Geochemical metallogenic information rapid extraction method based on element content gradient method
By using an elemental content gradient method, the problem of weak anomaly identification in complex geological backgrounds is solved by traditional methods. This enables rapid and effective extraction of geochemical mineralization information and generates intuitive mineralization prediction maps.
Patent Information
- Application Number
- CN202511747478.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-26
- Publication Date
- 2026-02-27
AI Technical Summary
Traditional geochemical anomaly identification methods suffer from several drawbacks when dealing with complex, irregular, and fractal geochemical element distributions. These include highly subjective setting of anomaly lower limits, poor ability to identify weak anomalies, sensitivity to shielding effects, and difficulty in accurately delineating concentration centers.
Using the elemental content gradient method, we collect and standardize geochemical sample data, construct a Kriging model to generate a content raster map, calculate the elemental gradient, combine fractal statistics and geological understanding to determine the lower limit of anomalies, generate a chemical elemental content gradient map, and integrate the information to generate the final mineralization prediction map.
It enables precise capture of weak anomalies, improves mineral exploration indication capabilities, enhances the efficiency and objectivity of mineralization information extraction, and generates intuitive and easy-to-interpret maps suitable for different geological backgrounds.
Smart Images
Figure CN121579609A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of geological exploration, in particular to a geochemical metallogenic information rapid extraction method based on element content gradient method. BACKGROUND
[0002] Traditional geochemical anomaly recognition methods are mostly based on classical statistics, such as mean plus-minus multiple standard deviation method, 85% cumulative frequency method, and background value method. These methods have certain effects when dealing with data with approximately normal or lognormal element content distribution, but when facing actual data with complex, irregular, and fractal characteristics of geochemical element content distribution, the following problems often exist: the abnormal lower limit setting is highly subjective and depends on experience, and has poor recognition ability for weak anomalies; sensitive to high-value area shielding effect, easy to cover weak anomaly information; unable to fully reflect the spatial gradient change characteristics of element content, and difficult to accurately delineate the concentration center; there are theoretical limitations when dealing with non-smooth and non-differentiable geochemical surfaces.
[0003] Therefore, there is an urgent need for a geochemical metallogenic information extraction method that can effectively identify weak anomalies, accurately extract concentration centers, and be applicable to complex geological backgrounds. SUMMARY
[0004] The purpose of the present application is to provide a geochemical metallogenic information rapid extraction method based on element content gradient method, aiming to solve or improve at least one of the above technical problems.
[0005] To achieve the above purpose, the present application provides the following scheme: A geochemical metallogenic information rapid extraction method based on element content gradient method, comprising: Collecting geochemical sample analysis data of original medium in the target area and performing standardization processing to obtain pre-processing data; the original medium includes stream sediments, rocks and soil; Constructing a Kriging model based on the pre-processing data and generating a geochemical content grid map; Calculating the gradient of element content at each grid pixel in the geochemical content grid map and generating a chemical element content gradient map; Using a comprehensive determination method combining fractal statistics method and geological understanding to determine the abnormal lower limit in the chemical element content gradient map, and delineating a gradient anomaly map according to the abnormal lower limit; Integrating the gradient anomaly maps corresponding to all target elements in the target area, and generating a final 1:250,000 geochemical metallogenic prediction map according to the information overlap degree.
[0006] Optionally, the collecting geochemical sample analysis data of original medium in the target area and performing standardization processing to obtain pre-processing data specifically comprises: Collecting geochemical sample analysis data of the original medium in the target area, and removing data points with obvious outliers or errors greater than a set threshold to obtain preliminary screening data; Standardizing the preliminary screening data by logarithmic conversion to obtain preprocessed data.
[0007] Optionally, the Krige model is constructed based on the preprocessed data, and a geochemical content grid map is generated, specifically including: Based on the preprocessed data, a variogram analysis is performed, and a best spatial structure model is fitted to obtain a Krige model, and the Krige model is used to perform Krige interpolation calculation on the target area to generate a geochemical content grid map of each element.
[0008] Optionally, the gradient of the element content at each grid pixel in the geochemical content grid map is calculated, and a chemical element content gradient map is generated, specifically including: The geochemical content grid map is imported into the ArcGIS platform, and the slope tool in the spatial analysis module is used to calculate the change rate of the central pixel in the x direction and the y direction; Based on the calculated change rate and a gradient value calculation formula, the gradient of the element content at each grid pixel in the geochemical content grid map is calculated, and a chemical element content gradient map is generated.
