A method for making a dynamic special effect of stereoscopic rendering of geological anomaly data

Abstract

The application discloses a kind of geologic anomaly data stereoscopic rendering dynamic special effect production methods, it is related to geological data visualization technical field.The method includes the following steps: collecting regionalized geological survey sample data and carrying out structured arrangement;Exploratory analysis and distribution transformation are carried out to data;Dimension reduction processing is handled by principal component analysis;Grid scalar field data is generated using Kriging interpolation or radial basis function interpolation;Separate background value and anomaly value and extract contour line;Combined with gradual change legend rendering, multi-sequence stereoscopic shadow map and image fusion technology, generate dynamic stereoscopic rendering special effect picture, and export as GIF or mp4 format.The application solves the problem that traditional two-dimensional display is not intuitive and dynamic effect is insufficient through multi-source data integration, intelligent algorithm and three-dimensional dynamic rendering, significantly improves the visualization expression and analysis efficiency of geologic anomaly data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data stereoscopic rendering dynamic special effect production, and particularly relates to a geological anomaly data stereoscopic rendering dynamic special effect production method. BACKGROUND

[0002] In geological exploration and research, the processing and analysis of geological anomaly data are crucial. Traditional geological data representation methods 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 technology, three-dimensional stereoscopic rendering of geological data has become possible. However, existing technologies still have problems such as insufficient realistic rendering effects and insufficient dynamic special effect expression when processing three-dimensional stereoscopic rendering of geological anomaly data. Therefore, a geological anomaly data stereoscopic rendering dynamic special effect production method is proposed to solve the above problems. SUMMARY

[0003] Therefore, the purpose of the present application is to provide a geological anomaly data stereoscopic rendering dynamic special effect production method to at least solve the above problems.

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

[0005] A geological anomaly data stereoscopic rendering dynamic special effect production method, comprising the following steps:

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

[0007] S2, structuring and organizing the sample data;

[0008] S3, performing exploratory analysis on the organized data;

[0009] S4, transforming non-normal distribution data based on the exploratory analysis results;

[0010] S5, performing principal component analysis on the transformed data, calculating the covariance matrix, eigenvalues, and eigenvectors, and determining the number of principal components and scores;

[0011] S6, generating scalar field data based on the principal component analysis results;

[0012] S7, separating background values and anomaly values in the gridded data, and identifying abnormal regions;

[0013] S8, generating contour maps based on the lower limit value of the anomaly, and extracting the contour lines using the grid sequence method or the Marching Cubes algorithm;

[0014] S9, rendering the gradient legend of the gridded data, generating a multi-sequence scalar field perspective shadow map, superimposing the contour map and the anomaly lower limit contour map, and generating a perspective rendering dynamic special effect map through image fusion;

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

[0016] Further, in step S1:

[0017] The geochemical survey includes soil, rock, water system sediments, plants, ecology, gas and nanometer gold related element data;

[0018] The natural heavy sand survey includes precious metal, non-ferrous metal, rare and radioactive element data;

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

[0020] Further, the data structure arrangement in step S2 specifically includes:

[0021] Eliminate duplicate and invalid data;

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

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

[0024] Further, the exploratory analysis of the arranged data in step S3 specifically includes:

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

[0026] Visualize the data spatial distribution characteristics through line chart, box plot or map.

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

[0028] Check whether the data in the exploratory analysis results is normally distributed, if the data is skewed, select and execute one or more transformation processing to transform the data to normal distribution; the transformation processing includes but is not limited to Box-Cox transformation, inverse sine transformation, logarithmic transformation, square root transformation, reciprocal transformation, standardization transformation, range standardization transformation, central tendency transformation, polynomial transformation, exponential transformation; wherein, the selection of transformation processing is based on the exploration of the effect of different transformations on the distribution of data set; if the constructed interpolation model selects the transformed data, the predicted value is transformed back to the original scale when generating the interpolation surface.

