Rock fracture prediction method and system based on resistivity data assimilation algorithm

Abstract

The invention discloses a rock fracture prediction method and system based on a resistivity data assimilation algorithm, and the method comprises the steps: building a numerical model for simulating the asymptotic fracture process of a rock mass under the load effect according to the boundary conditions and initial conditions of the rock mass; simulating the stress damage process of the rock mass by using a numerical calculation method to obtain prediction data of stress field and fracture damage field distribution of the rock mass; monitoring the damage process of the rock mass sample in real time by using a high-density electrical method to obtain observation data; rock mass fracture damage distribution is obtained based on observation data; an assimilation algorithm is adopted, information of observation data and information of prediction data are combined, and initial conditions or parameters of the numerical model are corrected; and then corrected rock fracture prediction data is obtained. Through the data assimilation algorithm, different data and model simulation results are fused in the power frame of rock fracture, the model track is automatically adjusted continuously depending on observation, the analysis result with higher precision and more consistency is obtained, and the prediction precision and predictability of the model are improved.

Description

Technical Field

[0001] This invention relates to the field of geotechnical engineering, and in particular to a method and system for predicting rock fracture based on a resistivity data assimilation algorithm. Background Technology

[0002] Deep underground engineering projects are situated in environments characterized by high altitude, high pollution levels, and high environmental disturbances. The hazard development process is the result of the interaction and co-evolution of the rock mass itself and environmental factors. As mining depth and conditions become more complex, rock mass stability deteriorates, leading to increasingly prominent engineering hazards such as landslides, subsidence, collapses, water inrushes, rock bursts, roof falls, and spalling induced by rock mass fracturing and instability. These hazards have become a major bottleneck restricting mineral resource extraction. These rock mass engineering hazards are closely related to the evolution of internal rock cracks and are the result of progressive rock damage and fracturing.

[0003] In the study of underground engineering activities, obtaining rock fracture information plays a crucial role in effectively predicting the state and evolution of underground behavior. There are two main methods for obtaining rock fracture information. One is through numerical simulation, which, based on different assumptions and boundary conditions, discretizes the medium into mesoscopic elements, introduces the inhomogeneity of the medium into the model, and incorporates reasonable constitutive relations and progressive failure criteria, thereby simulating the entire process of progressive material fracture. Numerical calculation methods in rock mechanics are mainly divided into two categories: one is based on continuum mechanics, such as the finite element method and the finite difference method; the other is based on discontinuous media mechanics, including the discrete element method and the discrete fracture network method. The second method involves inverting observational data collected by various sensors to obtain deep rock fracture information, such as acoustic emission / microseismic data and resistivity data during the rock fracture process. Rock acoustic emission / microseismic monitoring technology receives sound waves when micro-fractures occur inside the rock medium to understand the time, location, and intensity of the micro-fractures, as well as the physical properties of the rock (wave velocity, damping, etc.). It can achieve non-destructive, rapid, accurate, and real-time location of the seismic source and perform dynamic monitoring and evaluation of the rock. Electrical resistivity monitoring technology receives changes in resistivity during the rock failure process to understand the fractured and damaged areas and characteristics of the rock.

[0004] Existing methods have the following advantages and disadvantages: Both types of methods have their own advantages and applicable scenarios. For example, numerical simulation methods can flexibly design medium types and unit sizes, and can simulate the entire process of material progression, while inversion based on sensor observation data can realistically obtain rock fracture and damage information under specific scenarios. However, both methods contain errors in their representation of the rock damage and fracture process. Numerical simulation methods assume initial boundary conditions, but the control variables differ from actual deep-earth engineering, and the reliability of their models and parameters lacks verification from actual physical feedback information. This often leads to inconsistencies with the actual rock damage process. Rock damage and fracture obtained through inversion based on sensor observation data often suffer from errors due to the systematic errors of the sensors themselves and the non-uniqueness of the inversion algorithm. Furthermore, the rock damage and fracture mechanism lacks rigorous mechanical support, which also leads to inconsistencies with the actual rock damage and fracture process. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a rock fracture prediction method and system based on resistivity data assimilation algorithm, which addresses the deficiencies in the prior art.