[0009] Optionally, the gradient value calculation formula is specifically: wherein, and are the change rates of the central pixel in the horizontal and vertical directions.
[0010] Optionally, the slope tool uses a moving 3x3 window to traverse the entire grid.
[0011] Optionally, the comprehensive determination method combining fractal statistics with geological understanding is used to determine the anomaly lower limit in the chemical element content gradient map, and a gradient anomaly map is delineated according to the anomaly lower limit, specifically including: First, the generated gradient grid data is used to draw a curve on a double logarithmic coordinate using a C-A model, and the gradient value corresponding to the inflection point of the curve is a natural threshold for distinguishing background and anomalies; second, the natural threshold is adjusted in combination with the known deposit distribution, geological structure and lithology information of the target area to ensure that the delineated anomalies have a spatial coupling relationship with the ore-forming geological conditions; and finally, all pixels greater than or equal to the finally determined gradient anomaly lower limit are extracted to form a binary gradient anomaly map.
[0012] Optionally, the gradient anomaly map corresponding to all target elements in the target area is integrated, and a final 1:250,000 geochemical metallogenic prediction map is generated according to the information overlap degree. The gradient anomaly maps of all target elements in the target area are spatially superimposed and analyzed, and a 1:250,000 geochemical metallogenic prediction map is obtained according to the multi-element gradient anomaly overlap degree and gradient value.
[0013] According to the specific embodiments provided by the present application, the following technical effects are disclosed: The present application discloses a geochemical metallogenic information rapid extraction method based on element content gradient method, which comprises collecting geochemical sample analysis data of primary medium in a target area and performing standardization processing to obtain pre-processing data; constructing a Kriging model based on the pre-processing data and generating a geochemical content grid map; calculating the gradient of element content at each grid pixel in the geochemical content grid map and generating a chemical element content gradient map; determining the abnormal lower limit in the chemical element content gradient map by using a comprehensive determination method combining fractal statistics method and geological understanding, and delineating a gradient anomaly map according to the abnormal lower limit; and integrating the gradient anomaly maps corresponding to all target elements in the target area and generating a final 1:250,000 geochemical metallogenic prediction map according to the information overlap degree. The present application is guided by fractal theory, directly faces the essential characteristics of non-smooth and non-differentiable of geochemical field, accurately captures geochemical anomalies by quantifying the spatial gradient change of element content, especially the weak anomalies that are difficult to identify by traditional methods, so as to realize rapid and effective extraction of metallogenic information. BRIEF DESCRIPTION OF DRAWINGS
[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings described below are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.
[0015] Figure 1 The flowchart of the geochemical metallogenic information rapid extraction method based on element content gradient method of the present application. DETAILED DESCRIPTION
[0016] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.
[0017] The application aims to provide a geochemical metallogenic information rapid extraction method based on an element content gradient method, and aims to solve or improve at least one of the above technical problems.
[0018] In order to make the above-mentioned purposes, characteristics and advantages of the application more obvious and easy to understand, the application will be further described in detail below in combination with the drawings and specific embodiments.
[0019] As shown in the drawings, the application provides a geochemical metallogenic information rapid extraction method based on an element content gradient method, comprising: Figure 1 Step 100: Collecting geochemical sample analysis data of primary media in a target area and performing standardization processing to obtain pre-processing data; the primary media includes stream sediments, rocks and soil.
[0020] Step 200: Constructing a Kriging model based on the pre-processing data and generating a geochemical content grid map.
[0021] Step 300: Calculating the gradient of element content at each grid pixel in the geochemical content grid map and generating a chemical element content gradient map.
[0022] Step 400: Determining the anomaly lower limit in the chemical element content gradient map by using a comprehensive determination method combining fractal statistics and geological knowledge, and delineating a gradient anomaly map according to the anomaly lower limit.
[0023] Step 500: Integrating information of gradient anomaly maps corresponding to all target elements in the target area, and generating a final 1:250,000 geochemical metallogenic prediction map according to information overlap degree.
[0024] As a specific embodiment, data pre-processing and standardization are performed in step 100: Collecting geochemical sample analysis data of primary media such as stream sediments, rocks or soil in the study area, ensuring data quality, and eliminating obviously outlying or large error data points. In order to eliminate the differences in dimensions and background distribution between different elements, the original content data is standardized. It is recommended to use log transformation (Log-Transformation) or normal score transformation (Normal Score Transformation) to make the data distribution more normal, which is convenient for subsequent statistical analysis.
