A method for geological modeling

Abstract

This invention provides a method for geological modeling, comprising: S11, determining the range, size, and resolution levels of the grid subdivision of the study area according to the modeling task requirements, and acquiring geological body data points; S12, after processing the geological body data points, using crustal kinematics theory to restore the geological bodies of each grid to their undeformed initial state, generating an initial three-dimensional geological model; S13, performing forward-sequence evolution on the geological bodies restored to their initial state for each grid using crustal kinematics theory, identifying control data points in the geological body data points that influence crustal movement, and generating a three-dimensional geological model; S14, extracting the strata of each layer of the three-dimensional geological model from the grid, and automatically updating the three-dimensional geological model based on the control data points to guide the newly acquired geological body data points. This invention, by combining crustal kinematics theory and dynamic modeling technology, constructs an efficient, accurate, and scientific geological modeling method, thereby providing strong technical support for geological research.

Description

Technical Field

[0001] This invention relates to the field of geological modeling technology, and more particularly to a method for geological modeling. Background Technology

[0002] In the field of geological modeling, traditional 3D geological modeling methods mainly rely on the acquisition and processing of static data, typically generating the 3D structure of geological bodies through interpolation algorithms or numerical simulations. However, existing technologies suffer from the following major problems when dealing with complex geological conditions and dynamic geological evolution: insufficient modeling accuracy, lack of dynamic evolution capability, poor adaptability to complex geological conditions, insufficient resolution flexibility, and difficulties in data updates and model adjustments. Traditional methods cannot effectively simulate the dynamic evolution of crustal movement and require significant manual intervention when dealing with complex geological structures, resulting in low modeling efficiency. Furthermore, existing methods usually employ a fixed resolution, making it difficult to balance macroscopic and local modeling needs, and they cannot automatically update the model when acquiring new data. Summary of the Invention

[0003] The purpose of this invention is to provide a method for geological modeling, which combines crustal kinematics theory and dynamic modeling technology to construct an efficient, accurate and scientific geological modeling method.

[0004] To achieve the above-mentioned objective, the present invention provides a method for geological modeling, the method comprising:

[0005] S11. Determine the scope, size, and resolution level of the grid subdivision in the study area according to the modeling task requirements, and obtain the geological body data points of the completed grid subdivision in the study area.

[0006] S12. After processing the geological body data points of each grid using an interpolation algorithm, based on the acquired geological body data points, the geological body of each grid is restored to its undeformed initial state using the theory of crustal kinematics, generating an initial three-dimensional geological model.

[0007] S13. For each grid, the geological body that has recovered its initial state is subjected to forward-order evolution using the theory of crustal kinematics to determine the control data points that affect crustal movement in the geological body data points and generate a three-dimensional geological model.

[0008] S14. Extract the strata of each layer of the 3D geological model from the grid, and guide the newly acquired geological body data points based on the control data points to adjust the strata and automatically update the 3D geological model.

[0009] Furthermore, in step S11, the scope, size, and resolution level of the mesh subdivision for the study area are determined according to the requirements of the modeling task, specifically including:

[0010] The mesh division is defined by the smallest bounding rectangle covering the study area.

[0011] The triple (i, j, k) is used as the index number of the grid, where i, j, and k are all integers, representing the arrangement position of the grid cell relative to the origin in the X, Y, and Z coordinate axes, respectively.

[0012] Assuming the origin coordinates of the mesh are (x0, y0, z0), and the preset LOD level mesh cell size is (s x s y s z Then, the grid position of any point (x, y, z) can be calculated as follows:

[0013]

[0014] Where floor() rounds down, the minimum bounding rectangle region covered by grid (i, j, k) is:

[0015] x min =x0+i×s x ;y min =y0+j×s y ;z min =z0+k×s z

[0016] x max =x min +s x ;y max =y min +s y ;z max =z min +s z

[0017] Where x0, y0, and z0 are the coordinates of the origin of the grid, representing the initial position of the entire grid, and s x s y s z Let be the step size of the grid in the x, y, and z directions (the size of the grid cell), and let s be the length, width, and height of each grid cell. x s y s z Decision, x min y min z min x is the starting point of the grid. max y max z max This is the end point of the grid;

[0018] A multi-resolution grid is set based on a preset LOD level, and the rectangular area covered by the grid corresponds to different resolutions depending on the LOD level.

