A rare metal anomaly extraction method for regional geochemical data reuse

Abstract

The present invention provides a rare metal anomaly extraction method for regional geochemical data reuse, which relates to the field of regional geochemical exploration technology. The method includes: collecting regional geochemical data and determining target mineral species; sorting and checking data, statistically analyzing rare metal element parameters, and comparing with background values ​​to evaluate mineralization potential; transforming data to Euclidean space by central logarithmic ratio transformation; selecting elements related to target mineral species for principal component analysis, extracting rare metal mineralization element combinations and background element combinations; drawing element ratio anomaly maps to extract rare metal anomalies and improve the accuracy of prospecting. The present invention uses principal component analysis and element ratio method to extract rare metal anomalies, which can effectively reduce the influence of geochemical background differences and improve the accuracy of rare metal anomaly prospecting.

Description

Technical Field

[0001] The invention relates to the technical field of regional geochemical exploration, and in particular to a rare metal anomaly extraction method for regional geochemical data reuse. Background Art

[0002] In recent years, with the continuous development of emerging industries such as new energy, information technology, chips, and aerospace, the demand for "minerals" such as rare metals has also increased, which has led to a change in the goals or focus of mineral exploration. In addition, the advancement of mineral processing technology has also greatly reduced the boundary grade of the delineated mines during the prospecting process. These situations have made it necessary to review the geological exploration data accumulated in previous work.

[0003] A large amount of geochemical data is obtained in geological surveys. These geochemical data have played an important role in previous mineral exploration work (especially gold, copper, lead and zinc). However, as mentioned above, the prospecting information of rare metal mines such as lithium and beryllium in these data has not been paid attention to. The development and reuse of these data will provide important information for the prospecting planning of rare metals in a sustainable manner.

[0004] Regional geochemical background differences are one of the main difficulties faced in the delineation and interpretation of geochemical anomalies. When facing porphyry, skarn and other types of gold, copper, lead and zinc deposits, the geochemical anomalies formed by these types of minerals are generally characterized by high intensity and large area, and the impact of regional geochemical background differences on anomaly delineation is mostly acceptable. However, for rare metal ores, such as pegmatite-type lithium beryllium ores, such deposits are mostly produced in the surrounding rock in the form of veins, and the range of anomalies formed is often small. At the same time, granite surrounding rocks often have a high geochemical background for rare metals. In this case, continuing to use traditional methods to delineate geochemical anomalies is very likely to result in excessive anomalies in high background areas such as granite and excessively small anomalies in mineralized areas, which in turn affects the determination of prospective mineralization areas.

[0005] Geochemical data is a type of concentration ratio data, which is typical composition data. The "closure effect" of composition data may cause geochemical data to produce virtual correlation in the multivariate statistical analysis process, leading to unreasonable or even incorrect results. However, the "composition data" feature has been widely ignored in previous regional geochemical surveys, which may also lead to the omission of key abnormal information.

[0006] The existing patent "A prediction method suitable for prospecting and exploration of granite porphyry rare metal deposits (application number: 202110833923.5)" is mainly aimed at rubidium ore and does not involve the extraction of geochemical anomaly information. The existing patent "Method and system for identifying anomalies under complex geological and geochemical backgrounds (application number: 202210496794.X)" does not contain content for the extraction of rare metal anomalies. In addition, methods such as machine learning and neural networks have problems such as insufficient geological constraints and complex model parameters, requiring extensive human intervention and guidance, which limits their scope of application. Summary of the invention

[0007] In order to overcome the deficiencies of the prior art, the object of the present invention is to provide a rare metal anomaly extraction method for regional geochemical data reuse.

[0008] To achieve the above object, the present invention provides the following solutions:

[0009] A rare metal anomaly extraction method for regional geochemical data reuse, comprising:

[0010] Collect regional geochemical data based on the preset working area and determine the target mineral species;

[0011] Performing data collation and quality inspection on the regional geochemical data to obtain collated and inspected data;

[0012] Counting the basic parameters of each rare metal element in the collated and checked data, and comparing the basic parameters with regional background values, reference background values, crustal abundance and other regional statistical values, so as to preliminarily measure the rare metal mineralization potential within the scope of the work area;

[0013] transforming the collated and checked data from simplex space to Euclidean space using a centered logarithmic ratio transformation to obtain centered logarithmic ratio transformed data;

[0014] Select the elements that are closely related to the rare metal mineralization process and mineralization surrounding rocks of the target mineral species from among the various rare metal elements;

[0015] For closely related elements, principal component analysis is performed using the data after the central logarithmic ratio transformation to obtain principal component analysis results;

[0016] Drawing a principal component score biplot according to the principal component analysis result, and extracting the rare metal ore-forming element combination and the ore-forming background element combination of the target mineral species according to the aggregation characteristics of the elements on the principal component score biplot;

[0017] An element ratio anomaly map is drawn according to the rare metal ore-forming element combination and the ore-forming background element combination to extract rare metal anomalies.