[0029] Further, the transformed data in step S5 is subjected to principal component analysis, and a covariance matrix or a correlation matrix, eigenvalues and eigenvectors are calculated, and the number of principal components and scores are determined, which are specifically as follows:

[0030] The covariance matrix is calculated: for the standardized data matrix X in the transformed processing result, the covariance matrix C is calculated, and the elements C ij The calculation formula is:

[0031]

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

[0033] The correlation matrix is calculated: the correlation matrix R is calculated to replace the covariance matrix, and the elements r ij of the correlation matrix are calculated, 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, and the closer the absolute value is to 1, the stronger the correlation is;

[0036] Eigenvalue solving: the covariance matrix C or the correlation matrix R is subjected to eigenvalue decomposition, and 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, and the greater the eigenvalue, the more original data information the principal component carries;

[0037] Eigenvector solving: for each eigenvalue λ i , there is a corresponding eigenvector e i , and all eigenvectors corresponding to the eigenvalues are solved by using matrix operations in linear algebra, and 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;

[0038] Cumulative contribution rate calculation: the proportion of each eigenvalue in the sum of all eigenvalues, i.e. the contribution rate

[0039] is calculated, and the cumulative contribution rate is obtained by sequentially accumulating Table K shows the selected number of principal components;

[0040] Stone graph auxiliary judgment: a stone graph is drawn, i.e. the horizontal coordinate is the eigenvalue serial number, and the vertical coordinate is the broken line graph of the eigenvalue size, so as to observe the trend of the broken line;

[0041] ​Calculating principal component scores: according to the determined number of principal components and the corresponding eigenvectors, the principal component scores of each sample are calculated, and the principal component score vector of the ith sample is F i i1 i2 ik The calculation formula is: Where j = 1, 2,..., k represents the principal component number, e jl is the lth element of the eigenvector corresponding to the jth principal component, is the lth standardized variable of the ith sample.

[0042] Further, the scalar field data generated based on the principal component analysis result in step S6 is specifically:

[0043] The key index geological data in the principal component analysis result is gridded and interpolated to form a geological gridded scalar field data, and the interpolation method includes nearest neighbor interpolation, inverse distance weighted interpolation, Kriging interpolation, spline interpolation, radial basis function interpolation, triangular linear interpolation, polynomial regression interpolation, moving average interpolation, fractal interpolation, bilinear interpolation.

[0044] Further, the background value and the abnormal value in the gridded data are separated and the abnormal area is identified in step S7, which is specifically:

[0045] Determination of geological background value estimation method: the data generated after gridding and interpolation is used to estimate the geological background value, and the average method, or the median method, or the mode method, or the robust statistical method is used;

[0046] Background value calculation and determination: according to the selected background value estimation method, the data is calculated and operated;

[0047] Abnormal value identification and separation: the background value is reasonably verified, the abnormal data points or areas are identified, the data is judged one by one according to the set abnormal judgment standard, the data points or areas belonging to abnormal situation are identified, the background and abnormal data are separated, the identified abnormal data is separated from the original data set to form a separate abnormal data, and the background data is retained to form a binary gridded data with 0 and 1.

[0048] Further, the gradual change legend rendering in step S9 includes:

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

[0050] Adjusting the color gradient range, node and transparency parameters.

[0051] ​​​Further, the stereoscopic shadow map generation in step S9 includes:

[0052] The set point light source horizontal angle and vertical angle generate a multi-sequence shadow map according to a formula;

[0053] The brightness channel is replaced by an HSV color space, and the rendering image and the stereoscopic shadow data are fused.

[0054] Compared with the prior art, the present application has the following advantages:

[0055] The present application provides a method for producing a dynamic special effect of a geological anomaly data stereoscopic rendering,

[0056] 1. Stereoscopic and dynamic display: through multi-sequence scalar field stereoscopic shadow map generation and image fusion technology, the geological data is expanded from two-dimensional plane to three-dimensional dynamic rendering, which intuitively presents the spatial distribution and change trend of the geological anomaly, and significantly enhances the immersion and accuracy of data interpretation.

