Geological abnormal data three-dimensional rendering dynamic special effect making method

Abstract

The invention discloses a geological abnormal data three-dimensional rendering dynamic special effect making method, and relates to the technical field of geological data visualization. The method comprises the following steps: collecting regional geological survey sample data and carrying out structured arrangement; exploratory analysis and distribution transformation are carried out on the data; performing dimension reduction processing through principal component analysis; generating gridding scalar field data by adopting Kriging interpolation or radial basis function interpolation; separating a background value and an abnormal value and extracting a contour line; and generating a dynamic three-dimensional rendering special effect graph by combining gradient legend rendering, a multi-sequence three-dimensional shadow graph and an image fusion technology, and exporting the dynamic three-dimensional rendering special effect graph in a GIF or mp4 format. Through multi-source data integration, an intelligent algorithm and three-dimensional dynamic rendering, the problems that traditional two-dimensional display is not visual and the dynamic effect is insufficient are solved, and the visual expression and analysis efficiency of geological abnormal data is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data three-dimensional rendering dynamic special effect production, and particularly to a method for producing a three-dimensional rendering dynamic special effect of geological anomaly data. Background Art

[0002] In geological exploration and research, the processing and analysis of geological anomaly data are crucial. Traditional methods for representing geological data are often limited to two-dimensional planar graphics, making it difficult to intuitively display the distribution and characteristics of geological anomalies in three-dimensional space. With the development of computer graphics and visualization technologies, the three-dimensional rendering of geological data has become possible. However, when existing technologies handle the three-dimensional rendering of geological anomaly data, there are still problems such as insufficiently realistic rendering effects and inadequate expression of dynamic special effects. Therefore, a method for producing a three-dimensional rendering dynamic special effect of geological anomaly data is proposed to solve the above problems. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a method for producing a three-dimensional rendering dynamic special effect of geological anomaly data to solve at least the above problems.

[0004] The technical solution adopted by the present invention is as follows:

[0005] A method for producing a three-dimensional rendering dynamic special effect of geological anomaly data includes the following steps:

[0006] S1. Collect regionalized geological survey sample data, including geochemical survey, natural heavy sand survey, and geophysical survey data;

[0007] S2. Structurally organize the sample data;

[0008] S3. Conduct exploratory analysis on the organized data;

[0009] S4. According to the results of the exploratory analysis, perform transformation processing on non-normally distributed data;

[0010] S5. Perform principal component analysis on the transformed data, calculate the covariance matrix, eigenvalues, and eigenvectors, and determine the number of principal components and scores;

[0011] S6. Generate scalar field data based on the results of the principal component analysis;

[0012] S7. Separate the background values and anomaly values in the gridded data and identify the anomaly regions;

[0013] S8. Generate an isogram according to the anomaly lower limit value, and extract the isogram using the grid sequence method or the Marching Cubes algorithm;

[0014] S9. Render a gradient legend for the gridded data to generate a three-dimensional shaded map of a multi-series scalar field, overlay the contour map and the anomaly lower limit contour map, and generate a three-dimensional rendered dynamic special effect map through image fusion;

[0015] S10. Export the dynamic special effect map in GIF or mp4 format.

[0016] Further, in step S1:

[0017] The geochemical survey includes data on soil, rock, stream sediment, plant, ecology, gas, and nano-gold related elements;

[0018] The natural heavy mineral survey includes data on precious metals, non-ferrous metals, rare, and radioactive elements;

[0019] The geophysical survey includes data on gravity measurement, airborne magnetic measurement, and ground magnetic method measurement.

[0020] Further, the data structuring and sorting in step S2 specifically includes:

[0021] Eliminate duplicate and invalid data;

[0022] Design the table structure and header fields according to the data type;

[0023] Enter the data into the corresponding cells and perform cross-validation.

[0024] Further, the exploratory analysis of the sorted data in step S3 is specifically as follows:

[0025] By calculating the mean, variance, and range, draw a histogram or kernel density plot;

[0026] Visualize and display the data spatial distribution characteristics through line charts, box plots, or maps.

