Method for preparing a distribution map of mineralization centers based on spatial distribution regularities of anomalous elements

Abstract

This invention discloses a method for compiling mineralization center distribution maps based on the spatial distribution patterns of anomalous elements. The method involves collecting stream sediment data from the work area, performing principal component analysis, and extracting principal components with eigenvalues ​​greater than 1. It also distinguishes between medium-high temperature element combinations and medium-low temperature element combinations; calculates the mineralization center factor M; and calculates the outlier value M of the mineralization center factor. c Import the mineralization center factor M into the ZScape software and draw a contour map of the mineralization center factor in the working area; use the outlier value M of the mineralization center factor. c Create a mineralization center distribution map for the boundary; M≥M c The region is represented as the mineralization center; M≤M c The region is represented as the mineralization edge; based on the relationship between elemental zoning and temperature in stream sediments, this invention constructs mineralization center factors for magmatic-hydrothermal related deposits, realizes the quantitative compilation of mineralization center distribution maps, and innovates a new model for map compilation.

Description

Technical Field

[0001] This invention belongs to the field of mineral exploration technology, specifically relating to a method for compiling a distribution map of mineralization centers. Background Technology

[0002] In the field of mineral exploration, existing mapping primarily relies on existing geological, geophysical, geochemical, and remote sensing data of the work area to compile geological and mineral maps, structural maps, and comprehensive mineral exploration prediction maps. Through a series of maps, useful information such as ore-forming geological bodies and prospecting target areas can be obtained. However, the location of mineralization centers, which are most directly related to mineral exploration, cannot be accurately delineated. Existing geochemical methods have greatly aided our mineral exploration efforts, but they only show the anomaly range of a certain element within the work area and cannot represent the mineralization center for the specific deposit type within the work area.

[0003] Based on the thermodynamic theory of elements, during mineralization, the formation of compounds (minerals) by various elements occurs under relatively fixed conditions (such as pressure and temperature). Temperature is closely related to the chemical properties of elements and their distribution during mineralization. The source and temperature of mineralization determine the type and type of ore deposit, and also the location of element distribution. The zoning of elements due to temperature differences during mineralization provides a basis for anomaly or ore deposit evaluation. For example, Cu, W, Mo, Sn, and Bi generally form in the high-temperature stage (500~350℃), Pb, Zn, and Ag generally form in the high-to-medium-temperature stage (350~200℃), and Ba, As, Sb, Hg, and Au generally form in the medium-to-low-temperature stage (200~50℃). For magmatic-hydrothermal deposits, elements generally exhibit a zoning from the center to the periphery as follows: Cu-Mo → Cu, Mo, Ag, Rb → S, Co, As, F → Pb, Zn, Mn, Sr.

[0004] Therefore, how to utilize the temperature properties of elements and the spatial zonation of geochemical anomalies, as well as the differences between high-temperature and low-temperature element combinations, to establish a spatial distribution map of mineralization centers based on the spatial distribution patterns of anomalous elements has become an urgent technical problem to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide a method for compiling mineralization center distribution maps based on the spatial distribution patterns of anomalous elements. This method utilizes the zonation and differences of medium-high temperature and medium-low temperature elements in stream sediments to compile mineralization center distribution maps. By calculating mineralization center factors and factor anomalies, the location of mineralization centers can be quickly determined, providing a basis for tracing the direction of hydrothermal migration.

[0006] To achieve the above objectives, the following technical solution is adopted:

[0007] A method for compiling mineralization center distribution maps based on the spatial distribution patterns of anomalous elements includes the following steps:

[0008] (1) Principal component transformation of data

[0009] Principal component analysis was performed on the collected stream sediment data from the work area to extract principal components with eigenvalues ​​greater than 1. M intermediate-high temperature elemental combinations were identified, and the content of each element was denoted as follows: PC1 i Where i = 1...m; there are n combinations of elements at medium and low temperatures, and the content of each element is marked as follows: PC2 j , where j=1……n;

[0010] (2) Calculate the mineralization center factor

[0011] Will PC1 i and PC2 j The data for each element are tested for normality. For data that does not conform to a normal distribution, an iterative method is used to process it until it conforms to a normal distribution. The mean X1 and standard deviation Sd1 after the last iteration are taken, and the lower limit of abnormality is calculated as X1+2Sd1; (The data is then labeled.) PC1 i and PC2 j The abnormal lower bounds for each element are as follows: i and j ;

[0012] The mineralization center labeling factor is M, and its calculation formula is:

[0013] ;

[0014] The calculation formula means that the sum of the chromaticity values ​​of each element in the medium-high temperature element combination is divided by the sum of the chromaticity values ​​of each element in the medium-low temperature element combination, where the chromaticity value is the element value divided by the abnormal lower limit value.

