A dynamic inversion method and system for equivalent elastic parameters of a porous water-bearing rock
By collecting and processing data of porous water-bearing rock strata in real time, and combining the correlation between pore water pressure and equivalent elastic parameters of rocks, the equivalent elastic parameters are dynamically inverted using a particle swarm optimization algorithm. This overcomes the limitations of traditional methods, achieves efficient and accurate acquisition of rock mechanical parameters, and supports engineering safety assessment and design.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHANGSHA DESIGN & RES INST OF CHEM IND MIN
- Filing Date
- 2026-03-23
- Publication Date
- 2026-06-02
AI Technical Summary
Traditional methods for obtaining rock mechanics parameters are time-consuming and costly, and it is difficult to capture the dynamic changes in pore water pressure in real time. Existing inversion methods fail to fully consider the influence of pore water pressure on the equivalent elastic parameters of rocks, resulting in large discrepancies between the inversion results and the actual situation, which makes it difficult to meet the accuracy and timeliness requirements of engineering safety assessment.
By deploying distributed fiber optic strain sensors, laser displacement sensors, and pore water pressure gauges on-site to collect data in real time, and combining the correlation between pore water pressure and the equivalent elastic modulus and equivalent Poisson's ratio of rock, an inversion objective function is established. The particle swarm optimization algorithm is then used to solve and verify the function, and the equivalent elastic parameters are dynamically inverted.
It enables real-time tracking of changes in the mechanical properties of surrounding rock, improves the accuracy and timeliness of inversion results, simplifies the calculation process, facilitates engineering applications, and provides data support for engineering risk warning and support scheme adjustment.
Smart Images
Figure CN122132794A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of underground engineering technology, and more specifically to a dynamic inversion method and system for equivalent elastic parameters of porous water-bearing rocks. Background Technology
[0002] In geotechnical engineering practice, especially in the design of underground structures in porous aquifers, accurately obtaining rock mechanics parameters is crucial for ensuring the safety and stability of engineering projects. These parameters are essential for assessing the response of underground structures under hydraulic loads and directly relate to the safety and durability of the project.
[0003] However, traditional methods for obtaining rock mechanics parameters, such as laboratory tests and field sampling tests, have significant limitations. They are time-consuming, relatively costly, and increase the overall economic burden of the project. More importantly, traditional methods struggle to capture the dynamic changes in rock mechanics parameters with pore water pressure in real time. In porous aquifers, fluctuations in pore water pressure have a profound impact on the mechanical properties of the rock. Without timely access to this information, it is difficult to accurately assess the safety of underground structures under hydraulic loads, potentially leading to potential safety hazards.
[0004] Meanwhile, existing inversion methods have also revealed many shortcomings in dealing with the influence of pore water pressure on the equivalent elastic parameters of rocks: some methods fail to fully consider the close relationship between pore water pressure and rock elastic modulus and Poisson's ratio, resulting in a large discrepancy between the inversion results and the actual situation; some inversion processes are too complex, involving complex calculation steps for a large number of parameters, which not only increases the difficulty and uncertainty of the calculation, but also limits its widespread application in practical engineering to a certain extent.
[0005] Therefore, how to break through the limitations of traditional methods and existing inversion methods, provide a solid and reliable basis for the calculation of hydraulic loads of underground structures, and improve the accuracy and timeliness of underground engineering design and safety assessment are technical problems that urgently need to be solved by those skilled in the art. Summary of the Invention
[0006] In view of this, the present invention provides a dynamic inversion method and system for equivalent elastic parameters of porous water-bearing rocks, which solves the problems existing in the background technology.
[0007] To achieve the above objectives, the present invention provides the following technical solution: A dynamic inversion method for equivalent elastic parameters of porous water-bearing rocks includes the following steps: S1. Real-time data on the lining strain, displacement, and pore water pressure of underground structures in porous water-bearing rock strata are collected using on-site monitoring equipment. S2. Preprocess the raw data collected in S1 to remove outliers and noise; S3. Based on the preprocessed data, and combined with the correlation between pore water pressure and the equivalent elastic modulus and equivalent Poisson's ratio of rocks, an inversion objective function is established. S4. The inversion algorithm is used to solve the inversion objective function to obtain the equivalent elastic modulus and equivalent Poisson's ratio of the surrounding rock. S5. Verify and correct the equivalent elastic modulus and equivalent Poisson's ratio obtained by inversion, and output the final result.