[0027] Further, according to the exploratory analysis results in step S4, the transformation processing of non-normal distribution data is specifically as follows:

[0028] Check whether the data in the exploratory analysis results is normally distributed. If the data is skewed, select and perform one or more transformation processes to transform the data into a normal distribution; the transformation processes include, but are not limited to, Box-Cox transformation, arcsine transformation, logarithmic transformation, square root transformation, reciprocal transformation, standardization transformation, range standardization transformation, central tendency transformation, polynomial transformation, exponential transformation; among them, the selection of the transformation process is based on the exploration of the distribution effects of different transformations on the data set through histograms; if the constructed interpolation model selects the transformed data, when generating the interpolation surface, transform the predicted values back to the original scale.

[0029] Further, in step S5, principal component analysis is performed on the transformed data to calculate the covariance matrix or correlation matrix, eigenvalues, and eigenvectors, and to determine the number of principal components and scores specifically as follows:

[0030] Calculate the covariance matrix: For the standardized data matrix X in the transformation result, calculate its covariance matrix C. The elements C of the covariance matrix ij The calculation formula is:

[0031]

[0032] where n is the number of samples, are the i-th and j-th standardized variables of the k-th sample respectively;

[0033] Calculate the correlation matrix: Calculate the correlation matrix R to replace the covariance matrix. The elements r of the correlation matrix ij , and the calculation formula is:

[0034]

[0035] The correlation matrix focuses on reflecting the degree of linear correlation between variables, and its value range is between -1 and 1. The closer the absolute value is to 1, the stronger the correlation;

[0036] Eigenvalue solution: Perform eigenvalue decomposition on the covariance matrix C or the correlation matrix R to obtain a series of eigenvalues λ1, λ2..., λ p , where p is the number of variables. The size of the eigenvalue reflects the amount of information contained in the corresponding principal component. The larger the eigenvalue, the more original data information the principal component carries;

[0037] Eigenvector solution: For each eigenvalue λ i , there is a corresponding eigenvector e i . By using matrix operations in linear algebra, the eigenvectors corresponding to all eigenvalues are obtained. The eigenvectors will be used to construct the expression of the principal component, indicating the weight distribution of each original variable in the corresponding principal component;

[0038] Cumulative contribution rate calculation: Calculate the proportion of each eigenvalue in the sum of all eigenvalues, that is, the contribution rate

[0039] And accumulate them in turn to obtain the cumulative contribution rate Table k represents the number of selected principal components;

[0040] Scree plot for auxiliary judgment: Draw a scree plot, that is, a line graph with the eigenvalue number on the abscissa and the eigenvalue size on the ordinate, to observe the trend of the line;

[0041] Calculate the principal component scores: According to the determined number of principal components and their corresponding eigenvectors, calculate the principal component scores of each sample. Let the principal component score vector of the \(i\)th sample be \(F\). i = F i1 , F i2 ,..., F ik , and its calculation formula is: where \(j = 1, 2,\cdots, k\) represents the principal component serial number, \(e\) jl is the \(l\)th element of the eigenvector corresponding to the \(j\)th principal component, is the \(l\)th standardized variable of the \(i\)th sample.

[0042] Furthermore, in step S6, based on the principal component analysis results, generating scalar field data specifically includes:

[0043] Grid interpolation is performed on the key index geological data in the principal component analysis results to form geological grid scalar field data. The interpolation methods include nearest neighbor interpolation method, inverse distance weighted interpolation method, Kriging interpolation method, spline interpolation method, radial basis function interpolation method, triangulation linear interpolation method, polynomial regression interpolation method, moving average interpolation method, fractal interpolation method, and bilinear interpolation method.

[0044] Furthermore, in step S7, separating the background value and anomaly value in the grid data and identifying the anomaly area specifically includes:

[0045] Determine the geological background value estimation method: Estimate the geological background value for the data generated after grid interpolation by using the average value method, or median method, or mode method, or robust statistics method;

[0046] Background value calculation and determination: Perform calculation operations on the sorted data according to the selected background value estimation method;

[0047] Anomaly value identification and separation: Verify the rationality of the calculated background value, identify abnormal data points or areas, and judge the data one by one according to the set anomaly determination criteria. Identify the data points or areas that belong to the abnormal situation, separate the background and abnormal data, separate the identified abnormal data from the original data set to form separate abnormal data, and at the same time retain the background data to form binary grid data with 0 and 1 retained.

[0048] Furthermore, the gradient legend rendering in step S9 includes:

[0049] Select a single - tone, multi - tone or divergent color mapping scheme;

[0050] Adjust the color gradient range, nodes and transparency parameters.

