Calcite deposit prospecting prediction method and system based on dolomite geochemical analysis

Abstract

The invention discloses a calcite deposit prospecting prediction method and system based on dolomite geochemical analysis, and the method comprises the steps: S1, collecting a dolomite sample in a to-be-detected calcite mine ore containing dolomite region, and grinding the dolomite sample into a probe piece; s2, collecting a characteristic pattern of the probe piece under a cathode light-emitting mirror; s3, performing LA-ICP-MS test on the probe piece to obtain the trace element content and the rare earth element content of dolomite at different positions; and S4, predicting the position of the calcite deposit based on the feature map, the trace element content and the rare earth element content. According to the method disclosed by the invention, the technical effect of predicting the calcite deposit is achieved through dolomite LA-ICP-MS in-situ geochemical testing and carbonate rock cathode luminescence by utilizing a means of analyzing geochemical characteristics of the surrounding rock dolomite of the calcite deposit, and the technical problem of prospecting the calcite deposit is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of resource exploration, and in particular to a calcite deposit prospecting prediction method and system based on dolomite geochemical analysis. Background Art

[0002] Dolomite and calcite are both carbonate minerals with similar chemical compositions and physical properties, and their mineral assemblages overlap. Both are mainly calcium-based, and Ca anomalies in geochemical sampling may reflect dolomite or calcite mineralization at the same time, which needs to be further distinguished by combining other indicators such as changes in elements. In addition, in the process of trace element detection of carbonate rocks, the trace element content of samples is often lower than the detection line, which will increase the experimental error and cause deviation in the results. Summary of the invention

[0003] To solve the above problems, the present invention proposes a calcite deposit prospecting prediction method based on dolomite geochemical analysis, the method comprising:

[0004] Step S1, collecting dolomite samples in the dolomite area of ​​the calcite mine to be detected and grinding them into probe pieces;

[0005] Step S2, collecting a characteristic graph of the probe sheet;

[0006] Step S3, performing LA-ICP-MS testing on the probe sheet to obtain the trace element content and rare earth element content of each growth ring zone;

[0007] Step S4: predicting the location of the calcite deposit based on the characteristic map and the trace element content and rare earth element content of each growth ring zone.

[0008] Optionally, in step S1, the dolomite samples are dolomite samples of different contact types, and the contact types include: alteration halo formed by the contact between megacrystal calcite and dolomite and megacrystal calcite replacing dolomite.

[0009] Optionally, in step S2, a characteristic image of the probe sheet under a cathode luminescence microscope is collected.

[0010] Optionally, the specific process of step S3 includes:

[0011] Step S31, using Pearson correlation coefficient to calculate Mg 2+ Correlation with other trace elements, and obtaining trace elements and rare earth elements in the probe sheet that meet preset conditions based on the calculation results;

[0012] Step S32: Based on the obtained trace elements and rare earth elements, use LA-ICP-MS to obtain the trace element content and the rare earth element content.

[0013] Optionally, the specific process of step S31 includes:

[0014] Calculate Mg using Pearson correlation coefficient 2+ The correlation with other trace elements was used to obtain the calculated results;

[0015] Use two-tailed significance test to determine whether the calculation result is significant, and obtain a determination result;

[0016] The trace elements and the rare earth elements are obtained based on the judgment result.

[0017] Optionally, calculate Mg using Pearson correlation coefficient 2+ The correlation with other trace elements is as follows:

[0018]

[0019] Among them, cov(X,Y) is the covariance and σXσY is the standard deviation.

[0020] Optionally, the standard deviation is calculated as:

[0021]

[0022] Among them, n is the number of samples, x i is the sample value, x is the sample mean, y is i is the sample value; y is the sample mean of y.

