A background anomaly identification method based on isometric 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 equidistant logarithmic ratio and fractal theory, and mainly comprises the following characteristics: performing component data theory ILR 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 equidistant logarithmic ratio of all qualified samples of a research element m and the corresponding, and performing least square fitting on the scatter plot; the present application processes original concentration data through equidistant logarithmic ratio of component data; the threshold of background and anomaly is determined through fractal theory, the equidistant logarithmic ratio after the conversion of component data theory is subjected 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 equidistance logarithmic ratio and fractal theory. BACKGROUND

[0002] Identifying background and anomaly threshold is an important content of exploration geochemistry work. Accurate identification of background and anomaly threshold can effectively understand the element distribution characteristics and provide decision support for mineral exploration work. Traditionally, the threshold of element background and anomaly is based on the assumption that the original concentration data obeys Gaussian distribution, and is realized by mathematical statistics such as iterative removal of average value plus or minus several times (generally 1, 2, 2.5, 3 times) deviation. But the original concentration data is a component 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, and direct statistical analysis of the original concentration data may produce wrong conclusions. The component data theory can better solve this problem, but the ALR conversion of P-dimensional original concentration data can only produce P-1-dimensional effective variables, and the ALR conversion does not preserve distance. Although the CLR conversion can produce P-dimensional equidistant effective variables, it will lead to singular covariance, and the ILR not only preserves distance, but also converts the information consistent with the original information.

[0003] The distribution of elements in the earth does not conform to Gaussian distribution. The material composition of the earth is affected by multiple geological processes in the evolution history. The concentration of each element is the result of the superposition of multiple geological processes. Its distribution has nonlinear characteristics and obeys fractal statistics in most cases, not Gaussian distribution. The background and anomaly threshold identified by the traditional statistical analysis method may not conform to the geological facts.

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

[0005] The present application aims to provide a geochemical element background and anomaly threshold identification method based on equidistance logarithmic ratio and fractal theory to solve the above problems.

[0006] To achieve the above purpose, the present application provides the following technical scheme:

[0007] A background anomaly identification method based on equidistance logarithmic ratio and fractal theory, comprising: performing ILR conversion on chemical components according to component data theory, performing fractal processing on the ILR conversion data, determining the background anomaly boundary by a least square method fitting function, and determining the background and anomaly threshold by the data close to the third quantile (75%) on the Q-Q graph.

[0008] The specific steps of the present application are as follows:

[0009] S1. Clean the data and remove samples with element concentration values ​​below the detection limit;

[0010] S2. Calculate the logarithmic ratio of each element of the qualified sample , where m is the component to be analyzed, n is the number of components, and i is any other component not including m;

[0011] S3, statistics greater than or equal to a specific The number of samples N of the value, calculate its corresponding logarithm ;

[0012] S4. The logarithmic ratio of the element m to be studied for all qualified samples and its corresponding Perform scatter plotting;

[0013] 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:

[0014]

[0015] Among them, A and B are obtained by least squares fitting, and the slope A of different straight lines is different;

[0016] S6. Adjacent fitted straight lines will form intersections, and record the intersections they represent. value;

[0017] S7. Drawing QQ graph, determine the intersection points The first value close to the 3rd quantile 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.

[0018] Furthermore, the original concentration data are transformed into the central logarithm ratio (ILR) according to the compositional data theory, and the concentration and sample number are identified as abnormalities according to the fractal theory. The denominator in the ILR transformation step can be the content of all other 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.

