A real-time quantitative characterization method, device and medium for rock mass evolution

Abstract

The present application discloses a real-time quantitative characterization method, equipment and medium for rock mass evolution, and relates to the field of reservoir rock mass engineering geomechanics test and testing. The method comprises data fusion of multiphase field detection and monitoring data; establishing a physical principle-driven deep learning model using a deep learning method according to the physical principle of fluid density; training the deep learning model using real-time fused data as input and a corresponding evolution distribution image as output; obtaining an evolution distribution image using the trained deep learning model according to multiphase field detection and monitoring data of different times and types; and realizing quantitative characterization of physical and mechanical parameters in the whole process of dynamic evolution and progressive destruction of rock mass using a mathematical model according to macroscopic mechanical parameters and evolution distribution images synchronized with multiphase field detection and monitoring data; the present application can realize real-time transparent characterization of long-term coupled rock mass evolution of multiphase field.

Description

Technical Field

[0001] The present application relates to the field of reservoir rock mass engineering geomechanics test and testing, and in particular to a real-time quantitative characterization method, equipment and medium for rock mass evolution. Background Art

[0002] Carbon dioxide geological storage projects belong to deep geological projects that are "inaccessible to humans". The dynamic process and mechanism of fracture formation and evolution of deep rock mass under the multi-phase (supercritical-gas-liquid-solid) and multi-field (temperature-fluid-stress-chemistry-biology) multi-phase field coupling environment has always been a "black box" problem.

[0003] Realizing the real-time transparency of the evolution of multiphase field-coupled rock mass is the key to revealing the spatiotemporal response characteristics and evolution laws of the rock mass under the multiphase field coupling during the supercritical carbon dioxide injection disturbance. Although the current computer-aided X-ray tomography, nuclear magnetic resonance technology, geophysical detection and acoustic emission monitoring can provide key information on the evolution of rock mass structure, there are problems of structural imaging hysteresis or poor imaging accuracy, and it is impossible to achieve the instant transparency characterization of the evolution of multiphase field-coupled rock mass in carbon sequestration projects. At present, the research on high-precision imaging and real-time transparency characterization of the evolution of multiphase field-coupled rock mass is still blank.

[0004] Therefore, based on the above problems, there is an urgent need to provide a real-time quantitative characterization method suitable for the evolution of rock mass in carbon sequestration projects, so as to open the black box of the dynamic evolution of rock mass under the disturbance of supercritical carbon dioxide injection. Summary of the invention

[0005] The purpose of this application is to provide a real-time quantitative characterization method, equipment and medium for rock mass evolution, which can realize real-time transparent characterization of long-term coupled rock mass evolution in multi-phase fields.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a real-time quantitative characterization method for rock mass evolution, the real-time quantitative characterization method for rock mass evolution comprising:

[0008] Obtaining evolution distribution images of rock mass at different evolution stages and real-time multiphase field detection and monitoring data during the whole process of multiphase field coupling test; the evolution distribution images include: reconstructed pore distribution images, pore fluid pressure distribution images and supercritical carbon dioxide saturation distribution images; the multiphase field detection and monitoring data include: temperature, strain, resistivity, chemical ion concentration, microbial cell concentration, P-wave velocity and S-wave velocity and acoustic emission data;

[0009] Use feature extraction methods and Transformer deep learning models to fuse real-time multi-phase field detection and monitoring data;

[0010] Based on the physical principles of fluid density under the influence of temperature, chemical ion concentration, microbial cell concentration, and supercritical carbon dioxide, a deep learning model driven by physical principles is established using deep learning methods.

[0011] Using real-time fused data as input and corresponding evolving distribution images as output, we train a deep learning model driven by physical principles.

[0012] Based on multi-phase field detection and monitoring data of different types at different times, the trained deep learning model is used to obtain the evolution distribution image;

[0013] Based on the macroscopic mechanical parameters and evolution distribution images synchronized with the multiphase field detection and monitoring data, a mathematical model is used to achieve quantitative characterization of the physical and mechanical parameters in the entire process of dynamic evolution and progressive destruction of the rock mass; the macroscopic mechanical parameters are the macroscopic mechanical parameters of different stages of rock mass evolution under different loading paths and multiphase field coupling.

