Three-dimensional modeling method and rendering method of dynamic insertion of concealed fault into geological body

Abstract

The present application relates to the technical field of three-dimensional modeling of geological bodies, and discloses a three-dimensional modeling method and a rendering method for dynamic insertion of a concealed fault into a geological body, the modeling method comprising: based on geological body feature analysis and a fault identification algorithm, pre-processing geological body data, then using a dynamic insertion algorithm to simulate a fault to obtain fault simulation data; obtaining a fault insertion starting point in the fault simulation data, constructing an insertion model according to the fault insertion starting point, inserting the fault to both sides respectively, and obtaining fault insertion trajectory data; setting model parameters according to the fault insertion trajectory data to limit the simulation range of the dynamic insertion algorithm; under the criterion that the fault insertion trajectory has the highest matching degree with the geological body data, searching for optimal fault parameters based on a geological body structure model and a fault kinematics model, and correcting the geological body by fault insertion. The present application can improve the accuracy and efficiency of geological body modeling.

Description

Technical Field

[0001] This invention relates to the field of three-dimensional modeling technology for geological bodies, and in particular to a three-dimensional modeling and rendering method for dynamically inserting concealed faults into geological bodies. Background Technology

[0002] Existing 3D geological modeling techniques face numerous challenges when dealing with concealed faults. Traditional modeling methods often rely on limited geological data and empirical judgment, making it difficult to accurately simulate the dynamic insertion process of faults and their impact on the geological structure. Furthermore, these methods are inefficient when processing large-scale geological data and struggle to achieve real-time rendering and detailed display, limiting their application in complex geological environments. In terms of technical principles, many existing methods rely on static fault models, lacking in-depth analysis of fault kinematics and the dynamic changes in geological structure, leading to discrepancies between modeling results and actual geological conditions.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that the prior art has at least the following problems or defects: the existing modeling methods cannot effectively handle the dynamic insertion of concealed faults, resulting in the inability to accurately simulate the impact of faults on the geological structure; the existing technology is inefficient when processing large-scale geological data, making it difficult to meet the needs of real-time rendering and high-precision modeling; in addition, the existing technology lacks effective algorithm support in fault identification and parameter optimization, which limits the modeling accuracy and reliability. Summary of the Invention

[0004] The present invention aims to provide a three-dimensional modeling and rendering method for dynamically inserting concealed faults into geological bodies, in order to solve the technical problems proposed in the prior art.

[0005] The technical solutions adopted by the embodiments of the present invention to solve their technical problems are as follows:

[0006] Firstly, a three-dimensional modeling method for dynamically inserting concealed faults into geological bodies is provided, including:

[0007] Based on geological body feature analysis and fault identification algorithms, the geological body data is preprocessed, and then a dynamic interpolation algorithm is used to simulate faults to obtain fault simulation data.

[0008] Obtain the fault insertion starting point from the fault simulation data, construct an insertion model based on the fault insertion starting point, insert faults to both sides respectively, and obtain fault insertion trajectory data; set model parameters based on the fault insertion trajectory data to limit the simulation range of the dynamic insertion algorithm;

[0009] Under the criterion of maximizing the match between the fault insertion trajectory and the geological body data, the optimal fault parameters are searched based on the geological body structure model and the fault kinematic model to correct the fault insertion of the geological body.

[0010] Furthermore, the geological body feature analysis is performed using Fourier transform; the mathematical model of the fault identification algorithm is as follows:

[0011]

[0012] Where f(x) is the geological body data function, and F(x) is the Fourier transform result.

[0013] Furthermore, the preprocessing of the geological body data includes:

[0014] Using geological body data to which the window function is applied as input data, the fault location in the current stage is estimated based on the fault simulation results of the previous stage, and the optimal window function parameters are selected based on the location and geological body data characteristics.

[0015] The corresponding window function is obtained based on the window function parameters, and the amplitude-frequency response curve is obtained based on the shape of the window function;

[0016] Based on the amplitude-frequency response curve, the coefficients of the FIR filter are designed using the least squares method. The FIR filter is used to filter the geological body data, and then the fault identification coefficients of the window function with corresponding window function parameters are used to identify faults in the geological body data.

[0017] Furthermore, the optimal window function parameter is the window function parameter corresponding to the minimum error under different geological body characteristics.

