A method and system for predicting calcite deposits based on dolomite geochemical analysis

Abstract

This invention discloses a method and system for predicting calcite deposits based on dolomite geochemical analysis. The method includes: Step S1, collecting dolomite samples from the host dolomite area of ​​the calcite deposit to be detected and grinding them into probe slides; Step S2, acquiring feature images of the probe slides under a cathodoluminescence microscope; Step S3, performing LA-ICP-MS testing on the probe slides to obtain the trace element and rare earth element contents of dolomite at different locations; Step S4, predicting the location of calcite deposits based on the feature images and the trace element and rare earth element contents. This invention utilizes the geochemical feature analysis of dolomite in the host rock of calcite deposits, through in-situ geochemical testing of dolomite using LA-ICP-MS and cathodoluminescence of carbonate rocks, to achieve the technical effect of predicting calcite deposits and solve the technical problems of calcite deposit exploration.

Description

Technical Field

[0001] This invention relates to the field of resource exploration technology, specifically to a method and system for predicting calcite deposits based on dolomite geochemical analysis. Background Technology

[0002] Dolomite and calcite are both carbonate minerals with similar chemical compositions and physical properties, and their mineral assemblages overlap. Both are predominantly calcium-based, and Ca anomalies in geochemical sampling may simultaneously reflect mineralization in either dolomite or calcite, requiring further differentiation by considering other elemental variations. Furthermore, in the process of trace element analysis of carbonate rocks, samples often show trace element content below the detection threshold, which increases experimental error and leads to biased results. Summary of the Invention

[0003] To address the above problems, this invention proposes a method for predicting calcite deposits based on dolomite geochemical analysis, the method comprising:

[0004] Step S1: Collect dolomite samples from the dolomite area of ​​the calcite mine to be tested and grind them into probe sheets;

[0005] Step S2: Acquire feature maps of the probe sheet;

[0006] 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;

[0007] Step S4: Based on the feature map and the trace element content and rare earth element content of each growth zone, predict the location of the calcite deposit.

[0008] Optionally, in step S1, the dolomite sample is a dolomite sample with different contact types, including: alteration halo formed by contact between megacrystalline calcite and dolomite and megacrystalline calcite replacing dolomite.

[0009] Optionally, in step S2, a feature image under a cathode ray microscope is acquired on the probe sheet.

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

[0011] Step S31: Calculate Mg using the Pearson correlation coefficient. 2+ The correlation with other trace elements is used to calculate the trace elements and rare earth elements in the probe sheet that meet the preset conditions.

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

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

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

[0015] The significance of the calculation result is determined using two-tailed significance analysis to obtain the determination result.

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

[0017] Optionally, Mg can be calculated using the Pearson correlation coefficient. 2+ The correlation with other trace elements was calculated using the following specific method:

[0018]

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

[0020] Optional, the standard deviation is calculated as follows:

[0021]

[0022] Where n is the number of samples, x i Let x be the sample value, and y be the sample mean. i represents the values ​​of each sample; y represents the mean of the y samples.

[0023] This 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 from the host dolomite area of ​​the calcite mine to be tested and grind them into probe sheets.

[0025] The feature map acquisition module is used to acquire feature maps of the probe sheet;

[0026] The content acquisition module is 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;

[0027] The location prediction module is used to predict the location of calcite deposits based on the feature map and the trace element and rare earth element contents of each growth zone.

[0028] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0029] This invention observes the host dolomite region of calcite deposits and identifies different geochemical characteristics of the host dolomite. Dolomite samples with different contact types with the calcite deposit are collected, and dolomite probe specimens of different contact types are prepared. Cathodoluminescence microscopy is used to observe the different types of dolomite probe specimens to identify two types of contact between the calcite ore body and dolomite, from the distal end to the proximal end. Raw LA-ICP-MS data for each contact type are obtained through LA-ICP-MS testing. The raw LA-ICP-MS data are processed and analyzed to obtain the trace element and rare earth element contents of dolomite from the distal to the proximal end of the ore body. The correlation coefficient between the trace elements and Mg is calculated using the Pearson product-moment correlation coefficient. 2+ This invention analyzes elements closely related to calcite mineralization, including trace elements such as Si, Sc, Sr, Y, Sn, Tb, and Yb. It explores the patterns of elemental variation to predict the location of calcite deposits. Utilizing the geochemical characteristics of dolomite in the surrounding rocks of calcite deposits, this invention employs in-situ geochemical testing of dolomite using LA-ICP-MS and cathodoluminescence of carbonate rocks to achieve the technical effect of predicting calcite deposits and solves the technical problems in calcite deposit exploration. Attached Figure Description

[0030] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a flowchart illustrating the steps of a method for predicting calcite deposits based on dolomite geochemical analysis, according to an embodiment of the present invention.