[0023] The present invention also discloses a calcite deposit prospecting prediction system based on dolomite geochemical analysis, the system comprising:

[0024] The sample collection module is used to collect dolomite samples in the dolomite area of ​​the calcite mine to be tested and grind them into probe pieces;

[0025] A feature map acquisition module, used for acquiring a feature map of the probe sheet;

[0026] A content acquisition module, used to perform LA-ICP-MS testing on the probe sheet to obtain the trace element content and rare earth element content of each growth ring zone;

[0027] A location prediction module is used to predict the location of the calcite deposit based on the characteristic map and the trace element content and rare earth element content of each growth ring zone.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] The invention observes the ore-bearing dolomite area of ​​the calcite deposit and identifies different geochemical characteristics of the ore-bearing dolomite; collects different ore-bearing dolomite samples according to the contact type with the calcite deposit, and prepares dolomite probe sheet samples with different contact types; performs cathode luminescence microscope observation on different types of dolomite probe sheet samples respectively to identify two types of contact between the calcite ore body and the dolomite from the far end to the near end of the ore body; obtains LA-ICP-MS raw data of each contact type through LA-ICP-MS testing; processes and analyzes the LA-ICP-MS raw data to obtain the trace element content and rare earth element content of the dolomite from the far end to the near end of the ore body; and calculates the content of trace elements related to Mg by using the Pearson product-moment correlation coefficient. 2+ Closely related elements, analyze the elements closely related to calcite mineralization. For example, the trace elements are Si, Sc, Sr, Y, Sn, Tb, and Yb; explore the change rules of elements and predict the location of calcite deposits. The present invention uses the means of geochemical characteristic analysis of dolomite, the surrounding rock of calcite deposits, through dolomite LA-ICP-MS in-situ geochemical testing and carbonate rock cathode luminescence, to achieve the technical effect of predicting calcite deposits and solve the technical problems of calcite deposit exploration. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. 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.

[0031] Figure 1 A method step diagram of a calcite deposit prospecting prediction method based on dolomite geochemical analysis according to an embodiment of the present invention;

[0032] Figure 2 These are dolomite samples with different contact types and the corresponding local cathode luminescence images of the embodiments of the present invention, where: Figure 2 a is a schematic diagram of the alteration halo formed by the contact between megacrystal calcite and dolomite; Figure 2 b is a schematic diagram of cathode luminescence at the local location of the alteration halo; Figure 2 c is a schematic diagram of megacrystalline calcite replacing dolomite; Figure 2 d is a schematic diagram of the cathode luminescence of the local position of the replaced dolomite;

[0033] Figure 3 This is a schematic diagram of the results of processing and analyzing LA-ICP-MS raw data using ICPMSDataCal 12.2 software in an embodiment of the present invention;

[0034] Figure 4This is a schematic diagram of the change in the content of Si, Sc, Sr, Y, Sn, Tb, and Yb of the significant related elements of type I dolomite from the far end to the near end of the ore body in the embodiment of the present invention;

[0035] Figure 5 This is a schematic diagram of the change in the content of Si, Sc, Sr, Y, Sn, Tb, and Yb of the significant related elements of type II dolomite from the far end to the near end of the ore body in an embodiment of the present invention. DETAILED DESCRIPTION

[0036] 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.

[0037] First, the professional instruments and specialized terms used in the present invention are explained:

[0038] LA-ICP-MS, or Laser Ablation Inductively Coupled Plasma Mass Spectrometry, is an advanced analytical technique for highly sensitive elemental and isotopic analysis of solid samples. The technique focuses a laser beam on the sample surface to produce fine particles that are analyzed by an inductively coupled plasma mass spectrometer. This method does not require sample preparation and can directly test solid samples. It has the advantages of a wide range of detected elements, low detection limits, high sensitivity, and is almost non-destructive. LA-ICP-MS has a wide range of applications in many fields, including geology, archaeology, and materials science. In geology, it is used for in-situ microanalysis of rocks and minerals to reveal their composition and structure. In archaeology, it is particularly suitable for the analysis and research of precious silicate artifacts, and its chemical composition information can be obtained without destroying the sample. In addition, LA-ICP-MS is also used in gemology to quickly and accurately determine trace elements in gemstones, which helps to identify the authenticity and composition of gemstones. The main components of this technology include a laser ablation device, an inductively coupled plasma mass spectrometer, and a computer data processing system. The laser ablation device is responsible for heating, vaporizing and ionizing the material on the sample surface, producing a plume of particles and ions that then enter the ICPMS system for mass filtering and analysis.

