Three-dimensional numerical simulation system for fracture activity analysis

Abstract

The invention discloses a three-dimensional numerical simulation system for fracture activity analysis, and relates to the technical field of fracture activity analysis. Firstly, by analyzing historical terrain deformation data and fracture activity data and dynamically adjusting three-dimensional numerical model parameters based on an analysis result, the system can continuously optimize the model and improve the simulation precision and adaptability, and secondly, by developing a stress field solving algorithm suitable for a quantum computing platform, the stress field solving algorithm suitable for the quantum computing platform is developed. And three-dimensional numerical calculation of complex stress field distribution is carried out by using quantum calculation, so that the calculation speed and efficiency are greatly improved. And finally, by simulating the interaction of the microfractures and the three-dimensional influence of the microfractures on the macroscopic fracture behavior, the system can reproduce the dynamic behavior of the microfractures more truly. According to the stress concentration area provided by the quantum computing module, the three-dimensional grid density is automatically adjusted, dynamic optimal distribution of computing resources is achieved, and the computing precision of a key area is ensured.

Description

Technical Field

[0001] The invention belongs to the field of fault activity analysis, and in particular, relates to a three-dimensional numerical simulation system for fault activity analysis. Background Art

[0002] The existing three-dimensional numerical simulation system for fault activity analysis is a geological simulation tool based on traditional calculation methods and numerical simulation technology. The system aims to provide analysis of geological structure, stress distribution and potential geological disasters by simulating geological faults and related activities, and provide reference for geological research and engineering applications.

[0003] Existing technologies have many deficiencies in analyzing and simulating geological fault activity.

[0004] 1. When analyzing historical terrain deformation data and fault activity data, existing systems often rely on manual processing and simple statistical models, making it difficult to dynamically adjust the parameters of three-dimensional numerical models, resulting in low model accuracy and adaptability. Secondly, when traditional calculation methods deal with complex stress field distribution, the calculation speed is slow and inefficient, making it difficult to cope with large-scale and complex geological problems.

[0005] 2. Existing systems also have great limitations in simulating the interaction between microcracks and their three-dimensional influence on macroscopic fracture behavior, making it difficult to truly reproduce the dynamic behavior of microcracks. Finally, traditional technologies cannot automatically adjust the three-dimensional grid density according to the stress concentration area, resulting in insufficient optimization of computing resource allocation and failure to ensure the computing accuracy of key areas.

[0006] In view of this, the present invention is proposed. Summary of the invention

[0007] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a three-dimensional numerical simulation system for fault activity analysis, thereby solving the problems raised in the above-mentioned background technology.

[0008] In order to solve the above technical problems, the basic concept of the technical solution adopted by the present invention is:

[0009] A three-dimensional numerical simulation system for fault activity analysis, comprising: a data collection module, a three-dimensional coupling model module, a machine learning module, a quantum computing module, a multi-agent system module, and a visualization module;

[0010] a data collection module for collecting and integrating climate model data, including rainfall and temperature changes, and further including obtaining geological survey data and historical earthquake data, and inputting these data into the three-dimensional coupled model module;

[0011] The three-dimensional coupling model module is connected to the data collection module to develop a three-dimensional coupling model of the interaction between terrain and climate, and simulate the dynamic impact of climate change on terrain and fault activity, and finally send the simulation results to the machine learning module;

[0012] The machine learning module is connected to the 3D coupled model module to analyze historical terrain deformation data and fault activity data, and dynamically adjust the 3D numerical model parameters based on the analysis results;

[0013] The quantum computing module is connected to the machine learning module to develop a stress field solution algorithm suitable for the quantum computing platform and use quantum computing to perform three-dimensional numerical calculations of complex stress field distributions;

[0014] The multi-agent system module is connected to the quantum computing module to simulate the interaction between microcracks and their three-dimensional influence on macroscopic fracture behavior, and automatically adjust the three-dimensional grid density according to the stress concentration area provided by the quantum computing module;

[0015] The visualization module connects the multi-agent system module and is used to visualize the simulation results of terrain, stress distribution and fracture activity in three dimensions, providing a user interactive interface for data analysis and model tuning.

