1. A method of mineral content measurement of a rock block sample, comprising the steps of: :

step one, processing a massive rock sample into a cylindrical structure with the diameter of 30mm and the thickness of 2mm, and putting the cylindrical structure into a sample tray for vacuumizing;

starting an electron beam of an automatic analysis system to scan the surface of the vacuumized cylindrical sample, and obtaining a back scattering image after scanning;

thirdly, background subtraction is carried out based on gray scale distribution characteristics of the scanned back scattering image, minerals are divided into A and B as research objects through setting of a gray scale value range, namely, in the first step, an image with a gray scale value of 0-X of the minerals A is selected as a research object, a 255-X image part is subtracted as a background value, and the background value is used as a sample for measurement and analysis; secondly, selecting an image with a B gray value of 255-X as a research object, deducting the image with 0-X as a background value, and performing test analysis by using the image as a sample; wherein: x represents a selected gray segmentation value, and the value range of the selected gray segmentation value is 0-255;

step four: respectively measuring the mineral contents of the A part and the B part, and summarizing data of the mineral contents of the A part and the B part; area statistics for a and B minerals can be obtained based on the following equation (1):

C_m=(A+B)/(A_total+B_total)×100% (1)

calculating the area percentage content of different minerals in the whole block sample to obtain the mineral content data of the whole rock;

(1) in the formula, C_mThe area percentage content of a certain mineral in the whole block sample is indicated, and A and B are the total area of single mineral particles measured by two samples with different gray scale ranges respectively; a. the_totalAnd B_totalThe total area of all mineral particles measured for two different grey scale range samples respectively.

Background

The automatic analysis system for mineral parameters, model number MLA650, produced by FEI is a high-speed automated automatic quantitative analysis system for mineral parameters. The method is mainly used in the fields of mining industry, metallurgy, geology and the like. The method can automatically and quantitatively analyze important parameters such as mineral substance composition, component quantification, mineral embedding characteristics, mineral size distribution, mineral dissociation degree and the like of the sample. The working principle of the automatic quantitative analysis system for mineral analysis parameters is that after a mineral sample is scanned, background removal is carried out based on a back scattering image, then mineral phase separation is carried out after the mineral is granulated, and then information of mineral content is obtained through a statistical method. This method has significant advantages for granulation of significant samples, such as soil and sand content, but has significant limitations for monolithic rock samples. This is because the minerals in the whole rock are tightly connected together to form a glued state, and the background without impurities can be removed in the analysis process, which also results in that the mineral parameter automatic analysis system cannot detect and analyze the content information of the whole minerals.

Disclosure of Invention

In view of the above, the present invention aims to provide a method for determining the mineral content in a bulk rock sample using an automated mineral parameter analysis system. The method can be used for detecting and analyzing the whole mineral content, and adds a new function for the automatic mineral parameter analysis system to determine the blocky rock.

The purpose of the invention is realized by the following technical scheme:

a method of mineral content measurement of a rock block sample, comprising the steps of:

step one, processing a massive rock sample into a cylindrical structure with the diameter of 30mm and the thickness of 2mm, and putting the cylindrical structure into a sample tray for vacuumizing;

starting an electron beam of an automatic analysis system to scan the surface of the vacuumized cylindrical sample, and obtaining a back scattering image after scanning;

thirdly, background subtraction is carried out based on the gray scale distribution characteristics of the back scattering image, minerals are divided into A and B to be used as research objects through the setting of the gray scale value range, namely, in the first step, the image with the gray scale value of the minerals A being 0-X is selected to be used as the research object, the 255-X image part is used as the background value to be subtracted, and the background value is used as a sample to be measured and analyzed; secondly, selecting an image with a mineral B gray value of 255-X as a research object, deducting the image with the gray value of 0-X as a background value, and performing test analysis by taking the image as a sample; wherein: x represents a selected gray segmentation value, and the value range of the selected gray segmentation value is 0-255;

step four: and after the mineral contents of the A part and the B part are respectively measured, summarizing the data of the mineral contents of the A part and the B part. Area statistics for A and B minerals can be obtained based on the following equation (1)

C_m=(A+B)/(A_total+B_total)×100% (1)

Calculating the area percentage content of different minerals in the whole block sample to obtain the mineral content data of the whole rock;

(1) in the formula, C_mRefers to the area percentage content of a certain mineral in the whole block sample, and A and B are the total area of single mineral particles measured by two samples with different gray scale ranges respectively. A. the_totalAnd B_totalThe total area of all mineral particles measured for two different grey scale range samples respectively.

The advantages and the beneficial effects of the invention are as follows:

the method fills the defects of an automatic mineral parameter analysis system, and makes it possible to measure the mineral content of the massive rock sample based on the automatic quantitative mineral parameter analysis system. The whole process of measuring the mineral content of the massive rock sample is simple and quick, safe and economical, and the defects of long time consumption, complex operation and the like of mineral identification under the traditional microscope are overcome. The detection efficiency is effectively improved, and the method has the characteristics of accuracy of test results and timeliness of data feedback.

