Method and system for predicting blasting vibration velocity of deep-buried high-temperature tunnel

Abstract

The present application provides a kind of deep high ground temperature tunnel blasting vibration velocity prediction method and system, it is related to high temperature tunnel blasting engineering technical field, the present application obtains the wave velocity data along tunnel, and the discrete sequence of surrounding rock porosity is obtained by inversion, and based on sliding window detection method analysis porosity variation, dense and loose surrounding rock section is divided, vibration attenuation index is calculated in combination with thermal energy blasting theory and vibration attenuation model, and according to porosity data, the density of different section surrounding rock is estimated respectively, the initial vibration velocity of blasting is calculated, and vibration velocity prediction model is established in combination with wave equation and material damping characteristics, for high ground temperature environment, the temperature difference of loose surrounding rock section is analyzed, and the ground temperature correction coefficient is calculated according to thermal expansion theory and porosity characteristics, and the temperature of prediction model is corrected.The present application realizes the sectional accurate prediction of tunnel blasting vibration velocity under high ground temperature condition, and provides theoretical basis and technical support for tunnel engineering safety management and blasting optimization.

Description

Technical Field

[0001] This invention relates to the field of high-temperature tunnel blasting engineering technology, specifically to a method and system for predicting vibration velocity during blasting in deeply buried high-temperature tunnels. Background Technology

[0002] Due to the complex geological environment at depth, the high temperature of the surrounding rock and the significant changes in its mechanical properties, the vibrations generated during tunnel blasting not only affect construction safety but may also damage the surrounding environment and structures. Traditional blasting vibration velocity prediction methods are mostly based on shallow low-temperature conditions, ignoring the influence of high geothermal environment on the mechanical properties of rock mass and vibration propagation characteristics, resulting in insufficient prediction accuracy and difficulty in meeting the safety control requirements of deep-buried high geothermal tunnel construction.

[0003] While existing technologies for predicting tunnel blasting vibration velocity have provided theoretical and empirical models for energy attenuation and peak velocity of blasting vibrations, they generally lack a systematic consideration of the thermal expansion effect, elastic modulus changes, and strength attenuation of rock masses under high geothermal conditions. Furthermore, they typically predict the vibration velocity of the entire tunnel, making it difficult to effectively monitor and warn of vibration risks in different sections. Moreover, existing technologies often employ homogeneous equivalent models for vibration velocity prediction, assuming that the physical properties of the surrounding rock medium are uniform and stable, neglecting the spatial discrete distribution of porosity and its moderating effect on vibration wave velocity. This simplification makes it difficult for the model to accurately reflect the mechanical differences within different surrounding rock sections and their impact on vibration propagation, thus affecting the accurate prediction of peak velocity and vibration attenuation patterns.

[0004] Therefore, it is necessary to provide a method and system for predicting blasting vibration velocity in deeply buried high-temperature tunnels to solve the aforementioned problem.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for predicting blasting vibration velocity in deeply buried high-temperature tunnels, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A method for predicting blasting vibration velocity in deeply buried high-temperature tunnels, comprising the following steps:

[0009] Step 1: Based on the ultrasonic detection method, wave velocity is detected in the surrounding rock along the tunnel to obtain wave velocity data at each detection point along the entire length of the tunnel. A wave velocity transfer function is constructed based on the wave velocity data, and the porosity of the surrounding rock at different detection points is inverted to obtain a discrete sequence of the porosity of the surrounding rock.

[0010] Step 2: Detect the discrete sequence of surrounding rock porosity based on the sliding window detection method, divide the surrounding rock into sections based on the surrounding rock strength and determine the strength type of each section. The strength types include dense surrounding rock sections and loose surrounding rock sections. Construct the relationship between blasting energy density and vibration peak value based on thermal blasting theory, and calculate the vibration velocity attenuation index by combining the vibration attenuation model.

[0011] Step 3: Calculate the surrounding rock density of each section based on the discrete sequence of surrounding rock porosity, calculate the initial vibration velocity of blasting in different sections by combining the vibration velocity attenuation index, and establish a tunnel vibration velocity prediction model based on the wave equation and material damping characteristics to predict the first peak tunnel vibration velocity of each section.

[0012] Step 4: If the tunnel section belongs to the dense surrounding rock section, the first tunnel vibration velocity peak predicted by the model is taken as the tunnel blasting vibration velocity prediction result. If the tunnel section belongs to the loose surrounding rock section, the high geothermal environment data is analyzed, the average temperature of the loose surrounding rock section is compared with the preset benchmark temperature, and the geothermal correction coefficient is calculated based on the thermal expansion theory combined with the porosity of the loose surrounding rock section.

[0013] Step 5: Use the model corrected by the geothermal correction factor to predict the tunnel blasting vibration velocity in the loose surrounding rock section, so as to realize the segmented prediction of the blasting vibration velocity of the high geothermal tunnel.

[0014] Furthermore, a wave velocity transfer function is constructed based on the wave velocity data, and the porosity of the surrounding rock at different measuring points is inverted to obtain a discrete sequence of the porosity of the surrounding rock. The method used is as follows:

[0015] Ultrasonic detection was used to obtain continuous wave velocity distribution data along the tunnel. Measurement points were uniformly distributed along the tunnel axis. Based on geological literature, the wave velocities of fluids in pores and the wave velocities in the solid rock skeleton were obtained. In saturated porous media containing liquid, the porosity and wave velocity were correlated using a spatiotemporal averaging formula. The formula used is as follows:

[0016]

[0017] in, This represents the longitudinal wave velocity of ultrasonic waves as they pass through porous rock. Porosity The wave velocity of the fluid in the pores. The wave velocity of the solid rock skeleton;

[0018] The wave velocity distribution data in the tunnel is obtained, and the porosity distribution is inverted using the obtained wave velocity data. The formula used is as follows:

[0019]

[0020] in, Indicates the location of the tunnel Porosity of the surrounding rock at the measurement point Indicates the location of the tunnel Longitudinal wave velocity at the measurement point This is the index of the measurement points in the tunnel, and , This represents the total number of measurement points in the tunnel.