[0039] Embodiment 1

[0040] A method for prospecting and predicting calcite deposits based on geochemical analysis of dolomite, such as Figure 1 As shown, the method is as follows:

[0041] Step S1: In the dolomite area of ​​the calcite mine to be detected, collect dolomite samples and grind them into probe pieces.

[0042] The dolomite samples are dolomite samples with different contact types, including: alteration halo formed by the contact between megacrystalline calcite and dolomite and megacrystalline calcite replacing dolomite.

[0043] Step S2: Collecting characteristic images of the probe sheet. Collecting characteristic images of the probe sheet under a cathode luminescence microscope.

[0044] Observe the dolomite area of ​​the calcite deposit, collect dolomite samples of different contact types, and grind them into probe pieces; according to the different forms of dolomite in close contact with the calcite ore body, that is, the alteration halo formed by the contact between giant calcite and dolomite and the replacement of dolomite by giant calcite. In the first contact relationship, the mineral grain size in the altered surrounding rock has increased significantly, and the dolomite grain size is inversely proportional to the distance from the ore body. In the second contact relationship, the replacement effect of calcite and dolomite contact parts is obvious. The internal pore connectivity of dolomite is good, and the calcite filling phenomenon is significant. Cathodoluminescence shows the existence of rhombuses and triangles, and the core emits dark orange light. The characteristics of the two contact relationships are obvious.

[0045] Step S3: Perform LA-ICP-MS testing on the probe sheet to obtain the trace element content and rare earth element content of each growth ring zone.

[0046] The specific process of step S3 includes:

[0047] Step S31, using Pearson correlation coefficient to calculate Mg 2+ The correlation with other trace elements is based on the calculation results to obtain the trace elements and rare earth elements in the probe sheet that meet the preset conditions; step S32, based on the obtained trace elements and rare earth elements, use LA-ICP-MS to obtain the trace element content and rare earth element content.

[0048] The specific process of step S31 includes: using the Pearson correlation coefficient to calculate Mg 2+ The correlation with other trace elements is used to analyze the correlation between geological variables, assist in identifying the association between mineralizing element combinations, mineralization markers or geological processes, and obtain calculation results; use two-tailed significance judgment, that is, exclude the possibility that "correlation is randomly generated", and obtain the judgment result based on whether the calculation result is significant.

[0049] The LA-ICP-MS raw data were processed and analyzed using ICPMSDataCal 12.2 software to obtain the trace element content and rare earth element content of each growth ring; and the Pearson product-moment correlation coefficient Mg 2+ Correlation with the rest of the trace elements. Pearson's coefficient correlation is defined as the covariance divided by the product of the standard deviations of the variables:

[0050]

[0051] where cov(X,Y) is the covariance, calculated as (sample covariance), σX and σY are standard deviations, calculated as and

[0052] Among them, n is the number of samples, x i is the value of each sample, is the sample mean of x, y i is the value of each sample; is the sample mean of y.

[0053] The matrix is ​​represented as:

[0054] For a multivariate case X (n×p, where each column is a variable), the correlation case R can be calculated by the following steps:

[0055] Standardized data: Standardize each column of data (mean is 0, standard deviation is 1) to obtain the matrix Z.

[0056] Calculate the covariance matrix: The standardized covariance matrix is ​​the correlation coefficient:

[0057]

[0058] For two column vectors x and y (centered): Among them, ||x||, ||y|| are the vector moduli, r is the cosine similarity between vectors, and Z is the matrix obtained after standardizing the data.

[0059] r=1: perfect linear correlation; r>-1: perfect negative linear correlation; r=0: no linear correlation.

[0060] In correlation analysis, a two-tailed significance test is used to determine whether the Pearson correlation coefficient r is significantly different from zero (i.e., whether there is a statistically significant linear correlation between the variables). 2+ Pearson correlation coefficient exploration process and formula:

[0061] Null hypothesis and alternative hypothesis.

