Geochemical element layer distinguishing method and system

Abstract

The invention relates to the technical field of image processing, in particular to a geochemical element layer distinguishing method and system.The method comprises the steps that a copper element layer is subjected to grid division, the copper element color concentration change rate of adjacent grid points is calculated, and the grid points exceeding a set change rate threshold value are marked as mutation areas; and analyzing the copper element content difference of adjacent grid points in the abrupt change area, marking boundary points, and obtaining the distribution information of the copper element boundary points. According to the method, the mutation region is accurately recognized by calculating the copper element concentration change rate, and the problem of boundary fuzziness caused by data sparsity is reduced. A connection mode is optimized based on boundary curvature change, boundary information is ensured to be continuous and complete, and space expression precision is improved. The sampling point density calculation is clear in high and low density areas, the interpolation calculation is optimized, and errors caused by non-uniform data are reduced. The color concentration gradient of the abrupt change area is analyzed, the interpolation mode is adjusted, element transition is smoother, and error accumulation is reduced.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and in particular to a geochemical element layer resolution method and system. Background Art

[0002] The field of image processing technology includes a series of technical methods for analyzing, transforming, enhancing, recognizing and compressing image data. The core content of this technical field includes image data acquisition, image preprocessing, feature extraction, image reconstruction and classification recognition. Image data acquisition involves the use of sensors or scanning devices to obtain raw image data. Image preprocessing usually includes grayscale, denoising, contrast enhancement and other operations to improve image quality and usability. Feature extraction involves extracting information such as edges, textures, and color distribution from image data for subsequent analysis. Image reconstruction is mainly used in scenarios such as multi-view synthesis and super-resolution enhancement, and generates higher quality image data through mathematical modeling or neural network methods. Classification recognition is to analyze image content through pattern recognition technology or deep learning models to achieve target recognition, classification or target detection.

[0003] Among them, the geochemical element layer resolution method refers to a technical method that uses computer image processing to improve the resolution of the layer and optimize the expression of the spatial distribution of elements based on the spatial distribution characteristics of geochemical element data. This method covers the collection, standardization, feature extraction, interpolation calculation and high-resolution reconstruction of geochemical element data. Geochemical element data collection usually relies on remote sensing measurement or geological survey methods. Data standardization processing includes normalization, filtering, outlier removal and other operations to ensure data consistency and reliability. Feature extraction extracts element distribution characteristics through statistical analysis or spatial transformation methods. Interpolation calculation uses interpolation algorithms to supplement missing or sparse data points. Finally, combined with image reconstruction technology, high-resolution optimization of geochemical element layers is completed.

[0004] When optimizing layers based on the spatial distribution characteristics of geochemical element data, the relatively simple approach to handling outliers and sparse data during standardization and feature extraction can easily lead to insufficient expression of spatial details, especially in areas of mutation, where boundary positioning accuracy is limited. When dealing with missing or sparsely distributed data, interpolation calculations fail to fully consider the changing trends of local gradients, resulting in excessive smoothing or unreasonable mutations in boundary areas, leading to deviations in the transitional expression of element distribution. Although data standardization can improve consistency, its ability to distinguish between element-concentrated areas, transition areas, and background areas is limited, affecting the fine expression of layer data. Due to the failure to effectively introduce spatial structural information, the optimized data has deficiencies in boundary integrity and expression of spatial distribution characteristics, resulting in potential error accumulation even after resolution optimization, limiting the accurate depiction of spatial variations in geochemical elements. Summary of the Invention

[0005] In order to solve the technical problems existing in the prior art, the embodiments of the present invention provide a method and system for distinguishing geochemical elements from each other. The technical solution is as follows:

[0006] The geochemical element layer resolution method includes the following steps:

[0007] S1: Grid the copper element layer, calculate the rate of change of the copper element color concentration of adjacent grid points, mark the grid points that exceed the set change rate threshold as mutation areas, analyze the difference in copper element content between adjacent grid points in the mutation area, mark the boundary points, and obtain the distribution information of the copper element boundary points;

[0008] S2: connecting the boundary points in the copper element boundary point distribution information to form a boundary curve, calculating the curvature change of the boundary curve, determining whether the boundary curve has a break based on the smoothness of the change, adjusting the connection method of the boundary points based on the boundary point spacing in the break area, and obtaining the copper element boundary structure information;

[0009] S3: calling the coordinates and color density changes of the boundary points in the copper element boundary point distribution information, counting the number of sampling points in the grid, marking the copper element dense area and sparse area according to the number of sampling points, and obtaining the copper element sampling distribution information;

[0010] S4: combining the copper element boundary structure information and the copper element sampling distribution information, analyzing the offset of the concentration change of each grid point, identifying the element concentration area, transition area and background area according to the offset direction of the offset, and obtaining the copper element layer resolution result.

[0011] As a further solution of the present invention, the copper element boundary point distribution information includes boundary point coordinates, boundary point concentration change amplitude, and boundary point spacing change; the copper element boundary structure information includes boundary curve shape, boundary curve curvature change, and boundary point connection method; the copper element sampling distribution information includes data sampling point density, grid sampling point number, and copper element dense and sparse area distribution; the copper element layer resolution result includes element concentration area, transition area, and background area.

[0012] As a further solution of the present invention, the copper element layer is gridded, the rate of change of the copper element color concentration of adjacent grid points is calculated, the grid points that exceed a set change rate threshold are marked as mutation areas, the difference in copper element content of adjacent grid points in the mutation area is analyzed and the boundary points are marked. The specific steps for obtaining the distribution information of the copper element boundary points are as follows:

[0013] S101: Obtaining the coverage of the copper element layer and setting the grid division range, extracting the copper element color concentration data within the corresponding grid area, calculating the copper element color concentration change rate between grid points, marking the grid points whose change rate exceeds the set concentration change rate threshold as concentration mutation areas, and obtaining a concentration mutation grid point set;

[0014] S102: Based on the concentration mutation grid point set, calculating the copper element color concentration difference of each grid point in the mutation area, counting the copper element content difference between adjacent grid points within the mutation area, marking grid points whose content difference exceeds a difference threshold as concentration difference boundary points, and obtaining a concentration difference boundary point set;

[0015] S103: Analyze the copper element concentration distribution characteristics within the boundary point area based on the concentration difference boundary point set, calculate the spatial coherence of the copper element concentration within the boundary point area, extract the copper element concentration change trend along each direction of the boundary point, and obtain the copper element boundary point distribution information.