[0021] Furthermore, based on the discrete sequence of surrounding rock porosity detected by the sliding window detection method, the surrounding rock is divided into sections according to its strength, and the strength type of each section is determined. The method used is as follows:

[0022] A sliding window is selected on the discrete porosity sequence laid out along the tunnel. The length and step size of the sliding window are set, and the sliding window is moved point by point. At each sliding window position, for the porosity data subsequence covered by the window, the mean and standard deviation of the porosity within the window are calculated. For the mean of all adjacent windows, the rate of change is calculated using the following formula:

[0023]

[0024] in, Indicates the first The sliding window is relative to the first The relative change in the mean porosity of each window The index of the sliding window, and , This represents the total number of sliding windows. For the first Average porosity in each sliding window For the first Average porosity in each sliding window;

[0025] Based on the relative change in the mean values ​​of two adjacent windows, porosity abrupt change points are detected, and a threshold for the abrupt change is set. Traverse the sequence of rate of change of the mean, when at a certain position satisfy When a significant change in the surrounding rock strength occurs, it is considered a candidate abrupt change point. The standard deviation of porosity within two adjacent windows corresponding to the candidate abrupt change point is examined, and the relative percentage difference between the standard deviations of porosity in the two windows is calculated using the following formula:

[0026]

[0027] in, This represents the percentage difference in the standard deviation of porosity between the windows on either side of the abrupt change point. For the first Standard deviation of porosity within a sliding window For the first Standard deviation of porosity within a sliding window;

[0028] If the relative percentage difference in the standard deviation of porosity between two adjacent windows corresponding to a candidate mutation point does not exceed 20%, it indicates that the porosity data fluctuates to a consistent degree, and the candidate mutation point is the true mutation point of porosity change. If the relative percentage difference exceeds 20%, it indicates that the data fluctuation in one window is significantly higher than that in the other, and there is a local disturbance in the porosity data. In this case, the data in the adjacent windows are combined for smoothing, the smoothed mean and smoothed standard deviation are calculated, the smoothed values ​​are used to replace the original window values, the rate of change of the mean and the difference in standard deviation are recalculated, and the mutation point is determined.

[0029] The porosity data along the tunnel is divided into multiple sub-segments based on abrupt change points. The mean and standard deviation of the overall porosity data are calculated based on the discrete sequence of surrounding rock porosity. A critical porosity threshold is set based on the principle of three times the standard deviation. The average porosity of all windows within each sub-section is taken. ,like Then the sub-section is determined to be a tight surrounding rock section; if If so, the sub-section is determined to be a loose surrounding rock section.

[0030] Furthermore, a relationship between blast energy density and peak vibration was constructed, and the vibration velocity attenuation index was calculated using a vibration attenuation model. The method used was as follows:

[0031] Obtain blasting data from the tunnel construction site, including the total mass of explosives, the energy released per unit mass of explosives, the cross-sectional area of ​​the tunnel after blasting, and the total length of the blasted section. Calculate the overall energy density of the tunnel blast using thermal blasting theory, based on the following formula:

[0032]

[0033] in, This indicates the energy density of the overall tunnel blasting. The total mass of the explosives, The energy released per unit mass of explosive. The cross-sectional area of ​​the tunnel. The total length of the tunnel blasting section;

[0034] Based on the thermal explosion theory and vibration attenuation model, the peak vibration satisfies the following empirical relationship:

[0035]

[0036] in, Indicates reference distance Peak vibration velocity at that location These are empirical coefficients related to the medium and the blasting environment. The index representing the influence of blast energy density on peak vibration velocity. The geometric attenuation index of the rock mass. This is the reference distance between the measurement point and the blasting point;

[0037] Based on the obtained reference distance The vibration attenuation model is constructed based on the peak vibration velocity at a given location, using the following formula:

[0038]

[0039] in, Indicates the distance from the blast point Peak vibration velocity at that location The vibration velocity attenuation index is to be solved and is related to the physical properties of the surrounding rock;

[0040] The constructed vibration attenuation model is logarithmically processed to facilitate linear regression. The peak vibration velocities and their distances from multiple observation points are then substituted into the linearized model. Linear regression fitting parameters are set, and the model is solved using multiple linear regression. The formula used is:

[0041]

[0042] in, Indicates distance Peak vibration velocity at the location The value after performing a natural logarithmic transformation;

[0043] Construct a linear regression equation, let , , The equation is:

[0044]

[0045] in, , , ;

[0046] The peak vibration velocity and its distance at each observation point are substituted into the linear regression equation, and the solution is obtained by fitting the equation using the least squares method in multiple linear regression. The parameters are then fitted. , , The value of the vibration velocity decay index is... .

[0047] Furthermore, the density of the surrounding rock in the dense and loose sections was calculated, and the initial vibration velocity of the blasting in different sections was calculated based on the vibration velocity attenuation index. The method used was as follows:

[0048] The density of the surrounding rock is calculated as a weighted average of the density of solid particles and the pore volume, based on the following formula:

[0049]

[0050] in, The total density of the surrounding rock. Density of solid rock particles The density of the fluid in the pores;

[0051] Based on the above formula for calculating the surrounding rock density, the surrounding rock densities of the dense and loose surrounding rock sections are calculated separately. Combined with the obtained vibration velocity attenuation index, the initial vibration velocity during blasting of different sub-sections is calculated. The formula used is as follows:

[0052]

[0053]

[0054] in, This represents the initial vibration velocity during blasting of each dense surrounding rock section. This represents the initial vibration velocity during blasting in each section of loose surrounding rock. The blasting energy density generated during blasting of each dense surrounding rock section. The blasting energy density generated during blasting of each loose surrounding rock section. , The density of the surrounding rock is respectively for the dense surrounding rock section and the loose surrounding rock section.

[0055] Furthermore, a tunnel vibration velocity prediction model was established based on the wave equation and material damping characteristics to predict the first peak tunnel vibration velocity in each section. The method used is as follows:

[0056] Based on the wave equation of a tunnel, the relationship between the longitudinal wave velocity and the physical parameters of the surrounding rock is established. The formula used is as follows:

[0057]

[0058] in, This indicates the longitudinal wave velocity of the tunnel during blasting. It is the elastic modulus of the surrounding rock of the tunnel. It is the Poisson's ratio of the surrounding rock of the tunnel;

[0059] Based on the vibration velocity attenuation theoretical model, considering the geometric diffusion of vibration and the damping characteristics of surrounding rock, the damping coefficient of surrounding rock is calculated by combining the longitudinal wave velocity of the tunnel during blasting. The formula used is as follows:

[0060]

[0061] in, This represents the damping coefficient of the surrounding rock of the tunnel. It is the dominant frequency of the vibration wave during tunnel blasting. It is the surrounding rock quality factor;

[0062] The charge amount, length, explosive energy, and surrounding rock parameters of each tunnel section are obtained. The initial vibration velocity of each tunnel section is calculated. Using the initial vibration velocity of each tunnel section as input and the blasting vibration velocity of each tunnel section as output, a tunnel vibration velocity prediction model is established. If the tunnel section belongs to a dense surrounding rock section, the blasting vibration velocity of each dense surrounding rock section is directly predicted using the tunnel vibration velocity prediction model. The formula used is as follows:

[0063]

[0064] in, The model predicts the first Blasting vibration velocity in a dense surrounding rock section This represents the damping coefficient of the dense surrounding rock section within the tunnel. It is the geometric attenuation index of rock mass in dense surrounding rock sections.