[0018] Furthermore, obtaining the fault insertion point from the fault simulation data includes:

[0019] The fault locations of the fault domain are obtained by performing feature extraction, fault identification, and fault location estimation on the geological body data.

[0020] Search for the location of the largest fault in each geological body, and use it as the location of each fault. Perform curve fitting on the fault locations of multiple adjacent geological bodies in segments, and statistically analyze the relative deviation of each fault and the average deviation of the fault within the segment in the curve fitting results.

[0021] The fault segment with the smallest average deviation is selected as the target segment. Based on the relative deviation threshold, the fault location is selected, the fault domain feature information of each selected fault location is extracted, and the most significant feature is selected as the fault insertion starting point.

[0022] Furthermore, the parameter range of the tomographic insertion model is as follows:

[0023] P={(x,y,z)∣x∈[x1,x2],y∈[y1,y2],z∈[z1,z2]}

[0024] The fault insertion starting point coordinates are (x1, y1, z1), where x1, y1, and z1 are the minimum coordinate values ​​of the geological body data, and x2, y2, and z2 are the maximum coordinate values ​​of the geological body data.

[0025] Furthermore, the fault insertion correction of the geological body includes: dividing each geological body data into multiple segments along the fault insertion direction based on the geological body data; firstly, obtaining the geological body structure before and after the adjustment of the central fault parameters based on the differences in the geological body data before and after the fault insertion; and then obtaining the geological body structure and fault insertion position before and after the adjustment of other fault parameters.

[0026] In a second aspect of the invention, a three-dimensional modeling and rendering method for dynamically inserting concealed faults into geological bodies is provided, comprising:

[0027] Obtain fault simulation data from a three-dimensional modeling method that dynamically inserts geological bodies into concealed faults;

[0028] Based on the three-dimensional mesh generation method, the fault simulation data is divided into multiple data blocks with uniform structure;

[0029] Based on the viewpoint location, the CPU loads the corresponding data blocks into memory in real time, and loads the geological blocks to be rendered into the GPU's video memory.

[0030] Based on a GPU parallel computing platform, multi-resolution LOD models are constructed in parallel using GPU stream processors to render geological bodies.

[0031] Furthermore, the method based on three-dimensional mesh partitioning divides the fault simulation data into multiple data blocks with uniform structure, including: dividing the fault simulation data layer by layer according to a preset data block density threshold using a three-dimensional mesh partitioning method to obtain each sub-block; merging adjacent sub-blocks according to the number of data points in adjacent sub-blocks until the total number of points in the sub-blocks is reached to obtain multiple data blocks.

[0032] Furthermore, the step of loading corresponding data blocks into memory in real time using the CPU based on the viewpoint position, and loading the geological blocks to be rendered into the GPU's video memory, includes:

[0033] Calculate the file address of the geological body data block that needs to be loaded into memory and cache based on the current viewpoint position; construct the memory and cache data using a linked list data structure, and initialize and load the cache and memory geological body block data;

[0034] When the viewpoint changes, determine the type of viewpoint movement, update the memory data, calculate the cache update threshold, determine whether it is necessary to clear useless data in the cache and whether to load external storage data into the cache, and perform the necessary clearing or loading operations.

[0035] The above embodiments of the present invention have at least the following beneficial effects: the three-dimensional modeling method for dynamically inserting concealed faults into geological bodies according to the present invention can improve the accuracy and efficiency of geological body modeling. Through preprocessing steps based on geological body feature analysis and fault identification algorithms, combined with dynamic insertion algorithms for fault simulation, the insertion trajectory of faults and changes in geological body structure can be simulated more accurately. Furthermore, by obtaining the fault insertion starting point from the fault simulation data and constructing an insertion model, the insertion position and direction of the fault can be precisely controlled, thereby obtaining modeling results that better reflect actual geological conditions.

[0036] Furthermore, the 3D modeling and rendering method of this invention can effectively process large-scale geological data and achieve high-quality real-time rendering. By dividing the fault simulation data into multiple uniformly structured data blocks based on a 3D mesh generation method, and utilizing the collaborative work of CPU and GPU, geological blocks can be loaded and rendered quickly, improving rendering efficiency. Simultaneously, by using a GPU parallel computing platform to construct multi-resolution LOD models, rendering details can be dynamically adjusted according to different viewpoints and distances, optimizing visual effects and making the 3D display of geological bodies more realistic and intuitive. Attached Figure Description

[0037] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the drawings in the drawings are not to be limited by scale.