[0032] Figure 2 Examples of dolomite samples with different contact types and corresponding local cathodoluminescence images from embodiments of the present invention are shown below. Figure 2 a is a schematic diagram of the alteration halo formed by the contact between megacrystalline calcite and dolomite; Figure 2 b is a schematic diagram of cathodic emission at a 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 showing cathodic luminescence at a local location of the dolomite;

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

[0034] Figure 4This is a schematic diagram illustrating the changes in the contents of Si, Sc, Sr, Y, Sn, Tb, and Yb of dolomite from the far end to the near end of the ore body in Embodiment I of the present invention.

[0035] Figure 5 This is a schematic diagram illustrating the changes in the content of significantly relevant elements (Si, Sc, Sr, Y, Sn, Tb, Yb) in dolomite of type II in this embodiment of the invention from the far end to the near end of the ore body. Detailed Implementation

[0036] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0037] First, the professional instruments and proprietary terms used in this invention will be 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. This technique focuses a laser beam onto the sample surface, generating fine particles which are then analyzed by an inductively coupled plasma mass spectrometer. This method requires no sample preparation, allows direct testing of solid samples, and offers advantages such as a wide detection range, low detection limits, high sensitivity, and near-non-destructive nature. LA-ICP-MS has wide applications in various fields, including geology, archaeology, and materials science. In geology, it is used for in-situ micro-area analysis of rocks and minerals to reveal their composition and structure. In archaeology, it is particularly suitable for the analysis and study of precious silicate artifacts, obtaining information on their chemical composition without damaging the sample. Furthermore, LA-ICP-MS is used in gemology for the rapid and accurate determination of trace elements in gemstones, aiding in the identification of authenticity and composition. The main components of this technique 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, evaporating, and ionizing the material on the sample surface. The resulting particle and ion plumes then enter the ICPMS system for quality filtration and analysis.

[0039] Example 1

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

[0041] Step S1: Collect dolomite samples from the dolomite area of ​​the calcite mine to be tested and grind them into probe slides.

[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: Acquire feature images of the probe sheet. Acquire feature images of the probe sheet under a cathode ray microscope.

[0044] In the dolomite region hosting the calcite deposit, dolomite samples with different contact types were collected and ground into probe sections. Based on the different morphologies of dolomite in close contact with the calcite ore body—namely, alteration halos formed by the contact between megacrystalline calcite and dolomite, and the replacement of dolomite by megacrystalline calcite—the following findings were made. In the first contact relationship, the mineral grain size in the altered host rock was significantly increased, and the dolomite grain size was inversely proportional to the distance from the ore body. In the second contact relationship, the replacement effect at the contact site between calcite and dolomite was obvious. The internal pore connectivity of the dolomite was good, and the calcite filling phenomenon was significant. Cathodoluminescence revealed rhomboid and triangular shapes, with the core emitting a dark orange light. The characteristics of the two contact relationships were distinct.

[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 zone.

[0046] The specific process of step S3 includes:

[0047] Step S31: Calculate Mg using the Pearson correlation coefficient. 2+ Correlation with other trace elements, and based on the calculation results, 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: calculating Mg using the Pearson correlation coefficient. 2+ The correlation with other trace elements is used to analyze the correlation between geological variables, help identify the association between ore-forming element combinations, mineralization indicators or geological processes, and obtain the calculation results; the two-tailed significance judgment is used, that is, to exclude the possibility that "the correlation is randomly generated", and to obtain the judgment result based on whether the calculation result is significant.

[0049] The raw LA-ICP-MS data were processed and analyzed using ICPMSDataCal 12.2 software to obtain the trace element and rare earth element contents of each growth zone; and the Pearson product-moment correlation coefficient Mg was used as the basis for analysis. 2+ Correlation with other trace elements. The Pearson coefficient, or correlation coefficient, is defined as the product of covariance and the standard deviation of the variable.

[0050]

[0051] Where cov(X,Y) is the covariance, calculated as follows: (Sample covariance), σX and σY are the standard deviations, calculated as follows: and

[0052] Where n is the number of samples, x i For each sample value, Let x be the sample mean, and y be the sample mean. i For each sample value; Let y be the sample mean.