[0025] As a specific embodiment, spatial interpolation and gridding are performed in step 200: Based on the pretreated data, the variogram analysis is carried out, and the best spatial structure model (such as spherical model, exponential model or Gaussian model) is fitted, that is, the Kriging model. The above model is used to carry out Kriging interpolation calculation on the whole research area, and a high-precision geochemical content grid map (raster data) of each element is generated. This step can effectively suppress the "shielding effect" of the extremely high value, reduce the boundary and interpolation error, and provide stable and reliable basic data surface for gradient calculation.
[0026] As a specific embodiment, in step 300, the element content gradient raster map is generated: The core of this step is to calculate the change rate (i.e. gradient) of the element content at each raster pixel. The size of the gradient directly represents the degree of enrichment or depletion of the element at the point. The high gradient zone usually corresponds to the mutation boundary of the geochemical field, and the extreme value center of the gradient value often corresponds to the element enrichment center.
[0027] The Kriging grid data obtained in step 200 is imported into the ArcGIS or MapGIS platform, and the "slope (Slope)" tool in the spatial analysis module is used for calculation. The tool uses a moving 3x3 window to traverse the entire grid, and calculates the change rate of the center pixel in the x direction (dz / dx) and y direction (dz / dy).
[0028] In the process of software processing gradient change raster map set, if the processing pixel is NoData, the output of this position will be NoData. Since the unit of the gradient of this method is degree, the range of the calculated value is 0-90.
[0029] In each element geochemical gradient change raster map, the slope gradient (degrees) depends on the change rate (increment) of the surface from the center pixel in the horizontal (dz / dx) direction and the vertical (dz / dy) direction. The basic algorithm for calculating the slope is as follows (57.29578 is the value obtained by truncating the calculation result of 180 / π). The gradient value (in degrees) of each pixel is calculated using the following algorithm: wherein, and are the change rates of the center pixel in the horizontal and vertical directions. The calculation result is a value between 0° and 90°, and the larger the value, the more intense the change of the element content at the point.
[0030] As a specific embodiment, in step 400, the gradient anomaly lower limit is determined and the anomaly is extracted: The step discards the single threshold setting method in traditional statistics, and adopts a comprehensive determination method combining fractal statistics method (such as content-area fractal model C-A) with geological understanding, specifically as follows: Firstly, the generated gradient grid data is used to draw a curve on a double logarithmic coordinate (lg(Gradient) - lg(Area)) by using the C-A model, and the gradient value corresponding to the inflection point of the curve is the natural threshold value for distinguishing background and anomaly. Secondly, the threshold value is fine-tuned in combination with the distribution of known deposits (points) in the region, geological structure and lithology information, so as to ensure that the delineated anomaly has a good spatial coupling relationship with the ore-forming geological conditions. Finally, all pixels greater than or equal to the finally determined lower limit of the gradient anomaly are extracted to form a binary geochemical gradient anomaly map.
[0031] As a specific implementation, multi-element information integration and ore-forming prospect delineation are performed in step 500: The gradient anomaly maps of all target elements (such as Cu, Pb, Zn, Au, Ag, etc.) in the study area are subjected to spatial superposition analysis. Then, the regions with high multi-element gradient anomaly overlap degree and large gradient value are identified. These regions are usually strong element enrichment centers and focuses of ore-forming fluid activities, and are important ore-forming favorable areas and priority prospecting target areas, and finally a 1:25,000 comprehensive geochemical anomaly and ore-forming prediction map is generated.
[0032] In summary, the fractal theory is applied throughout the method, starting from recognizing the complexity of the geochemical field, and using gradient calculation to directly depict its nonlinearity and heterogeneity, thereby avoiding the information loss caused by the "smoothing" processing of traditional methods. Moreover, since the gradient value is extremely sensitive to small spatial changes in element content, it can effectively amplify and identify weak and slow anomalies that are masked by high background or traditional methods, thereby significantly improving the ore-prospecting indication ability.
