Mineral trace element analysis method using microscopic Raman spectrum

Abstract

The invention discloses a method for analyzing mineral trace elements by utilizing a micro-Raman spectrum, which comprises the following steps of: fixing a mineral sample on a rotary sample table of a micro-Raman spectrometer, selecting an area to be detected by combining a micro-imaging technology, collecting a Raman background signal under the pre-illumination of low-power laser, and analyzing the trace elements in the area to be detected according to the Raman background signal. The laser focal length, the spot size and the incident angle are adjusted accordingly, the laser is focused on a target area, under the calibrated laser condition, through a multi-angle excitation micro-area, after an algorithm is applied to optimize signals, reduce impurity interference and integrate multi-angle Raman signals, the characteristic spectrum information of the microelements can be extracted, and the microelements can be accurately detected. High-resolution analysis of sample components is realized, and a three-dimensional distribution model of microelements in a microcell is constructed in combination with microscopic imaging data. The method has the characteristics of non-destructiveness, high resolution and rapid detection, and is suitable for mineral component analysis in the fields of geology, material science and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of geology and analytical chemistry, and in particular to a method for analyzing trace elements in minerals using micro-Raman spectroscopy. Background Art

[0002] In the analysis of trace elements in minerals, traditional technical methods mostly rely on chemical digestion, electron probe, mass spectrometry and X-ray diffraction. Although these methods have high accuracy in macro-composition analysis, they have certain limitations, especially in non-destructive detection and micro-area component distribution analysis. It is difficult to meet the needs of high-resolution spatial analysis. Chemical digestion methods usually require sample pretreatment, and the samples will be dissolved or partially damaged, and the integrity of the samples cannot be guaranteed. This has great limitations for rare minerals or rare geological samples. Although electron probe and mass spectrometry analysis methods have certain trace element detection accuracy, their analysis depth and resolution are limited by the energy of the electron beam or laser, making it difficult to achieve fine component distribution analysis of complex mineral micro-areas. In addition, these methods are often affected by background noise and impurity interference, resulting in unstable detection results.

[0003] Although existing micro-Raman spectroscopy technology is a relatively mature non-destructive detection method, it has problems with low signal intensity and insufficient sensitivity when detecting trace elements. Raman scattering is essentially a weak scattering phenomenon. The Raman signals of trace elements in minerals are usually weak and easily affected by background noise, resulting in a low signal-to-noise ratio. Existing technologies make it difficult to increase the intensity of Raman signals without destroying the sample. Especially when the trace element content in the sample is low, the Raman signal is often masked by the stronger background signal, resulting in detection sensitivity and accuracy that are difficult to meet analysis requirements. The intensity of the Raman signal is also affected by factors such as the physical properties of the sample, laser power and spot size. These parameters are difficult to accurately control with existing technologies, further affecting the detection effect.

[0004] Background signals and impurity interference are also a major technical bottleneck in the application of micro-Raman spectroscopy technology in the analysis of trace elements in minerals. The Raman signal in mineral samples is easily interfered by background impurities, especially when the sample contains impurities or has a complex structure. The background signal in the Raman spectrum will significantly mask the characteristic spectrum of the trace elements, making the detection results unstable and the data reliability poor. Existing technologies are difficult to effectively suppress the interference of background signals, which often leads to a large amount of noise components in the spectrum, making it difficult to distinguish the characteristic peaks of trace elements, affecting the detection accuracy of trace elements.

[0005] Multi-angle signal acquisition is of great significance in trace element analysis. However, due to sample heterogeneity, variations in laser incident angle, and differences in spot size and laser power, the consistency of multi-angle Raman signals is difficult to ensure. Existing technologies suffer from poor signal consistency during multi-angle detection, resulting in large deviations between data from different angles, affecting the accuracy of the final analysis results. This is especially true when the laser incident angle is unstable or the sample position is slightly offset. The deviations in multi-angle signals are exacerbated, increasing the complexity of data processing and reducing the reliability and reproducibility of detection. Existing Raman spectroscopy processes are relatively complex, especially when processing trace element signals from multiple angles and regions. These processes often require a series of steps, including signal enhancement, background correction, and data normalization. Existing technologies lack effective optimization algorithms to support these processes, resulting in low data processing efficiency. Especially in complex mineral samples, background correction, signal enhancement, and noise filtering are often inaccurate, easily leading to data loss or error accumulation, affecting the accuracy and reproducibility of detection results. Existing signal processing algorithms lack adaptive adjustment capabilities, making it difficult to automatically optimize parameters based on sample characteristics, resulting in unstable data processing results and further limiting the application of the technology.

[0006] The spatial distribution of trace elements in mineral samples is of great value for composition and structure analysis, but existing Raman spectroscopy technology is difficult to directly obtain the three-dimensional distribution information of trace elements in samples. Traditional microscopic imaging and two-dimensional spectroscopy technology can only provide Raman signal information in a two-dimensional plane, and it is difficult to realize three-dimensional distribution analysis of trace elements in micro areas. Three-dimensional reconstruction has high requirements for data consistency and integrity. Existing technologies have limited means for multi-angle data integration and three-dimensional modeling, and cannot effectively realize accurate spatial analysis of trace elements. The quantitative accuracy of existing Raman spectroscopy technology for trace elements is not ideal. Since the Raman peak intensity of trace elements in mineral samples is weak and is interfered by factors such as background noise and signal consistency, existing technologies have large errors in peak area calculation and signal intensity quantification of trace elements. In particular, existing quantitative methods are mostly based on simple peak intensity calculation, which fails to consider the depth distribution of trace elements in the sample and signal attenuation factors, resulting in insufficient accuracy of quantitative results.

[0007] Therefore, how to provide a method for analyzing trace elements in minerals using micro-Raman spectroscopy is an urgent problem that needs to be solved by those skilled in the art. Summary of the Invention

[0008] One objective of the present invention is to propose a method for analyzing trace elements in minerals using micro-Raman spectroscopy. This method fixes the mineral sample on a rotating sample stage, combines microscopic imaging to achieve precise positioning of micro-areas, and utilizes multi-angle excitation and an adaptive algorithm to optimize Raman signal acquisition and processing. The method describes in detail the process for achieving three-dimensional distribution analysis of trace elements, and has the advantages of non-destructive, high-resolution, and rapid detection.