[0019] Compared with the existing technical methods, the present invention can achieve the following beneficial effects:

[0020] The isometric log ratio converted by the component data theory obeys Gaussian distribution, and the statistical result can better reflect the element distribution characteristics than the original concentration; the isometric log ratio processed by the component data theory is consistent with the information of the original concentration data, and the information quantity of the result is better than that of the additive log ratio and the central log ratio. Through the isometric log ratio and the fractal study of the sample number logarithm, the background formed by non-mineralization process and the anomaly formed by mineralization process can be accurately identified. BRIEF DESCRIPTION OF DRAWINGS

[0021] Fig. 1 is a flow chart of the method of the present application;

[0022] Fig. 2 is a relationship diagram of the embodiment of the present application;

[0023] Fig. 3 is a Q-Q diagram of the data; DETAILED DESCRIPTION

[0024] 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, rather than all the embodiments. 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 present application, but only represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

[0025] A background anomaly identification method based on the isometric log ratio and the fractal theory, comprising the following steps:

[0026] Step one, cleaning the data and removing the samples with element concentration values lower than the detection limit;

[0027] Step two, calculating the isometric log ratio corresponding to each element of the qualified samples , wherein m is the component to be analyzed, n is the number of components, and i is any other component excluding m;

[0028] Step three, counting the number N of samples greater than or equal to a specific value, and calculating the corresponding log .

[0029] Step four, performing scatter plot on the isometric log ratio of the component m of all qualified samples and the corresponding . ​​

[0030] Step five, least square fitting is performed on the scatter plot of S4, the number of fitted straight lines is determined by the specific shape of the scatter plot, and the fitting standard is the determination coefficient R of each straight line 2 greater than or equal to 0.75, that is:

[0031]

[0032] wherein A and B are obtained by least square fitting, and the slope A of different straight lines is different;

[0033] Step six, adjacent fitted straight lines will form intersection points, and the value represented by the intersection points is recorded;

[0034] Step seven, a Q-Q plot of C is drawn, and the quantile of the value of each intersection point is determined, and the original concentration C corresponding to the first m value close to the third quantile is determined as the background and anomaly threshold, and the remaining intersection points are sequentially set as anomaly thresholds of different levels according to the size of the original concentration value represented by the intersection points. The meaning of "close to the third quantile" is that the distance between the quantile value and the third quantile is the shortest.

[0035] In addition, it should be understood that although the present specification is described in terms of embodiments, not every embodiment contains only one independent technical solution, and the description manner of the specification is only for the sake of clarity, and the person skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by the person skilled in the art.

Claims

1. A background anomaly recognition method based on isometric log ratio and fractal theory, characterized in that: The chemical composition is subjected to component data theory isometric log ratio (ILR) transformation; the ILR data and the sample number N have a relationship that , , m is a component to be studied, , n is the number of components, N is the sample number greater than or equal to a specific ilr(m) value, A and B are obtained by least square fitting; the ILR transformed data and the sample number are subjected to fractal processing; the background anomaly boundary is determined by least square fitting function; and the background and anomaly threshold values are determined by data close to the 0.75 quantile on a Q-Q plot.

2. The background anomaly identification method based on the isometric log ratio and fractal theory according to claim 1, characterized in that Comprise the following specific steps: S1, cleaning the data, eliminating the sample with the element concentration value lower than the detection limit; S2, calculating the isometric log ratio of each element of the qualified sample wherein m is the component to be analyzed, n is the number of components, and i is any other component not including m. S3, counting the number of samples N greater than or equal to a certain value, calculating the corresponding logarithm of the number of samples N ; S4, the isometric logarithmic ratio of the research element m of all qualified samples and its corresponding scatter plot; S5, least square fitting is performed on the scatter plot of S4, the number of straight lines to be fitted is determined by the specific shape of the scatter plot, and the fitting standard is the determination coefficient R of each straight line 2 greater than or equal to 0.75, that is: wherein A and B are obtained by least square 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, plot Q-Q plot, determine the quantile of each intersection point value corresponding to the first intersection point close to quantile number 0.75 value corresponding to the first intersection point close to quantile number 0.75 m background and anomaly threshold, and the rest of the intersection points are set as anomaly thresholds of different levels according to the size of the original concentration value represented, and the meaning of "close to quantile number 0.75" is that the quantile value is closest to quantile number 0.

75.

3. The background anomaly identification method based on the isometric log ratio and fractal theory according to claim 1 or 2, characterized in that: According to the component data theory, the original concentration data is converted by ILR logarithmic ratio, and the concentration and sample number are identified according to the fractal theory. The identified background and anomaly have nonlinear distribution properties.

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

Images