[0006] The technical solution adopted by this invention to solve its technical problem is: This invention provides a rock fracture prediction method based on a resistivity data assimilation algorithm, the method comprising the following steps: Based on the boundary and initial conditions of the rock mass, a numerical model is established to simulate the asymptotic fracture process of the rock mass under load; the stress failure process of the rock mass is simulated using numerical calculation methods to obtain predicted data on the distribution of stress field and fracture damage field of the rock mass. The failure process of rock mass samples was monitored in real time using high-density electrical resistivity tomography (EDT) to obtain observational data; the distribution of rock mass fracture damage was obtained based on the observational data. An assimilation algorithm is used to combine information from observational and prediction data to correct the initial conditions or parameters of the numerical model, thereby obtaining corrected rock fracture prediction data.

[0007] Furthermore, the method for obtaining prediction data in the method of the present invention specifically includes: A numerical model was established using a continuous / discontinuous method. The geometric model of the rock mass was divided into solid elements by finite element meshing. Joint elements without thickness were inserted between adjacent solid elements to simulate the discontinuous deformation behavior of crack initiation and fracture. The joint element initially has no thickness. The stress-displacement constitutive model of the fracture plastic zone theory is used to describe the mechanical behavior of the joint element, and the internal forces of the joint element are obtained. When a joint element fails, the connected solid elements form a potential contact pair. These potential contact pairs are identified by a contact search algorithm and contact detection is performed. Finally, contact force is calculated for all detected contact pairs, thus obtaining predicted data on the stress field and fracture damage field distribution of the rock mass.

[0008] Furthermore, the continuous / discontinuous method of the present invention specifically includes: The motion control equations for the continuous / discontinuous method are expressed as follows:

[0009] in, and These are the mass matrix and damping matrix of the finite element mesh; It is the nodal displacement vector; , , and These are the internal forces of solid elements, the internal forces of joint elements, the contact forces, and the resultant external forces; Based on the finite element method of the continuity approach and the elastoplastic constitutive theory, the internal forces of solid elements are expressed as:

[0010] in, It is a certain entity. It is the gradient matrix of the shape function of the entity unit node. Let be the Cauchy stress tensor of the solid element.

[0011] Furthermore, the method for obtaining the internal forces of joint elements according to the present invention specifically includes: The stress of the joint element between two adjacent solid elements can be calculated by the following formula:

[0012]

[0013] The internal forces of a joint element are expressed as follows:

[0014] in, and These are the normal and shear forces of the joint element; and These are the relative normal displacement and tangential displacement between two adjacent solid elements, respectively. and That is and The elastic limit; and These are the plastic fracture limits for normal and tangential displacements; and These are the tensile strength and shear strength of the joint element. It is the material softening coefficient.

[0015] Furthermore, the method for calculating contact force according to the present invention specifically includes: The contact force is calculated using a potential-based method, and is expressed as follows:

[0016]

[0017] in, It is the contact force in the normal direction between the contact points. For normal contact stiffness, The contact potential gradient inside the contact block. The contact potential gradient inside the target block.

[0018] Furthermore, the method for obtaining observation data according to the present invention specifically includes: Electrical conductivity or resistivity is used to represent changes in the electrical properties of rock masses, thereby reflecting changes in the development and propagation of internal fractures and quantifying internal rock damage. Acoustic electrode sensors are deployed on the surface of rock samples, and external forces are applied to the rock using a loading device to simulate real underground stress conditions; the resistivity signal of the rock mass is collected by the acoustic electrode sensors, and the resistivity is calculated. The inversion calculation minimizes the potential difference between the observed potential difference and the potential difference obtained from the forward calculation. By continuously iterating and updating the resistivity calculation results, the objective function value is gradually reduced, thereby obtaining a rock mass failure resistivity cloud map as observation data.

[0019] Furthermore, the formula for calculating resistivity in this invention is as follows:

[0020] in: Apparent resistivity Geometric factor The measured potential difference, For the injected current; The objective function used in the inversion calculation is expressed as:

[0021] in, It is the first The potential difference measured in this step It is the potential difference calculated based on resistivity. It is a regularization term used to smooth resistivity calculations. It is a regularization parameter that controls the weight of the regularization term.