[0008] Optionally, in S1, the monitoring equipment includes: distributed fiber optic strain sensors, laser displacement sensors, and pore water pressure gauges; the specific deployment method of the monitoring equipment is as follows: Distributed fiber optic strain sensors are deployed at fixed intervals along the circumferential direction of the underground structure lining. The length of each distributed fiber optic strain sensor covers the full width of the lining and is attached to the lining surface with epoxy resin adhesive. Laser displacement sensors are deployed at the arch top, arch waist and sidewalls of the lining, and are fixed to the lining surface with expansion bolts. The pore water pressure gauge is buried in the porous aquifer, with the burial depth corresponding to the position of the arch, waist and sidewall of the underground structure. It is implanted by drilling and the hole is filled with cement grout for fixation.
[0009] Optionally, the specific steps of S1 are as follows: Distributed fiber optic strain sensors detect changes in lining strain in real time and convert strain signals into optical signals; The laser displacement sensor emits a laser beam onto the lining surface, receives the reflected light, and calculates the displacement change. The pore water pressure gauge senses the pore water pressure through a pressure-bearing diaphragm, which drives the vibrating wire to vibrate. The electromagnetic coil converts the vibration signal into an electrical signal. The system uses a data acquisition card, storage module, and wireless transmission module to acquire, convert, and store optical signals, displacement changes, and electrical signals to form raw data.
[0010] Optionally, the specific steps of S2 are as follows: Criterion for removing outliers: Calculate the mean and standard deviation of the original data, and remove data that are outside the range of mean ± 3 times the standard deviation; Wavelet transform noise reduction: The original data is decomposed into wavelets, and the wavelet coefficients are processed by selecting a soft threshold before wavelet reconstruction.
[0011] Optionally, in S3, the correlation between pore water pressure and the equivalent elastic modulus and equivalent Poisson's ratio of the rock can be obtained as follows: Rock samples from the same origin as the aquifer in the field were selected for indoor triaxial compression tests. The equivalent elastic modulus and equivalent Poisson's ratio of the rock samples were determined under different pore water pressure conditions. Representative test sections were selected on-site, and pore water pressure data of the representative test sections and the equivalent elastic modulus and equivalent Poisson's ratio of the rock obtained by inversion under the corresponding conditions were collected simultaneously. By combining indoor test data and field monitoring data, the least squares method was used for fitting, and the correlation was obtained that within the range of pore water pressure variation, the equivalent elastic modulus decreases linearly with the increase of pore water pressure, while the equivalent Poisson's ratio increases linearly with the increase of pore water pressure.
[0012] Optionally, in S3, the specific steps for establishing the inversion objective function are as follows: Using the equivalent elastic modulus and equivalent Poisson's ratio as the parameters to be inverted, the parameters to be inverted are substituted into the preset underground structural mechanics calculation model to obtain the theoretical lining strain value and the theoretical lining displacement value. Error terms between theoretical and measured lining strain values and between theoretical and measured lining displacement values are constructed. The sum of the squares of the two error terms is used as the inversion objective function to minimize the objective function value.
[0013] Optionally, the specific steps of S4 are as follows: The equivalent elastic modulus and equivalent Poisson's ratio are used as optimization variables, and their value ranges are set respectively; Using the inversion objective function as the fitness index, the particle swarm is initialized and the combination of variables that minimizes the fitness value is searched iteratively. During the iteration process, each generation of variables is substituted into the underground structure mechanics calculation model to obtain the corresponding theoretical strain data and displacement data, and the objective function value is calculated. The iteration terminates when the number of iterations reaches the preset maximum or the objective function value is within the preset error. At this point, the variable values are the equivalent elastic modulus and equivalent Poisson's ratio of the surrounding rock.
[0014] Optionally, the specific steps for S5 are as follows: The equivalent elastic parameters of rock samples were obtained through field sampling tests as benchmark values, and the theoretical strain and displacement corresponding to the inversion parameters were calculated using historical monitoring data. Set an error threshold. If the deviations between the inversion parameters and the benchmark values, and between the theoretical values and the historical measured values are all within the error threshold, then the verification is successful. If the deviation exceeds the error threshold, adjust the correlation between pore water pressure and elastic parameters or the inversion algorithm parameters according to the source of the error, re-execute the inversion process until the verification is successful, and output the final result.