[0019] Furthermore, in step S12, the geological body data points of each grid are processed using an interpolation algorithm, specifically including:

[0020] Geological body data points are divided into continuous data and discrete data. Continuous data is processed using three-dimensional kriging and sequential Gaussian simulation interpolation, while discrete data is processed using indicator kriging and sequential indicator simulation interpolation.

[0021] Furthermore, in step S12, the geological bodies of each grid are restored to their undeformed initial state using the theory of crustal kinematics, specifically including:

[0022] A geological model reflecting the initial state of a geological body before tectonic movement is constructed, and the boundary conditions, magnitude of forces, and direction of forces of the initial three-dimensional geological model are determined based on the geological body data points of the study area.

[0023] The irregular initial three-dimensional geological model before tectonic movement is divided into a finite number of regular mesh elements for stress-strain simulation in order to calculate the deformation after tectonic movement.

[0024] Based on the calculated deformation, the geological body after tectonic movement is restored to its initial undeformed state through inversion, thus completing the construction of the initial three-dimensional geological model.

[0025] Furthermore, in step S13, the geological bodies in the initial state of each grid are subjected to forward-sequence evolution using crustal kinematics theory, specifically including:

[0026] Based on the calculated deformation, the evolution of the geological body during tectonic movement is simulated using an initial three-dimensional geological model. The evolution process includes the deformation, displacement, and stress changes of the geological body at different stages.

[0027] By using forward-order modeling technology, combined with geothermal gradient calculation and 3D modeling technology, 3D geological models of geological bodies at different evolutionary stages are generated.

[0028] Furthermore, in step S13, the control data points affecting crustal movement in the geological body data points are determined, and a three-dimensional geological model is generated, specifically including:

[0029] S21. Set the fitness function to the mean square error that controls the prediction error of the data points, initialize the particle swarm, and give the initial estimated value of the particles according to the magnitude of the deformation and the range of influence.

[0030] S22. Calculate the initial fitness value for each particle and use it as the individual best value for that particle. Find the optimal fitness value among all particles and set it as the global best value.

[0031] S23. Update the particle velocity based on the individual best and global best values ​​of the particles, recalculate the fitness value. If the new fitness value of a particle is better than its historical best value, update its individual best value. If a better global fitness value appears, update the global best value.

[0032] S24. If the preset number of iterations is reached or the global optimum value no longer improves, stop the iteration; otherwise, return to step S23.

[0033] S25. Return the optimal control data points and use them for displacement calculation of the three-dimensional geological model at different stages. Use the updated data points to determine the three-dimensional geological model at the final stage.

[0034] Furthermore, in step S14, the strata of each layer of the initial three-dimensional geological model are extracted from the mesh, specifically including:

[0035] Noise reduction of three-dimensional geological models is achieved through anisotropic diffusion.

[0036] Traverse the profile attribute information data in the 3D geological model, determine the relationship between the reading points and feature points, and if they are the same, interpolate the stratigraphic lines using the same reading points and feature points.

[0037] Read the information of adjacent strata, connect the corresponding stratigraphic lines to form a complete stratigraphic plane.

[0038] Furthermore, in step S14, the newly acquired geological body data points are guided based on the control data points to adjust the strata, specifically including:

[0039] Based on the geological information of the control data points, stratigraphic matching is performed on the new data points. The control data points are used as constraints to adjust the stratigraphic morphology matched with the new data points.

[0040] The adjusted geological morphology is integrated into the three-dimensional geological model to update the various layers of the three-dimensional geological model.