[0053] The matrix is ​​represented as:

[0054] For multivariate burden of proof X (n×p, each column being a variable), the relevance burden of proof R can be calculated using the following steps:

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

[0056] Calculate the covariance matrix: The standardized covariance matrix is ​​the correlation coefficient. (Evidence:)

[0057]

[0058] For two column vectors x and y (already centered): Where ||x|| and ||y|| are vector magnitudes, 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, the 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). The following is an example of using the two-tailed significance test Mg... 2+ The process and formula for exploring the Pearson correlation coefficient:

[0061] Null hypothesis and alternative hypothesis.

[0062] Null hypothesis H0: The population correlation coefficient ρ = 0, indicating no correlation between variables; H1: The population correlation coefficient ρ ≠ 0, indicating a correlation between variables. Two-tailed test.

[0063] Test statistic (t). The formula for testing the significance of the correlation coefficient using t is:

[0064]

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

[0066] Calculate the two-tailed p-value. Find the corresponding two-tailed 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 the significance. For a given significance level α = 0.01, find the two-tailed critical value t by consulting the t-distribution table. 临界 If |t|>t 临界 We reject the null hypothesis. The critical value satisfies...

[0070] Based on the above formula, with the corresponding degrees of freedom df = 45 (47 elements in total), the significance level α = 0.01 and the key value is 0.372. The trace elements are thus determined to be Si, Sc, Sr, Y, Sn, Tb, and Yb.

[0071] Step S4: Based on the feature map and the trace element and rare earth element contents of each growth zone, predict the location of the calcite deposit.

[0072] Based on the study of the variation patterns of the contents of the aforementioned elements Si, Sc, Sr, Y, Sn, Tb, and Yb from the far end to the near end of the ore deposit, the location of calcite deposits can be predicted. The specific patterns of elemental content variation are as follows:

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

[0074] The possible reasons for this phenomenon are:

[0075] The formation of calcite (CaCO3) requires a high concentration of Ca and CO3. 2- The environment in which calcite is abundant, and Si is a major component of quartz or silicate minerals (such as feldspar and mica). During calcite precipitation, silicate minerals may be dissolved or their formation inhibited, leading to Si being "diluted" near the ore end. Alternatively, due to fluid migration, the solubility of SiO2 in hydrothermal or ore-forming fluids is low, and calcite precipitation may be accompanied by an increase in pH, prompting Si to be carried away by the fluid in colloidal or silicate form. The significant increase in Sc and Sr elements may be due to isomorphous substitution of Sr: Sr 2+ With Ca 2+ Similar ionic radii Ca in easily substituted calcite lattice 2+ This leads to Sr enrichment near the ore end. The gradual increase in Y, Sn, Tb, and Yb may be due to the fractionation of rare earth elements (Y, Tb, Yb): Y and heavy rare earth elements (Yb) often form carbonate or fluorine complexes in fluids (such as Y(CO3)3). 3- ) migration, CO3 during calcite precipitation 2-Decreasing concentration may inhibit dissolution, leading to slow enrichment. Light rare earth elements (such as La and Ce) are more easily adsorbed by clay or iron-manganese oxides, while heavy rare earth elements have stronger migration capabilities, resulting in smaller enrichment of Y and Yb. Sn, however, is chemically inert. 4+ In oxidizing environments, it readily forms insoluble cassiterite (SnO2), exhibiting weak migration ability and only slowly releasing and accumulating under localized reducing conditions. Fluid activity of Sc: Sc 3+ In hydrothermal fluids, calcite often migrates as hydroxyl or chlorine complexes (such as Sc(OH)3, ScCl3). When calcite precipitates, an increase in fluid pH or a decrease in temperature may disrupt the stability of the complexes, promoting the accumulation of Sc as an independent mineral (such as scandium) or adsorbed onto the surface of clay minerals.

