A background anomaly identification method based on center log ratio and fractal theory

Abstract

The present application relates to the technical field of exploration geochemistry, and especially relates to a background anomaly identification method based on a center logarithmic ratio value and a fractal theory, and mainly comprises the following characteristics: performing component data theory CLR conversion on chemical components, counting the number N of samples greater than or equal to a specific value, calculating the corresponding logarithm, performing scatter plot mapping on the center logarithmic ratio value of all qualified samples of a research element m and the corresponding logarithm, and performing least square fitting on the scatter plot; the present application processes original concentration data through a component data center logarithmic ratio value; a background and an anomaly threshold value are determined through a fractal theory, the center logarithmic ratio value after the component data theory conversion is subject to Gaussian distribution, and the statistical analysis result can better reflect the characteristics of element distribution than the original concentration.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of exploration geochemistry, and particularly to a background anomaly identification method based on a center logarithmic ratio value and a fractal theory. BACKGROUND

[0002] Identifying background and anomaly thresholds is an important part of exploration geochemistry. Accurate identification of background and anomaly thresholds can effectively understand element distribution characteristics and provide decision support for mineral exploration. Tradically, the thresholds of element background and anomaly are based on the assumption that the original concentration data obeys Gaussian distribution, and are realized by mathematical statistics such as iterative removal of several times (generally 1, 2, 2.5, 3 times) of the average value plus or minus the deviation. However, the original concentration data is compositional data, and the closure operation causes the element concentration to be not an independent random variable, which does not meet the premise requirement of some mathematical statistics methods. Direct statistical analysis of the original concentration data may produce incorrect conclusions. Compositional data theory can better solve such problems, but the ALR transformation of P-dimensional original concentration data can only produce P-1-dimensional effective variables, and the ALR transformation does not preserve distance, while the ILR transformation can only generate an effective variable consistent with the original information, and it is relatively difficult to explain. The CLR transformation can produce effective variables with the same dimension as the original data and preserve distance.

[0003] The distribution of elements in the earth does not conform to Gaussian distribution. The composition of matter on the earth is affected by multiple geological processes in the evolution history, and the concentration of each element is the result of the superposition of multiple geological processes. Its distribution has nonlinear characteristics and is subject to fractal statistics rather than Gaussian distribution. The background and anomaly thresholds identified by traditional statistical analysis methods may not conform to geological facts.

[0004] Therefore, a background anomaly identification method based on a center logarithmic ratio value and a fractal theory is proposed. SUMMARY

[0005] The present application aims to solve the above problems and provides a background anomaly identification method based on a center logarithmic ratio value and a fractal theory.

[0006] The present application realizes the above-mentioned purpose through the following technical scheme: a background anomaly identification method based on a center logarithmic ratio value and a fractal theory, comprising: performing CLR transformation on chemical components according to compositional data theory, performing fractal processing on the CLR transformed data, determining a background anomaly boundary through a least square method fitting function, and determining background and anomaly thresholds through data close to the third quantile (75%) on a Q-Q graph.

[0007] The specific steps of the present application are as follows:

[0008] S1. Clean the data and remove samples with element concentration values ​​below the detection limit;

[0009] S2. Calculate the concentration of each component of the qualified sample The corresponding central logarithmic ratio , where n is the number of components, m is the component to be studied, 1 ≤ i ≤ n;

[0010] S3, statistics greater than or equal to a specific The number of samples N of the value, calculate its corresponding logarithm ;

[0011] S4, the central logarithm ratio of all qualified samples to be studied component m and its corresponding Perform scatter plotting;

[0012] S5. Perform least squares fitting on the scatter plot from S4. The number of fitted lines is determined by the specific shape of the scatter plot, and the fitting criterion is the coefficient of determination R of each line. 2 Greater than or equal to 0.75, that is:

[0013]

[0014] Among them, A and B are obtained by least squares fitting, and the slope A of different straight lines is different;

[0015] S6. Adjacent fitting lines will form intersections, and record the intersections they represent. value;

[0016] S7. Drawing QQ graph, determine the intersection points The first value close to the 3rd quartile The original concentration C m The background and anomaly thresholds are defined, and the remaining intersection points are set as anomaly thresholds of varying levels, based on the size of the original concentration values ​​they represent. "Close to the 3rd quantile" means that the distance between its quantile value and the tertile is the shortest.

[0017] Furthermore, the original concentration data are transformed into central logarithmic ratio (CLR) according to the compositional data theory, and anomalies of the concentration and sample number are identified based on the fractal theory. The denominator in the CLR transformation step can be the content of all elements, or the content of the element to be studied and the constraint constant 1 minus the content of the element to be studied m. The identified background and anomalies have nonlinear distribution properties.