[0014] Optionally, the device for acquiring real-time multi-phase field detection and monitoring data includes: an optical fiber sensor, an electrode sensor, and an ultrasonic transducer.

[0015] Optionally, the deep learning method includes: a Bayesian neural network, a convolutional neural network, a deep residual network, a recurrent neural network and a generative adversarial network.

[0016] Optionally, the mathematical model is established by logistic regression analysis, support vector machine SVM, random forest or neural network.

[0017] In a second aspect, the present application provides a real-time quantitative characterization device for rock mass evolution, the real-time quantitative characterization device for rock mass evolution comprising:

[0018] The data acquisition module is used to obtain the evolution distribution images of the rock mass at different evolution stages during the whole process of the multiphase field coupling test and the real-time multiphase field detection and monitoring data; the evolution distribution images include: reconstructed pore distribution images, pore fluid pressure distribution images and supercritical carbon dioxide saturation distribution images; the multiphase field detection and monitoring data include: temperature, strain, resistivity, chemical ion concentration, microbial cell concentration, P-wave velocity and S-wave velocity and acoustic emission data;

[0019] Data fusion module, used to fuse real-time multi-phase field detection and monitoring data using feature extraction methods and Transformer deep learning models;

[0020] A deep learning model building module driven by physical principles, which is used to build a deep learning model driven by physical principles using deep learning methods based on the physical principles of fluid density under the influence of temperature, chemical ion concentration, microbial cell concentration, and supercritical carbon dioxide;

[0021] The deep learning model training module driven by physical principles is used to train the deep learning model driven by physical principles with real-time fused data as input and corresponding evolution distribution images as output;

[0022] The evolution distribution image inversion module is used to obtain the evolution distribution image based on the multi-phase field detection and monitoring data of different types and at different times using the trained deep learning model;

[0023] The physical and mechanical parameter quantitative characterization module is used to achieve the quantitative characterization of the physical and mechanical parameters in the whole process of dynamic evolution and progressive destruction of rock mass by using mathematical models based on the macroscopic mechanical parameters and evolution distribution images synchronized with multiphase field detection and monitoring data.

[0024] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the real-time quantitative characterization method of rock mass evolution.

[0025] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the real-time quantitative characterization method of rock mass evolution.

[0026] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0027] The present application provides a real-time quantitative characterization method, device and medium for rock mass evolution. The established deep learning model driven by physical principles embeds the controlling physical laws (i.e., partial differential equations) as prior information, combines the differentiable programming method with the traditional numerical simulation method (such as finite element method, finite difference method or discrete element method), so that all numerical operations are implemented with differentiable building blocks. The differentiable mechanism of the deep learning model driven by physical principles in the present application solves the forward and reverse problems through automatic differentiation methods, and is relatively easy to combine with deep neural networks, so that it has universal applicability. The present application uses feature extraction methods and Transformer deep learning models to perform data fusion on real-time multi-phase field detection and monitoring data, that is, to fuse and collaboratively invert multi-source heterogeneous and heterogeneous massive information, so as to realize the efficient use of data and images of different formats, different magnitudes, different dimensions and different resolutions obtained by different detection and monitoring means; to overcome the key scientific problems of the difficulty of training deep learning models driven by physical principles, and to realize the real-time transparent quantitative characterization of the fine structure field, effective stress field and supercritical carbon dioxide migration of multi-phase field coupled rock mass. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0029] Figure 1 This is an application environment diagram of a real-time quantitative characterization method of rock mass evolution in one embodiment of the present application;

[0030] Figure 2 A schematic flow chart of a real-time quantitative characterization method for rock mass evolution provided in one embodiment of the present application;

[0031] Figure 3 Schematic diagram of the deep learning model principle driven by physical principles;