[0041] Compared with the prior art, the beneficial effects of the present invention are:

[0042] This invention provides a method for geological modeling that, through interpolation algorithms and crustal kinematics theory, more scientifically explains the evolution of geological bodies, restoring them to their undeformed initial state. Combined with forward-sequence evolution, this accurately reconstructs the true morphology and evolutionary process of geological bodies, significantly improving model accuracy. By identifying and utilizing control data points, the model can better reflect the changing patterns of geological bodies during crustal movements, thus providing a more reliable basis for geological research. It supports multi-resolution modeling, allowing for flexible adjustment of model accuracy according to research needs, adapting to complex geological conditions, reducing manual intervention, and improving modeling efficiency. It can dynamically adjust strata based on newly acquired data points, ensuring the timeliness and reliability of the model. This invention provides an efficient, accurate, and dynamic modeling solution for practical applications such as resource exploration and geological disaster prediction. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0044] Figure 1 This is a schematic diagram of a method for geological modeling provided in an embodiment of the present invention. Detailed Implementation

[0045] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0046] Reference Figure 1 This embodiment provides a method for geological modeling, the method comprising:

[0047] S11. Determine the scope, size, and resolution level of the grid subdivision in the study area according to the modeling task requirements, and obtain the geological body data points of the completed grid subdivision in the study area.

[0048] In this embodiment, based on the requirements of the modeling task, the geographical boundaries of the study area are first defined. Field investigations, analysis of historical geological data, and review of relevant literature are conducted to determine the specific area to be modeled. Based on the research objectives and the accuracy of available data, an appropriate grid size and resolution level are selected. For example, for macroscopic geological structure analysis, a larger grid and lower resolution can be chosen; while for local areas requiring detailed analysis, a smaller grid and higher resolution are needed. Geological body data points within the study area are collected using various methods, such as drilling, seismic exploration, and remote sensing, including but not limited to information on stratigraphic thickness, rock type, and fault location. The acquired geological body data points are preprocessed, including data cleaning, outlier handling, and format standardization, to ensure data quality and consistency.

[0049] In step S11, the scope, size, and resolution level of the mesh subdivision for the study area are determined according to the requirements of the modeling task, specifically including:

[0050] The range of the grid division is defined by the smallest bounding rectangle covering the study area.

[0051] The triple (i, j, k) is used as the index number of the grid, where i, j, and k are all integers, representing the arrangement position of the grid cell relative to the origin in the X, Y, and Z coordinate axes, respectively.

[0052] Assuming the origin coordinates of the mesh are (x0, y0, z0), and the preset LOD level mesh cell size is (s x s y s z Then, the grid position of any point (x, y, z) can be calculated as follows:

[0053]

[0054] Where floor() rounds down, the minimum bounding rectangle region covered by grid (i, j, k) is:

[0055] x min =x0+i×s x ;y min =y0+j×s y ;z min =z0+k×s z

[0056] x max =x min +s x ;y max =y min +s y ;z max =z min +sz

[0057] Where x0, y0, and z0 are the coordinates of the origin of the grid, representing the initial position of the entire grid, and s x s y s z Let be the step size of the grid in the x, y, and z directions (the size of the grid cell), and let s be the length, width, and height of each grid cell. x s y s z Decision, x min y min z min x is the starting point of the grid. max y max z max This is the end point of the grid.

[0058] A multi-resolution grid is set based on a preset LOD level, and the rectangular area covered by the grid corresponds to different resolutions depending on the LOD level.

[0059] In this embodiment, common mesh units used in 3D modeling include tetrahedrons, parallelepipeds (cubes, cuboids), and corner meshes. This invention uses cuboid meshes. Within the same LOD level, the mesh size is consistent. Triples (i, j, k) are used as the mesh index. The origin of the mesh division coincides with the lower left corner vertex of the mesh (0, 0, 0), thus allowing the calculation of the mesh's coordinates and position index. The smaller the mesh volume, the closer it is to meeting the requirement of uniform internal properties. Simultaneously, the number or density of meshes also increases. The choice of mesh size is related to the application requirements of the model, the quality of the available data for modeling, and the processing capabilities of the information system.