[0057] 2. Algorithm optimization improves accuracy: combined with principal component analysis, various interpolation algorithms (such as Kriging interpolation, radial basis function interpolation) and fractal trend surface method, the data redundancy is effectively reduced, the accuracy of background separation and anomaly recognition is improved, and the human error is reduced.

[0058] 3. High-efficiency data processing capability: through data structure arrangement, exploratory analysis and distribution transformation (such as Box-Cox transformation, standardization transformation), the non-normal data is quickly corrected to ensure the reliability of subsequent analysis; automatic contour extraction (Marching Cubes algorithm) and gradual rendering technology greatly shorten the data processing period.

[0059] 4. Multi-dimensional visualization support: single-tone, multi-tone and divergent color mapping schemes are provided, combined with HSV color space fusion technology, high-contrast and multi-level visualization effects are realized to meet the diversified needs of complex geological scenes.

[0060] 5. Convenient output of results: support for GIF and mp4 format dynamic picture export, convenient for cross-platform sharing and display, and provide efficient tools for geological research, mineral exploration and environmental assessment. BRIEF DESCRIPTION OF DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only preferred embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0062] Figure 1It is a kind of geological anomaly data stereoscopic rendering dynamic special effect production method overall flow schematic diagram proposed in the embodiment of the application. DETAILED DESCRIPTION

[0063] The principles and characteristics of the application are described below in conjunction with the accompanying drawings, and the listed embodiments are only used to explain the application and not to limit the scope of the application.

[0064] Reference Figure 1 A kind of geological anomaly data stereoscopic rendering dynamic special effect production method, comprising the following steps:

[0065] S1, collect regionalized geological survey sample data, including geochemical survey, natural heavy sand survey and geophysical survey data;

[0066] S2, the sample data is structured and arranged;

[0067] S3, exploratory analysis is carried out on the arranged data;

[0068] S4, according to the exploratory analysis result, non-normal distribution data is transformed;

[0069] S5, principal component analysis is carried out on the transformed data, covariance matrix, eigenvalue and eigenvector are calculated, and the number of principal components and scores are determined;

[0070] S6, based on the principal component analysis result, scalar field data is generated;

[0071] S7, background value and abnormal value in gridding data are separated, and abnormal area is identified;

[0072] S8, contour map is generated according to abnormal lower limit value, and contour is extracted using grid sequence method or Marching Cubes algorithm;

[0073] S9, the gridding data is rendered by gradual change legend, and multi-sequence scalar field stereoscopic shadow map is generated, contour map and abnormal lower limit contour map are superimposed, and stereoscopic rendering dynamic special effect map is generated by image fusion;

[0074] S10, the dynamic special effect map is exported as GIF or mp4 format.

[0075] In step S1,

[0076] The geochemical survey includes soil, rock, water system sediments, plants, ecology, gas and nanometer gold related element data;

[0077] The natural heavy sand survey includes noble metal, non-ferrous metal, rare and radioactive element data;

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

[0079] The data structuring in step S2 specifically includes:

[0080] Removing duplicate and invalid data;

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

[0082] Entering data into corresponding cells and cross-verifying.

[0083] For example, the data of geochemical survey, natural heavy sand survey and geophysical survey in regional geological survey sample sampling data are screened, and duplicate and invalid data are removed;

[0084] Designing table structure and table header according to the type of sampling data and subsequent use purpose;

[0085] Entering the combed data one by one into the corresponding cells of the designed table;

[0086] Data auditing and verification: verifying the data in the arranged data table with the original collected sampling data.

[0087] The exploratory analysis of the arranged data in step S3 specifically includes:

[0088] Drawing a histogram or kernel density plot by calculating the mean, variance and range;

[0089] Visualizing the spatial distribution characteristics of data through line chart, box plot or map.