[0018] Preferably, the regional geochemical data is collated and quality checked to obtain the collated and checked data, including:

[0019] The average value and median value of the content of each rare metal element in the regional geochemical data are counted by map sheet, and variance analysis is used to compare whether there is a significant difference in the average value and median value of the content of rare metal elements between different map sheets. If there is a significant difference, it is judged whether it is caused by the geological background based on the geological conditions of the map sheet. For the differences between the map sheets that are determined not to be caused by the geological background, correction is performed according to the map sheet to obtain the data after sorting and checking; if there is no significant difference, the regional geochemical data is determined to be the data after sorting and checking;

[0020] Check whether there are any rare metal element content values ​​below the detection limit, and replace the rare metal element content values ​​below the detection limit with 1 / 2 of the detection limit to obtain the sorted and checked data.

[0021] Preferably, the basic parameters include the mean and median of the content.

[0022] Preferably, the formula for the center logarithmic ratio transformation is: ;in, is the geometric mean of all element contents at the ith data point: , is the number of elements in the data point, is the data value after logarithmic ratio transformation of the center of element j, is the sorted and checked data corresponding to the jth element in the i-th data point.

[0023] Preferably, drawing an element ratio anomaly map according to the rare metal ore-forming element combination and the ore-forming background element combination to extract rare metal anomalies includes:

[0024] Standardizing the contents of the rare metal ore-forming element combination and the ore-forming background element combination;

[0025] Calculate the ratio of the arithmetic mean of the standardized values ​​of the rare metal ore-forming elements and the ore-forming background elements at each data point;

[0026] Calculate the logarithm of the ratio of the arithmetic mean; the logarithm reflects the difference in the content of ore-forming elements and ore-forming background elements. Combined with the distribution of the target mineral species and the geological background characteristics, a value greater than 0 is set as the lower limit of the anomaly, and an element ratio anomaly map is drawn based on the lower limit of the anomaly.

[0027] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0028] The present invention provides a rare metal anomaly extraction method for regional geochemical data reuse, comprising: collecting regional geochemical data according to a preset working area range and determining target mineral species; performing data sorting and quality inspection on the regional geochemical data to obtain sorted and inspected data; statistically analyzing basic parameters of each rare metal element in the sorted and inspected data, and comparing the basic parameters with regional background values, reference background values, crustal abundance and other regional statistical values ​​to preliminarily measure the rare metal mineralization potential of the working area; transforming the sorted and inspected data from a simplex space to a central logarithmic ratio transformation; Euclidean space, obtain data after central logarithm ratio transformation; select elements closely related to the rare metal mineralization process and mineralization surrounding rocks of the target mineral species from each rare metal element; for closely related elements, use the central logarithm ratio transformed data to perform principal component analysis to obtain principal component analysis results; draw a principal component score biplot according to the principal component analysis results, and extract the rare metal mineralization element combination and mineralization background element combination of the target mineral species according to the clustering characteristics of the elements on the principal component score biplot; draw an element ratio anomaly map according to the rare metal mineralization element combination and the mineralization background element combination to extract rare metal anomalies. The method of the present invention uses principal component analysis and element ratio method to extract rare metal anomalies, which can effectively reduce the influence of geochemical background differences and improve the accuracy of rare metal anomaly prospecting. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0030] Figure 1 A flow chart of a method provided by an embodiment of the present invention;

[0031] Figure 2 A schematic diagram of a technical route provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0032] 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. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0034] Figure 1 A flow chart of a method provided by an embodiment of the present invention, such as Figure 1 As shown, the present invention provides a rare metal anomaly extraction method for regional geochemical data reuse, comprising:

[0035] Step 100: Collect regional geochemical data according to the preset working area range and determine the target mineral species;

[0036] Step 200: sorting and quality checking the regional geochemical data to obtain sorted and checked data;

[0037] Step 300: Counting the basic parameters of each rare metal element in the collated and checked data, and comparing the basic parameters with regional background values, reference background values, crustal abundance and other regional statistical values, so as to preliminarily measure the rare metal mineralization potential of the working area;

[0038] Step 400: transforming the sorted and checked data from simplex space to Euclidean space using centered logarithmic ratio transformation to obtain centered logarithmic ratio transformed data;

[0039] Step 500: selecting elements closely related to the rare metal mineralization process and mineralization surrounding rocks of the target mineral species from among the various rare metal elements;

[0040] Step 600: For closely related elements, principal component analysis is performed using the data after the central logarithmic ratio transformation to obtain a principal component analysis result;

[0041] Step 700: drawing a principal component score biplot according to the principal component analysis result, and extracting the rare metal ore-forming element combination and the ore-forming background element combination of the target mineral species according to the clustering characteristics of the elements on the principal component score biplot;

[0042] Step 800: Draw an element ratio anomaly map based on the rare metal ore-forming element combination and the ore-forming background element combination to extract rare metal anomalies.