[0009] A method for analyzing trace elements in minerals using micro-Raman spectroscopy according to an embodiment of the present invention includes the following steps:

[0010] S1. Fix the mineral sample on the rotatable sample stage of the micro-Raman spectrometer and select the micro area to be measured by microscopic imaging;

[0011] S2, performing low-power laser pre-illumination treatment on the selected micro-area and recording the generated Raman background signal;

[0012] S3. According to the feedback of the pre-illumination signal, adjust the laser focal length, spot size and incident angle to focus the laser on the target micro area;

[0013] S4. Under the calibrated laser conditions, the micro-area is excited at different angles, and the Raman spectrum signals are collected and temporarily stored at each angle;

[0014] S5. Apply an adaptive Raman signal enhancement algorithm to optimize multi-angle excitation data, filter out impurity interference by adjusting laser power and focal length in real time, and output optimized Raman spectrum data;

[0015] S6. Integrate the optimized multi-angle Raman signals to extract the characteristic spectral information of trace elements in the micro area and perform composition analysis;

[0016] S7. Combining multi-angle signals and microscopic imaging data, the trace element distribution and crystal structure in the micro area are reconstructed in three dimensions.

[0017] Optionally, the S1 specifically includes:

[0018] S11. Clean the surface of the mineral sample using a low-temperature plasma cleaning method with a cleaning time set to 5 to 20 seconds and a plasma power controlled at 10 to 50 watts to remove surface impurities while maintaining the integrity of the sample's microstructure;

[0019] S12. Fix the mineral sample on the rotatable sample stage of the Raman microscope. The sample stage is made of non-conductive material, with a rotation angle range of 0° to 360° and a rotation accuracy controlled within 0.1°. It is also equipped with an angle locking function to meet the positioning requirements of the micro area under multi-angle excitation.

[0020] S13, optically imaging the sample surface using a microscope system, with the magnification set between 10x and 100x and the focus adjusted from 1 to 10 microns, to identify the sample surface structure and select the location of the micro-region to be measured;

[0021] S14. In the selected target micro-area, the laser spot diameter is set to 1 to 5 microns, the incident angle is set to 45° to 90°, and the positioning accuracy of the micro-area is controlled within ±0.5 microns to limit the focal area of ​​the laser.

[0022] Optionally, the S3 specifically includes:

[0023] S21, pre-illuminating the selected target micro-area with a low-power laser, with the laser power set at 1.5 to 4 mW and the laser wavelengths at 532, 633, and 785 nm, to optimize thermal damage and obtain a stable background signal;

[0024] S22. The laser pre-irradiation time is controlled within 3 to 10 seconds. The pre-irradiation time for brittle minerals is 3 to 5 seconds, and for denser minerals it is 6 to 10 seconds, to meet the surface impurity removal requirements of different mineral characteristics.

[0025] S23. During the low-power laser pre-illumination process, real-time acquisition of background Raman spectral signals is performed, with the sampling frequency set at 10 to 50 times per second and the baseline noise controlled within 0.1%. The intensity and noise information of the background signal are recorded and stored as a background reference.

[0026] S24. Apply the dynamic background correction adaptive algorithm to optimize the collected background Raman spectrum signal, and adjust the sampling frequency f, laser power P and baseline threshold T in real time to match the background signal characteristics of different mineral samples:

[0027]

[0028] Among them, S bg is the optimized background signal intensity, α, β, and γ are adaptive coefficients related to the characteristics of the mineral sample;

[0029] S25, by using the optimized background signal obtained by the dynamic background correction adaptive algorithm, the background reference is updated in real time to provide a stable Raman spectrum reference signal;

[0030] S26. Perform preliminary data processing on the optimized background reference signal, set the peak sensitivity within 0.05%, and use an adaptive filtering algorithm to extract the peak position, peak shape and peak intensity information of the Raman peak.

[0031] Optionally, the S3 specifically includes:

[0032] S31. Based on the obtained background signal data, adjust the focal length of the laser, and control the focal length range to be 1 to 10 microns to match the microstructural characteristics of different mineral samples and achieve preliminary focusing on the target area;

[0033] S32. Based on the focal length adjustment, further adjust the laser spot diameter. The spot diameter is set to 1 to 5 microns to limit the laser action range, focus on the target micro area, and avoid irradiation interference of non-target areas;

[0034] S33. Optimize the incident angle of the laser according to the structural characteristics of the sample. The incident angle range is controlled to be 45° to 90° with an angle adjustment accuracy of ±1°. Adjust the laser incident conditions within this range to provide the required Raman scattering signal intensity and uniformity.

[0035] S34, through the focus optimization adaptive algorithm, combined with the collected background signal, adjust the laser power P and focal length d parameters in real time to optimize the laser focusing effect:

[0036]

[0037] Among them, F opt is the optimized laser focusing intensity, θ is the laser incident angle, η and δ are coefficients adaptively adjusted according to the characteristics of the mineral sample;

[0038] S35. After completing the parameter adjustment of the focus optimization adaptive algorithm, the optimized laser parameters are applied to the target micro-area to achieve spatial overlap between the laser focus and the target micro-area.

[0039] Optionally, the S4 specifically includes:

[0040] S41. After the laser focal length and spot diameter are adjusted, different laser incident angles are sequentially selected for multi-angle excitation, with the incident angle range being 20° to 80° and the angle interval of each excitation being 5° to 10°, to collect multidimensional Raman signal data of the target microregion;

[0041] S42. Collect Raman spectral signals in real time at each excitation angle, record the signal intensity I, signal-to-noise ratio S / N, and peak position λ, where the signal-to-noise ratio measurement accuracy is within 0.1%, and store the data as an initial signal reference;

[0042] S43. Based on the initial signal reference, the angle optimization adaptive algorithm is applied to adjust the laser power P, incident angle θ and spot diameter D in real time to optimize the stability of the collected signal:

[0043]

[0044] Among them, I optis the optimized signal intensity, α, β, and γ are adaptive coefficients related to sample characteristics;

[0045] S44, normalizing the optimized signal data, with the normalization range being 0 to 1, and calculating based on the ratio of signal intensity to background noise, optimizing the signal deviation caused by the angle difference, and unifying the signal data;

[0046] S45. Integrate the normalized signal data at each angle to construct a multidimensional data matrix, including signal intensity, incident angle, and peak position information, to form a comprehensive Raman signal spectrum of the target micro-area, providing complete spectral data for trace element analysis.