[0015] A system for implementing the dynamic inversion method for equivalent elastic parameters of porous water-bearing rocks as described in any of the above claims, comprising: The data acquisition module is used to collect in real time the lining strain data, displacement data, and pore water pressure data of underground structures in porous water-bearing rock strata through monitoring equipment deployed on site. The data purification module is used to preprocess the collected raw data to remove outliers and noise; The inversion model construction module is used to establish an inversion objective function by combining the preprocessed data with the correlation between pore water pressure and the equivalent elastic modulus and equivalent Poisson's ratio of rocks. The parameter solving module is used to solve the inversion objective function using an inversion algorithm to obtain the equivalent elastic modulus and equivalent Poisson's ratio of the surrounding rock. The verification and optimization module is used to verify and correct the equivalent elastic modulus and equivalent Poisson's ratio obtained by inversion, and output the final result.
[0016] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a method and system for dynamic inversion of equivalent elastic parameters of porous water-bearing rocks, which has the following beneficial effects: (1) By establishing the correlation law between pore water pressure and the equivalent elastic modulus and equivalent Poisson's ratio of rocks, this invention breaks through the limitation of ignoring the influence of water pressure on rock mechanical parameters in traditional inversion, making the inversion model more consistent with the actual mechanical properties of porous water-bearing rock layers, and reducing the error caused by the omission of environmental factors from a theoretical perspective. (2) The present invention uses particle swarm optimization algorithm to solve the inversion objective function. The optimal parameter combination is found quickly through particle iterative search. Compared with the traditional numerical solution method, the calculation time is greatly shortened while ensuring accuracy, which meets the timeliness requirements of engineering for parameter inversion. (3) This invention can dynamically track the changing trend of the mechanical properties of the surrounding rock through real-time inversion and parameter correction. When the parameters fluctuate abnormally, it can provide data support for early warning of engineering risks, assist the construction party in adjusting the support scheme in a timely manner, and ensure the safety of the construction process. Attached Figure Description
[0017] 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 embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.
[0018] Figure 1 A flowchart illustrating the dynamic inversion method for equivalent elastic parameters of porous water-bearing rock strata provided by this invention; Figure 2 The diagram shows the structure of the dynamic inversion system for equivalent elastic parameters of porous water-bearing rock strata provided by this invention. Detailed Implementation
[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0020] To address the problems of traditional methods for obtaining rock mechanics parameters being time-consuming, costly, and unable to reflect the dynamic changes of parameters with pore water pressure in real time, as well as existing inversion methods failing to consider the influence of pore water pressure, having complex inversion processes, and being difficult to apply to practical engineering, this invention discloses a dynamic inversion method for the equivalent elastic parameters of porous water-bearing rocks, such as... Figure 1 As shown, it includes the following steps: S1. Real-time data on the lining strain, displacement, and pore water pressure of underground structures in porous water-bearing rock strata are collected using on-site monitoring equipment. S2. Preprocess the raw data collected in S1 to remove outliers and noise; S3. Based on the preprocessed data, and combined with the correlation between pore water pressure and the equivalent elastic modulus and equivalent Poisson's ratio of rocks, an inversion objective function is established. S4. The inversion algorithm is used to solve the inversion objective function to obtain the equivalent elastic modulus and equivalent Poisson's ratio of the surrounding rock. S5. Verify and correct the equivalent elastic modulus and equivalent Poisson's ratio obtained by inversion, and output the final result.
[0021] based on Figure 1 As shown in the flowchart, this embodiment collects data in real time through on-site monitoring equipment, which can reflect the dynamic changes of on-site rock mechanical parameters with pore water pressure. In the inversion process, the influence of pore water pressure on the equivalent elastic parameters of the rock is considered, and the established inversion objective function is more in line with the actual situation, thus improving the accuracy of the inversion results. The inversion algorithm with simple operation and fast convergence speed is adopted, which simplifies the inversion process and facilitates its application in actual engineering, thereby improving the accuracy and timeliness of underground engineering design and safety assessment.
[0022] Next, for Figure 1 The process shown will be described in detail to further understand the technical solution of the present invention.