[0043] To achieve the above purpose, Figure 2 As shown, the technical route of this embodiment includes the following steps:

[0044] Step 1: Collect regional geochemical data based on the scope of the work area. You can query from systems such as Geological Cloud, Geological Science Data Publishing System, and obtain from the "Regional Geochemical Exploration Database" or natural resources management department.

[0045] Step 2: Organize and check the acquired geochemical data.

[0046] ① Systematic error check: Calculate the mean and median content of each element by map sheet; use variance analysis to compare whether there are significant differences in the mean and median content of elements between different map sheets; if there are significant differences, determine whether they are caused by the geological background based on the geological conditions of the map sheet; if the differences between map sheets are determined not to be caused by the geological background, make corrections according to the map sheet.

[0047] ② Detection limit check: Check whether there are element content values ​​below the detection limit; replace the element content values ​​below the detection limit with 1 / 2 detection limit.

[0048] Step 3: Perform basic parameter statistics on the data sorted in step 2, including mean, median and coefficient of variation, and compare them with regional background values, reference background values, crustal abundance and other regional statistical values ​​to preliminarily measure the rare metal mineralization potential of the working area.

[0049] Step 4: Use the central logarithmic ratio transformation (CLR) to transform the geochemical data from simplex space to Euclidean space. Taking geochemical data with n data points and m elements as an example, the CLR transformation formula is:

[0050]

[0051] Represents the value of the jth element of the i-th data point after transformation; Represents the original value of the jth element of the i-th data point; is the geometric mean of all element contents at the ith data point: .

[0052] Step 5: Regional geochemical surveys generally test 39 indicators. These geochemical indicators include information on hydrothermal activity, tectonic activity, geological unit background, and surface activity. Rare metal ores generally do not have geochemical halos, and the scale of anomalies is generally small. In order to avoid other irrelevant information covering up the anomalies of rare metal ores, elements closely related to the mineralization process and mineralization surrounding rocks of rare metal ores are selected for principal component analysis. For example, for pegmatite-type lithium-beryllium deposits, it is recommended to select the following elements: B, Be, Bi, F, La, Li, Nb, P, Sb, Th, U, Y, Zr.

[0053] Step 6: For the elements selected in step 5, use the corresponding data after the central logarithm ratio transformation in step 4 to perform principal component analysis. According to the number of selected elements, the number of principal components should not be too large, and the total variance explanation rate should be greater than 70%.

[0054] Step 7, draw a biplot of the principal component scores based on the scores of each element on each principal component obtained in step 6. According to the clustering characteristics of the elements on the biplot, extract the rare metal ore-forming element combination and the ore-forming background element combination. The former rare metal ore-forming element combination is mainly composed of ore-forming elements and their closely related elements, and the ore-forming background element combination is mainly composed of elements reflecting the characteristics of the ore-forming surrounding rocks. For example, for pegmatite-type lithium-beryllium deposits, the rare metal ore-forming element combination is selected as: Li, Be, B, and the ore-forming background element combination is: La, Y, Zr, Nb.

[0055] Step 8: Draw an element ratio anomaly map based on the elements selected in step 7. First, standardize the original content value of each data point of each element. For example, the mean standardization is calculated as follows: , represents the content value of the jth data point of element x, Represents the arithmetic mean of all data points of element x. Then calculate the ratio of the arithmetic mean of the standardized values ​​of the rare metal ore-forming elements and the ore-forming background elements at each data point, and then calculate the logarithm of the ratio. For example, Li', Be', La', Y', Zr', Nb' are the standardized element content values ​​of the ore-forming elements Li and Be and the ore-forming background elements La, Y, Zr, Nb, respectively. The logarithm of the element ratio includes: , , . Since the coefficients are adjusted and the logarithmic values ​​are obtained, when the logarithmic value of the element ratio obtained above is close to 0, it means that the content of the ore-forming elements and the ore-forming background elements is not much different; when the logarithmic value of the element ratio is greater than 0, it means that the content of the ore-forming elements is higher than the content of the ore-forming background elements; when the logarithmic value of the element ratio is less than 0, it means that the content of the ore-forming elements is lower than the content of the ore-forming background elements. In combination with the distribution of the target mineral species and the characteristics of the geological background, a value greater than 0 is set as the lower limit of the abnormality, and the element ratio anomaly map is drawn based on the lower limit of the abnormality. The beneficial effects of the present invention are as follows:

[0056] (1) The present invention proposes a method for extracting rare metal anomalies in the reuse of regional geochemical data, which reuses the existing regional geochemical data and extracts effective rare metal anomalies, providing a basis for the delineation of rare metal mineralization prospecting areas.