[0022] Furthermore, the assimilation algorithm of the present invention specifically includes: In the prediction phase, based on the current state variables or parameter estimates, the predicted data is calculated using a numerical model. In the analysis / update phase, the predicted data is compared with the observed data, the difference between the two is calculated, the gain matrix is ​​used to adjust the weights of the predicted data and the observed data, and the updated state variables or parameter estimates are generated. The iterative process involves repeatedly executing the steps of the prediction and analysis / update phases until the convergence condition or the number of iterations is reached.

[0023] Furthermore, the assimilation algorithm of the present invention is expressed in the following form:

[0024] in, It is the first The optimal estimate of the state variables or parameter variables in the next iteration, i.e., the optimal estimate of the model's state variables or parameters. It is the updated state variable or parameter vector, i.e., the first... The optimal estimate in the next iteration It is the first The gain matrix of the next iteration is used to adjust the weights of the observed data and the model prediction data. It is a vector of measured observation data, acquired by the electrode sensor. It is a model operator that converts state variables or parameter variables. The predicted values ​​mapped into the observation space are the observed data.

[0025] This invention provides a rock fracture prediction system based on a resistivity data assimilation algorithm, comprising: a temperature and pressure control device, a loading device, an acoustic electrode sensor, an electrode switching switch, a high-density electrical resistivity meter, and a data processing device; wherein: Loading equipment is used to apply external force to rock samples; Temperature and pressure control equipment, connected to the loading equipment, is used to control the temperature and pressure of the loading equipment to simulate real underground stress conditions; Acoustic electrode sensors are deployed on the surface of rock samples to collect the electrical conductivity or resistivity of the rock samples. The electrode switching switch is connected at one end to the acoustic electrode sensor and at the other end to the high-density electrical resistivity meter. A high-density resistivity transilluminator is used to receive resistivity signals. The data processing equipment is connected to a high-density electrical resistivity meter and contains a computer program for implementing the rock fracture prediction method based on resistivity data assimilation algorithm described above.

[0026] The beneficial effects of this invention are: 1. This invention integrates resistivity observation data from different sources and at different resolutions with model simulation results within the dynamic framework of rock fracture through a data assimilation algorithm. This allows the rock fracture process model and various observation operator sets to continuously and automatically adjust the model trajectory based on observations, resulting in more accurate and consistent analysis results, and improving the model's prediction accuracy and predictability.

[0027] 2. The method of the present invention adjusts the boundary conditions by comparing with measured data, and provides a true feedback on the rock failure process caused by damage and fracture, providing sufficient experimental evidence for the rock damage and fracture mechanism, and can effectively predict rock failure.

[0028] 3. In establishing the numerical model, this invention employs a continuous / discontinuous method. A solid element model is formed by meshing the rock mass geometry into a finite element mesh. Then, joint elements with no thickness are inserted between adjacent solid elements, effectively simulating discontinuous deformation behaviors such as crack initiation and fracture. Furthermore, unlike solid elements, joint elements initially have no thickness, and their internal forces cannot be directly analyzed using existing constitutive theories. Therefore, the stress-displacement constitutive model of the fracture plastic zone theory is used to describe the mechanical behavior of the joint elements.

[0029] 4. This invention combines actual observation data (resistivity data) with information from the driving numerical model (predicted values) through an assimilation algorithm to correct the initial conditions or parameters of the numerical model, making it better match the actual observation data, thereby improving the accuracy and reliability of the model. Attached Figure Description

[0030] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 This is a flowchart of the assimilation process according to an embodiment of the present invention; Figure 2 This is an observation data acquisition and processing system according to an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the principle of potential-based contact force calculation in an embodiment of the present invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0032] Example 1 like Figure 1As shown in the figure, the rock fracture prediction method based on resistivity data assimilation algorithm of the present invention includes the following steps: Step 1: Based on the boundary and initial conditions of the rock mass, establish a numerical model to simulate the asymptotic fracture process of the rock mass under load; use numerical calculation methods to simulate the stress failure process of the rock mass and obtain predicted data on the stress field and fracture damage field distribution of the rock mass. Step 2: Use high-density electrical resistivity tomography (EDT) to monitor the failure process of rock mass samples in real time and obtain observation data; obtain the distribution of rock mass fracture damage based on the observation data; Step 3: Use an assimilation algorithm to combine the information from the observation data and the prediction data to correct the initial conditions or parameters of the numerical model; thus obtaining the corrected rock fracture prediction data.