[0060] The requirements for grid size in model applications are similar to the requirements for geological maps or map scales in different tasks. Multi-resolution LOD models can meet the different grid size requirements of various applications. When available data for modeling is sparse or has low accuracy, it is suitable to build a low-resolution model; when data is dense and reliability is high, it is suitable to build a high-resolution model. Based on these considerations, the 3D geological model constructed in this paper is a multi-resolution LOD model.

[0061] The mesh model is multi-resolution, with different Level of Dimension (LOD) levels corresponding to different resolutions. LOD levels are labeled with non-negative integers: 0 represents the lowest resolution level, and increasing the level indicates higher resolution (i.e., smaller mesh cells). All LOD levels use a unified mesh origin, but the mesh cell size varies across levels. Furthermore, high-resolution meshes are used only in localized areas with high-quality original data. The mesh origin is not required to be the smallest X, Y, Z coordinate point of the modeling area, allowing for easy expansion of the modeling range in all directions (up, down, left, right, front, back). Otherwise, when expanding the model range, the origin needs to be reset and the index numbers of all meshes updated. If the modeling area is fixed, then the smallest X, Y, Z coordinate value of the area is the best choice for the mesh origin.

[0062] S12. After processing the geological body data points of each grid using an interpolation algorithm, based on the acquired geological body data points, the geological body of each grid is restored to its undeformed initial state using the theory of crustal kinematics, generating an initial three-dimensional geological model.

[0063] In this embodiment, interpolation algorithms are used to process data points, enabling more accurate estimation of the geological properties of unknown points within the grid and reducing errors caused by missing or unevenly distributed data. When dealing with complex geological deformation problems, such as folds and faults, inverse analysis based on crustal kinematics theory can effectively reconstruct the original morphology of geological bodies before crustal movement, providing strong support for analyzing the evolutionary history of geological bodies and predicting future changes, while also providing an accurate foundation for subsequent geological evolution simulations. Combined with geological kinematics theory, the model can better reflect the actual deformation process of geological bodies, improving the model's scientific validity and credibility.

[0064] In step S12, the geological body data points of each grid are processed using an interpolation algorithm, specifically including:

[0065] Geological body data points are divided into continuous data and discrete data. Continuous data is processed using three-dimensional kriging and sequential Gaussian simulation interpolation, while discrete data is processed using indicator kriging and sequential indicator simulation interpolation.

[0066] In this embodiment, the geological body data points for each grid may originate from raw data, such as boreholes, cross-sections, or existing 3D geological models, or from interpolation results. For raw data, grid values ​​can be assigned by comparing the positional relationship between the grid and the data. For interpolation calculations, relevant algorithms from geostatistics are used, and the grid attributes provided by the raw data are the sample data upon which the interpolation depends. Geological body data points can be divided into two main categories: discrete attributes, such as lithology, and continuous attributes, such as resistivity. For discrete geological body data points, two interpolation methods are used: indicator kriging and sequential indicator simulation. For continuous geological body data points, two interpolation methods are used: 3D kriging and sequential Gaussian simulation. After configuring the parameters, the process of assigning values ​​to the grid from raw data or performing interpolation calculations can be executed automatically.

[0067] Specifically, when assigning values ​​to the grid based on the original data or through interpolation, the matching LOD level is specified according to the data quality level. For example, if LOD6 is specified as the matching working level, the calculation and assignment are performed according to the grid size parameters of LOD6 level. Then, the geological body data points of the grid in the updated area of ​​LOD6 level are merged and assigned to the LOD5 level grid, and then merged and assigned to the LOD4 level grid, and so on, until the geological body data points of the corresponding LOD0 level grid are updated. The merging rule for discrete geological body data points is to take the dominant value in the high-resolution grid as the geological body data point of the low-resolution grid. The merging rule for continuous geological body data points is to take the arithmetic mean of the geological body data points in the high-resolution grid as the geological body data point of the low-resolution grid.