[0016] Optionally, collect and integrate climate model data by:

[0017] Collect climate model data, including rainfall and temperature change data, geological structure information, stratum distribution, lithology characteristics, and historical earthquake data;

[0018] Integrate climate model data, geological survey data and historical earthquake data, and input the integrated data into the three-dimensional coupled model module.

[0019] The optional 3D coupled model module develops a 3D coupled model of topography-climate interaction and simulates the dynamic impact of climate change on topography and fault activity. The specific steps are:

[0020] Based on the geological survey data and remote sensing technology data provided by the data collection module, a high-resolution three-dimensional terrain model is constructed, and based on the meteorological data, a three-dimensional climate model is constructed;

[0021] The terrain model and the climate model are coupled and their interaction is described by the following formula:

[0022]

[0023] Among them, U represents the terrain change vector, U0 is the initial terrain, f(x, t′) is the force of climate factors, α is the coupling coefficient, and σ(x, t′) is the geological stress tensor;

[0024] Numerical simulations are performed on the coupled model to solve the dynamic evolution of topography and fault activity under climate change. Finally, the simulation results are processed and sent to the machine learning module.

[0025] Optionally, the steps of analyzing historical terrain deformation data and fault activity data and dynamically adjusting the parameters of the three-dimensional numerical model based on the analysis results are:

[0026] Clean and standardize historical terrain deformation data and fault activity data to remove noise and outliers to ensure data quality. Extract key features from preprocessed data and use deep learning models to train feature data to build prediction models. Use cross-validation methods to optimize model parameters during training.

[0027] Based on the trained model, the simulation results of the three-dimensional coupling model are analyzed in real time, and the numerical model parameters are adjusted dynamically. The adjustment process can be described by the following formula: Among them, ΔP is the parameter adjustment amount, η is the learning rate, L is the loss function, and P is the three-dimensional numerical model parameter;

[0028] The adjusted parameters are fed back to the three-dimensional coupling model module for iterative update until the simulation results converge and reach the expected accuracy.

[0029] Optionally, the steps for developing a stress field solving algorithm suitable for a quantum computing platform are:

[0030] Define a quantum computing model for stress field distribution problems and convert classical computing problems into quantum computing problems, such as using quantum tensor networks to represent stress field distribution;

[0031] Set the initial stress distribution state |ψ0>;

[0032] According to the Hamiltonian To describe the evolution of the stress field, the variational quantum solver (VQE) is used to perform quantum state evolution and solve the stress field distribution. The formula is as follows: Where |ψ(t)> is the quantum state at time t

[0033] Measure the quantum state and obtain the expected value of the stress field distribution in is the observation operator;

[0034] The quantum computation results are fed back to the multi-agent system module to simulate the interaction of microcracks and their impact on macroscopic fracture behavior.

[0035] Optionally, to simulate the interaction of microcracks and their 3D effects on macroscopic fracture behavior, the steps to automatically adjust the 3D mesh density based on the stress concentration areas provided by the quantum computing module are:

[0036] Initialize the agents, each agent represents a microcrack, and assign specific physical and mechanical properties to each agent during initialization, such as position, size, direction, and strength;

[0037] Set the interaction rules between multiple agents. Based on the stress field distribution results provided by the quantum computing module, use the following formula to describe the interaction between microcracks: Among them, F ij is the interaction force between the i-th and j-th agents, k is the force constant, q i and q j is the crack strength, r ij is the distance between agents, λ is the viscosity coefficient, υ i and j is the agent speed.

[0038] Optionally, based on the set agent interaction rules, the evolution of microcracks in the stress field is simulated, and the growth, interaction and aggregation of cracks are solved using the multi-agent system (MAS) method;

[0039] The 3D mesh density is dynamically adjusted based on the stress concentration area provided by the quantum computing module. The refinement process is described by the following formula: Among them, Δh is the mesh adjustment amount, γ is the refinement coefficient, σ is the stress distribution, and x, y, and z are the spatial coordinates;

[0040] Based on the results of the adaptive refinement algorithm, the allocation of computing resources is optimized to ensure the calculation accuracy of high stress concentration areas, and the results are sent to the visualization module.