[0033] In a preferred embodiment of the present invention, the method for obtaining the prediction data in step 1 specifically includes: A numerical model was established using a continuous / discontinuous method. The geometric model of the rock mass was divided into solid elements by finite element meshing. Joint elements without thickness were inserted between adjacent solid elements to simulate the discontinuous deformation behavior of crack initiation and fracture. The joint element initially has no thickness. The stress-displacement constitutive model of the fracture plastic zone theory is used to describe the mechanical behavior of the joint element, and the internal forces of the joint element are obtained. When a joint element fails, the connected solid elements form a potential contact pair. These potential contact pairs are identified by a contact search algorithm and contact detection is performed. Finally, contact force is calculated for all detected contact pairs, thus obtaining predicted data on the stress field and fracture damage field distribution of the rock mass.

[0034] In a preferred embodiment of the present invention, the method for obtaining observation data in step 2 specifically includes: Electrical conductivity or resistivity is used to represent changes in the electrical properties of rock masses, thereby reflecting changes in the development and propagation of internal fractures and quantifying internal rock damage. Acoustic electrode sensors are deployed on the surface of rock samples, and external forces are applied to the rock using a loading device to simulate real underground stress conditions; the resistivity signal of the rock mass is collected by the acoustic electrode sensors, and the resistivity is calculated. The inversion calculation minimizes the potential difference between the observed potential difference and the potential difference obtained from the forward calculation. By continuously iterating and updating the resistivity calculation results, the objective function value is gradually reduced, thereby obtaining a rock mass failure resistivity cloud map as observation data.

[0035] In a preferred embodiment of the present invention, the assimilation algorithm in step 3 specifically includes: In the prediction phase, based on the current state variables or parameter estimates, the predicted data is calculated using a numerical model. In the analysis / update phase, the predicted data is compared with the observed data, the difference between the two is calculated, the gain matrix is ​​used to adjust the weights of the predicted data and the observed data, and the updated state variables or parameter estimates are generated. The iterative process involves repeatedly executing the steps of the prediction and analysis / update phases until the convergence condition or the number of iterations is reached.

[0036] Example 2 Based on Example 1, this invention provides specific algorithms and processes for each step. The rock fracture process prediction method and system based on data assimilation mainly includes three parts: driving numerical model, observation data, and assimilation algorithm.

[0037] 1. Driving numerical models In the study of deep underground engineering activities, one of the main research methods is to simulate the asymptotic fracturing process of deep rocks under load using numerical models to study the rock damage and fracturing mechanism. The fracturing behavior during the rock simulation process will cause changes in the model boundary conditions and physical fields. By comparing with measured data and adjusting the boundary conditions, the rock failure process caused by damage and fracturing can be realistically reflected, providing sufficient experimental evidence for the rock damage and fracturing mechanism and enabling the prediction of rock failure.

[0038] Numerical methods in rock mechanics are mainly divided into two categories: continuous methods, such as the finite element method and the finite difference method, which are based on continuum mechanics; and discontinuous methods, which simulate the discontinuous mechanical behavior of materials using discrete elements and discrete particles. The continuous / discontinuous method combines the advantages of both traditional and discontinuous media mechanics, and can simulate the process of brittle materials from microcrack initiation to macroscopic fracture, and is widely used in various fields. In establishing the numerical model, the continuous / discontinuous method mainly involves two steps: first, the geometric model is meshed into a solid element model using finite element meshes; then, joint elements with no thickness are inserted between adjacent solid elements to simulate discontinuous deformation behaviors such as crack initiation and fracture. Based on Newton's second law, the main governing equations of the continuous / discontinuous method can be expressed as follows:

[0039] in, and These are the mass matrix and damping matrix of the finite element mesh; It is the nodal displacement vector; , , and These are the internal forces of solid elements, the internal forces of joint elements, the contact forces, and the resultant external forces.