[0023] (a) Data collection
[0024] The data acquisition step in S1, as the initial step of the entire dynamic inversion method, is the foundation for accurately inverting the equivalent elastic parameters of porous water-bearing rocks. Its core lies in providing on-site data support for subsequent parameter inversion through real-time, multi-dimensional data acquisition. Lining strain data, displacement data, and pore water pressure data directly reflect the interaction state between the underground structure and the porous water-bearing rock strata. These are the core basis for establishing the inversion model and verifying the inversion results, determining the feasibility and accuracy of the entire technical solution.
[0025] Based on a comprehensive consideration of engineering requirements and technical characteristics, the monitoring equipment selected in this embodiment includes: a distributed fiber optic strain sensor, a laser displacement sensor, and a pore water pressure gauge, covering the core physical quantities required for inversion. Among them, the distributed fiber optic strain sensor (composed of an optical fiber body, a protective encapsulation layer, and a connection interface) features high sensitivity, fast response speed, and resistance to harsh environments, making it suitable for underground humid and vibrating environments. It can directly monitor the strain on the lining surface, reflecting the microscopic deformation of the structure caused by the stress on the surrounding rock. The laser displacement sensor (including a laser emitting module, a receiving module, and a data conversion module) has an accuracy of up to 0.01 mm, capable of capturing minute displacement changes. The pore water pressure gauge (composed of a pressure-bearing diaphragm, a vibrating wire, an electromagnetic coil, and a signal processing circuit) has good long-term stability and can directly measure the pore water pressure in the rock strata, continuously tracking the dynamics of water pressure.
[0026] Furthermore, based on the structural mechanical properties and the distribution patterns of rock strata, the specific deployment method for the monitoring equipment was determined as follows: Distributed fiber optic strain sensors are deployed at fixed intervals along the circumferential direction of the underground structure lining. The length of each distributed fiber optic strain sensor covers the full width of the lining. They are adhered to the lining surface with epoxy resin, which can resist moisture on the lining surface. Since the underground structure lining is a ring-shaped stress structure, the circumferential strain distribution in this embodiment can reflect the uniformity of the radial pressure of the surrounding rock. The fixed interval deployment should balance the monitoring density and cost. Laser displacement sensors are deployed at the arch top, arch waist, and sidewalls of the lining, and are fixed to the lining surface with expansion bolts to ensure that the monitoring direction is perpendicular to the lining surface. Since the arch top is the part of the lining most prone to subsidence, and the arch waist and sidewalls are areas of concentrated shear deformation, the displacement of these parts is directly related to the stability of the surrounding rock. In this embodiment, deploying laser displacement sensors here can capture the response of the most dangerous points of the structure. The pore water pressure gauge is buried in the porous aquifer, with the burial depth corresponding to the position of the arch, waist, and sidewall of the underground structure. It is implanted by drilling and the hole is filled with cement grout to fix it, ensuring close contact with the rock layer and avoiding distortion of water pressure transmission. In fact, the range of 2-5m away from the lining is the stress-affected zone of the surrounding rock. The sensor buried here can reflect the water pressure environment that has the greatest impact on the stress of the lining. Corresponding to the arch and other parts, a position-matched water pressure-structure response relationship can be established to improve the accuracy of the correlation law.
[0027] Therefore, in this embodiment, the data acquisition of S1 specifically includes the following steps: Distributed fiber optic strain sensors detect changes in lining strain in real time and convert strain signals into optical signals; The laser displacement sensor emits a laser beam onto the lining surface, receives the reflected light, and calculates the displacement change. The pore water pressure gauge senses the pore water pressure through a pressure-bearing diaphragm, which drives the vibrating wire to vibrate. The electromagnetic coil converts the vibration signal into an electrical signal. The system uses a data acquisition card, storage module, and wireless transmission module to acquire, convert, and store optical signals, displacement changes, and electrical signals to form raw data.
[0028] In this embodiment, the selection, deployment, and data acquisition scheme of the monitoring equipment in S1 not only solves the problems of insufficient timeliness and representativeness of existing data, but also provides comprehensive and reliable input data for subsequent inversion through multi-parameter collaborative monitoring, which is a key prerequisite for realizing dynamic inversion.