[0041] Optionally, the visualization module supports virtual reality and augmented reality technologies to provide users with an immersive three-dimensional visualization experience.

[0042] After adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art. Of course, any product implementing the present invention does not necessarily need to achieve all the advantages described below at the same time:

[0043] By analyzing historical terrain deformation data and fracture activity data, and dynamically adjusting the three-dimensional numerical model parameters based on the analysis results, the system can continuously optimize the model and improve the accuracy and adaptability of the simulation. By developing a stress field solution algorithm suitable for the quantum computing platform and using quantum computing to perform three-dimensional numerical calculations of complex stress field distributions, the calculation speed and efficiency have been greatly improved. By simulating the interaction of microcracks and their three-dimensional influence on macroscopic fracture behavior, the system can more realistically reproduce the dynamic behavior of microcracks. According to the stress concentration area provided by the quantum computing module, the three-dimensional grid density is automatically adjusted to achieve dynamic optimization allocation of computing resources and ensure the calculation accuracy of key areas.

[0044] The specific implementation modes of the present invention are further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The drawings described below are only some embodiments. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0046] In the figure:

[0047] Figure 1 Block diagram of the three-dimensional numerical simulation system.

[0048] It should be noted that these drawings and textual descriptions are not intended to limit the conceptual scope of the present invention in any way, but are intended to illustrate the concept of the present invention for those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0049] The present invention will now be described in further detail with reference to the accompanying drawings.

[0050] See also Figure 1 As shown, in this embodiment, a three-dimensional numerical simulation system for fault activity analysis is provided, including a data collection module, a three-dimensional coupling model module, a machine learning module, a quantum computing module, a multi-agent system module, and a visualization module;

[0051] The data collection module is used to collect and integrate climate model data, including rainfall and temperature changes, and further includes obtaining geological survey data and historical earthquake data, and inputting these data into the three-dimensional coupled model module; the data collection module integrates multi-source data to provide a rich information basis for the model, thereby improving the accuracy and reliability of the simulation. Moreover, by including multi-dimensional data such as climate change, geological surveys, and earthquake history, the data collection module can more comprehensively reflect the dynamic changes in the geological environment and support more complex fault activity analysis. Finally, the automated processing and standardized steps of data input reduce the risk of errors caused by human intervention and improve the efficiency and consistency of data processing, thereby making subsequent three-dimensional coupled model calculations more accurate and efficient.

[0052] The three-dimensional coupling model module is connected to the data collection module to develop a three-dimensional coupling model of the interaction between terrain and climate, and simulate the dynamic impact of climate change on terrain and fault activity, and finally send the simulation results to the machine learning module; by directly connecting to the data collection module, seamless data transmission and real-time update are achieved to ensure the accuracy and timeliness of the model input data. Secondly, the three-dimensional coupling model can comprehensively consider the interaction between terrain and climate factors, provide a more accurate and comprehensive geological environment simulation, and thus improve the ability to predict fault activity. Simulating the dynamic impact of climate change on terrain and fault activity helps to identify potential geological disaster risks and provide a scientific basis for disaster prevention and mitigation. Finally, the simulation results are sent to the machine learning module to further analyze and optimize the model parameters, realize dynamic adjustment and optimization, and improve the overall system intelligence level and prediction accuracy.

[0053] The machine learning module is connected to the three-dimensional coupling model module to analyze historical terrain deformation data and fault activity data, and dynamically adjust the three-dimensional numerical model parameters based on the analysis results; by analyzing historical terrain deformation and fault activity data, it can automatically identify and extract important features to improve the efficiency and accuracy of data processing and analysis. Secondly, connecting the three-dimensional coupling model module realizes the seamless integration of data and models, ensuring that model parameters can be updated and adjusted in real time according to the latest analysis results. This dynamic adjustment mechanism enables the three-dimensional numerical model to better adapt to changes in actual geological conditions and improve the accuracy of simulation and prediction. In addition, the self-learning and optimization capabilities of the machine learning algorithm can continuously improve the performance of the model and reduce deviations and errors caused by human intervention.

