CN111505101A

CN111505101A - Uranium ore producing area classification method based on principal component analysis

Info

Publication number: CN111505101A
Application number: CN202010352579.3A
Authority: CN
Inventors: 邵学鹏; 樊怡辰; 龙开明; 卜文庭; 汤磊; 刘雪梅; 郝樊华; 谢波
Original assignee: Institute of Nuclear Physics and Chemistry China Academy of Engineering Physics
Current assignee: Institute of Nuclear Physics and Chemistry China Academy of Engineering Physics
Priority date: 2020-04-29
Filing date: 2020-04-29
Publication date: 2020-08-07
Anticipated expiration: 2040-04-29
Also published as: CN111505101B

Abstract

The invention discloses a uranium ore producing area classification method based on principal component analysis. The method comprises the following steps: (1) collecting a uranium ore sample, and carrying out pretreatment to obtain a uranium ore sample solution; (2) measuring the content of rare earth elements in a uranium ore sample by adopting an inductively coupled plasma mass spectrometer; (3) analyzing the Nd isotope ratio in the uranium ore sample by adopting a thermal surface ionization mass spectrometer; (4) and (3) performing principal component analysis on the content of the rare earth elements and the Nd isotope ratio, and classifying the uranium ore samples according to production places according to calculation results. Aiming at the problems of nuclear material responsibility tracing and the like, the invention adopts a multivariate statistical analysis method, improves the classification accuracy of uranium ores, and adapts to the requirements of nuclear evidence obtaining application.

Description

Uranium ore producing area classification method based on principal component analysis

Technical Field

The invention relates to the technical field of ore classification, in particular to a uranium ore producing area classification method based on principal component analysis.

Background

In recent years, the nuclear activities of countries and regions around China have become a focus of international attention, countries such as korea, japan, india and pakistan have a sufficient amount of nuclear materials, the risk of the nuclear materials flowing into the surrounding countries and regions is high, China should pay attention to the nuclear diffusion situation of the surrounding regions, and once the nuclear materials are diffused to the country, the nuclear activities are a great hidden danger of national stability and safety. Therefore, mastering the nuclear evidence obtaining technology is helpful for protecting the nuclear safety benefits of China and maintaining the international reputation of China.

For the reasons, a comprehensive subject involving multiple subjects, namely the nuclear evidence collection, is generated. The purpose of nuclear forensics is to provide characteristic quantities and process history information of intercepted nuclear materials as much as possible, provide evidences for tracing the origin, production process and transportation route of the materials, and provide technical support for proving the responsibility of exploring illegal possession of the nuclear materials and radioactive materials. Among them, the traceability analysis of the geographical source of the nuclear material is the key point of the nuclear evidence research, and uranium ore as the basic raw material of the nuclear material becomes the important analysis object of the nuclear evidence research.

In nuclear evidence research, a characteristic fingerprint that provides accurate and reliable geographic traceability information is generally referred to as a regional indicator. At present, the traditional uranium ore producing area classification method mainly adopts trace element content or stable isotope composition as a region indicator for research, however, if the stable isotope composition is independently used as the region indicator, the defects of extremely limited contained information and low accuracy rate exist; if the trace elements are used as the regional indicators independently, the method generally classifies the producing areas by comparing the distribution curves of the trace elements, and because the trace elements have various types and large content difference, the method is difficult to directly classify the trace elements according to the producing areas, has strong subjectivity, is difficult to quantify and has large errors. In addition, because the chemical properties of the rare earth elements in the trace elements are relatively close, the correlation coefficient is relatively large, namely, the contained information is relatively overlapped, the traditional method for classifying the nuclear material producing areas by taking the rare earth elements as the regional indicators has the problems of relatively large calculated amount and low analysis efficiency.

Disclosure of Invention

In view of the above, the invention aims to provide an effective and high-accuracy uranium ore production place classification method based on principal component analysis.