[0029] (ii) Data Cleaning
[0030] S2, as a crucial step after data acquisition, is a data purification step that ensures the accuracy of the inversion results. Its core is to remove outliers and noise from the original data, providing a high-quality and reliable data foundation for subsequent inversion modeling.
[0031] Due to the complex underground environment, sensors are susceptible to factors such as humidity, vibration, and electromagnetic interference, inevitably introducing noise into the collected data. Furthermore, sensor installation misalignment and poor cable contact can lead to sudden anomalies, distorting the true trend of the data. Therefore, this embodiment selects to preprocess the collected raw data. The specific steps in S2 are as follows: Outlier removal criteria: Calculate the mean and standard deviation of the original data, and remove data that exceed the mean ± 3 times the standard deviation. This method achieves objective determination of outliers through statistical principles, avoiding the subjectivity of manual screening. Wavelet transform noise reduction: By performing wavelet decomposition on the original data, selecting a soft threshold to process the wavelet coefficients, and then performing wavelet reconstruction; compared with traditional filtering methods, wavelet transform has the characteristics of multi-resolution analysis, which can decompose the data into different frequency components. By setting a threshold for the high-frequency noise component, noise can be removed while retaining the abrupt change characteristics in the low-frequency real signal.
[0032] In this embodiment, S2 combines the two methods to form a progressive processing flow of "outlier removal - noise elimination", which not only solves the discrete interference in the data, but also eliminates continuous noise, providing clean and reliable data input for the subsequent establishment of the objective function.
[0033] (III) Construction of Inversion Model
[0034] S3 is the core modeling step in the entire inversion method, and its significance lies in building a bridge connecting "data, parameters, and mechanical response." By establishing the inversion objective function through preprocessed data and the correlation between pore water pressure and the equivalent elastic parameters of the rock, the abstract problem of rock mechanical parameter inversion is transformed into a quantifiable mathematical optimization problem. This provides a scientific and accurate solution object for subsequent inversion algorithms, and determines the physical rationality and solution effectiveness of the inversion model.
[0035] Traditional inversion methods often use objective functions based on mechanical models of dry rock masses, neglecting the dynamic influence of pore water pressure on the rock's equivalent elastic modulus and equivalent Poisson's ratio. This leads to a disconnect between the objective function and the actual mechanical properties of porous aquifers. Pore water pressure changes in real time, and the corresponding equivalent elastic parameters of the rock also adjust dynamically. Correlation mechanisms allow the objective function to update parameter relationships in real time as pore water pressure changes, ensuring that the inversion model always matches the dynamic conditions in the field.
[0036] Specifically, the correlation between pore water pressure and the equivalent elastic modulus and equivalent Poisson's ratio of rocks is obtained as follows: Rock samples from the same origin as the aquifer in the field were selected for indoor triaxial compression tests. The equivalent elastic modulus and equivalent Poisson's ratio of the rock samples were determined under different pore water pressure conditions. Representative test sections were selected on-site, and pore water pressure data of the representative test sections and the equivalent elastic modulus and equivalent Poisson's ratio of the rock obtained by inversion under the corresponding conditions were collected simultaneously. By combining indoor test data and field monitoring data, the least squares method was used for fitting, and the correlation was obtained that within the range of pore water pressure variation, the equivalent elastic modulus decreases linearly with the increase of pore water pressure, while the equivalent Poisson's ratio increases linearly with the increase of pore water pressure.
[0037] Furthermore, incorporating the correlation between pore water pressure and the equivalent elastic parameters of the rock into the inversion objective function is the core logic for ensuring the physical rationality of the objective function and the accuracy of the solution. Specifically, the steps for establishing the inversion objective function are as follows: Using the equivalent elastic modulus and equivalent Poisson's ratio as the parameters to be inverted, the parameters to be inverted are substituted into the preset underground structural mechanics calculation model to obtain the theoretical lining strain value and the theoretical lining displacement value. Error terms between theoretical and measured lining strain values and between theoretical and measured lining displacement values are constructed. The sum of the squares of the two error terms is used as the inversion objective function to minimize the objective function value.
[0038] In this embodiment, S3 establishes an inversion objective function based on correlation rules, which not only solves the defect of traditional models ignoring the influence of water pressure, but also provides physical constraints and optimization directions for parameter inversion. It is the core technical support for achieving accurate and dynamic inversion of equivalent elastic parameters of porous water-bearing rocks.