[0051] Further, the generation of the three-dimensional shadow map in step S9 includes:

[0052] Set the horizontal angle and vertical angle of the point light source, and generate multi-sequence shadow maps according to the formula;

[0053] Replace the luminance channel through the HSV color space, and fuse the rendering map and the three-dimensional shadow data.

[0054] Compared with the prior art, the beneficial effects of the present invention are:

[0055] The present invention proposes a method for making three-dimensional rendering dynamic special effects of geological anomaly data,

[0056] 1. Three-dimensional and dynamic display: Through the generation of multi-sequence scalar field three-dimensional shadow maps and image fusion technology, the geological data is extended from a two-dimensional plane to three-dimensional dynamic rendering, intuitively presenting the spatial distribution and change trend of geological anomalies, and significantly enhancing the immersion and accuracy of data interpretation.

[0057] 2. Algorithm optimization to improve accuracy: Combining principal component analysis, various interpolation algorithms (such as Kriging interpolation, radial basis function interpolation) and fractal trend surface method, effectively reducing data redundancy, improving the accuracy of background separation and anomaly recognition, and reducing human error.

[0058] 3. High-efficiency data processing ability: Through data structured arrangement, exploratory analysis and distribution transformation (such as Box-Cox transformation, standardization transformation), realize the rapid correction of non-normal data, ensure the reliability of subsequent analysis; automated contour extraction (Marching Cubes algorithm) and gradient rendering technology, greatly shortening the data processing cycle.

[0059] 4. Multi-dimensional visualization support: Provide single-tone, multi-tone and divergent color mapping schemes, combined with HSV color space fusion technology, to achieve high-contrast and multi-level visualization effects, meeting the diverse needs of complex geological scenarios.

[0060] 5. Convenience of result output: Support the export of dynamic images in GIF and mp4 formats, which is convenient for cross-platform sharing and display, and provides an efficient tool for geological scientific research, mineral exploration and environmental assessment. Description of the Drawings

[0061] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only the preferred embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0062] Figure 1It is a schematic diagram of the overall process of a method for making three-dimensional rendering dynamic special effects of geological anomaly data proposed in an embodiment of the present invention. Specific embodiments

[0063] The principles and features of the present invention will be described below with reference to the accompanying drawings. The listed embodiments are only used to explain the present invention and are not intended to limit the scope of the present invention.

[0064] Refer to Figure 1 , a method for making three-dimensional rendering dynamic special effects of geological anomaly data, including the following steps:

[0065] S1. Collect regional geological survey sample data, including geochemical survey, natural heavy sand survey and geophysical survey data;

[0066] S2. Structurally organize the sample data;

[0067] S3. Conduct exploratory analysis on the organized data;

[0068] S4. According to the results of exploratory analysis, perform transformation processing on non-normal distribution data;

[0069] S5. Perform principal component analysis on the transformed data, calculate the covariance matrix, eigenvalues and eigenvectors, and determine the number and scores of principal components;

[0070] S6. Generate scalar field data based on the results of principal component analysis;

[0071] S7. Separate the background values and anomaly values in the gridded data and identify the anomaly areas;

[0072] S8. Generate contour maps according to the anomaly lower limit values, and extract contours using the grid sequence method or the Marching Cubes algorithm;

[0073] S9. Perform gradient legend rendering on the gridded data, generate a multi-sequence scalar field three-dimensional shadow map, overlay the contour map and the anomaly lower limit contour map, and generate a three-dimensional rendering dynamic special effect map through image fusion;

[0074] S10. Export the dynamic special effect map in GIF or mp4 format.

[0075] In step S1:

[0076] The geochemical survey includes data related to soil, rock, stream sediment, plant, ecology, gas and nano-gold related elements;

[0077] The natural heavy sand survey includes data on precious metals, non-ferrous metals, rare and radioactive elements;

[0078] The geophysical survey includes gravity measurement, aeromagnetic measurement and ground magnetic method measurement data.

[0079] The data structured arrangement described in step S2 specifically includes:

[0080] Eliminating duplicate and invalid data;

[0081] Designing the table structure and header fields according to the data type;

[0082] Entering the data into the corresponding cells and performing cross-verification.

