in sequence. The memory stores a computer program, the transceiver transmits and receives data, and the processor reads the computer program and executes the method for estimating the longitudinal profile in-situ stress parameters of a deep-buried long tunnel as described in the first aspect or a possible design. Specifically, the memory may include, but is not limited to, random-access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out (FIFO) memory, and / or first-in-last-out (FILO) memory, etc.; the processor may include, but is not limited to, a microprocessor of the STM32F105 series. Furthermore, the computer device may also include, but is not limited to, a power module, a display screen, and other necessary components.

[0194] The working process, working details and technical effects of the aforementioned computer equipment provided in the third aspect of this embodiment can be found in the method for estimating the geostress parameter values ​​of the longitudinal profile of a deep-buried long tunnel as described in the first aspect or possible design, and will not be repeated here.

[0195] This fourth aspect of the embodiment provides a computer-readable storage medium storing instructions comprising a method for estimating the longitudinal profile in-situ stress parameters of a deep-buried long tunnel as described in the first aspect or a possible design. Specifically, the computer-readable storage medium stores instructions that, when executed on a computer, perform the method for estimating the longitudinal profile in-situ stress parameters of a deep-buried long tunnel as described in the first aspect or a possible design. The computer-readable storage medium refers to a data storage medium, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or Memory Sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0196] The working process, working details and technical effects of the aforementioned computer-readable storage medium provided in the fourth aspect of this embodiment can be found in the method for estimating the geostress parameter values ​​of the longitudinal profile of a deep-buried long tunnel as described in the first aspect or possible design, and will not be repeated here.

[0197] This fifth aspect of the embodiment provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform the method for estimating the geostress parameter values ​​of the longitudinal profile of a deep-buried long tunnel as described in the first aspect or a possible design. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0198] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for estimating the geostress parameters of a longitudinal profile of a deep-buried, long tunnel, characterized in that, include: Obtain measured values ​​of geostress parameters at multiple measured points on the longitudinal profile of a long, deeply buried tunnel; Based on the measured values ​​of the geostress parameters at the multiple measured points, the semivariance of the geostress parameters for each pair of measured points is calculated to obtain the semivariance values ​​of the geostress parameters for multiple pairs of measured points. In addition, based on the known coordinates of the multiple measured points, the distance values ​​of the multiple pairs of measured points are calculated. Determine the lag distance h1 and the longest distance H among the distance values ​​of the multiple pairs of measured points. max Where h1 represents a positive number, H max This represents a positive number greater than h1; Based on the hysteresis h1, the first interval (0, H) max The interval is divided into the following first subintervals: (0,h1],(h1,2*h1],…,((k-1)*h1,k*h1],…,((K-1)*h1,H max ], where K = Ceiling(H max / h1), Ceiling() represents the floor function, and k represents a positive integer less than k; Based on the distance values ​​of the multiple pairs of measured points and the attribution relationship of the multiple first sub-intervals, the multiple pairs of measured points are divided into multiple first groups that correspond one-to-one with the multiple first sub-intervals; Based on the semivariance values ​​and distance values ​​of the geostress parameters of the multiple pairs of measured points, the average semivariance values ​​and average distance values ​​of the geostress parameters of each of the multiple first groups are calculated. Based on the average semivariogram and average distance of the geostress parameters of each first group, the model coefficients of multiple experimental variation function models are fitted. The average semivariogram of the geostress parameters is used as the experimental variation function value during the fitting process, and the average distance is used as the distance to the point to be estimated as a regional variable during the fitting process. Based on the measured values ​​of geostress parameters at the multiple measured points, error analysis is performed using the model coefficients of the multiple experimental variogram models to obtain the model quality evaluation index value of each experimental variogram model in the multiple experimental variogram models. Based on the model quality evaluation index values ​​of each experimental variation function model, the optimal experimental variation function model that best meets the preset conditions for model optimization is determined from the multiple experimental variation function models. Based on the known coordinates of the plurality of measured points and the known coordinates of the target measuring point on the longitudinal profile of the deep-buried long tunnel, m measured points located around the target measuring point are determined from the plurality of measured points, where m represents a positive integer greater than 2; Based on the known coordinates of the target measurement point and the known coordinates of the m measured points, the distance from the target measurement point to each of the m measured points is calculated. This distance is then used as a regional variable to represent the distance to the point to be estimated and substituted into the optimal experimental variation function model. Finally, the model coefficients of the optimal experimental variation function model are applied to calculate the experimental variation function values ​​between the target measurement point and each of the m measured points. Based on the semivariance values ​​of the geostress parameters of each pair of measured points in the m measured points and the experimental variation function values ​​of the target measuring point and each of the m measured points, the following ordinary Kriging equations are established: In the formula, i and j represent positive integers, and λ i γ(x) represents the weight coefficient to be solved for the i-th measured point among the m measured points. i ,x j ) represents the semivariance value of the geostress parameter corresponding to the i-th measured point and the j-th measured point among the m measured points, u represents the Lagrange multiplier factor to be solved, and γ(x) represents the semivariance value of the geostress parameter corresponding to the i-th measured point and the j-th measured point among the m measured points. i (x0) represents the experimental variation function value between the target measurement point and the i-th measured point; Solving the ordinary Kriging equations yields m weighting coefficients that correspond one-to-one with the m measured points. Based on the measured values ​​of the geostress parameters at the m measured points, the estimated value of the geostress parameter Z(x0) at the target measuring point is calculated according to the following formula: In the formula, Z(x) i ) represents the measured value of the geostress parameter at the i-th measured point; Based on the estimated values ​​of the geostress parameters of all target measuring points in the longitudinal profile of the deep-buried long tunnel, a geostress parameter estimation contour map is drawn on the longitudinal profile of the deep-buried long tunnel, and the geostress parameter estimation contour map is output and displayed.