[0016] As a further solution of the present invention, the boundary points in the copper element boundary point distribution information are connected to form a boundary curve, the curvature change of the boundary curve is calculated, and whether there is a break in the boundary curve is determined based on the smoothness of the change. The connection method of the boundary points is adjusted according to the boundary point spacing in the break area. The specific steps of obtaining the copper element boundary structure information are as follows:

[0017] S201: Calling the boundary points of the mutation area in the copper element boundary point distribution information, connecting adjacent boundary points in coordinate arrangement order to form a boundary curve, calculating the curvature change amplitude of the boundary curve, analyzing the curvature fluctuation range of each section of the boundary curve, evaluating the smoothness of the boundary curve, screening the area where the curvature change amplitude exceeds a set smoothness threshold, and obtaining boundary curvature change data;

[0018] S202: Based on the boundary curvature change data, determine whether there is a fracture area on the boundary curve, detect the coordinates of the boundary points of the fracture area, calculate the spatial distance between adjacent fracture boundary points, count the distribution range of the boundary point distance of each fracture area, and obtain boundary fracture point distance data;

[0019] S203: adjusting the boundary line segment connection mode of the fracture area according to the boundary fracture point spacing data, calculating the continuity of the corrected boundary curve, detecting the adjusted boundary curvature distribution, extracting the corrected boundary point coordinates, and obtaining the copper element boundary structure information.

[0020] As a further solution of the present invention, the coordinates and color density changes of the boundary points in the copper element boundary point distribution information are called, the number of sampling points in the grid is counted, and the copper element dense area and sparse area are marked according to the number of sampling points. The specific steps of obtaining the copper element sampling distribution information are as follows:

[0021] S301: Calling the coordinates of the boundary points and the corresponding color density change values ​​in the copper element boundary structure information, obtaining copper element data sampling points in the boundary area and the adjacent area, calculating the sampling point density of the grids in each area, counting the number of copper element sampling points in each grid, screening the areas in the adjacent grids where the number of sampling points changes by more than a set sampling point number change threshold, marking the distribution boundary between the dense sampling area and the sparse sampling area, and obtaining sampling density distribution data;

[0022] S302: Based on the sampling density distribution data, extract the color concentration change value of the copper element in the sparse area, calculate the color concentration gradient change range in the sparse area, count the gradient fluctuation interval of each grid area, screen the grid areas whose gradient change amplitude exceeds the gradient change amplitude threshold, and obtain gradient change interval data;

[0023] S303: According to the gradient change interval data, the sampling point distribution in the sparse area is adjusted, the copper element sampling points in the sparse area are increased according to the color concentration gradient change range, and the sampling point calculation method in the dense area is corrected. The number distribution of sampling points in each grid area is updated to obtain the copper element sampling distribution information.

[0024] As a further solution of the present invention, the formula is used to calculate the color density gradient change in the sparse area:

[0025]

[0026] Among them, T v represents the gradient fluctuation index of the grid area, N represents the number of sampling points in the grid area, T i Represents the color density gradient value of the i-th sampling point, T avg Represents the mean value of the color density gradient of all sampling points in the grid area, T j Represents the color density gradient value of the j-th sampling point in the grid area.

[0027] As a further solution of the present invention, the copper element boundary structure information and the copper element sampling distribution information are combined to analyze the offset of the concentration change of each grid point, and the element concentration area, transition area and background area are identified according to the offset direction of the offset. The specific steps for obtaining the copper element layer resolution result are as follows:

[0028] S401: Analyzing the copper element color concentration at each grid point based on the copper element boundary structure information and the copper element sampling distribution information, calculating the offset of the grid point concentration change, marking the grid points whose offset exceeds a set offset threshold as error points, and statistically analyzing the distribution characteristics of the error points in space to obtain spatial distribution data of the error points;

[0029] S402: Based on the spatial distribution data of the error point, the copper element concentration difference between the error point and the adjacent grid points is calculated, the direction and intensity of the error point offset are analyzed, the offset trend within the error point area is statistically analyzed, the element concentration area, the element transition area, and the background area are identified, and the spatial position of each area is marked to obtain copper element area classification data;

[0030] S403: establishing a layer structure correspondence relationship based on the copper element region classification data, integrating the feature type of each region, and adjusting the boundary information within the layer to obtain a copper element layer resolution result.

[0031] As a further solution of the present invention, the transition deviation of adjacent grid points in the mutation area is calculated using the formula:

[0032]

[0033] Where, ΔC nei represents the composite transition deviation of adjacent grid points in the mutation region, N represents the number of adjacent grid points in the mutation region, C i represents the copper concentration value of the i-th grid point, C i+1 represents the copper concentration value of its adjacent grid points, D i Represents the local concentration gradient variation at the i-th grid point, C j represents the copper concentration value of the jth grid point in the mutation area, C j+1 represents the copper concentration value of its adjacent grid points, C m Represents the copper concentration value of the mth grid point in the mutation area, Represents the mean of the copper concentration values ​​of all grid points in the mutation area.

[0034] As a further embodiment of the present invention, the method further includes: S5: calling the mutation region information in the copper element layer resolution result, analyzing the color concentration change gradient of adjacent sampling points in the copper element mutation region, screening the grid points whose concentration change gradient exceeds the gradient threshold, identifying the transition trend of the mutation region, calculating the transition deviation of adjacent grid points in the mutation region, adjusting the concentration transition interpolation method of the mutation region based on the transition deviation, and correcting the concentration change in the mutation region to obtain optimized copper element layer data;

[0035] The optimized copper element layer data includes the concentration change of the grid points in the mutation area, the gradient change in the transition area, and the interpolation method adjustment result.

[0036] S501: Calling the mutation area information in the copper element layer resolution result, calculating the color density change gradient between adjacent sampling points, screening the grid points where the color density change gradient exceeds the gradient threshold, analyzing the transition trend in the mutation area, and obtaining the mutation area transition trend data;

[0037] S502: Based on the transition trend data of the mutation region, calculate the transition deviations of adjacent grid points in the mutation region, count the transition deviation range of each region, filter out grid points whose transition deviations exceed a set deviation threshold, mark transition abnormal regions, and obtain transition deviation data of the mutation region;

[0038] S503: adjusting the color density transition interpolation method of the mutation area according to the mutation area transition deviation data, correcting the density change in the mutation area, calculating the adjusted grid color density value, and obtaining optimized copper element layer data.

