Application of seven-level anomaly division method to medium-large scale geochemical exploration data

Abstract

The application discloses a method for applying seven-grade anomaly division method to medium-large scale geochemical exploration data, and comprises the following steps: step S1: the point position of the discrete data of the medium-large scale regional investigation is generally not located at the center position of the regular grid, and the average value gridding interpolation can be adopted to make the data points be distributed on the grid center; step S2: the WIG, Al2O3 / TiO2 and K2O / SiO2 three parameters of the 1:200,000 geochemical exploration data of the research area are calculated to obtain the three parameters of the 1:200,000 data; step S3: the nearest point gridding interpolation is performed on the three parameters of the 1:200,000 data according to the above-mentioned regular grid of the medium-large scale, so that the data gridding densities of the two are same; and step S4: the two kinds of grid data are matched, and then the seven-grade anomaly division method is used to determine and grade the evaluation of the medium-large scale geochemical exploration data; the problem that the medium-large scale geochemical exploration data lacking of background values can also use the seven-grade anomaly division method to determine and grade the evaluation is solved.

Description

Technical Field

[0001] This invention relates to the field of geochemical exploration, and in particular to a method for applying the seven-level anomaly classification method to medium- and large-scale geochemical data. Background Technology

[0002] To address the issue that the commonly used weathering index CIA, used to measure the degree of weathering in samples, requires CO2 content data for calculation, but CO2 is not included in the 39 elements analyzed in the national regional geochemical survey plan, the applicant's research team proposed the granite weathering index WIG (Weathering Index of Granite) in 2013.

[0003] WIG=100[Na2O+K2O+(CaO-10 / 3P2O5)] / (Al2O3+Fe2O3+TiO2)

[0004] In the formula, the oxide content is expressed in moles; (CaO-10 / 3P2O5) is non-negative, meaning it is 0 when it is negative; Fe2O3 represents total iron oxide, i.e., TFe2O3. The smaller the WIG value, the higher the degree of weathering of the sample.

[0005] Based on the discovery of an exponential relationship between the trace element content and the weathering index (WIG) of the background area samples, the applicant's research team proposed an original empirical equation for trace element background values ​​in 2015:

[0006] lg(c)=A*(1.2-WIG / 100)+B*lg(Al2O3 / Ti)+C*lg(K2O / SiO2)+D

[0007] In the formula, c is the content of trace elements (Au, Ag, Hg, Cd are in ng / g, and the content of other trace elements is in μg / g), the WIG value should be less than 120, the content of Al2O3, K2O, and SiO2 is in mass percentage, the content of Ti is in μg / g, and A, B, C, and D are fitting parameters.

[0008] The empirical equation for elemental background values ​​combines three indicators—WIG, Al2O3 / TiO2, and K2O / SiO2—to quantitatively characterize the geochemical background values ​​of trace elements in a sample. It is a variable method for determining trace element background values, meaning that the trace element background value in each sample can be quantitatively calculated using the above empirical equation.

[0009] Based on empirical equations for trace element background values, the applicant's research team proposed an original geochemical level 7 anomaly classification method in 2018 and developed the GBAL software for calculating elemental background values ​​and level 7 anomalies.

[0010]

[0011] In the formula, i represents the anomaly level in the seven-level anomaly category, and C... b C is the background value of a trace element in the sample obtained from the empirical equation for background values. ai C represents the lower limit of the i-th level anomaly of the element in the sample. g Given the boundary grade of this element under normal technical conditions at this stage, when i is 7, C a7 =C g That is, when the elemental anomaly reaches level seven, it indicates that its content has reached the minimum acceptable grade, while when i is 1, C a1 This is the first-level lower limit of anomaly for the element, or simply the lower limit of anomaly for that element. The actual measured content (C) of a trace element in a sample is compared with the calculated lower limits of anomalies at each level (e.g., C0). ai By comparing these, the anomaly level to which it belongs can be determined. For example, C <C a1 At that time, it belongs to the background sample; C is between C a1 and C a2 If it falls between these two points, it belongs to Level 1 anomaly; if C falls between C... a2 and C a3 When C > C, it belongs to Level 2 exception; and so on; when C > C a7 At that time, it belonged to level 7 anomaly. This shows that the level 7 anomaly classification method can not only identify anomalies but also classify and evaluate them. When classifying anomalies, two scales need to be used: background value and boundary quality.