[0047] Optionally, the S5 specifically includes:

[0048] S51. Apply an adaptive Raman signal enhancement algorithm to the data collected from multi-angle excitation. Adjust the laser power and spot diameter based on real-time signal feedback. The laser power range is 1.5 to 4 milliwatts, and the spot diameter range is 1 to 5 microns. This adapts to the Raman signal requirements under different signal conditions, so that the collected Raman signal achieves the required sensitivity and consistency at all angles.

[0049] S52. Based on the feedback of the collected signal intensity I and signal-to-noise ratio, the combination of laser power, spot diameter, and incident angle is optimized and calculated using the adaptive adjustment formula:

[0050]

[0051] Among them, I enhanced is the signal strength after enhancement, α, β, γ, δ and ∈ are coefficients adaptively adjusted according to the sample characteristics;

[0052] S53, performing signal smoothing processing on the obtained optimized signal data, by calculating the ratio of signal intensity to background noise, so as to remove noise interference and retain the main characteristic information of the Raman peak, thereby providing consistency of signal data collected at different angles;

[0053] S54, performing weighted superposition of multi-angle signals on the smoothed signal data, calculating a weighted average of the signal data at different angles, wherein the weight coefficient is set according to the signal intensity and the signal-to-noise ratio, and optimizing the signal deviation caused by the angle difference. The multi-angle superposition data is stored in a matrix form, and the matrix dimensions include the signal intensity, the incident angle, and the spot characteristics, thereby forming a comprehensive Raman signal data set;

[0054] S55. The obtained Raman signal comprehensive data set is used as the final signal benchmark. The benchmark data contains the optimized multi-angle Raman signal characteristics, providing complete spectral feature information for quantitative and qualitative detection in trace element analysis.

[0055] Optionally, the S6 specifically includes:

[0056] S61. Based on the generated Raman signal comprehensive data set and in combination with the Raman signal comprehensive spectrum, characteristic spectrum extraction is performed on the Raman data. Based on the changes in the Raman peak position λ, peak intensity I, and peak width w in the micro-region, characteristic spectrum information of the trace element is identified, and interference signals are removed using an adaptive filtering algorithm.

[0057] S62. Based on the characteristic spectrum, the dynamic weight component separation algorithm is applied to separate the independent Raman peak signals of different trace elements:

[0058]

[0059] Among them, C element is the characteristic spectrum component of the separated trace elements, I i is the peak intensity of the i-th Raman peak, λ is the measured peak position, and λ i is the theoretical peak position, w i is the peak width, θ is the laser incident angle, and α, β, γ, and η are coefficients that are adaptively adjusted according to the sample characteristics;

[0060] S63. Calculate the peak area of ​​the separated characteristic spectrum components using the Gaussian-Laplace mixed integration method to make the peak area calculation more suitable for complex spectra:

[0061]

[0062] Among them, A element is the characteristic spectrum peak area of ​​the trace element, I is the peak intensity, λ0 is the central peak position of the characteristic spectrum, w is the peak width, and δ is the weight coefficient of the Laplace operator;

[0063] S64. Integrate the calculated relative concentration information of each trace element to generate a trace element distribution map within the target micro-area. The distribution map is presented in the form of a multi-dimensional matrix, including the concentration, position and Raman signal intensity information of each trace element.

[0064] Optionally, the S7 specifically includes:

[0065] S71. Performing three-dimensional coordinate registration of the generated trace element distribution map with the microscopic imaging data, using an adaptive coordinate mapping algorithm to make the concentration distribution of the trace elements correspond to the position of the mineral microregions in the microscopic image, and establishing a spatial correspondence between the composition and structure through a multi-layer correction model;

[0066] S72. Based on the registered three-dimensional coordinates, a weighted multidimensional data interpolation algorithm is applied to interpolate the Raman signal intensity, peak position, and concentration information to generate a continuous distribution image of the Raman signal in the micro-area:

[0067]

[0068] Among them, I interp is the signal strength after interpolation, I k is the distance r k The original signal strength at W k is the adaptive weight coefficient, r k is the distance between the interpolation point and the data point, and σ is a parameter that controls the smoothness of the interpolation;

[0069] S73, performing multi-level three-dimensional reconstruction on the generated continuous distribution image, accumulating and adaptively adjusting layer by layer to generate a three-dimensional distribution model of trace elements in the micro-region, and adjusting the Raman peak position and intensity according to the signal deviation during the reconstruction process;

[0070] S74. Based on the three-dimensional model, the layered integration method is used to analyze the concentration distribution of trace elements at different depths in the micro-area, and the layered depth weight calculation is used to generate a depth profile:

[0071]

[0072] Among them, C depth is the trace element concentration in the depth direction, I(z) is the signal intensity at depth z, z0 is the reference depth, τ is the control parameter of the depth distribution, and δ is the Laplace term weight coefficient;

[0073] S75. Fusing the generated depth profile data with the surface data of the microscopic imaging to present a spatial distribution map of the trace elements in the target micro-area in the form of a three-dimensional matrix. The distribution map includes the concentration, position, depth and signal intensity information of each trace element.

[0074] The beneficial effects of the present invention are:

[0075] The present invention uses micro-Raman spectroscopy technology to achieve non-destructive and high-resolution detection of trace elements in minerals, overcoming the sample damage problem caused by traditional chemical digestion and physical sampling. During the detection process, low-power laser pre-illumination and multi-angle excitation are used, so that the sample can be analyzed in micro-area components without destroying the original structure. It is particularly suitable for rare or difficult-to-destroy geological samples, expands the application of micro-Raman spectroscopy in geology and materials science, and provides a reliable technical means for non-destructive analysis. At the same time, the present invention combines adaptive signal enhancement and dynamic background correction algorithms, and significantly improves the intensity and signal-to-noise ratio of the Raman signal by precisely adjusting the laser focal length, spot diameter and incident angle. During the multi-angle signal acquisition and optimization process, the interference of background signals is eliminated, ensuring the sensitivity and resolution of the trace element signal. Even under low concentration conditions, the characteristic spectrum can be clearly identified, ensuring the accuracy of the detection data.