In order to achieve the above purpose, the technical scheme of the invention is as follows:

a uranium ore producing area classification method based on principal component analysis is characterized by comprising the following steps:

(1) collecting m uranium ore samples, and carrying out pretreatment to obtain uranium ore sample solutions;

(2) respectively measuring the content of rare earth elements in m uranium ore samples by adopting an inductively coupled plasma mass spectrometer;

(3) respectively measuring Nd isotope ratios in the m uranium ore samples by adopting a thermal surface ionization mass spectrometer;

(4) performing principal component analysis according to the rare earth element content and Nd isotope ratio of each uranium ore sample, and classifying the uranium ore samples according to the production place sources; wherein, the step (4) specifically comprises the following steps:

step (4.1) utilizes the content of rare earth elements and Nd isotope ratio of each uranium ore sample¹⁴³Nd/¹⁴⁴Nd, solving a covariance matrix;

step (4.2) combining the covariance matrix with the extreme value theorem to obtain a principal component coefficient matrix, and solving the characteristic root and the variance contribution rate of each principal component;

step (4.3) sorting the variance contribution rates from large to small, and selecting principal components with the accumulated variance contribution rate larger than 80% and the characteristic root larger than 1;

determining corresponding principal component coefficients of the selected principal components according to the principal component coefficient matrix, multiplying the content of each rare earth element and the Nd isotope ratio with the corresponding principal component coefficients of the selected principal components respectively, and summing the products to obtain corresponding principal component values of each uranium ore sample respectively;

and (4.5) drawing a uranium ore sample distribution map by taking each principal component as a coordinate axis, and classifying the geographical sources of the uranium ore samples according to the spatial positions of the sample points in the distribution map.

Further, the step (1) specifically comprises the following steps:

step (1.1) grinding a uranium ore sample into powder with uniform particles by using a ball grinder;

weighing a uranium ore powder sample and placing the uranium ore powder sample in a polytetrafluoroethylene beaker;

digesting: sequentially adding HNO into a polytetrafluoroethylene beaker₃HF and HClO₄Heating the beaker on an electric heating plate for a certain time;

step (1.4) after the sample solution is completely evaporated to dryness, utilizing concentrated HNO₃Dissolving;

step (1.5) repeating operation step (1.4) for multiple times until the dissolution is complete;

step (1.6) after the sample solution is evaporated to dryness again, HCl and H are used₃BO₃And dissolving the mixed acid again to obtain a uranium ore sample solution.

Further, in the step (1.3), HNO₃The concentration is 15 mol/L;

further, in the step (1.3), the concentration of HF is 20 mol/L;

further, in the step (1.3), HClO₄The concentration is 12.4 mol/L;

further, in the step (1.6), H₃BO₃The concentration is 0.5 mol/L;

further, the inductively coupled plasma mass spectrometry in the step (2) is carried out under the conditions that the sample introduction speed is 0.3rps, the sample is stable for 35 seconds before analysis, the He gas mode is adopted, the collection point number of the collection number of the unit mass number is 3, the data collection repetition time is 3 times, the radio frequency power is 1550W, the carrier gas flow rate is 5.5m L/min, and the atomization chamber temperature is 2 ℃.

Further, the step (3) of measuring the Nd isotope ratio in the uranium ore sample by adopting a thermal surface ionization mass spectrometer specifically comprises the following steps:

step (3.1) coating a sample solution on the center of a rhenium strip filament by using a micropipette;

step (3.2) the filament after sample coating is loaded into an ion source, the filament is heated, and a central Faraday cup is adopted for receiving¹⁴⁴Nd⁺Adjusting ion flow peak shape and peak center;

step (3.3) adjusting the focusing parameters of the electron lens of the thermal surface ionization mass spectrometer, and heating the filament at a low speed to obtain a stable and strong filament¹⁴³Nd⁺And¹⁴⁴Nd⁺ion current intensity;

step (3.4) when the vacuum degree of the ion source reaches a certain value, obtaining¹⁴³Nd/¹⁴⁴And obtaining the Nd isotope ratio.

Further, in the step (3.4), the vacuum degree of the ion source is more than or equal to 1.1 × 10^-7mbar, is obtained¹⁴³Nd/¹⁴⁴The ratio of Nd.

Compared with the prior art, the invention has the following advantages and beneficial effects:

1. in the selection of the regional indicator, the regional indicator of the uranium ore is formed by adopting the content of the rare earth element and the Nd isotope, so that the problems that other common regional indicator elements are easily influenced by a mining and metallurgy process or have poor synchronism and the like are solved;

2. according to the method, the classification research of the producing areas of the uranium ores is carried out by using a principal component analysis method, most information of original variables is kept, data are effectively compressed, and the rapid and accurate producing area classification can be realized.

3. The uranium ore producing area classification method provided by the invention realizes accurate producing area classification of the uranium ore by combining rare earth element content and Nd isotope composition, and can be used for tracing analysis of nuclear materials, radioactive materials and other related materials.

