Method for indicating hydrothermal center based on chlorite short-wave spectrum and Raman parameters

Abstract

The invention discloses a method for indicating a hydrothermal center based on chlorite short-wave spectrum and Raman parameters, and belongs to the technical field of mineral exploration. The method comprises the following steps: carrying out geological exploration on a target mining area; determining chlorite sample sampling points based on the exploration result; acquiring chlorite SWIR spectral data based on the chlorite sample; analyzing and interpreting SWIR spectrum data, establishing a mining area altered mineral spectrum matching library, and analyzing spectrum parameters of chlorite samples; performing laser Raman spectrum scanning on chlorite samples with different depths to obtain a change curve of Si-Obr-Si telescopic vibration of the chlorite samples along with the depths; sWIR and Raman parameter prospecting indexes are extracted, and comprehensive prospecting indexes are established to determine the hydrothermal center. The method has the characteristics of high recognition degree, convenience in operation, reliable result, short interpretation time, high test speed, relatively strong short wave infrared parameters and Raman spectrum characteristic signals and high signal-to-noise ratio.

Description

Technical Field

[0001] This invention relates to the field of mineral exploration technology, specifically a method for indicating hydrothermal centers based on chlorite shortwave spectroscopy and Raman parameters. Background Technology

[0002] Traditional mineral exploration methods rely on the experience of professional geologists or on rock geochemical analysis and geophysical equipment detection, which are time-consuming and labor-intensive.

[0003] For a long time, the application of shortwave infrared spectroscopy (SWIR) in mineral exploration has mainly focused on light-colored hydroxyl-containing minerals such as muscovite and sericite. Their SWIR spectra exhibit a distinct Al–OH absorption peak near 2200 nm, and their crystallinity index (peak shift and peak depth ratio, SWIR-IC) is used to indicate hydrothermal centers. However, another widely developed but long-neglected dark-colored mineral—chlorite—has low reflectivity, complex composition, and often occurs in association with other minerals, making its SWIR signal difficult to interpret. Therefore, a new technical method is needed that can accurately identify mineralization clues and provide reliable results.

[0004] Chlorite is a representative alteration mineral found in porphyry deposits, widely distributed in magmatic hydrothermal and porphyry-type deposits, and present at multiple stages of deposit formation. The mineral structure and chemical composition of chlorite itself sensitively reflect the temperature and pressure conditions of the ore-forming hydrothermal fluids, as indicated by characteristic parameters in short-wave infrared (SWIR) and laser Raman spectroscopy. It serves as an important mineral for indicating the migration direction of magmatic hydrothermal fluids (heat sources) and is a crucial research subject for identifying hydrothermal centers during the exploration and prospecting of magmatic hydrothermal deposits. Therefore, the hydrothermal alteration zoning of deposits can be identified using the short-wave infrared (SWIR) and Raman parameters of chlorite group minerals.

[0005] Short-wave infrared spectroscopy is a novel mineral exploration technique that has rapidly developed in the Western mining industry in recent years. Common hydrothermal alteration minerals lie in the short-wave infrared spectral range (1300–2500 nm), primarily based on the different molecular groups or the same molecular groups (-OH, H₂O, NH₄) formed under different physicochemical conditions. + CO3 2- SO4 2-The absorption characteristics of Fe-OH and Mg-OH minerals to short-wave infrared light differ significantly, which can be used to determine the type and content of target minerals. Although laser Raman spectroscopy has been rapidly adopted in the mining industry in recent years, and is mostly used for Raman imaging studies of fluid inclusions, there is limited research on the identification of hydrothermal centers in magmatic hydrothermal deposits using mineral Raman parameters. On the other hand, existing identification methods mainly rely on field observations of alteration assemblages of hydrothermal minerals to analyze the hydrothermal centers of porphyry copper deposits and magmatic hydrothermal deposits. This process is complex, and the accuracy and efficiency are not high enough. Summary of the Invention

[0006] The purpose of this invention is to provide a method for indicating hydrothermal centers based on the short-wavelength spectroscopy and Raman parameters of chlorite. This method can rapidly identify the hydrothermal centers of magmatic hydrothermal gold deposits by using the short-wavelength infrared spectroscopy and Raman parameters of chlorite, which can effectively improve the reliability of delineating the hydrothermal centers of deposits. At the same time, it can be cross-verified with the short-wavelength infrared spectral characteristics of chlorite, thereby improving the accuracy and efficiency of exploration results.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A method for indicating hydrothermal centers based on chlorite shortwave spectroscopy and Raman parameters includes: Geological surveys were conducted on the target mining area to obtain exploration results; Based on the exploration results, the sampling points for chlorite samples were determined. SWIR spectral data of chlorite were obtained based on the chlorite sample; The SWIR spectral data were analyzed and interpreted to establish a spectral matching library of altered minerals in the mining area, and the spectral parameters of the chlorite samples were analyzed. Laser Raman spectroscopy was performed on chlorite samples at different depths to obtain Si-O content. br -Si stretching vibration variation curve with depth; Extract SWIR and Raman parameters as prospecting indicators, establish a comprehensive prospecting index based on SWIR and Raman parameters, and then determine the hydrothermal center.