[0090] For example, the univariate descriptive statistical analysis is performed, the mean, median, mode, variance, standard deviation and range of each geological variable are calculated to understand the central position and dispersion degree of the variable data; the histogram and kernel density plot are drawn to observe the data distribution form of the variable; the data is visually presented through line chart, box plot and map visualization according to the data characteristics and analysis purpose; the results of univariate descriptive statistics and visualization are combined to analyze the geological data, comb out the main characteristics of the data, the key relationship between variables and the existing abnormal conditions, and interpret them according to geological professional knowledge to show the distribution characteristics of geological data in geographical space and obtain exploratory analysis results.

[0091] In step S4, the non-normal distribution data is transformed according to the exploratory analysis results, specifically:

[0092] Check whether the data in the exploratory analysis result is normally distributed, if the data is skewed, select and execute 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, inverse sine transformation, logarithmic transformation, square root transformation, reciprocal transformation, standardization transformation, range standardization transformation, central tendency transformation, polynomial transformation, exponential transformation; wherein the selection of the transformation process is based on the exploration of the effect of different transformations on the distribution of the data set; if the constructed interpolation model selects the transformed data, the predicted value is transformed back to the original scale when generating the interpolation surface.

[0093] In step S5, principal component analysis is performed on the transformed data, and the covariance matrix or the correlation matrix, the eigenvalue and the eigenvector are calculated, and the number of principal components and scores are determined. Specifically:

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

[0095]

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

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

[0098]

[0099] 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 is;

[0100] Eigenvalue solution: eigenvalue decomposition is performed 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 All eigenvectors corresponding to the eigenvalues are solved by using matrix operations in linear algebra, and 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;

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

[0103] And then get the cumulative contribution rate Table K shows the selected principal component Number;

[0104] Gravel map auxiliary judgment: draw a gravel map, that is, the horizontal coordinate is the eigenvalue sequence number, and the vertical coordinate is the eigenvalue size broken line graph, to observe the trend of the broken line;

[0105] Calculate the principal component score: according to the determined number of principal components and the corresponding characteristic vector, calculate the principal component score of each sample, and let the principal component score vector of the ith sample be F i i1 i2 ik The calculation formula is: Where j = 1, 2,..., k represents the principal component sequence number, e jl Is the lth element of the jth principal component corresponding to the characteristic vector, Is the lth standardized variable of the ith sample.

[0106] In step S6, the scalar field data is generated based on the principal component analysis result, which is specifically:

[0107] The key index geological data in the principal component analysis result is gridded and interpolated to form a geological gridded scalar field data, and the interpolation method includes nearest neighbor interpolation method, inverse distance weighted interpolation method, Kriging interpolation method, spline interpolation method, radial basis function interpolation method, triangular linear interpolation method, polynomial regression interpolation method, moving average interpolation method, fractal interpolation method and bilinear interpolation method.

[0108] In step S7, the background value and the abnormal value in the gridded data are separated, and the abnormal area is identified, which is specifically:

[0109] Determination of geological background value estimation method: the data generated after gridding and interpolation is used to estimate the geological background value, which is calculated by using the average value method, or the median method, or the mode method, or the robust statistical method.

[0110] Background value calculation and determination: according to the selected background value estimation method, the data is calculated and operated;

[0111] ​​​Anomaly value identification and separation: the calculated background value is verified for reasonableness, abnormal data points or regions are identified, and the data is judged one by one according to the set abnormality judgment standard, the data points or regions belonging to abnormal conditions are identified, the background and abnormal data are separated, the identified abnormal data is separated from the original data set to form a separate abnormal data, while the background data is retained to form a binary gridding data retaining 0 and 1.