[0068] In step S12, the geological bodies of each grid are restored to their undeformed initial state using the theory of crustal kinematics, specifically including:

[0069] A geological model reflecting the initial state of a geological body before tectonic movement is constructed, and the boundary conditions, magnitude of forces, and direction of forces of the initial three-dimensional geological model are determined based on the geological body data points of the study area.

[0070] The irregular initial three-dimensional geological model before tectonic movement is divided into a finite number of regular grid cells for stress-strain simulation in order to calculate the deformation after tectonic movement.

[0071] Based on the calculated deformation, the geological body after tectonic movement is restored to its initial undeformed state through inversion, thus completing the construction of the initial three-dimensional geological model.

[0072] In this embodiment, by combining crustal kinematics theory and employing precise stress-strain simulation and inversion methods, the true deformation process of geological bodies can be better reflected, and the initial state of geological bodies before tectonic movements can be more accurately restored, thus improving the model's accuracy. The initial three-dimensional geological model can serve as the basis for subsequent geological evolution simulations, supporting the dynamic evolution simulation of geological bodies at different geological periods. Simultaneously, it provides strong support for analyzing the evolutionary history of geological bodies and predicting future changes when dealing with complex geological deformation problems.

[0073] Specifically, based on geological body data points and geological history data, the boundary conditions of the initial 3D geological model are determined, such as stratigraphic boundaries and fault locations. The tectonic history of the study area is analyzed to determine the magnitude and direction of the forces acting on the geological body before tectonic movements, including crustal uplift, compression, and tension. The irregular initial 3D geological model is divided into a finite number of regular grid cells, each representing a smaller geological block. Stress-strain simulations are performed on each grid cell using numerical simulation methods such as finite element analysis to calculate the deformation of each grid cell under tectonic activity. Using the inversion method, an inversion model is established based on the calculated deformation. Through iterative calculations, the geological body after tectonic movement is gradually restored to its undeformed initial state. The restored geological body data is integrated into a unified 3D coordinate system. Using 3D modeling software or algorithms, the processed data points are connected to form a continuous geological body surface and structure, generating the initial 3D geological model.

[0074] S13. For each grid, the geological body that has recovered its initial state is subjected to forward-order evolution using crustal kinematics theory to determine the control data points that affect crustal movement in the geological body data points and generate a three-dimensional geological model.

[0075] In this embodiment, a forward-sequence evolution simulation is performed using crustal kinematics theory to more scientifically simulate the evolution process of geological bodies, improve the accuracy of the model, identify key geological structures that significantly affect crustal movement by identifying control data points, and provide important information for geological research and resource exploration. The generated three-dimensional geological model serves as the basis for subsequent geological evolution simulations, supporting the dynamic evolution simulation of geological bodies in different geological periods. The three-dimensional geological model provides a reliable geological basis for practical applications such as resource exploration, geological disaster prediction, and engineering design, and has broad application value.

[0076] In step S13, the geological bodies in the initial state of each grid are reconstructed using the theory of crustal kinematics for forward-order evolution, specifically including:

[0077] Based on the calculated deformation, the evolution of the geological body during tectonic movement is simulated using an initial three-dimensional geological model. The evolution process includes the deformation, displacement, and stress changes of the geological body at different stages.

[0078] By using forward-order modeling technology, combined with geothermal gradient calculation and 3D modeling technology, 3D geological models of geological bodies at different evolutionary stages are generated.

[0079] In this embodiment, based on the initial three-dimensional geological model generated in the preceding steps, model parameters are initialized, including the physical properties of the geological body and the initial stress state. According to the theory of crustal kinematics and the deformation calculated in previous steps, the evolution of the geological body during tectonic movements is simulated, including determining the type of tectonic movement (such as compression, tension, shear, etc.), its direction, and rate. The evolution process of the geological body is divided into different stages, each stage representing the main tectonic movements experienced by the geological body within a specific time period. Stress-strain simulations are performed for each evolution stage to calculate the deformation, displacement, and stress changes of the geological body at different stages.