[0032] Figure 4 Schematic diagram of the transparency representation of multiphase field coupled rock mass evolution. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0034] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0035] The real-time quantitative characterization method of rock mass evolution provided in the embodiments of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the multiphase field detection and monitoring data to be processed to the server 104. After the server 104 receives the multiphase field detection and monitoring data to be processed, for the multiphase field detection and monitoring data to be processed, the server 104 obtains an evolution distribution image based on the trained deep learning model. According to the macroscopic mechanical parameters and evolution distribution images synchronized with the multiphase field detection and monitoring data, a mathematical model is used to realize the quantitative characterization of the physical and mechanical parameters in the whole process of dynamic evolution and progressive destruction of the rock mass; the server 104 can feedback the obtained quantitative characterization of the physical and mechanical parameters in the whole process of dynamic evolution and progressive destruction of the rock mass to the terminal 102. In addition, in some embodiments, the real-time quantitative characterization method of rock mass evolution can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly process the multi-phase field detection and monitoring data to be processed, or the server 104 can obtain the multi-phase field detection and monitoring data to be processed from the data storage system and process the multi-phase field detection and monitoring data to be processed.

[0036] In an exemplary embodiment, Figure 2 and Figure 4 As shown, a real-time quantitative characterization method for rock mass evolution is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate, including the following S201 to S206. Among them:

[0037] S201, obtaining the evolution distribution images of the rock mass at different evolution stages and real-time multiphase field detection and monitoring data during the whole process of the multiphase field coupling test; the evolution distribution images include: reconstructed pore distribution images (fine structure field), pore fluid pressure distribution images (effective stress field) and supercritical carbon dioxide saturation distribution images; the multiphase field detection and monitoring data include: temperature, strain, resistivity, chemical ion concentration, microbial cell concentration, P-wave velocity and S-wave velocity and acoustic emission data; the multiphase field detection and monitoring data are optical-electrical-acoustic signals;

[0038] S202, using feature extraction methods and Transformer deep learning models to perform data fusion on real-time multi-phase field detection and monitoring data; feature extraction methods include but are not limited to: principal component analysis method.

[0039] S203, based on the physical principles of temperature, chemical ion concentration, microbial cell concentration and fluid density under the influence of supercritical carbon dioxide, a deep learning model driven by physical principles is established using a deep learning method, and Figure 3 As shown; the deep learning methods include: Bayesian neural network, convolutional neural network, deep residual network, recurrent neural network and generative adversarial network. Among them, the physical principles include: the equation of sound wave propagation in the medium, the equation of resistivity tomography and the equation of fiber optic sensing technology.

[0040] S204, using the real-time fused data as input and the corresponding evolution distribution image as output, training a deep learning model driven by physical principles;

[0041] S205, based on multiphase field detection and monitoring data of different times and types, uses the trained deep learning model to obtain evolution distribution images; S205 realizes real-time and accurate imaging of the entire evolution process of long-term coupled multiphase field rock deformation, damage, and fracture, and transparent representation of the fine structure field, effective stress field, and supercritical carbon dioxide migration inside the rock mass (transparent results);

[0042] S206. Based on the macroscopic mechanical parameters and evolution distribution images synchronized with the multiphase field detection and monitoring data, a mathematical model is used to quantitatively characterize the physical and mechanical parameters in the whole process of dynamic evolution and progressive destruction of the rock mass. The macroscopic mechanical parameters are the macroscopic mechanical parameters of the rock mass at different stages of evolution under different loading paths and multiphase field coupling, thereby revealing the multiphase field long-term coupling mechanism of the reservoir rock matrix evolution and caprock structure degradation of the carbon sequestration project under the true triaxial stress state.

[0043] The mathematical model is established by logistic regression analysis, support vector machine SVM, random forest or neural network.

[0044] S201-S206 proposed a multi-source heterogeneous data fusion and real-time joint inversion method of multi-phase field parameters based on big data analysis, data mining and machine learning, in order to achieve the immediate and transparent characterization of the long-term coupled rock mass evolution of multi-phase fields.

[0045] Devices for acquiring real-time multi-phase field detection and monitoring data include, but are not limited to: optical fiber sensors, electrode sensors, and ultrasonic transducers.