[0008] As a further aspect of the present invention: geological surveys are conducted on the target mining area to obtain exploration results, including: Collect historical exploration reports, geological maps, and other relevant documents for the target mining area; Select characteristic surface outcrops for reconnaissance and observation, and record the geological occurrence, alteration, and mineralization characteristics of the surface outcrops.

[0009] As a further aspect of the present invention: determining chlorite sample sampling points based on the exploration results, including: Based on the known occurrence of ore bodies and the layout of exploration projects within the target mining area; Select exploration line profiles that cut across and longitudinally through the ore body; The surface outcrops and boreholes within the obtained profile are selected and used as sampling points.

[0010] As a further aspect of the present invention: obtaining SWIR spectral data of chlorite based on the chlorite sample includes: Determine the sampling interval for surface outcrops and boreholes within the selected exploration line profile, and begin sampling; Samples were numbered and their locations were recorded during sampling. Connect the infrared spectrometer, computer, and probe. After 30 minutes of warm-up, open the data collection software and set the infrared spectrometer's dark current, spectral averaging, and reference white parameters according to the properties of the sample. The samples were cleaned and dried. Each sample was measured 2-3 times at different locations. The infrared spectrometer was calibrated every 20-30 minutes after each test.

[0011] As a further aspect of the present invention: analyzing and interpreting the SWIR spectral data, establishing a spectral matching library of altered minerals in the mining area, and analyzing the spectral parameters of chlorite samples, including: Based on the exploration lines and boreholes, the selected samples were classified and documented, and engineering files for each sample injection were created using spectral interpretation software. After re-injection, the standard library built into the interpretation software is selected and set, and minerals that are likely to appear are selected, while minerals that will not appear under specific deposit conditions are blocked. Among them, minerals that are likely to appear include mica group minerals, illite, kaolinite, and chlorite, while minerals that will not appear include garnet, pyroxene, and potassium feldspar. Target spectral parameters are obtained by screening SWIR spectral data; Characteristic parameters of SWIR spectral data are extracted, including absorption depth at specific locations, absorption depth, and crystallinity.

[0012] As a further aspect of the present invention: Engineering files for each injection are created using spectral interpretation software, including: The spectral parameters were standardized and re-injected, the injection intervals were set sequentially, and homogenization was performed. Create project documents.

[0013] As a further aspect of the present invention: obtaining target spectral parameters by screening SWIR spectral data includes: SWIR spectral data were interpreted using interpretation software. The interpreted SWIR spectral data were then examined, and spectral lines with low reliability, inconsistent repeat samples, and low signal-to-noise ratio were selected. The spectral lines are processed by manual input or deletion, and the cause of the error is obtained according to the sample position.

[0014] As a further aspect of the present invention: Characteristic parameters of the SWIR spectral data are extracted, including absorption depth at specific locations, absorption depth, and crystallinity, including: Import a .TXT format data file of an entire borehole or tunnel; Import the depth data of the sample; Based on the depth data, the SWIR wavelength characteristic parameters of the sample are extracted; Extract the absorption depth characteristic parameters of SWIR at the corresponding wavelength; Constructing a crystallinity algorithm: Calculate the crystallinity of mica group minerals, where IC is the crystallinity of mica group minerals, Dep2200 is the absorption wavelength data of mica group minerals at 2200nm, and Dep1900 is the absorption wavelength data of mica group minerals at 1900nm.

[0015] As a further aspect of the present invention: laser Raman spectroscopy scanning was performed on chlorite samples at different depths to obtain Si-O from the chlorite samples. br -The Si stretching vibration variation curve with depth includes: Ar using a wavelength of 532nm + The incident light source was used to scan chlorite samples at different depths to obtain the spectra of each chlorite sample containing Raman characteristic peaks. Choose 786cm -1 Displaced Si-O interlayer in chlorite br -Si stretching vibration as a relevant Raman parameter; Using chlorite Si-O br -Raman parameters related to Si stretching vibrations were plotted, and the Raman displacements of chlorite samples at each depth were expressed as Cartesian coordinate curves with respect to depth. Si-O from chlorite samples br - The Raman displacement curve of Si stretching vibration with respect to depth is used to analyze the pressure variation of the alteration zone where chlorite is located.

[0016] As a further aspect of the present invention: when Si-O br When the Raman displacement of the Si stretching vibration increases, it indicates that the pressure forming the alteration zone of the chlorite is higher. Further analysis of the pressure change based on the maximum and minimum values ​​of the Raman displacement with respect to depth can help to classify different alteration zones.