[0076] The present invention also uses multi-angle signal acquisition and dynamic weight adjustment to ensure that the Raman signals collected from multiple angles have high consistency, effectively reducing the signal deviation caused by changes in laser power or incident angle. By processing signal data through normalization and weighted superposition algorithms, the stability and reproducibility of the data are improved, and the reliability of the detection results is guaranteed. Combining microscopic imaging data and multi-angle Raman signals, the present invention further realizes the three-dimensional spatial distribution reconstruction of trace elements. Based on multi-level three-dimensional reconstruction and depth profile analysis, a three-dimensional distribution map is generated in the micro area. Compared with traditional two-dimensional analysis, the present invention provides sample depth and spatial distribution information, provides rich spatial data for the structural and composition difference analysis of complex minerals, and significantly enhances the application effect of micro-Raman spectroscopy in spatial structure analysis.

[0077] The present invention introduces adaptive filtering, dynamic background correction and Gaussian-Laplace hybrid integration algorithms, which enhance the noise suppression and background signal optimization capabilities in signal processing. Parameter settings are automatically adjusted through the adaptive algorithm, simplifying the multi-angle data processing process, significantly improving data processing efficiency and accuracy, and reducing the risk of error accumulation. The Gaussian-Laplace hybrid integration is highly accurate in peak area calculation and quantitative analysis, ensuring the accuracy of trace element quantitative detection. The combination of peak area calculation and hierarchical integration methods enables the present invention to achieve high precision in the precise quantitative analysis of trace elements. By calculating the signal intensity to background noise ratio, interference from non-target areas can be effectively eliminated, and trace element concentration data can be accurately obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0079] Figure 1 This is a flow chart of a method for analyzing trace elements in minerals using micro-Raman spectroscopy proposed by the present invention;

[0080] Figure 2 Schematic diagram of multi-angle excitation Raman signal acquisition and adaptive signal enhancement algorithm optimization processing in the present invention;

[0081] Figure 3 This is a flow chart for extracting trace element characteristic spectra and constructing three-dimensional spatial distribution maps based on multi-angle signals in the present invention. DETAILED DESCRIPTION

[0082] The present invention will now be described in further detail with reference to the accompanying drawings, which are simplified schematic diagrams that illustrate the basic structure of the present invention in a schematic manner.

[0083] refer to Figure 1-3 A method for analyzing trace elements in minerals using micro-Raman spectroscopy comprises the following steps:

[0084] S1. Fix the mineral sample on the rotatable sample stage of the micro-Raman spectrometer and select the micro area to be measured by microscopic imaging;

[0085] S2, performing low-power laser pre-illumination treatment on the selected micro-area and recording the generated Raman background signal;

[0086] S3. According to the feedback of the pre-illumination signal, adjust the laser focal length, spot size and incident angle to focus the laser on the target micro area;

[0087] S4. Under the calibrated laser conditions, the micro-area is excited at different angles, and the Raman spectrum signals are collected and temporarily stored at each angle;

[0088] S5. Apply an adaptive Raman signal enhancement algorithm to optimize multi-angle excitation data, filter out impurity interference by adjusting laser power and focal length in real time, and output optimized Raman spectrum data;

[0089] S6. Integrate the optimized multi-angle Raman signals to extract the characteristic spectral information of trace elements in the micro area and perform composition analysis;

[0090] S7. Combining multi-angle signals and microscopic imaging data, the trace element distribution and crystal structure in the micro area are reconstructed in three dimensions.

[0091] In this embodiment, S1 specifically includes:

[0092] S11. Clean the surface of the mineral sample using a low-temperature plasma cleaning method with a cleaning time set to 5 to 20 seconds and a plasma power controlled at 10 to 50 watts to remove surface impurities while maintaining the integrity of the sample's microstructure;

[0093] S12. Fix the mineral sample on the rotatable sample stage of the Raman microscope. The sample stage is made of non-conductive material, with a rotation angle range of 0° to 360° and a rotation accuracy controlled within 0.1°. It is also equipped with an angle locking function to meet the positioning requirements of the micro area under multi-angle excitation.

[0094] S13, optically imaging the sample surface using a microscope system, with the magnification set between 10x and 100x and the focus adjusted from 1 to 10 microns, to identify the sample surface structure and select the location of the micro-region to be measured;

[0095] S14. In the selected target micro-area, the laser spot diameter is set to 1 to 5 microns, the incident angle is set to 45° to 90°, and the positioning accuracy of the micro-area is controlled within ±0.5 microns to limit the focal area of ​​the laser.

[0096] In this embodiment, S3 specifically includes:

[0097] S21, pre-illuminating the selected target micro-area with a low-power laser, with the laser power set at 1.5 to 4 mW and the laser wavelengths at 532, 633, and 785 nm, to optimize thermal damage and obtain a stable background signal;

[0098] S22. The laser pre-irradiation time is controlled within 3 to 10 seconds. The pre-irradiation time for brittle minerals is 3 to 5 seconds, and for denser minerals it is 6 to 10 seconds, to meet the surface impurity removal requirements of different mineral characteristics.

[0099] S23. During the low-power laser pre-illumination process, real-time acquisition of background Raman spectral signals is performed, with the sampling frequency set at 10 to 50 times per second and the baseline noise controlled within 0.1%. The intensity and noise information of the background signal are recorded and stored as a background reference.

[0100] S24. Apply the dynamic background correction adaptive algorithm to optimize the collected background Raman spectrum signal, and adjust the sampling frequency f, laser power P and baseline threshold T in real time to match the background signal characteristics of different mineral samples:

[0101]

[0102] Among them, S bg is the optimized background signal intensity, α, β, and γ are adaptive coefficients related to the characteristics of the mineral sample;

[0103] S25, by using the optimized background signal obtained by the dynamic background correction adaptive algorithm, the background reference is updated in real time to provide a stable Raman spectrum reference signal;

[0104] S26. Perform preliminary data processing on the optimized background reference signal, set the peak sensitivity within 0.05%, and use an adaptive filtering algorithm to extract the peak position, peak shape and peak intensity information of the Raman peak.

[0105] In this embodiment, S3 specifically includes:

[0106] S31. Based on the obtained background signal data, adjust the focal length of the laser, and control the focal length range to be 1 to 10 microns to match the microstructural characteristics of different mineral samples and achieve preliminary focusing on the target area;

[0107] S32. Based on the focal length adjustment, further adjust the laser spot diameter. The spot diameter is set to 1 to 5 microns to limit the laser action range, focus on the target micro area, and avoid irradiation interference of non-target areas;

[0108] S33. Optimize the incident angle of the laser according to the structural characteristics of the sample. The incident angle range is controlled to be 45° to 90° with an angle adjustment accuracy of ±1°. Adjust the laser incident conditions within this range to provide the required Raman scattering signal intensity and uniformity.

