Geophysical 3D visualization and geological model building system

Abstract

The application discloses a geophysical three-dimensional visualization and geological model construction system, and belongs to the technical field of geophysical data processing and three-dimensional geological modeling. The system comprises a multi-source data acquisition module, a volume rendering module, an interactive segmentation module, an uncertainty expression module, a multi-scale fusion module, an attribute interpolation module and a model construction module. Multi-source geophysical data is converted into an intuitive three-dimensional visualization model through the volume rendering technology. An interactive segmentation algorithm is adopted to accurately extract the geological body boundary. An uncertainty expression mechanism is innovatively introduced to quantize the model reliability. Multi-scale fusion technology is integrated to realize seamless connection from regional structure to deposit fine structure. Kriging interpolation function is provided to generate a three-dimensional distribution field of physical property parameters. The application significantly improves the spatial cognition ability and decision efficiency under complex geological conditions, and provides a powerful visualization analysis platform for geological exploration and resource evaluation.

Description

Technical Field

[0001] This invention relates to the field of geophysical data processing and 3D geological modeling technology, specifically to a geophysical 3D visualization and geological model construction system based on volumetric rendering and multi-scale fusion. Background Technology

[0002] Geophysical exploration is a crucial means of understanding subsurface geological structures, assessing mineral resources, and guiding engineering construction. With the development of various geophysical detection technologies, such as seismic, gravity, magnetic, and electromagnetic methods, we are able to acquire vast amounts of subsurface information. However, transforming this multi-source, heterogeneous geophysical data into intuitive and accurate three-dimensional geological models remains a significant challenge for the field of earth sciences.

[0003] For example, Chinese Patent Publication No. CN119273864A discloses a complex geological model generation system based on 3D visualization and GIS technology. This system includes a modeling acquisition module, a terrain annotation module, a boundary extraction module, a boundary verification module, and a model generation module. The system acquires a topographic map of the area to be modeled, determines the location of boundary points, the shape of the boundary, and the terrain features, extracts and verifies the boundary features, and finally generates a local geological model.

[0004] However, existing technologies have the following shortcomings: First, they lack volumetric rendering technology for multi-source geophysical data, making it impossible to intuitively display the spatial distribution characteristics of three-dimensional geophysical fields; second, they lack uncertainty expression mechanisms, failing to quantify the reliability of each part of the model, resulting in decision-makers being unable to accurately assess the model's confidence level; third, they lack multi-scale fusion capabilities, failing to achieve seamless integration from regional tectonic frameworks to the fine structure of ore deposits, thus limiting the model's practical value; fourth, they lack attribute interpolation modules based on limited observation points, making it difficult to generate continuous three-dimensional distributions of physical parameters, affecting the accuracy of resource estimation; fifth, boundary extraction methods rely on simple gradient calculations, which are insufficiently accurate under complex geological conditions and prone to misjudgment. Summary of the Invention

[0005] To address the aforementioned technical issues, this invention provides a geophysical 3D visualization and geological model construction system. It employs volumetric rendering technology to achieve 3D visualization of geophysical data, introduces an uncertainty expression mechanism to quantify model reliability, integrates multi-scale fusion technology to achieve cross-scale modeling, and provides attribute interpolation functionality to support resource evaluation, significantly improving spatial cognitive ability and decision-making efficiency under complex geological conditions.

[0006] The technical solution adopted in this invention is: a geophysical three-dimensional visualization and geological model construction system, comprising:

[0007] The multi-source data acquisition module is used to collect seismic data, gravity and magnetic data, and electromagnetic data of the area to be modeled. If the collected data types conform to the preset data type set, the data is normalized to obtain standardized geophysical data.

[0008] The volume rendering module is used to generate a three-dimensional data volume based on the standardized geophysical data. If the data density in the three-dimensional data volume meets the first density threshold range, the volume rendering of the three-dimensional data volume is performed using a ray casting algorithm to obtain a three-dimensional visualization model with opacity information.

[0009] The interactive segmentation module is used to receive user interaction commands for the 3D visualization model. If the boundary clarity of the target area indicated by the interaction command meets the first clarity threshold, the region growing algorithm is used to extract the set of geological body boundary points from the target area.

[0010] The uncertainty expression module is used to calculate the reliability coefficient of each boundary point in the set of boundary points of the geological body. If the reliability coefficient is lower than the first reliability threshold, the degree of uncertainty is expressed by adjusting the opacity value and color coding value of the corresponding boundary point.

[0011] The multi-scale fusion module is used to acquire regional tectonic framework data and ore deposit fine structure data. If the difference between the spatial scale of the regional tectonic framework data and the spatial scale of the ore deposit fine structure data exceeds the preset scale difference threshold, a pyramid hierarchical fusion strategy is adopted to fuse data of different scales to generate a cross-scale geological model.

[0012] The attribute interpolation module is used to determine the spatial distribution density of observation points based on the observation point data set. If the spatial distribution density is lower than the first density threshold, the Kriging interpolation method is used to generate a three-dimensional distribution field of physical property parameters. If there are numerical discontinuities in the generated three-dimensional distribution field and the degree of discontinuity exceeds the preset continuity threshold, local re-interpolation processing is performed on the region.

[0013] The model building module is used to integrate the three-dimensional visualization model, the set of geological body boundary points, uncertainty information, cross-scale geological model and three-dimensional distribution field to generate a complete three-dimensional geological model with reliability annotations.

[0014] In one possible implementation, when the multi-source data acquisition module acquires seismic data, if the signal-to-noise ratio of the seismic data is lower than a first signal-to-noise ratio threshold, it uses distributed acoustic sensing technology to enhance the seismic data, replacing the traditional detector data with fiber optic strain rate data. If the enhancement of the signal-to-noise ratio exceeds a preset enhancement threshold, the enhanced seismic data is used as the seismic component of the standardized geophysical data.