[0017] As a further aspect of the present invention: extracting SWIR and Raman parameters as mineral exploration indicators, establishing a comprehensive mineral exploration index based on SWIR and Raman parameters, and then determining hydrothermal centers, including: Based on the zoning characteristics of hydrous alteration minerals established by SWIR technology and the chloritization zone markers established by Raman parameters, an alteration zoning standard for magmatic hydrothermal deposits is established. Based on the established alteration zoning standards, alteration zones are divided on the planar geological map and the geological profile map of the exploration line, and the alteration mineral mapping is completed. The extracted SWIR characteristic parameters, Raman parameter values, and elemental content data were compared one-to-one with the locations of ore bodies on geological maps to obtain the correspondence between ore body locations and chlorite parameters. This determined the location of the alteration zone where the chlorite was situated and extracted comprehensive prospecting indicators based on SWIR technology and Raman parameters. These comprehensive prospecting indicators included the shift of the Fe-OH absorption peak position, the shift of the Mg-OH absorption peak position, and the Si-O absorption peak position in chlorite. br -Si stretching vibration.

[0018] Compared with the prior art, the beneficial effects of the present invention are: This invention relates to a method for identifying hydrothermal centers in magmatic hydrothermal deposits based on short-wave infrared spectral parameters and Raman parameters of chlorite. It utilizes the changes in chemical composition and structure of chlorite due to variations in temperature, pressure, and other chemical conditions during its formation, and identifies favorable ore bodies and hydrothermal centers through relevant spectroscopic characteristics. This method features high identification accuracy, convenient operation, reliable results, quantitative analysis and estimation, short data interpretation time, fast testing speed, strong short-wave infrared and Raman spectral characteristic signals, high signal-to-noise ratio, and high specificity, significantly improving the accuracy of hydrothermal center delineation. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the identification of alteration zonation in low-temperature hydrothermal magmatic deposits according to the present invention. Figure 2 This invention is based on chlorite Pos2250 mapping; Figure 3 This is a schematic diagram showing the temperature of chlorite at different locations as calculated in this invention. Figure 4 This is a graph showing the Raman displacement of chlorite at different locations as a function of depth, according to the present invention. Figure 5 This is a diagram illustrating the method steps of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example: It should be noted that for most mineral deposits, the types of alteration minerals that appear are related to the temperature, pressure, and genesis during the formation of the deposit. Therefore, the alteration minerals observed during testing can indicate certain genetic information about the deposit. Minerals that are most likely to appear in short-wave infrared spectroscopy testing are generally mica group minerals, illite, kaolinite, chlorite, etc. For specific deposits, the alteration minerals will also exhibit certain characteristics. The main alteration mineral characteristics are as follows: Shallow low-temperature hydrothermal deposit systems typically contain sericite, illite, calcite, chlorite, montmorillonite, kaolinite, and dolomite, but generally do not contain garnet, pyroxene, or potassium feldspar. Porphyry deposit systems typically contain sericite, illite, chlorite, dickite, and kaolinite, but generally do not contain or rarely contain andalusite, amphibole, kyanite, or cordierite. Skarn-type mineral deposit systems typically contain sericite, chlorite, epidote, actinolite, phlogopite, feldspar, and carbonate minerals, but rarely or never contain kaolinite group minerals, alunite, gypsum, etc.

[0022] Please see Figures 1-5 In this embodiment of the invention, a method for indicating hydrothermal centers based on chlorite shortwave spectroscopy and Raman parameters includes the following steps: S1: Conduct geological surveys of the target mining area to obtain exploration results.

[0023] Collect historical exploration reports and geological maps of the target mining area; select characteristic surface outcrops for reconnaissance and observation, and record the geological occurrence, alteration, and mineralization characteristics of the surface outcrops. Step 1.1: Through literature review, understand the geotectonic background of the relevant mineral deposit and collect previously published literature on the deposit. In addition, actively communicate with the mining company, review existing exploration reports and geological maps, and follow up on the exploration projects and mining progress already implemented by the mine. Step 1.2: After collecting data, conduct preliminary analysis to determine the targets for field observation, based on an understanding of the basic geological conditions. Select a suitable exploration route on the surface to observe as many geological outcrops as possible, and ensure the route layout maximizes control over the mining area's surface. Simultaneously, select boreholes from completed drilling projects that cut across and longitudinally into the main ore body, forming a "cross"-shaped exploration profile to comprehensively reveal the geological information of the deeper parts of the mining area. During observation, keep detailed records, including outcrop location, lithology, alteration type, and mineralization degree, to provide necessary geological information for laboratory analysis. Step 1.3: After completing data processing and field observation records, it is necessary to have a general understanding of the basic situation within the mining area. First, it is essential to identify the main intrusive rocks developing in the mining area and their interpenetrating relationships. It is also necessary to understand the main alteration types and their approximate distribution range. Simultaneously, it is necessary to gain a certain understanding of the location, occurrence, and scale of the ore bodies. Based on the above foundation, subsequent testing work will be more purposeful and targeted.

[0024] S2: Determine the sampling points for chlorite samples based on the exploration results.