[0109] S34, through the focus optimization adaptive algorithm, combined with the collected background signal, adjust the laser power P and focal length d parameters in real time to optimize the laser focusing effect:

[0110]

[0111] Among them, F opt is the optimized laser focusing intensity, θ is the laser incident angle, η and δ are coefficients adaptively adjusted according to the characteristics of the mineral sample;

[0112] S35. After completing the parameter adjustment of the focus optimization adaptive algorithm, the optimized laser parameters are applied to the target micro-area to achieve spatial overlap between the laser focus and the target micro-area.

[0113] In this embodiment, the S4 specifically includes:

[0114] S41. After the laser focal length and spot diameter are adjusted, different laser incident angles are sequentially selected for multi-angle excitation, with the incident angle range being 20° to 80° and the angle interval of each excitation being 5° to 10°, to collect multidimensional Raman signal data of the target microregion;

[0115] S42. Collect Raman spectral signals in real time at each excitation angle, record the signal intensity I, signal-to-noise ratio S / N, and peak position λ, where the signal-to-noise ratio measurement accuracy is within 0.1%, and store the data as an initial signal reference;

[0116] S43. Based on the initial signal reference, the angle optimization adaptive algorithm is applied to adjust the laser power P, incident angle θ and spot diameter D in real time to optimize the stability of the collected signal:

[0117]

[0118] Among them, I opt is the optimized signal intensity, α, β, and γ are adaptive coefficients related to sample characteristics;

[0119] S44, normalizing the optimized signal data, with the normalization range being 0 to 1, and calculating based on the ratio of signal intensity to background noise, optimizing the signal deviation caused by the angle difference, and unifying the signal data;

[0120] S45. Integrate the normalized signal data at each angle to construct a multidimensional data matrix, including signal intensity, incident angle, and peak position information, to form a comprehensive Raman signal spectrum of the target micro-area, providing complete spectral data for trace element analysis.

[0121] In this embodiment, the S5 specifically includes:

[0122] S51. Apply an adaptive Raman signal enhancement algorithm to the data collected from multi-angle excitation. Adjust the laser power and spot diameter based on real-time signal feedback. The laser power range is 1.5 to 4 milliwatts, and the spot diameter range is 1 to 5 microns. This adapts to the Raman signal requirements under different signal conditions, so that the collected Raman signal achieves the required sensitivity and consistency at all angles.

[0123] S52. Based on the feedback of the collected signal intensity I and signal-to-noise ratio, the combination of laser power, spot diameter, and incident angle is optimized and calculated using the adaptive adjustment formula:

[0124]

[0125] Among them, I enhanced is the signal strength after enhancement, α, β, γ, δ and ∈ are coefficients adaptively adjusted according to the sample characteristics;

[0126] S53, performing signal smoothing processing on the obtained optimized signal data, by calculating the ratio of signal intensity to background noise, so as to remove noise interference and retain the main characteristic information of the Raman peak, thereby providing consistency of signal data collected at different angles;

[0127] S54, performing weighted superposition of multi-angle signals on the smoothed signal data, calculating a weighted average of the signal data at different angles, wherein the weight coefficient is set according to the signal intensity and the signal-to-noise ratio, and optimizing the signal deviation caused by the angle difference. The multi-angle superposition data is stored in a matrix form, and the matrix dimensions include the signal intensity, the incident angle, and the spot characteristics, thereby forming a comprehensive Raman signal data set;

[0128] S55. The obtained Raman signal comprehensive data set is used as the final signal benchmark. The benchmark data contains the optimized multi-angle Raman signal characteristics, providing complete spectral feature information for quantitative and qualitative detection in trace element analysis.

[0129] In this embodiment, S6 specifically includes:

[0130] S61. Based on the generated Raman signal comprehensive data set and in combination with the Raman signal comprehensive spectrum, characteristic spectrum extraction is performed on the Raman data. Based on the changes in the Raman peak position λ, peak intensity I, and peak width w in the micro-region, characteristic spectrum information of the trace element is identified, and interference signals are removed using an adaptive filtering algorithm.

[0131] S62. Based on the characteristic spectrum, the dynamic weight component separation algorithm is applied to separate the independent Raman peak signals of different trace elements:

[0132]

[0133] Among them, C element is the characteristic spectrum component of the separated trace elements, I i is the peak intensity of the i-th Raman peak, λ is the measured peak position, and λ i is the theoretical peak position, w i is the peak width, θ is the laser incident angle, and α, β, γ, and η are coefficients that are adaptively adjusted according to the sample characteristics;

[0134] S63. Calculate the peak area of ​​the separated characteristic spectrum components using the Gaussian-Laplace mixed integration method to make the peak area calculation more suitable for complex spectra:

[0135]

[0136] Among them, A element is the characteristic spectrum peak area of ​​the trace element, I is the peak intensity, λ0 is the central peak position of the characteristic spectrum, w is the peak width, and δ is the weight coefficient of the Laplace operator;

[0137] S64. Integrate the calculated relative concentration information of each trace element to generate a trace element distribution map within the target micro-area. The distribution map is presented in the form of a multi-dimensional matrix, including the concentration, position and Raman signal intensity information of each trace element.

[0138] In this embodiment, the S7 specifically includes:

[0139] S71. Performing three-dimensional coordinate registration of the generated trace element distribution map with the microscopic imaging data, using an adaptive coordinate mapping algorithm to make the concentration distribution of the trace elements correspond to the position of the mineral microregions in the microscopic image, and establishing a spatial correspondence between the composition and structure through a multi-layer correction model;

[0140] S72. Based on the registered three-dimensional coordinates, a weighted multidimensional data interpolation algorithm is applied to interpolate the Raman signal intensity, peak position, and concentration information to generate a continuous distribution image of the Raman signal in the micro-area:

[0141]

[0142] Among them, I interp is the signal strength after interpolation, I k is the distance r k The original signal strength at W k is the adaptive weight coefficient, r k is the distance between the interpolation point and the data point, and σ is a parameter that controls the smoothness of the interpolation;

[0143] S73, performing multi-level three-dimensional reconstruction on the generated continuous distribution image, accumulating and adaptively adjusting layer by layer to generate a three-dimensional distribution model of trace elements in the micro-region, and adjusting the Raman peak position and intensity according to the signal deviation during the reconstruction process;

[0144] S74. Based on the three-dimensional model, the layered integration method is used to analyze the concentration distribution of trace elements at different depths in the micro-area, and the layered depth weight calculation is used to generate a depth profile:

[0145]

[0146] Among them, C depth is the trace element concentration in the depth direction, I(z) is the signal intensity at depth z, z0 is the reference depth, τ is the control parameter of the depth distribution, and δ is the Laplace term weight coefficient;

[0147] S75. Fusing the generated depth profile data with the surface data of the microscopic imaging to present a spatial distribution map of the trace elements in the target micro-area in the form of a three-dimensional matrix. The distribution map includes the concentration, position, depth and signal intensity information of each trace element.