[0112] In step S8, an isogram is generated according to the lower limit value of the anomaly, and the isogram is extracted using a grid sequence method or a Marching Cubes algorithm. Specifically, the isogram is extracted as follows:

[0113] Setting the value of the isogram: the lower limit value of the anomaly extracted according to the gridding data background and the anomaly separation algorithm is used as input for the isogram extraction operation;

[0114] Using the isogram extraction algorithm: according to the complexity of the gridding data anomaly, the precision requirement and the calculation efficiency of the isogram extraction algorithm, the isogram extraction algorithm includes: a grid sequence method or an active contour model method or a Marching Cubes algorithm;

[0115] Performing the isogram extraction operation: using the selected algorithm and the set parameters, the isogram extraction operation is performed on the prepared gridding data, the intersection of the data points, grid cells, etc. and the given isogram value is automatically judged by the program according to the algorithm flow, the intersection point coordinates are calculated, the line segments are connected, and the complete isogram is gradually constructed.

[0116] The gradual change legend rendering in step S9 includes:

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

[0118] Adjusting the color gradient range, node and transparency parameters.

[0119] The solid shadow map generation in step S9 includes:

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

[0121] Replacing the brightness channel through the HSV color space, and fusing the rendered map and the solid shadow data.

[0122] Exemplarily, according to the characteristics of the gridded data and the requirements of the visualization, a color mapping type is performed, wherein the color mapping type includes a single-tone gradient mapping, a multi-tone gradient mapping, a divergent color mapping, and other contrast color mappings; a stretching method is selected to linearly or nonlinearly map the numerical range of the data to a color gradient range or to divide the data into several intervals, or a classified gradient rendering method is selected to correspond each interval to a color; a color gradient is applied: a determined color gradient scheme is applied to the rendering of the gridded data, so that the gridded data displays a corresponding color according to the size of the value; and rendering parameters are adjusted: the transparency, brightness, and contrast parameters of the rendering are adjusted to optimize the rendering effect.

[0123] For performing the multi-sequence scalar field stereoscopic shading map generation, the following steps are performed:

[0124] By specifying the height and other information 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 the adjacent grid cell, the assumed brightness is obtained. The generated shading and light after calculation are the gray scale gradients (from black to white) related to the integers between 0 and 255. In the rendering map display, especially after using the transparency, the "stereoscopic shading" can greatly enhance the visualization effect of the surface.

[0125] Before the stereoscopic shading calculation of the geological anomaly scalar field data, the position of the simulated point light source in space needs to be specified, which can be described by two parameters of the horizontal angle and the vertical angle of the point light source. The horizontal angle of the point light source (Horizontal) refers to the angle direction of the simulated point light source, which is measured clockwise in the range of 0 to 360 degrees based on the north as the reference direction, and the horizontal angle of 90 degrees is east. The initial horizontal angle is set to 0 degrees. The vertical angle of the point light source (Altitude) refers to the angle or inclination angle of the point light source above the horizontal plane, and the unit of the height is degree, ranging from 0 to 90 degrees. 0 degrees is located on the horizontal plane, and 90 degrees is located in the direction perpendicular to the horizontal plane. The initial vertical angle is set to 45 degrees.

[0126] The calculation formula of the stereoscopic shading of the geological anomaly scalar field is as follows:

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

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

[0129] where Vertical represents the zenith angle, Horizontal represents the horizontal angle, Pitch and Orientation represent the tilt angle and tilt azimuth, respectively. The angles are in radians.

[0130] Calculate the point light source incidence angle, the zenith angle: The vertical angle of the point light source must be greater than the angle of the horizontal plane (that is, 0 degrees). However, the formula for the stereononic value of the geological anomaly scalar field requires that the angle be expressed in radians and that it be the angle from the vertical. Let the direction perpendicular to the horizontal plane be the zenith, and the point light source zenith angle be the angle between the direction from the zenith point to the point light source, that is, the complementary angle of the point light source vertical angle (that is, 90 degrees minus the solar altitude angle). To calculate the point light source incidence angle, the first step is to convert the point light source vertical angle to the point light source zenith angle. The second step is to convert the point light source zenith angle to radians.