[0039] A geochemical element layer resolution system, comprising:

[0040] The grid mutation region detection module divides the copper element layer into grids, calculates the rate of change of the copper element color concentration of adjacent grid points, marks the grid points that exceed the set change rate threshold as mutation regions, analyzes the difference in copper element content between adjacent grid points in the mutation region, marks the boundary points, and obtains the distribution information of the copper element boundary points;

[0041] The boundary structure analysis module connects the boundary points in the copper element boundary point distribution information to form a boundary curve, calculates the curvature change of the boundary curve, determines whether there is a break in the boundary curve based on the smoothness of the change, adjusts the connection method of the boundary points based on the boundary point spacing in the break area, and obtains the copper element boundary structure information;

[0042] The sampling distribution calculation module calls the coordinates and color concentration changes of the boundary points in the copper element boundary point distribution information, counts the number of sampling points in the grid, marks the copper element dense area and sparse area according to the number of sampling points, and obtains the copper element sampling distribution information;

[0043] The layer resolution result generation module combines the copper element boundary structure information and the copper element sampling distribution information, analyzes the offset of the concentration change of each grid point, identifies the element concentration area, transition area and background area according to the offset direction of the offset, and obtains the copper element layer resolution result;

[0044] The transition region adjustment module calls the mutation region information in the copper element layer resolution result, analyzes the color concentration change gradient of adjacent sampling points in the copper element mutation region, screens the grid points whose concentration change gradient exceeds the gradient threshold, identifies the transition trend of the mutation region, calculates the transition deviation of adjacent grid points in the mutation region, adjusts the concentration transition interpolation method of the mutation region according to the transition deviation, corrects the concentration change in the mutation region, and obtains optimized copper element layer data.

[0045] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0046] In the present invention, the mutation area is accurately identified by calculating the rate of change of copper element concentration, reducing the boundary fuzzy problem caused by data sparsity. The connection method is optimized based on the change of boundary curvature to ensure the continuity and completeness of boundary information and improve the accuracy of spatial expression. The sampling point density calculation clarifies the high and low density areas, optimizes the interpolation calculation, and reduces the error caused by uneven data. The color concentration gradient of the mutation area is analyzed and the interpolation method is adjusted to make the element transition smoother and reduce error accumulation. Combined with the optimized data and boundary structure, the concentration area, transition area and background area are identified based on the concentration offset, making the element spatial distribution more intuitive and improving the accuracy of data expression. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0048] Figure 1 Flow chart of the method of the present invention.

[0049] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0050] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0051] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0052] In the embodiments of the present invention, the terms "image" and "picture" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same. The terms "of," "corresponding," and "corresponding" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same.

[0053] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0054] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0055] See also Figure 1 The present invention provides a technical solution: a geochemical element layer resolution method, comprising the following steps:

[0056] S1: Set the grid division range for the copper element layer, obtain the copper element color concentration data of each grid point, calculate the copper element color concentration change rate between adjacent grid points, mark the grid points whose color concentration change rate exceeds the set concentration change rate threshold as concentration mutation areas, calculate the concentration change amplitude within the mutation area, analyze the copper element content difference between adjacent grid points in the mutation area, select grid points whose content difference exceeds the difference threshold, mark the concentration difference boundary points, analyze the coherence of the copper element concentration distribution in the boundary point area, and obtain the copper element boundary point distribution information;

[0057] S2: Calling the boundary points of the mutation area in the copper element boundary point distribution information, connecting the boundary points in order to form a boundary curve, calculating the curvature change amplitude of the boundary curve, evaluating the smoothness of the curvature change of the boundary area, screening the area whose smoothness exceeds the set smoothness threshold, determining whether there is a broken area in the boundary curve, calculating the boundary point spacing in the broken area, adjusting the connection method of the boundary line segments, and obtaining the copper element boundary structure information;

[0058] S3: Recall the coordinates and color concentration changes of the boundary points in the copper element boundary point distribution information, calculate the copper element data sampling point density in the boundary area and adjacent areas, count the number of copper element sampling points in each grid, screen the areas in the adjacent grids where the change in the number of copper element sampling points exceeds the set sampling point number change threshold, mark the distribution boundary between the data dense area and the sparse area, analyze the gradient change range of the copper element color concentration in the sparse area, increase the copper element sampling points in the sparse area according to the gradient change range, and adjust the sampling point calculation method in the dense area to obtain the copper element sampling distribution information;

[0059] S4: Combine the copper element boundary structure information and copper element sampling distribution information, analyze the copper element color concentration change of each grid point, calculate the offset of the grid point concentration change, select the grid points whose offset exceeds the set offset threshold as error points, obtain the spatial distribution of the error points, analyze the difference in copper element concentration change between the error point and the adjacent grid points, determine the direction and intensity of the error offset, identify the element concentration area, element transition area and background area, mark the feature type of each area, establish the layer structure correspondence, and obtain the copper element layer resolution result;

[0060] S5: Call the mutation area information in the copper element layer resolution result, analyze the color concentration change gradient of adjacent sampling points in the copper element mutation area, screen the grid points whose concentration change gradient exceeds the gradient threshold, identify the transition trend of the mutation area, calculate the transition deviation of adjacent grid points in the mutation area, adjust the concentration transition interpolation method of the mutation area according to the transition deviation, and correct the concentration change in the mutation area to obtain the optimized copper element layer data.

[0061] The copper element boundary point distribution information includes the boundary point coordinates, the boundary point concentration change amplitude, and the boundary point spacing change. The copper element boundary structure information includes the boundary curve shape, the boundary curve curvature change, and the boundary point connection method. The copper element sampling distribution information includes the data sampling point density, the number of grid sampling points, and the distribution of copper element dense and sparse areas. The copper element layer resolution results include element concentration areas, transition areas, and background areas.

[0062] Grid the copper element layer, calculate the rate of change of the copper element color concentration of adjacent grid points, mark the grid points that exceed the set change rate threshold as the mutation area, analyze the difference in copper element content between adjacent grid points in the mutation area and mark the boundary points. The specific steps for obtaining the distribution information of copper element boundary points are as follows:

[0063] S101: Obtaining the coverage of the copper element layer and setting the grid division range, extracting the copper element color concentration data within the corresponding grid area, calculating the copper element color concentration change rate between grid points, marking the grid points whose change rate exceeds the set concentration change rate threshold as concentration mutation areas, and obtaining a concentration mutation grid point set;

[0064] To obtain the coverage of the copper element layer, first determine the initial area of ​​copper element distribution, call remote sensing images or geographic information system (GIS) data to analyze the spatial distribution of copper elements, and convert the image data into a standard raster format. Each raster cell represents the copper element content area within a certain range. The threshold judgment method is used to set the minimum copper element presence value as the boundary of the area coverage. The set of all pixel points that meet the threshold is calculated to obtain the complete copper element coverage range. When setting the grid division range, a fixed grid size is set according to the spatial distribution characteristics of the copper element layer. For example, if the total area of ​​the area is 100 square kilometers, it can be divided into 10×10 grids, and each grid cell area is 1 square kilometer. Ensure that the accuracy of the grid division meets the calculation requirements. When extracting the copper element color concentration data in the corresponding grid area, traverse all grid cells. Calculate the average color concentration value of each pixel in the grid, and use the change in the ratio of the green and blue channels in the RGB color space to obtain the copper concentration value. For example, set the copper concentration calculation formula C = k × (G / B), where C is the copper color concentration, G and B are the green and blue channel values ​​of the pixel, respectively, and k is the normalization coefficient (set range 0.5-2.0, based on experimental calibration). When calculating the copper color concentration change rate between grid points, use the differential calculation method and set the concentration change rate between adjacent grid units to V = (C2-C1) / d, where V is the change rate, C2 and C1 are the copper concentration values ​​of adjacent grids, respectively, and d is the distance between the grid center points (in kilometers). When marking the grid points whose change rate exceeds the set concentration change rate threshold as concentration mutation areas, set the concentration change rate threshold V th , if V>V th , then mark the grid point as the concentration mutation area, assuming V th =0.3, if the V calculated at a certain grid point is 0.45, then the point is determined to be a mutation point, and finally the concentration mutation grid point set is obtained.