[0076] In summary, as Figure 2 As shown, in the first type of contact relationship, gray medium- to coarse-grained dolomite containing calcite nodules is observed at the edge of the ore body. Microscopic observation reveals a "bimodal" distribution of dolomite crystal size, i.e., two types with different crystal sizes and degrees of euhedrality (morphology), and different pore types and degrees of internal pore connectivity. Alizarin red staining can clearly distinguish it from calcite. Figure 2 The data shows that the hydrothermal dolomite (Dol) near the calcite side exhibits straight-faced euhedral crystals, with calcite filling the intercrystalline spaces. When the host rock is dolomite, the grain size of minerals in the altered host rock is significantly increased, a result of hydrothermal recrystallization, and the dolomite directly affected by the hydrothermal fluids shows coarser grain size. Larger, straight-faced euhedral crystals show significantly increased intergranular porosity and permeability. 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 both straight-faced subhedral and curved-faced anhedral structures, exhibiting characteristics of high-temperature regeneration (some overgrown crystals begin to show curved faces). This reflects the rapid crystallization and attached crystal growth characteristics, with numerous lattice defects and a certain degree of wavy extinction. Dolomite zoning is visible under cathodoluminescence.

[0077] In the second type of contact relationship, gray medium- to coarse-grained dolomite containing calcite nodules is observed at the edge of the ore body. Microscopic observation shows obvious metasomatism at the contact points between calcite and dolomite. The internal pores of the dolomite are well interconnected, with significant calcite infilling. Cathodoluminescence reveals rhomboid and triangular nodules, with the core emitting a dark orange light.

[0078] These two contact relationships indicate that dolomite is affected by Ca-rich water. 2+ Hydrothermal ore-forming fluid alteration occurred, during which dolomite (MgCa(CO3)) underwent de-dolomitization. Therefore, tracers and Mg... 2+ The relevant elements are of great significance for finding calcite deposits of this type.

[0079] like Figure 3As shown, the raw LA-ICP-MS 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 the calcite. The Pearson product-moment correlation coefficient was used to investigate the Mg content. 2+ Correlation with other trace elements is used to analyze the correlation between geological variables, aiding in the identification of associations between ore-forming element assemblages, mineralization markers, or geological processes. Calculation results are obtained; a two-tailed significance test is used to exclude the possibility that "the correlation is randomly generated," and the judgment result is obtained based on whether the calculation results are significant. The correlation between each element and Mg is then analyzed. 2+ A two-tailed significance test was performed on the correlation between the elements Si, Sc, Sr, Y, Sn, Tb, Yb and Mg, and the critical value at the p=0.01 level was 0.372. 2+ Significant correlations exist. 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 Figures 4-5 As shown in the figure, G-Cal represents megacrystalline calcite, i.e., the calcite ore body; Dol represents dolomite, i.e., the main host rock of the calcite ore body. The diagram illustrates the trace element and rare earth element content from far to near the calcite, analyzed from right to left. Based on 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 Si content decreases significantly; the other elements increase to varying degrees; (2) In the first contact type, the elements in dolomite fluctuate greatly due to the influence of hydrothermal fluids.

[0082] Example 2

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

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

[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 feature maps of the probe sheet.

[0087] In the dolomite region hosting the calcite deposit, dolomite samples with different contact types were collected and ground into probe sections. Based on the different morphologies of dolomite in close contact with the calcite ore body—namely, alteration halos formed by the contact between megacrystalline calcite and dolomite, and the replacement of dolomite by megacrystalline calcite—the following findings were made. In the first contact relationship, the mineral grain size in the altered host rock was significantly increased, and the dolomite grain size was inversely proportional to the distance from the ore body. In the second contact relationship, the replacement effect at the contact site between calcite and dolomite was obvious. The internal pore connectivity of the dolomite was good, and the calcite filling phenomenon was significant. Cathodoluminescence revealed rhomboid and triangular shapes, with the core emitting a dark orange light. The characteristics of the two contact relationships were distinct.

[0088] The content acquisition module is 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 zone.

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

[0090] Mg was calculated using the Pearson correlation coefficient. 2+ Correlation with other trace elements is used to calculate the trace elements and rare earth elements in the probe slice that meet the preset conditions. Based on the obtained trace elements and rare earth elements, the content of trace elements and rare earth elements is obtained using LA-ICP-MS.

[0091] Mg was calculated using the Pearson correlation coefficient. 2+ The correlation with other trace elements is used to obtain the calculation results; the significance of the calculation results is determined by using two-tailed significance test to obtain the judgment results; based on the judgment results, the trace elements and the rare earth elements are obtained.

[0092] The raw LA-ICP-MS data were processed and analyzed using ICPMSDataCal 12.2 software to obtain the trace element and rare earth element contents of each growth zone; and the Pearson product-moment correlation coefficient Mg was used as the basis for analysis. 2+ Correlation with other trace elements. The Pearson coefficient, or correlation coefficient, is defined as the product of covariance and the standard deviation of the variable.