[0148] Example 1:

[0149] In order to verify the feasibility of the present invention in practice, the present invention was applied to rare mineral samples from Alxa, Inner Mongolia, for trace element composition analysis. The mineral samples contained trace elements such as molybdenum and manganese with low concentrations and uneven distribution. Traditional detection methods made it difficult to obtain accurate micro-region composition information while ensuring sample integrity. The present invention utilizes multi-angle excitation and adaptive algorithm optimization of micro-Raman spectroscopy to achieve high-resolution detection and spatial distribution reconstruction of mineral trace elements under non-destructive conditions.

[0150] In the sample fixing step, the mineral sample is placed on the rotating sample stage of the micro-Raman spectrometer, and the microscope imaging system captures the sample surface image to help select the micro-area to be measured. The sample stage has a rotatable function with an angle range of 0° to 360° and a rotation accuracy of 0.1° to ensure the accurate positioning of the micro-area during multi-angle excitation. A low-power laser of 532 nm is used for pre-illumination, with the power set at 3 mW and the time controlled at 5 seconds. Through this pre-illumination process, the background signal is recorded and the interference of surface impurities is reduced, thereby obtaining a stable initial Raman signal baseline. Through the collection and analysis of the background signal, it is found that the background noise of the sample is mainly concentrated in the low-frequency area, and there are differences at different angles, which interferes with the Raman signal of trace elements. On this basis, the system automatically adjusts the focal length, spot size and incident angle of the laser to make the laser accurately focus on the target micro-area and avoid excitation of non-target areas.

[0151] In the process of formally collecting Raman signals, the present invention adopts multi-angle excitation collection, collecting Raman signal data at each angle from 20° to 80° at intervals of 5°. The collection time for each angle is controlled within 1 second, and the signal sampling frequency is 20 times / second to ensure the integrity and real-time performance of the data. The signal intensity, signal-to-noise ratio and peak position data collected at each angle are recorded and temporarily stored by the system for subsequent signal optimization and integration processing. Experimental data show that there are slight differences in Raman signal intensity at different angles, but after applying the adaptive signal enhancement algorithm, the signal-to-noise ratio is significantly improved, especially at incident angles of 45° and 65°, the signal-to-noise ratio is increased by 12% and 15% respectively, effectively reducing the interference of background noise and making the characteristic spectrum peaks of trace elements more clearly distinguishable.

[0152] During the data processing stage, the present invention optimizes the adaptive algorithm to adjust the laser power and incident angle in real time, so that the collected Raman signal achieves the required sensitivity and consistency at each angle. Through normalization processing and weighted superposition of multi-angle signals, the system forms a multidimensional data matrix containing signal intensity, incident angle and peak position information. The dynamic background correction algorithm is applied to further optimize the data set, and the stability of the signal baseline is significantly improved. Compared with traditional methods, the drift rate of the signal baseline is reduced from 0.05% to 0.02%, thereby enhancing the reliability of the detection data.

[0153] During the three-dimensional distribution reconstruction phase, the present invention reconstructed a three-dimensional distribution map of trace elements based on the collected multi-angle Raman signals and microscopic imaging data. Through three-dimensional coordinate mapping, the corresponding relationship between the concentration of each element and its spatial position within the microregion was established. Peak area calculations were performed using a Gaussian-Laplace mixed integral method. The results showed that the concentration of molybdenum varied significantly across different depth levels. At a depth of 5 microns from the surface, the molybdenum concentration was 0.45%, rising to 0.62% at a depth of 10 microns. This depth distribution information is difficult to obtain using traditional two-dimensional analysis, but the present invention provides detailed spatial distribution data. To further verify the stability of the data, the present invention combined the Raman signals with microscopic imaging data for depth profiling. The results showed that within the 5 to 10 micron range of the sample, the molybdenum concentration variation was consistent with the data in the three-dimensional model, with an error of less than 0.03%, demonstrating the accuracy of the three-dimensional model.

[0154] In this experiment, the detection efficiency of the present invention was also significantly improved. The entire Raman signal acquisition, signal enhancement, data integration and three-dimensional reconstruction process was completed within 30 minutes, which is 50% shorter than the traditional method. During the data processing process, the system automatically performs signal enhancement and background correction, avoiding the complex manual processing process. In the construction of spatial distribution maps, traditional methods rely on multiple sampling and manual adjustment. The present invention uses an adaptive algorithm to realize automatic data integration and three-dimensional reconstruction in a single acquisition process, which not only greatly improves detection efficiency, but also significantly reduces data deviations caused by human errors.

[0155] Table 1 Related data table

[0156]

[0157] It can be seen from the table data that the laser power is set to 3 milliwatts and the pre-illumination time is 5 seconds. The low-power laser effectively reduces sample damage and ensures the stability of the background signal, providing a reliable foundation for subsequent high-precision Raman signal acquisition. Through multi-angle excitation acquisition, the incident angle range is between 20° and 80°, the angle interval is 5°, the single-angle acquisition time is controlled at 1 second, and the signal sampling frequency is set to 20 times / second, ensuring the real-time and integrity of the collected data. This multi-angle acquisition mode ensures the stability and consistency of the signal, and significantly improves the sensitivity of trace element detection. The present invention uses an adaptive signal enhancement algorithm to significantly improve the signal-to-noise ratio at specific angles, especially at angles of 45° and 65°, the signal-to-noise ratio is increased by 12% and 15% respectively. Compared with traditional methods, the signal reference drift rate of the present invention is reduced from 0.05% to 0.02%, showing excellent signal consistency and stability, effectively reducing background noise interference, and in three-dimensional spatial distribution and depth. In terms of depth analysis, the three-dimensional model constructed by the present invention shows the variation of molybdenum concentration at different depth levels. The concentrations at depths of 5 microns and 10 microns are 0.45% and 0.62%, respectively. The concentration error range of the data model is controlled within 0.03%, which further verifies the precision and accuracy of the three-dimensional reconstruction model, so that the spatial distribution information of the sample can be clearly presented. The entire detection process takes 30 minutes, which is 50% shorter than the 60-minute detection time of the traditional method, effectively improving the experimental efficiency and reducing the complexity of manual operation. The data in the table intuitively demonstrates the significant improvements of the present invention in non-destructive detection, high-resolution analysis, detection sensitivity and operational efficiency, and fully verifies the efficiency and reliability of this method in mineral trace element analysis.