[0062] Null hypothesis H 0 : Overall correlation coefficient ρ = 0, there is no correlation between variables; H 1 : The overall correlation coefficient ρ≠0, there is correlation between variables, two-tailed test.

[0063] Test statistic (t). Test the significance of the correlation coefficient of t, the formula is:

[0064]

[0065] Where r is the sample Pearson correlation coefficient and n is the sample size.

[0066] Two-tailed p-value calculation. Find the corresponding two-sided probability in the t distribution:

[0067] p=2P(T≥|t|)

[0068] Where T follows a t-distribution with n-2 degrees of freedom df.

[0069] Determine significance. For a given significance level α = 0.01, find the two-tailed critical value t by looking up the t distribution table. 临界 , if |t|>t 临界 , reject the null hypothesis. The critical value satisfies

[0070] According to the above formula, the critical value of the significance level α=0.01 is 0.372 when the corresponding degree of freedom df=45 (47 elements in total). It is concluded that the trace elements are Si, Sc, Sr, Y, Sn, Tb, and Yb.

[0071] Step S4: predicting the location of the calcite deposit based on the characteristic map and the trace element content and rare earth element content of each growth ring zone.

[0072] Based on the study of the change in the content of the above elements Si, Sc, Sr, Y, Sn, Tb, and Yb from the far end to the near end of the mine, the location of the calcite deposit is predicted. The specific changes in the element content are as follows:

[0073] The Si element decreases significantly near the ore end; the Sc and Sr elements increase significantly; the Y, Sn, Tb, and Yb increase relatively slowly.

[0074] The possible reasons for this phenomenon are:

[0075] Calcite (CaCO 3 ) requires rich Ca, CO 3 2- environment, and Si is the main component of quartz or silicate minerals (such as feldspar, mica). During the precipitation of calcite, silicate minerals may be dissolved or inhibited from forming, resulting in Si being "diluted" near the ore end. Or due to the effect of fluid migration, SiO in hydrothermal or ore-forming fluids 2 The solubility is low, and the precipitation of calcite may be accompanied by an increase in pH, which causes Si to be carried away by the fluid in the form of colloid or silicate. The reason for the significant increase of Sc and Sr elements may be the isomorphous substitution of Sr: Sr 2+ With Ca 2+ Similar ionic radius Easy to replace Ca in the calcite lattice 2+ , resulting in Sr enrichment near the ore end. The slow rise of Y, Sn, Tb, and Yb may be due to the fractionation of rare earth elements (Y, Tb, and Yb): Y and heavy rare earth (Yb) are often present in the fluid as carbonate or fluorine complexes (such as Y(CO 3 ) 33- ) migration, calcite precipitation CO 3 2- The reduction in concentration may inhibit its dissolution, resulting in slow enrichment. Light rare earths (such as La and Ce) are more easily adsorbed by clay or iron-manganese oxides, while heavy rare earths have stronger migration ability, resulting in smaller enrichment of Y and Yb. Among them, Sn is chemically inert. 4+ In an oxidizing environment, it is easy to form insoluble cassiterite (SnO 2 ), has weak migration ability, and is only slowly released and enriched under local reducing conditions. Fluid activity of Sc: Sc 3+ In hydrothermal fluids, it is often present as hydroxyl or chlorine complexes (such as Sc(OH) 3 ScCl 3 When calcite precipitates, the increase in fluid pH or the decrease in temperature may destroy the stability of the complex and promote the enrichment of Sc in the form of independent minerals (such as scandium) or adsorbed on the surface of clay minerals.