[0038] Figure 1 This is a flowchart illustrating the three-dimensional modeling method for dynamically inserting concealed faults into geological bodies provided by the present invention.

[0039] Figure 2 This is a flowchart illustrating the three-dimensional modeling and rendering method for dynamically inserting hidden faults into geological bodies provided by this invention.

[0040] Figure 3 A schematic diagram of the structure of an electronic device according to an embodiment of the present invention is shown. Detailed Implementation

[0041] To facilitate understanding of the present invention, a more detailed description is provided below with reference to the accompanying drawings and specific embodiments. It should be noted that when an element is described as "connected" to another element, it can be directly on the other element, or one or more intermediate elements may exist between them. The terms "upper," "lower," "left," "right," "upper end," "lower end," "top," and "bottom," etc., used in this specification indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings. They are used only for the convenience of describing the present invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0042] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention.

[0043] The following is combined Figure 1 The three-dimensional modeling method 100 for dynamically inserting hidden faults into geological bodies provided in this application will be described in detail through specific embodiments.

[0044] Figure 1 This is a flowchart illustrating the three-dimensional modeling method for dynamically inserting concealed faults into geological bodies provided by the present invention. One embodiment of the three-dimensional modeling method for dynamically inserting concealed faults into geological bodies provided by the present invention includes:

[0045] Step 101: Based on geological body feature analysis and fault identification algorithm, the geological body data is preprocessed, and then the fault simulation is performed using dynamic insertion algorithm to obtain fault simulation data;

[0046] Step 102: Obtain the fault insertion starting point from the fault simulation data, construct an insertion model based on the fault insertion starting point, insert faults to both sides respectively, and obtain fault insertion trajectory data; set model parameters based on the fault insertion trajectory data to limit the simulation range of the dynamic insertion algorithm;

[0047] Step 103: Under the criterion of the highest matching degree between the fault insertion trajectory and the geological body data, based on the geological body structure model and the fault kinematic model, search for the optimal fault parameters and perform fault insertion correction on the geological body.

[0048] It should be noted that this method first preprocesses the geological body data based on geological body feature analysis and fault identification algorithms, which is a crucial step in the modeling process. This step identifies potential faults within the geological body and provides an accurate data foundation for subsequent fault simulation.

[0049] Specifically, geological body feature analysis typically employs mathematical methods such as Fourier transform to convert geological body data into the frequency domain, facilitating the analysis and identification of fault features. Fault identification algorithms then determine the location and characteristics of faults based on specific patterns in the geological body data, such as discontinuities or anomalous variations. This process may require setting specific algorithm parameters, such as the window size for the Fourier transform and the threshold for fault identification, to ensure the accuracy of the analysis results.

[0050] Preferably, to improve the efficiency and accuracy of preprocessing, an adaptive algorithm can be used to dynamically adjust the parameters of the Fourier transform, or machine learning techniques can be used to optimize the fault identification algorithm. Furthermore, parallel computing techniques can be used to accelerate the data processing, or cloud computing resources can be utilized to process large-scale geological data.

[0051] In some embodiments, the geological body feature analysis is performed using Fourier transform; the mathematical model of the fault identification algorithm is:

[0052]

[0053] Where f(x) is the geological body data function, and F(x) is the Fourier transform result.

[0054] It should be noted that the geological body feature analysis in this embodiment uses Fourier transform, a commonly used mathematical tool, to convert geological body data from the time domain to the frequency domain in order to better identify and analyze fault features in the geological body.

[0055] Specifically, the Fourier transform result F(x) is obtained by combining the geological body data function f(x) with the Fourier transform kernel e. -2πix The result is obtained through integration. In this process, it is necessary to select an appropriate window function to process the geological body data in order to reduce spectral leakage and improve frequency resolution. The choice of window function and its parameter settings have a significant impact on the Fourier transform results, and may include the type of window function, such as the Hanning window or the Hamming window, as well as its length and shape.