[0012] The proposed seven-level anomaly classification method addresses the problem that currently used methods for determining geochemical anomalies do not consider the influence of sample weathering degree and parent rock lithology on elemental content. Furthermore, it combines three indicators—WIG, Al2O3 / TiO2, and K2O / SiO2—to quantitatively characterize the geochemical background values ​​of trace elements in samples. This method is a multivariable, variable-value-based geochemical anomaly delineation method that does not require specific data distribution patterns and is applicable to general geological samples such as rocks, soils, and stream sediments.

[0013] The seven-level anomaly classification method has a good application effect in the determination of geochemical anomalies. However, when applying this method, it is necessary to first use the empirical equation of trace element background value to calculate the background value of trace elements in the sample. In this process, it is necessary to first determine the three parameters (hereinafter referred to as the three parameters) of WIG, Al2O3 / TiO2 and K2O / SiO2 in the sample.

[0014] Although the analytical items of my country's 1:200,000 regional geochemical survey data can meet the needs of the above empirical equations and the calculation of the seventh-order anomaly, the test data analyzed in the 1:50,000 and larger scale regional geochemical surveys cannot meet the needs of the three-parameter calculation (or lack background values). This means that the seventh-order anomaly classification method cannot be directly applied to the determination of geochemical anomalies at the 1:50,000 and larger scales.

[0015] Therefore, it is necessary to propose a method that allows elemental content data from regional geochemical surveys at scales of 1:50,000 and larger to be identified and evaluated using a seven-level anomaly classification method.

[0016] This invention aims to propose a method for identifying and evaluating anomalies in medium-to-large scale geochemical exploration data lacking background values ​​using a seven-level anomaly classification method. Seven-level anomaly classification requires two benchmarks: background value and boundary grade. Since the boundary grade of elements can be considered a constant under current technical conditions, i.e., a known parameter, this invention addresses the problem of finding a reasonable background value for medium-to-large scale geochemical exploration data lacking background values, thereby enabling the identification and evaluation of anomalies using the seven-level anomaly classification method. Summary of the Invention

[0017] To address the problems existing in the prior art, this invention provides a method for applying the seven-level anomaly classification method to medium- and large-scale geochemical data. This method solves the problem of how to find a reasonable background value for medium- and large-scale geochemical data that lacks a background value, and thus the seven-level anomaly classification method can be used to identify and evaluate anomalies.

[0018] The method of applying the seven-level anomaly classification method to medium-to-large scale geochemical data includes the following steps:

[0019] Step S1: The sampling points for discrete data in medium and large scale regional surveys are the sampling locations of water system sediments or soil, which are generally not located at the center of a regular grid. The discrete data are averaged and interpolated using a regular grid of appropriate size, so that the data points are regularly distributed at the center of the grid. At the same time, the averaged and interpolated algorithm also ensures that the data of each data point participates in the calculation.

[0020] Step S2: Using the 1:200,000 regional geochemical data of the study area, calculate the three parameters WIG, Al2O3 / TiO2, and K2O / SiO2 to obtain the three parameters of the 1:200,000 geochemical data;

[0021] Step S3: Perform nearest-point grid interpolation on the three parameters of the 1:200,000 geochemical data according to the same regular grid size as the medium and large scale data mentioned above, so that the grid density of the 1:200,000 regional geochemical data and the medium and large scale geochemical data is the same.

[0022] Step S4: Within the study area, match the medium-to-large scale element content data after average grid interpolation with the three parameters or data items required to calculate the three parameters of the 1:200,000 regional geochemical data after grid interpolation from the nearest point, or the background value data, and use the seven-level anomaly classification method to determine and classify the anomalies in the medium-to-large scale geochemical data.