[0080] By combining geothermal gradient data, the temperature variations of geological bodies at different evolutionary stages are simulated, and these temperature changes affect the physical properties and deformation behavior of the geological bodies. Using 3D modeling technology, 3D models of the geological bodies at different evolutionary stages are generated based on the simulation results, including adjustments to the shape, location, and properties of the geological bodies to reflect their changes during tectonic movements. The simulated evolutionary process is integrated into a continuous 3D geological model, demonstrating the evolution of the geological body from its initial state to its final state.

[0081] In step S13, the control data points affecting crustal movement in the geological body data points are determined, and a three-dimensional geological model is generated, specifically including:

[0082] S21. Set the fitness function to the mean square error that controls the prediction error of the data points, initialize the particle swarm, and give the initial estimated value of the particles according to the magnitude of the deformation and the range of influence.

[0083] S22. Calculate the initial fitness value for each particle and use it as the individual best value for that particle. Find the optimal fitness value among all particles and set it as the global best value.

[0084] S23. Update the particle velocity based on the individual best and global best values ​​of the particles, recalculate the fitness value. If the new fitness value of a particle is better than its historical best value, update its individual best value. If a better global fitness value appears, update the global best value.

[0085] S24. If the preset number of iterations is reached or the global optimum value no longer improves, stop the iteration; otherwise, return to step S23.

[0086] S25. Return the optimal control data points and use them for displacement calculation of the three-dimensional geological model at different stages. Use the updated data points to determine the three-dimensional geological model at the final stage.

[0087] In this embodiment, control data points help identify and locate key geological structures that significantly influence crustal movement and geological body morphology, such as faults, fold axes, and stratigraphic boundaries, providing important reference data for the deformation and evolution of geological bodies. Particle swarm optimization algorithms are used to accurately identify control data points, reducing prediction errors and improving the accuracy of the 3D geological model. By utilizing fitness functions and iterative optimization processes, control data points that significantly impact crustal movement can be effectively identified.

[0088] Specifically, the fitness function is set as the mean squared error of the prediction error of the control data points to quantify the difference between the predicted and actual values. The particle swarm is initialized, with each particle representing a potential control data point. Initial estimates for the particles are given based on prior knowledge or random initialization, according to the magnitude and influence range of the deformation. An initial fitness value is calculated for each particle and used as its individual optimal value.

[0089] Find the optimal fitness value among all particles and set it as the global optimum. Update the particle's velocity based on the individual optimum and the global optimum, and recalculate the fitness value of each particle using the updated velocity. If a particle's new fitness value is better than its historical optimum, update its individual optimum; if a better global fitness value emerges, update the global optimum. Check if the preset number of iterations has been reached or if the global optimum has stopped improving. If the termination condition is met, stop the iteration; otherwise, continue iterating. After iteration, return the optimal control data point. Use the optimal control data point to calculate the displacement of the 3D geological model at different stages, and use the updated data point to determine the 3D geological model for the final stage.

[0090] S14. Extract the strata of each layer of the 3D geological model from the grid, and guide the newly acquired geological body data points based on the control data points to adjust the strata and automatically update the 3D geological model.

[0091] In this embodiment, by extracting the ground plane from the grid and adjusting it based on control data points, the true shape and structure of the geological body are more accurately reflected, improving the accuracy of the model. The three-dimensional geological model is automatically updated and can quickly respond to newly acquired geological body data points, ensuring the timeliness and reliability of the model.

[0092] In step S14, the strata of each layer of the initial three-dimensional geological model are extracted from the mesh, specifically including:

[0093] Noise reduction of three-dimensional geological models is achieved through anisotropic diffusion.

[0094] Traverse the profile attribute information data in the 3D geological model, determine the relationship between the reading points and feature points, and if they are the same, interpolate the stratigraphic lines using the same reading points and feature points.

[0095] Read the information of adjacent strata, connect the corresponding stratigraphic lines to form a complete stratigraphic plane.