[0015] In one possible implementation, when the volume rendering module uses a ray casting algorithm, it sets multiple viewpoint positions and emits rays from each viewpoint position to the three-dimensional data volume. If the value of the data voxel through which the ray passes meets a first value range, it accumulates the color value and opacity value corresponding to the data voxel. If the accumulated opacity reaches a first opacity threshold, it terminates the subsequent sampling of the ray and generates a two-dimensional projection image corresponding to the viewpoint.

[0016] In one possible implementation, after receiving the interaction instruction, the interactive segmentation module determines the location of the seed point indicated by the interaction instruction. Starting from the seed point, if the difference between the geophysical attribute value of the adjacent data point and the attribute value of the seed point is less than a first similarity threshold, the adjacent data points are added to the same geological body region. If the number of added data points exceeds a first scale threshold, the region growth is stopped and the set of geological body boundary points is output.

[0017] In one possible implementation, when calculating the reliability coefficient, the uncertainty expression module obtains the number and distribution uniformity of observation points around the boundary point. If the number of observation points is greater than a first quantity threshold and the distribution uniformity is greater than a first uniformity threshold, the reliability coefficient is set to a high value. If the number of observation points is less than a second quantity threshold or the distribution uniformity is less than a second uniformity threshold, the reliability coefficient is set to a low value.

[0018] In one possible implementation, when the multi-scale fusion module adopts a pyramid hierarchical fusion strategy, it uses regional tectonic framework data as the bottom coarse-scale data and ore deposit fine structure data as the top fine-scale data. If the ratio of spatial resolution of adjacent levels meets the first ratio range, spatial registration between levels is achieved through upsampling or downsampling operations. If the registration error is less than the first error threshold, weighted fusion of multi-level data is performed.

[0019] The beneficial effects of this invention are as follows:

[0020] First, this invention transforms multi-source geophysical data into an intuitive 3D visualization model using volumetric rendering technology. It employs a ray casting algorithm to achieve real-time rendering of the 3D data field, supporting interactive exploration and significantly improving geologists' efficiency in understanding subsurface structures. Compared to existing technologies, this invention does not rely on 2D profiles or simple isosurface displays, but instead uses volumetric rendering to directly present the internal structure of the 3D data field, making the spatial morphology of complex geological bodies readily apparent.

[0021] Secondly, this invention innovatively introduces an uncertainty representation mechanism. By calculating the reliability coefficients of boundary points and using opacity and color coding, it visually displays the confidence level of each part of the model. This mechanism helps decision-makers identify areas of high uncertainty, rationally arrange supplementary exploration work, avoid making major decisions in low-reliability areas, and reduce exploration risks. Existing technologies typically present geological models in a deterministic manner, which can easily give users the false impression of "what you see is what you get." However, the uncertainty representation mechanism of this invention can truly reflect the confidence level of the model.

[0022] Third, this invention integrates multi-scale fusion technology, employing a pyramid-layered fusion strategy to achieve seamless integration from regional tectonic frameworks to fine-grained mineral deposit structures. Through spatial registration and weighted fusion, this invention can simultaneously present macroscopic tectonic patterns and local mineralization details within the same model, meeting the application needs at different levels. Compared to existing technologies that can only establish models at a single scale, the multi-scale fusion capability of this invention significantly enhances the model's applicability and practical value.

[0023] Fourth, this invention provides an attribute interpolation module that uses the Kriging interpolation method based on limited observation point data to generate a continuous three-dimensional distribution field of physical property parameters, supporting resource estimation and development scheme design. Geological constraints are considered during the interpolation process to ensure the physical rationality of the interpolation results. Compared with existing technologies that lack attribute interpolation functionality, this invention can construct a complete physical property parameter model even with sparse observation data, providing a quantitative basis for resource evaluation.

[0024] Fifth, this invention employs interactive segmentation technology to accurately extract geological body boundaries from a 3D visualization model. Combined with a region growing algorithm and user interaction, it can accurately delineate the morphology of geological bodies under complex geological conditions. Compared to existing boundary extraction methods that rely on simple gradient calculations, the interactive segmentation technology of this invention has higher accuracy and greater adaptability, and can effectively handle situations where geological body boundaries are blurred. Attached Figure Description

[0025] Figure 1 This is a system architecture diagram of the geophysical three-dimensional visualization and geological model construction system of the present invention.

[0026] Figure 2 This is a flowchart of the multi-source data acquisition module in the system of this invention.

[0027] Figure 3 This is a schematic diagram illustrating the working principle of the volume rendering module in the system of this invention.

[0028] Figure 4 This is the computational logic diagram of the uncertainty expression module in the system of this invention.

[0029] Figure 5This is a diagram of the pyramid-shaped hierarchical fusion structure of the multi-scale fusion module in the system of this invention.

[0030] Figure 6 This is a flowchart of the Kriging interpolation process of the attribute interpolation module in the system of this invention. Detailed Implementation

[0031] Please refer to the attached document. Figures 1-6 The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0032] See Figure 1 This invention provides a geophysical 3D visualization and geological model building system, including a multi-source data acquisition module, a volume rendering module, an interactive segmentation module, an uncertainty expression module, a multi-scale fusion module, an attribute interpolation module, and a model building module. These modules are interconnected through data flow, forming a complete geological modeling workflow.

[0033] See Figure 2 The multi-source data acquisition module is responsible for collecting seismic, gravity, magnetic, and electromagnetic data for the area to be modeled. In geophysical exploration practice, different physical field methods exhibit varying response characteristics to subsurface media; therefore, comprehensively utilizing multiple methods can yield more comprehensive geological information.