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

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

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

[0134] Convert the point light source horizontal angle: 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 value of the grid cell of the geological anomaly scalar field, and is expressed in degrees. The tilt angle is calculated using the inverse trigonometric function, and its formula is as follows: tan a (Pitch) = grid value difference / horizontal distance, so a (Pitch) = arctan (grid value difference / horizontal distance).

[0139] Calculate the tilt azimuth:

[0140] Orientation is the orientation of the slope of the geological anomaly scalar field. Orientation is used to identify the direction of the steepest descent from each grid cell to its neighboring grid cells. Orientation can be considered as the direction of the tilt angle. Orientation is an angle that will be measured in a clockwise direction, with a range of 0 (due east) to 360 (still due east).

[0141] The tilt angle is generally calculated by using the fitting surface method. The fitting surface is generally a quadratic surface, that is, a window of 3*3 grid cells is taken on the grid data of the geological anomaly scalar field. The grid cells are sequentially counted as cell5, cell2, cell6, cell 1, cell, cell3, cell8, cell4, cell7 from left to right and then from top to bottom. The center of each window is a grid cell point cell. The calculation formula of the tilt angle and the tilt orientation of the center point cell is as follows:

[0142] Pitch=tan;

[0143] Orientation=Pitchsn / Pitchwe;

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

[0145] The calculation of Pitchwe and Pitchsn can use the following several commonly used methods:

[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] In the above formula, cellsize is the length of the grid size of the geological anomaly scalar field.

[0149] The formula for calculating the volume shadow value of each grid cell is traversed through the geological anomaly scalar field by sliding the 3*3 window from left to right and then from top to bottom.

[0150] The horizontal angle and the vertical angle of the point light source are generated by specifying two parameters, a series of stereoscopic shadow maps are generated by modifying the input value of the horizontal angle of the point light source, assuming that the number of the series is n, according to the formula Horizontal = i*360 / n, i = 0, 1, 2, …, n-1, n stereoscopic shadow maps are generated;

[0151] For generating a stereoscopic rendering dynamic special effect map by image fusion, and exporting the stereoscopic rendering dynamic special effect map, specifically: taking the gridded data gradient map as the first layer, superimposing the gridded data contour map as the second layer, and superimposing the gridded data abnormal lower limit contour map as the third layer, three layers are combined into a rendering map, and the rendering map is stored in the memory as RGBA four-channel image data;

[0152] The RGBA four-channel rendering map in the memory is set to 80% transparency, the rendering map is converted from the RGB color space to the HSV color space, the V waveband data in the rendering map in the HSV color space is replaced with the scalar field data stereoscopic shadow map data, the rendering map is converted from the HSV color space to the RGB color space, a plurality of sequence rendering maps are fused with stereoscopic maps to generate n stereoscopic rendering dynamic special effect maps, and the n stereoscopic rendering dynamic special effect maps are output in GIF picture format or mp4 video format.

[0153] The above only describes the preferred embodiments of the present application, and is not intended to limit the present application, any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for producing dynamic special effects of geological anomaly data three-dimensional rendering, 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. Based on 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. generating scalar field data based on the principal component analysis results; S7, separating background values ​​and outliers in the gridded data and identifying outlier 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. Perform gradient legend rendering on the gridded data to generate a multi-sequence scalar field stereo shadow map, superimpose the contour map and the anomaly lower limit contour map, and generate a stereo rendering dynamic special effect map through image fusion; wherein, generating a multi-sequence scalar field stereo shadow map specifically includes: specifying the height information of the simulated point light source for each grid unit based on the geological anomaly scalar field data and calculating its surface assumed brightness value, setting the position of the simulated point light source and calculating the brightness value of each grid unit according to the relationship with the adjacent grid units to obtain the assumed brightness, the generated shadow corresponds to the integer grayscale gradient between 0 and 255 and gradually changes from black to white, and the stereo shadow Transparency is combined with rendering to enhance surface visualization. Before calculation, the position of the simulated point light source in space must be specified. The position is described by the horizontal angle and vertical angle of the point light source. The horizontal angle is measured clockwise from 0 to 360 degrees with north as the reference direction, and 90 degrees indicates the east direction. The vertical angle represents the angle above the horizontal plane and is measured in degrees from 0 to 90 degrees, with 0 degrees in the horizontal plane and 90 degrees in the vertical direction. The calculation formula for stereo shadows is: GeoAnomaly3DShader=255.0*((cos(Vertical)*cos(Pitch))+ (sin(Vertical) * sin(Pitch) * cos(Horizontal - Orientation))) Where Vertical represents the zenith angle, Horizontal represents the horizontal angle, Pitch represents the tilt angle, and Orientation represents the tilt direction, with all angles in radians. The calculation first converts the vertical angle of the point light source to the zenith angle, i.e., Vertical_deg = 90 – Altitude, and then to radians, Vertical_rad = Vertical × π / 180.