[0046] The process of determining the evolution distribution image is:

[0047] Real-time monitoring of the different evolution stages of rock deformation, damage and crack initiation, cracking, expansion and penetration during the whole process of multiphase field coupling test, and use CT technology to scan and obtain a large number of evolution distribution images before, during and after the test; the evolution distribution images are digital cores;

[0048] In order to realize the instant transparent characterization of the fine structure field, effective stress field and supercritical carbon dioxide migration inside the rock mass under the long-term coupling of multiphase fields, this application establishes a deep learning method driven by physical principles and a deep learning model with physical significance. The traditional method of training deep learning models requires a large amount of data. The images of the internal structure and fluid migration changes of the rock mass obtained by CT scanning tests and the corresponding multiphase field detection and monitoring data are limited. Therefore, it is difficult to build a high-precision and universal deep learning model with existing training methods, and the established deep learning models are often only statistically significant and lack physical significance and explanation. The deep learning model driven by physical principles to be established by the present invention establishes a Bayesian neural network by embedding the controlling physical laws (i.e., partial differential equations) as prior information, and combines the differentiable programming method with the traditional numerical simulation method (such as finite element method, finite difference method or discrete element method), so that all numerical operations are implemented with differentiable building blocks. This differentiable mechanism can solve forward and reverse problems through automatic differentiation methods, and can be easily combined with deep neural networks to build a universal deep learning model driven by physical principles.

[0049] This application will intelligently integrate and collaboratively invert multi-source heterogeneous and heterogeneous massive information to achieve efficient use of data and images of different formats, magnitudes, dimensions, and resolutions obtained by different detection and monitoring methods; overcome the key scientific problem of the difficulty in training universal deep learning models driven by physical principles, and achieve real-time transparent quantitative characterization of the fine structure field, effective stress field, and supercritical carbon dioxide migration of multi-phase field-coupled rock masses.

[0050] Based on the same inventive concept, the embodiment of the present application also provides a real-time quantitative characterization device for rock mass evolution for implementing the real-time quantitative characterization method of rock mass evolution involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the real-time quantitative characterization device for rock mass evolution provided below can refer to the limitations of the real-time quantitative characterization method of rock mass evolution above, and will not be repeated here.

[0051] In an exemplary embodiment, a real-time quantitative characterization device for rock mass evolution is provided, comprising:

[0052] The data acquisition module is used to obtain the evolution distribution images of the rock mass at different evolution stages during the whole process of the multiphase field coupling test and the real-time multiphase field detection and monitoring data; the evolution distribution images include: reconstructed pore distribution images, pore fluid pressure distribution images and supercritical carbon dioxide saturation distribution images; the multiphase field detection and monitoring data include: temperature, strain, resistivity, chemical ion concentration, microbial cell concentration, P-wave velocity and S-wave velocity and acoustic emission data;

[0053] Data fusion module, used to fuse real-time multi-phase field detection and monitoring data using feature extraction methods and Transformer deep learning models;

[0054] A deep learning model building module driven by physical principles, which is used to build a deep learning model driven by physical principles using deep learning methods based on the physical principles of fluid density under the influence of temperature, chemical ion concentration, microbial cell concentration, and supercritical carbon dioxide;

[0055] The deep learning model training module driven by physical principles is used to train the deep learning model driven by physical principles with real-time fused data as input and corresponding evolution distribution images as output;

[0056] The evolution distribution image inversion module is used to obtain the evolution distribution image based on the multi-phase field detection and monitoring data of different types and at different times using the trained deep learning model;

[0057] The physical and mechanical parameter quantitative characterization module is used to achieve the quantitative characterization of the physical and mechanical parameters in the whole process of dynamic evolution and progressive destruction of rock mass by using mathematical models based on the macroscopic mechanical parameters and evolution distribution images synchronized with multiphase field detection and monitoring data.

[0058] In an exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a real-time quantitative characterization method of rock mass evolution is implemented.