[0096] In this embodiment, non-isotropic diffusion denoising process effectively removes noise from the model, improving the model's accuracy and clarity. Traversing profile attribute information data and stratigraphic line interpolation ensures the continuity and smoothness of stratigraphic lines, avoiding stratigraphic fractures or discontinuities.

[0097] Specifically, based on the model's complexity and noise level, an isotropic diffusion algorithm is applied and its parameters are adjusted to denoise the 3D geological model, removing noise and artifacts while preserving stratigraphic edges and important features. Each profile attribute data point in the 3D geological model is traversed, extracting its geological attributes (such as lithology and stratigraphic number) to determine if the current reading point is a feature point (such as a stratigraphic boundary or fault). If the reading point and a feature point have the same relationship, they are considered to belong to the same stratum. For reading points and feature points belonging to the same stratum, interpolation algorithms (such as linear interpolation and spline interpolation) are used to interpolate the stratigraphic lines, generating smooth stratigraphic lines. Information from adjacent strata is read, including the position and shape of stratigraphic lines. The endpoints of the stratigraphic lines are adjusted to connect adjacent stratigraphic lines, ensuring a seamless spatial connection and guaranteeing the continuity and integrity of the stratigraphic planes. Using the connected stratigraphic lines, a triangulated network (TIN) or rasterization method is used to convert the stratigraphic lines into stratigraphic planes to generate complete stratigraphic planes.

[0098] In step S14, the newly acquired geological body data points are guided based on the control data points to adjust the strata, specifically including:

[0099] Based on the geological information of the control data points, stratigraphic matching is performed on the new data points. The control data points are used as constraints to adjust the stratigraphic morphology matched with the new data points.

[0100] The adjusted geological morphology is integrated into the three-dimensional geological model to update the various layers of the three-dimensional geological model.

[0101] In this embodiment, based on the geological information (such as stratigraphic age and lithology) of the control data points, stratigraphic attribute matching is performed on newly acquired geological body data points. By comparing the stratigraphic attributes of the new data points with those of the control data points, the stratigraphic morphology to which the new data points belong is determined. Combined with the spatial location information of the control data points, spatial location correction is performed on the new data points to ensure spatial consistency with the control data points. Using the control data points as constraints, the morphology of the stratigraphic plane matched by the new data points is adjusted. Using interpolation algorithms (such as kriging, inverse distance weighted interpolation, etc.), the morphology of the stratigraphic plane is recalculated under the constraints of the control data points. The adjusted stratigraphic plane is smoothed to eliminate outliers or noise, ensuring the continuity and smoothness of the stratigraphic plane. The adjusted stratigraphic plane morphology is integrated into the three-dimensional geological model, updating each layer of the model.

[0102] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for geological modeling, characterized in that, The method includes: S11. Determine the scope, size, and resolution level of the grid subdivision in the study area according to the modeling task requirements, and obtain the geological body data points of the completed grid subdivision in the study area. S12. After processing the geological body data points of each grid using an interpolation algorithm, based on the acquired geological body data points, the geological body of each grid is restored to its undeformed initial state using the theory of crustal kinematics, generating an initial three-dimensional geological model. S13. For each grid, the geological body that has recovered its initial state is subjected to forward-order evolution using the theory of crustal kinematics to determine the control data points that affect crustal movement in the geological body data points and generate a three-dimensional geological model. Identify the control data points affecting crustal movement within the geological body data points, and generate a three-dimensional geological model, specifically including: S21. Set the fitness function to the mean square error that controls the prediction error of the data points, initialize the particle swarm, and give the initial estimated value of the particles according to the magnitude of the deformation and the range of influence. S22. Calculate the initial fitness value for each particle and use it as the individual best value for that particle. Find the optimal fitness value among all particles and set it as the global best value. S23. Update the particle velocity based on the individual best and global best values ​​of the particles, recalculate the fitness value. If the new fitness value of a particle is better than its historical best value, update its individual best value. If a better global fitness value appears, update the global best value. S24. If the preset number of iterations is reached or the global optimum value no longer improves, stop the iteration; otherwise, return to step S23. S25. Return the optimal control data points and use them for displacement calculation of the three-dimensional geological model at different stages. Use the updated data points to determine the three-dimensional geological model at the final stage. S14. Extract the strata of each layer of the 3D geological model from the grid, and guide the newly acquired geological body data points based on the control data points to adjust the strata and automatically update the 3D geological model.