[0056] Preferably, to improve the efficiency and accuracy of the Fourier transform, the Fast Fourier Transform (FFT) algorithm can be used to reduce computational load. Furthermore, an adaptive window function technique can be introduced to dynamically adjust the window function parameters based on the characteristics of the geological body data, thereby obtaining a more accurate frequency response. In the mathematical model of the fault identification algorithm, a regularization term can also be considered to suppress the influence of noise and outliers, improving the accuracy and robustness of fault identification. These improvements contribute to enhancing the performance and reliability of the overall modeling process.

[0057] In some embodiments, the preprocessing of geological body data includes:

[0058] Using geological body data to which the window function is applied as input data, the fault location in the current stage is estimated based on the fault simulation results of the previous stage, and the optimal window function parameters are selected based on the location and geological body data characteristics.

[0059] The corresponding window function is obtained based on the window function parameters, and the amplitude-frequency response curve is obtained based on the shape of the window function;

[0060] Based on the amplitude-frequency response curve, the coefficients of the FIR filter are designed using the least squares method. The FIR filter is used to filter the geological body data, and then the fault identification coefficients of the window function with corresponding window function parameters are used to identify faults in the geological body data.

[0061] Specifically, the window function mentioned in the preprocessing step is a mathematical function used to weight signals in the frequency domain to reduce edge effects and improve the accuracy of signal analysis. Window functions include the Hanning window, Hamming window, and Blackman window, each with its specific frequency characteristics and application scenarios. Fault identification relies on the characteristics of geological data, such as discontinuities or anomalous changes, and identifies the location of faults through set algorithm parameters. Parameter settings may involve the width and shape of the window function and the sensitivity of fault identification.

[0062] Preferably, during preprocessing, an adaptive window function technique can be employed to dynamically adjust the parameters of the window function based on the characteristics of the geological body data to obtain the optimal frequency response. Furthermore, machine learning algorithms can be combined to optimize the fault identification process, training a model to identify fault features in the geological body data. When designing the FIR filter, the window function method or frequency sampling method can be used to determine the filter coefficients to achieve effective filtering of the geological body data.

[0063] In some embodiments, the optimal window function parameter is the window function parameter corresponding to the minimum error under different geological body characteristics.

[0064] It should be noted that the optimal window function parameters described in this embodiment refer to the window function settings that minimize errors and most effectively identify faults under different geological body characteristics. This is achieved by adjusting the window function parameters to optimize the fault identification process and ensure the accuracy of fault simulation.

[0065] Specifically, window function parameters include the type, length, shape, and smoothness of the window function. These parameters directly affect the frequency domain characteristics of the window function, thus impacting the accuracy of fault identification. For example, the length of the window function determines the frequency domain resolution, while the shape and smoothness affect the size of the window function's side lobes, which in turn affect the identification of fault features. In practice, the window function parameters that minimize error under specific geological body characteristics can be determined through experiments or optimization algorithms.

[0066] Preferably, to further improve the accuracy of window function parameter selection, machine learning or artificial intelligence techniques can be used to analyze the relationship between different geological body characteristics and window function parameters. By training the model, the optimal window function parameters under specific geological body conditions can be automatically learned and predicted. Furthermore, multi-scale analysis methods can be considered, applying different window function parameters at different scales to more comprehensively identify and simulate faults. The application of these methods can further improve the accuracy and efficiency of fault identification.

[0067] In some embodiments, obtaining the fault insertion point in the fault simulation data includes:

[0068] The fault locations of the fault domain are obtained by performing feature extraction, fault identification, and fault location estimation on the geological body data.

[0069] Search for the location of the largest fault in each geological body, and use it as the location of each fault. Perform curve fitting on the fault locations of multiple adjacent geological bodies in segments, and statistically analyze the relative deviation of each fault and the average deviation of the fault within the segment in the curve fitting results.

[0070] The fault segment with the smallest average deviation is selected as the target segment. Based on the relative deviation threshold, the fault location is selected, the fault domain feature information of each selected fault location is extracted, and the most significant feature is selected as the fault insertion starting point.

[0071] It should be noted that in this embodiment, the process of obtaining the fault insertion point is achieved through feature extraction, fault identification, and fault location estimation operations on geological body data. This step is crucial to ensuring the accuracy of fault simulation, as it involves accurately locating the fault from complex geological data.

