Method for determining and verifying non-centralized logarithmic ratio transformation denominator of geochemical component data

Abstract

The invention discloses a method for determining and verifying a non-centralized logarithmic ratio transformation denominator of geochemical component data, which comprises the following steps of: performing Spearman correlation analysis on exploration geochemical component data, constructing a first standard on the basis of a correlation analysis result, removing elements which are not suitable for serving as the non-centralized logarithmic ratio transformation denominator, and determining a second standard on the basis of elements which are not suitable for serving as the non-centralized logarithmic ratio transformation denominator; obtaining candidate elements conforming to a first standard; calculating an enrichment coefficient of each element in the exploration geochemical component data, constructing a second standard based on the enrichment coefficient, and performing priority ranking on each element according to the second standard; selecting the element with the highest priority level in the candidate elements as a non-centralized logarithm ratio transformation denominator according to the priority level sequence; and verifying the rationality and effectiveness of the non-centralized logarithmic ratio transformation of the component data. According to the method disclosed by the invention, the non-centralized logarithmic ratio transformation denominator of the component data is reasonably and effectively determined by constructing two standards, and the rationality and effectiveness of geochemical component data processing are improved.

Description

Technical Field

[0001] The present invention relates to the field of geological and mineral exploration, and in particular to a method for determining and verifying the denominator of a non-centered logarithmic ratio transformation of geochemical composition data. Background Art

[0002] Because geochemical composition data often sum to a constant value, they must be transformed to address the closure effect. This closure effect, where the total amount of all geochemical components (element content) is constant, results in mutual constraints between components, resulting in negative or positive correlations. These correlations are spurious and have no geological significance. This is a common problem encountered in geochemical composition data analysis.

[0003] The uncentered log-ratio transformation (ALR) is the earliest method proposed to open the closed effect of compositional data. However, this method requires the selection of one of the components as the denominator. Different denominators will lead to certain differences in the final factor analysis results. Based on experience, predecessors have proposed some standards for determining the denominator of the uncentered log-ratio transformation (ALR), including: (1) the content values ​​of the selected elements (denominators) should all be higher than the detection limit to ensure their accuracy; (2) the selected elements (denominators) should be relatively stable in various geological and geochemical processes, such as Si, Al, Ti, Zr, etc.; (3) the selected elements should be unrelated to mineralization elements; (4) the selected elements (denominators) should be trace elements rather than major elements. In fact, the above standards for determining the denominator of the uncentered log-ratio transformation (ALR) are too subjective and there is no quantitative indicator to measure or screen them.

[0004] In summary, there is an urgent need to establish relevant standards to effectively and reasonably determine the denominator of the decentralized log-ratio transformation (ALR) of component data, so as to solve the dilemma faced by the existing selection of the denominator of the decentralized log-ratio transformation (ALR) of component data. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for determining the denominator of the uncentered logarithmic ratio transformation (ALR) of composition data. The method reasonably and effectively determines the denominator of the uncentered logarithmic ratio transformation (ALR) of composition data by constructing two standards, thereby improving the rationality and effectiveness of geochemical composition data processing.

[0006] In order to solve the above technical problems, the present invention specifically provides the following technical solutions: A method for determining the denominator of a non-centered logarithmic ratio transformation of geochemical composition data comprises the following steps: Collect exploration geochemical composition data of the study area, including stream sediment, soil or rock geochemical data.

[0007] Performing Spearman correlation analysis on the exploration geochemical composition data, constructing a first criterion based on the correlation analysis results, removing elements that are not suitable as denominators of the decentered log-ratio transformation, and obtaining candidate elements that meet the first criterion; Calculating the enrichment coefficient of each element in the exploration geochemical composition data, constructing a second standard based on the enrichment coefficient, and prioritizing each element according to the second standard; According to the priority ranking, the element with the highest priority among the candidate elements is selected as the denominator of the decentralized logarithmic ratio transformation.

[0008] Furthermore, the geochemical composition data include geochemical data of regional soil, rock, stream sediment, and plant and their component data, and the component data include soil fine particles and active state partial extraction.

[0009] Furthermore, the exploration geochemical composition data includes laboratory analysis data of multiple elements. If the laboratory analysis result of a certain element is zero or below the detection limit, half of the corresponding detection limit of the element is used as the final laboratory analysis data to ensure that the laboratory analysis results of all elements are not missing or zero.