[0039] (iv) Parameter solution
[0040] S4 is the core solution step that transforms the theoretical model into actual parameters. Its significance lies in efficiently solving the objective function established in S3 through an inversion algorithm, ultimately obtaining the equivalent elastic modulus and equivalent Poisson's ratio that reflect the true mechanical properties of porous water-bearing rock strata. The solution efficiency and accuracy of S4 directly determine the timeliness and reliability of the inversion results, and are a key guarantee for realizing dynamic inversion and supporting real-time engineering decision-making.
[0041] Traditional inversion methods often employ local optimization algorithms such as gradient descent, which are sensitive to initial values, prone to getting trapped in local optima, and have slow convergence speeds when multiple parameters are coupled. For massive amounts of data updated in real time in porous aquifers, traditional algorithms often require hours or even days to complete a single solution, failing to meet the timeliness requirements of dynamic parameter updates in engineering projects.
[0042] Therefore, this embodiment employs a particle swarm optimization algorithm when solving the inversion objective function. The specific steps are as follows: The equivalent elastic modulus and equivalent Poisson's ratio are used as optimization variables, and their value ranges are set respectively; Using the inversion objective function as the fitness index, the particle swarm is initialized and the combination of variables that minimizes the fitness value is searched iteratively. During the iteration process, each generation of variables is substituted into the underground structure mechanics calculation model to obtain the corresponding theoretical strain data and displacement data, and the objective function value is calculated. The iteration terminates when the number of iterations reaches the preset maximum or the objective function value is within the preset error. At this point, the variable values are the equivalent elastic modulus and equivalent Poisson's ratio of the surrounding rock.
[0043] Particle swarm optimization (PSO) is a global optimization algorithm that effectively escapes local optima through iterative optimization via parallel searching of a swarm of particles, making it particularly suitable for nonlinear and multimodal objective functions established by S3. In the specific solution process, the correlation between pore water pressure and parameters obtained from S3 is used as a constraint to narrow the parameter search range. Setting dual termination conditions—either "the number of iterations reaches its maximum value" or "the objective function value is within a preset error range"—allows for flexible adjustment based on engineering needs. For example, in emergency scenarios, reducing the number of iterations allows for rapid acquisition of approximate solutions, while in fine-design scenarios, increasing the threshold requirement ensures solution accuracy.
[0044] In this embodiment, S4 effectively solves the problems of low efficiency, poor adaptability and unreliable results of traditional solution methods by selecting algorithms, designing constraints and coupling models. It is the core technical link to realize the dynamic and accurate inversion of equivalent elastic parameters of porous water-bearing rocks.
[0045] (v) Verification optimization
[0046] S5, as the verification and optimization link in the entire dynamic inversion process, is the key to ensuring the engineering applicability of the inversion results. Its significance lies in verifying and correcting the equivalent elastic modulus and equivalent Poisson's ratio obtained by inversion, forming a closed-loop system of "inversion-verification-correction". This ensures that the output parameters not only conform to the theoretical calculation logic, but also fit the actual engineering conditions on site. It is the core guarantee for improving the credibility of the inversion results and realizing the engineering implementation of the technical solution.
[0047] Specifically, the verification and correction process in S5 is as follows: The equivalent elastic parameters of rock samples obtained through field sampling tests are used as benchmark values. The theoretical strain and displacement corresponding to the inversion parameters are calculated using historical monitoring data. This embodiment uses the dual verification of field sampling test comparison and engineering monitoring data backtracking to take into account both point data and surface response, which can cover the entire chain of "rock mass characteristics-structural response" and avoid the limitations of single verification. Set an error threshold. If the deviations between the inversion parameters and the benchmark values, and between the theoretical values and the historical measured values are all within the error threshold, then the verification is successful. If the deviation exceeds the error threshold, the correlation between pore water pressure and elastic parameters or the parameters of the inversion algorithm are adjusted according to the source of the error, and the inversion process is re-executed until the verification is passed and the final result is output. This closed-loop mechanism of "verification result - model correction - re-inversion - re-verification" enables the inversion system to adapt to changes in field conditions, gradually improve parameter accuracy, and form dynamic learning capabilities.