[0065] Furthermore, the geothermal correction coefficient is calculated based on the thermal expansion theory and the porosity of the loose surrounding rock section. The method used is as follows:

[0066] The average ground temperature of each loose surrounding rock section of the tunnel is collected, and the temperature difference between each loose surrounding rock section is calculated based on a preset reference temperature. The formula used is as follows:

[0067]

[0068] in, Indicates the first The temperature difference in the loose surrounding rock section Indicates the first The average ground temperature of the loose surrounding rock section It is the preset reference temperature;

[0069] Under the influence of temperature difference, the loose surrounding rock sections experience additional linear thermal expansion strain as follows:

[0070]

[0071] in, Indicates the first Thermal expansion strain of the surrounding rock in a loose surrounding rock section The linear thermal expansion coefficient of the surrounding rock;

[0072] A temperature correction model was used to obtain the temperature of each loose surrounding rock section. The formula used to determine the elastic modulus of the surrounding rock is:

[0073]

[0074] in, Indicates the first The elastic modulus of the surrounding rock in a loose surrounding rock section It is the elastic modulus of the surrounding rock at the reference temperature. It is the temperature sensitivity coefficient of the elastic modulus of the surrounding rock;

[0075] Based on the temperature of each loose surrounding rock section The geothermal correction coefficient is generated by combining the elastic modulus and thermal expansion strain of the surrounding rock, based on the following formula:

[0076]

[0077] in, Indicates the first Geothermal correction coefficient for each loose surrounding rock section.

[0078] Furthermore, the tunnel vibration velocity prediction model is modified using the geothermal correction coefficient for each loose surrounding rock section, and the blasting vibration velocity in the loose surrounding rock section is predicted based on the modified model. The method used is as follows:

[0079] If the tunnel section belongs to the loose surrounding rock section, then based on the established tunnel vibration velocity prediction model, a ground temperature correction coefficient is introduced to correct the model. The corrected model is then used to predict the vibration velocity of the blasted tunnel in the loose surrounding rock section. The formula used is as follows:

[0080]

[0081] in, Indicates the revised version of the first... Blasting vibration velocity in a loose surrounding rock section This represents the damping coefficient of the loose surrounding rock section within the tunnel. It represents the geometric attenuation index of the rock mass in the loose surrounding rock section.

[0082] The present invention also provides a blasting vibration velocity prediction system for deep-buried high-geothermal tunnels. The blasting vibration velocity prediction system is used to execute the above-described blasting vibration velocity prediction method for deep-buried high-geothermal tunnels, and includes:

[0083] The wave velocity detection and porosity inversion module uses ultrasonic detection to detect the wave velocity of the surrounding rock along the tunnel, obtains wave velocity data at various detection points along the entire length of the tunnel, constructs a wave velocity transfer function based on the wave velocity data, and inverts the porosity of the surrounding rock at different detection points to obtain a discrete sequence of surrounding rock porosity.

[0084] The blasting vibration model construction module detects the discrete sequence of surrounding rock porosity based on the sliding window detection method. It divides the surrounding rock into sections based on the surrounding rock strength and determines the strength type of each section. The strength types include dense surrounding rock sections and loose surrounding rock sections. It constructs the relationship between blasting energy density and vibration peak value based on thermal blasting theory, and calculates the vibration velocity attenuation index by combining the vibration attenuation model.

[0085] The tunnel vibration velocity initial prediction module calculates the surrounding rock density of each section based on the discrete sequence of surrounding rock porosity, calculates the initial vibration velocity of blasting in different sections by combining the vibration velocity attenuation index, and establishes a tunnel vibration velocity prediction model based on the wave equation and material damping characteristics to predict the first peak tunnel vibration velocity of each section.

[0086] The geothermal correction coefficient calculation module uses the first tunnel vibration velocity peak predicted by the model as the tunnel blasting vibration velocity prediction result if the tunnel section belongs to the dense surrounding rock section. If the tunnel section belongs to the loose surrounding rock section, the high geothermal environment data is analyzed, the average temperature of the loose surrounding rock section is compared with the preset benchmark temperature, and the geothermal correction coefficient is calculated based on the thermal expansion theory combined with the porosity of the loose surrounding rock section.

[0087] The high-temperature tunnel vibration velocity zoning prediction and correction module is used to predict the tunnel blasting vibration velocity in loose surrounding rock sections using a model corrected by a geothermal correction coefficient, so as to achieve zoning prediction of the blasting vibration velocity in high-temperature tunnels.

[0088] Compared with the prior art, the beneficial effects of the present invention are:

[0089] This invention fully considers the complex and variable geological environment of deeply buried high-temperature tunnels. It addresses factors such as the spatial variation of surrounding rock porosity, thermal expansion effect, and decay of surrounding rock mechanical properties. Through multi-dimensional modeling including wave velocity detection and porosity inversion, sliding window partitioning, correlation between energy density and surrounding rock physical parameters, and geothermal correction, it achieves accurate segmented prediction of tunnel blasting vibration velocity. This invention overcomes the problem of low accuracy in blasting vibration velocity prediction caused by the use of homogeneous models and neglect of surrounding rock physical properties in existing technologies.

[0090] Furthermore, in response to the dynamic changes in the elastic modulus of rock mass, thermal expansion strain, and damping characteristics of surrounding rock under high geothermal conditions, this invention scientifically corrects the vibration velocity prediction model by introducing thermal expansion theory and temperature correction coefficient. This effectively reflects the coupling effect of temperature on vibration propagation characteristics and solves the problem of prediction deviation caused by neglecting the temperature effect in the prior art. Attached Figure Description

[0091] Figure 1 This is a schematic diagram of the overall method flow of the present invention.

[0092] Figure 2 This is a schematic diagram of the system module flow of the present invention. Detailed Implementation

[0093] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0094] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0095] Example:

[0096] Please see Figure 1 A method for predicting blasting vibration velocity in deeply buried high-temperature tunnels, the specific steps of which include:

[0097] Step 1: Based on the ultrasonic detection method, wave velocity is detected in the surrounding rock along the tunnel to obtain wave velocity data at each detection point along the entire length of the tunnel. A wave velocity transfer function is constructed based on the wave velocity data, and the porosity of the surrounding rock at different detection points is inverted to obtain a discrete sequence of the porosity of the surrounding rock.

[0098] In a specific embodiment of the present invention, since the propagation speed of ultrasonic waves in rock mass directly reflects the elastic properties and structural characteristics of rock mass, the porosity, fracture distribution and saturation state of rock will significantly affect the magnitude and change of wave velocity. The higher the porosity, the larger the proportion of void volume in rock mass, the lower the elastic modulus and stiffness of rock mass, resulting in a decrease in the propagation speed of ultrasonic waves.