[0076] In summary, if Figure 2 As shown in the figure, in the first type of contact relationship, gray medium-coarse crystal dolomite containing calcite clumps are seen at the edge of the ore body. Microscopic observation shows that the dolomite crystal size has a "double mode" distribution feature, that is, two types with different crystal size and degree of crystallization (morphology), and different pore types and internal pore connectivity. It can be clearly distinguished from calcite by alizarin red staining. Figure 2 a shows that the hydrothermal dolomite (Dol) near the calcite side is a straight crystal face euhedral crystal, and the crystals are filled with calcite. When the ore-bearing host rock is dolomite, the mineral grain size in the altered host rock is significantly increased, which is the result of hydrothermal recrystallization, and the grain size of dolomite directly affected by hydrothermal fluid is coarse. The intercrystalline pores and permeability of euhedral crystals with larger crystals and straight crystal faces are significantly improved. The dolomite grain size is inversely proportional to the distance from the ore body. The coarse-grained saddle-shaped dolomite crystals in close contact with the calcite ore body have a flat crystal face semi-automorphic and curved crystal face anhedral structure, and show the characteristics of high-temperature regrowth (some overgrown crystals begin to show curved crystal faces). This reflects the rapid crystallization and attached crystal growth characteristics of the crystal, with many lattice defects and a certain wavy extinction. Dolomite rings can be seen under cathode luminescence.

[0077] In the second type of contact relationship, gray medium-coarse crystal dolomite containing calcite clumps are seen at the edge of the ore body. Microscopic observation shows that the contact between calcite and dolomite has obvious metasomatism. The internal pore connectivity of dolomite is good, and the calcite filling phenomenon is significant. Cathodoluminescence shows the presence of rhombuses and triangles, and the core emits dark orange light.

[0078] These two contact relationships show that dolomite is rich in Ca 2+ Hydrothermal ore-forming fluid transformation, during which dolomite (MgCa(CO 3) showed a dolomitization phenomenon. Therefore, the tracer and Mg 2+ The related elements are of great significance in the search for calcite deposits of this origin.

[0079] like Figure 3 As shown in the figure, the LA-ICP-MS raw data were processed and analyzed using ICPMSDataCal 12.2 software to obtain the contents of trace elements and rare earth elements from far to near calcite; and the Pearson product-moment correlation coefficient was used to explore the Mg 2+ The correlation with other trace elements is used to analyze the correlation between geological variables and assist in identifying the association between ore-forming element combinations, mineralization markers or geological processes. The calculation results are obtained; a two-tailed significance judgment is used, that is, the possibility of "correlation is randomly generated" is excluded, and the judgment result is obtained based on whether the calculation result is significant. 2+ The correlation was tested with two tails, and the key value at the level of p = 0.01 was 0.372 under the corresponding degree of freedom. 2+ There is a significant correlation. Among them, Si and Sr are positively correlated with Mg ions, while Sc, Y, Sn, Tb, and Yb are negatively correlated with Mg ions.

[0080] like Figure 4-Figure 5 As shown in the figure, G-Cal is giant crystal calcite, that is, calcite ore body; Dol is dolomite, that is, the main host rock of calcite ore body. The schematic diagram of trace element and rare earth element content from far to near from calcite is obtained by analyzing from right to left; according to the changes in Si, Sc, Ni, Zn, Sr, Y, Sn, Tb, Ho, Er and Yb content from the far end to the near end of the ore body, the following conclusions can be drawn:

[0081] (1) The closer to the ore body, the more obvious the Si content decreases; the other elements increase to varying degrees; (2) In the first contact type, affected by the hydrothermal fluid, the elements in the dolomite fluctuate greatly.

[0082] Embodiment 2

[0083] A calcite deposit prospecting prediction system based on dolomite geochemical analysis, the system comprising:

[0084] The sample collection module is used to collect dolomite samples in the dolomite area of ​​the calcite mine to be tested and grind them into probe pieces.

[0085] The dolomite samples are dolomite samples with different contact types, including: alteration halo formed by the contact between megacrystalline calcite and dolomite and megacrystalline calcite replacing dolomite.

[0086] The feature map acquisition module is used to acquire the feature map of the probe sheet.

[0087] Observe the dolomite area of ​​the calcite deposit, collect dolomite samples of different contact types, and grind them into probe pieces; according to the different forms of dolomite in close contact with the calcite ore body, that is, the alteration halo formed by the contact between giant calcite and dolomite and the replacement of dolomite by giant calcite. In the first contact relationship, the mineral grain size in the altered surrounding rock has increased significantly, and the dolomite grain size is inversely proportional to the distance from the ore body. In the second contact relationship, the replacement effect of calcite and dolomite contact parts is obvious. The internal pore connectivity of dolomite is good, and the calcite filling phenomenon is significant. Cathodoluminescence shows the existence of rhombuses and triangles, and the core emits dark orange light. The characteristics of the two contact relationships are obvious.