[0040] Based on the finite element method of the continuity approach and the elastoplastic constitutive theory, the internal forces of solid elements can be expressed as:

[0041] in It is a certain entity. It is the gradient matrix of the shape function of the entity unit node. Let be the Cauchy stress tensor of the solid element.

[0042] Unlike solid elements, joint elements initially have no thickness, and their internal forces cannot be directly analyzed using existing constitutive theories. Therefore, the stress-displacement constitutive model of the fracture plastic zone theory is used to describe the mechanical behavior of joint elements. The stress of a joint element between two adjacent solid elements can be calculated by the following formula:

[0043]

[0044] in, and These are the normal and shear forces of the joint element; and These are the relative normal displacement and tangential displacement between two adjacent solid elements, respectively. and That is and The elastic limit; and These are the plastic fracture limits for normal and tangential displacements. and These are the tensile strength and shear strength of the joint element. This is the material softening coefficient. Therefore, the internal forces within the joint element can be expressed as:

[0045] When a joint element fails, the connected solid elements form a potential contact pair. Contact search algorithms can effectively identify these potential contact pairs, perform contact detection, and finally calculate the contact force for all detected contact pairs. Current methods for calculating contact force are mainly divided into two categories: geometry-based contact force calculation and potential-based contact force calculation. For example... Figure 3 As shown, potential-based contact force calculation is widely used in continuous / discontinuous contact force calculations due to its simplicity and stability. Its main principle can be expressed as:

[0046]

[0047] in, It is the contact force in the normal direction between the contact points. For normal contact stiffness, The contact potential gradient inside the contact block. The contact potential gradient inside the target block.

[0048] 2. Observational Data Acquisition The electrical properties of rocks are one of their inherent physical properties. Under constant environmental conditions, they are closely related only to the rock's porosity, fracture propagation, and deformation. However, when rocks are subjected to various factors such as loads, environmental changes, and chemical reactions, internal defects can be generated, propagated, and penetrate, altering their electrical properties and affecting related electrical parameters. Therefore, electrical conductivity or resistivity can be used to represent changes in the electrical properties of rocks, reflecting the development and propagation of internal fractures and quantifying internal rock damage.

[0049] Specifically, acoustic electrode sensors need to be deployed on the surface of the rock sample. An external force is applied to the rock using a loading device to simulate real underground stress conditions. The acoustic electrode sensors are connected to a selector switch via high-temperature, high-pressure resistant cables, which then connect to a resistivity acquisition instrument to receive resistivity signals, including potential difference, apparent resistivity, and acquisition errors. Finally, algorithms are used to process and analyze the signals to extract useful signals, such as... Figure 2 As shown.

[0050] Electrical resistivity measurement calculates the resistivity of rocks by measuring the potential difference and current between electrodes. The specific formula for calculating resistivity depends on the electrode arrangement used. Common arrangements include the Winner method and the Schlumberger method.

[0051] Regardless of the arrangement method, the general formula for calculating resistivity is:

[0052] in: apparent resistivity ( · ), Geometric factor The measured potential difference (V), The injected current (A).

[0053] The objective function is to minimize the difference between the observed potential difference and the potential difference obtained from the forward modeling through inversion calculation, which is to minimize the error between the observed potential difference and the calculated potential difference.

[0054] in, It is the first The potential difference measured in this step It is the potential difference calculated based on the resistivity model. It is a regularization term used to smooth the resistivity model. It is a regularization parameter that controls the weight of the regularization term.

[0055] By continuously iterating and updating the resistivity model, the objective function value is gradually reduced to obtain a rock failure resistivity contour map as an observation value.

[0056] 3. Data assimilation algorithm The core idea of ​​the assimilation algorithm is to combine the actual observation data (resistivity data) with the information of the driving numerical model (predicted value) through optimization methods, so as to correct the initial conditions or parameters of the numerical model, making it better match the actual observation data, thereby improving the accuracy and reliability of the model.