Drawings

Fig. 1 is a back-scattered electron image of a bulk sample.

FIG. 2 is a mineral profile plot of a portion of a block sample having gray scale values of 0-125.

FIG. 3 is a mineral distribution graph of a portion of a block sample with gray scale values of 125-255.

Detailed Description

The invention is explained in detail below with reference to the figures and examples:

example (b): bulk sandstone sample mineralogical composition analysis

A method of mineral content measurement of a rock block sample, comprising the steps of:

step one, processing a massive rock sample into a cylindrical structure with the diameter of 30mm and the thickness of 2mm, putting the cylindrical structure into a sample disc, and vacuumizing to 5 Mpa;

starting an electron beam of an automatic analysis system to scan the surface of the vacuumized cylindrical sample, and obtaining a back scattering image (see figure 1) after scanning;

thirdly, based on the fact that the mineral particles on the surface of the rock are in a connected state, performing background subtraction on the gray scale distribution characteristics of the scanned back scattering image, namely performing background subtraction on minerals A and B, namely selecting an image with a gray scale value of 0-X of the mineral A as a research object, subtracting an image part with a gray scale value of 255-X as a background value, and performing measurement analysis on the image part serving as a sample; secondly, selecting an image with a B gray value of 255-X as a research object, deducting the image with 0-X as a background value, and performing test analysis by using the image as a sample; wherein: and X represents a selected gray segmentation value, the value range of the selected gray segmentation value is 0-255 (the selected gray segmentation value of the mineral is reasonably selected according to the distribution characteristics of the gray values of the specific sample), and the value of X suitable for the sandstone sample is selected to be 125 after the comparison of the X value taking effects. FIGS. 2 and 3 show the results of the background subtraction mineral measurement, in which the white part is the background subtracted in several times;

step four: after the mineral contents of the mineral A and the mineral B are respectively measured, the data of the mineral contents of the mineral A and the mineral B are summarized, so that the area statistical data of the mineral A and the mineral B can be obtained, and the area statistical data are based on the following formula:

C_m=(A+B)/(A_total+B_total)×100%

calculating the area percentage content of different minerals in the whole block sample to obtain the mineral content data of the whole rock;

wherein, C_mRefers to the area percentage content of a certain mineral in the whole block sample, and A and B are the total area of single mineral particles measured by two samples with different gray scale ranges respectively. A. the_totalAnd B_totalThe total area of all mineral particles measured for two different grey scale range samples respectively.

In the following, table 1 and table 2 respectively list the mineral contents of quartz, muscovite, plagioclase, waintianite, jade, chlorite and potash feldspar with gray values between 0 and 125 and gray values between 125 and 255.

TABLE 1 mineral content (. mu.m) of part A (between grey values 0-125)²）

Mineral name	Mineral	Area
			Quartz	Quartz	387420.84
White mica	Muscovite	103416.46
			Plagioclase feldspar	Plagioclase	167985.10
Calcium antimonite	Romeite	5435.28
			Jadeite	Jadeite	20429.53
Chlorite (chlorite)	Chamosite	5230.55
			Potassium feldspar	K-feldspar	17104.47
Background	Background	31050.36
			Total of	Total	738072.58

Table 2 mineral content (. mu.m) of section B (gray scale values between 125 and 255)²）

Mineral name	Mineral	Area
			Quartz	Quartz	1383158.31
White mica	Muscovite	635661.74
			Plagioclase feldspar	Plagioclase	167985.10
Calcium antimonite	Romeite	179429.33
			Jadeite	Jadeite	169260.68
Chlorite (chlorite)	Chamosite	362190.45
			Potassium feldspar	K-feldspar	367225.28
Calcite	Calcite	62336.94
			Iron dolomite	Ankerite	41990.66
Aluminum cerium apatite	Aluminium britholite	3241.82
			Albite	Albite	484605.63
Background	Background	42571.07
			Total of	Total	3899656.99

Table 3 shows the percentage by area of the monolithic quartz, muscovite, plagioclase, wainite, emerald, chlorite, and potash feldspar minerals.

TABLE 3 mineral area percentage content (%)

Table 1, table 2 and table 3 can give: according to the method, the background of the minerals A and the background of the minerals B are deducted, the image with the gray value of 0-X of the minerals A is used as a research object, the image part of 255-X is deducted as a background value, the images with the gray value of 255-X of the minerals B are used as a research object, and the image with the gray value of 0-X is deducted as a background value, so that the total area of single mineral particles measured by two samples with different gray ranges is obtained respectively.

And the data of the mineral contents of the minerals A and B are summarized to obtain the data of the mineral content of the whole rock after the mineral contents of the minerals A and B are respectively measured in the table 3.