[0018] Compared with the existing technical methods, the present invention can achieve the following beneficial effects:

[0019] The central log ratio value of the component data theory processing center The data obeys Gaussian distribution, and statistical results can better reflect element distribution characteristics than original concentration; the central log ratio value of the component data theory processing center The dimension of the result is consistent with that of the original concentration data, the information dimension of the result is better than that of the additive log ratio value, and the interpretability is better than that of the isometric log ratio value; the central log ratio value The fractal research of the central log ratio value and the sample number log can accurately identify the background formed by non-mineralization process and the anomaly formed by mineralization process. BRIEF DESCRIPTION OF DRAWINGS

[0020] Fig. 1 is a flow chart of the method of the present application;

[0021] Fig. 2 is a relationship diagram of an embodiment of the present application;

[0022] Fig. 3 is a Q-Q plot of the data. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. The detailed description of the embodiments of the present application provided in the drawings below is not intended to limit the scope of the claimed application, but only to represent selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor are within the scope of the present application.

[0024] A background anomaly identification method based on central log ratio value and fractal theory, comprising the following steps:

[0025] Step 1: clean the data, and remove samples with element concentration values below the detection limit to ensure that all sample data are qualified;

[0026] Step 2: calculate the central log ratio value of all qualified samples of the studied component m .

[0027] Step 3: count the number of samples N greater than or equal to a certain value, and calculate the corresponding log .

[0028] Step 4: scatter plot the central log ratio value of all qualified samples of the studied component m and its corresponding .

[0029] ​​Step 5: Perform least squares fitting on the scatter plot obtained in step 4. The number of fitted lines is determined by the specific shape of the scatter plot, and the fitting criterion is the coefficient of determination R of each fitted line. 2 Greater than or equal to 0.75, that is:

[0030]

[0031] In the formula, A is the fractal index and B is the constant term. Both are obtained by least squares fitting. The slope A of different straight lines is different.

[0032] Step 6: Adjacent fitted lines will form intersections, and record the intersections they represent. value;

[0033] Step 7: Drawing QQ graph, determine the intersection points The first value close to the 3rd quartile The original concentration C m The other intersection points are set as abnormal thresholds of different levels according to the concentration values ​​they represent.

[0034] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Ordinary technicians in this field should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by ordinary technicians in this field.

Claims

1. A background anomaly recognition method based on central logarithmic ratio and fractal theory, characterized by: The chemical composition data is converted to the theoretical central logarithm ratio CLR; the CLR data and the sample number N exist relationship, in which , m is the component to be studied, is the component concentration, n is the number of components, 1 ≤ i ≤ n, N is the number of samples greater than or equal to a specific clr(m) value, A and B are obtained by least squares fitting; fractal processing is performed on the CLR conversion data and the number of samples; the background anomaly boundary is determined by the least squares fitting function; The background and anomaly thresholds were determined by the data close to the quantile 0.75 on the QQ plot.

2. The background anomaly identification method according to claim 1, characterized in that: The specific steps include: S1. Clean the data and remove samples with element concentration values ​​below the detection limit; S2. Calculate the concentration of each component of the qualified sample The corresponding central logarithmic ratio , where n is the number of components, m is the component to be studied, 1 ≤ i ≤ n; S3, statistics greater than or equal to a specific The number of samples N of the value, calculate its corresponding logarithm ; S4, the central logarithm ratio of all qualified samples to be studied component m and its corresponding Perform scatter plotting; S5. Perform least squares fitting on the scatter plot generated in S4. The number of fitted lines is determined by the specific shape of the scatter plot, and the fitting criterion is the coefficient of determination R of each line. 2 Greater than or equal to 0.75, that is: Among them, A and B are obtained by least squares fitting, and the slope A of different straight lines is different; S6. Adjacent fitted straight lines will form intersections, and record the intersections they represent. value; S7. Drawing QQ graph, determine the intersection points The first value close to the quantile 0.75 The component concentration C corresponding to the value m are the background and anomaly thresholds, and the remaining intersection points are set as anomaly thresholds of different levels according to the size of the original concentration values ​​they represent. "Close to quantile 0.75" means that the distance between its quantile value and quantile 0.75 is the shortest.

3. The background anomaly recognition method based on central logarithmic ratio and fractal theory according to any one of claims 1 or 2, characterized in that: The original concentration data is converted into central logarithmic ratio (CLR) based on the compositional data theory. The concentration and sample number are anomaly identified based on the fractal theory. The identified background and anomalies have nonlinear distribution properties.

4. The background anomaly recognition method based on central logarithmic ratio and fractal theory according to claim 2, characterized in that: In step S5, A and B are obtained by least square fitting, and the slopes A of different straight lines are different.

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