[0034] Seismic data reflects the wave impedance structure of underground rock strata. By analyzing the propagation time and amplitude characteristics of seismic waves, stratigraphic interfaces and fault structures can be identified. For example, the seismic data sampling interval is 5 meters, the time sampling rate is 4 milliseconds, and the coverage depth range is 0 to 5000 meters.

[0035] Gravity and magnetic data reflect the density and magnetic susceptibility distribution of underground rocks, and are particularly effective for identifying geological bodies with significant density or magnetic differences, such as intrusive intrusive bodies and mineralized zones. For example, gravity data is measured with an accuracy of 0.01 milligal, and magnetic data with an accuracy of 0.1 nanot.

[0036] Electromagnetic data reflects the resistivity distribution of underground media and is sensitive to low-resistivity bodies such as aquifers, clay layers, and mineralized alteration zones. For example, magnetotelluric sounding, with a frequency range of 320 Hz to 0.0001 Hz, can detect depths up to 10,000 meters.

[0037] The multi-source data acquisition module first checks the type of the acquired data to confirm whether the data type belongs to a preset data type set. The preset data type set includes seismic reflection data, seismic refraction data, gravity anomaly data, magnetic anomaly data, magnetotelluric data, and controlled-source audio-visual magnetotelluric data, among others. If the acquired data type conforms to the preset data type set, the data is then normalized.

[0038] The purpose of normalization is to eliminate the influence of different physical dimensions and numerical magnitudes, enabling comprehensive analysis of different types of geophysical data within a unified numerical space. The normalization formula is as follows:

[0039] ,

[0040] in: These are the normalized data values; The original data value; The minimum value of the original data; This represents the maximum value of the original data.

[0041] Through normalization, all geophysical data are mapped to the range of 0 to 1, which facilitates subsequent data fusion and visualization.

[0042] It should be noted that a low signal-to-noise ratio in seismic data can negatively impact subsequent processing. This invention introduces Distributed Acoustic Sensing (DAS) technology to enhance seismic data. DAS technology utilizes optical fiber as the sensing medium, recording seismic wave signals by measuring strain rate changes along the fiber. Compared to traditional detectors, DAS technology offers advantages such as high spatial resolution, low cost, and suitability for large-scale deployment.

[0043] For example, when the signal-to-noise ratio (SNR) of the seismic data is lower than a first SNR threshold (e.g., 5 dB), the system automatically activates fiber optic strain rate data acquired using DAS technology. By integrating and filtering the fiber optic strain rate data, signal quality comparable to or even better than traditional seismic data can be obtained. If the enhanced SNR improvement exceeds a preset improvement threshold (e.g., 3 dB), the enhanced seismic data is used as the seismic component of the standardized geophysical data for subsequent volumetric rendering and modeling processes.

[0044] See Figure 3 The volume rendering module generates a 3D data volume based on standardized geophysical data and uses a ray casting algorithm to perform volume rendering, thereby obtaining a 3D visualization model with opacity information.

[0045] A 3D data volume is a three-dimensional array obtained by regularly gridding standardized geophysical data according to spatial coordinates. Each grid cell (voxel) corresponds to a small cubic region in space, storing the geophysical attribute values ​​of that region. For example, the spatial resolution of a 3D data volume is 10 meters × 10 meters × 10 meters, the coverage area is 5000 meters × 5000 meters × 3000 meters, and the total number of voxels is 5 × 10^8.

[0046] Volumetric rendering is a visualization technique that directly generates two-dimensional images from a three-dimensional data field without extracting intermediate geometric surfaces, thus preserving the internal structural information of the data field. This invention employs a ray casting algorithm to achieve volumetric rendering.

[0047] The basic principle of the ray casting algorithm is as follows: several rays are emitted from the observer's viewpoint toward the 3D data volume. When each ray passes through the data volume, voxels are sampled along the ray path. The corresponding color and opacity are determined based on the voxel values. Then, the colors and opacities of each voxel are accumulated and synthesized according to the direction of ray propagation to finally obtain the color value of the pixel corresponding to the ray.

[0048] For example, the viewpoint position is set as (x_view, y_view, z_view), and the screen plane is perpendicular to the viewing direction. For each pixel (i, j) on the screen, calculate the ray direction vector from the viewpoint through that pixel pointing into the data body. .

[0049] Along the light With fixed step size (For example Sampling was conducted at a distance of 1 meter, with the sampling points located at:

[0050] ,

[0051] in: The spatial location of the k-th sampling point; Viewpoint position; The sampling point number; This is the sampling step size; This is the direction vector of the light ray.

[0052] For each sampling point Geophysical property values ​​of a point are obtained from a 3D data volume using trilinear interpolation. Then, the attribute values ​​are transferred according to the transfer function. Mapped to color values and opacity value .

[0053] The transfer function is a mapping relationship defined by the user according to geological interpretation needs. For example, for seismic impedance data, the transfer function can be set as follows:

[0054] when (For example When the impedance layer is low, the color is set to blue. Opacity ;

[0055] when (For example When the impedance layer is medium, the color is set to green. Opacity ;

[0056] when When the corresponding high impedance layer is used, the color is set to red. Opacity .

[0057] Color and opacity are accumulated along the direction of the light source using a front-to-back compositing method. The accumulation formula is as follows:

[0058] ,

[0059] ,

[0060] in: For cumulative color values; To accumulate opacity; The color value of the kth sampling point ; Let be the opacity of the k-th sampling point.