[0054] The quantum computing module is connected to the machine learning module to develop a stress field solving algorithm suitable for the quantum computing platform, and use quantum computing to perform three-dimensional numerical calculations of complex stress field distributions; the quantum computing module uses its powerful parallel computing capabilities to greatly improve the speed and efficiency of complex stress field distribution calculations. Compared with traditional computing methods, it can handle larger-scale and more complex geological problems. Secondly, by connecting to the machine learning module, quantum computing can combine the adaptive and optimization capabilities of machine learning to develop more accurate and efficient stress field solving algorithms and achieve higher calculation accuracy. Using quantum computing for three-dimensional numerical calculations can more accurately simulate stress distribution and changes in complex geological environments, providing more detailed and reliable data support for fault activity analysis.

[0055] The multi-agent system module is connected to the quantum computing module to simulate the interaction between microcracks and their three-dimensional influence on macroscopic fracture behavior, and automatically adjust the three-dimensional grid density according to the stress concentration area provided by the quantum computing module; the multi-agent system (MAS) can simulate the complex interaction between microcracks, accurately reproduce the influence of microscopic cracks on macroscopic fracture behavior, and improve the details and accuracy of the model. Secondly, the quantum computing module is connected to use the powerful computing power of quantum computing to quickly identify and calculate stress concentration areas and achieve efficient data processing and analysis. This combination can give full play to the respective technical advantages and greatly improve the speed and accuracy of the simulation. In addition, by automatically adjusting the three-dimensional grid density, the system can dynamically optimize computing resources, allocate more computing power to key areas, and improve the precision and reliability of the simulation.

[0056] The visualization module connects the multi-agent system module and is used to visualize the simulation results of terrain, stress distribution and fracture activity in three dimensions, providing a user interactive interface for data analysis and model tuning.

[0057] In this embodiment, the steps of collecting and integrating climate model data are as follows:

[0058] Collect climate model data, including rainfall and temperature change data, geological structure information, stratum distribution, lithology characteristics, and historical earthquake data;

[0059] Integrate climate model data, geological survey data and historical earthquake data, and input the integrated data into the three-dimensional coupled model module.

[0060] In this embodiment, the three-dimensional coupling model module develops a three-dimensional coupling model of the interaction between terrain and climate, and simulates the specific steps of the dynamic impact of climate change on terrain and fault activity:

[0061] Based on the geological survey data and remote sensing technology data provided by the data collection module, a high-resolution three-dimensional terrain model is constructed, and based on the meteorological data, a three-dimensional climate model is constructed;

[0062] The terrain model and the climate model are coupled and their interaction is described by the following formula:

[0063]

[0064] Among them, U represents the terrain change vector, U0 is the initial terrain, f(x, t′) is the force of climate factors, α is the coupling coefficient, and σ(x, t′) is the geological stress tensor;

[0065] Numerical simulations are performed on the coupled model to solve the dynamic evolution of topography and fault activity under climate change. Finally, the simulation results are processed and sent to the machine learning module.

[0066] It should be noted that the three-dimensional model constructed using high-resolution geological and meteorological data improves the accuracy and detail presentation of the model. Secondly, by coupling the terrain and climate models, the interaction of multiple environmental factors can be comprehensively considered, providing a more comprehensive perspective for simulating geological changes. The use of formulas to describe the interaction ensures the scientificity and rigor of the model. The application of numerical simulation can dynamically interpret the impact of climate change on terrain and fault activity, and improve the accuracy and reliability of predictions. Finally, the processed simulation results are sent to the machine learning module for further analysis and optimization to form a closed-loop automation and intelligent system, which improves the efficiency of overall analysis and decision-making.