[0025] Based on the known occurrence of ore bodies and the layout of exploration projects within the target mining area; select exploration line profiles that cut across and longitudinally through the ore bodies; select surface outcrops and boreholes within the obtained profiles, and use these surface outcrops and boreholes as sampling points, specifically: Step 2.1: Based on the topography and geological conditions of the mining area, samples are taken along the same exploration line on the surface, with a basic spacing of 50-100m. In areas with abundant geological phenomena, diverse alteration types, and visible lithological and alteration boundaries, the sampling spacing is increased to 10-20m. In areas with relatively uniform geological phenomena and alteration changes, or in areas with poor topographical conditions, the sampling interval can be appropriately expanded. The sampling spacing of boreholes is initially maintained at 1-2m, and can be increased to 5-10m after a basic understanding of alteration information is obtained. Similarly, the sampling spacing needs to be adjusted according to changes in geological information. This sampling spacing setting maximizes the collection of geological information while saving time and effort, ensuring high efficiency. During sampling, samples need to be systematically numbered and recorded in detail.

[0026] S3: Obtain SWIR spectral data of chlorite based on chlorite samples.

[0027] For the selected exploration line profile, determine the sampling interval for surface outcrops and boreholes, and begin sampling; number and record the location of each sample during sampling; connect the infrared spectrometer, computer, and probe, and after a 30-minute warm-up period, open the data collection software and set the infrared spectrometer's dark current, spectral average, and reference white parameters according to the sample properties; clean and dry the samples, and measure each sample 2-3 times at different locations, calibrating the infrared spectrometer every 20-30 minutes. Step 3.1: There are various commonly used infrared spectrometers. Here, we will use the TerraSpec 4, the latest model manufactured by Analytical Spectral Devices, Inc. (ASD), as an example to explain its usage and parameter settings. When using it, the spectrometer, computer, and probe must be correctly connected. Pay attention to the fragility of the optical cable during connection. Allow the instrument to warm up for 30 minutes before opening the data collection software to ensure it reaches optimal operating condition. Set the instrument's dark current, spectrum average, and white reference parameters according to the sample's properties. Taking a blocky, light-colored sample as an example, such samples have high reflectivity. Therefore, set the dark current to 25 and keep it constant, the spectrum average to 200 (test time 20s), and the white reference to 400. If the properties of the sample change, multiple tests are required, and the parameters must be reset to ensure the accuracy of the collected data. Step 3.2: First, systematically number the samples and provide a brief lithological description. Then, clean them thoroughly with a soft brush and sun-dry them for at least 8 hours to eliminate the influence of "free water" on spectral depth and position. Before testing, optimization calibration using a standard white board is required. After confirming the instrument is functioning correctly, further white reference calibration using the standard white board is performed. Only after obtaining a straight and smooth line can sample testing proceed. When testing samples, align the probe with the straight surface of the borehole sample, avoiding areas with well-developed sulfides and quartz veins to prevent them from affecting the overall reflectance of the spectrum and causing inaccurate test results. To reduce the influence of sample inhomogeneity and random errors, each sample is generally tested 2-3 times, and the average value is taken to ensure data reliability. To avoid the influence of instrument heating over time, the instrument should be calibrated every 20-30 minutes to monitor its operating status.

[0028] S4: Analyze and interpret SWIR spectral data, establish a spectral matching library of altered minerals in the mining area, and analyze the spectral parameters of chlorite samples.

[0029] Based on the exploration lines and boreholes, the selected samples were classified and documented. Spectral parameters were standardized and re-injected, with injection intervals set sequentially and homogenization performed. An engineering file was created. After re-injection, the standard library built into the interpretation software was selected, choosing minerals with high probability of occurrence and masking minerals that would not appear under specific deposit conditions. The SWIR spectral data was interpreted using the interpretation software, and the interpreted SWIR spectral data was checked, selecting spectral lines with low reliability, inconsistencies in duplicate samples, and low signal-to-noise ratios. Spectral lines were processed using manual input or deletion methods, and the specific reasons for errors in the spectral lines were obtained corresponding to the sample locations. Step 4.1: Place SWIR data from the same exploration line or borehole in the same folder and create an engineering file using spectral interpretation software (e.g., The Spectral Geologist (TSG) v.8). Re-sampling is required when importing data; set the sampling interval to 1 nm and smooth the high-noise regions at 350–400 nm and 2450–2500 nm. Step 4.2: After the project file is created, set the number of mineral types for automatic unmixing in the software to 3, and the detection threshold to 5%. In addition, select common alteration minerals such as mica, chlorite, and kaolinite from magmatic hydrothermal deposit systems as the standard mineral library, and filter out minerals with very low occurrence rates, such as phlogopite and serpentine, to reduce the error rate of the unmixing results. After completing the settings, the software will automatically interpret the data. Step 4.3: After obtaining the automatic interpretation results, firstly, perform signal-to-noise ratio and quality checks on the acquired spectral curves, discarding spectral data with low signal-to-noise ratio and reliability, as well as meaningless data. Then, extract minerals by category, and perform extraction checks on each mineral category separately, correcting inaccurate and erroneous spectra. Step 4.4: Combine the automatically interpreted hyperspectral alteration mineral data with geological data to delineate basic alteration mineral zones on the geological map and complete the alteration mineral mapping work.