[0065] S102: Based on the concentration mutation grid point set, calculate the copper element color concentration difference of each grid point in the mutation area, count the copper element content differences between adjacent grid points within the mutation area, mark the grid points whose content differences exceed the difference threshold as concentration difference boundary points, and obtain the concentration difference boundary point set;

[0066] Based on the concentration mutation grid point set, the copper element color concentration difference of each grid point in the mutation area is calculated. All mutation grid point sets are traversed to calculate the concentration difference ΔC between each grid point and the adjacent grid unit. i -C j |, where C i and C jThe copper concentration value of adjacent grid cells is calculated, and the copper content difference of adjacent grid points in the mutation area is counted. The concentration difference of all adjacent grid pairs is calculated. The statistical range is set to all grid cells in the mutation area. The concentration distribution is analyzed using the mean and standard deviation. The grid points with content differences exceeding the difference threshold are marked as concentration difference boundary points. The concentration difference threshold ΔC is set. th , if the concentration difference ΔC between adjacent grids is greater than ΔC th , then the grid point is marked as the concentration difference boundary point, for example, setting ΔC th =0.5, if the concentration values ​​of two adjacent grid cells are 2.1 and 1.4 respectively, then their difference ΔC = 0.7, which exceeds the threshold, so the point is determined to be a concentration difference boundary point, and finally the concentration difference boundary point set is obtained.

[0067] S103: Analyzing the copper element concentration distribution characteristics within the boundary point area based on the concentration difference boundary point set, calculating the spatial coherence of the copper element concentration within the boundary point area, extracting the copper element concentration change trend along each direction of the boundary point, and obtaining the copper element boundary point distribution information;

[0068] Based on the set of concentration difference boundary points, the copper concentration distribution characteristics in the boundary point area are analyzed, the concentration values ​​around the boundary points are counted, the concentration change gradient is calculated, and the directional gradient calculation method is used to determine the copper concentration change trend of the boundary points. The spatial coherence of the copper concentration in the boundary point area is calculated. The copper concentration value sequence of the grid cells in the adjacent area of ​​the boundary point is selected, and its coefficient of variation CV = σ / μ is calculated, where σ is the concentration standard deviation and μ is the concentration mean. If CV>0.6, the concentration variation in this area is large. The copper concentration change trend of the boundary point along each direction is extracted, and the four-neighborhood method is used to calculate the concentration gradient in each direction to obtain the copper boundary point distribution information, including the spatial distribution density of the boundary point and the boundary coherence coefficient.

[0069] Connect the boundary points in the copper element boundary point distribution information to form a boundary curve, calculate the curvature change of the boundary curve, determine whether there is a break in the boundary curve based on the smoothness of the change, and adjust the connection method of the boundary points based on the boundary point spacing in the break area. The specific steps for obtaining the copper element boundary structure information are as follows:

[0070] S201: Calling the boundary points of the mutation area in the copper element boundary point distribution information, connecting adjacent boundary points in coordinate arrangement order to form a boundary curve, calculating the curvature change amplitude of the boundary curve, analyzing the curvature fluctuation range of each segment of the boundary curve, evaluating the smoothness of the boundary curve, screening the area where the curvature change amplitude exceeds a set smoothness threshold, and obtaining boundary curvature change data;

[0071] Call the mutation area boundary points in the copper element boundary point distribution information, connect adjacent boundary points in the coordinate arrangement order to form a boundary curve, traverse all boundary point data, and arrange them in ascending order of horizontal and vertical coordinate values ​​or angle sorting method to ensure that there is no intersection or break when the boundary points are connected. When calculating the curvature change amplitude of the boundary curve, calculate the local curvature of the boundary point set by the three-point fitting method and set the curvature calculation formula Where x′ and y′ are first-order derivatives, x″ and y″ are second-order derivatives. All points on the curve are traversed to calculate the local curvature value. When analyzing the curvature fluctuation range of each segment of the boundary curve, the boundary curve is divided into equally spaced segments (for example, every 5 points are divided into a segment), and the maximum and minimum values ​​of all curvature values ​​in the segment are counted, and the curvature variation range within the segment is calculated as ΔK=K max -K min When evaluating the smoothness of the boundary curve, the standard deviation of the curvature change of all segments is calculated K , set the smoothness judgment standard, if σ K >σ th , it is considered that the curvature fluctuation of the region is large. When screening the region where the curvature change amplitude exceeds the set smoothness threshold, the curvature change threshold ΔK is set. th , if the curvature variation range of a certain section ΔK>ΔK th , then mark the section as an abnormal curvature change area, for example, set ΔK th =0.05. If ΔK=0.08 is calculated for a certain section, the area is identified as a section with significant curvature change, and the boundary curvature change data is finally obtained.

[0072] S202: Based on the boundary curvature change data, determine whether there is a fracture area on the boundary curve, detect the coordinates of the boundary points in the fracture area, calculate the spatial distance between adjacent fracture boundary points, count the distribution range of the boundary point distances in each fracture area, and obtain boundary fracture point distance data;

[0073] Based on the boundary curvature change data, when judging whether there is a broken area on the boundary curve, detect the segment with significant curvature change and calculate the Euclidean distance between the starting and ending points of the segment Among them, x1, y1 and x2, y2 are the coordinates of the starting and ending points respectively. If the distance is greater than the set fracture judgment threshold D th , then it is determined that there is a boundary fracture in the area. When detecting the coordinates of the boundary points of the fracture area, all the segments determined to be fracture areas are traversed, the coordinates of the first and last points of the segment are extracted, and stored in the fracture boundary point set. When calculating the spatial spacing between adjacent fracture boundary points, the distances between the start and end points of all fracture segments are counted, and the mean D is calculated. avg and standard deviation σ D , when counting the distribution range of the boundary points of each fracture area, set the judgment interval [Davg -σ D ,D avg +σ D ], filter out abnormal breakpoints beyond this interval, for example, set D th =0.2, if the distance D between the start and end points of a fracture area is =0.35, then the distance between the area is determined to be abnormal, and finally the boundary fracture point distance data is obtained.