[0067] In this embodiment, the steps of analyzing historical terrain deformation data and fault activity data and dynamically adjusting the parameters of the three-dimensional numerical model based on the analysis results are:

[0068] Clean and standardize historical terrain deformation data and fault activity data to remove noise and outliers to ensure data quality. Extract key features from preprocessed data and use deep learning models to train feature data to build prediction models. Use cross-validation methods to optimize model parameters during training.

[0069] Based on the trained model, the simulation results of the three-dimensional coupling model are analyzed in real time, and the numerical model parameters are adjusted dynamically. The adjustment process can be described by the following formula: Among them, ΔP is the parameter adjustment amount, η is the learning rate, L is the loss function, and P is the three-dimensional numerical model parameter.

[0070] The adjusted parameters are fed back to the three-dimensional coupling model module for iterative update until the simulation results converge and reach the expected accuracy.

[0071] It should be noted that: through data cleaning and standardization, the high quality of input data is ensured, and the interference of noise and outliers on the model is eliminated. Secondly, the application of feature extraction and deep learning models can extract key information from a large amount of data, improving the prediction ability and accuracy of the model. The cross-validation method is used to optimize the model parameters to prevent overfitting and ensure the generalization ability of the model. Based on the trained model, real-time analysis and dynamic adjustment of the three-dimensional numerical model parameters can timely reflect the latest geological changes and improve the accuracy and real-time performance of the simulation results.

[0072] In this embodiment, the steps of developing a stress field solution algorithm suitable for a quantum computing platform are as follows:

[0073] Define a quantum computing model for stress field distribution problems and convert classical computing problems into quantum computing problems, such as using quantum tensor networks to represent stress field distribution;

[0074] Set the initial stress distribution state |ψ0>;

[0075] According to the Hamiltonian To describe the evolution of the stress field, the variational quantum solver (VQE) is used to perform quantum state evolution and solve the stress field distribution. The formula is as follows: Where |ψ(t)> is the quantum state at time t

[0076] Measure the quantum state and obtain the expected value of the stress field distribution in is the observation operator;

[0077] The quantum computation results are fed back to the multi-agent system module to simulate the interaction of microcracks and their impact on macroscopic fracture behavior.

[0078] It should be noted that by converting classical computing problems into quantum computing problems and using quantum tensor networks to represent stress field distribution, the powerful parallel processing capabilities and efficient solving capabilities of quantum computing can be fully utilized. Setting the initial stress distribution state ensures the accurate starting point of the calculation. Using Hamiltonian to describe the evolution of the stress field and applying the variational quantum solver (VQE) for quantum state evolution can effectively solve complex stress field distribution problems and improve calculation accuracy and efficiency. The quantum state is measured to obtain the expected value of the stress field distribution, providing specific calculation results. Finally, the quantum computing results are fed back to the multi-agent system module, which can simulate the interaction of microcracks and their impact on macroscopic fracture behavior, achieving seamless connection from quantum computing to macroscopic simulation, and enhancing the comprehensive analysis capability of the entire system.

[0079] In this embodiment, the steps of simulating the interaction between microcracks and their three-dimensional influence on macroscopic fracture behavior and automatically adjusting the three-dimensional grid density according to the stress concentration area provided by the quantum computing module are:

[0080] Initialize the agents, each agent represents a microcrack, and assign specific physical and mechanical properties to each agent during initialization, such as position, size, direction, and strength;

[0081] Set the interaction rules between multiple agents. Based on the stress field distribution results provided by the quantum computing module, use the following formula to describe the interaction between microcracks: Among them, F ij is the interaction force between the i-th and j-th agents, k is the force constant, q i and q j is the crack strength, r ij is the distance between agents, λ is the viscosity coefficient, υ i and jThe speed of the agent. By initializing the agent and giving it specific physical and mechanical properties, the behavior of each microcrack can be simulated in detail, improving the details and accuracy of the simulation. Secondly, by setting the interaction rules between agents, the complex interactions between microcracks can be realistically reproduced based on the precise stress field distribution results provided by the quantum computing module. The use of formulas to describe the interaction forces of microcracks ensures the scientificity and rigor of the simulation. Automatically adjusting the density of the three-dimensional grid can dynamically optimize computing resources according to the stress concentration area, allocate more computing power to key areas, and improve the efficiency and accuracy of the simulation.