[0083] Exemplarily, screening the data of geochemical survey, natural heavy sand survey and geophysical survey in the sampling data of regionalized geological survey samples, and eliminating the duplicate-recorded data and invalid data;

[0084] Planning the table structure and header design according to the type of sampling data and the subsequent usage purpose;

[0085] Sequentially entering the sorted data into the corresponding cells of the designed table;

[0086] Data review and verification: Verifying the data in the sorted data table with the originally collected sampling data materials.

[0087] The exploratory analysis of the sorted data in step S3 is specifically as follows:

[0088] Calculating the mean, variance and range, and drawing a histogram or kernel density plot;

[0089] Visualizing and displaying the data spatial distribution characteristics through line charts, box plots or maps.

[0090] Exemplarily, performing univariate descriptive statistical analysis, calculating the central tendency indicators such as the mean, median and mode of each geological variable, and the dispersion degree indicators such as variance, standard deviation and range, to understand the central position and dispersion degree of the variable data; observing the data distribution pattern of the variable by drawing histograms and kernel density graphs; performing visual display and comprehensive analysis, selecting visualization means such as line charts, box plots and maps according to the data characteristics and analysis purposes to visually present the data, and combining the results of univariate descriptive statistics and visual display to comprehensively analyze the geological data, sort out the main characteristics of the data, the key relationships between variables and the existing abnormal situations, and interpret them based on geological professional knowledge to display the distribution characteristics of geological data in the geographical space and obtain the exploratory analysis results.

[0091] According to the exploratory analysis results in step S4, the transformation processing of non-normal distribution data is specifically as follows:

[0092] Check whether the data in the exploratory analysis results is normally distributed. If the data is skewed, select and perform one or more transformation processes to transform the data into a normal distribution; the transformation processes include, but are not limited to, Box-Cox transformation, arcsine transformation, logarithmic transformation, square root transformation, reciprocal transformation, standardization transformation, range standardization transformation, central tendency transformation, polynomial transformation, exponential transformation; among them, the selection of the transformation process is based on the exploration of the distribution effect of different transformations on the data set by the histogram; if the constructed interpolation model selects the transformed data, when generating the interpolation surface, transform the predicted value back to the original scale.

[0093] In step S5, perform principal component analysis on the transformed data, calculate the covariance matrix or correlation matrix, eigenvalues, and eigenvectors, and determine the number of principal components and scores specifically as follows:

[0094] Calculate the covariance matrix: For the standardized data matrix X in the transformation result, calculate its covariance matrix C, and the elements C of the covariance matrix ij The calculation formula is:

[0095]

[0096] where n is the number of samples, are the i-th and j-th standardized variables of the k-th sample respectively;

[0097] Calculate the correlation matrix: Calculate the correlation matrix R to replace the covariance matrix, and the elements r of the correlation matrix ij , and the calculation formula is:

[0098]

[0099] The correlation matrix focuses on reflecting the linear correlation degree between variables, and its value range is between -1 and 1. The closer the absolute value is to 1, the stronger the correlation;

[0100] Eigenvalue solution: Perform eigenvalue decomposition on the covariance matrix C or the correlation matrix R to obtain a series of eigenvalues λ1, λ2..., λ p , p is the number of variables, and the size of the eigenvalue reflects the amount of information contained in the corresponding principal component. The larger the eigenvalue, the more original data information the principal component carries;

[0101] Eigenvector solution: For each eigenvalue λ i , there is a corresponding eigenvector e i , and all eigenvectors corresponding to the eigenvalues are obtained by using matrix operations in linear algebra. The eigenvectors will be used to construct the expression of the principal component, indicating the weight distribution of each original variable in the corresponding principal component;

[0102] Cumulative contribution rate calculation: Calculate the proportion of each eigenvalue to the sum of all eigenvalues, that is, the contribution rate

[0103] And accumulate them in sequence to obtain the cumulative contribution rate Table k represents the number of principal components selected ;

[0104] Scree plot for auxiliary judgment: Draw a scree plot, that is, a line chart with the eigenvalue number on the abscissa and the eigenvalue size on the ordinate, to observe the trend of the line;

[0105] Calculate the principal component scores: According to the determined number of principal components and their corresponding eigenvectors, calculate the principal component scores of each sample. Let the principal component score vector of the i-th sample be F i =F i1 ,F i2 ,...,F ik , and its calculation formula is: where j = 1, 2..., k represents the principal component number, and e jl is the l-th element of the eigenvector corresponding to the j-th principal component, is the l-th standardized variable of the i-th sample.