[0030] Step 4.5: Conduct a comprehensive analysis of all spectral data within the mining area, classify the data of different lithologies, and select various SWIR spectral lines based on the interpreted mineral assemblage, spectral line shape, characteristic parameters, etc.

[0031] This invention identifies the hydrothermal center of magmatic hydrothermal gold deposits by utilizing the short-wave infrared and Raman parameters of chlorite. Compared with conventional petrographic identification methods, it overcomes the problem of misleading the reasonable delineation of hydrothermal centers due to rock heterogeneity and the superposition of multiple hydrothermal phases during mineralization. Accurate identification can be performed simply by locating the measurement point. The operation is simple, the data interpretation time is short, and the testing speed is fast. On the other hand, the Raman spectral feature signal is strong, the signal-to-noise ratio is high, and the specificity is strong, which can greatly improve the accuracy of hydrothermal center delineation and mineralization rate.

[0032] In this embodiment, the specific method for importing a .TXT format data file of an entire borehole or tunnel is as follows: ① Click the white file button, select General for Format: ASCII files of [x,y] pains (includes Ore Xpress and Agilent) ②The Select option selects the entire .TXT data of a borehole or tunnel; ③ Select the desired wavelength range, generally 350-2500nm; ④ Select the file save path and file name. The file name must be in English characters and cannot contain Chinese characters; ⑤ After completing the above steps, the software will automatically generate the interpreted alteration mineral data in the Summary interface, including the types of alteration minerals and semi-quantitative data.

[0033] ⑥ The Spectum interface will generate individual spectral data for each data point, and you can obtain the absorption peak wavelength of the corresponding mineral by clicking on the corresponding absorption peak position. The Stack interface will generate spectral data for all data points, and you can obtain the corresponding spectral characteristic data by clicking on them.

[0034] Importing sample depth data, primarily done within the Scatter interface, is accomplished as follows: ① Double-click the first scatter plot to import the depth data. Save the sample depth data as a Csv file. Only the depth data needs to be saved; the sample number does not need to be saved. ② Click Edit, select the New Scalar option, enter the file name of the sample depth, such as xx-Depth, Group is the path of the parameter, you can choose General or choose Method yourself, select IMPORT: a numeric or class scalar from a Csv file or the Clipboard. Then click Next Page; ③ Select the .Csv format sample depth file, and then click Finish.

[0035] The specific method for extracting the SWIR wavelength characteristic parameters of a sample based on depth data is as follows: ① Extract SWIR wavelength characteristic parameters of the sample: Click Edit, select the New Scalar option, enter the file name of the characteristic parameters of the sample data, such as Chlorite-Wvelength2200, and select PROFILE for Method: a spectral index from the spectral curves themselves. Then click Next Page. ② Select the required wavelength and wavelength error range. Enter the wavelength of the desired alteration mineral in Centre wavelength, such as 2350nm for chlorite. Select the error range for Radius, which can be chosen by yourself, generally 10-20nm. Select Wavelength at minimum for Profile type. Other options can be ignored. Then click Finish. ③ In the Scatter interface, X can be selected for depth data, Y can be selected for wavelength data, and Aux can be selected for wavelength data. The selection of the above coordinate axes is in the Group parameter path selected when creating a new Scalar. Fit is the expression for the correlation of the selectable parameters and the value of the correlation coefficient R. Then all the data will be presented in the scatter plot. ④ All the parameters set above can be copied into a table or CorelDRAW. Right-click on the scatter plot and select Graphics-Copy to paste the wavelength and other related data into the table or CorelDRAW for processing into a graph. ⑤ After mapping, analyze the correspondence between the wavelengths of altered minerals and the ore body. Generally, the closer to the ore body, the more the absorption peak of the mica group minerals at 2200nm shifts towards the direction of shorter wavelengths.

[0036] The specific method for extracting the absorption depth characteristic parameters of SWIR at the corresponding wavelength is as follows: ① Extract the SWIR wavelength characteristic parameters of the sample. Click Edit, select the New Scalar option, enter the file name of the characteristic parameters of the sample data, such as Chlorite-Dep2250, select PROFILE for Method: a spectral index from the spectral curves themselves, and then click the next page; ② Select the required absorption depth and error range for the wavelength. Enter the wavelength of the desired altered mineral in Centre wavelength, such as 2200nm for sericite. Select the error range for Radius, which can be chosen by yourself, generally 10-20nm. Select Ralative absorption depth for Profile type. Other options can be ignored. Then click Finish. ③ Select the required data, and the system will automatically generate a scatter plot. This plot can be copied to tables and CorelDRAW for data processing and plotting. Generally, the greater the depth of the mineral absorption peak, the higher its relative content.

[0037] The specific method for constructing the crystallinity algorithm is as follows: ① Obtain absorption wavelength data at 1900nm and 2200nm for mica group minerals; ② Copy the obtained data into a table and use the formula to calculate the crystallinity of the mica group minerals; Where IC represents the crystallinity of mica minerals, Dep2200 represents the absorption depth data of mica minerals at 2200 nm, and Dep1900 represents the absorption depth data of mica minerals at 1900 nm. ③ The higher the crystallinity, the higher the mineralization temperature.