[0158] In summary, the present invention effectively improves the accuracy, data stability and spatial resolution of mineral trace element detection, provides strong technical support for the precise analysis of mineral trace elements, has significant technical advantages in signal enhancement, background noise suppression and three-dimensional reconstruction, and shows broad application prospects in the composition analysis and spatial structure analysis of rare minerals.

[0159] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for analyzing trace elements in minerals using micro-Raman spectroscopy, characterized in that: The steps include: S1. Fix the mineral sample on the rotatable sample stage of the micro-Raman spectrometer and select the micro-area to be measured by microscopic imaging; S2, performing low-power laser pre-illumination treatment on the selected micro-area and recording the generated Raman background signal; S3, according to the feedback of the pre-illumination signal, adjusting the laser focal length, spot size and incident angle to focus the laser on the target micro area; S4, under the calibrated laser condition, the micro-area is excited at different angles, and the Raman spectrum signal is collected and temporarily stored at each angle; S5. Apply the adaptive Raman signal enhancement algorithm to optimize the multi-angle excitation data, filter out impurity interference and output the optimized Raman spectrum data by adjusting the laser power and focal length in real time; S6. Integrate the optimized multi-angle Raman signals, extract the characteristic spectrum information of trace elements in the micro area, and perform component analysis; S7. Combining multi-angle signals and microscopic imaging data, the distribution of trace elements and crystal structure in the micro area are reconstructed in three dimensions.

2. The method for analyzing trace elements in minerals using micro-Raman spectroscopy according to claim 1, characterized in that: The S1 specifically includes: S11. Clean the surface of the mineral sample using a low-temperature plasma cleaning method, with the cleaning time set to 5 to 20 seconds and the plasma power controlled to 10 to 50 watts to remove surface impurities while maintaining the integrity of the sample's microstructure; S12. Fix the mineral sample on the rotatable sample stage of the micro-Raman spectrometer. The sample stage is made of non-conductive material, with a rotation angle range of 0° to 360°, a rotation accuracy controlled within 0.1°, and an angle locking function to meet the positioning requirements of the micro-area under multi-angle excitation; S13, using a microscope system to perform optical imaging on the sample surface, with the magnification set between 10x and 100x and the focus adjustment range between 1 and 10 microns, to identify the sample surface structure and select the position of the micro-area to be measured; S14. In the selected target micro-area, the laser spot diameter is set to 1 to 5 microns, the incident angle is set to 45° to 90°, and the positioning accuracy of the micro-area is controlled within ±0.5 microns to limit the focal area of ​​the laser.

3. The method for analyzing trace elements in minerals using micro-Raman spectroscopy according to claim 1, characterized in that: The S3 specifically includes: S21, pre-illuminating the selected target micro-area with low-power laser, with the laser power set at 1.5 to 4 mW and the laser wavelengths at 532, 633, and 785 nm, to optimize thermal damage and obtain a stable background signal; S22, the laser pre-illumination time is controlled within 3 to 10 seconds, wherein the pre-illumination time for brittle minerals is 3 to 5 seconds, and the pre-illumination time for minerals with higher density is 6 to 10 seconds, so as to meet the surface impurity removal requirements of different mineral characteristics; S23, during the low-power laser pre-illumination process, real-time collection of background Raman spectrum signals, with the sampling frequency set at 10 to 50 times / second, the baseline noise controlled within 0.1%, the intensity and noise information of the background signal recorded, and stored as the background reference; S24. Apply the dynamic background correction adaptive algorithm to optimize the collected background Raman spectrum signal, and match the background signal characteristics of different mineral samples by adjusting the sampling frequency f, laser power P and baseline threshold T in real time: Among them, S bg is the optimized background signal intensity, α, β, and γ are adaptive coefficients related to the characteristics of mineral samples; S25, by using the optimized background signal obtained by the dynamic background correction adaptive algorithm, the background reference is updated in real time to provide a stable Raman spectrum reference signal; S26. Perform preliminary data processing on the optimized background reference signal, set the peak sensitivity within 0.05%, and use an adaptive filtering algorithm to extract the peak position, peak shape and peak intensity information of the Raman peak.

4. The method for analyzing trace elements in minerals using micro-Raman spectroscopy according to claim 1, characterized in that: The S3 specifically includes: S31. Based on the background signal data obtained, the focal length of the laser is adjusted, and the focal length range is controlled to be 1 to 10 microns to match the microstructure characteristics of different mineral samples and achieve preliminary focusing on the target area; S32. On the basis of adjusting the focal length, further adjusting the laser spot diameter, the spot diameter is set to 1 to 5 microns, so as to limit the laser action range, focus on the target micro area, and avoid irradiation interference of non-target areas; S33. Optimize the incident angle of the laser according to the structural characteristics of the sample. The incident angle range is controlled to be 45° to 90°, and the angle adjustment accuracy is ±1°. Adjust the laser incident conditions within this range to provide the required Raman scattering signal intensity and uniformity. S34, through the focus optimization adaptive algorithm, combined with the collected background signal, the laser power P and focal length d parameters are adjusted in real time to optimize the laser focusing effect: Among them, F opt is the optimized laser focusing intensity, θ is the laser incident angle, η and δ are coefficients adaptively adjusted according to the characteristics of the mineral sample; S35. After completing the parameter adjustment of the focus optimization adaptive algorithm, the optimized laser parameters are applied to the target micro-area to achieve spatial overlap between the laser focus and the target micro-area.