[0106] In step S6, based on the principal component analysis results, generating scalar field data specifically is:

[0107] Grid the key index geological data in the principal component analysis results for interpolation to form geological gridded scalar field data. The interpolation methods include nearest neighbor interpolation method, inverse distance weighted interpolation method, Kriging interpolation method, spline interpolation method, radial basis function interpolation method, triangulation linear interpolation method, polynomial regression interpolation method, moving average interpolation method, fractal interpolation method, bilinear interpolation method.

[0108] In step S7, separating the background value and anomaly value in the gridded data and identifying the anomaly area specifically is:

[0109] Determine the geological background value estimation method: Estimate the geological background value for the data generated after gridding interpolation by using the average method, or median method, or mode method, or robust statistics method;

[0110] Background value calculation and determination: Perform calculation operations on the sorted data according to the selected background value estimation method;

[0111] Outlier identification and separation: Verify the rationality of the calculated background value, identify abnormal data points or regions, and judge the data one by one according to the set abnormal judgment criteria. Identify that the data points or regions are abnormal, separate the background and abnormal data, separate the identified abnormal data from the original data set to form separate abnormal data, and at the same time retain the background data to form a binary grid data with 0 and 1 retained.

[0112] In step S8, generate an isogram according to the abnormal lower limit value. The specific method of extracting the isogram using the grid sequence method or the Marching Cubes algorithm is as follows:

[0113] Set the value of the isogram: Using the background of the grid data and the abnormal lower limit value extracted by the abnormal separation algorithm as input, perform the isogram extraction operation;

[0114] Use the isogram extraction algorithm: According to the complexity of the grid data abnormality, the accuracy requirements and calculation efficiency of extracting the isogram by using the isogram extraction algorithm. The isogram extraction algorithm includes: the grid sequence method, the active contour model method, or the Marching Cubes algorithm;

[0115] Execute the isogram extraction operation: Use the selected algorithm and set parameters to perform the isogram extraction operation on the prepared grid data. Automatically judge the intersection of data points, grid cells, etc. with the given isogram value through the program according to the algorithm flow, calculate the intersection coordinates, connect the line segments, and gradually construct a complete isogram.

[0116] The gradient legend rendering described in step S9 includes:

[0117] Select a single-tone, multi-tone or divergent color mapping scheme;

[0118] Adjust the color gradient range, nodes and transparency parameters.

[0119] The generation of the three-dimensional shadow map described in step S9 includes:

[0120] Set the horizontal angle and vertical angle of the point light source, and generate a multi-sequence shadow map according to the formula;

[0121] Replace the brightness channel through the HSV color space, and fuse the rendering map and the three-dimensional shadow data.

[0122] Exemplarily, according to the characteristics of the gridded data and the requirements of visualization, color mapping types are carried out, where the color mapping types include single-tone gradient mapping, multi-tone gradient mapping, divergent color mapping, and other contrast color mappings; select stretching to linearly or non-linearly map the numerical range of the data into the color gradient range or divide the data into several intervals, or use the classified gradient rendering method to correspond each interval to a color; apply the color gradient: apply the determined color gradient scheme to the rendering of the gridded data, so that the gridded data shows corresponding colors according to the magnitude of the numerical value; adjust the rendering parameters: adjust the transparency, brightness, and contrast parameters of the rendering to optimize the rendering effect;

[0123] Specifically for generating a multi-sequence scalar field stereo shadow map:

[0124] By specifying information such as the height of the simulated point light source for each grid cell in the geological anomaly scalar field data, the assumed brightness value of the surface is calculated. By setting the position of the simulated point light source and calculating the brightness value of each grid cell related to adjacent grid cells, the assumed brightness can be obtained. The generated shadows and light are gray gradients (changing from black to white) related to integers between 0 and 255. When displaying the rendered image, especially when using transparency, "stereo shadow" can greatly enhance the visualization effect of the surface.

[0125] Before calculating the stereo shadow of the geological anomaly scalar field data, the position of the simulated point light source in space needs to be specified, and this position can be described by two parameters: the horizontal angle and the vertical angle of the point light source. The horizontal angle (Horizontal) of the point light source refers to the angular direction of the simulated point light source, which is measured clockwise in the range of 0 degrees to 360 degrees with north as the reference direction. A horizontal angle of 90 degrees is east, and the initial horizontal angle is set to 0 degrees. The vertical angle (Altitude) of the point light source refers to the angle or inclination angle of the point light source above the horizontal plane, and the unit of height is degrees, with a range of 0 to 90 degrees. 0 degrees is on the horizontal plane, and 90 degrees is in the direction perpendicular to the horizontal plane. The initial vertical angle is set to 45 degrees.