[0038] S5: Laser Raman spectroscopy was performed on chlorite samples at different depths to obtain Si-O from the chlorite samples. br -Si stretching vibration variation curve with depth.

[0039] Ar using a wavelength of 532nm + The incident light source was used to scan chlorite samples at different depths, obtaining spectra of each chlorite sample containing Raman characteristic peaks; in applied research on magmatic hydrothermal deposits and porphyry deposits, the Si-O content of chlorite was studied. br -Si stretching vibration Raman parameters are often used to identify hydrothermal centers, with 786 cm⁻¹ being a preferred value. -1 Displaced Si-O interlayer in chlorite br -Si stretching vibrations were used as relevant Raman parameters; chlorite Si-O was employed. br Raman parameters related to Si stretching vibrations were used to plot the Raman displacements of chlorite samples at each depth in a Cartesian coordinate system. The Si-O content of the chlorite samples was also analyzed. br - The Raman displacement curve of Si stretching vibration with respect to depth is used to analyze the pressure variation in the alteration zone of chlorite. Laser Raman data of the chlorite sample is used to analyze the distance of the alteration zone from the hydrothermal center. Specifically: Step 5.1: Perform Raman spectroscopy scanning: Use a 532nm wavelength incident light source to scan the chlorite samples at various depths to obtain the spectra of each chlorite sample containing Raman characteristic peaks. Select 786cm⁻¹ as the focal length. -1 Si-O in the displaced chlorite interlayer M4 octahedron br -Raman parameters related to Si stretching vibrations, because the hydrogen bond (O…H) bond lengths in the interlayer domain structure exhibit strong compressibility as pressure increases; Step 5.2: Raman laser testing was performed on chlorite samples at various depths to determine the Si-O content in the chlorite tetrahedra. br -Raman parameters related to Si stretching vibrations were used to plot the Raman displacement curves of chlorite samples at each depth as a function of depth. Step 5.3: Si-O analysis of chlorite samples br -Analyze the changes in pressure during the formation of the alteration zone of chlorite by using the Raman displacement curve of Si stretching vibration with respect to depth. Further, in step 5.1, the chlorite sample was analyzed using a JEOL JXA-8100 instrument with the following parameters: accelerating voltage of 20 kV, beam current of 10 nA, and beam spot diameter of 2 μm. The main elements analyzed included Si, Ti, Fe, As, Mg, Pb, Sb, Zn, and Cu. The mineral sample was scanned using a La bR AM HREvolution laser confocal micro-Raman spectrometer manufactured by HORI BA JO BIN YVON (France), with a spectral resolution of 0.65 cm⁻¹. -1 The spectrometer has a focal length of 800mm and uses a 532nm A wavelength. r+ Laser, 50x Leica objective lens, scanning range 200–1000 -1 cm; Furthermore, in steps 5.2 and 5.3, when Si-O br When the Raman displacement of the Si stretching vibration increases, it indicates that the pressure forming the alteration zone of chlorite is higher; according to the bond force constant k (as shown in Equation II, where N is Avogadro's constant, r is the distance between atoms, a and B are constants, X... A X B Based on the electronegativity of elements A and B and the Raman shift formula (as shown in Equation III, where c is the speed of light, μ is the reduced mass, and k is the bond constant), it can be seen that Si-O br During bond stretching vibrations, the intermolecular distance decreases, leading to an increase in the bond force constant k. For the same molecule, whose reduced mass μ is constant, an increase in the bond force constant k results in an increase in the Raman shift. Therefore, when 786 cm⁻¹... -1 Si-O on displacement brWhen the Raman displacement of the Si stretching vibration increases, it indicates that the pressure formed by the alteration zone is relatively large; conversely, the pressure formed by the alteration zone is relatively small.

[0040]

[0041] .

[0042] S6: Extract SWIR and Raman parameters as prospecting indicators, establish a comprehensive prospecting index based on SWIR and Raman parameters, and then determine the hydrothermal center.

[0043] Based on the zoning characteristics of hydrous alteration minerals established by SWIR technology and the chloritization zone markers established by Raman parameters, an alteration zoning standard for magmatic hydrothermal deposits was established. According to the established alteration zoning standard, alteration zones were divided on planar geological maps and exploration line profile geological maps, and alteration mineral mapping was completed. The extracted SWIR characteristic parameters, Raman parameter values, and elemental content data were compared with the locations of ore bodies on the geological map to obtain the correspondence between ore body locations and chlorite parameters, determining the location of the alteration zone where chlorite is located. A comprehensive prospecting index based on SWIR technology and Raman parameters was extracted, including the shift of the Fe-OH absorption peak position, the shift of the Mg-OH absorption peak position, and the Si-O absorption peak position. br The Si stretching vibration quantity is used to establish an alteration zoning standard for porphyry deposits by combining the zoning characteristics of hydrous alteration minerals established based on SWIR technology with the chloritization zone markers established through Raman parameters. This organic combination of the two testing techniques fully leverages the advantages of SWIR technology in identifying hydrous alteration minerals while supplementing its limitations in testing anhydrous minerals through Raman parameter testing, thus achieving the goal of accurately and efficiently identifying alteration zoning in porphyry deposits.