[0048] In the above process, corrections are made according to the source of the verification deviation. For example, if the deviation stems from the correlation between pore water pressure and parameters, the correlation equation is refitted based on the verification data; if the deviation is due to insufficient iteration of the inversion algorithm, the particle swarm optimization algorithm parameters are optimized; if the deviation is due to residual data noise, the preprocessing operations are strengthened. This method of adjusting the correction scheme according to the source of error can efficiently reduce the deviation.
[0049] In this embodiment, S5 solves the problems of one-sided verification, blind correction, and rigid system of traditional technology through multi-dimensional verification, targeted correction, and closed-loop optimization, providing the final guarantee for the engineering applicability of the inversion results, and enabling the entire dynamic inversion method to stably output high-quality rock equivalent elastic parameters.
[0050] and Figure 1 Corresponding to the method described above, this embodiment of the invention also provides a dynamic inversion system for equivalent elastic parameters of porous water-bearing rocks, used for... Figure 1 The specific implementation of the method, the dynamic inversion system for equivalent elastic parameters of porous water-bearing rocks provided in this embodiment of the invention, can be applied to computer terminals or various mobile devices, such as... Figure 2 As shown, it specifically includes: The data acquisition module is used to collect in real time the lining strain data, displacement data, and pore water pressure data of underground structures in porous water-bearing rock strata through monitoring equipment deployed on site. The data purification module is used to preprocess the collected raw data to remove outliers and noise; The inversion model construction module is used to establish an inversion objective function by combining the preprocessed data with the correlation between pore water pressure and the equivalent elastic modulus and equivalent Poisson's ratio of rocks. The parameter solving module is used to solve the inversion objective function using an inversion algorithm to obtain the equivalent elastic modulus and equivalent Poisson's ratio of the surrounding rock. The verification and optimization module is used to verify and correct the equivalent elastic modulus and equivalent Poisson's ratio obtained by inversion, and output the final result.
[0051] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.
[0052] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A method for dynamic inversion of equivalent elastic parameters of porous water-bearing rocks, characterized in that, Includes the following steps: S1. Real-time data on the lining strain, displacement, and pore water pressure of underground structures in porous water-bearing rock strata are collected using on-site monitoring equipment. S2. Preprocess the raw data collected in S1 to remove outliers and noise; S3. Based on the preprocessed data, and combined with the correlation between pore water pressure and the equivalent elastic modulus and equivalent Poisson's ratio of rocks, an inversion objective function is established. S4. The inversion algorithm is used to solve the inversion objective function to obtain the equivalent elastic modulus and equivalent Poisson's ratio of the surrounding rock. S5. Verify and correct the equivalent elastic modulus and equivalent Poisson's ratio obtained by inversion, and output the final result.
2. The method for dynamic inversion of equivalent elastic parameters of porous water-bearing rocks according to claim 1, characterized in that, In S1, the monitoring equipment includes: distributed fiber optic strain sensors, laser displacement sensors, and pore water pressure gauges; the specific deployment method of the monitoring equipment is as follows: Distributed fiber optic strain sensors are deployed at fixed intervals along the circumferential direction of the underground structure lining. The length of each distributed fiber optic strain sensor covers the full width of the lining and is attached to the lining surface with epoxy resin adhesive. Laser displacement sensors are deployed at the arch top, arch waist and sidewalls of the lining, and are fixed to the lining surface with expansion bolts. The pore water pressure gauge is buried in the porous aquifer, with the burial depth corresponding to the position of the arch, waist and sidewall of the underground structure. It is implanted by drilling and the hole is filled with cement grout for fixation.
3. The method for dynamic inversion of equivalent elastic parameters of porous water-bearing rocks according to claim 2, characterized in that, The specific steps of S1 are as follows: Distributed fiber optic strain sensors detect changes in lining strain in real time and convert strain signals into optical signals. The laser displacement sensor emits a laser beam onto the lining surface, receives the reflected light, and calculates the displacement change. The pore water pressure gauge senses the pore water pressure through a pressure-bearing diaphragm, which drives the vibrating wire to vibrate. The electromagnetic coil converts the vibration signal into an electrical signal. The system uses a data acquisition card, storage module, and wireless transmission module to acquire, convert, and store optical signals, displacement changes, and electrical signals to form raw data.