[0074] S203: adjusting the boundary line segment connection mode of the fracture area according to the boundary fracture point spacing data, calculating the continuity of the corrected boundary curve, detecting the adjusted boundary curvature distribution, extracting the corrected boundary point coordinates, and obtaining the copper element boundary structure information;

[0075] When adjusting the boundary segment connection mode of the fractured area based on the boundary fracture point spacing data, interpolation correction or least squares fitting method is used to calculate the new boundary point coordinates to ensure the continuity of the boundary segment. When calculating the continuity of the corrected boundary curve, all points on the corrected curve are traversed and the curvature change amplitude between adjacent points is calculated to ensure that the curvature change meets the continuity judgment standard, that is, |ΔK| <K th When detecting the adjusted boundary curvature distribution, calculate the local curvature of the corrected curve and calculate its standard deviation σ K,new , if σ K,new <σ th , then it is determined that the boundary has been smoothed. When extracting the corrected boundary point coordinates, all the adjusted boundary point data are stored and arranged in coordinate order to finally obtain the copper element boundary structure information.

[0076] The specific steps for obtaining the copper element sampling distribution information are as follows:

[0077] S301: Retrieving the coordinates of the boundary points and the corresponding color density change values ​​in the copper element boundary point distribution information, obtaining copper element data sampling points in the boundary area and adjacent areas, calculating the sampling point density of the grids in each area, counting the number of copper element sampling points in each grid, screening areas in adjacent grids where the change in the number of sampling points exceeds a set sampling point number change threshold, marking the distribution boundary between the densely sampled area and the sparsely sampled area, and obtaining sampling density distribution data;

[0078] Call the coordinates of the boundary points and the corresponding color concentration change values ​​in the copper element boundary structure information, traverse all boundary point data, and extract the corresponding space coordinates (X i ,Y i ) and color density change value C iWhen obtaining the copper element data sampling points in the boundary area and the adjacent area, all sampling points are retrieved in the boundary area grid and the adjacent grid. When calculating the sampling point density of the grid in each area, the number of copper element data points in each grid is counted. g , set the grid area A g , calculate the sampling point density D of each grid g =N g / A g When counting the number of copper element sampling points in each grid, traverse all grid cells and record the total number of sampling points in each grid. When filtering the area where the number of sampling points in the adjacent grids exceeds the set sampling point number change threshold, calculate the point number change of the adjacent grid ΔN=|N g1 -N g2 |, set the change threshold ΔN th , if ΔN>ΔN th , it is determined that there is a sudden change in the sampling point density in the area. For example, if ΔN th =5. If the number of sampling points in a grid is 12 and the number of sampling points in the adjacent grid is 4, then ΔN=8, which exceeds the threshold. The area is marked as the junction of the dense area and the sparse area. When marking the distribution boundary of the dense sampling area and the sparse sampling area, all grid boundaries that meet the mutation conditions are traversed and their coordinate information is stored to finally obtain the sampling density distribution data.

[0079] S302: Based on the sampling density distribution data, extract the color concentration change value of the copper element in the sparse area, calculate the color concentration gradient change range in the sparse area, count the gradient fluctuation interval of each grid area, select the grid areas where the gradient change amplitude exceeds the gradient change amplitude threshold, and obtain gradient change interval data;

[0080] Calculate the range of color density gradient variation in the sparse area using the formula:

[0081]

[0082] Among them, T v represents the gradient fluctuation index of the grid area, N represents the number of sampling points in the grid area, T i Represents the color density gradient value of the i-th sampling point, T avg Represents the mean value of the color density gradient of all sampling points in the grid area, T j Represents the color density gradient value of the jth sampling point in the grid area;

[0083] Detailed explanation of the formula and the process of formula calculation and derivation:

[0084] For the purpose of specific calculation, it is assumed that an actual grid area contains 5 sampling points, and the measured color density gradient value T jThey are: 5, 3, 8, 6, 2. First, calculate the average value T of the color density gradient avg :

[0085]

[0086] Next, we calculate the absolute value of the difference between each gradient and the average gradient, and then sum them up:

[0087]

[0088] Then, calculate the sum of squares:

[0089]

[0090] And the square of the sum of all gradient values ​​divided by N:

[0091]

[0092] Substituting these values ​​into the normalized denominator:

[0093]

[0094] Finally, calculate T v :

[0095]

[0096] The results show that there are significant fluctuations in the color concentration gradient within the selected grid area. Compared with the average value of the color concentration gradient within the grid area, this fluctuation indicates that the degree of variation in the color concentration gradient within the grid area is large, which is important for identifying the non-uniformity of the copper element distribution. This value can be used to further decide whether special attention should be paid to this area or further sampling enhancement should be performed.

[0097] S303: Adjust the sampling point distribution in the sparse area based on the gradient change interval data, increase the copper element sampling points in the sparse area according to the color concentration gradient change range, and correct the sampling point calculation method in the dense area. Update the number of sampling points in each grid area to obtain copper element sampling distribution information;

[0098] According to the gradient change interval data, when adjusting the sampling point distribution in the sparse area, add sampling points in the area marked as gradient mutation. When increasing the copper element sampling points in the sparse area according to the color concentration gradient change range, use the interpolation sampling method to evenly increase the number of sampling points in the mutation interval and set the number of new points N. add Calculated based on ΔT, if ΔT>ΔT th ,but When modifying the calculation method of sampling points in dense areas, the repeated sampling points in the dense areas are aggregated, and the adjacent data points are merged using a grid grouping method to calculate the average concentration value C avg =∑C i / N g , when updating the number of sampling points in each grid area, recalculate the D of each grid g , and finally obtain the optimized copper element sampling distribution information.

[0099] Combining the optimized copper element layer data and copper element boundary structure information, the offset of the concentration change of the grid points in the mutation area before and after optimization is analyzed and the error points are marked. The spatial distribution, concentration difference and offset direction of the copper element at the error points are analyzed, and the element concentration area, transition area and background area are identified. The corresponding relationship of the layer structure is established. The specific steps for obtaining the copper element layer resolution result are as follows:

[0100] S401: combining the optimized copper element layer data and the copper element boundary structure information, analyzing the copper element color concentration of the same grid point before and after optimization, calculating the offset of the grid point concentration change, marking the grid points whose offset exceeds the set offset threshold as error points, and statistically analyzing the distribution characteristics of the error points in space to obtain spatial distribution data of the error points;

[0101] Call the optimized copper element layer data to get the copper element color concentration of the same grid point before and after optimization, traverse the layer data before and after optimization, and extract the color concentration value C of the same grid point. before and C after When calculating the offset of the grid point concentration change, the difference calculation formula ΔC=C after -C before When the grid points whose offset exceeds the set offset threshold are selected as error points, the offset threshold ΔC is set. th , if the calculated offset of a grid point ΔC>ΔC th , then the point is determined to be an error point, for example, setting ΔC th =0.5, if the calculation result of a grid point ΔC = 0.8, then the point is marked as an error point. When the distribution characteristics of the error points in space are counted, the spatial distribution density D of the error points is calculated. m =N m / A, where N m is the number of error points, A is the total area, and the spatial autocorrelation coefficient of the error points is calculated. Determine whether the error points have clustered distribution characteristics. If R>0.6, it is considered that the error points are clustered in space, and finally obtain the spatial distribution data of the error points.