[0059] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0060] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0061] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0062] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0063] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0064] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0065] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0066] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A real-time quantitative characterization method for rock mass evolution, characterized in that: The real-time quantitative characterization method of rock mass evolution includes: Obtaining evolution distribution images of rock mass at different evolution stages and real-time multiphase field detection and monitoring data during the whole process of multiphase field coupling test; the evolution distribution images include: reconstructed pore distribution images, pore fluid pressure distribution images and supercritical carbon dioxide saturation distribution images; the multiphase field detection and monitoring data include: temperature, strain, resistivity, chemical ion concentration, microbial cell concentration, P-wave velocity and S-wave velocity and acoustic emission data; Use feature extraction methods and Transformer deep learning models to fuse real-time multi-phase field detection and monitoring data; Based on the physical principles of temperature, chemical ion concentration, microbial cell concentration, and fluid density under the influence of supercritical carbon dioxide, a deep learning model driven by physical principles is established using a deep learning method; the physical principles include: equations for sound wave propagation in a medium, equations for resistivity tomography, and equations for fiber optic sensing technology; Using real-time fused data as input and corresponding evolving distribution images as output, we train a deep learning model driven by physical principles. Based on multi-phase field detection and monitoring data of different types at different times, the trained deep learning model is used to obtain the evolution distribution image; Based on the macroscopic mechanical parameters and evolution distribution images synchronized with the multiphase field detection and monitoring data, a mathematical model is used to achieve quantitative characterization of the physical and mechanical parameters in the entire process of dynamic evolution and progressive destruction of the rock mass; the macroscopic mechanical parameters are the macroscopic mechanical parameters of different stages of rock mass evolution under different loading paths and multiphase field coupling.

2. The real-time quantitative characterization method of rock mass evolution according to claim 1, characterized in that: The devices for acquiring real-time multi-phase field detection and monitoring data include: optical fiber sensors, electrode sensors and ultrasonic transducers.

3. The real-time quantitative characterization method of rock mass evolution according to claim 1, characterized in that: The deep learning methods include: Bayesian neural network, convolutional neural network, deep residual network, recurrent neural network and generative adversarial network.

4. The real-time quantitative characterization method of rock mass evolution according to claim 1, characterized in that: The mathematical model is established by logistic regression analysis, support vector machine SVM, random forest or neural network.

5. A real-time quantitative characterization device for rock mass evolution, characterized in that: The real-time quantitative characterization equipment for rock mass evolution includes: The data acquisition module is used to obtain the evolution distribution images of the rock mass at different evolution stages during the whole process of the multiphase field coupling test and the real-time multiphase field detection and monitoring data; the evolution distribution images include: reconstructed pore distribution images, pore fluid pressure distribution images and supercritical carbon dioxide saturation distribution images; the multiphase field detection and monitoring data include: temperature, strain, resistivity, chemical ion concentration, microbial cell concentration, P-wave velocity and S-wave velocity and acoustic emission data; Data fusion module, used to fuse real-time multi-phase field detection and monitoring data using feature extraction methods and Transformer deep learning models; A physical principle driven deep learning model building module is used to establish a physical principle driven deep learning model using a deep learning method based on the physical principles of temperature, chemical ion concentration, microbial cell concentration and fluid density under the influence of supercritical carbon dioxide; the physical principles include: equations for sound wave propagation in a medium, equations for resistivity tomography, and equations for fiber optic sensing technology; The deep learning model training module driven by physical principles is used to train the deep learning model driven by physical principles with real-time fused data as input and corresponding evolution distribution images as output; The evolution distribution image inversion module is used to obtain the evolution distribution image based on the multi-phase field detection and monitoring data of different types and at different times using the trained deep learning model; The physical and mechanical parameter quantitative characterization module is used to achieve the quantitative characterization of the physical and mechanical parameters in the whole process of dynamic evolution and progressive destruction of rock mass by using mathematical models based on the macroscopic mechanical parameters and evolution distribution images synchronized with multiphase field detection and monitoring data.

6. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the real-time quantitative characterization method for rock mass evolution according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the real-time quantitative characterization method of rock mass evolution described in any one of claims 1 to 4 is implemented.

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