2. The method for estimating the geostress parameters of a long, deeply buried tunnel longitudinal profile according to claim 1, characterized in that, Before calculating the semivariance of the geostress parameters for each pair of measured points based on the measured values ​​of the geostress parameters at the plurality of measured points, the method further includes: Determine whether the measured values ​​of the geostress parameters at the multiple measured points follow a normal distribution; If not, then the measured values ​​of the geostress parameters at the multiple measured points are subjected to power transformation or logarithmic transformation to obtain new measured values ​​of the geostress parameters at the multiple measured points that follow a normal distribution.

3. The method for estimating the geostress parameters of a long, deeply buried tunnel longitudinal profile according to claim 1, characterized in that, Determining the lag distance h1 includes the following steps S31 to S34: S31. In the interval (0, H) max Select a value from the [value] as the current value of the hysteresis distance h1, and then execute step S32, where H max This represents the longest distance value among the distance values ​​of the multiple pairs of measured points; S32. For each experimental variogram model in multiple experimental variogram models, based on the current value of the lag distance h1 and the measured values ​​of the geostress parameters at the multiple measured points, cross-validation is used to obtain the corresponding estimated values ​​of the geostress parameters at the multiple measured points, and then step S33 is executed. S33. For each experimental variogram model, calculate the corresponding model quality evaluation index value based on the measured values ​​of the geostress parameters at the multiple measured points and the corresponding estimated values ​​of the geostress parameters at the multiple measured points, and then execute step S34. S34. Determine whether the model quality assessment index values ​​of each experimental variation function model meet the preset iteration stopping condition. If yes, determine the current value of the lag distance h1 as the final value; otherwise, in the interval (0, H... max Select a new value as the current value of the hysteresis distance h1, and then execute step S32.