[0093]

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

[0095] For multivariate burden of proof X (n×p, each column being a variable), the relevance burden of proof R can be calculated using the following steps:

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

[0097] Calculate the covariance matrix: The standardized covariance matrix is ​​the correlation coefficient. (Evidence:)

[0098]

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

[0100] r = 1: Perfect linear correlation; r > -1: Perfect negative linear correlation; r = 0: No linear correlation.

[0101] In correlation analysis, the 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). The following is an example of using the two-tailed significance test Mg... 2+ The process and formula for exploring the Pearson correlation coefficient:

[0102] Null hypothesis and alternative hypothesis.

[0103] Null hypothesis H0: The population correlation coefficient ρ = 0, indicating no correlation between variables; H1: The population correlation coefficient ρ ≠ 0, indicating a correlation between variables. Two-tailed test.

[0104] Test statistic (t). The formula for testing the significance of the correlation coefficient using t is:

[0105]

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

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

[0108] p = 2P (T ≥ |t|)

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

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

[0111] Based on the above formula, with the corresponding degrees of freedom df = 45 (47 elements in total), the significance level α = 0.01 and the key value is 0.372. The trace elements are thus determined to be Si, Sc, Sr, Y, Sn, Tb, and Yb.

[0112] The location prediction module is used to predict the location of calcite deposits based on the feature map and the trace element and rare earth element contents of each growth zone.

[0113] Based on the study of the variation patterns of the contents of the aforementioned elements Si, Sc, Sr, Y, Sn, Tb, and Yb from the far end to the near end of the ore deposit, the location of calcite deposits can be predicted. The specific patterns of element content variation are as follows: Si decreases significantly near the ore end; Sc and Sr increase significantly; Y, Sn, Tb, and Yb increase relatively gradually.

[0114] The possible reasons for this phenomenon are:

[0115] The formation of calcite (CaCO3) requires a high concentration of Ca and CO3. 2- The environment in which calcite is abundant, and Si is a major component of quartz or silicate minerals (such as feldspar and mica). During calcite precipitation, silicate minerals may be dissolved or their formation inhibited, leading to Si being "diluted" near the ore end. Alternatively, due to fluid migration, the solubility of SiO2 in hydrothermal or ore-forming fluids is low, and calcite precipitation may be accompanied by an increase in pH, prompting Si to be carried away by the fluid in colloidal or silicate form. The significant increase in Sc and Sr elements may be due to isomorphous substitution of Sr: Sr 2+ With Ca 2+ Similar ionic radii Ca in easily substituted calcite lattice 2+ This leads to Sr enrichment near the ore end. The gradual increase in Y, Sn, Tb, and Yb may be due to the fractionation of rare earth elements (Y, Tb, Yb): Y and heavy rare earth elements (Yb) often form carbonate or fluorine complexes in fluids (such as Y(CO3)3). 3- ) migration, CO3 during calcite precipitation 2- Decreasing concentration may inhibit dissolution, leading to slow enrichment. Light rare earth elements (such as La and Ce) are more easily adsorbed by clay or iron-manganese oxides, while heavy rare earth elements have stronger migration capabilities, resulting in smaller enrichment of Y and Yb. Sn, however, is chemically inert. 4+ In oxidizing environments, it readily forms insoluble cassiterite (SnO2), exhibiting weak migration ability and only slowly releasing and accumulating under localized reducing conditions. Fluid activity of Sc: Sc 3+ In hydrothermal fluids, calcite often migrates as hydroxyl or chlorine complexes (such as Sc(OH)3, ScCl3). When calcite precipitates, an increase in fluid pH or a decrease in temperature may disrupt the stability of the complexes, promoting the accumulation of Sc as an independent mineral (such as scandium) or adsorbed onto the surface of clay minerals.

[0116] In summary, as Figure 2 As shown, in the first type of contact relationship, gray medium- to coarse-grained dolomite containing calcite nodules is observed at the edge of the ore body. Microscopic observation reveals a "bimodal" distribution of dolomite crystal size, i.e., two types with different crystal sizes and degrees of euhedrality (morphology), and different pore types and degrees of internal pore connectivity. Alizarin red staining can clearly distinguish it from calcite. Figure 2 The data shows that the hydrothermal dolomite (Dol) near the calcite side exhibits straight-faced euhedral crystals, with calcite filling the interstices. When the host rock is dolomite, the grain size of minerals in the altered host rock is significantly increased, a result of hydrothermal recrystallization, and the dolomite directly affected by the hydrothermal fluids has a coarser grain size. The intergranular porosity and permeability of the larger, straight-faced euhedral crystals 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 straight-faced subhedral and curved-faced anhedral structure, showing characteristics of high-temperature regeneration (some overgrown crystals begin to show curved crystal faces). This reflects the rapid crystallization and attached crystal growth characteristics, with numerous lattice defects and a certain degree of wavy extinction. Dolomite zoning is visible under cathodoluminescence.