[0015] (3) Calculate the outliers of mineralization center factors

[0016] The outlier value M of the mineralization center factor was calculated using an iterative method. c ;

[0017] (4) Compile a distribution map of mineralization centers

[0018] Import the mineralization center factor M into the ZScape software and plot the mineralization center factor contour map of the working area; use the mineralization center factor outliers M c Create a mineralization center distribution map for the boundary; M≥M cThe region is represented as the mineralization center; M≤M c The area is represented as a mineralized edge.

[0019] According to the above scheme, when distinguishing between medium-high temperature element combinations and medium-low temperature element combinations in step 1, the element combination situation of positive loading reaction is taken into account, combined with the geological background and deposit type of the working area.

[0020] According to the above scheme, the iterative processing in step 2 includes: converting the original data of each element in the working area into logarithmic data, calculating its mean X1 and standard deviation Sd1, removing extremely high and low values ​​according to X1±3Sd1, and repeating the iteration until no points can be removed.

[0021] According to the above scheme, the specific iterative method for step 3 is as follows:

[0022] The data for the mineralization center factor M are tested for normality. For data that does not conform to a normal distribution, an iterative method is used to process it. The original data of M is converted into logarithmic data, and its mean (X1) and standard deviation (Sd1) are calculated. Extremely high and low values ​​are removed according to X1±3Sd1, and this process is repeated iteratively until no data can be removed. After the data of M conforms to a normal distribution, the mean (X1) and standard deviation (Sd1) after the last iteration are taken, and the outlier value of the mineralization center factor M is determined. c It is calculated as X1+2Sd1.

[0023] According to the above scheme, step 4 includes setting M≥M c The area filled with reddish-brown indicates the mineralization center; M≤M c The areas are filled with blue-yellow and light red to indicate mineralized edges.

[0024] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0025] Based on the relationship between elemental zonation and temperature in stream sediments, this invention constructs mineralization center factors for magmatic-hydrothermal related deposits, enabling the quantitative compilation of mineralization center distribution maps and innovating a new model for map compilation.

[0026] This invention transforms traditional geological mapping from intuitive understanding to quantitative description, endows geochemical element data anomalies with certain indicative significance, effectively connects the relationship between elemental zonation and temperature, and transforms anomalies for a certain element into mineralization center factors applicable to all magmatic-hydrothermal deposits, providing an important basis for the identification of mineralization centers and the determination of hydrothermal migration directions. Attached Figure Description

[0027] Figure 1 Principal component analysis (PCA) of geochemical data from the Juno mineralization area.

[0028] Figure 2 Map showing the distribution of mineralization centers in the Juno mining area. Detailed Implementation

[0029] The following embodiments further illustrate the technical solution of the present invention, but are not intended to limit the scope of protection of the present invention.

[0030] The specific implementation provides a method for compiling a mineralization center distribution map of the Juno mineralization cluster area using geochemical data:

[0031] (1) Principal Component Analysis (PCA)

[0032] 1415 stream sediment data points from the Juno mineralization area were collected using a standard 50,000 map sheet. Principal component analysis (PCA) was performed on all data. Figure 1 As shown, the principal component analysis results for all elements in the working area were obtained, and principal components with eigenvalues ​​greater than 1 were extracted. Based on the elemental combinations of positive loading reactions, combined with the geological background and deposit type of the working area, four medium-to-high temperature elemental combinations were identified in this area: Cu, Mo, W, and Bi. The content of each element is labeled as follows: PC1 1. PC1 2. PC1 3. PC1 4. Nine low-temperature elemental combinations were identified in this region: Sn, Ba, Hg, Sb, As, Zn, Pb, Au, and Ag. The content of each element is labeled as follows: PC2 1 PC2 2 PC2 3 PC2 4 PC2 5 PC2 6 PC2 7 PC2 8 PC2 9.

[0033] (2) Calculate the mineralization center factor

[0034] Will PC1 i and PC2 j The data of each element are tested for normality. For data that do not conform to normality, an iterative method is used for processing. The specific steps are to convert the original data of each element in the working area into logarithmic data, calculate its mean (X1) and standard deviation (Sd1), remove extremely high and low values ​​according to X1±3Sd1, and repeat the iteration until no points can be removed.