4. The method for dynamic inversion of equivalent elastic parameters of porous water-bearing rocks according to claim 1, characterized in that, The specific steps of S2 are as follows: Criterion for removing outliers: Calculate the mean and standard deviation of the original data, and remove data that are outside the range of mean ± 3 times the standard deviation; Wavelet transform noise reduction: The original data is decomposed into wavelets, and the wavelet coefficients are processed by selecting a soft threshold before wavelet reconstruction.
5. The method for dynamic inversion of equivalent elastic parameters of porous water-bearing rocks according to claim 1, characterized in that, In S3, the correlation between pore water pressure and the equivalent elastic modulus and equivalent Poisson's ratio of the rock is obtained as follows: Rock samples from the same origin as the aquifer in the field were selected for indoor triaxial compression tests. The equivalent elastic modulus and equivalent Poisson's ratio of the rock samples were determined under different pore water pressure conditions. Representative test sections were selected on-site, and pore water pressure data of the representative test sections and the equivalent elastic modulus and equivalent Poisson's ratio of the rock obtained by inversion under the corresponding conditions were collected simultaneously. By combining indoor test data and field monitoring data, the least squares method was used for fitting, and the correlation was obtained that within the range of pore water pressure variation, the equivalent elastic modulus decreases linearly with the increase of pore water pressure, while the equivalent Poisson's ratio increases linearly with the increase of pore water pressure.
6. The method for dynamic inversion of equivalent elastic parameters of porous water-bearing rocks according to claim 1, characterized in that, In S3, the specific steps for establishing the inversion objective function are as follows: Using the equivalent elastic modulus and equivalent Poisson's ratio as the parameters to be inverted, the parameters to be inverted are substituted into the preset underground structural mechanics calculation model to obtain the theoretical lining strain value and the theoretical lining displacement value. Error terms between theoretical and measured lining strain values and between theoretical and measured lining displacement values are constructed. The sum of the squares of the two error terms is used as the inversion objective function to minimize the objective function value.
7. The method for dynamic inversion of equivalent elastic parameters of porous water-bearing rocks according to claim 1, characterized in that, The specific steps of S4 are as follows: The equivalent elastic modulus and equivalent Poisson's ratio are used as optimization variables, and their value ranges are set respectively; Using the inversion objective function as the fitness index, the particle swarm is initialized and the combination of variables that minimizes the fitness value is searched iteratively. During the iteration process, each generation of variables is substituted into the underground structure mechanics calculation model to obtain the corresponding theoretical strain data and displacement data, and the objective function value is calculated. The iteration terminates when the number of iterations reaches the preset maximum or the objective function value is within the preset error. At this point, the variable values are the equivalent elastic modulus and equivalent Poisson's ratio of the surrounding rock.
8. The method for dynamic inversion of equivalent elastic parameters of porous water-bearing rocks according to claim 1, characterized in that, The specific steps of S5 are as follows: The equivalent elastic parameters of rock samples were obtained through field sampling tests as benchmark values, and the theoretical strain and displacement corresponding to the inversion parameters were calculated using historical monitoring data. Set an error threshold. If the deviations between the inversion parameters and the benchmark values, and between the theoretical values and the historical measured values are all within the error threshold, then the verification is successful. If the deviation exceeds the error threshold, adjust the correlation between pore water pressure and elastic parameters or the inversion algorithm parameters according to the source of the error, re-execute the inversion process until the verification is successful, and output the final result.
9. A system for implementing the dynamic inversion method for equivalent elastic parameters of porous water-bearing rocks as described in any one of claims 1-8, characterized in that, include: The data acquisition module is used to collect in real time the lining strain data, displacement data, and pore water pressure data of underground structures in porous water-bearing rock strata through monitoring equipment deployed on site. The data purification module is used to preprocess the collected raw data to remove outliers and noise; The inversion model construction module is used to establish an inversion objective function by combining the preprocessed data with the correlation between pore water pressure and the equivalent elastic modulus and equivalent Poisson's ratio of rocks. The parameter solving module is used to solve the inversion objective function using an inversion algorithm to obtain the equivalent elastic modulus and equivalent Poisson's ratio of the surrounding rock. The verification and optimization module is used to verify and correct the equivalent elastic modulus and equivalent Poisson's ratio obtained by inversion, and output the final result.