Drawings

FIG. 1 is a graph showing the distribution of the main components based on the content of rare earth elements and the ratio of Nd isotope in one example;

fig. 2 is a partial diagram of the distribution of the main component based on the content of the rare earth element and the Nd isotope ratio provided in the first embodiment.

Detailed Description

The present invention will now be described more fully hereinafter with reference to the accompanying examples, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

(4) and (3) performing principal component analysis according to the rare earth element content and Nd isotope ratio of each uranium ore sample, and classifying the uranium ore samples according to the production place sources.

And (4) performing principal component analysis according to the rare earth element content and Nd isotope ratio of each uranium ore sample, and classifying the uranium ore samples according to the production place sources. The method specifically comprises the following steps:

and (4.5) drawing a uranium ore sample distribution map by taking each principal component as a coordinate axis, and carrying out production place classification on the uranium ore sample according to the spatial position of a sample point in the distribution map.

Further, m uranium ore samples are collected in the step (1) and are pretreated to obtain uranium ore sample solutions.

The method specifically comprises the following steps:

step (1.1) grinding a uranium ore sample into powder with uniform particles with the particle size of about 10 microns by using a spherical grinder;

step (1.2) weighing a uranium ore powder sample with a certain mass by using a high-precision balance and placing the uranium ore powder sample into a polytetrafluoroethylene beaker;

step (1.6) after the sample solution is evaporated to dryness again, HCl and H are used₃BO₃And dissolving the mixed acid again to eliminate fluorinated complexes possibly generated in the digestion process of the uranium ore sample, thereby obtaining the uranium ore sample solution.

Further, in the step (1.3), HNO₃The concentration is 15 mol/L;

further, in the step (1.3), the concentration of HF is 20 mol/L;

further, in the step (1.3), HClO₄The concentration is 12.4 mol/L;

further, in the step (1.6), H₃BO₃The concentration is 0.5 mol/L;

Further, the step (3) adopts a thermal surface ionization mass spectrometer to measure the Nd isotope ratio in the uranium ore sample. The method specifically comprises the following steps:

step (3.1) coating the sample solution on the center of a rhenium strip filament by using a micropipette, which is beneficial to improving the ionization efficiency of the sample;

step (3.4) when the vacuum degree of the ion source reaches a certain value, adopting a Faraday cup to receive the ion source simultaneously¹⁴³Nd⁺And¹⁴⁴Nd⁺ion flow to obtain¹⁴³Nd/¹⁴⁴And obtaining the Nd isotope ratio.

Further, the rhenium filament current rising speed in the step (3.2) is 200 mA/min-800 mA/min;

further, the rhenium filament current rising speed in the step (3.3) is 30 mA/min-80 mA/min, the rhenium filament current rising speed is not too fast, otherwise, the too fast evaporation consumption of the sample is easily caused;

further, in the step (3.4), the vacuum degree of the ion source is more than or equal to 1.1 × 10^-7mbar, is obtained¹⁴³Nd/¹⁴⁴The Nd ratio;

further, in the step (3.4), each measurement consists of 10-20 blocks, and each block contains 10-16 cycles;

furthermore, the rhenium filament current increasing speed in the step (3.2) is 400 mA/min;

further, the rhenium filament current rising speed in the step (3.3) is 60 mA/min;

further, each measurement in step (3.4) consists of 16 blocks, each block containing 11 cycles.

Example 1

In this embodiment, a uranium ore origin classification method based on principal component analysis specifically includes the following steps:

collecting a uranium ore sample, and carrying out pretreatment to obtain a uranium ore sample solution;

the total of 25 uranium ore samples collected in the laboratory, which are respectively from four continents of europe, africa, north america and oceania, wherein 10 uranium ore samples produced in the united states, 7 uranium ore samples produced in congo, 3 uranium ore samples produced in australia, 2 uranium ore samples produced in macagasco, 1 uranium ore sample produced in germany, 1 uranium ore sample produced in mexico and 1 uranium ore sample produced in nanobira are contained.