[0088] The content acquisition module is used to perform LA-ICP-MS test on the probe sheet to obtain the trace element content and rare earth element content of each growth ring zone.

[0089] The specific process of the content acquisition module includes:

[0090] Calculate Mg using Pearson correlation coefficient 2+ The correlation with other trace elements is based on the calculation results to obtain the trace elements and rare earth elements in the probe sheet that meet the preset conditions; based on the obtained trace elements and rare earth elements, LA-ICP-MS is used to obtain the trace element content and rare earth element content.

[0091] Calculate Mg using Pearson correlation coefficient 2+ The correlation with other trace elements is obtained to obtain a calculation result; whether the calculation result is significant is judged by two-tail significance to obtain a judgment result; and the trace elements and the rare earth elements are obtained based on the judgment result.

[0092] The LA-ICP-MS raw data were processed and analyzed using ICPMSDataCal 12.2 software to obtain the trace element content and rare earth element content of each growth ring; and the Pearson product-moment correlation coefficient Mg 2+ Correlation with the rest of the trace elements. Pearson's coefficient correlation is defined as the covariance divided by the product of the standard deviations of the variables:

[0093]

[0094] where cov(X,Y) is the covariance, calculated as (Sample covariance) σX and σY are standard deviations, calculated as and The matrix is ​​represented as:

[0095] For a multivariate case X (n×p, where each column is a variable), the correlation case R can be calculated by the following steps:

[0096] Standardized data: Standardize each column of data (mean is 0, standard deviation is 1) to obtain the matrix Z.

[0097] Calculate the covariance matrix: The standardized covariance matrix is ​​the correlation coefficient:

[0098]

[0099] For two column vectors x and y (centered): Where ||x||, ||y|| are the vector moduli, and r is the cosine similarity between vectors.

[0100] r=1: perfect linear correlation; r>-1: perfect negative linear correlation; r=0: no linear correlation.

[0101] In correlation analysis, a two-tailed significance test is used to determine whether the Pearson correlation coefficient r is significantly different from zero (i.e., whether there is a statistically significant linear correlation between the variables). 2+ Pearson correlation coefficient exploration process and formula:

[0102] Null hypothesis and alternative hypothesis.

[0103] Null hypothesis H 0 : Overall correlation coefficient ρ = 0, there is no correlation between variables; H 1 : The overall correlation coefficient ρ≠0, there is correlation between variables, two-tailed test.

[0104] Test statistic (t). Test the significance of the correlation coefficient of t, the formula is:

[0105]

[0106] Where r is the sample Pearson correlation coefficient and n is the sample size

[0107] Two-tailed p-value calculation. Find the corresponding two-sided probability in the t distribution:

[0108] p=2P(T≥|t|)

[0109] Where T follows a t distribution with degrees of freedom df of n-2

[0110] Determine significance. For a given significance level α = 0.01, find the two-tailed critical value t by looking up the t distribution table. 临界 , if |t|>t 临界 , reject the null hypothesis. The critical value satisfies

[0111] According to the above formula, the critical value of the significance level α=0.01 is 0.372 when the corresponding degree of freedom df=45 (47 elements in total). It is concluded that the trace elements are Si, Sc, Sr, Y, Sn, Tb, and Yb.

[0112] A location prediction module is used to predict the location of the calcite deposit based on the characteristic map and the trace element content and rare earth element content of each growth ring zone.

[0113] The location of the calcite deposit is predicted by studying the change of the content of the above elements Si, Sc, Sr, Y, Sn, Tb, and Yb from the far end to the near end. The specific changes in the element content are as follows: the Si element decreases significantly near the mine; the Sc and Sr elements increase significantly; the Y, Sn, Tb, and Yb increase more slowly.