[0072] Specifically, feature extraction typically involves statistical analysis and pattern recognition of geological body data to identify potential fault features. Fault identification may involve using specific algorithms, such as machine learning models, to analyze the extracted features and determine the presence of faults. Fault location estimation requires spatial localization of the identified fault features, which may involve spatial analysis and modeling of the geological body data. Parameters that may need to be set in this process include feature extraction thresholds, fault identification sensitivity, and location estimation accuracy.

[0073] Preferably, to improve the accuracy of fault insertion initiation, multi-source data fusion technology can be employed, combining various data types from the geological body, such as seismic data and geological exploration data, to enhance fault identification accuracy. Furthermore, advanced machine learning algorithms, such as deep learning networks, can be introduced to automatically learn and identify fault features. For fault location estimation, high-precision Geographic Information System (GIS) technology can be used to improve positioning accuracy. The introduction of these technologies can further enhance the quality and efficiency of fault simulation.

[0074] In some embodiments, the parameter range of the tomographic insertion model is:

[0075] P={(x,y,z)∣x∈[x1,x2],y∈[y1,y2],z∈[z1,z2]}

[0076] The fault insertion starting point coordinates are (x1, y1, z1), where x1, y1, and z1 are the minimum coordinate values ​​of the geological body data, and x2, y2, and z2 are the maximum coordinate values ​​of the geological body data.

[0077] It should be noted that the parameter range of the fault insertion model in this embodiment is determined by defining a parameter space, which limits the possible parameter values ​​during the fault insertion process.

[0078] Specifically, the parameter range of the fault insertion model is defined as P = {(x,y,z) | x∈[x1,x2],y∈[y1,y2],z∈[z1,z2]}, where x1,y1,z1 are the minimum coordinate values ​​of the geological body data, and x2,y2,z2 are the maximum coordinate values ​​of the geological body data. This parameter space is set based on the actual range of the geological body data, ensuring that the fault insertion simulation is performed within a reasonable spatial range. In practice, the settings of these parameters need to be determined based on the specific geological body data, which may involve statistical analysis of the geological body data to determine the minimum and maximum coordinate values.

[0079] Preferably, to improve the flexibility and adaptability of fault insertion simulation, an adaptive parameter adjustment mechanism can be introduced. For example, the parameter range can be dynamically adjusted based on real-time feedback during the simulation process to better adapt to changes in geological body data. Furthermore, multi-scale simulation techniques can be introduced, allowing fault insertion simulations to be performed at different spatial scales to accommodate geological body structures of varying complexity. The introduction of these techniques can further improve the accuracy and efficiency of the simulation, while increasing the model's applicability.

[0080] In some embodiments, the fault insertion correction of the geological body includes: dividing each geological body data into multiple segments along the fault insertion direction based on the geological body data; first, obtaining the geological body structure before and after the adjustment of the central fault parameters based on the differences in the geological body data before and after the fault insertion; and then obtaining the geological body structure and fault insertion position before and after the adjustment of other fault parameters.

[0081] It should be noted that the fault insertion correction process in this embodiment is a process of segmenting the geological body data along the fault insertion direction and adjusting the geological body structure according to the differences in the geological body data before and after fault insertion.

[0082] Specifically, fault insertion correction involves dividing the geological body data into multiple segments along the fault insertion direction. This may involve spatial analysis of the geological body data to determine the appropriate segmentation method. Adjustments to the central fault parameters and other fault parameters are based on changes in the geological body data before and after fault insertion, which may include changes in the physical properties and structural characteristics of the geological body. In practice, parameters that may need to be set include the granularity of segmentation, and the algorithms and methods for adjusting fault parameters.

[0083] Preferably, to improve the accuracy and efficiency of fault insertion correction, advanced numerical simulation techniques, such as finite element analysis or discrete element method, can be used to simulate the impact of fault insertion on the geological structure. Furthermore, machine learning algorithms can be introduced to analyze changes in geological data before and after fault insertion and automatically adjust fault parameters. Regarding geological structure adjustment, multi-scale modeling techniques can be considered to accurately simulate changes in the geological body at different scales.