2. The method for geological modeling according to claim 1, characterized in that, In step S11, the scope, size, and resolution level of the mesh subdivision for the study area are determined according to the requirements of the modeling task, specifically including: The mesh division is defined by the smallest bounding rectangle covering the study area. The triple (i, j, k) is used as the index number of the grid, where i, j, and k are all integers, representing the arrangement position of the grid cell relative to the origin in the X, Y, and Z coordinate axes, respectively. Assume the coordinates of the origin of the grid are ( , , The preset size of the LOD level grid cell is ( , , Then, the grid position of any point (x, y, z) can be calculated as follows: in, () represents rounding down. The minimum bounding rectangle region covered by grid (i, j, k) is: in, , , The coordinates of the origin of the grid represent the initial position of the entire grid. , The step size of the grid is defined by the x, y, and z directions, where the step size is the size of the grid cell. The length, width, and height of each grid cell are respectively determined by... , , Decide, , , As the starting point of the grid, , , This is the end point of the grid; A multi-resolution grid is set based on a preset LOD level, and the rectangular area covered by the grid corresponds to different resolutions depending on the LOD level.

3. The method for geological modeling according to claim 1, characterized in that, In step S12, the geological body data points of each grid are processed using an interpolation algorithm, specifically including: Geological body data points are divided into continuous data and discrete data. Continuous data is processed using three-dimensional kriging and sequential Gaussian simulation interpolation, while discrete data is processed using indicator kriging and sequential indicator simulation interpolation.

4. The method for geological modeling according to claim 1, characterized in that, In step S12, the geological bodies of each grid are restored to their undeformed initial state using the theory of crustal kinematics, specifically including: A geological model reflecting the initial state of a geological body before tectonic movement is constructed, and the boundary conditions, magnitude of forces, and direction of forces of the initial three-dimensional geological model are determined based on the geological body data points of the study area. The irregular initial three-dimensional geological model before tectonic movement is divided into a finite number of regular mesh elements for stress-strain simulation in order to calculate the deformation after tectonic movement. Based on the calculated deformation, the geological body after tectonic movement is restored to its initial undeformed state through inversion, thus completing the construction of the initial three-dimensional geological model.

5. The method for geological modeling according to claim 4, characterized in that, In step S13, the geological bodies in the initial state of each grid are reconstructed using the theory of crustal kinematics for forward-order evolution, specifically including: Based on the calculated deformation, the evolution of the geological body during tectonic movement is simulated using an initial three-dimensional geological model. The evolution process includes the deformation, displacement, and stress changes of the geological body at different stages. By using forward-order modeling technology, combined with geothermal gradient calculation and 3D modeling technology, 3D geological models of geological bodies at different evolutionary stages are generated.

6. The method for geological modeling according to claim 1, characterized in that, In step S14, the strata of each layer of the initial three-dimensional geological model are extracted from the mesh, specifically including: Noise reduction of three-dimensional geological models is achieved through anisotropic diffusion. Traverse the profile attribute information data in the 3D geological model, determine the relationship between the reading points and feature points, and if they are the same, interpolate the stratigraphic lines using the same reading points and feature points. Read the information of adjacent strata, connect the corresponding stratigraphic lines to form a complete stratigraphic plane.

7. The method for geological modeling according to claim 1, characterized in that, In step S14, the newly acquired geological body data points are guided based on the control data points to adjust the strata, specifically including: Based on the geological information of the control data points, stratigraphic matching is performed on the new data points. The control data points are used as constraints to adjust the stratigraphic morphology matched with the new data points. The adjusted geological morphology is integrated into the three-dimensional geological model to update the various layers of the three-dimensional geological model.

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