Pre-treating a uranium ore sample: it was first ground to a uniform particle size of about 10 microns using a ball mill and the digestion process was as follows:

(1) weighing a 500mg uranium ore powder sample by using a high-precision balance and placing the sample in a polytetrafluoroethylene beaker;

(2) 9m L HNO with a concentration of 15 mol/L are added in sequence₃HF at a concentration of 6m L of 20 mol/L and HClO at a concentration of 3m L of 12.4 mol/L₄Digesting, namely heating the polytetrafluoroethylene beaker on an electric hot plate to 210 ℃, and keeping the temperature for 24 hours;

(3) after the sample solution was completely evaporated to dryness, 10m L of concentrated HNO was used₃Dissolving;

(4) repeating the operation step (1.4) for multiple times until the dissolution is complete;

(5) after the sample solution had been evaporated to dryness again, the solution was again brought to dryness with 2.5 mol/L HCl 4m L and 0.5 mol/L H2 m L₃BO₃The mixed acid is redissolved at 90 ℃ to eliminate fluorinated complexes which may remain during digestion of the uranium ore sample.

And (4) completely digesting the uranium ore powder sample to obtain a sample solution.

And (2) measuring the content of rare earth elements in 25 uranium ore samples by adopting an inductively coupled plasma mass spectrometer:

the inductively coupled plasma mass spectrometer is used for measuring the content of L a, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb and L u of 14 rare earth elements in each uranium ore sample solution, and the conditions of the inductively coupled plasma mass spectrometer are that the sample introduction speed is 0.3rps, the analysis is stable for 35 seconds, the He gas mode is that the collection point number of the unit mass number is 3, the data collection repetition times is 3, the radio frequency power is 1550W, the carrier gas flow rate is 5.5m L/min, and the atomization chamber temperature is 2 ℃ and the ICP-MS measurement results of the content of each rare earth element in 25 uranium ore samples are shown in Table 1.

TABLE 1 measurement of the content (ppm) of each rare earth element in uranium ore samples and the Nd isotope ratio

TABLE 1 measurement of the ratio of the content (ppm) of each rare earth element to Nd isotope in uranium ore samples

And (3) measuring the Nd isotope ratio in the uranium ore sample by adopting a thermal surface ionization mass spectrometer:

in the coating process of the thermal ionization mass spectrum in the step (3.1), a rhenium strip is generally adopted as a sample strip, the length of the sample strip is about 10mm, the width of the sample strip is about 0.1mm, a micropipette is adopted to dropwise add the sample solution onto the rhenium strip, and the sample solution is heated and evaporated to dryness under appropriate current, because the rhenium strip is very narrow, only 1 mu L sample solution can be dropwise added each time, and therefore, the sample is repeatedly added.

Before the sample is coated, the blank rhenium belt is placed in vacuum of 10^-5Pa, vacuum cleaning and degassing with a heating current through the rhenium strip, typically 5.5A for a duration of about 30 min. The program can effectively reduce the measurement background caused by hydrocarbon, reduce the interference of isobaric ions with the same quantity, improve the vacuum degree of the ion source during measurement and be beneficial to maintaining a good measurement environment. It is noted that to increase the ionic current strength, improve ionization efficiency and reduce isotope fractionation, micropipettes should be used to coat the sample solution in the center of the rhenium band as much as possible.

The specific sample coating conditions are that 1 mu L sample solution is dripped on a rhenium band under the current of 0.9A, after the sample solution is dried by distillation, the current is added to 1.3A and kept for 2min, the current is added to 1.8A and kept for 30s, the current is added to 2A and kept for 2s, the current is slowly reduced to 1.3A and kept for 30s, the current is slowly reduced to 0A, and the rhenium band is taken down to be measured.

Step (3.2) the filament after sample coating is loaded into an ion source, the filament is heated to about 1000 ℃ at the speed of 200mA/min, and a central Faraday cup is adopted for receiving¹⁴⁴Nd⁺Adjusting ion flow peak shape and peak center;

step (3.3) repeatedly adjusting the gathering parameters of the electron lens of the thermal surface ionization mass spectrometer, and optimizing the state of the electron lens of the thermal surface ionization mass spectrometer; the filament is heated at a low speed of 60mA/min to obtain the stable and strong filament¹⁴³Nd⁺And¹⁴⁴Nd⁺ion current intensity;

step (3.4) when the vacuum degree of the ion source is better than 1.1 × 10^-7measurement at mbar Nd⁺Ion flow, obtaining¹⁴³Nd/¹⁴⁴The ratio of Nd. Each measurement consists of 16 blocks, each containing 11 cycles. The integration time and dead time for each cycle were set to 4.914s and 3s, respectively.