[0010] Furthermore, before performing Spearman correlation analysis on the exploration geochemical composition data, the collected geochemical composition data are also checked for consistency of sample type and data quality.

[0011] Among them, consistency means that all samples in the data set remain uniform in collection media, processing procedures, and analysis methods to avoid the introduction of systematic biases due to sampling or experimental differences; the inspection content includes medium consistency, unified sampling depth, and pretreatment standardization.

[0012] Numerical quality refers to the accuracy, completeness, and reliability of the data, ensuring that the analysis results truly reflect the geochemical processes; the inspection content includes data integrity, analysis accuracy verification, detection limit, rationality of element content, etc.

[0013] Furthermore, constructing the first standard includes: calculating the correlation coefficient between statistical elements, setting a threshold based on the theoretical geochemical behavior characteristics of the elements and the geological and geochemical characteristics of the study area, and when the correlation coefficient between two elements is greater than the threshold, the two elements are not suitable as the denominator of the decentralized logarithmic ratio transformation.

[0014] Theoretically, when the absolute value of the correlation coefficient is greater than 0.7, it can be considered that a high correlation exists; therefore, the threshold value of the correlation coefficient is a value ≥ 0.7, which depends on the theoretical geochemical behavior characteristics of the elements and the geological and geochemical characteristics of the study area.

[0015] Geochemical composition data usually do not conform to the normal distribution. Spearman correlation analysis can be performed directly to preliminarily explore the correlation between elements. Elements with high correlation are not suitable as the denominator of the uncentered log-ratio transformation (ALR).

[0016] Furthermore, the second criterion includes: calculating the enrichment coefficient of each element and prioritizing the candidate elements in ascending order of enrichment coefficient, where the element with the smaller enrichment coefficient has the higher priority. It is ensured that the denominator of the non-centered logarithmic ratio transformation (ALR) of the selected composition data has the weakest metallogenic relationship with the study area and is evenly distributed, ensuring the rationality and effectiveness of the non-centered logarithmic ratio transformation (ALR) of the composition data.

[0017] The enrichment coefficient is a commonly used parameter to measure the concentration of the measured element in a region. It is influenced by many factors, including bedrock, weathering, mineralization, and sample size. However, a high enrichment coefficient for an element indicates a close association with mineralization. Elements with high enrichment levels often indicate a possible link to mineralization and warrant further investigation.

[0018] The present invention also provides a verification method for the denominator of the non-centered logarithmic ratio transformation of geochemical composition data, which is used to verify the feasibility and effectiveness of the non-centered logarithmic ratio transformation denominator obtained by the above method, and specifically includes the following steps: A plot with known geological characteristics was selected as the study area. Based on the non-centered logarithmic ratio transformation denominator obtained by the above method and the commonly used denominator in the non-centered logarithmic ratio transformation of current component data, non-centered logarithmic ratio transformation was performed respectively, and a comparative experiment was conducted with the original data. The comparative experiment includes: Combinations of elements known to have strong correlations were selected to test the linear relationship of the log-ratio transformation results; Test whether the logarithmic ratio transformation results conform to or approximately conform to the normal distribution; Data factor analysis was performed to extract factors, and their rationality was verified from the perspective of geology and geochemistry.

[0019] Compared with the prior art, the present invention has the following beneficial effects: 1. The method of the present invention constructs a method for determining the denominator of the uncentered logarithmic ratio transformation (ALR) of geochemical composition data. The method reasonably and effectively determines the denominator of the uncentered logarithmic ratio transformation (ALR) of composition data by constructing two standards, thereby improving the rationality and effectiveness of geochemical composition data processing.

[0020] 2. The method of the present invention can be used to open the closed effect of regional geochemical exploration data, so that the extracted factors have clearer geological and geochemical significance, greatly improving data utilization efficiency.

[0021] 3. The method of the present invention is highly operable. Currently, this method has been used to process the soil fine particles, rock cores and soil geochemical data of the Qujia gold mine and the Shansonggang mining area, opening the closure effect. The extracted factors have clear geological and geochemical significance, which greatly promotes mineral exploration. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other implementation drawings based on the provided drawings without inventive effort.