4. The method for estimating the geostress parameters of a long, deeply buried tunnel longitudinal profile according to claim 3, characterized in that, For each experimental variogram model in multiple experimental variogram models, based on the current value of the lag distance h1 and the measured values ​​of the geostress parameters at the multiple measured points, cross-validation is used to obtain the corresponding estimated values ​​of the geostress parameters at the multiple measured points, including: For each target measured point among the plurality of measured points, all other measured points among the plurality of measured points are determined as corresponding plurality of reference measured points; For a specific experimental variation function model among multiple experimental variation function models and a specific target measured point among multiple measured points, based on the current value of the lag distance h1, the model parameters of the specific experimental variation function model, and the measured values ​​of the geostress parameters at multiple reference measured points of the specific target measured point, the corresponding geostress parameter estimates are calculated according to the following steps: Determine the longest distance value among multiple pairs of reference measured points. Among them, the multiple pairs of reference measured points refer to a point pair among the multiple reference measured points of a certain target measured point; Based on the current value of the lag distance h1, the second interval It is divided into the following multiple second sub-intervals: in, Ceiling() is a function that rounds up the value. Indicates less than Positive integers; Based on the distance values ​​of the multiple pairs of reference measured points and the attribution relationship of the multiple second sub-intervals, the multiple pairs of reference measured points are divided into multiple second groups that correspond one-to-one with the multiple second sub-intervals; Based on the semivariance values ​​and distance values ​​of the geostress parameters of the multiple pairs of reference measured points, the average semivariance values ​​and average distance values ​​of the geostress parameters of each of the multiple second groups are calculated. Based on the average semivariogram and average distance of the geostress parameters of each second group, the model coefficients of a certain experimental variation function model are fitted, wherein the average semivariogram of the geostress parameters is used as the experimental variation function value during the fitting process, and the average distance is used as the distance to the point to be estimated as a regional variable during the fitting process. Based on the known coordinates of multiple reference measured points of a certain target measured point and the known coordinates of the target measured point, the distance value from the target measured point to each of the multiple reference measured points is calculated. This distance value is then used as a regional variable to the distance to the point to be estimated and substituted into the experimental variation function model. Then, the model parameters of the experimental variation function model are applied to calculate the experimental variation function value between the target measured point and each of the multiple reference measured points of the target measured point. Based on the semivariance values ​​of geostress parameters of each pair of reference measured points among multiple reference measured points of a certain target measured point and the experimental variability function values ​​of each reference measured point among multiple reference measured points of the certain target measured point, the ordinary Kriging equation system is established and solved to obtain multiple reference weight coefficients that correspond one-to-one with the multiple reference measured points of the certain target measured point. Based on the measured values ​​of geostress parameters of multiple reference measured points of a certain target measured point and the multiple reference weight coefficients, the estimated value of geostress parameters of the certain target measured point is calculated.

5. The method for estimating the geostress parameters of a long, deeply buried tunnel longitudinal profile according to claim 1, characterized in that, Based on the measured values ​​of geostress parameters at the multiple measured points, error analysis is performed using the model coefficients of the multiple experimental variogram models to obtain the model quality evaluation index values ​​for each experimental variogram model, including: For each experimental variation function model in the plurality of experimental variation function models, based on the measured values ​​of the geostress parameters at the plurality of measured points and the corresponding model coefficients, the cross-validation method is used to obtain the corresponding estimated values ​​of the geostress parameters at the plurality of measured points. For each experimental variation function model, the corresponding model quality evaluation index value is calculated based on the measured values ​​of the geostress parameters at the multiple measured points and the corresponding estimated values ​​of the geostress parameters at the multiple measured points.

6. The method for estimating the geostress parameters of a long, deeply buried tunnel longitudinal profile according to claim 1, characterized in that, The multiple experimental variation function models include any combination of the following models (A) to (C): (A) Spherical model, expressed as: (B) Exponential model, expressed as: (C) Gaussian model, expressed as: In the above expression, γ(h) represents the experimental variogram value, h represents the distance from the regionalized variable to the point to be estimated, C0 represents the sill value as the first model coefficient, C represents the camber as the second model coefficient, and a represents the range as the third model coefficient.

7. The method for estimating the geostress parameters of a long, deeply buried tunnel longitudinal profile according to claim 1, characterized in that, The model quality evaluation index values ​​include the average error value, root mean square error value, standardized root mean square error value and / or average standard error value. The preferred preset conditions of the model include the average error value being close to 0, the standardized root mean square error value being close to 1 and / or the root mean square error value being close to the average standard error value.