[0114] The possible reasons for this phenomenon are:

[0115] Calcite (CaCO 3 ) requires rich Ca, CO 3 2- environment, and Si is the main component of quartz or silicate minerals (such as feldspar, mica). During the precipitation of calcite, silicate minerals may be dissolved or inhibited from forming, resulting in Si being "diluted" near the ore end. Or due to the effect of fluid migration, SiO in hydrothermal or ore-forming fluids 2 The solubility is low, and the precipitation of calcite may be accompanied by an increase in pH, which causes Si to be carried away by the fluid in the form of colloid or silicate. The reason for the significant increase of Sc and Sr elements may be the isomorphous substitution of Sr: Sr 2+ With Ca 2+ Similar ionic radius Easy to replace Ca in the calcite lattice 2+ , resulting in Sr enrichment near the ore end. The slow rise of Y, Sn, Tb, and Yb may be due to the fractionation of rare earth elements (Y, Tb, and Yb): Y and heavy rare earth (Yb) are often present in the fluid as carbonate or fluorine complexes (such as Y(CO 3 ) 3 3- ) migration, calcite precipitation CO 3 2- The reduction in concentration may inhibit its dissolution, resulting in slow enrichment. Light rare earths (such as La and Ce) are more easily adsorbed by clay or iron-manganese oxides, while heavy rare earths have stronger migration ability, resulting in smaller enrichment of Y and Yb. Among them, Sn is chemically inert. 4+ In an oxidizing environment, it is easy to form insoluble cassiterite (SnO 2 ), has weak migration ability, and is only slowly released and enriched under local reducing conditions. Fluid activity of Sc: Sc3+ In hydrothermal fluids, it is often present as hydroxyl or chlorine complexes (such as Sc(OH) 3 ScCl 3 When calcite precipitates, the increase in fluid pH or the decrease in temperature may destroy the stability of the complex and promote the enrichment of Sc in the form of independent minerals (such as scandium) or adsorbed on the surface of clay minerals.

[0116] In summary, if Figure 2 As shown in the figure, in the first type of contact relationship, gray medium-coarse crystal dolomite containing calcite clumps are seen at the edge of the ore body. Microscopic observation shows that the dolomite crystal size has a "double mode" distribution feature, that is, two types with different crystal size and degree of crystallization (morphology), and different pore types and internal pore connectivity. It can be clearly distinguished from calcite by alizarin red staining. Figure 2 a shows that the hydrothermal dolomite (Dol) near the calcite side is a straight crystal face euhedral crystal, and the crystals are filled with calcite. When the ore-bearing host rock is dolomite, the mineral grain size in the altered host rock is significantly increased, which is the result of hydrothermal recrystallization, and the grain size of dolomite directly affected by hydrothermal fluid is coarse. The intercrystalline pores and permeability of euhedral crystals with large crystals and straight crystal faces are significantly improved. The dolomite grain size is inversely proportional to the distance from the ore body. The coarse-grained saddle-shaped dolomite crystals in close contact with the calcite ore body have a flat crystal face semi-automorphic and curved crystal face anhedral structure, and show the characteristics of high-temperature regrowth (some overgrown crystals begin to show curved crystal faces). This reflects the rapid crystallization and attached crystal growth characteristics of the crystal, with many lattice defects and a certain wavy extinction. Dolomite rings can be seen under cathode luminescence.

[0117] In the second type of contact relationship, gray medium-coarse crystal dolomite containing calcite clumps are seen at the edge of the ore body. Microscopic observation shows that the contact between calcite and dolomite has obvious metasomatism. The internal pore connectivity of dolomite is good, and the calcite filling phenomenon is significant. Cathodoluminescence shows the presence of rhombuses and triangles, and the core emits dark orange light.

[0118] These two contact relationships show that dolomite is rich in Ca 2+ Hydrothermal ore-forming fluid transformation, during which dolomite (MgCa(CO 3 ) showed a dolomitization phenomenon. Therefore, the tracer and Mg 2+ The related elements are of great significance in the search for calcite deposits of this origin.