[0084] The various embodiments of the present invention have the following beneficial effects: The three-dimensional modeling method for dynamically inserting concealed faults into geological bodies according to the present invention can solve the accuracy and efficiency problems existing in traditional geological body modeling. By preprocessing the geological body data using geological body feature analysis and fault identification algorithms, the location and morphology of faults can be identified and simulated more accurately. The application of the dynamic insertion algorithm makes fault simulation more flexible and realistic, and by setting model parameters through fault insertion trajectory data, the simulation range can be limited, improving computational efficiency. In addition, by searching for the optimal fault parameters under the criterion of the highest matching degree between the fault insertion trajectory and the geological body data, accurate fault insertion correction can be performed on the geological body, thereby obtaining a three-dimensional model that more closely matches the actual geological conditions.

[0085] Furthermore, the technical solution proposed in this invention can optimize the processing flow of geological body data. For example, by using Fourier transform for geological body feature analysis, a deeper understanding of the intrinsic characteristics of geological body data can be achieved. The application of window functions and the design of FIR filters in the preprocessing steps can improve the accuracy of fault identification. The accurate acquisition of fault insertion starting points and the construction of fault insertion models can ensure the accuracy and controllability of the fault insertion process. The combined use of these technologies can not only improve the accuracy of modeling but also enhance the automation and intelligence of the modeling process while ensuring modeling quality.

[0086] Please refer to the following: Figure 2 , Figure 2 This is a flowchart illustrating the three-dimensional modeling and rendering method 200 for dynamically inserting concealed faults into geological bodies provided by the present invention, including:

[0087] Step 201: Obtain fault simulation data obtained by the three-dimensional modeling method of dynamically inserting hidden faults into geological bodies;

[0088] Step 202: Based on the three-dimensional mesh generation method, the fault simulation data is divided into multiple data blocks with uniform structure;

[0089] Step 203: Based on the viewpoint position, use the CPU to load the corresponding data blocks into memory in real time, and load the geological blocks to be rendered into the GPU's video memory.

[0090] Step 204: Based on the GPU parallel computing platform, use GPU stream processors to construct multi-resolution LOD models in parallel and render the geological bodies.

[0091] Specifically, acquiring fault simulation data is the starting point of the rendering process, which typically involves reading geological body data corrected for fault insertion from a database or file system. 3D mesh generation methods are used to divide this data into multiple uniformly structured data blocks, which helps improve the efficiency of data processing and rendering. Consideration of viewpoint location is to ensure that the rendered geological blocks match the user's perspective, improving the real-time performance and accuracy of the rendering. Parameters that may need to be set in this process include the size of the data blocks, the precision of the mesh generation, and the viewpoint update frequency.

[0092] Preferably, to further improve rendering efficiency and quality, Hierarchical Level of Detail (LOD) technology can be employed to dynamically adjust the level of detail in the rendering based on the viewpoint distance. Furthermore, multi-threading or multi-processing techniques can be introduced to accelerate the loading and processing of data blocks. For GPU rendering, shader programs and rendering pipelines can be optimized to fully utilize the parallel computing capabilities of the GPU. The introduction of these technologies makes the rendering of geological objects more efficient and realistic, while also enhancing the user experience.

[0093] In some embodiments, the method of dividing fault simulation data into multiple uniformly structured data blocks based on a three-dimensional mesh partitioning method includes: dividing the fault simulation data layer by layer according to a preset data block density threshold using a three-dimensional mesh partitioning method to obtain each sub-block; merging adjacent sub-blocks according to the number of data points in adjacent sub-blocks until the total number of points in the sub-blocks is reached to obtain multiple data blocks.

[0094] It should be noted that the 3D meshing method in this embodiment is used to effectively organize the fault simulation data into uniformly structured data blocks to facilitate subsequent rendering processing. This method ensures that each data block maintains consistency in size and complexity by dividing the data layer by layer, thereby improving rendering efficiency and quality.

[0095] Specifically, the 3D mesh generation method involves dividing the fault simulation data according to a preset data block density threshold. This typically includes determining the size, shape, and distribution of the data blocks, and how to optimize these parameters based on the characteristics of the data. Data block merging is based on the number of data points within adjacent sub-blocks; by merging adjacent sub-blocks, a threshold for the total number of points in each sub-block is reached, thus forming the final data block. Parameters that may need to be set in this process include the minimum and maximum size of the data blocks, the merging threshold, and the segmentation precision.