[0057] Assimilation algorithms can be represented in the following basic form:

[0058] in, It is the first The state variables or parameter variables of the next iteration (the optimal estimates of the model state variables or parameters). It is the updated state variable or parameter vector, i.e., the first... The optimal estimate in the next iteration It is the first The gain matrix of the next iteration is used to adjust the weights of the observed data and the model prediction data. It is a vector of measured observation data, acquired by the electrode sensor. It is a model operator that converts state variables or parameter variables. The predicted values ​​mapped into the observation space are the model prediction data.

[0059] The main steps are as follows: (1) Prediction phase: Estimate based on current state variables or parameters Predicted data is calculated using numerical models.

[0060] (2) In the analysis / update phase, the predicted data will be... Compared with measured data Compare them, calculate the difference between them, and use the gain matrix. This is used to adjust the weights of predicted and observed data to generate updated state variables or parameter estimates. .

[0061] (3) Iterative process: repeatedly execute the prediction and analysis steps until the convergence condition or the number of iterations is reached.

[0062] Example 3 This invention provides a rock fracture prediction system based on a resistivity data assimilation algorithm, comprising: a temperature and pressure control device, a loading device, an acoustic electrode sensor, an electrode switching switch, a high-density electrical resistivity meter, and a data processing device; wherein: Loading equipment is used to apply external force to rock samples; Temperature and pressure control equipment, connected to the loading equipment, is used to control the temperature and pressure of the loading equipment to simulate real underground stress conditions; Acoustic electrode sensors are deployed on the surface of rock samples to collect the electrical conductivity or resistivity of the rock samples. The electrode switching switch is connected at one end to the acoustic electrode sensor and at the other end to the high-density electrical resistivity meter. A high-density resistivity transilluminator is used to receive resistivity signals. The data processing equipment is connected to a high-density electrical resistivity meter and contains a computer program for implementing the rock fracture prediction method based on resistivity data assimilation algorithm described above.

[0063] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0064] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A rock fracture prediction method based on resistivity data assimilation algorithm, characterized in that, The method includes the following steps: Based on the boundary and initial conditions of the rock mass, a numerical model is established to simulate the asymptotic fracture process of the rock mass under load; the stress failure process of the rock mass is simulated using numerical calculation methods to obtain predicted data on the distribution of stress field and fracture damage field of the rock mass. The failure process of rock mass samples was monitored in real time using high-density electrical resistivity tomography (EDT) to obtain observational data; the distribution of rock mass fracture damage was obtained based on the observational data. An assimilation algorithm is used to combine information from observational and prediction data to correct the initial conditions or parameters of the numerical model, thereby obtaining corrected rock fracture prediction data.

2. The rock fracture prediction method based on resistivity data assimilation algorithm according to claim 1, characterized in that, The specific methods for obtaining prediction data in this method include: A numerical model was established using a continuous / discontinuous method. The geometric model of the rock mass was divided into solid elements by finite element meshing. Joint elements without thickness were inserted between adjacent solid elements to simulate the discontinuous deformation behavior of crack initiation and fracture. The joint element initially has no thickness. The stress-displacement constitutive model of the fracture plastic zone theory is used to describe the mechanical behavior of the joint element, and the internal forces of the joint element are obtained. When a joint element fails, the connected solid elements form a potential contact pair. These potential contact pairs are identified by a contact search algorithm and contact detection is performed. Finally, contact force is calculated for all detected contact pairs, thus obtaining predicted data on the stress field and fracture damage field distribution of the rock mass.

3. The rock fracture prediction method based on resistivity data assimilation algorithm according to claim 2, characterized in that, The continuous / discontinuous method specifically includes: The motion control equations for the continuous / discontinuous method are expressed as follows: in, and These are the mass matrix and damping matrix of the finite element mesh; It is the nodal displacement vector; , , and These are the internal forces of solid elements, the internal forces of joint elements, the contact forces, and the resultant external forces; Based on the finite element method of the continuity approach and the elastoplastic constitutive theory, the internal forces of solid elements are expressed as: in, It is a certain entity unit. It is the gradient matrix of the shape function of the entity unit node. Let be the Cauchy stress tensor of the solid element.