[0061] When cumulative opacity Reaching the first opacity threshold (e.g.) When the light source is almost completely absorbed, subsequent sampling of that light source is terminated to improve rendering efficiency. Finally, the accumulated color values ​​are... That is, the color of the screen pixel (i, j).

[0062] By performing the aforementioned light projection process on all pixels on the screen, a two-dimensional projection image of the three-dimensional data volume can be generated, i.e., a three-dimensional visualization model. Users can change the viewpoint position and transfer function parameters through interactive operations to update the visualization model in real time, observing underground geological structures from different angles and display methods.

[0063] It should be noted that before performing volumetric rendering, the system checks the data density of the 3D data volume. If the data density is too low (for example, below the first density threshold, set to less than 10 valid data points per 100m × 100m × 100m cube), the data volume is too sparse, making it difficult to generate a high-quality rendered image. In this case, the system will prompt the user to supplement the observation data or adjust the rendering parameters.

[0064] The interactive segmentation module is used to extract geological body boundaries from 3D visualization models. A geological body refers to a spatially connected region with similar geophysical properties, such as a stratigraphic unit, intrusive rock mass, or mineralized zone. Accurately identifying geological body boundaries is a crucial step in constructing geological models.

[0065] This invention employs an interactive segmentation algorithm based on region growing. Users specify one or more seed points within the target geological body using interactive methods such as mouse clicks in a 3D visualization model. The system expands outwards from the seed points, incorporating data points with similar geophysical properties into the same geological body region.

[0066] Specifically, the interactive segmentation module first receives the user's interaction command and determines the seed point location indicated by the interaction command. Next, examine the boundary sharpness of the region surrounding the seed point. Boundary sharpness can be measured by calculating the gradient of geophysical property values ​​around the seed point:

[0067] ,

[0068] in: The gradient value; These are geophysical attribute values; , , These are the partial derivatives of the attribute value in the x, y, and z directions, respectively.

[0069] If the average gradient value within the target region is lower than the first sharpness threshold (e.g.) This indicates that the region's internal attributes change gradually and its boundaries are clear, making it suitable for region growth segmentation.

[0070] The steps of the region growing algorithm are as follows:

[0071] 1. Initialization: Set the seed point Add to pending queue Mark the seed point as visited;

[0072] 2. Loop processing: from the queue Take a data point from Obtain its six neighboring data points (top, bottom, left, right, front, and back);

[0073] 3. Similarity determination: For each neighboring data point If it has not been accessed, calculate its attribute value. With seed point attribute value The difference: ;

[0074] 4. Condition extension: If (First similarity threshold, for example) This indicates that the neighboring data point and the seed point belong to the same geological body, and the neighboring data point is added to the queue. And mark it as visited;

[0075] Size control: Record the number of labeled data points. If the number exceeds the first size threshold (e.g., 10^6 data points), stop region growth to prevent the algorithm from expanding indefinitely.

[0076] 5. Boundary extraction: Repeat steps 2 to 5 until the queue is full. Empty. Ultimately, all data points marked as visited constitute the target geological body region, and the data points on the region boundary form the set of geological body boundary points.

[0077] Through the interactive segmentation algorithm described above, the system can automatically identify and extract the boundaries of geological bodies based on the seed points specified by the user, which greatly improves the efficiency and accuracy of boundary delineation.

[0078] See Figure 4 The uncertainty expression module is used to quantify the reliability of various parts of the geological model and visually display the distribution of uncertainty. During geological modeling, due to limited observational data, approximate interpolation methods, and subjective geological interpretation, the model inherently contains uncertainties. Ignoring uncertainty increases decision-making risk.

[0079] This invention employs a reliability assessment method based on data coverage. For each boundary point in the set of geological body boundary points, the system calculates its reliability coefficient. The reliability coefficient is calculated by considering two factors: the number of observation points around the boundary point and the spatial uniformity of the observation points.

[0080] Number of observation points This reflects the data density around the boundary points. A higher number of observation points indicates more sufficient data constraints in that region, and thus higher model reliability. For example, a first quantity threshold can be set. Second quantity threshold .

[0081] Spatial distribution uniformity of observation points This reflects whether the spatial distribution of observation points is uniform. If observation points are concentrated in one direction while data is lacking in other directions, the model will have higher uncertainty in the directions with insufficient data. Uniformity can be measured by calculating the variance of the observation point locations.

[0082] ,

[0083] in: For uniformity; The standard deviation of the observation point location; This is the maximum possible standard deviation (e.g., the standard deviation when all observations are concentrated at one point).

[0084] Uniformity The value ranges from 0 to 1, with values ​​closer to 1 indicating a more uniform distribution. For example, a first uniformity threshold is set. Second uniformity threshold .

[0085] Reliability coefficient The calculation formula is as follows:

[0086] ,

[0087] in: Reliability coefficient; Weighted by the number of observation points; The uniformity weight; The normalization function for the number of observation points; This is the normalization function for uniformity.

[0088] normalization function The definition is as follows:

[0089] like ,but ;

[0090] like ,but ;

[0091] like ,but .

[0092] normalization function The definition is as follows:

[0093] like ,but ;

[0094] like ,but ;

[0095] like ,but .

[0096] For example, take , Reliability coefficient The value ranges from 0 to 1, with the value closer to 1 indicating higher reliability.

[0097] If the reliability coefficient of the boundary points Below the first reliability threshold (e.g.) This indicates that the uncertainty at this point is relatively high. The system expresses the degree of uncertainty by adjusting the opacity and color coding values ​​of this point.

[0098] Specifically, the opacity value With reliability coefficient Inversely proportional:

[0099] ,

[0100] in: Due to uncertainty and lack of transparency; This represents the reliability coefficient.