[0099] The wave velocity transfer function is constructed using a spatiotemporal averaging formula, enabling wave velocity detection data to not only reflect the elastic properties of the rock mass but also to accurately infer porosity, thus achieving a quantitative description of the rock mass pore structure. Furthermore, based on the porosity sequence derived from the wave velocity transfer function, the physical differences in different surrounding rock sections along the tunnel can be accurately identified, supporting subsequent zoned vibration velocity prediction and risk classification. This method, combined with ultrasonic detection technology, rapidly and non-destructively obtains porosity distribution, providing an efficient and accurate technical means for vibration velocity safety assessment of deeply buried high-geothermal tunnels.

[0100] Furthermore, a wave velocity transfer function is constructed based on the wave velocity data, and the porosity of the surrounding rock at different measuring points is inverted to obtain a discrete sequence of the porosity of the surrounding rock. The method used is as follows:

[0101] Ultrasonic detection was used to obtain continuous wave velocity distribution data along the tunnel. Measurement points were uniformly distributed along the tunnel axis. Based on geological literature, the wave velocities of fluids in pores and the wave velocities in the solid rock skeleton were obtained. In saturated porous media containing liquid, the porosity and wave velocity were correlated using a spatiotemporal averaging formula. The formula used is as follows:

[0102]

[0103] in, This represents the longitudinal wave velocity of ultrasonic waves as they pass through porous rock. Porosity The wave velocity of the fluid in the pores. The wave velocity of the solid rock skeleton;

[0104] The wave velocity distribution data in the tunnel is obtained, and the porosity distribution is inverted using the obtained wave velocity data. The formula used is as follows:

[0105]

[0106] in, Indicates the location of the tunnel Porosity of the surrounding rock at the measurement point Indicates the location of the tunnel Longitudinal wave velocity at the measurement point This is the index of the measurement points in the tunnel, and , This represents the total number of measurement points in the tunnel.

[0107] Step 2: Detect the discrete sequence of surrounding rock porosity based on the sliding window detection method. Divide the surrounding rock into sections based on the surrounding rock strength and determine the strength type of each section. The strength types include dense surrounding rock sections and loose surrounding rock sections. Construct the relationship between blasting energy density and vibration peak value based on thermal blasting theory, and calculate the vibration velocity attenuation index by combining the vibration attenuation model.

[0108] In a specific embodiment of the present invention, based on the obtained discrete sequence of porosity of the surrounding rock of the tunnel, the sliding window method is used in combination with the dual criteria of mean change rate and standard deviation difference to capture the abrupt change points in the porosity sequence. By evaluating and smoothing the difference in porosity standard deviation on both sides of the abrupt change point, the interference of local data anomalies and noise on the partitioning results is suppressed, making the partitioning results more stable and continuous.

[0109] By using the sliding window technique, we can ensure the spatial continuous sampling of porosity data, flexibly capture local anomalies, adapt to the heterogeneous distribution characteristics of the surrounding rock, improve the spatiotemporal resolution of abrupt change detection, and highlight significant changes in porosity by using the relative change rate of the mean of adjacent windows. This is consistent with the abrupt change characteristics of common rock mass structure changes and is an important basis for judging the transformation of the physical properties of the surrounding rock.

[0110] It should be noted that, on the discrete sequence of porosity laid out along the tunnel, a sliding window is selected, with the window length set to the interval between 10 measurement points and the step size set to the interval between 1 measurement point. The sequence is slid point by point. At each window position, the mean and standard deviation of the porosity data covered by the window are calculated to reflect the concentration characteristics and fluctuation of the porosity of the surrounding rock in that section. Then, the relative rate of change of the mean of two adjacent windows is calculated and compared with the threshold to determine the abrupt change point.

[0111] Furthermore, based on the discrete sequence of surrounding rock porosity detected by the sliding window detection method, the surrounding rock is divided into sections according to its strength, and the strength type of each section is determined. The method used is as follows:

[0112] A sliding window is selected on the discrete porosity sequence laid out along the tunnel. The length and step size of the sliding window are set, and the sliding window is moved point by point. At each sliding window position, for the porosity data subsequence covered by the window, the mean and standard deviation of the porosity within the window are calculated. For the mean of all adjacent windows, the rate of change is calculated using the following formula:

[0113]

[0114] in, Indicates the first The sliding window is relative to the first The relative change in the mean porosity of each window The index of the sliding window, and , This represents the total number of sliding windows. For the first Average porosity in each sliding window For the first Average porosity in each sliding window;

[0115] Based on the relative change in the mean values ​​of two adjacent windows, porosity abrupt change points are detected, and a threshold for the abrupt change is set. Traverse the sequence of rate of change of the mean, when at a certain position satisfy When a significant change in the surrounding rock strength occurs, it is considered a candidate abrupt change point. The standard deviation of porosity within two adjacent windows corresponding to the candidate abrupt change point is examined, and the relative percentage difference between the standard deviations of porosity in the two windows is calculated using the following formula:

[0116]

[0117] in, This represents the percentage difference in the standard deviation of porosity between the windows on either side of the abrupt change point. For the first Standard deviation of porosity within a sliding window For the first Standard deviation of porosity within a sliding window;

[0118] If the relative percentage difference in the standard deviation of porosity between two adjacent windows corresponding to a candidate mutation point does not exceed 20%, it indicates that the porosity data fluctuates to a consistent degree, and the candidate mutation point is the true mutation point of porosity change. If the relative percentage difference exceeds 20%, it indicates that the data fluctuation in one window is significantly higher than that in the other, and there is a local disturbance in the porosity data. In this case, the data in the adjacent windows are combined for smoothing, the smoothed mean and smoothed standard deviation are calculated, the smoothed values ​​are used to replace the original window values, the rate of change of the mean and the difference in standard deviation are recalculated, and the mutation point is determined.

[0119] The porosity data along the tunnel is divided into multiple sub-segments based on abrupt change points. The mean and standard deviation of the overall porosity data are calculated based on the discrete sequence of surrounding rock porosity. A critical porosity threshold is set based on the principle of three times the standard deviation. The average porosity of all windows within each sub-section is taken. ,like Then the sub-section is determined to be a tight surrounding rock section; if If so, the sub-section is determined to be a loose surrounding rock section.

[0120] It should be noted that by calculating the blasting energy density, the density characteristics of energy release during tunnel blasting were clarified. This energy density index comprehensively considers factors such as explosive mass, energy release, tunnel cross-section, and blasting length, making the prediction of vibration peak value more consistent with actual working conditions. Based on the vibration attenuation model, the blasting energy density and measurement distance are incorporated into the vibration peak value calculation, which integrates medium properties and geometric attenuation effects, enabling rapid and effective prediction of vibration velocity peak values ​​at different locations.