To correct for mass fractionation effects, to¹⁴⁶Nd/¹⁴⁴Nd-0.7219 as correction parameterAnd (4) correcting the measured Nd isotope ratio by adopting an index law to obtain the Nd isotope ratio in each uranium ore sample¹⁴³Nd/¹⁴⁴The measurement results of the ratio of Nd (hereinafter, Nd was used instead) are shown in Table 1.

And (4) performing principal component analysis according to the rare earth element content and Nd isotope ratio of each uranium ore sample, and classifying the uranium ore samples according to the production place sources.

And (3) calculating covariance of all the quantities by using SPSS 20 statistical analysis software and taking the content information of the 14 rare earth elements and the Nd isotope ratio of each uranium ore sample as input quantities to obtain covariance matrixes of 25 uranium ore samples (table 2). As can be seen from the matrix: except Nd, Sm and Eu, the absolute values of correlation coefficients of Nd and the contents of other 12 rare earth elements are less than 0.2, which shows that the fingerprint information contained in Nd isotope is less compressible.

Table 2 covariance matrix based on rare earth element content and Nd isotope ratio

	La	Ce	Pr	Nd	Sm	Eu	Gd	Tb
									La	1.000	.907	.506	.292	.070	.129	.483	.274
Ce	.907	1.000	.820	.651	.279	.109	.805	.612
									Pr	.506	.820	1.000	.957	.540	.063	.998	.913
Nd	.292	.651	.957	1.000	.715	.056	.964	.985
									Sm	.070	.279	.540	.715	1.000	.000	.557	.819
Eu	.129	.109	.063	.056	.000	1.000	.051	.051
									Gd	.483	.805	.998	.964	.557	.051	1.000	.923
Tb	.274	.612	.913	.985	.819	.051	.923	1.000
									Dy	.231	.456	.676	.807	.971	-.011	.690	.892
Ho	.268	.451	.620	.745	.966	-.033	.633	.844
									Er	.317	.625	.885	.958	.852	.002	.895	.983
Tm	.325	.678	.959	.989	.695	-.053	.970	.972
									Yb	.224	.605	.937	.977	.643	-.063	.953	.950
Lu	.203	.495	.778	.898	.929	-.071	.796	.955
									εNd	.114	.130	.086	-.006	-.243	-.396	.078	-.072

The covariance matrix is combined with the extreme value principle to obtain principal components, the characteristic root and variance contribution rate of each principal component are calculated to obtain a table 3, and the table shows that: the characteristic root values corresponding to the first three principal components are all larger than 1, wherein the characteristic root value of the first principal component reaches 10.222, and the contribution rate reaches 68.148%; the second principal component has a characteristic root value of 1.974, a contribution rate of 13.161%, the third principal component has a value of 1.424, and the contribution rate of 9.496%, wherein the cumulative contribution rate of the first two principal components has reached 81.309%, indicating that the first two principal components already contain most of the original information. And selecting the first two principal components for analysis according to the requirements that the accumulated contribution rate of the principal components is more than 80% and the characteristic root is more than 1.

TABLE 3 characteristic root and contribution rate of each main component based on rare earth element content and Nd isotope ratio

Principal component	Characteristic root	Contribution ratio (%)	Cumulative contribution ratio (%)
				1	10.222	68.148	68.148
2	1.974	13.161	81.309
				3	1.424	9.496	90.805
4	.864	5.763	96.568
				5	.468	3.117	99.685
6	.020	.133	99.818
				7	.015	.098	99.916
8	.012	.082	99.998
				9	.000	.001	99.999
10	7.629E-005	.001	100.000
				11	1.762E-005	.000	100.000
12	6.711E-006	4.474E-005	100.000
				13	3.146E-006	2.097E-005	100.000
14	4.184E-007	2.789E-006	100.000
				15	1.726E-007	1.151E-006	100.000

And (3) obtaining a coefficient matrix of the first two selected principal components by combining a covariance matrix with an extreme value principle as shown in a table 4, multiplying the content of each rare earth element and the Nd isotope ratio with the corresponding principal component coefficient in the principal component coefficient matrix respectively, and summing the products to obtain the corresponding principal component value of each uranium ore sample respectively.