[0101] The lower the reliability coefficient, the higher the opacity value, and the more transparent the boundary points are in the visualization model, intuitively indicating to the user that the model reliability of that area is low.

[0102] In addition, the system can also use color coding to express uncertainty. For example, a warm / cool color mapping can be used:

[0103] High reliability Warm colors (red, orange) indicate sufficient data and a reliable model;

[0104] Medium reliability Neutral tones (yellow, green);

[0105] Low reliability Cool colors (blue, purple) indicate sparse data and high model uncertainty.

[0106] By combining opacity and color coding, users can intuitively identify areas of high uncertainty in geological models, providing guidance for supplementary exploration work and avoiding major decisions in areas of low reliability.

[0107] See Figure 5 The multi-scale fusion module is used to integrate geological data at different spatial scales, achieving a seamless connection from regional tectonic frameworks to the fine structure of ore deposits. Geological phenomena exhibit significant scale effects; the regional-scale tectonic pattern controls the macroscopic distribution of ore deposits, while the fine structure within the ore deposit determines the grade and morphology of the ore body. Single-scale modeling struggles to simultaneously capture both macroscopic and microscopic information; multi-scale fusion enables the construction of more comprehensive geological models.

[0108] This invention employs a pyramid-style hierarchical fusion strategy. The pyramid model contains multiple levels, each corresponding to a different spatial resolution. The bottom level contains coarse-scale data with a large coverage area but low resolution; the top level contains fine-scale data with a small coverage area but high resolution; and the middle levels have a moderate spatial range and resolution.

[0109] For example, the regional structural framework data serves as the bottom layer L0, with a spatial resolution of 1000m×1000m and a coverage area of ​​100km×100km; the middle layer L1 has a spatial resolution of 100m×100m and a coverage area of ​​10km×10km; and the top layer L2 is the fine structure data of the ore deposit, with a spatial resolution of 10m×10m and a coverage area of ​​1km×1km.

[0110] The multi-scale fusion module first examines the spatial scale difference between data at different levels. The scale difference is defined as the ratio of the spatial resolution of adjacent levels:

[0111] ,

[0112] in: Scale ratio; Let i be the spatial resolution of the i-th layer; Let be the spatial resolution of the (i+1)th layer.

[0113] If scale ratio Exceeding a preset scale difference threshold (e.g.) This indicates that there are significant scale differences between adjacent levels, necessitating spatial registration and fusion.

[0114] Spatial registration unifies data from different levels into the same spatial coordinate system. For coarse-scale data, upsampling is used to increase its resolution to match that of fine-scale data; for fine-scale data, downsampling can be used to reduce its resolution to match that of coarse-scale data, or it can be directly embedded into the corresponding region of the coarse-scale data.

[0115] Upsampling methods include nearest neighbor interpolation, bilinear interpolation, and cubic spline interpolation. For example, using cubic spline interpolation for upsampling can improve resolution while maintaining data smoothness.

[0116] Registration error is assessed by calculating the differences in the overlapping areas of the registered data:

[0117] ,

[0118] in: Registration error; This represents the number of data points in the overlapping region. For the i-th layer at position The value; For the (i+1)th layer at position The value.

[0119] If the registration error Less than the first error threshold (e.g.) This indicates that the registration effect is good and that weighted fusion of multi-layer data can be performed.

[0120] The weighted fusion formula is as follows:

[0121] ,

[0122] in: In order to merge in position The value; The weight coefficients of the i-th layer; For the i-th layer at position The value; This represents the total number of levels in the pyramid.

[0123] Weighting coefficient The weight is determined based on the spatial resolution and data quality of each layer. Layers with higher spatial resolution and better data quality have larger weight coefficients. For example, the formula for calculating the weight coefficient is:

[0124] ,

[0125] in: The weight coefficients of the i-th layer; Let i be the spatial resolution of the i-th layer; Score the data quality of the i-th layer; It represents the reciprocal of the resolution; the higher the resolution, the larger the reciprocal.

[0126] Data quality score Taking into account factors such as signal-to-noise ratio, coverage density, and measurement accuracy, the value ranges from 0 to 1. For example, if the data quality score of a certain layer is lower than the first quality threshold (e.g., ...), ... This indicates that the data quality of this layer is poor, and the system will reduce the weight coefficient of this layer; if the reduced weight coefficient is less than the preset minimum weight value (e.g., ...), it indicates that the data quality of this layer is poor. If the data in that layer is removed from the fusion process, it will prevent low-quality data from negatively impacting the fusion results.

[0127] Through pyramid-layered fusion, the system can simultaneously present macroscopic tectonic patterns and local fine structures in the same model, generating cross-scale geological models to meet the application needs of different levels.

[0128] See Figure 6The attribute interpolation module is used to generate a continuous three-dimensional distribution field of physical property parameters based on limited observation point data, supporting resource estimation and development scheme design. In geological exploration practice, data obtained by direct observation methods such as boreholes and tunnels are often discrete and limited, while resource assessment and engineering design require an understanding of the distribution of physical property parameters across the entire area. Attribute interpolation can infer parameter values ​​at unobserved locations based on observation point data, constructing a continuous three-dimensional distribution model.

[0129] This invention employs the Kriging interpolation method. Kriging interpolation is an optimal linear unbiased estimation method based on geostatistics, capable of estimating parameter values ​​at unobserved locations while considering spatial correlation, and providing the estimation error.

[0130] The attribute interpolation module first obtains the observation point data set, which includes the spatial coordinates of each observation point. and physical property parameter values Then, calculate the spatial distribution density of the observation points:

[0131] ,

[0132] in: Spatial distribution density; This represents the number of observation points; Let V be the volume of the region to be modeled.