[0033] The entire process of the present application highly depends on the mature GIS platform for implementation, and the operation process is streamlined and the calculation is automated, thereby greatly improving the efficiency and objectivity of information extraction, and the result map is intuitive and convenient for geological interpretation and decision-making. Moreover, the method of the present application has been successfully applied and verified in typical metallogenic belts such as the East Kunlun, and it has been proved to have good adaptability to different landscape areas and deposit types, and is a high-efficiency and reliable geochemical ore-forming information rapid extraction technology.
[0034] Each embodiment in the specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between each embodiment can be mutually referred to.
[0035] The principles and implementations of the present application are described in the specific examples in this article, and the above examples are only used to help understand the core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation and application range will be changed. Therefore, the content of the specification should not be understood as a limitation of the present application.
Claims
1. A geochemical metallogenic information rapid extraction method based on an element content gradient method, characterized in that, The method comprises the following steps: Collecting geochemical sample analysis data of the original medium in the target area and performing standardization processing to obtain pre-processing data; The original medium comprises stream sediments, rocks and soil; Constructing a Kriging model based on the pre-processing data and generating a geochemical content grid map; Calculating the gradient of element content at each grid pixel in the geochemical content grid map and generating a chemical element content gradient map; Determining the anomaly lower limit in the chemical element content gradient map by using a comprehensive determination method combining fractal statistics and geological knowledge, and delineating a gradient anomaly map according to the anomaly lower limit; Integrating the gradient anomaly maps corresponding to all target elements in the target area and generating a final 1:250,000 geochemical metallogenic prediction map according to the information overlap degree.
2. The method for rapid extraction of geochemical ore-forming information based on the elemental content gradient method according to claim 1, characterized in that, The step of collecting geochemical sample analysis data of the original medium in the target area and performing standardization processing to obtain pre-processing data specifically comprises the following steps: Collecting geochemical sample analysis data of the original medium in the target area and removing data points with obvious outliers or errors greater than a set threshold to obtain preliminary screening data; Performing standardization processing on the preliminary screening data by using logarithmic conversion to obtain pre-processing data.
3. The method according to claim 1, wherein, The step of constructing a Kriging model based on the pre-processing data and generating a geochemical content grid map specifically comprises the following steps: Performing variogram analysis based on the pre-processing data, fitting a best spatial structure model to obtain a Kriging model, performing Kriging interpolation calculation on the target area by using the Kriging model, and generating a geochemical content grid map of each element.
4. The method according to claim 1, wherein, The step of calculating the gradient of element content at each grid pixel in the geochemical content grid map and generating a chemical element content gradient map specifically comprises the following steps: Importing the geochemical content grid map into an ArcGIS platform and calculating the change rate of the central pixel in the x direction and the y direction by using a slope tool in a spatial analysis module; Calculating the gradient of element content at each grid pixel in the geochemical content grid map based on the calculated change rate and a gradient value calculation formula, and generating a chemical element content gradient map.
5. The method according to claim 4, wherein the method is characterized by, The gradient value calculation formula is specifically as follows: wherein and is the rate of change of the central pixel in the horizontal and vertical directions.
6. The method according to claim 4, wherein, The slope tool uses a moving 3*3 window to traverse the entire grid.
7. The method according to claim 1, wherein the method is characterized by, The step of determining the anomaly lower limit in the chemical element content gradient map by using a comprehensive determination method combining fractal statistics and geological knowledge and delineating a gradient anomaly map according to the anomaly lower limit specifically comprises the following steps: Firstly, a curve is plotted on a double logarithmic coordinate by using a C-A model for the generated gradient grid data, the gradient value corresponding to the inflection point of the curve is a natural threshold value for distinguishing background and anomaly; secondly, the natural threshold value is adjusted in combination with the known deposit distribution, geological structure and lithology information of the target area to ensure that the delineated anomaly has a spatial coupling relationship with the metallogenic geological condition; and finally, all pixels greater than or equal to the finally determined gradient anomaly lower limit are extracted to form a binary gradient anomaly map.
8. The method for rapid extraction of geochemical ore-forming information based on the elemental content gradient method according to claim 1, characterized in that, The step of integrating the gradient anomaly maps corresponding to all target elements in the target area and generating a final 1:250,000 geochemical metallogenic prediction map according to the information overlap degree specifically comprises the following steps: The gradient anomaly maps of all target elements in the target area are subjected to spatial superposition analysis, and are screened according to the multi-element gradient anomaly overlap degree and gradient value, so as to obtain a 1:250,000 geochemical metallogenic prediction map.