[0121] The vibration velocity decay index can be used to quantify the physical properties of vibration energy decaying with propagation distance. The vibration velocity decay index characterizes the exponential decay intensity caused by medium damping, scattering and energy dissipation when blasting vibration propagates in the surrounding rock.

[0122] Furthermore, a relationship between blast energy density and peak vibration was constructed, and the vibration velocity attenuation index was calculated using a vibration attenuation model. The method used was as follows:

[0123] Obtain blasting data from the tunnel construction site, including the total mass of explosives, the energy released per unit mass of explosives, the cross-sectional area of ​​the tunnel after blasting, and the total length of the blasted section. Calculate the overall energy density of the tunnel blast using thermal blasting theory, based on the following formula:

[0124]

[0125] in, This indicates the energy density of the overall tunnel blasting. The total mass of the explosives, The energy released per unit mass of explosive. The cross-sectional area of ​​the tunnel. The total length of the tunnel blasting section;

[0126] Based on the thermal explosion theory and vibration attenuation model, the peak vibration satisfies the following empirical relationship:

[0127]

[0128] in, Indicates reference distance Peak vibration velocity at that location These are empirical coefficients related to the medium and the blasting environment. The index representing the influence of blast energy density on peak vibration velocity. The geometric attenuation index of the rock mass. This is the reference distance between the measurement point and the blasting point;

[0129] Based on the obtained reference distance The vibration attenuation model is constructed based on the peak vibration velocity at a given location, using the following formula:

[0130]

[0131] in, Indicates the distance from the blast point Peak vibration velocity at that location The vibration velocity attenuation index is to be solved and is related to the physical properties of the surrounding rock;

[0132] The constructed vibration attenuation model is logarithmically processed to facilitate linear regression. The peak vibration velocities and their distances from multiple observation points are then substituted into the linearized model. Linear regression fitting parameters are set, and the model is solved using multiple linear regression. The formula used is:

[0133]

[0134] in, Indicates distance Peak vibration velocity at the location The value after performing a natural logarithmic transformation;

[0135] Construct a linear regression equation, let , , The equation is:

[0136]

[0137] in, , , ;

[0138] The peak vibration velocity and its distance at each observation point are substituted into the linear regression equation, and the solution is obtained by fitting the equation using the least squares method in multiple linear regression. The parameters are then fitted. , , The value of the vibration velocity decay index is... .

[0139] Step 3: Calculate the surrounding rock density of each section based on the discrete sequence of surrounding rock porosity, calculate the initial vibration velocity of blasting in different sections by combining the vibration velocity attenuation index, and establish a tunnel vibration velocity prediction model based on the wave equation and material damping characteristics to predict the first peak tunnel vibration velocity of each section.

[0140] In a specific embodiment of the present invention, dense and loose surrounding rock sections are divided by a discrete sequence of porosity to accurately reflect the spatial heterogeneity of the surrounding rock and obtain the actual surrounding rock density data corresponding to the sections. Combined with the surrounding rock density and vibration velocity attenuation index of different sections, the initial vibration velocity of each section is calculated to accurately reflect the influence of the physical properties of the surrounding rock on the intensity of the blasting vibration source. Then, based on the wave equation and the damping characteristics of the material, a vibration velocity prediction model considering the elasticity, damping and wave attenuation of the surrounding rock is established to ensure that the model has a solid theoretical foundation and can simulate the energy dissipation and waveform changes in the actual vibration propagation process.

[0141] Furthermore, the density of the surrounding rock in the dense and loose sections was calculated, and the initial vibration velocity of the blasting in different sections was calculated based on the vibration velocity attenuation index. The method used was as follows:

[0142] The density of the surrounding rock is calculated as a weighted average of the density of solid particles and the pore volume, based on the following formula:

[0143]

[0144] in, The total density of the surrounding rock. Density of solid rock particles The density of the fluid in the pores;

[0145] Based on the above formula for calculating the surrounding rock density, the surrounding rock densities of the dense and loose surrounding rock sections are calculated separately. Combined with the obtained vibration velocity attenuation index, the initial vibration velocity during blasting of different sub-sections is calculated. The formula used is as follows:

[0146]

[0147]

[0148] in, This represents the initial vibration velocity during blasting of each dense surrounding rock section. This represents the initial vibration velocity during blasting in each section of loose surrounding rock. The blasting energy density generated during blasting of each dense surrounding rock section. The blasting energy density generated during blasting of each loose surrounding rock section. , The density of the surrounding rock is respectively for the dense surrounding rock section and the loose surrounding rock section.

[0149] Furthermore, a tunnel vibration velocity prediction model was established based on the wave equation and material damping characteristics to predict the first peak tunnel vibration velocity in each section. The method used is as follows:

[0150] Based on the wave equation of a tunnel, the relationship between the longitudinal wave velocity and the physical parameters of the surrounding rock is established. The formula used is as follows:

[0151]

[0152] in, This indicates the longitudinal wave velocity of the tunnel during blasting. It is the elastic modulus of the surrounding rock of the tunnel. It is the Poisson's ratio of the surrounding rock of the tunnel;

[0153] Based on the vibration velocity attenuation theoretical model, considering the geometric diffusion of vibration and the damping characteristics of surrounding rock, the damping coefficient of surrounding rock is calculated by combining the longitudinal wave velocity of the tunnel during blasting. The formula used is as follows:

[0154]

[0155] in, This represents the damping coefficient of the surrounding rock of the tunnel. It is the dominant frequency of the vibration wave during tunnel blasting. It is the surrounding rock quality factor;

[0156] The charge amount, length, explosive energy, and surrounding rock parameters of each tunnel section are obtained. The initial vibration velocity of each tunnel section is calculated. Using the initial vibration velocity of each tunnel section as input and the blasting vibration velocity of each tunnel section as output, a tunnel vibration velocity prediction model is established. If the tunnel section belongs to a dense surrounding rock section, the blasting vibration velocity of each dense surrounding rock section is directly predicted using the tunnel vibration velocity prediction model. The formula used is as follows:

[0157]

[0158] in, The model predicts the first Blasting vibration velocity in a dense surrounding rock section This represents the damping coefficient of the dense surrounding rock section within the tunnel. It is the geometric attenuation index of rock mass in dense surrounding rock sections.

[0159] The initial vibration velocity of each dense surrounding rock section during blasting is input into the constructed tunnel vibration velocity prediction model, and then the blasting velocity of each measurement point in each dense surrounding rock section is obtained.