[0096] Preferably, to further improve the rendering efficiency of data blocks, spatial partitioning algorithms, such as octrees or quadtrees, can be used to optimize the organization of data blocks. Furthermore, adaptive mesh partitioning techniques can be considered to dynamically adjust the size and number of data blocks based on the local complexity of the data. When merging adjacent sub-blocks, error analysis can be introduced to ensure that the merging operation does not negatively impact the rendering results.

[0097] In some embodiments, the step of loading corresponding data blocks into memory in real time using the CPU based on the viewpoint position, and loading the geological blocks to be rendered into the GPU's video memory, includes:

[0098] Calculate the file address of the geological body data block that needs to be loaded into memory and cache based on the current viewpoint position; construct the memory and cache data using a linked list data structure, and initialize and load the cache and memory geological body block data;

[0099] When the viewpoint changes, determine the type of viewpoint movement, update the memory data, calculate the cache update threshold, determine whether it is necessary to clear useless data in the cache and whether to load external storage data into the cache, and perform the necessary clearing or loading operations.

[0100] Specifically, the file addresses of geological body data blocks that need to be loaded into memory and cache are calculated based on the current viewpoint location. This involves determining the spatial relationship between the user's viewpoint and the geological body data, and how to select the data blocks to be loaded based on this relationship. A linked list data structure is constructed to manage memory and cached data, which helps to efficiently organize and access data blocks.

[0101] Furthermore, when the viewpoint changes, the memory data needs to be updated, which may involve loading, unloading, and replacing data blocks. Parameters that may need to be set during this process include the viewpoint update detection frequency, data block loading and unloading strategies, and memory and cache management strategies.

[0102] Preferably, to improve the efficiency of data processing and rendering, a prediction algorithm can be used to predict the changing trends of the user's perspective, thereby preloading and rendering the corresponding data blocks. Furthermore, data prefetching technology can be introduced to preload potentially needed data blocks based on the user's interaction patterns and historical data.

[0103] Furthermore, in terms of memory and cache management, intelligent cache replacement algorithms, such as the Least Recently Used (LRU) algorithm, can be employed to optimize the storage and replacement of data blocks. The introduction of these technologies makes viewpoint location processing more efficient and intelligent, thereby improving the user experience.

[0104] It should be noted that the three-dimensional modeling and rendering method 200 for dynamically inserting concealed faults into geological bodies provided in this embodiment of the invention only shows the part related to the technical problem to be solved by this embodiment of the invention. It can be understood that the three-dimensional modeling and rendering method 200 for dynamically inserting concealed faults into geological bodies provided in this embodiment of the invention also includes other steps for implementing the function of the three-dimensional modeling and rendering method 200 for dynamically inserting concealed faults into geological bodies, which will not be described in detail here.

[0105] The following is for reference. Figure 3 The diagram illustrates a structural schematic of an electronic device 300 suitable for implementing some embodiments of the present invention. The electronic devices in some embodiments of the present invention may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 3 The terminal device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0106] like Figure 3 As shown, the electronic device 300 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the electronic device 300. The processing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0107] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 An electronic device 300 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 3 Each box shown can represent a device or multiple devices as needed.

[0108] Furthermore, the storage medium in the embodiments of this application stores program instructions capable of implementing all the above methods. These program instructions can be stored in the storage medium in the form of a software product, including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, or terminal devices such as computers, servers, mobile phones, and tablets.

[0109] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; under the concept of the present invention, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the present invention as described above, which are not provided in detail for the sake of brevity; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A three-dimensional modeling method for dynamically inserting concealed faults into geological bodies, characterized in that, Includes the following steps: Based on geological body feature analysis and fault identification algorithms, the geological body data is preprocessed, and then a dynamic interpolation algorithm is used to simulate faults to obtain fault simulation data. Obtain the fault insertion starting point from the fault simulation data, construct an insertion model based on the fault insertion starting point, insert faults to both sides respectively, and obtain fault insertion trajectory data. Model parameters are set based on fault insertion trajectory data to limit the simulation range of the dynamic insertion algorithm; Under the criterion of maximizing the match between the fault insertion trajectory and the geological body data, the optimal fault parameters are searched based on the geological body structure model and the fault kinematic model to correct the fault insertion of the geological body.

2. The three-dimensional modeling method for dynamically inserting concealed faults into geological bodies according to claim 1, characterized in that, The geological body feature analysis was performed using Fourier transform.