5. The method for analyzing trace elements in minerals using micro-Raman spectroscopy according to claim 1, characterized in that: The S4 specifically includes: S41, after the laser focal length and the spot diameter are adjusted, different laser incident angles are selected in turn for multi-angle excitation, the incident angle range is 20° to 80°, the angle interval of each excitation is 5° to 10°, and the multi-dimensional Raman signal data of the target micro-area are collected; S42, collecting Raman spectrum signals in real time at each excitation angle, recording the signal intensity I, signal-to-noise ratio S / N and peak position λ, wherein the signal-to-noise ratio measurement accuracy is within 0.1%, and storing the data as an initial signal reference; S43. Based on the initial signal reference, the angle optimization adaptive algorithm is applied to adjust the laser power P, incident angle θ and spot diameter D in real time to optimize the collected signal stability: Among them, I opt is the optimized signal intensity, α, β, and γ are adaptive coefficients related to sample characteristics; S44, normalizing the optimized signal data, wherein the normalization range is 0 to 1, and calculating based on the ratio of signal intensity to background noise, optimizing the signal deviation caused by the angle difference, and unifying the signal data; S45. Integrate the normalized signal data at each angle to construct a multidimensional data matrix, including signal intensity, incident angle and peak position information, to form a comprehensive Raman signal spectrum of the target micro-area, providing complete spectral data for trace element analysis.

6. The method for analyzing trace elements in minerals using micro-Raman spectroscopy according to claim 1, characterized in that: The S5 specifically includes: S51. Apply an adaptive Raman signal enhancement algorithm to the data collected by multi-angle excitation, and adjust the laser power and spot diameter according to real-time signal feedback. The laser power range is 1.5 to 4 milliwatts, and the spot diameter range is 1 to 5 microns to adapt to the Raman signal requirements under different signal conditions, so that the collected Raman signal can achieve the required sensitivity and consistency at each angle; S52, based on the feedback of the collected signal intensity I and signal-to-noise ratio, optimize the combination of laser power, spot diameter and incident angle, and calculate through the adaptive adjustment formula: Among them, I enhanced is the signal strength after enhancement, α, β, γ, δ and ∈ are coefficients adaptively adjusted according to the sample characteristics; S53, performing signal smoothing processing on the obtained optimized signal data, by calculating the ratio of signal intensity to background noise, so as to remove noise interference and retain the main characteristic information of the Raman peak, and provide consistency of signal data collected at different angles; S54, performing weighted superposition of multi-angle signals on the smoothed signal data, calculating a weighted average of the signal data at different angles, wherein the weight coefficient is set according to the signal strength and the signal-to-noise ratio, and optimizing the signal deviation caused by the angle difference. The multi-angle superposition data is stored in a matrix form, and the matrix dimensions include the signal strength, the incident angle and the spot characteristics, so as to form a comprehensive Raman signal data set; S55. The obtained Raman signal comprehensive data set is used as the final signal benchmark. The benchmark data contains optimized multi-angle Raman signal characteristics, providing complete spectral feature information for quantitative and qualitative detection in trace element analysis.

7. The method for analyzing trace elements in minerals using micro-Raman spectroscopy according to claim 1, characterized in that: The S6 specifically includes: S61, based on the generated Raman signal comprehensive data set and in combination with the Raman signal comprehensive spectrum, extract the characteristic spectrum of the Raman data, identify the characteristic spectrum information of the trace elements based on the changes of the Raman peak position λ, peak intensity I and peak width w in the micro-region, and use an adaptive filtering algorithm to remove interference signals; S62. Based on the characteristic spectrum, the dynamic weight component separation algorithm is applied to separate the independent Raman peak signals of different trace elements: Among them, C element is the characteristic spectrum component of the separated trace elements, I i is the peak intensity of the ith Raman peak, λ is the measured peak position, and λ i is the theoretical peak position, w i is the peak width, θ is the laser incident angle, α, β, γ, η are coefficients that are adaptively adjusted according to the sample characteristics; S63. Calculate the peak area of ​​the separated characteristic spectrum components, and use the Gaussian-Laplace mixed integration method to make the peak area calculation more suitable for complex spectra: Among them, A element is the characteristic spectrum peak area of ​​the trace element, I is the peak intensity, λ0 is the central peak position of the characteristic spectrum, w is the peak width, and δ is the weight coefficient of the Laplace operator; S64, integrating the calculated relative concentration information of each trace element to generate a trace element distribution map in the target micro-area, wherein the distribution map is presented in the form of a multi-dimensional matrix, including the concentration, position and Raman signal intensity information of each trace element.

8. The method for analyzing trace elements in minerals using micro-Raman spectroscopy according to claim 1, characterized in that: The S7 specifically includes: S71, performing three-dimensional coordinate registration of the generated trace element distribution map and the microscopic imaging data, using an adaptive coordinate mapping algorithm to make the concentration distribution of the trace elements correspond to the position of the mineral micro-region in the microscopic image, and establishing a spatial correspondence between the composition and the structure through a multi-layer correction model; S72. Based on the registered three-dimensional coordinates, a weighted multidimensional data interpolation algorithm is applied to interpolate the Raman signal intensity, peak position and concentration information to generate a continuous distribution image of the Raman signal in the micro-area: Among them, I interp is the signal strength after interpolation, I k The distance r k The original signal strength at W k is the adaptive weight coefficient, r k is the distance between the interpolation point and the data point, and σ is a parameter that controls the smoothness of the interpolation; S73, performing multi-faceted three-dimensional reconstruction on the generated continuous distribution image, accumulating layer by layer and adaptively adjusting, to generate a three-dimensional distribution model of trace elements in the micro-region, and adjusting the Raman peak position and intensity according to the signal deviation during the reconstruction process; S74. Based on the three-dimensional model, the layered integration method is used to analyze the concentration distribution of trace elements at different depths in the micro-area, and the layered depth weight calculation is used to generate the depth profile: Among them, C depth is the trace element concentration in the depth direction, I(z) is the signal intensity at depth z, z0 is the reference depth, τ is the control parameter of the depth distribution, and δ is the weight coefficient of the Laplace term; S75. The generated depth profile data is merged with the surface data of the microscopic imaging to present a spatial distribution map of the trace elements in the target micro-area in the form of a three-dimensional matrix. The distribution map includes the concentration, position, depth and signal intensity information of each trace element.

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