[0160] Step 4: If the tunnel section belongs to the dense surrounding rock section, the first tunnel vibration velocity peak predicted by the model is taken as the tunnel blasting vibration velocity prediction result. If the tunnel section belongs to the loose surrounding rock section, the high geothermal environment data is analyzed, the average temperature of the loose surrounding rock section is compared with the preset benchmark temperature, and the geothermal correction coefficient is calculated based on the thermal expansion theory and the porosity of the loose surrounding rock section.

[0161] In a specific embodiment of the present invention, due to the deep-buried high-temperature environment in which the tunnel is located, especially in the loose surrounding rock section, the elastic modulus and strain characteristics of the surrounding rock material will change significantly due to temperature changes, which directly affects the propagation of blasting vibration and the stability of the surrounding rock. Therefore, by introducing a ground temperature correction coefficient, the influence of ground temperature on the elastic modulus and thermal expansion strain of the surrounding rock can be incorporated into vibration analysis and structural mechanics calculation, thereby improving the physical authenticity and accuracy of vibration peak prediction and safety assessment.

[0162] It should be noted that the construction logic of the geothermal correction coefficient is based on the theory of thermal expansion and the temperature effect in materials mechanics. First, changes in geothermal temperature cause additional linear thermal expansion strain in the surrounding rock, affecting its deformation state. Second, the elastic modulus of the surrounding rock exhibits temperature sensitivity and typically decreases with temperature changes, which reduces its stiffness and vibration transmission capacity. By comprehensively considering the thermal expansion strain and the temperature-corrected elastic modulus, a mathematical model couples the two to form the geothermal correction coefficient, which is the ratio of the corrected elastic modulus to the baseline state. This adjusts the vibration propagation parameters, thereby more scientifically simulating the dynamic response characteristics of the surrounding rock under different temperature conditions and achieving temperature adaptability and refinement of the vibration prediction model.

[0163] Furthermore, the geothermal correction coefficient is calculated based on the thermal expansion theory and the porosity of the loose surrounding rock section. The method used is as follows:

[0164] The average ground temperature of each loose surrounding rock section of the tunnel is collected, and the temperature difference between each loose surrounding rock section is calculated based on a preset reference temperature. The formula used is as follows:

[0165]

[0166] in, Indicates the first The temperature difference in the loose surrounding rock section Indicates the first The average ground temperature of the loose surrounding rock section It is the preset reference temperature;

[0167] Under the influence of temperature difference, the loose surrounding rock sections experience additional linear thermal expansion strain as follows:

[0168]

[0169] in, Indicates the first Thermal expansion strain of the surrounding rock in a loose surrounding rock section The linear thermal expansion coefficient of the surrounding rock;

[0170] A temperature correction model was used to obtain the temperature of each loose surrounding rock section. The formula used to determine the elastic modulus of the surrounding rock is:

[0171]

[0172] in, Indicates the first The elastic modulus of the surrounding rock in a loose surrounding rock section It is the elastic modulus of the surrounding rock at the reference temperature. It is the temperature sensitivity coefficient of the elastic modulus of the surrounding rock;

[0173] Based on the temperature of each loose surrounding rock section The geothermal correction coefficient is generated by combining the elastic modulus and thermal expansion strain of the surrounding rock, based on the following formula:

[0174]

[0175] in, Indicates the first The geothermal correction coefficient for each loose surrounding rock section; in the above formula, as the temperature increases, the surrounding rock undergoes thermal expansion, and the additional thermal expansion strain... The increase leads to changes in the effective stress-bearing section and deformation mode of the structure, and the denominator part The increase implies that the geometric "expansion" effect introduced by thermal expansion has been taken into account, which is equivalent to reducing the stiffness response of the surrounding rock to vibration; the increase in temperature causes the elastic modulus to increase. The decrease in temperature, from a physical perspective, reflects a softening of the surrounding rock stiffness, resulting in a more compliant response to blasting vibrations and a reduced ability to transmit vibrations; the geothermal correction coefficient... As an overall correction factor, it is the product of the ratio of change in elastic modulus and the thermal expansion strain correction term. The combined effect of increased expansion strain and decreased elastic modulus makes... The significant reduction reasonably reflects the weakening of the mechanical properties of the surrounding rock under high-temperature conditions.

[0176] Step 5: Use the model corrected by the geothermal correction factor to predict the tunnel blasting vibration velocity in the loose surrounding rock section, so as to realize the segmented prediction of the blasting vibration velocity of the high geothermal tunnel.

[0177] In a specific embodiment of the present invention, the temperature effect of the elastic modulus and thermal expansion strain of the surrounding rock is incorporated into the vibration velocity model by means of a geothermal correction coefficient. This accurately reflects the changes in the mechanical properties of the surrounding rock material under high temperature conditions, avoids the prediction deviation caused by the neglect of temperature influence in traditional homogeneous models, and outputs more targeted peak vibration velocity predictions for different geothermal conditions in loose surrounding rock sections. This reflects the influence of geothermal heterogeneity of the surrounding rock on vibration propagation and improves the distinguishability of vibration characteristics in different sections of the tunnel.

[0178] It should be noted that the porosity of the surrounding rock is affected by temperature. As the geothermal temperature rises, the pore structure in the surrounding rock undergoes microscopic adjustments due to thermal expansion and pyrolysis, typically manifesting as a decrease in porosity or local closure. This reduction in porosity leads to an increase in the density of the surrounding rock, thereby altering its elastic modulus and making its mechanical properties more rigid. Conversely, as the temperature decreases, the porosity may increase, making the surrounding rock relatively loose, reducing the elastic modulus, and increasing vibration propagation damping. Combined with the temperature correction model, the geothermal correction coefficient not only reflects the trend of the elastic modulus of the surrounding rock changing with temperature but also implicitly considers the influence of temperature on porosity, as changes in porosity directly affect the actual values ​​of the elastic modulus and thermal expansion strain. By correcting the elastic modulus parameters of the surrounding rock and introducing thermal expansion strain, the model can dynamically adjust the impact of temperature-induced porosity changes on the rigidity and vibration transmission characteristics of the surrounding rock, thus achieving more accurate and physically reasonable tunnel vibration velocity prediction.

[0179] Furthermore, the tunnel vibration velocity prediction model is modified using the geothermal correction coefficient for each loose surrounding rock section, and the blasting vibration velocity in the loose surrounding rock section is predicted based on the modified model. The method used is as follows:

[0180] If the tunnel section belongs to the loose surrounding rock section, then based on the established tunnel vibration velocity prediction model, a ground temperature correction coefficient is introduced to correct the model. The corrected model is then used to predict the vibration velocity of the blasted tunnel in the loose surrounding rock section. The formula used is as follows:

[0181]

[0182] in, Indicates the revised version of the first... Blasting vibration velocity in a loose surrounding rock section This represents the damping coefficient of the loose surrounding rock section within the tunnel. It represents the geometric attenuation index of the rock mass in the loose surrounding rock section.