3. The three-dimensional modeling method for dynamically inserting concealed faults into geological bodies according to claim 2, characterized in that, The preprocessing of geological body data includes: Using geological body data to which the window function is applied as input data, the fault location in the current stage is estimated based on the fault simulation results of the previous stage, and the optimal window function parameters are selected based on the location and geological body data characteristics. The corresponding window function is obtained based on the window function parameters, and the amplitude-frequency response curve is obtained based on the shape of the window function; Based on the amplitude-frequency response curve, the coefficients of the FIR filter are designed using the least squares method. The FIR filter is used to filter the geological body data, and then the fault identification coefficients of the window function with corresponding window function parameters are used to identify faults in the geological body data.

4. The three-dimensional modeling method for dynamically inserting concealed faults into geological bodies according to claim 3, characterized in that, The optimal window function parameter is the window function parameter corresponding to the minimum error under different geological body characteristics.

5. The three-dimensional modeling method for dynamically inserting concealed faults into geological bodies according to claim 1, characterized in that, The step of obtaining the fault insertion point in the fault simulation data includes: The fault locations of the fault domain are obtained by performing feature extraction, fault identification, and fault location estimation on the geological body data. Search for the location of the largest fault in each geological body, and use it as the location of each fault. Perform curve fitting on the fault locations of multiple adjacent geological bodies in segments, and statistically analyze the relative deviation of each fault and the average deviation of the fault within the segment in the curve fitting results. The fault segment with the smallest average deviation is selected as the target segment. Based on the relative deviation threshold, the fault location is selected, the fault domain feature information of each selected fault location is extracted, and the most significant feature is selected as the fault insertion starting point.

6. The three-dimensional modeling method for dynamically inserting concealed faults into geological bodies according to claim 1, characterized in that, The parameter range of the insertion model is: The coordinates of the fault insertion start point are: , These are the minimum coordinate values ​​of the geological body data; These are the maximum coordinate values ​​of the geological body data.

7. The three-dimensional rendering method for dynamically inserting concealed faults into geological bodies according to claim 1, characterized in that, The fault insertion correction of the geological body includes: dividing each geological body data into multiple segments along the fault insertion direction based on the geological body data; first, obtaining the geological body structure before and after the adjustment of the central fault parameters based on the difference in geological body data before and after fault insertion; and then obtaining the geological body structure and fault insertion position before and after the adjustment of other fault parameters.

8. A three-dimensional rendering method for dynamically inserting concealed faults into geological bodies, characterized in that, Includes the following steps: Obtain fault simulation data obtained by the three-dimensional modeling method for dynamically inserting geological bodies into concealed faults according to any one of claims 1-7; Based on the three-dimensional mesh generation method, the fault simulation data is divided into multiple data blocks with uniform structure; Based on the viewpoint location, the CPU loads the corresponding data blocks into memory in real time, and loads the geological blocks to be rendered into the GPU's video memory. Based on a GPU parallel computing platform, multi-resolution LOD models are constructed in parallel using GPU stream processors to render geological bodies.

9. The three-dimensional rendering method for dynamically inserting concealed faults into geological bodies according to claim 8, characterized in that, The method based on three-dimensional mesh partitioning divides fault simulation data into multiple data blocks with uniform structure. This includes: dividing the fault simulation data layer by layer according to a preset data block density threshold using a three-dimensional mesh partitioning method to obtain each sub-block; merging adjacent sub-blocks according to the number of data points in adjacent sub-blocks until the total number of points in the sub-blocks is reached to obtain multiple data blocks.

10. The three-dimensional rendering method for dynamically inserting concealed faults into geological bodies according to claim 9, characterized in that, The process of loading corresponding data blocks into memory in real time using the CPU based on the viewpoint position, and loading the geological blocks to be rendered into the GPU's video memory, includes: Calculate the file address of the geological body data block that needs to be loaded into memory and cache based on the current viewpoint position; construct the memory and cache data using a linked list data structure, and initialize and load the cache and memory geological body block data; When the viewpoint changes, determine the type of viewpoint movement, update the memory data, calculate the cache update threshold, determine whether it is necessary to clear useless data in the cache and whether to load external storage data into the cache, and perform the necessary clearing or loading operations.

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