[0102] S402: Based on the spatial distribution data of the error point, the copper element concentration difference between the error point and the adjacent grid points is calculated, the direction and intensity of the error point offset are analyzed, the offset trend within the error point area is statistically analyzed, the element concentration area, element transition area, and background area are identified, and the spatial position of each area is marked to obtain copper element area classification data;

[0103] Based on the spatial distribution data of the error points, when calculating the copper element concentration difference between the error point and the adjacent grid points, all error points are traversed to calculate the color concentration difference ΔC between them and the adjacent grid points. nei =|C m -C n |, where C m is the error point concentration value, C n When analyzing the direction and intensity of the error point offset, the gradient of the concentration change of the error point along the four directions (east, south, west, and north) is calculated. dir -C m ) / M, where C dir is the concentration of adjacent directional grid points, M is the distance between adjacent grid points, and the mean directional gradient T of all error points is calculated. avg and standard deviation σ T , when the deviation trend within the error point area is statistically analyzed, the concentration change deviation ΔC of the error point within the area is calculated region =∑|ΔC i | / N m , set the offset trend threshold ΔC trend , if ΔC region >ΔC trend , then the area is judged as a high offset area. When identifying element concentration areas, element transition areas and background areas, the classification standard is set according to the concentration offset and spatial distribution characteristics of the error points. If ΔC>1.0 and the error point density D m >0.8, it is marked as element concentration area. If 0.5<ΔC≤1.0 and 0.4 <D m ≤0.8, it is marked as the element transition zone. If ΔC≤0.5 and D m If the value is ≤0.4, it is marked as the background area. When marking the spatial position of each area, the classification result corresponding to each grid point is recorded and a spatial distribution matrix is ​​generated to finally obtain the regional classification data of copper elements.

[0104] S403: Establishing a layer structure correspondence based on the copper element region classification data, integrating the feature types of each region, and adjusting the boundary information within the layer to obtain the copper element layer resolution result;

[0105] According to the copper element regional classification data, when establishing the layer structure correspondence, the classification results of the element concentration area, transition area and background area are mapped to the layer structure, the boundary grid points of each area are extracted and the spatial topological relationship is established. When integrating the feature types of each area, the average concentration value C in each area is calculated. avg =∑C i / N r , where N r is the number of grids in the region, and the concentration gradient T inside the region is calculated. r =(C max -C min ) / M r , where M r The maximum distance within the region. When adjusting the boundary information within the layer, compare the boundary concentration difference ΔC of adjacent regions. bound =|C r1 -C r2 |, set the boundary smoothing adjustment threshold ΔC smooth , if ΔC bound >ΔC smooth , the boundary concentration value is adjusted by interpolation, and finally the copper element layer resolution result is obtained.

[0106] The method further includes, S5: calling mutation region information in the copper element layer resolution result, analyzing the color concentration change gradient of adjacent sampling points in the copper element mutation region, screening grid points whose concentration change gradient exceeds a gradient threshold, identifying a transition trend of the mutation region, calculating a transition deviation of adjacent grid points in the mutation region, adjusting a concentration transition interpolation method of the mutation region based on the transition deviation, and correcting the concentration change in the mutation region to obtain optimized copper element layer data;

[0107] The optimized copper element layer data includes the concentration change of grid points in the mutation area, the gradient change in the transition area, and the interpolation method adjustment results;

[0108] S501: Calling the mutation area information in the copper element layer resolution result, calculating the color density change gradient between adjacent sampling points, screening the grid points where the color density change gradient exceeds the gradient threshold, analyzing the transition trend in the mutation area, and obtaining the mutation area transition trend data;

[0109] Call the number of sampling points and corresponding color concentration data in the mutation area in the optimized copper element sampling distribution information, traverse all grids in the mutation area, and extract the number of sampling points N in each grid i and its corresponding color density data C i When calculating the color density gradient between adjacent sampling points, the adjacent point difference method is used to calculate the gradient T = |C i+1 -C i | / L, where Ci+1 and C i are the color density values ​​of adjacent sampling points, L is the distance between the two points, and when screening the grid points where the color density change gradient exceeds the gradient threshold, the gradient change threshold T is set. th , if the gradient T>T th , then the point is determined to be a mutation point of color concentration change, for example, set T th =0.4, if the calculated gradient value T of a point is 0.6, then the point is marked as a mutation point. When analyzing the transition trend in the mutation area, the gradient mean T of all mutation points is calculated. avg and standard deviation σ T , the fluctuation range of the gradient in the statistical mutation interval ΔT = T max -T min , and judge the spatial consistency of concentration changes in the region, and finally obtain the transition trend data of the mutation area.

[0110] S502: Based on the transition trend data of the mutation area, calculate the transition deviations of adjacent grid points in the mutation area, count the transition deviation range of each area, filter out grid points whose transition deviations exceed a set deviation threshold, mark the transition abnormal area, and obtain the transition deviation data of the mutation area;

[0111] Calculate the transition deviation of adjacent grid points in the mutation area using the formula:

[0112]

[0113] Where, ΔC nei represents the composite transition deviation of adjacent grid points in the mutation region, N represents the number of adjacent grid points in the mutation region, C i represents the copper concentration value of the i-th grid point, C i+1 represents the copper concentration value of its adjacent grid points, D i Represents the local concentration gradient variation at the i-th grid point, C j represents the copper concentration value of the jth grid point in the mutation area, C j+1 represents the copper concentration value of its adjacent grid points, C m Represents the copper concentration value of the mth grid point in the mutation area, Represents the mean value of the copper concentration of all grid points in the mutation area;

[0114] Detailed explanation of the formula and the process of formula calculation and derivation:

[0115] This formula calculates the composite transition deviation for adjacent grid points within the abrupt region. The first part calculates the weighted average transition deviation, accounting for the effect of the variation and relative change in copper concentration between adjacent grid points. The second part calculates the normalized transition deviation, accounting for the sum of the adjacent and average differences across all grid points.

[0116] To this end, consider an actual monitoring area consisting of four grid points, and the copper concentration value C i 20, 15, 10, 5 ppm respectively. In addition, each grid point D i The magnitude of the local concentration gradient changes (expressed as percentage changes) are 0.1, 0.2, 0.15, and 0.05, respectively.

[0117] First calculate the average copper concentration:

[0118]

[0119] Calculate the first part:

[0120]

[0121] The average transition deviation is:

[0122] Calculate the second part:

[0123]

[0124] The normalized transition deviation is:

[0125] The final formula value is:

[0126] ΔC nei =5.75+0.714=6.464;

[0127] The results showed that the average transition deviation between adjacent grid points in the selected mutation area was 5.75, and when the average difference in all concentrations within the area was taken into account, the adjusted composite transition deviation was 6.464. This reflects the large heterogeneity of copper distribution within the area, and further research or monitoring may be needed to determine whether there are environmental factors or other intervening factors.