[0126] The calculation formula for the stereo shadow of the geological anomaly scalar field:

[0127] GeoAnomaly3DShader = 255.0 * ((cos(Vertical) * cos(Pitch)) +

[0128] (sin(Vertical) * sin(Pitch) * cos(Horizontal - Orientation)))

[0129] In the formula, Vertical represents the zenith angle, Horizontal represents the horizontal angle, Pitch and Orientation represent the tilt angle and tilt azimuth respectively. All the angular vertical angles are in radians.

[0130] Calculate the incident angle of the point light source, that is, the zenith angle: The vertical angle of the specified point light source must be greater than the angle of the horizontal plane (that is, 0 degrees). However, the formula for the stereoscopic shadow value of the geological anomaly scalar field requires the angle to be expressed in radians and the angle deflected from the vertical direction. Set the direction perpendicular to the horizontal plane as the zenith. The zenith angle of the point light source is the angle between the zenith point and the direction to the point light source, that is, the complementary angle of the vertical angle of the point light source (that is, 90 degrees minus the solar altitude angle). The first step in calculating the incident angle of the point light source is to convert the vertical angle of the point light source to the zenith angle of the point light source. The second step is to convert the zenith angle of the point light source to radians.

[0131] Convert the vertical angle of the point light source to the zenith angle: Vertical_deg = 90 – Altitude;

[0132] Convert to radians: Vertical_rad = Vertical * pi / 180.0.

[0133] Calculate the horizontal angle of the point light source: The stereoscopic shadow formula requires the horizontal angle to be in radians. First, convert the zenith angle from geographical units (compass direction) to mathematical units (right angle). Then convert the horizontal angle to radians.

[0134] Method for converting the horizontal angle of the point light source: Horizontal_math = 360.0 - Horizontal + 90;

[0135] If, Horizontal_math >= 360.0; Horizontal_math = Horizontal_math - 360.0;

[0136] Convert to radians: Horizontal_rad = Horizontal_math * pi / 180.0;

[0137] Calculate the tilt angle:

[0138] The tilt angle (Pitch) is the degree of steepness of the grid cell value of the geological anomaly scalar field, and the tilt angle is expressed in degrees. It is calculated using the inverse trigonometric function, and the formula is as follows: tanα (Pitch) = grid value difference / horizontal distance, so α (Pitch) = arctan (grid value difference / horizontal distance).

[0139] Calculate the tilt azimuth:

[0140] The dip orientation refers to the orientation of the slope of the geological anomaly scalar field. The dip orientation is used to identify the downhill direction with the largest rate of change of values in the direction from each grid cell to its adjacent grid cell. The dip orientation can be regarded as the direction of the dip angle. The dip orientation is an angle, which is measured in the clockwise direction, and the angle range is between 0 (due east) and 360 (still due east).

[0141] The dip angle calculation generally uses the fitting surface method. The fitting surface generally uses a quadratic surface, that is, a window with a size of 3×3 grid cells is taken on the grid data of the geological anomaly scalar field. The grid cells are counted in the order from left to right and then from top to bottom, and are denoted as cell5, cell2, cell6, cell 1, cell, cell3, cell8, cell4, cell7 in turn. The center of each window is a grid cell point cell. The calculation formulas for the dip angle and dip orientation of the center point cell are as follows:

[0142] Pitch = tan;

[0143] Orientation = Pitchsn / Pitchwe;

[0144] In the above formula: Pitch is the dip angle, Orientation is the dip orientation, Pitchwe is the dip angle in the X direction, and Pitchsn is the dip angle in the Y direction.

[0145] For the calculation of Pitchwe and Pitchsn, the following several common methods can be used:

[0146] Pitchwe = (cell8 + 2 * cell 1 + cell5) - (cell7 + 2 * cell3 + cell6) / 8 * cellsize;

[0147] Pitchsn = (cell7 + 2 * cell4 + cell8) - (cell6 + 2 * cell2 + cell5) / 8 * cellsize;

[0148] The cellsize in the above formula is the length of the grid size of the geological anomaly scalar field.