[0044] In this embodiment, the target mining area is a geological area in the field.

[0045] In this embodiment, when Si-O br When the Raman displacement of the Si stretching vibration increases, it indicates that the pressure forming the alteration zone of the chlorite is higher. Further analysis of the pressure change based on the maximum and minimum values ​​of the Raman displacement with respect to depth can help to classify different alteration zones.

[0046] In this embodiment, the prospecting indicators of chlorite-related parameters can be determined according to the following criteria. 1) The shift between the Fe-OH absorption peak position (Pos2250) and the Mg-OH absorption peak position (2235) of chlorite is the key to the location of its hydrothermal center. That is, the closer to the hydrothermal center or ore body, the shorter the Pos2250 of chlorite.

[0047] 2) When Si-O br An increase in the Raman displacement parameter of the Si stretching vibration indicates a higher mineralization pressure in the alteration zone where the chlorite is located.

[0048] 3) Based on the above parameters, the hydrothermal center of the magmatic hydrothermal deposit is delineated in a comprehensive manner to more accurately delineate the hydrothermal center of the magmatic hydrothermal deposit.

[0049] Specifically, step 6.1 involves dividing alteration zones on the planar geological map and exploration line profile geological map based on the standards established in the previous step and the automatically interpreted spectral data obtained in step 4.3. This completes the mapping of alteration minerals in two-dimensional planar space. With technical support, the alteration zoning can be extended to three-dimensional space. The distribution of chlorite within the alteration zones is then analyzed, such as... Figure 2 As shown, the wavelength range of chlorite is between 2239-2254nm. The chlorite closer to the ore body has a shorter wavelength (<2246nm), while the chlorite farther from the ore body has a longer Pos2250 (greater than 2246nm).

[0050] Step 6.2: Analyze the correlation curves between chlorite temperature and depth to determine the temperature range of chlorite at different depths. For example... Figure 3 As shown, the temperature range of the chlorite samples is 194℃~287℃, with the chlorite samples closer to the ore body exhibiting significantly higher temperatures. This indicates that the chlorite samples closer to the ore body are more closely aligned with the hydrothermal center.

[0051] Step 6.3: Compare the Raman spectra of chlorite near the ore body with those far from the ore body, such as... Figure 4 As shown, 786 nm of chlorite was found at a depth of 630 m near the ore body. -1 Raman shifts are more pronounced, while chlorite at depths of 708m and 521m, far from the ore body, shows a wavelength of 786nm. -1 The short Raman displacement indicates that the alteration zone near the ore body at a depth of 630m was formed under relatively high pressure, which was higher than the pressure at the alteration zone far from the ore body.

[0052] Step 6.4: Compare the extracted SWIR feature parameters and Raman parameters with the locations of ore bodies on the geological map to obtain the correspondence between the ore body locations and the above parameters. Extract comprehensive prospecting indicators based on SWIR technology and Raman, delineate suitable ore-forming hydrothermal centers, and provide timely and effective technical support for prospecting and exploration in the periphery and deep parts of the mining area.

[0053] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for indicating hydrothermal centers based on chlorite shortwave spectroscopy and Raman parameters, characterized in that, include: Geological surveys were conducted on the target mining area to obtain exploration results; Based on the exploration results, the sampling points for chlorite samples were determined. SWIR spectral data of chlorite were obtained based on the chlorite sample; The SWIR spectral data were analyzed and interpreted to establish a spectral matching library of altered minerals in the mining area, and the spectral parameters of the chlorite samples were analyzed. Laser Raman spectroscopy was performed on chlorite samples at different depths to obtain Si-O content. br -Si stretching vibration variation curve with depth; Extract SWIR and Raman parameters as prospecting indicators, establish a comprehensive prospecting index based on SWIR and Raman parameters, and then determine the hydrothermal center.

2. The method for indicating hydrothermal centers based on chlorite short-wavelength spectroscopy and Raman parameters according to claim 1, characterized in that, Geological surveys were conducted on the target mining area, and the exploration results were obtained, including: Collect historical exploration reports, geological maps, and other relevant documents for the target mining area; Select characteristic surface outcrops for reconnaissance and observation, and record the geological occurrence, alteration, and mineralization characteristics of the surface outcrops.

3. The method for indicating hydrothermal centers based on chlorite short-wavelength spectroscopy and Raman parameters according to claim 2, characterized in that, Based on the exploration results, the sampling points for chlorite samples were determined, including: Based on the known occurrence of ore bodies and the layout of exploration projects within the target mining area; Select exploration line profiles that cut across and longitudinally through the ore body; The surface outcrops and boreholes within the obtained profile are selected and used as sampling points.