[0133] If spatial distribution density Below the first density threshold (e.g.) The number of observation points (per cubic meter) indicates that the observation points are relatively sparse, and spatial correlation information needs to be fully utilized during the interpolation process.

[0134] The core of Kriging interpolation is the semivariogram model. The semivariogram describes the relationship between the spatial variability of physical parameters and distance:

[0135] ,

[0136] in: The distance is The value of the semivariogram at time; The distance is The number of observation point pairs; For position The physical property parameters; Distance for The physical property parameters of the location.

[0137] Based on the empirical semivariogram, a theoretical semivariogram model is fitted. Commonly used theoretical models include the spherical model, the exponential model, and the Gaussian model. For example, a spherical model is used:

[0138] ,

[0139] in: The value is the semi-variogram value; This is known as the Nugget Effect. Sill value; For range; For distance.

[0140] nugget effect Reflects small-scale variability and measurement error; sill value Reflects the overall degree of variation; range This reflects the effective distance of spatial correlation; once the distance exceeds the range, the parameter values ​​are no longer correlated.

[0141] The parameters are obtained by fitting the theoretical semivariogram model using the least squares method. , and The optimal estimate. Goodness of fit. Used to evaluate the fit:

[0142] ,

[0143] in: For goodness of fit; It represents the distance series; The observed semivariogram value; This represents the semi-variogram value of the theoretical model; To observe the average value of the semivariogram.

[0144] If the goodness of fit Greater than the first fit threshold (e.g.) This indicates that the theoretical model can effectively describe the spatial variability of the data, and kriging interpolation can be performed based on this model.

[0145] For the position to be interpolated Kriging interpolation estimate A linear combination of the observed values:

[0146] ,

[0147] in: For position The interpolated estimate; The weight coefficient for the i-th observation point; Let be the physical property parameter value of the i-th observation point; This represents the total number of observation points.

[0148] Weighting coefficient By solving the Kriging equations, we obtain:

[0149] ,

[0150] ,

[0151] in: For observation point With observation point The semivariogram values ​​between; For observation point With observation point The distance between them; It is a Lagrange multiplier; For observation point The semivariogram value between the interpolation point and the interpolation point; For observation point The distance between the point to be interpolated and the point to be interpolated.

[0152] Solving the above system of equations yields the weighting coefficients. Then calculate the interpolation estimate. .

[0153] Kriging interpolation can also provide the estimation error (Kriging variance):

[0154] ,

[0155] in: Kriging variance; These are the weighting coefficients; The semivariogram value between the observation point and the point to be interpolated; It is a Lagrange multiplier.

[0156] Kriging variance reflects the uncertainty of interpolation estimation; the smaller the variance, the more reliable the estimation.

[0157] By performing kriging interpolation on all grid points within the modeling area, a three-dimensional distribution field of physical property parameters can be generated.

[0158] It should be noted that the interpolation results must meet physical reasonableness requirements. If the interpolated physical property parameters exceed the physical reasonable range (e.g., density less than 0 or porosity greater than 1), it indicates a problem with the interpolation process. In this case, the system will perform constrained interpolation processing on that point, introducing upper and lower bound constraints to ensure that the interpolation results conform to geological laws.

[0159] Furthermore, if there are regions of numerical discontinuity in the generated 3D distribution field (e.g., the difference in parameter values ​​between adjacent grid points exceeds a preset continuity threshold, such as...), This indicates that there may be geological faults or missing data in the area. The system will perform local re-interpolation on this area, using a higher sampling density or introducing geological constraints to improve interpolation accuracy.

[0160] The model building module is responsible for integrating the outputs of the aforementioned modules to generate a complete 3D geological model with reliability annotations. This module manages the 3D visualization model, the set of geological body boundary points, uncertainty information, cross-scale geological models, and 3D distribution fields in a unified manner, forming a comprehensive geological model database.

[0161] The model building module first establishes a unified spatial coordinate system to ensure that all data are within the same coordinate framework. Then, the area to be modeled is divided into a regular three-dimensional grid, with the grid resolution determined according to application requirements; an example is 10 meters × 10 meters × 10 meters.

[0162] For each grid cell (spatial cell), the model building module assigns an attribute vector containing the following information:

[0163] Geophysical properties: derived from a 3D visualization model, including seismic wave velocity, density, resistivity, etc.

[0164] Geological attributes: derived from the set of boundary points of geological bodies, identifying the type of geological body to which this unit belongs (stratigraphic unit, rock mass, ore body, etc.).

[0165] Uncertainty index: derived from the uncertainty expression module, quantifying the reliability of the unit model;

[0166] Data source identifier: Records the main source of data for this unit (direct observation, interpolation calculation, cross-scale fusion, etc.).

[0167] The model building module also supports model visualization. During visualization, if the uncertainty index of a spatial unit exceeds the second reliability threshold (e.g., uncertainty index...), ... The system will add a special marker to the cell (e.g., using a grid wireframe, flashing effect, or color warning) to alert the user that the model in that area has low reliability and should be used with caution.

[0168] In addition, the model building module provides model export functionality, supporting the export of geological models to various common formats, such as VTK (Visualization Toolkit) format, GOCAD format, and Petrel format, which facilitates interaction with other geological modeling software and numerical simulation software.

[0169] In one possible implementation, the system also includes a surface reconstruction module for extracting smooth boundary surfaces from the set of boundary points of the geological body. Boundary surfaces are the outer surfaces of the geological body in three-dimensional space and are of great significance for geological body volume calculation, resource estimation, and visualization.