[0023] Preferably, steps S2 and S3 serve to provide background values ​​for medium-to-large scale geochemical data using 1:200,000 regional geochemical data, or to provide necessary parameters for calculating background values, such as three parameters or data items required for calculating the three parameters.

[0024] Preferably, in steps S1 and S3, the 1:200,000 regional geochemical data and the medium-to-large scale geochemical data are gridded according to the same grid density, such as a regular grid of 1:50,000 or larger scale, and then the two are matched into a set of data.

[0025] Preferably, step S4 involves matching the two sets of data and using the geochemical level seven anomaly classification method to determine and classify anomalies, thereby enabling the application of the geochemical level seven anomaly classification method in medium- and large-scale geochemical exploration.

[0026] Preferably, for medium-to-large scale geochemical data, it is recommended to use the average value grid interpolation method, and for 1:200,000 regional geochemical data, it is recommended to use the nearest point grid interpolation method.

[0027] The beneficial effects of applying the seven-level anomaly classification method to medium-to-large scale geochemical data in this invention are as follows:

[0028] 1. The key innovation of this invention is that it uses test data from 1:200,000 regional geochemical exploration to supplement medium- and large-scale geochemical exploration data that lack major oxide data and cannot calculate the three parameters or background values, thereby realizing the application of the seven-level anomaly classification method in medium- and large-scale geochemical exploration.

[0029] 2. The original large-scale geochemical data could not use the seven-level anomaly classification method when identifying anomalies, which made it impossible to eliminate the influence of parent rock lithology and sample weathering degree on element content.

[0030] 3. The medium-to-large scale geochemical data after the necessary parameters are supplemented by the present invention can be classified into seven levels of anomalies. This not only identifies anomalies but also evaluates them in a graded manner.

[0031] 4. The anomalies identified in this invention are objective anomalies after effectively eliminating the influence of parent rock lithology and sample weathering degree. They are not relative anomalies between samples within the study area, nor do they need to consider the distribution pattern of elemental content data within the study area. A single sample or a few samples (as long as they meet the matching requirements of 1:200,000 regional geochemical data and medium-to-large scale data at the same location) can also be identified and evaluated using the seven-level anomaly classification method. Attached Figure Description

[0032] Figure 1 This is a distribution diagram of 1:200,000 data points in the study area (+ indicates the location of the sample point).

[0033] Figure 2 This is a diagram showing the distribution of 1:50,000 data points in the study area (+ indicates the location of the sample point).

[0034] Figure 3 This is a graph showing the distribution of the average data points (1:50,000) after gridding in the study area according to the present invention.

[0035] Figure 4 This is a diagram showing the distribution of the nearest point without null values ​​in the study area after the 1:50,000 data points of this invention are gridded.

[0036] Figure 5 This is a 1:50,000 geochemical anomaly map of tungsten, using tungsten as an example, according to the present invention. Detailed Implementation

[0037] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0038] Taking medium-to-large scale (1:50,000) geochemical data covering a 30km×20km area in Chenzhou City, Hunan Province as an example, this paper describes the specific implementation method of using the seven-level anomaly classification method after applying the 1:200,000 regional geochemical data of this area.

[0039] (1) Retrieve 1:200,000 and 1:50,000 geochemical survey data within the study area from the database, and obtain 176 1:200,000 regional geochemical data and 1,738 1:50,000 geochemical data.

[0040] (2) Project the 1:200,000 and 1:50,000 data points of this area onto the geological map and browse the distribution of the data points. It can be found that the 1:200,000 data points are distributed according to a 2km×2km grid, while the 1:50,000 data points are not strictly distributed according to a 0.5km×0.5km regular grid.

[0041] (3) Discretize the 1:50,000 data that is not distributed according to the regular grid by using a grid spacing of 0.5km×0.5km. The calculation mode is the average value. Then, the gridded grid data is converted into a discrete data table and the data table is exported. At this time, the data are distributed on a 0.5km×0.5km grid, and the number of data rows is 1269.

[0042] (4) Null values ​​exist in some grids after the average value is gridded. The discrete data exported after the average value is gridded with a grid spacing of 0.5km×0.5km to eliminate null values. The calculation mode is the nearest point. Then, the gridded data is converted into discrete data again. At this time, there are no blank data on the 0.5km×0.5km grid. The number of data rows is 61×41=2501 (rows).