[0057] (2) The method of the present invention utilizes the acquired geochemical data to obtain abnormal information that has not been paid attention to in previous work, which can greatly improve the utilization efficiency of massive geochemical data. At the same time, it reduces the time and cost required for traditional field surveys, which can greatly improve the efficiency of rare metal prospecting.

[0058] (3) The method of the present invention utilizes principal component analysis and element ratio method to extract rare metal anomalies, which can effectively reduce the impact of geochemical background differences and improve the accuracy of rare metal anomaly prospecting. Principal component analysis (PCA) converts related geochemical variables in high-dimensional space into a small number of unrelated principal components based on covariance or correlation coefficient matrix. The result can extract two or more pairs of element groups related to geological processes. The element ratio is a numerical reflection of element combination or specific geochemical process, and is widely used in geochemical surveys and research. Using elements reflecting geological and geochemical background as the denominator can reduce the impact of geochemical background differences.

[0059] (4) The method of the present invention is mainly based on a method combining data processing techniques such as composition data and principal component analysis with mineralization background theories such as rare metal mineralization background and element combination characteristics, and has strong operability.

[0060] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0061] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A rare metal anomaly extraction method for regional geochemical data reuse, characterized in that: include: Collect regional geochemical data based on the preset working area and determine the target mineral species; Performing data collation and quality inspection on the regional geochemical data to obtain collated and inspected data; Count the basic parameters of each rare metal element in the collated and checked data, and compare the basic parameters with regional background values, reference background values, crustal abundance and other regional statistical values ​​to preliminarily measure the rare metal mineralization potential within the working area; transforming the collated and checked data from simplex space to Euclidean space using a centered logarithmic ratio transformation to obtain centered logarithmic ratio transformed data; Select the elements that are closely related to the rare metal mineralization process and mineralization surrounding rocks of the target mineral species from among the various rare metal elements; For closely related elements, principal component analysis is performed using the data after the central logarithmic ratio transformation to obtain principal component analysis results; Drawing a principal component score biplot according to the principal component analysis result, and extracting the rare metal ore-forming element combination and the ore-forming background element combination of the target mineral species according to the aggregation characteristics of the elements on the principal component score biplot; Drawing an element ratio anomaly map according to the rare metal ore-forming element combination and the ore-forming background element combination to extract rare metal anomalies; The geochemical data of the region are collated and quality checked to obtain the collated and checked data, including: The average value and median value of the content of each rare metal element in the regional geochemical data are counted by map sheet, and variance analysis is used to compare whether there is a significant difference in the average value and median value of the content of rare metal elements between different map sheets. If there is a significant difference, it is judged whether it is caused by the geological background based on the geological conditions of the map sheet. For the differences between the map sheets that are determined not to be caused by the geological background, correction is performed according to the map sheet to obtain the data after sorting and checking; if there is no significant difference, the regional geochemical data is determined to be the data after sorting and checking; Check whether there are rare metal element content values ​​below the detection limit, and replace the rare metal element content values ​​below the detection limit with 1 / 2 of the detection limit to obtain the sorted and checked data; Drawing an element ratio anomaly map according to the rare metal ore-forming element combination and the ore-forming background element combination to extract rare metal anomalies includes: Standardizing the contents of the rare metal ore-forming element combination and the ore-forming background element combination; Calculate the ratio of the arithmetic mean of the standardized values ​​of the rare metal ore-forming elements and the ore-forming background elements at each data point; Calculate the logarithm of the ratio of the arithmetic mean; the logarithm reflects the difference in the content of ore-forming elements and ore-forming background elements. Combined with the distribution of the target mineral species and the geological background characteristics, a value greater than 0 is set as the lower limit of the anomaly, and an element ratio anomaly map is drawn based on the lower limit of the anomaly.

2. The rare metal anomaly extraction method for regional geochemical data reuse according to claim 1, characterized in that: The basic parameters include the average and median of the element contents.

3. The rare metal anomaly extraction method for regional geochemical data reuse according to claim 1, characterized in that: The formula for the centered log ratio transform is: ;in, is the geometric mean of all element contents at the ith data point: , is the number of elements in the data point, is the data value after logarithmic ratio transformation of the center of element j, is the number of the ith data point j The collated and checked data corresponding to each element.

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