[0183] Please see Figure 2 The present invention also provides a blasting vibration velocity prediction system for deep-buried high-temperature tunnels. The blasting vibration velocity prediction system is used to execute the above-described blasting vibration velocity prediction method for deep-buried high-temperature tunnels, and includes:

[0184] The wave velocity detection and porosity inversion module uses ultrasonic detection to detect the wave velocity of the surrounding rock along the tunnel, obtains continuous distribution data of wave velocity over the entire length of the tunnel, constructs a wave velocity transfer function based on the wave velocity data, and inverts the porosity of the surrounding rock at different measuring points to obtain a discrete sequence of surrounding rock porosity.

[0185] The blasting vibration model construction module uses the discrete sequence of surrounding rock porosity detected by the sliding window detection method to divide the surrounding rock strength of the tunnel, thereby determining the dense surrounding rock section and the loose surrounding rock section in the tunnel. It also constructs the relationship between blasting energy density and vibration peak value based on thermal blasting theory, and calculates the vibration velocity attenuation index by combining the vibration attenuation model.

[0186] The tunnel vibration velocity initial prediction module calculates the surrounding rock density of dense and loose surrounding rock sections based on the discrete sequence of surrounding rock porosity. It also calculates the initial vibration velocity of blasting in different sections by combining the vibration velocity attenuation index. Finally, it establishes a tunnel vibration velocity prediction model based on the wave equation and material damping characteristics to predict the first peak tunnel vibration velocity in the dense surrounding rock section.

[0187] The geothermal correction coefficient calculation module is used to analyze high geothermal environment data, compare the average temperature of the loose surrounding rock section with the preset benchmark temperature, and calculate the geothermal correction coefficient based on the thermal expansion theory and the porosity of the loose surrounding rock section.

[0188] The high-temperature tunnel vibration velocity zoning prediction and correction module is used to correct the tunnel vibration velocity prediction model using a geothermal correction coefficient. Based on the corrected model, the first tunnel vibration velocity peak value in the loose surrounding rock section is predicted, so as to realize the zoning prediction of blasting vibration velocity in high-temperature tunnels.

[0189] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0190] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0191] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0192] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for predicting blasting vibration velocity in deeply buried high-temperature tunnels, characterized in that, The specific steps include: Step 1: Based on the ultrasonic detection method, wave velocity is detected in the surrounding rock along the tunnel to obtain wave velocity data at each detection point along the entire length of the tunnel. A wave velocity transfer function is constructed based on the wave velocity data, and the porosity of the surrounding rock at different detection points is inverted to obtain a discrete sequence of the porosity of the surrounding rock. Step 2: Detect the discrete sequence of surrounding rock porosity based on the sliding window detection method, divide the surrounding rock into sections based on the surrounding rock strength and determine the strength type of each section. The strength types include dense surrounding rock sections and loose surrounding rock sections. Construct the relationship between blasting energy density and vibration peak value based on thermal blasting theory, and calculate the vibration velocity attenuation index by combining the vibration attenuation model. Step 3: Calculate the surrounding rock density of each section based on the discrete sequence of surrounding rock porosity, calculate the initial vibration velocity of blasting in different sections by combining the vibration velocity attenuation index, and establish a tunnel vibration velocity prediction model based on the wave equation and material damping characteristics to predict the first peak tunnel vibration velocity of each section. Step 4: If the tunnel section belongs to the dense surrounding rock section, the first tunnel vibration velocity peak predicted by the model is taken as the tunnel blasting vibration velocity prediction result. If the tunnel section belongs to the loose surrounding rock section, the high geothermal environment data is analyzed, the average temperature of the loose surrounding rock section is compared with the preset benchmark temperature, and the geothermal correction coefficient is calculated based on the thermal expansion theory combined with the porosity of the loose surrounding rock section. Step 5: Use the model corrected by the geothermal correction factor to predict the tunnel blasting vibration velocity in the loose surrounding rock section, so as to realize the segmented prediction of the blasting vibration velocity of the high geothermal tunnel.

2. The method for predicting blasting vibration velocity in a deep-buried high-geothermal tunnel according to claim 1, characterized in that, A wave velocity transfer function is constructed based on wave velocity data, and the porosity of the surrounding rock at different measuring points is inverted to obtain a discrete sequence of surrounding rock porosity. The method used is as follows: Ultrasonic detection was used to obtain continuous wave velocity distribution data along the tunnel. Measurement points were evenly distributed along the tunnel axis. Based on geological literature, the wave velocity of fluid in pores and the wave velocity in the solid rock skeleton were obtained. In a saturated porous medium containing liquid, the porosity and wave velocity were linked by the spatiotemporal averaging formula to construct a wave velocity transfer function. The wave velocity distribution data in the tunnel is obtained based on the constructed wave velocity transfer function, and the porosity distribution is inverted using the obtained wave velocity data.

3. The method for predicting blasting vibration velocity in deeply buried high-temperature tunnels according to claim 2, characterized in that, Discrete sequences of surrounding rock porosity detected using the sliding window detection method are used to divide the surrounding rock into sections based on its strength and determine the strength type of each section. The method used is as follows: A sliding window is selected on the discrete sequence of porosity laid out along the tunnel. The length and step size of the sliding window are set, and the sliding window is moved point by point. At each sliding window position, for the porosity data subsequence covered by the window, the mean and standard deviation of porosity within the window are calculated, and the rate of change of the mean of all adjacent windows is calculated. Based on the relative change of the mean values ​​of two adjacent windows, porosity mutation points are detected. A mutation threshold is set, and the mean change rate sequence is traversed. When the calculated change rate of the mean values ​​of adjacent windows at a certain position is greater than the set mutation threshold, it is regarded as a candidate mutation point in which the surrounding rock strength has changed significantly. The porosity standard deviation in the two adjacent windows corresponding to the candidate mutation point is checked, and the relative percentage difference of the porosity standard deviation of the two windows is calculated. If the relative percentage difference in the standard deviation of porosity between two adjacent windows corresponding to a candidate mutation point does not exceed 20%, it indicates that the porosity data fluctuates to a consistent degree, and the candidate mutation point is the true mutation point of porosity change. If the relative percentage difference exceeds 20%, it indicates that the data fluctuation in one window is significantly higher than that in the other, and there is a local disturbance in the porosity data. In this case, the data in the adjacent windows are combined for smoothing, the smoothed mean and smoothed standard deviation are calculated, the smoothed values ​​are used to replace the original window values, the rate of change of the mean and the difference in standard deviation are recalculated, and the mutation point is determined. The porosity data along the tunnel is divided into multiple sub-segments based on abrupt change points. The mean and standard deviation of the overall porosity data are calculated based on the discrete sequence of surrounding rock porosity. A critical porosity threshold is set based on the principle of three times the standard deviation. The average porosity of all windows within each sub-section is taken. ,like Then the sub-section is determined to be a tight surrounding rock section; if If so, the sub-section is determined to be a loose surrounding rock section.