[0170] The surface reconstruction module first checks the spatial sparsity of the boundary points. Sparsity is defined as the ratio of the average spacing between boundary points to the size of the geological body features:

[0171] ,

[0172] in: Sparsity; The average spacing between boundary points; The characteristic dimensions of a geological body (e.g., the maximum length or thickness of the geological body).

[0173] If sparsity Exceeding the first sparsity threshold (e.g.) This indicates that the boundary points are sparsely distributed, and directly connecting the boundary points may result in an uneven surface. In this case, the system uses the Delaunay triangulation algorithm to generate a triangular mesh surface.

[0174] Delaunay triangulation is a method for connecting discrete point sets into triangular meshes. It maximizes the minimum angle and can generate triangles with good shapes. For a set of boundary points in 3D space, the system first projects the boundary points onto a local tangent plane, performs Delaunay triangulation on the 2D plane, and then maps the triangle vertices back to 3D space to form a triangular mesh surface.

[0175] The generated triangular mesh needs to meet certain geometric quality standards. The system checks the minimum angle of each triangle. ,like Less than a preset angle threshold (e.g.) This indicates that the triangle is too flat, which affects the smoothness of the surface and the accuracy of calculations. The system optimizes and adjusts these low-quality triangles by using mesh optimization techniques such as edge flipping and vertex insertion to improve the quality of the triangles.

[0176] The optimized triangular mesh surface has good smoothness and geometric accuracy, and can accurately represent the spatial morphology of geological bodies.

[0177] The implementation process of the present invention will be further illustrated below through a specific embodiment.

[0178] A three-dimensional geological model needs to be built for a mining area to guide mine development. The mining area covers 5 square kilometers and has an exploration depth of 2000 meters. Existing data includes:

[0179] Seismic reflection data: covering the entire area, with a spatial sampling interval of 10 meters and a temporal sampling rate of 4 milliseconds;

[0180] Gravity data: 200 measurement points, with an average spacing of 100 meters;

[0181] Magnetic data: 150 measurement points, with an average spacing of 120 meters;

[0182] Drilling data: 30 boreholes were drilled to obtain physical property parameters such as lithology, density, and porosity.

[0183] First, the multi-source data acquisition module normalizes the seismic and gravity / magnetic data. The signal-to-noise ratio (SNR) of the seismic data is assessed at 4.5 dB, which is lower than the first SNR threshold of 5 dB. The system then uses fiber optic strain rate data acquired via DAS technology for enhancement processing. After enhancement, the SNR is increased to 7.8 dB, an increase of 3.3 dB, exceeding the preset enhancement threshold of 3 dB. The enhanced seismic data is then used as the seismic component of the standardized geophysical data.

[0184] Then, the volume rendering module generates a 3D data volume based on standardized geophysical data, with a spatial resolution of 10m × 10m × 10m. A ray casting algorithm is used for volume rendering, with the viewpoint set 2000 meters above the center of the mining area. The transfer function maps seismic wave impedance to color and opacity. The generated 3D visualization model clearly displays the spatial distribution and fault structures of the underground strata.

[0185] Geologists use an interactive segmentation module to specify seed points for ore body regions in a visualization model. The system then uses a region growing algorithm to automatically extract ore body boundaries, identifying three main ore bodies with a set of boundary points containing approximately 50,000 data points.

[0186] The uncertainty expression module calculates the reliability coefficient for each boundary point. There are 15 observation points around orebody 1, with a uniformity of 0.75, and the reliability coefficient is calculated as follows: The reliability is high; there are 5 observation points around ore body 2, with a uniformity of 0.45, and the reliability coefficient is calculated to be... If the opacity of the boundary of ore body 2 is below the first reliability threshold of 0.5, the system sets the opacity of the boundary of ore body 2 to 0.56 and colors it as a cool tone, which visually indicates that the ore body has a high degree of uncertainty.

[0187] The multi-scale fusion module integrates regional tectonic framework data (spatial resolution 1000 meters) and fine-structure data of ore deposits (spatial resolution 10 meters), with a scale ratio of 100, exceeding the preset scale difference threshold of 5. The system adopts a pyramid hierarchical fusion strategy, establishing a three-level pyramid model. Through upsampling and weighted fusion, a cross-scale geological model is generated, which can not only show the macroscopic pattern of regional fault structures controlling the distribution of ore deposits, but also present the fine structure of grade variations within the ore body.

[0188] The attribute interpolation module generates a three-dimensional density distribution field based on physical property parameter data from 30 boreholes using the Kriging interpolation method. The goodness of fit of the semivariogram is then evaluated. The interpolated density distribution field has a spatial resolution of 10 m × 10 m × 10 m, covering the entire area. During the interpolation process, the system found that the density difference between adjacent grid points in a certain area reached 0.8 g / cm³, exceeding the preset continuity threshold of 0.5. Local re-interpolation was then performed on this area, introducing geological fault constraints to improve the interpolation accuracy.

[0189] Finally, the model building module integrates all data to generate a complete 3D geological model containing geophysical attributes, geological attributes, uncertainty indicators, and data source identifiers. The model contains 5 × 10^7 spatial units, with each unit having an attribute vector length of 20. The model is exported in VTK format for subsequent resource estimation and mining design.

[0190] This embodiment successfully integrates multi-source geophysical data and borehole data through the geophysical 3D visualization and geological model construction system of the present invention, and constructs a high-precision, multi-scale, three-dimensional geological model with uncertainty annotation, providing a scientific basis for mine development and significantly improving the accuracy of resource evaluation and the reliability of decision-making.