[0043] (5) Use GBAL software to calculate the three parameters of 1:200,000 regional geochemical data.

[0044] (6) The three-parameter data of the 1:200,000 geochemical data are discretized by gridding with a grid spacing of 0.5km×0.5km. The calculation mode is the nearest point. Then the gridded data is converted into discrete data. At this time, the 1:200,000 three-parameter data is converted into a 1:50,000 scale. The data points are distributed according to the regular grid of 0.5km×0.5km. The number of data points is 61×41=2501 (points).

[0045] (7) Match the 2501 records of 1:50,000 data after interpolation without missing values ​​with the 2501 records of three parameters calculated and interpolated from 1:200,000 geochemical data according to spatial coordinates to form 1:50,000 geochemical data after compensating for the three parameters. Alternatively, you can directly use other software to match the grid data in steps (3) and (6) to form a new data table, and the operation in step (4) can be omitted.

[0046] (8) Use GBAL software to calculate the background value and anomaly classification of each element in 1:50,000 geochemical data containing three parameters (or the data items required to calculate the three parameters). The geochemical level 7 anomaly map of each trace element can be made using the anomaly classification value.

[0047] Based on the above eight steps, regional geochemical data can be used to identify and evaluate anomalies in medium-to-large scale geochemical data that lack background values, using a seven-level anomaly classification method.

Claims

1. A method for applying the seven-level anomaly classification method to medium-to-large scale geochemical exploration data, characterized in that... Includes the following steps: Step S1: The sampling locations of discrete data points in medium-to-large scale regional surveys are the sampling locations of water system sediments or soil, which are not located at the center of a regular grid. The discrete data are then subjected to mean grid interpolation using a regular grid of appropriate size, so that the data points are regularly distributed at the center of the grid. At the same time, the mean grid interpolation algorithm also ensures that the data of each data point participates in the calculation. Step S2: Using the 1:200,000 regional geochemical data of the study area, calculate the three parameters WIG, Al2O3 / TiO2, and K2O / SiO2 to obtain the three parameters of the 1:200,000 geochemical data; Step S3: Perform nearest-point grid interpolation on the three parameters of the 1:200,000 geochemical data or the data items required to calculate the three parameters, or the background values, according to a regular grid of the same size as the medium and large scale data, so that the grid density of the 1:200,000 regional geochemical data is the same as that of the medium and large scale geochemical data. Step S4: Within the study area, match the three parameters of the medium-to-large scale element content data obtained by mean-value grid interpolation with the three parameters of the regional geochemical data obtained by nearest-point gridding, or the data items required to calculate the three parameters, or the background value data, and use the seven-level anomaly classification method to determine and classify the anomalies in the medium-to-large scale geochemical data.

2. The method for applying the seven-level anomaly classification method to medium-to-large scale geochemical exploration data according to claim 1, characterized in that, The purpose of steps S2 and S3 is to use 1:200,000 regional geochemical data to provide background values ​​for medium and large scale geochemical data or the three parameters required to calculate the background values, or the data items required to calculate the three parameters.

3. The method for applying the seven-level anomaly classification method to medium-to-large scale geochemical data according to claim 1, characterized in that, Steps S1 and S3 involve gridding the 1:200,000 regional geochemical data and medium-to-large scale geochemical data with the same grid density, thereby matching the two into a single set of data.

4. The method for applying the seven-level anomaly classification method to medium-to-large scale geochemical exploration data according to claim 1, characterized in that, Step S4 involves matching the two sets of data and using a seven-level anomaly classification method to determine and classify anomalies, thereby realizing the application of the seven-level anomaly classification method in medium- and large-scale geochemical exploration.

5. The method for applying the seven-level anomaly classification method to medium-to-large scale geochemical data according to claim 1, characterized in that, The grids for the medium-to-large scale geochemical data are calculated using the average value grid interpolation method, while the 1:200,000 regional geochemical data are calculated using the nearest point grid interpolation method.

Images