4. The method for predicting blasting vibration velocity in deeply buried high-temperature tunnels according to claim 3, characterized in that, A formula relating blast energy density to peak vibration was constructed, and the vibration attenuation index was calculated using a vibration attenuation model. The method used was as follows: Obtain blasting data at the tunnel construction site, including the total mass of explosives, the energy released per unit mass of explosives, the cross-sectional area of ​​the tunnel after blasting, and the total length of the tunnel blasting section. Calculate the overall energy density of the tunnel blasting using thermal blasting theory. Based on thermal explosion theory and vibration attenuation model, the peak vibration velocity at a reference distance from the explosion point is obtained. The vibration attenuation model is constructed by combining the peak vibration velocity at a given location with an exponential construction. The constructed vibration attenuation model is logarithmically processed to facilitate linear regression. The peak vibration velocities and their distances from multiple observation points are then substituted into the linearized model. Linear regression fitting parameters are set, and the model is solved using multiple linear regression fitting. A linear regression equation was constructed, and the peak velocity and distance of each observation point were substituted into the linear regression equation. The solution was obtained by fitting the equation using the least squares method in multiple linear regression, and the values ​​of the parameters and the velocity decay index were obtained.

5. The method for predicting blasting vibration velocity in a deep-buried high-geothermal tunnel according to claim 4, characterized in that, The density of the surrounding rock in the dense and loose sections was calculated, and the initial blasting velocity in different sections was calculated based on the vibration velocity attenuation index. The method used was as follows: The surrounding rock density is calculated by weighted average of solid particle density and pore volume. Based on the calculation formula of surrounding rock density, the surrounding rock density of dense surrounding rock section and loose surrounding rock section are calculated separately. Combined with the obtained vibration velocity attenuation index, the initial vibration velocity during blasting of different sub-sections is calculated.

6. The method for predicting blasting vibration velocity in deeply buried high-geothermal tunnels according to claim 5, characterized in that, A tunnel vibration velocity prediction model based on the wave equation and material damping characteristics is established to predict the first peak tunnel vibration velocity in each section. The method used is as follows: Based on the wave equation of the tunnel, the relationship between the longitudinal wave velocity and the physical parameters of the surrounding rock is established. On the basis of the vibration velocity attenuation theoretical model, considering the geometric diffusion of vibration and the damping characteristics of the surrounding rock, the damping coefficient of the surrounding rock is calculated in combination with the longitudinal wave velocity of the tunnel during blasting. The charge amount, length, explosive energy, and surrounding rock parameters of each tunnel section are obtained. The initial vibration velocity of each tunnel section is calculated. Using the initial vibration velocity of each tunnel section as input and the blasting vibration velocity of each tunnel section as output, a tunnel vibration velocity prediction model is established. If the tunnel section belongs to a dense surrounding rock section, the blasting vibration velocity of each dense surrounding rock section is directly predicted using the tunnel vibration velocity prediction model.

7. The method for predicting blasting vibration velocity in a deep-buried high-geothermal tunnel according to claim 6, characterized in that, The geothermal correction factor is calculated based on the theory of thermal expansion and the porosity of the loose surrounding rock section. The method used is as follows: The average ground temperature of each loose surrounding rock section of the tunnel is collected. The ground temperature difference of each loose surrounding rock section is calculated based on the preset reference temperature. Under the action of the temperature difference, the additional linear thermal expansion strain generated in each loose surrounding rock section is calculated. The temperature correction model is used to obtain the elastic modulus of the surrounding rock in each loose surrounding rock section under high ground temperature. Based on the elastic modulus and thermal expansion strain of the surrounding rock in each loose surrounding rock section under high geothermal conditions, a geothermal correction coefficient is generated.

8. The method for predicting blasting vibration velocity in a deep-buried high-geothermal tunnel according to claim 7, characterized in that, The tunnel vibration velocity prediction model is corrected using the geothermal correction coefficient for each loose surrounding rock section, and the blasting vibration velocity in the loose surrounding rock section is predicted based on the corrected model. The method used is as follows: If the tunnel section belongs to the loose surrounding rock section, the model is modified by introducing a ground temperature correction coefficient based on the established tunnel vibration velocity prediction model, and the modified model is used to predict the vibration velocity of the blasted tunnel in the loose surrounding rock section.

9. A blasting vibration velocity prediction system for deeply buried high-temperature tunnels, characterized in that, The blasting vibration velocity prediction system is used to execute the blasting vibration velocity prediction method for deep-buried high-geothermal tunnels as described in any one of claims 1-8, including: The wave velocity detection and porosity inversion module uses ultrasonic detection to detect the wave velocity of the surrounding rock along the tunnel, obtains wave velocity data at various detection points along the entire length of the tunnel, constructs a wave velocity transfer function based on the wave velocity data, and inverts the porosity of the surrounding rock at different detection points to obtain a discrete sequence of surrounding rock porosity. The blasting vibration model construction module detects the discrete sequence of surrounding rock porosity based on the sliding window detection method. It divides the surrounding rock into sections based on the surrounding rock strength and determines the strength type of each section. The strength types include dense surrounding rock sections and loose surrounding rock sections. It constructs the relationship between blasting energy density and vibration peak value based on thermal blasting theory, and calculates the vibration velocity attenuation index by combining the vibration attenuation model. The tunnel vibration velocity initial prediction module calculates the surrounding rock density of each section based on the discrete sequence of surrounding rock porosity, calculates the initial vibration velocity of blasting in different sections by combining the vibration velocity attenuation index, and establishes a tunnel vibration velocity prediction model based on the wave equation and material damping characteristics to predict the first peak tunnel vibration velocity of each section. The geothermal correction coefficient calculation module uses the first tunnel vibration velocity peak predicted by the model as the tunnel blasting vibration velocity prediction result if the tunnel section belongs to the dense surrounding rock section. If the tunnel section belongs to the loose surrounding rock section, the high geothermal environment data is analyzed, the average temperature of the loose surrounding rock section is compared with the preset benchmark temperature, and the geothermal correction coefficient is calculated based on the thermal expansion theory combined with the porosity of the loose surrounding rock section. The high-temperature tunnel vibration velocity zoning prediction and correction module is used to predict the tunnel blasting vibration velocity in loose surrounding rock sections using a model corrected by a geothermal correction coefficient, so as to achieve zoning prediction of the blasting vibration velocity in high-temperature tunnels.

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