[0149] Slide and traverse the grid of the geological anomaly scalar field in turn according to the above formula for calculating the stereoscopic shadow value of each grid cell in a 3*3 window from left to right and then from top to bottom.

[0150] Generated by specifying two parameters, namely the horizontal angle and the vertical angle of the point light source. By modifying the input value of the horizontal angle of the point light source, a series of three-dimensional shadow maps are generated. Assuming the number parameter of the series is n, according to the formula Horizontal = i * 360 / n, where i = 0, 1, 2, …, n - 1, n three-dimensional shadow maps are generated;

[0151] For image fusion to generate a three-dimensional rendering dynamic special effect map, and the export of the three-dimensional rendering dynamic special effect map is specifically as follows: Use the gridded data gradient map as the first layer, superimpose the gridded data contour map as the second layer, and then superimpose the gridded data lower anomaly limit contour map as the third layer. The three layers are combined into a rendering map, and the rendering map is stored in memory as RGBA four-channel image data;

[0152] For the RGBA four-channel rendering map in memory, set the transparency to 80%. Transform the rendering map from the RGB color space to the HSV color space. Replace the V-band data in the rendering map in the HSV color space with the three-dimensional shadow map data of the scalar field data. Transform the rendering map from the HSV color space to the RGB color space. Fuse the multi-sequence rendering maps with the three-dimensional maps to generate n three-dimensional rendering dynamic special effect maps, and output the n three-dimensional rendering dynamic special effect maps in the GIF image format or the mp4 video format.

[0153] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for producing dynamic special effects of three-dimensional rendering of geological anomaly data, characterized in that: The following steps are involved: S1. Collect regional geological survey sample data, including geochemical survey, natural heavy sand survey and geophysical survey data; S2, structuring the sample data; S3, conduct exploratory analysis on the collated data; S4. According to the results of exploratory analysis, transform the non-normal distribution data; S5. Perform principal component analysis on the transformed data, calculate the covariance matrix, eigenvalues ​​and eigenvectors, and determine the number and score of principal components; S6. Generate scalar field data based on the principal component analysis results; S7, separating background values ​​and abnormal values ​​in the gridded data and identifying abnormal areas; S8. Generate a contour map based on the lower limit of the anomaly and extract the contour lines using the grid sequence method or the Marching Cubes algorithm; S9, performing gradient legend rendering on the gridded data, generating a multi-series scalar field stereoscopic shadow map, superimposing the contour map and the anomaly lower limit contour map, and generating a stereoscopic rendering dynamic special effects map through image fusion; S10, exporting the dynamic special effects image into GIF or mp4 format.

2. The method for producing dynamic special effects of geological anomaly data three-dimensional rendering according to claim 1, characterized in that: In step S1: The geochemical survey includes soil, rock, stream sediment, plant, ecology, gas and nano-gold related element data; The natural heavy sand survey includes data on precious metals, non-ferrous metals, rare and radioactive elements; The geophysical survey includes gravity survey, aeromagnetic survey and ground magnetic survey data.

3. The method for producing dynamic special effects of geological anomaly data three-dimensional rendering according to claim 2, characterized in that: The data structuring in step S2 specifically includes: Eliminate duplicate and invalid data; Design table structure and header fields according to data type; Enter the data into the corresponding cells and perform cross-validation.

4. The method for producing dynamic special effects of geological anomaly data three-dimensional rendering according to claim 3 is characterized in that: The exploratory analysis of the sorted data in step S3 is specifically as follows: Draw a histogram or kernel density plot by calculating the mean, variance, and range; Visualize the spatial distribution characteristics of data through line charts, box plots or maps.

5. A method for producing dynamic special effects of three-dimensional rendering of geological anomaly data according to claim 4, characterized in that: In step S4, according to the exploratory analysis results, the transformation processing of the non-normal distribution data is specifically as follows: Check whether the data in the exploratory analysis results are normally distributed. If the data is skewed, select and perform one or more transformation processes to transform the data into a normal distribution; the transformation processes include but are not limited to Box-Cox transformation, arcsine transformation, logarithmic transformation, square root transformation, reciprocal transformation, standardized transformation, range standardized transformation, central trend transformation, polynomial transformation, and exponential transformation; wherein the selection of transformation processing is based on the exploration of the distribution effects of different transformations on the data set by the histogram; if the constructed interpolation model selects the transformed data, then when generating the interpolation surface, the predicted value is transformed back to the original proportion.