[0128] S503: adjusting the color density transition interpolation method of the mutation area according to the transition deviation data of the mutation area, correcting the density change in the mutation area, calculating the adjusted grid color density value, and obtaining the optimized copper element layer data;

[0129] According to the transition deviation data of the mutation area, when adjusting the color density transition interpolation method of the mutation area, for the area marked as abnormal, use linear interpolation or bilinear interpolation to recalculate the density value of the grid point. When correcting the concentration change in the mutation area, traverse all adjustment grids and calculate the corrected color density mean C new =(C old +C adj ) / 2, where C old is the original grid concentration, C adj For the adjacent grid density, when calculating the adjusted grid color density value, ensure that the concentration gradient change of all grids after adjustment meets T <T th , and finally obtain the optimized copper element layer data.

[0130] See also Figure 2 , geochemical element layer resolution system, the system includes:

[0131] The grid mutation region detection module divides the copper element layer into grids, calculates the rate of change of the copper element color concentration of adjacent grid points, marks the grid points that exceed the set change rate threshold as mutation regions, analyzes the difference in copper element content between adjacent grid points in the mutation region, marks the boundary points, and obtains the distribution information of the copper element boundary points;

[0132] The boundary structure analysis module connects the boundary points in the copper element boundary point distribution information to form a boundary curve, calculates the curvature change of the boundary curve, determines whether there is a break in the boundary curve based on the smoothness of the change, adjusts the connection method of the boundary points according to the boundary point spacing in the break area, and obtains the copper element boundary structure information;

[0133] The sampling distribution calculation module calls the coordinates and color concentration changes of the boundary points in the copper element boundary point distribution information, counts the number of sampling points in the grid, marks the copper element dense and sparse areas according to the number of sampling points, and obtains the copper element sampling distribution information;

[0134] The layer resolution result generation module combines the copper element boundary structure information and copper element sampling distribution information to analyze the offset of the concentration change at each grid point. Based on the offset direction of the offset, it identifies the element concentration area, transition area, and background area to obtain the copper element layer resolution result.

[0135] The transition area adjustment module calls the mutation area information in the copper element layer resolution result, analyzes the color concentration change gradient of adjacent sampling points in the copper element mutation area, screens the grid points whose concentration change gradient exceeds the gradient threshold, identifies the transition trend of the mutation area, calculates the transition deviation of adjacent grid points in the mutation area, adjusts the concentration transition interpolation method of the mutation area according to the transition deviation, and corrects the concentration change in the mutation area to obtain the optimized copper element layer data.

[0136] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A geochemical element layer resolution method, characterized in that: The following steps are involved: S1: Grid the copper element layer, calculate the rate of change of the copper element color concentration of adjacent grid points, mark the grid points that exceed the set change rate threshold as mutation areas, analyze the difference in copper element content between adjacent grid points in the mutation area, mark the boundary points, and obtain the distribution information of the copper element boundary points; S2: connecting the boundary points in the copper element boundary point distribution information to form a boundary curve, calculating the curvature change of the boundary curve, determining whether the boundary curve has a break based on the smoothness of the change, adjusting the connection method of the boundary points based on the boundary point spacing in the break area, and obtaining the copper element boundary structure information; S3: calling the coordinates and color density changes of the boundary points in the copper element boundary point distribution information, counting the number of sampling points in the grid, marking the copper element dense area and sparse area according to the number of sampling points, and obtaining the copper element sampling distribution information; S4: combining the copper element boundary structure information and the copper element sampling distribution information, analyzing the offset of the concentration change of each grid point, identifying the element concentration area, transition area and background area according to the offset direction of the offset, and obtaining the copper element layer resolution result.

2. The geochemical element layer resolution method according to claim 1, characterized in that: The copper element boundary point distribution information includes boundary point coordinates, boundary point concentration change amplitude, and boundary point spacing change; the copper element boundary structure information includes boundary curve shape, boundary curve curvature change, and boundary point connection method; the copper element sampling distribution information includes data sampling point density, grid sampling point number, and copper element dense and sparse area distribution; the copper element layer resolution result includes element concentration area, transition area, and background area.

3. The geochemical element layer resolution method according to claim 1, characterized in that: Grid the copper element layer, calculate the rate of change of the copper element color concentration of adjacent grid points, mark the grid points that exceed the set change rate threshold as the mutation area, analyze the difference in copper element content between adjacent grid points in the mutation area and mark the boundary points. The specific steps for obtaining the distribution information of copper element boundary points are as follows: S101: Obtaining the coverage of the copper element layer and setting the grid division range, extracting the copper element color concentration data within the corresponding grid area, calculating the copper element color concentration change rate between grid points, marking the grid points whose change rate exceeds the set concentration change rate threshold as concentration mutation areas, and obtaining a concentration mutation grid point set; S102: Based on the concentration mutation grid point set, calculating the copper element color concentration difference of each grid point in the mutation area, counting the copper element content difference between adjacent grid points within the mutation area, marking grid points whose content difference exceeds a difference threshold as concentration difference boundary points, and obtaining a concentration difference boundary point set; S103: Analyze the copper element concentration distribution characteristics within the boundary point area based on the concentration difference boundary point set, calculate the spatial coherence of the copper element concentration within the boundary point area, extract the copper element concentration change trend along each direction of the boundary point, and obtain the copper element boundary point distribution information.

4. The geochemical element layer resolution method according to claim 1, wherein: The specific steps of connecting the boundary points in the copper element boundary point distribution information to form a boundary curve, calculating the curvature change of the boundary curve, determining whether the boundary curve has a break based on the smoothness of the change, and adjusting the connection method of the boundary points based on the boundary point spacing in the break area to obtain the copper element boundary structure information are as follows: S201: Calling the boundary points of the mutation area in the copper element boundary point distribution information, connecting adjacent boundary points in coordinate arrangement order to form a boundary curve, calculating the curvature change amplitude of the boundary curve, analyzing the curvature fluctuation range of each section of the boundary curve, evaluating the smoothness of the boundary curve, screening the area where the curvature change amplitude exceeds a set smoothness threshold, and obtaining boundary curvature change data; S202: Based on the boundary curvature change data, determine whether there is a fracture area on the boundary curve, detect the coordinates of the boundary points of the fracture area, calculate the spatial distance between adjacent fracture boundary points, count the distribution range of the boundary point distance of each fracture area, and obtain boundary fracture point distance data; S203: adjusting the boundary line segment connection mode of the fracture area according to the boundary fracture point spacing data, calculating the continuity of the corrected boundary curve, detecting the adjusted boundary curvature distribution, extracting the corrected boundary point coordinates, and obtaining the copper element boundary structure information.