[0023] Figure 1 A flowchart of a method for determining the denominator of a non-centered logarithmic ratio transformation (ALR) of component data provided by an embodiment of the present invention; Figure 2 A geological characteristic map of the study area provided in an embodiment of the present invention; Figure 3 A linear comparison diagram of the elements with strong correlation provided by the present invention; Figure 4 This is a comparison chart of the component data distribution provided by the present invention. DETAILED DESCRIPTION

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0025] The following first describes the concepts involved in this application with reference to the accompanying drawings. It should be noted that the following description of each concept is intended only to make the content of this application easier to understand and does not limit the scope of protection of this application. At the same time, the embodiments and features in the embodiments of this application can be combined with each other unless there is a conflict. The following detailed description of this application will be made with reference to the accompanying drawings and in conjunction with the embodiments.

[0026] like Figure 1 As shown, the present invention discloses a method for determining the denominator of a decentered logarithmic ratio transformation of geochemical composition data. In this example, the method for determining the denominator of a decentered logarithmic ratio transformation (ALR) of composition data provided by the present invention is further described in detail, taking the Qujia hidden gold deposit research area in Laizhou, Jiaodong, my country as an example.

[0027] The geological characteristics of the study area are shown in Figure 2 below. They are relatively simple. The study area is covered by Quaternary alluvial deposits, and the bedrock is diorite. The mineralization is relatively simple, mainly gold mineralization, and the ore minerals are gold and silver.

[0028] Geochemical measurement data of fine particles in soil from Qujia, Laizhou, Shandong Province were collected and checked. If the measurement value of a sample was zero or below the detection limit, half of the detection limit of the corresponding element was taken as its corresponding content.

[0029] Since the geochemical composition data do not conform to the normal distribution or approximately conform to the normal distribution, factor analysis cannot be performed directly. However, Spearman correlation analysis does not require the data to conform to the normal distribution assumption. Therefore, Spearman correlation analysis is performed on the soil fine-grained geochemical data to preliminarily determine the correlation between elements. Spearman correlation analysis can be performed using SPSS software.

[0030] Table 1 Spearman correlation analysis results

[0031] Based on Table 1, a Spearman correlation coefficient of 0.75 between two elements was selected as the threshold. That is, if the Spearman correlation coefficient between two elements is greater than 0.75, indicating a strong correlation between them, then these two elements are not suitable as denominators for the uncentered log-ratio transformation (ALR), as this destroys the original correlation in the data. The following elements are found to be unsuitable as denominators for the uncentered log-ratio transformation (ALR): Cr, Co, Cu, Ni, I, V, W, Fe₂O₃, MgO, Na₂O, K₂O, Ag, Bi, Sb, Ti, and Br.

[0032] Statistical analysis was performed on the soil fine-grain geochemical data, and the enrichment coefficient was calculated using the Northeast China continental crust as the background value, as shown in Table 2 below.

[0033] Table 2 Enrichment coefficient

[0034] The denominator of the uncentered logarithmic ratio transformation (ALR) of the element with the smallest enrichment coefficient was selected. Since MgO had a high Spearman correlation coefficient with Sb, V, Ti, and Fe2O3 and could not be used as the denominator of the uncentered logarithmic ratio transformation (ALR), CaO was selected as the denominator of the uncentered logarithmic ratio transformation (ALR).

[0035] The present invention also discloses a verification method for the denominator of the non-centered logarithmic ratio transformation of geochemical composition data, which is used to verify the feasibility and effectiveness of the non-centered logarithmic ratio transformation denominator obtained by the above method.

[0036] The principles for judging the effect of denominator determination are: first, whether the correlation between elements is further improved; second, whether the element combination extracted by factor analysis has a clearer geological and geochemical significance, that is, it can effectively indicate the bedrock and mineralization processes in the study area.

[0037] (1) Linear comparison experiment of strongly correlated elements In the uncentered log-ratio transformation (ALR) of compositional data, Ti is often used as the denominator. Therefore, we compared the results of the original data, the uncentered log-ratio transformation with Ti as the denominator (ALR: Ti), and the uncentered log-ratio transformation with CaO as the denominator (ALR: CaO). First, we selected element combinations known to have strong correlations as a test of the rationality and effectiveness of the log-ratio transformation results, including Au-Ag (the main mineralization element combination, the ore minerals of the Laizhou Qujia gold mine are gold and silver mines), Na2O-K2O (highly active in the surface environment, with similar geochemical behavior characteristics), Ba-Sr and Co-Ni (can exist in isomorphous forms), Br-I (both volatile elements, with similar geochemical behavior characteristics) The results are as follows Figure 3 shown.