6. A method for producing dynamic special effects of geological anomaly data three-dimensional rendering according to claim 5, characterized in that: In step S5, principal component analysis is performed on the transformed data to calculate the covariance matrix or correlation matrix, eigenvalues ​​and eigenvectors, and the number of principal components and scores are determined as follows: Calculate the covariance matrix: For the standardized data matrix X in the transformation processing result, calculate its covariance matrix C. The element C of the covariance matrix ij The calculation formula is: Where n is the number of samples, are the i-th and j-th standardized variables of the i-th sample respectively; Calculate the correlation matrix: Calculate the correlation matrix R to replace the covariance matrix. The element r of the correlation matrix ij , the calculation formula is: The correlation matrix focuses on reflecting the degree of linear correlation between variables. Its value range is between -1 and 1. The closer the absolute value is to 1, the stronger the correlation is. Eigenvalue solution: Perform eigenvalue decomposition on the covariance matrix C or the correlation matrix R to obtain the series eigenvalues ​​λ1, λ2...,λ p , p is the number of variables, the size of the eigenvalue reflects the amount of information contained in the corresponding principal component. The larger the eigenvalue, the more original data information the principal component carries; Eigenvector solution: For each eigenvalue λ i , all have corresponding eigenvectors e i , by using matrix operations in linear algebra to find the eigenvectors corresponding to all eigenvalues, the eigenvectors will be used to construct the expression of the principal component, which represents the weight distribution of each original variable in the corresponding principal component; Cumulative contribution rate calculation: Calculate the proportion of each eigenvalue to the sum of all eigenvalues, that is, the contribution rate And add them up in sequence to get the cumulative contribution rate k represents the selected main component Divide the number; Scree chart aids judgment: Draw a scree chart, that is, a line chart with the horizontal axis as the eigenvalue number and the vertical axis as the eigenvalue size, to observe the trend of the line; Calculate the principal component score: According to the determined number of principal components and their corresponding eigenvectors, calculate the principal component score of each sample. Let the principal component score vector of the i-th sample be F i =F i1 ,F i2 ,...,F ik , and its calculation formula is: Where j = 1, 2..., k represents the number of the principal component, e jl is the lth element of the eigenvector corresponding to the jth principal component, is the lth standardized variable of the ith sample.

7. A method for producing dynamic special effects of geological anomaly data three-dimensional rendering according to claim 6, characterized in that: In step S6, based on the principal component analysis results, scalar field data is generated as follows: The key indicator geological data in the principal component analysis results are gridded and interpolated to form geological gridded scalar field data. The interpolation methods include nearest neighbor interpolation, inverse distance weighted interpolation, Kriging interpolation, spline interpolation, radial basis function interpolation, triangulation linear interpolation, polynomial regression interpolation, moving average interpolation, fractal interpolation, and bilinear interpolation.

8. The method for producing dynamic special effects of geological anomaly data three-dimensional rendering according to claim 7, characterized in that: In step S7, the background values ​​and abnormal values ​​in the gridded data are separated, and the abnormal areas are identified as follows: Determine the method for estimating geological background value: use the data generated after gridding interpolation to estimate the geological background value by using the mean value method, median method, mode method, or robust statistical method; Calculation and determination of background value: Perform calculation operations on the sorted data according to the selected background value estimation method; Outlier identification and separation: verify the rationality of the calculated background value, identify abnormal data points or areas, judge the data one by one according to the set abnormal judgment criteria, identify the data points or areas that belong to abnormal situations, separate the background and abnormal data, separate the identified abnormal data from the original data set to form separate abnormal data, and retain the background data at the same time to form binary grid data that retains 0 and 1.

9. A method for producing dynamic special effects of three-dimensional rendering of geological anomaly data according to claim 8, characterized in that: The gradient legend rendering in step S9 includes: Choose between monochromatic, multi-tone, or diverging color mapping schemes; Adjust color gradient range, nodes and transparency parameters.

10. A method for producing dynamic special effects of geological anomaly data three-dimensional rendering according to claim 9, characterized in that: The generation of the three-dimensional shadow image in step S9 includes: Set the horizontal angle and vertical angle of the point light source and generate multiple sequence shadow maps according to the formula; Replace the brightness channel with the HSV color space and fuse the rendered image with the stereo shadow data.

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