8. A device for estimating the geostress parameters of a long, deeply buried tunnel longitudinal profile, characterized in that, It includes a measured data acquisition module, an intermediate data calculation module, an intermediate data determination module, a numerical interval division module, a measured point pair grouping module, a mean data calculation module, a model coefficient fitting module, a model error analysis module, an optimal model selection module, a surrounding measurement point determination module, a variation function calculation module, an equation system construction module, an equation system solution module, an estimated data calculation module, and a contour map drawing module, all connected in sequence. The measured data acquisition module is used to acquire the measured values ​​of geostress parameters at multiple measured points on the longitudinal profile of a deep-buried long tunnel. The intermediate data calculation module is used to calculate the semivariance of the geostress parameters of each pair of measured points according to the measured values ​​of the geostress parameters of the multiple measured points, to obtain the semivariance values ​​of the geostress parameters of multiple pairs of measured points, and also to calculate the distance values ​​of the multiple pairs of measured points according to the known coordinates of the multiple measured points. The intermediate data determination module is used to determine the lag distance h1 and the longest distance value H among the distance values ​​of the multiple pairs of measured points. max Where h1 represents a positive number, H max This represents a positive number greater than h1; The numerical interval division module is used to divide the first interval (0, H) according to the lag distance h1. max The interval is divided into the following first subintervals: (0,h1],(h1,2*h1],…,((k-1)*h1,k*h1],…,((K-1)*h1,H max ], where K = Ceiling(H max / h1), Ceiling() represents the floor function, and k represents a positive integer less than k; The measured point pair grouping module is used to divide the multiple pairs of measured points into multiple first groups corresponding one-to-one with the multiple first sub-intervals based on the distance values ​​of the multiple pairs of measured points and the attribution relationship of the multiple first sub-intervals; The mean data calculation module is used to calculate the average value of the half variance of the ground stress parameters and the average value of the distance in each of the multiple first groups based on the half variance values ​​and distance values ​​of the ground stress parameters of the multiple pairs of measured points. The model coefficient fitting module is used to fit the model coefficients of multiple experimental variation function models based on the average semivariogram and average distance of the geostress parameters of each first group. The average semivariogram of the geostress parameters is used as the experimental variation function value during the fitting process, and the average distance is used as the distance to the point to be estimated as a regional variable during the fitting process. The model error analysis module is used to perform error analysis based on the measured values ​​of the geostress parameters at the multiple measured points, applying the model coefficients of the multiple experimental variogram models, and obtaining the model quality evaluation index value of each experimental variogram model in the multiple experimental variogram models. The optimal model selection module is used to determine the optimal experimental variation function model that best meets the preset conditions for model optimization from the multiple experimental variation function models based on the model quality evaluation index value of each experimental variation function model. The surrounding measuring point determination module is used to determine m measuring points located around the target measuring point from the plurality of measuring points based on the known coordinates of the plurality of measuring points and the known coordinates of the target measuring point on the longitudinal profile of the deep-buried long tunnel, where m represents a positive integer greater than 2; The variation function calculation module is used to calculate the distance value from the target measurement point to each of the m measured points based on the known coordinates of the target measurement point and the known coordinates of the m measured points, and to substitute this distance value as the distance to the point to be estimated into the optimal experimental variation function model. Then, the model coefficients of the optimal experimental variation function model are applied to calculate the experimental variation function value between the target measurement point and each of the m measured points. The equation system construction module is used to establish the following ordinary Kriging equation system based on the semivariance values ​​of the geostress parameters of each pair of measured points in the m measured points and the experimental variation function values ​​of the target measuring point and each of the m measured points: In the formula, i and j represent positive integers, and λ i γ(x) represents the weight coefficient to be solved for the i-th measured point among the m measured points. i ,x j ) represents the semivariance value of the geostress parameter corresponding to the i-th measured point and the j-th measured point among the m measured points, u represents the Lagrange multiplier factor to be solved, and γ(x) represents the semivariance value of the geostress parameter corresponding to the i-th measured point and the j-th measured point among the m measured points. i (x0) represents the experimental variation function value between the target measurement point and the i-th measured point; The equation solving module is used to solve the ordinary Kriging equations to obtain m weight coefficients that correspond one-to-one with the m measured points. The estimation data calculation module is used to calculate the estimated value Z(x0) of the geostress parameter of the target measuring point according to the measured values ​​of the geostress parameters of the m measured points, using the following formula: In the formula, Z(x) i ) represents the measured value of the geostress parameter at the i-th measured point; The contour map drawing module is used to draw a contour map of estimated ground stress parameters on the longitudinal profile of the deep-buried long tunnel based on the estimated ground stress parameters of all target measuring points in the entire region on the longitudinal profile of the deep-buried long tunnel, and output and display the contour map of estimated ground stress parameters.

9. A computer device, characterized in that, The device includes a memory, a processor, and a transceiver connected in sequence, wherein the memory is used to store a computer program, the transceiver is used to send and receive data, and the processor is used to read the computer program and execute the method for estimating the geostress parameter values ​​of the longitudinal profile of a deep-buried long tunnel as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that... The computer-readable storage medium stores instructions that, when executed on a computer, perform the method for estimating the geostress parameters of the longitudinal profile of a deep-buried long tunnel as described in any one of claims 1 to 7.

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