[0191] 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 geophysical three-dimensional visualization and geological model construction system, characterized in that, include: The multi-source data acquisition module is used to collect seismic data, gravity and magnetic data, and electromagnetic data of the area to be modeled. If the collected data types conform to the preset data type set, the data is normalized to obtain standardized geophysical data. The volume rendering module is used to generate a three-dimensional data volume based on the standardized geophysical data. If the data density in the three-dimensional data volume meets the first density threshold range, the volume rendering module uses a ray casting algorithm to render the three-dimensional data volume and obtain a three-dimensional visualization model with opacity information. When using the ray casting algorithm, the volume rendering module sets multiple viewpoint positions and emits rays from each viewpoint position to the three-dimensional data volume. If the value of the data voxel that the ray passes through meets the first value range, the color value and opacity value corresponding to the data voxel are accumulated. If the accumulated opacity reaches the first opacity threshold, the subsequent sampling of the ray is terminated, and a two-dimensional projection image corresponding to the viewpoint is generated. An interactive segmentation module is used to receive user interaction commands for the 3D visualization model. If the boundary clarity of the target area indicated by the interaction command meets the first clarity threshold, a region growing algorithm is used to extract the set of geological body boundary points from the target area. An uncertainty expression module is used to calculate the reliability coefficient of each boundary point in the set of boundary points of the geological body. If the reliability coefficient is lower than the first reliability threshold, the degree of uncertainty is expressed by adjusting the opacity value and color coding value of the corresponding boundary point. When calculating the reliability coefficient, the uncertainty expression module obtains the number and distribution uniformity of observation points around the boundary point. If the number of observation points is greater than the first number threshold and the distribution uniformity is greater than the first uniformity threshold, the reliability coefficient is set to a high value. If the number of observation points is less than the second number threshold or the distribution uniformity is less than the second uniformity threshold, the reliability coefficient is set to a low value. The multi-scale fusion module is used to acquire regional tectonic framework data and ore deposit fine structure data. If the difference between the spatial scale of the regional tectonic framework data and the spatial scale of the ore deposit fine structure data exceeds the preset scale difference threshold, a pyramid hierarchical fusion strategy is adopted to fuse data of different scales to generate a cross-scale geological model. The attribute interpolation module is used to determine the spatial distribution density of observation points based on the observation point data set. If the spatial distribution density is lower than a first density threshold, the Kriging interpolation method is used to generate a three-dimensional distribution field of physical property parameters. If there are numerical discontinuities in the generated three-dimensional distribution field and the degree of discontinuity exceeds a preset continuity threshold, local re-interpolation processing is performed on the region. When using the Kriging interpolation method, the attribute interpolation module establishes a semi-variogram model. If the goodness of fit of the semi-variogram is greater than a first goodness of fit threshold, the physical property parameter values ​​of the point to be interpolated are calculated based on the semi-variogram model. If the calculated physical property parameter values ​​exceed the physically reasonable range, constrained interpolation processing is performed on the point to ensure that the interpolation results conform to geological laws. The model building module is used to integrate the three-dimensional visualization model, the set of geological body boundary points, the degree of uncertainty, the cross-scale geological model and the three-dimensional distribution field to generate a complete three-dimensional geological model with reliability annotations. The surface reconstruction module is used to extract the boundary surface from the set of boundary points of the geological body. If the spatial distribution sparsity of the boundary points exceeds the first sparsity threshold, the Delaunay triangulation algorithm is used to generate a triangular mesh surface. If the minimum angle of the triangular mesh is less than the preset angle threshold, the mesh is optimized and adjusted to ensure the smoothness and geometric accuracy of the surface.

2. The geophysical three-dimensional visualization and geological model construction system according to claim 1, characterized in that: When acquiring seismic data, if the signal-to-noise ratio (SNR) of the seismic data is lower than the first SNR threshold, the multi-source data acquisition module uses distributed acoustic sensing technology to enhance the seismic data, replacing the traditional geophone data with fiber optic strain rate data. If the enhancement of the SNR exceeds the preset enhancement threshold, the enhanced seismic data is used as the seismic component of the standardized geophysical data.

3. The geophysical three-dimensional visualization and geological model construction system according to claim 1, characterized in that: After receiving the interaction command, the interactive segmentation module determines the location of the seed point indicated by the interaction command. Starting from the seed point, if the difference between the geophysical attribute value of the adjacent data point and the attribute value of the seed point is less than the first similarity threshold, the adjacent data points are added to the same geological body region. If the number of added data points exceeds the first scale threshold, the region growth is stopped and the set of geological body boundary points is output.

4. The geophysical three-dimensional visualization and geological model construction system according to claim 1, characterized in that: When the multi-scale fusion module adopts the pyramid hierarchical fusion strategy, it uses regional tectonic framework data as the bottom coarse-scale data and ore deposit fine structure data as the top fine-scale data. If the ratio of spatial resolution of adjacent levels meets the first ratio range, spatial registration between levels is achieved through upsampling or downsampling operations. If the registration error is less than the first error threshold, weighted fusion of multi-level data is performed.

5. The geophysical three-dimensional visualization and geological model construction system according to claim 4, characterized in that: When performing weighted fusion of multi-layer data, the multi-scale fusion module determines the weight coefficient based on the spatial resolution and data quality of each layer. If the data quality score of a certain layer is lower than the first quality threshold, the weight coefficient of that layer is reduced. If the reduced weight coefficient is less than the preset minimum weight value, the data of that layer is removed from the fusion process.

6. The geophysical three-dimensional visualization and geological model construction system according to claim 1, characterized in that: When generating a complete 3D geological model, the model building module assigns an attribute vector to each spatial unit in the model, which includes geophysical attributes, geological attributes, uncertainty indicators, and data source identifiers. If the uncertainty indicator of a spatial unit is higher than the second reliability threshold, a special mark is added to the unit when the model is visualized to prompt the user that the model reliability of the area is low.

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