[0082] In this embodiment, based on the set agent interaction rules, the evolution process of microcracks in the stress field is simulated, and the growth, interaction and aggregation of cracks are solved using the multi-agent system (MAS) method;

[0083] The 3D mesh density is dynamically adjusted based on the stress concentration area provided by the quantum computing module. The refinement process is described by the following formula: Among them, Δh is the mesh adjustment amount, γ is the refinement coefficient, σ is the stress distribution, and x, y, and z are the spatial coordinates;

[0084] According to the results of the adaptive refinement algorithm, the allocation of computing resources is optimized to ensure the calculation accuracy of high stress concentration areas, and the results are sent to the visualization module. Through the intelligent agent interaction rules, the dynamic evolution process of microcracks in the stress field can be simulated in detail, and the growth, interaction and aggregation of cracks can be truly reflected, which improves the authenticity and accuracy of the simulation. Secondly, the MAS method is used to systematically solve the crack behavior to ensure a comprehensive analysis of the complex crack system. Based on the stress concentration area provided by quantum computing, the three-dimensional grid density is dynamically adjusted to achieve refined calculations in key areas, optimize computing resources, and improve computing efficiency and accuracy. The formula description of the refinement process ensures the scientificity and accuracy of the adjustment process. Finally, the allocation of computing resources is optimized according to the adaptive refinement algorithm to ensure high-precision calculations in high stress concentration areas, further improving the reliability and accuracy of the simulation.

[0085] In this embodiment, the visualization module supports virtual reality and augmented reality technologies to provide users with an immersive three-dimensional visualization experience.

[0086] The present invention is not limited to the above-mentioned embodiments. Anyone should be aware that any structural changes made under the enlightenment of the present invention, and any technical solutions that are the same or similar to the present invention, fall within the protection scope of the present invention. The technology, shape, and structural parts not described in detail in the present invention are all well-known technologies.

Claims

1. A three-dimensional numerical simulation system for fault activity analysis, characterized in that: include: Data collection module, three-dimensional coupling model module, machine learning module, quantum computing module, multi-agent system module, visualization module; A data collection module is used to collect and integrate climate model data and input these data into the three-dimensional coupled model module; The three-dimensional coupling model module is connected to the data collection module to develop a three-dimensional coupling model of the interaction between terrain and climate, and simulate the dynamic impact of climate change on terrain and fault activity, and finally send the simulation results to the machine learning module; The machine learning module is connected to the 3D coupled model module to analyze historical terrain deformation data and fault activity data, and dynamically adjust the 3D numerical model parameters based on the analysis results; The quantum computing module is connected to the machine learning module to develop a stress field solution algorithm suitable for the quantum computing platform and use quantum computing to perform three-dimensional numerical calculations of complex stress field distributions; The multi-agent system module is connected to the quantum computing module to simulate the interaction between microcracks and their three-dimensional influence on macroscopic fracture behavior, and automatically adjust the three-dimensional grid density according to the stress concentration area provided by the quantum computing module; The visualization module connects the multi-agent system module and is used to visualize the simulation results of terrain, stress distribution and fracture activity in three dimensions, providing a user interactive interface for data analysis and model tuning.

2. A three-dimensional numerical simulation system for fault activity analysis according to claim 1, characterized in that: The steps to collect and integrate climate model data are: Collect climate model data, including rainfall and temperature change data, geological structure information, stratum distribution, lithology characteristics, and historical earthquake data; Integrate climate model data, geological survey data and historical earthquake data, and input the integrated data into the three-dimensional coupled model module.