0. The horizontal angle of the point light source is also converted to mathematical units and adjusted to radians. The pitch angle indicates the steepness of the change in the grid cell value. The orientation indicates the direction of the slope and is measured clockwise from 0 to 360 degrees. It is used to identify the downslope direction with the largest rate of change. The pitch angle and orientation are calculated using a surface fitting method based on a 3×3 grid cell window. Each cell in the grid is sequentially traversed and the tilt values ​​in the X and Y directions are calculated based on the relationship between adjacent cells. Multiple stereo shadow maps are generated by varying the horizontal angle of the simulated point light source. The sequence number n is set and the formula Horizontal = i × 360 / n is used. Calculate the horizontal angle of each sequence, where i is an integer from 0 to n-1, and finally generate n stereo shadow images to form a multi-sequence scalar field stereo shadow map; S10: export the dynamic special effects image into GIF or MP4 format.

2. The method for producing dynamic special effects of geological anomaly data stereo rendering according to claim 1, characterized in that: In step S1: The geochemical survey includes data on soil, rock, stream sediment, plants, ecology, gas and nano-gold related elements; 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. A method for producing dynamic special effects of geological anomaly data three-dimensional rendering according to claim 3, characterized in that: The exploratory analysis of the sorted data in step S3 is 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. The method for producing dynamic special effects of geological anomaly data stereo rendering according to claim 4, characterized in that: In step S4, based on 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 are 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; among which, the selection of transformation processes is based on the exploration of the effects of different transformations on the distribution of the data set through histograms; if the constructed interpolation model chooses to use the transformed data, then when generating the interpolation surface, the predicted values ​​will be 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 elements of the covariance matrix are 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 elements of the correlation matrix , 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 correlation matrix R to obtain the series eigenvalues , 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 , have corresponding eigenvectors , 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 total of all eigenvalues, that is, the contribution rate , and then add them up to get the cumulative contribution rate , indicating that k represents the selected main component Divide into numbers; Scree chart aided 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: Calculate the principal component score of each sample based on the determined number of principal components and their corresponding eigenvectors. Let the principal component score vector of the i-th sample be , and its calculation formula is: ,in represents the principal component number, is the first eigenvector corresponding to the jth principal component elements, It is The first sample standardized variables.

7. The method for producing dynamic special effects of geological anomaly data stereo 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 stereo 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 grid interpolation to estimate the geological background value by using the mean method, median method, mode method, or robust statistical method; Background value calculation and determination: 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 abnormality judgment criteria, identify the data points or areas that are abnormal, separate the background and abnormal data, separate the identified abnormal data from the original data set to form separate abnormal data, while retaining the background data to form binary grid data with 0 and 1 retained.

9. The method for producing dynamic special effects of geological anomaly data stereo rendering 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. The method for producing dynamic special effects of geological anomaly data stereo rendering according to claim 9, characterized in that: The generation of the three-dimensional shadow image in step S9 includes: Set the horizontal and vertical angles 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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