[0035] After assuming the data follows a normal distribution, the mean (X1) and standard deviation (Sd1) after the last iteration are taken. The lower bound of outlier is X1 + 2Sd1. PC1 i and PC2j The abnormal lower bounds for each element are as follows: i (i=1...4) and j (j=1……9). The calculated lower bounds for each element are as follows: 1 2 3 4 1 2 3 4 5 6 7 8 9

[0036] The mineralization center factor is denoted as M, and its calculation formula is the sum of the contrast values ​​of each element in the medium-high temperature element combination obtained from the principal component analysis above, divided by the sum of the contrast values ​​of each element in the medium-low temperature element combination, where the contrast value is the element value divided by the lower limit of the anomaly. The formula for calculating the mineralization center factor is as follows:

[0037] ;

[0038] (3) Calculate the outliers of mineralization center factors

[0039] The outlier value M of the mineralization center factor is calculated using the iterative method described in step (2). c The specific calculation process is as follows: The data of the mineralization center factor M is tested for normality. For data that does not conform to a normal distribution, an iterative method is used to process it. The original data of M is converted into logarithmic data, and its mean (X1) and standard deviation (Sd1) are calculated. Extremely high and low values ​​are removed at a rate of X1 ± 3Sd1, and this process is repeated iteratively until no data can be removed. After the data of M conforms to a normal distribution, the mean (X1) and standard deviation (Sd1) after the last iteration are taken. The lower limit of the mineralization center factor anomaly is X1 + 2Sd1. The calculated mineralization center outlier M... c =3.17.

[0040] (4) Compile a distribution map of mineralization centers

[0041] Import the mineralization center factor M calculated in step (2) into the software ZScape, draw the mineralization center factor contour map of the working area, and use the mineralization center factor outlier M calculated in step (3) as the basis for the plotting. c Create a distribution map of mineralization centers at the boundary.

[0042] M≥M cThe region with a value of 3.17 is filled with reddish-brown, representing a mineralization center; M≤M c The area with a value of 3.17 is filled with blue-yellow and light red to indicate the mineralization edge, such as... Figure 2 As shown in the figure, the compiled mineralization center distribution map is basically consistent with the actual mineralization centers, further proving the effectiveness of the method.

Claims

1. A method for compiling a mineralization center distribution map based on the spatial distribution patterns of anomalous elements, characterized in that... Includes the following steps: (1) Collect sediment data from the working area's water system and perform principal component analysis to extract principal components with eigenvalues ​​greater than 1; identify m combinations of medium- to high-temperature elements, with the content of each element labeled as follows. PC1 i Where i = 1...m; there are n combinations of elements at medium and low temperatures, and the content of each element is marked as follows: PC2 j , where j=1……n; (2) PC1 i and PC2 j The data for each element are tested for normality. For data that does not conform to a normal distribution, an iterative method is used to process it until it conforms to a normal distribution. The mean X1 and standard deviation Sd1 after the last iteration are taken, and the lower limit of abnormality is calculated as X1+2Sd1; (The data is then labeled.) PC1 i and PC2 j The abnormal lower bounds for each element are as follows: i and j ; The mineralization center labeling factor is M, and its calculation formula is: ； The calculation formula means that the sum of the chromaticity values ​​of each element in the medium-high temperature element combination is divided by the sum of the chromaticity values ​​of each element in the medium-low temperature element combination, where the chromaticity value is the element value divided by the abnormal lower limit value. (3) Calculate the outlier value M of the mineralization center factor using the iterative method. c ; (4) Import the mineralization center factor M into the ZScape software and draw the contour map of the mineralization center factor in the working area; use the outlier value M of the mineralization center factor as the plotting point. c Create a mineralization center distribution map for the boundary; M≥M c The region is represented as the mineralization center; M≤M c The area is represented as a mineralized edge.

2. The method for compiling a mineralization center distribution map based on the spatial distribution law of anomalous elements as described in claim 1, characterized in that... In step 1, when distinguishing between medium-high temperature element combinations and medium-low temperature element combinations, the element combinations of positive loading reactions are taken into account, combined with the geological background and deposit type of the working area.

3. The method for compiling a mineralization center distribution map based on the spatial distribution law of anomalous elements as described in claim 1, characterized in that... The iterative processing described in step 2 includes: converting the original data of each element in the working area into logarithmic data, calculating its mean X1 and standard deviation Sd1, removing extremely high and low values ​​according to X1±3Sd1, and repeating the iteration until no points can be removed.

4. The method for compiling a mineralization center distribution map based on the spatial distribution law of anomalous elements as described in claim 1, characterized in that... The specific iterative method for step 3 is as follows: The data for the mineralization center factor M are tested for normality. For data that does not conform to a normal distribution, an iterative method is used to process it. The original data of M is converted into logarithmic data, and its mean X1 and standard deviation Sd1 are calculated. Extremely high and low values ​​are removed according to X1±3Sd1, and the iteration is repeated until no points can be removed. After the data of M conforms to a normal distribution, the mean X1 and standard deviation Sd1 after the last iteration are taken, and the outlier value of the mineralization center factor M is determined. c It is calculated as X1+2Sd1.

5. The method for compiling a mineralization center distribution map based on the spatial distribution law of anomalous elements as described in claim 1, characterized in that... Step 4 includes setting M≥M c The area filled with reddish-brown indicates the mineralization center; M≤M c The areas filled with blue-yellow and light red indicate mineralized edges.

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