4. The method for indicating hydrothermal centers based on chlorite short-wavelength spectroscopy and Raman parameters according to claim 3, characterized in that, Based on the chlorite sample, SWIR spectral data of chlorite were obtained, including: Determine the sampling interval for surface outcrops and boreholes within the selected exploration line profile, and begin sampling; Samples were numbered and their locations were recorded during sampling. Connect the infrared spectrometer, computer, and probe. After 30 minutes of warm-up, open the data collection software and set the infrared spectrometer's dark current, spectral averaging, and reference white parameters according to the properties of the sample. The samples were cleaned and dried. Each sample was measured 2-3 times at different locations. The infrared spectrometer was calibrated every 20-30 minutes after each test.

5. The method for indicating hydrothermal centers based on chlorite short-wavelength spectroscopy and Raman parameters according to claim 4, characterized in that, The SWIR spectral data were analyzed and interpreted to establish a spectral matching library for altered minerals in the mining area. The spectral parameters of the chlorite samples were analyzed, including: Based on the exploration lines and boreholes, the selected samples were classified and documented, and engineering files for each sample injection were created using spectral interpretation software. After re-injection, the standard library built into the interpretation software is selected and set, and minerals that are likely to appear are selected and minerals that will not appear under specific deposit conditions are blocked. Among them, the minerals that are likely to appear include mica group minerals, illite, kaolinite and chlorite, and the minerals that will not appear include garnet, pyroxene and potassium feldspar. Target spectral parameters are obtained by screening SWIR spectral data; Characteristic parameters of SWIR spectral data are extracted, including absorption depth at specific locations, absorption depth, and crystallinity.

6. The method for indicating hydrothermal centers based on chlorite short-wavelength spectroscopy and Raman parameters according to claim 5, characterized in that, Engineering files for each injection were created using spectral interpretation software, including: The spectral parameters were standardized and re-injected, the injection intervals were set sequentially, and homogenization was performed. Create project documents.

7. The method for indicating hydrothermal centers based on chlorite short-wavelength spectroscopy and Raman parameters according to claim 6, characterized in that, Screening SWIR spectral data yields target spectral parameters, including: SWIR spectral data were interpreted using interpretation software. The interpreted SWIR spectral data were then examined, and spectral lines with low reliability, inconsistent repeat samples, and low signal-to-noise ratio were selected. The spectral lines are processed by manual input or deletion, and the cause of the error is obtained according to the sample position.

8. The method for indicating hydrothermal centers based on chlorite short-wavelength spectroscopy and Raman parameters according to claim 7, characterized in that, Feature parameters of SWIR spectral data were extracted, including absorption depth at specific locations, absorption depth, and crystallinity, including: Import a .TXT format data file of an entire borehole or tunnel; Import the depth data of the sample; Based on the depth data, the SWIR wavelength characteristic parameters of the sample are extracted; Extract the absorption depth characteristic parameters of SWIR at the corresponding wavelength; Constructing a crystallinity algorithm: Calculate the crystallinity of mica group minerals, where IC is the crystallinity of mica group minerals, Dep2200 is the absorption depth data of mica group minerals at 2200 nm, and Dep1900 is the absorption depth data of mica group minerals at 1900 nm.

9. The method for indicating hydrothermal centers based on chlorite short-wavelength spectroscopy and Raman parameters according to claim 8, characterized in that, Laser Raman spectroscopy was performed on chlorite samples at different depths to obtain Si-O content. br -The Si stretching vibration variation curve with depth includes: Ar using a wavelength of 532nm + The incident light source was used to scan chlorite samples at different depths to obtain the spectra of each chlorite sample containing Raman characteristic peaks. Choose 786cm -1 Displaced Si-O interlayer in chlorite br -Si stretching vibration as a relevant Raman parameter; Using chlorite Si-O br -Raman parameters related to Si stretching vibrations were plotted, and the Raman displacements of chlorite samples at each depth were expressed as Cartesian coordinate curves with respect to depth. Si-O from chlorite samples br - The Raman displacement curve of Si stretching vibration with respect to depth is used to analyze the pressure variation of the alteration zone where chlorite is located.

10. The method for indicating hydrothermal centers based on chlorite short-wavelength spectroscopy and Raman parameters according to claim 1, characterized in that, Extracting SWIR and Raman parameters as prospecting indicators, establishing a comprehensive prospecting index based on SWIR and Raman parameters, and then identifying hydrothermal centers, including: Based on the zoning characteristics of hydrous alteration minerals established by SWIR technology and the chloritization zone markers established by Raman parameters, an alteration zoning standard for magmatic hydrothermal deposits is established. Based on the established alteration zoning standards, alteration zones are divided on the planar geological map and the geological profile map of the exploration line, and the alteration mineral mapping is completed. The extracted SWIR characteristic parameters, Raman parameter values, and elemental content data were compared one-to-one with the locations of ore bodies on geological maps to obtain the correspondence between ore body locations and chlorite parameters. This determined the location of the alteration zone where the chlorite was situated and extracted comprehensive prospecting indicators based on SWIR technology and Raman parameters. These comprehensive prospecting indicators included the shift of the Fe-OH absorption peak position, the shift of the Mg-OH absorption peak position, and the Si-O absorption peak position in chlorite. br -Si stretching vibration.

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