Background anomaly recognition method based on equidistant logarithmic ratio and fractal theory

Abstract

The invention relates to the technical field of exploration geochemistry, in particular to a background anomaly recognition method based on an equidistant logarithmic ratio and a fractal theory, which is mainly characterized by comprising the following steps of: performing component data theory ILR conversion on chemical components, counting the number N of samples greater than or equal to a specific # imgabs0 # value, calculating the corresponding logarithm # imgabs1 #, and calculating the number N of samples greater than or equal to the specific # imgabs0 # value according to the logarithm # imgabs1 # and the logarithm # imgabs1 #; carrying out scatter projection on the equidistant logarithmic ratio # imgabs2 # of all qualified sample to-be-studied elements m and the corresponding # imgabs3 #, and carrying out least square fitting on the projected scatter diagram; the method comprises the following steps: processing original concentration data through an equidistant logarithmic ratio of component data; a background and an abnormal threshold value are determined through a fractal theory, an equidistant logarithm ratio # imgabs4 # subjected to theoretical conversion of component data obeys Gaussian distribution, and a statistical analysis result can better reflect element distribution characteristics compared with original concentration.

Description

Technical Field

[0001] The invention relates to the technical field of exploration geochemistry, and in particular to a background anomaly recognition method based on equidistant logarithmic ratios and fractal theory. Background Art

[0002] Identifying background and anomaly thresholds is an important part of geochemical exploration. Accurately identifying background and anomaly thresholds can effectively understand the distribution characteristics of elements and provide decision support for mineral exploration. Traditionally, the thresholds of element background and anomaly are based on the assumption that the original concentration data follows a Gaussian distribution, and are achieved through mathematical statistics, such as iterative elimination of deviations that are higher than the average plus or minus several times (generally 1, 2, 2.5, 3 times). However, the original concentration data is component data, and the closed operation causes the element concentration to not be an independent random variable, which does not meet the prerequisite requirements of some mathematical statistics methods. Direct statistical analysis of the original concentration data may produce erroneous conclusions. The compositional data theory can solve this type of problem well, but the ALR transformation of the P-dimensional original concentration data can only produce P-1-dimensional effective variables, and the ALR transformation does not retain the distance. Although the CLR change can produce P-dimensional equidistant effective variables, it will lead to singular covariance, while the ILR not only retains the distance, but also the converted information is consistent with the original information.

[0003] The distribution of elements in the earth does not conform to the Gaussian distribution. The material composition of the earth is affected by multiple geological processes in its evolutionary history. The concentration of each element is the result of the superposition of multiple geological processes. Its distribution has nonlinear characteristics and in most cases obeys 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 recognition method based on equidistant logarithmic ratio and fractal theory is proposed. Summary of the invention

[0005] The present invention aims to solve the above problems and provide a method for identifying geochemical element background and anomaly threshold based on equidistant logarithmic ratio and fractal theory.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A background anomaly recognition method based on equidistant log ratio and fractal theory comprises: performing theoretical ILR conversion on chemical components, performing fractal processing on ILR conversion data, determining background anomaly boundaries by fitting functions using the least square method, and determining background and anomaly thresholds by using data close to the third quartile (75%) on a QQ plot.

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

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

[0010] S2. Calculate the equidistant logarithmic ratios 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 obtained 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] A and B are obtained by least squares fitting, and the slopes A of different straight lines are 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 abnormal thresholds are the other intersection points, and the other intersection points are set as abnormal thresholds of different levels according to the size of the original concentration values ​​they represent. "Close to the third quantile" means that the distance between its quantile value and the third quantile is the shortest.

[0018] Furthermore, the original concentration data are converted into central logarithmic ratio (ILR) according to the compositional data theory, and the concentration and sample number are identified as abnormal according to the fractal theory. The denominator in the ILR conversion 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 present invention uses the isometric logarithmic ratio after the component data theory conversion Obeying Gaussian distribution, its statistical results can better reflect the distribution characteristics of elements than the original concentration; the equidistant logarithmic ratio processed by composition data theory The information of the original concentration data is consistent, and the information content of the results is better than that of the additive logarithmic ratio and the central logarithmic ratio. The fractal study of the logarithm of the sample number can accurately identify the background formed by non-metallogenic processes and the anomalies formed by metallogenic processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] FIG1 is a flow chart of the method of the present invention;

[0022] FIG. 2 is an embodiment of the present invention. Relationship diagram;

[0023] Figure 3 shows QQ plot of the data. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0025] A background anomaly recognition method based on equidistant logarithmic ratio and fractal theory comprises the following steps:

[0026] Step 1: Clean the data and remove samples with element concentration values ​​below the detection limit;

[0027] Step 2: Calculate the equidistant logarithmic ratios 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;

[0028] Step 3: Count the values ​​greater than or equal to a specific value The number of samples N of the value, calculate its corresponding logarithm ;

[0029] Step 4: Take the equidistant logarithmic ratio of the element m to be studied for all qualified samples and its corresponding Perform scatter plotting;

[0030] Step 5: Perform least squares fitting on the scatter plot of S4. The number of fitted lines is determined by the specific shape of the scatter plot, and the fitting criterion is the determination coefficient R of each line. 2 Greater than or equal to 0.75, that is:

[0031]

[0032] A and B are obtained by least squares fitting, and the slopes A of different straight lines are different;

[0033] Step 6: Adjacent fitted straight lines will form intersections, and record their representations. value;

[0034] Step 7: Drawing QQ graph, determine the intersection points The first value close to the 3rd quantile The original concentration C m The background and abnormal thresholds are the other intersection points, and the other intersection points are set as abnormal thresholds of different levels according to the size of the original concentration values ​​they represent. "Close to the third quantile" means that the distance between its 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 implementation modes, not every implementation mode 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 modes that can be understood by ordinary technicians in this field.

Claims

1. A background anomaly recognition method based on equidistant logarithmic ratio and fractal theory, characterized by: The chemical composition data were converted into theoretical isometric logarithmic ratios (ILRs). The ILR converted data and sample size were fractal processed. The background anomaly boundary was determined by fitting the function using the least squares method. The background and anomaly thresholds were determined by the data close to the third quartile (75%) on the QQ plot.

2. The background anomaly recognition method based on equidistant logarithmic ratio and fractal theory according to claim 1 is 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 equidistant logarithmic ratios 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; S3, statistics greater than or equal to a specific The number of samples N of the value, calculate its corresponding logarithm ; S4. The logarithmic ratio of the element m to be studied for all qualified samples and its corresponding Perform scatter plotting; S5. Perform least squares fitting on the scatter plot obtained 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: , where A and B are obtained by least squares fitting, and the slopes A of different straight lines are 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 3rd quantile The original concentration C m is the background and abnormal threshold, and the other intersection points are set as abnormal thresholds of different levels according to the size of the original concentration values ​​they represent. "Close to 3 quantile" means that the distance between its quantile value and the third quantile is the shortest.

3. The background anomaly recognition method based on equidistant logarithmic ratio and fractal theory according to claim 1 or 2, characterized in that: The original concentration data is converted into ILR logarithmic ratios based on the compositional data theory, and the concentration and sample number are identified as abnormal based on the fractal theory; the identified background and abnormalities have nonlinear distribution properties.

4. The background anomaly recognition method based on equidistant logarithmic ratio and fractal theory according to claim 2 is 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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