[0038] Linear fitting R of each element combination of original data, uncentered logarithmic ratio transformed Ti as denominator (ALR:Ti) and uncentered logarithmic ratio transformed CaO as denominator (ALR:CaO) 2 As shown in Table 3, it can be seen that the linearity of the transformed data with the uncentered logarithmic ratio CaO as the denominator (ALR:CaO) is closer to 1.

[0039] Table 3 Linear fitting data

[0040] (2) Normal distribution comparison experiment Factor analysis of component data is a widely used statistical technique that aims to transform high-dimensional data into low-dimensional data through linear transformation while preserving the variability of the data as much as possible. However, the premise of factor analysis is that the component data conforms to or approximately conforms to the normal distribution. After the original data, the uncentered logarithmic ratio transformation Ti as the denominator (ALR:Ti) and the uncentered logarithmic ratio transformation CaO as the denominator (ALR:CaO), Au, Ag, and I are normally distributed as follows Figure 4 As shown in the figure, it can be seen that the data after the decentralization logarithmic ratio transformation CaO as the denominator (ALR:CaO) is closer to the normal distribution. (3) Comparative test of factor analysis results Factor analysis was performed using the original data, the uncentered log-ratio transformed Ti as the denominator (ALR:Ti), and the uncentered log-ratio transformed CaO as the denominator (ALR:CaO).

[0041] The results of factor analysis of the original data are shown in Table 4. Eight factors were extracted from the factor analysis, namely: F1 (Fe2O3-MgO-V-Ti-W-Sb-Ni-Cr-Co-(Na2O-K2O-Sr-Ba)), F2 (Br-I-Se), F3 (F-Al2O3-(SiO2)), F4 (Bi-Hg-S), F5 (Au-Ag), F6 (Sn-Zn), F7 (CaO) and F8 (Cl).

[0042] Table 4 Factor analysis results of original data

[0043] The results of the factor analysis with uncentered log-ratio transformation Ti as the denominator (ALR: Ti) are shown in Table 5. Five factors were extracted from the factor analysis, namely: F1 (TFe2O3-MgO-VW-Zn-Sn-Sb-Ni-Mn-IF-Cu-Cr-Co-Cd), F2 (Ba-Pb-Sr-SiO2-Al2O3-Na2O-K2O), F3 (As-Br-Se), F4 (Au-Ag), and F5 (Cl).

[0044] Table 5 Factor analysis results with decentered log-ratio transformation Ti as the denominator (ALR:Ti)

[0045] The results of the factor analysis with uncentered log-ratio transformation of CaO as the denominator (ALR:CaO) are shown in Table 6. The factor analysis extracted three factors: F1 (As-Bi-Br-Cd-Cl-Co-Cr-Cu-F-Hg-I-Mn-Mo-Ni-S-Sb-Se-Sn-VW-Zn-Fe2O3-MgO), F2 (Ba-Mo-Pb-Sr-Ti-SiO2-Al2O3-Na2O-K2O), and F3 (Au-Ag). Table 6 Factor analysis results with uncentered log-ratio transformed CaO as the denominator (ALR:CaO)

[0046] From the comparison of factor analysis results in Table 4-6, we can find that: (1) Dimensionality reduction: The factor analysis with uncentered log-ratio transformation of CaO as the denominator (ALR:CaO) yielded the best results. The number of factors extracted from the original data, ALR:Ti, and ALR:CaO factor analyses was 8, 5, and 3, respectively.

[0047] (2) Data interpretability: Factor analysis of the original data extracted a total of 8 factors. Some factors (such as F2 (Br-I-Se), F3 (F-Al2O3-(SiO2)), F4 (Bi-Hg-S), F6 (Sn-Zn), F7 (CaO), and F8 (Cl)) could not be reasonably explained from a geological and geochemical perspective. ALR: Ti factor analysis extracted 5 factors, namely F2 (Ba-Pb-Sr-SiO2-Al2O3-Na2O-K2O), F3 (As-Br-Se), and F5 (Cl), which had poor interpretability. ALR: The CaO factor analysis extracts F1 (As-Bi-Br-Cd-Cl-Co-Cr-Cu-F-Hg-I-Mn-Mo-Ni-S-Sb-Se-Sn-VW-Zn-Fe2O3-MgO), which indicates areas with thin alluvial deposits, reflecting the bedrock monzogranite. F2 (Ba-Mo-Pb-Sr-Ti-SiO2-Al2O3-Na2O-K2O) indicates areas with thick alluvial deposits, primarily reflecting alluvial sands. Pb and Mo may have originated from mining contamination. F3 (Au-Ag) indicates gold mineralization in the region. This result significantly improves the interpretability of the results and can better reflect the main geological and geochemical processes in the study area.