Expression of the first principal component:

PC1＝0.039×La+0.068×Ce+0.091×Pr+0.095×Nd+0.079×Sm+0.001×Eu+0.091×Gd+0.097×Tb+0.088×Dy+0.084×Ho+0.097×Er+0.095×Tm+0.091×Yb+0.093×Lu–0.004×Nd；

expression of the second principal component:

PC2＝0.356×La+0.325×Ce+0.161×Pr+0.020×Nd–0.261×Sm+0.006×Eu+0.147×Gd–0.041×Tb–0.176×Dy–0.180×Ho–0.045×Er+0.044×Tm+0.034×Yb–0.135×Lu+0.265×Nd；

TABLE 4 principal component coefficient matrix based on rare earth element content and Nd isotope ratio

	Coefficient of first principal component	Coefficient of second principal component
			La	.039	.356
Ce	.068	.325
			Pr	.091	.161
Nd	.095	.020
			Sm	.079	-.261
Eu	.001	.006
			Gd	.091	.147
Tb	.097	-.041
			Dy	.088	-.176
Ho	.084	-.180
			Er	.097	-.045
Tm	.095	.044
			Yb	.091	.034
Lu	.093	-.135
			εNd	-.004	.265

Except for Nd, PC1 is positively correlated with other statistical variables, PC2 is negatively correlated with Sm, Tb, Dy, Ho, Er and L u and is positively correlated with other statistical variables, and the accumulated contribution rate of the first two main components exceeds 80%, so that the two-dimensional distribution graph of the uranium ore sample is drawn by taking the first main component and the second main component as coordinate axes.

As can be seen from the principal component distribution diagram 1:

(1) the method comprises the following steps that 25 uranium ore samples from different geographical sources are obviously distributed and gathered, and the uranium ore samples are mainly classified into 5 types, and the specific distribution is shown in a circle surrounded part in a figure;

(2) the uranium ore samples from the motor Gassaka, Australia, Germany and Nanbia have better aggregation condition and are easy to distinguish;

(3) samples of uranium ore from congo, usa, mexico are grouped together in a class and closely spaced from each other.

For ease of observation, the region of uranium ore sample collection from congo and usa was magnified to give fig. 2, and it can be clearly seen that 10 us samples, 7 congo samples and 1 mexico sample were mixed and distributed in the region-0.6 < PC1< -0.35, -0.5< PC2<0.8, and the us samples and congo samples can be distinguished by the dotted lines in the figure.

Claims

1. A uranium ore producing area classification method based on principal component analysis is characterized by comprising the following steps:

(4) performing principal component analysis according to the rare earth element content and Nd isotope ratio of each uranium ore sample, and classifying the uranium ore samples according to the production place sources;

wherein, the step (4) specifically comprises the following steps:

step (4.1) solving a covariance matrix by utilizing the rare earth element content and Nd isotope ratio of each uranium ore sample;

2. The method for classifying uranium ore producing regions based on principal component analysis according to claim 1, wherein: the step (1) specifically comprises the following steps:

3. The method for classifying uranium ore producing regions based on principal component analysis according to claim 2, wherein: in the step (1.3), the HNO₃The concentration was 15 mol/L.

4. The method for classifying uranium ore producing regions based on principal component analysis according to claim 2, wherein in the step (1.3), the HF concentration is 20 mol/L.

5. The method for classifying uranium ore producing regions based on principal component analysis according to claim 2, wherein: in the step (1.3), the HClO₄The concentration was 12.4 mol/L.

6. The method for classifying uranium ore producing regions based on principal component analysis according to claim 2, wherein: in the step (1.3), the H₃BO₃The concentration was 0.5 mol/L.

7. The uranium ore producing area classification method based on principal component analysis according to claim 1, wherein the inductively coupled plasma mass spectrometry of step (2) is performed under conditions of a sample introduction speed of 0.3rps and a stability of 35 seconds before analysis, a He gas mode with a number of acquisition points per unit mass number of 3, a data acquisition repetition number of 3, a radio frequency power of 1550W, a carrier gas flow rate of 5.5m L/min, and an atomization chamber temperature of 2 ℃.

8. The method for classifying uranium ore producing regions based on principal component analysis according to claim 1, wherein: the step (3) of measuring the Nd isotope ratio in the uranium ore sample by adopting a thermal surface ionization mass spectrometer specifically comprises the following steps:

9. The method for classifying uranium ore producing areas based on principal component analysis according to claim 8, wherein the step (3.4) is specifically performed such that an ion source vacuum degree is 1.1 × 10 or more^-7mbar, is obtained¹⁴³Nd/¹⁴⁴The ratio of Nd.