[0119] like Figure 3 As shown in the figure, the LA-ICP-MS raw data were processed and analyzed using ICPMSDataCal 12.2 software to obtain the contents of trace elements and rare earth elements from far to near calcite; and the Pearson product-moment correlation coefficient was used to explore the Mg 2+ Correlation with other trace elements.2+ The correlation was tested with two tails, and the key value at the level of p = 0.01 was 0.384 under the corresponding degree of freedom. 2+ There is a significant correlation. Among them, Si and Sr are positively correlated with Mg ions, while Sc, Y, Sn, Tb, and Yb are negatively correlated with Mg ions.

[0120] like Figure 4 As shown in the figure, according to the changes in the contents of Si, Sc, Ni, Zn, Sr, Y, Sn, Tb, Ho, Er and Yb from the distal end to the proximal end of the ore body, the following conclusions can be drawn:

[0121] (1) The closer to the ore body, the more obvious the Si content decreases; the other elements increase to varying degrees. (2) In the first contact type, affected by the hydrothermal fluid, the elements in the dolomite fluctuate greatly.

[0122] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A calcite deposit prospecting prediction method based on dolomite geochemical analysis, characterized in that: The method comprises: Step S1, collecting dolomite samples in the dolomite area of ​​the calcite mine to be detected and grinding them into probe pieces; Step S2, collecting a characteristic graph of the probe sheet; Step S3, performing LA-ICP-MS testing on the probe sheet to obtain the trace element content and rare earth element content of dolomite at different positions; Step S4: predicting the location of the calcite deposit based on the characteristic graph and the trace element content and rare earth element content.

2. The calcite deposit prospecting prediction method based on dolomite geochemical analysis according to claim 1, characterized in that: In the step S1, the dolomite samples are dolomite samples of different contact types, and the contact types include: alteration halo formed by contact between megacrystal calcite and dolomite and megacrystal calcite replacing dolomite.

3. The calcite deposit prospecting prediction method based on dolomite geochemical analysis according to claim 1, characterized in that: In the step S2, a characteristic image of the probe sheet under a cathode luminescence microscope is collected.

4. The calcite deposit prospecting prediction method based on dolomite geochemical analysis according to claim 1, characterized in that: The specific process of step S3 includes: Step S31, using Pearson correlation coefficient to calculate Mg 2+ Correlation with other trace elements, and obtaining trace elements and rare earth elements in the probe sheet that meet preset conditions based on the calculation results; Step S32: Based on the obtained trace elements and rare earth elements, use LA-ICP-MS to obtain the trace element content and the rare earth element content.

5. The calcite deposit prospecting prediction method based on dolomite geochemical analysis according to claim 4, characterized in that: The specific process of step S31 includes: Calculate Mg using Pearson correlation coefficient 2+ The correlation with other trace elements was used to obtain the calculated results; Use two-tailed significance test to determine whether the calculation result is significant, and obtain a determination result; The trace elements and the rare earth elements are obtained based on the judgment result.

6. The calcite deposit prospecting prediction method based on dolomite geochemical analysis according to claim 5, characterized in that: Calculate Mg using Pearson correlation coefficient 2+ The correlation with other trace elements is as follows: Among them, cov(X,Y) is the covariance and σXσY is the standard deviation.

7. The calcite deposit prospecting prediction method based on dolomite geochemical analysis according to claim 6, characterized in that: The standard deviation is calculated as: σX: σY: Among them, n is the number of samples, x i is the value of each sample, is the sample mean of x, y i is the value of each sample; is the sample mean of y.

8. A calcite deposit prospecting prediction system based on dolomite geochemical analysis, used to implement the calcite deposit prospecting prediction method according to any one of claims 1 to 7, characterized in that: The system includes: The sample collection module is used to collect dolomite samples in the dolomite area of ​​the calcite mine to be tested and grind them into probe pieces; A feature map acquisition module, used for acquiring a feature map of the probe sheet; A content acquisition module, used to perform LA-ICP-MS testing on the probe sheet to obtain the trace element content and rare earth element content of dolomite at different positions; A location prediction module is used to predict the location of the calcite deposit based on the characteristic map and the trace element content and rare earth element content.

Images