[0117] In the second type of contact relationship, gray medium- to coarse-grained dolomite containing calcite nodules is observed at the edge of the ore body. Microscopic observation shows obvious metasomatism at the contact points between calcite and dolomite. The internal pores of the dolomite are well interconnected, with significant calcite infilling. Cathodoluminescence reveals rhomboid and triangular nodules, with the core emitting a dark orange light.

[0118] These two contact relationships indicate that dolomite is affected by Ca-rich water. 2+ Hydrothermal ore-forming fluid alteration occurred, during which dolomite (MgCa(CO3)) underwent de-dolomitization. Therefore, tracers and Mg... 2+ The relevant elements are of great significance for finding calcite deposits of this type.

[0119] like Figure 3 As shown, the raw LA-ICP-MS 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 the calcite. The Pearson product-moment correlation coefficient was used to investigate the Mg content. 2+ Correlation with other trace elements. Through the relationship between each element and Mg. 2+ A two-tailed significance test was performed on the correlation between the elements Si, Sc, Sr, Y, Sn, Tb, Yb and Mg, and the critical value at the p=0.01 level was 0.384. 2+Significant correlations exist. 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, based on the changes in the contents of Si, Sc, Ni, Zn, Sr, Y, Sn, Tb, Ho, Er, and Yb from the far end to the near end of the ore body, the following conclusions can be drawn:

[0121] (1) The closer to the ore body, the Si content decreases significantly; the other elements increase to varying degrees. (2) In the first contact type, the elements in dolomite fluctuate greatly due to the influence of hydrothermal fluids.

[0122] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for predicting calcite deposits based on dolomite geochemical analysis, characterized in that, The method includes: Step S1: Collect dolomite samples from the dolomite area of ​​the calcite mine to be tested and grind them into probe sheets; Step S2: Acquire feature maps of the probe sheet; Step S3: Perform LA-ICP-MS testing on the probe sheet to obtain the trace element content and rare earth element content of dolomite at different locations; Step S4: Based on the feature map and the contents of trace elements and rare earth elements, predict the location of the calcite deposit; In step S2, a feature image under a cathode ray microscope is acquired on the probe sheet; The specific process of step S3 includes: Step S31: Calculate Mg using the Pearson correlation coefficient. 2+ The correlation with other trace elements is used to calculate 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 content of trace elements and rare earth elements; The specific process of step S31 includes: Mg was calculated using the Pearson correlation coefficient. 2+ The correlation with other trace elements was used to obtain the calculation results; The significance of the calculation result is determined using two-tailed significance analysis to obtain the determination result. The trace elements and rare earth elements are obtained based on the judgment results.

2. The method for predicting calcite deposits based on dolomite geochemical analysis according to claim 1, characterized in that, In step S1, the dolomite sample is a dolomite sample with different contact types, including: alteration halo formed by contact between megacrystalline calcite and dolomite and megacrystalline calcite replacing dolomite.

3. The method for predicting calcite deposits based on dolomite geochemical analysis according to claim 1, characterized in that, Mg was calculated using the Pearson correlation coefficient. 2+ The correlation with other trace elements was calculated using the following specific method: in, For covariance, The standard deviation is denoted as .

4. The method for predicting calcite deposits based on dolomite geochemical analysis according to claim 3, characterized in that, The standard deviation is calculated as follows: : ； : ； in, n For the number of samples, For each sample value, for x Sample mean For each sample value; for y Sample mean.

5. A calcite deposit prospecting and prediction system based on dolomite geochemical analysis, used to implement the calcite deposit prospecting and prediction method according to any one of claims 1-4, characterized in that, The system includes: The sample collection module is used to collect dolomite samples from the host dolomite area of ​​the calcite mine to be tested and grind them into probe sheets. The feature map acquisition module is used to acquire feature maps of the probe sheet; The content acquisition module is 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 locations; The location prediction module is used to predict the location of calcite deposits based on the feature map and the contents of trace elements and rare earth elements.

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