4. The rock fracture prediction method based on resistivity data assimilation algorithm according to claim 3, characterized in that, The method for obtaining the internal forces of joint elements specifically includes: The stress of the joint element between two adjacent solid elements is calculated by the following formula: The internal forces of a joint element are expressed as follows: in, and These are the normal and shear forces of the joint element; and These are the relative normal displacement and tangential displacement between two adjacent solid elements, respectively. and That is and The elastic limit; and These are the plastic fracture limits for normal and tangential displacements; and These are the tensile strength and shear strength of the joint element. It is the material softening coefficient.

5. The rock fracture prediction method based on resistivity data assimilation algorithm according to claim 4, characterized in that, The method for calculating contact force specifically includes: The contact force is calculated using a potential-based method, and is expressed as follows: in, It is the contact force in the normal direction between the contact points. For normal contact stiffness, This represents the contact potential gradient inside the contact block. The contact potential gradient inside the target block.

6. The rock fracture prediction method based on resistivity data assimilation algorithm according to claim 1, characterized in that, The method for obtaining observation data specifically includes: Electrical conductivity or resistivity is used to represent changes in the electrical properties of rock masses, thereby reflecting changes in the development and propagation of internal fractures and quantifying internal rock damage. Acoustic electrode sensors are deployed on the surface of rock samples, and external forces are applied to the rock using a loading device to simulate real underground stress conditions; the resistivity signal of the rock mass is collected by the acoustic electrode sensors, and the resistivity is calculated. The inversion calculation minimizes the potential difference between the observed potential difference and the potential difference obtained from the forward calculation. By continuously iterating and updating the resistivity calculation results, the objective function value is gradually reduced, thereby obtaining a rock mass failure resistivity cloud map as observation data.

7. The rock fracture prediction method based on resistivity data assimilation algorithm according to claim 6, characterized in that, The formula for calculating resistivity is: in: Apparent resistivity Geometric factor The measured potential difference, For the injected current; The objective function used in the inversion calculation is expressed as: in, It is the first The potential difference measured in this step It is the potential difference calculated based on resistivity. It is a regularization term used to smooth resistivity calculations. It is a regularization parameter that controls the weight of the regularization term.

8. The rock fracture prediction method based on resistivity data assimilation algorithm according to claim 1, characterized in that, The assimilation algorithm specifically includes the following methods: In the prediction phase, based on the current state variables or parameter estimates, the predicted data is calculated using a numerical model. In the analysis / update phase, the predicted data is compared with the observed data, the difference between the two is calculated, the gain matrix is ​​used to adjust the weights of the predicted data and the observed data, and the updated state variables or parameter estimates are generated. The iterative process involves repeatedly executing the steps of the prediction and analysis / update phases until the convergence condition or the number of iterations is reached.

9. The rock fracture prediction method based on resistivity data assimilation algorithm according to claim 8, characterized in that, The assimilation algorithm is expressed in the following form: in, It is the first The optimal estimate of the state variables or parameter variables in the next iteration, i.e., the optimal estimate of the model's state variables or parameters. It is the updated state variable or parameter vector, i.e., the first... The optimal estimate in the next iteration It is the first The gain matrix of the next iteration is used to adjust the weights of the observed data and the model prediction data. It is a vector of measured observation data, acquired by the electrode sensor. It is a model operator that converts state variables or parameter variables. The predicted values ​​mapped into the observation space are the observed data.

10. A rock fracture prediction system based on resistivity data assimilation algorithm, characterized in that, include: Temperature and pressure control equipment, loading equipment, acoustic electrode sensors, electrode switching devices, high-density electrical resistivity tomography (EDT) instruments, and data processing equipment; among which: Loading equipment is used to apply external force to rock samples; Temperature and pressure control equipment, connected to the loading equipment, is used to control the temperature and pressure of the loading equipment to simulate real underground stress conditions; Acoustic electrode sensors are deployed on the surface of rock samples to collect the electrical conductivity or resistivity of the rock samples. The electrode switching switch is connected at one end to the acoustic electrode sensor and at the other end to the high-density electrical resistivity meter. A high-density resistivity transducer is used to receive resistivity signals. A data processing device, connected to a high-density electrical resistivity meter, is equipped with a computer program for implementing the rock fracture prediction method based on resistivity data assimilation algorithm as described in any one of claims 1 to 9.

Images