3. A three-dimensional numerical simulation system for fault activity analysis according to claim 1, characterized in that: The three-dimensional coupled model module develops a three-dimensional coupled model of the interaction between topography and climate, and simulates the dynamic impact of climate change on topography and fault activity. The specific steps are: Based on the geological survey data and remote sensing technology data provided by the data collection module, a high-resolution three-dimensional terrain model is constructed, and based on the meteorological data, a three-dimensional climate model is constructed; The terrain model and the climate model are coupled and their interaction is described by the following formula: Among them, U represents the terrain change vector, U0 is the initial terrain, f(x, t′) is the force of climate factors, α is the coupling coefficient, and σ(x, t′) is the geological stress tensor; Numerical simulations are performed on the coupled model to solve the dynamic evolution of topography and fault activity under climate change. Finally, the simulation results are processed and sent to the machine learning module.

4. A three-dimensional numerical simulation system for fault activity analysis according to claim 1, characterized in that: The steps for analyzing historical terrain deformation data and fault activity data and dynamically adjusting the parameters of the three-dimensional numerical model based on the analysis results are as follows: Clean and standardize historical terrain deformation data and fault activity data to remove noise and outliers to ensure data quality. Extract key features from preprocessed data and use deep learning models to train feature data to build prediction models. Use cross-validation methods to optimize model parameters during training. Based on the trained model, the simulation results of the three-dimensional coupling model are analyzed in real time, and the numerical model parameters are adjusted dynamically. The adjustment process can be described by the following formula: Among them, ΔP is the parameter adjustment amount, η is the learning rate, L is the loss function, and P is the three-dimensional numerical model parameter; The adjusted parameters are fed back to the three-dimensional coupling model module for iterative update until the simulation results converge and reach the expected accuracy.

5. A three-dimensional numerical simulation system for fault activity analysis according to claim 1, characterized in that: The steps to develop a stress field solving algorithm suitable for quantum computing platforms are: Define a quantum computing model for stress field distribution problems and convert classical computing problems into quantum computing problems, such as using quantum tensor networks to represent stress field distribution; Set the initial stress distribution state |ψ0>; According to the Hamiltonian To describe the evolution of the stress field, the variational quantum solver (VQE) is used to perform quantum state evolution and solve the stress field distribution. The formula is as follows: Where |ψ(t)> is the quantum state at time t Measure the quantum state and obtain the expected value of the stress field distribution in is the observation operator; The quantum computation results are fed back to the multi-agent system module to simulate the interaction of microcracks and their impact on macroscopic fracture behavior.

6. A three-dimensional numerical simulation system for fault activity analysis according to claim 1, characterized in that: To simulate the interaction of microcracks and their three-dimensional effects on macroscopic fracture behavior, the steps to automatically adjust the three-dimensional mesh density according to the stress concentration area provided by the quantum computing module are: Initialize the agents, each agent represents a microcrack, and assign specific physical and mechanical properties to each agent during initialization; Set the interaction rules between multiple agents. Based on the stress field distribution results provided by the quantum computing module, use the following formula to describe the interaction between microcracks: Among them, F ij is the interaction force between the i-th and j-th agents, k is the force constant, q i and q j is the crack strength, r ij is the distance between agents, λ is the viscosity coefficient, v i and v j is the agent speed.

7. A three-dimensional numerical simulation system for fault activity analysis according to claim 6, characterized in that: Based on the set agent interaction rules, the evolution of microcracks in the stress field is simulated, and the growth, interaction and aggregation of cracks are solved using the multi-agent system (MAS) method. The density of the 3D mesh is dynamically adjusted according to the stress concentration area provided by the quantum computing module. The refinement process is described by the following formula: Among them, Δh is the mesh adjustment amount, γ is the refinement coefficient, σ is the stress distribution, and x, y, and z are the spatial coordinates; Based on the results of the adaptive refinement algorithm, the allocation of computing resources is optimized to ensure the calculation accuracy of high stress concentration areas, and the results are sent to the visualization module.

8. A three-dimensional numerical simulation system for fault activity analysis according to claim 1, characterized in that: The visualization module supports virtual reality and augmented reality technologies, providing users with an immersive three-dimensional visualization experience.

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