5. The geochemical element layer resolution method according to claim 1, characterized in that: The specific steps of calling the coordinates and color density changes of the boundary points in the copper element boundary point distribution information, counting the number of sampling points in the grid, marking the copper element dense area and sparse area according to the number of sampling points, and obtaining the copper element sampling distribution information are as follows: S301: Calling the coordinates of the boundary points and the corresponding color density change values ​​in the copper element boundary structure information, obtaining copper element data sampling points in the boundary area and the adjacent area, calculating the sampling point density of the grids in each area, counting the number of copper element sampling points in each grid, screening the areas in the adjacent grids where the number of sampling points changes by more than a set sampling point number change threshold, marking the distribution boundary between the dense sampling area and the sparse sampling area, and obtaining sampling density distribution data; S302: Based on the sampling density distribution data, extract the color concentration change value of the copper element in the sparse area, calculate the color concentration gradient change range in the sparse area, count the gradient fluctuation interval of each grid area, screen the grid areas whose gradient change amplitude exceeds the gradient change amplitude threshold, and obtain gradient change interval data; S303: According to the gradient change interval data, the sampling point distribution in the sparse area is adjusted, the copper element sampling points in the sparse area are increased according to the color concentration gradient change range, and the sampling point calculation method in the dense area is corrected. The number distribution of sampling points in each grid area is updated to obtain the copper element sampling distribution information.

6. The geochemical element layer resolution method according to claim 5, characterized in that: To calculate the color density gradient change in a sparse area, the formula is used: Among them, T v represents the gradient fluctuation index of the grid area, N represents the number of sampling points in the grid area, T i Represents the color density gradient value of the i-th sampling point, T avg Represents the mean value of the color density gradient of all sampling points in the grid area, T j Represents the color density gradient value of the j-th sampling point in the grid area.

7. The geochemical element layer resolution method according to claim 1, characterized in that: Combining the copper element boundary structure information and copper element sampling distribution information, analyzing the offset of the concentration change of each grid point, identifying the element concentration area, transition area and background area according to the offset direction of the offset, and obtaining the copper element layer resolution result in the following specific steps: S401: Analyzing the copper element color concentration at each grid point based on the copper element boundary structure information and the copper element sampling distribution information, calculating the offset of the grid point concentration change, marking the grid points whose offset exceeds a set offset threshold as error points, and statistically analyzing the distribution characteristics of the error points in space to obtain spatial distribution data of the error points; S402: Based on the spatial distribution data of the error point, the copper element concentration difference between the error point and the adjacent grid points is calculated, the direction and intensity of the error point offset are analyzed, the offset trend within the error point area is statistically analyzed, the element concentration area, the element transition area, and the background area are identified, and the spatial position of each area is marked to obtain copper element area classification data; S403: establishing a layer structure correspondence relationship based on the copper element region classification data, integrating the feature type of each region, and adjusting the boundary information within the layer to obtain a copper element layer resolution result.

8. The geochemical element layer resolution method according to claim 1, characterized in that: The method also The method comprises the following steps: S5: calling the mutation region information in the copper element layer resolution result, analyzing the color concentration change gradient of adjacent sampling points in the copper element mutation region, screening the grid points whose concentration change gradient exceeds the gradient threshold, identifying the transition trend of the mutation region, calculating the transition deviation of adjacent grid points in the mutation region, adjusting the concentration transition interpolation method of the mutation region according to the transition deviation, and correcting the concentration change in the mutation region to obtain optimized copper element layer data; The optimized copper element layer data includes the concentration change of the grid points in the mutation area, the gradient change in the transition area, and the interpolation method adjustment result. S501: Calling the mutation area information in the copper element layer resolution result, calculating the color density change gradient between adjacent sampling points, screening the grid points where the color density change gradient exceeds the gradient threshold, analyzing the transition trend in the mutation area, and obtaining the mutation area transition trend data; S502: Based on the transition trend data of the mutation region, calculate the transition deviations of adjacent grid points in the mutation region, count the transition deviation range of each region, filter out grid points whose transition deviations exceed a set deviation threshold, mark transition abnormal regions, and obtain transition deviation data of the mutation region; S503: adjusting the color density transition interpolation method of the mutation area according to the mutation area transition deviation data, correcting the density change in the mutation area, calculating the adjusted grid color density value, and obtaining optimized copper element layer data.

9. The geochemical element layer resolution method according to claim 8, characterized in that: To calculate the transition deviation of adjacent grid points in the mutation area, the formula is used: Where, ΔC nei represents the composite transition deviation of adjacent grid points in the mutation region, N represents the number of adjacent grid points in the mutation region, C i represents the copper concentration value of the i-th grid point, C i+1 represents the copper concentration value of its adjacent grid points, D i Represents the local concentration gradient variation at the i-th grid point, C j represents the copper concentration value of the jth grid point in the mutation area, C j+1 represents the copper concentration value of its adjacent grid points, C m Represents the copper concentration value of the mth grid point in the mutation area, Represents the mean of the copper concentration values ​​of all grid points in the mutation area.

10. Geochemical element layer resolution system, characterized by: The method for geochemical element layer resolution according to any one of claims 1 to 9 is performed, wherein the system comprises: The grid mutation region detection module divides the copper element layer into grids, calculates the rate of change of the copper element color concentration of adjacent grid points, marks the grid points that exceed the set change rate threshold as mutation regions, analyzes the difference in copper element content between adjacent grid points in the mutation region, marks the boundary points, and obtains the distribution information of the copper element boundary points; The boundary structure analysis module connects the boundary points in the copper element boundary point distribution information to form a boundary curve, calculates the curvature change of the boundary curve, determines whether there is a break in the boundary curve based on the smoothness of the change, adjusts the connection method of the boundary points based on the boundary point spacing in the break area, and obtains the copper element boundary structure information; The sampling distribution calculation module calls the coordinates and color concentration changes of the boundary points in the copper element boundary point distribution information, counts the number of sampling points in the grid, marks the copper element dense area and sparse area according to the number of sampling points, and obtains the copper element sampling distribution information; The layer resolution result generation module combines the copper element boundary structure information and the copper element sampling distribution information, analyzes the offset of the concentration change of each grid point, identifies the element concentration area, transition area and background area according to the offset direction of the offset, and obtains the copper element layer resolution result; The transition region adjustment module calls the mutation region information in the copper element layer resolution result, analyzes the color concentration change gradient of adjacent sampling points in the copper element mutation region, screens the grid points whose concentration change gradient exceeds the gradient threshold, identifies the transition trend of the mutation region, calculates the transition deviation of adjacent grid points in the mutation region, adjusts the concentration transition interpolation method of the mutation region according to the transition deviation, corrects the concentration change in the mutation region, and obtains optimized copper element layer data.

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