[0048] In summary, the decentralized logarithmic ratio transformation denominator obtained by the method disclosed in the present invention has strong feasibility and effectiveness.

[0049] The embodiments and / or implementation methods described above are only used to illustrate the preferred embodiments and / or implementation methods for realizing the technology of the present invention, and do not impose any form of limitation on the implementation methods of the technology of the present invention. Any person skilled in the art may make slight changes or modifications to other equivalent embodiments without departing from the scope of the technical means disclosed in the content of the present invention, but they should still be regarded as technologies or embodiments that are essentially the same as the present invention.

[0050] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. The above is only the preferred implementation method of this application. It should be pointed out that due to the limitations of textual expression, there are objectively infinite specific structures. For ordinary technicians in this technical field, without departing from the principles of this application, they can also make several improvements, modifications or changes, and can also combine the above technical features in an appropriate manner; these improvements, modifications, changes or combinations, or the direct application of the inventive concept and technical solution to other occasions without improvement, should be regarded as the scope of protection of this application.

Claims

1. A method for determining the denominator of a decentralised logarithmic ratio transformation of geochemical composition data, characterized in that: The steps include: Collect exploration geochemical composition data of the study area; Performing Spearman correlation analysis on the exploration geochemical composition data, constructing a first criterion based on the correlation analysis results, removing elements that are not suitable as denominators of the decentered log-ratio transformation, and obtaining candidate elements that meet the first criterion; Calculating the enrichment coefficient of each element in the exploration geochemical composition data, constructing a second standard based on the enrichment coefficient, and prioritizing each element according to the second standard; According to the priority ranking, the element with the highest priority among the candidate elements is selected as the denominator of the decentralized logarithmic ratio transformation.

2. The method for determining the denominator of the decentralized logarithmic ratio transformation of geochemical composition data according to claim 1, characterized in that: The exploration geochemical data includes geochemical data of stream sediments, soil or rocks.

3. The method for determining the denominator of the decentralized logarithmic ratio transformation of geochemical composition data according to claim 1 or 2, characterized in that: The exploration geochemical composition data includes the analysis data of multiple elements. If the analysis result of a certain element is zero or below the detection limit, half of the detection limit of the element is used as the final analysis data.

4. The method for determining the denominator of the decentralized logarithmic ratio transformation of geochemical composition data according to claim 1, characterized in that: Before performing Spearman correlation analysis on the exploration geochemical composition data, the collected geochemical composition data were also checked for consistency of sample type and data quality.

5. The method for determining the denominator of the decentralized logarithmic ratio transformation of geochemical composition data according to claim 1, characterized in that: The construction of the first standard includes: statistically calculating the correlation coefficient between elements, setting a threshold based on the theoretical geochemical behavior characteristics of the elements and the geological and geochemical characteristics of the study area. When the absolute value of the correlation coefficient between two elements is greater than the threshold, the two elements are not suitable as the denominator of the decentralized logarithmic ratio transformation.

6. The method for determining the denominator of the decentralized logarithmic ratio transformation of geochemical composition data according to claim 5, characterized in that: The threshold value is ≥ 0.

7.

7. The method for determining the denominator of the decentralized logarithmic ratio transformation of geochemical composition data according to claim 1, characterized in that: Constructing the second criterion includes: calculating the enrichment coefficient of each element, and prioritizing the candidate elements in ascending order of the enrichment coefficient, wherein the element with the smaller enrichment coefficient has a higher priority.

8. A verification method for the denominator of the decentralised logarithmic ratio transformation of geochemical composition data, characterized in that: The steps include: A plot with known geological characteristics is selected as the study area. Based on the non-centered logarithmic ratio transformation denominator obtained by the method according to any one of claims 1 to 7 and the commonly used denominator in the non-centered logarithmic ratio transformation of current component data, a non-centered logarithmic ratio transformation is performed, and a comparative experiment is performed with the original data. The comparative experiment includes: Combinations of elements known to have strong correlations were selected to test the linear relationship of the log-ratio transformation results; Test whether the logarithmic ratio transformation results conform to or approximately conform to the normal distribution; Data factor analysis was performed to extract factors, and their rationality was verified from the perspective of geology and geochemistry.

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