A scanning electron microscope dynamic simulation method and system of regional mineral alteration process

Abstract

This invention discloses a scanning electron microscope (SEM) dynamic simulation method and system for regional mineral alteration processes, belonging to the field of materials characterization technology. The method includes: analyzing the changes in phase boundary interface positions during mineral alteration to obtain instantaneous migration rates, and correlating these rates with synchronized mineral alteration data tuples to generate a quantitative relationship between environmental parameters and corrosion rates; based on this quantitative relationship, analyzing the morphological changes of nanospeckled patterns on the mineral sample surface to generate a full-field strain distribution map; and based on the full-field strain distribution map, analyzing multi-parameter evolution relationships through spatiotemporal registration and data correlation to generate a mineral alteration process analysis report. This invention achieves high-precision time alignment of morphology, composition, and environmental parameters through fully automated time-series acquisition and multimodal data synchronization processing, solving the problem of data asynchrony and improving the reliability of dynamic correlation analysis.

Description

Technical Field

[0001] This invention relates to the field of materials characterization technology, and in particular to a scanning electron microscope dynamic simulation method and system for regional mineral alteration processes. Background Technology

[0002] Mineral alteration, as a crucial manifestation of matter and energy exchange within geological systems, is widely present in natural processes such as mineralization, rock weathering, and fluid-rock interactions within the Earth's crust. With the development of micro-nano observation technologies, scanning electron microscopy (SEM), due to its high spatial resolution and multimodal signal acquisition capabilities, has become one of the core tools for studying the evolution of mineral surface microstructures. In recent years, the maturity of environmental scanning electron microscopy (ESEM) has made it possible to observe the dynamic changes of minerals in aqueous or reactive atmospheres in real time under near-in-situ conditions, driving a technological shift from static morphology analysis to dynamic process analysis. Simultaneously, by combining focused ion beam (FIB) processing, energy dispersive spectroscopy (EDS) elemental mapping, and time-series image acquisition techniques, researchers have achieved preliminary qualitative tracking of alteration behavior in localized areas.

[0003] Traditional SEM observations typically rely on intermittent imaging under human intervention, resulting in discontinuous sampling of time-series data, making it difficult to capture rapidly occurring interface migration events. Furthermore, multi-source data (such as morphology, composition, and environmental parameters) lack strict time synchronization and spatial registration mechanisms, limiting the accuracy of cross-modal correlation analysis. Existing studies generally neglect the evolution of the strain field in the micro-regions of materials during the alteration process and have failed to establish a full-field quantitative strain characterization method based on nanospeckle correlation technology, leading to insufficient understanding of the mechanisms of alteration-induced stress accumulation and fracture precursors. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a scanning electron microscope dynamic simulation method for regional mineral alteration processes to solve the problems of low cross-modal correlation accuracy and difficulty in quantitatively characterizing the evolution of micro-region strain fields.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a scanning electron microscope dynamic simulation method for regional mineral alteration processes, which includes micro-nano processing of mineral samples and data analysis through a scanning electron microscope environmental chamber to generate a spatiotemporal reference initial dataset.

[0008] Based on the initial spatiotemporal reference dataset, a fully automated time-series acquisition method is used to synchronously acquire multimodal mineral alteration data and generate synchronized mineral alteration data tuples.

[0009] The changes in the position of the phase boundary interface during the mineral alteration process are analyzed to obtain the instantaneous migration rate, which is then correlated with the synchronized mineral alteration data tuple to generate a quantitative relationship between environmental parameters and corrosion rate.

[0010] Based on the quantitative relationship between environmental parameters and corrosion rate, the morphological changes of nanospeck patterns on the surface of mineral samples are analyzed to generate a full-field strain distribution map.

[0011] Based on the full-field strain distribution map, the evolution relationship of multiple parameters is analyzed through spatiotemporal registration and data correlation to generate a mineral alteration process analysis report.

[0012] As a preferred embodiment of the scanning electron microscope dynamic simulation method for the regional mineral alteration process described in this invention, the micro-nano processing of the mineral sample refers to performing three-stage precision polishing and micro-nano processing on the regional mineral sample by focused ion beam vapor deposition, and obtaining nano-spot patterns on the surface of the regional mineral sample through a deposition process to generate a functionalized mineral sample.

[0013] As a preferred embodiment of the scanning electron microscope (SEM) dynamic simulation method for regional mineral alteration processes described in this invention, the steps for generating a spatiotemporal reference initial dataset through data analysis in a scanning electron microscope environmental chamber are as follows:

[0014] Functionalized mineral samples are positioned in a vacuum isolation environment via a vacuum sample transfer node and installed in a scanning electron microscope environment chamber to generate a parameter-responsive mineral sample assembly.

[0015] Based on the parameter response mineral sample assembly, high-resolution backscattered electron images and energy spectrum elemental distribution data were acquired, and environmental pH and temperature baseline values ​​were obtained to generate an initial spatiotemporal baseline dataset.

[0016] As a preferred embodiment of the scanning electron microscopy dynamic simulation method for regional mineral alteration processes described in this invention, the steps of acquiring the initial dataset based on a spatiotemporal reference using a fully automated time-series acquisition method are as follows:

[0017] Based on the initial dataset of the spatiotemporal reference, a time series acquisition task is created, acquisition task parameters are set, and the configured acquisition task parameters are compiled into a machine-executable instruction set;

[0018] The machine executes a set of machine-executable instructions, acquires backscattered electron images and energy spectrum elemental characteristic peak intensities through three acquisition channels, and binds them with timestamp markers to generate a raw data set with timestamps.

[0019] As a preferred embodiment of the scanning electron microscope dynamic simulation method for regional mineral alteration processes described in this invention, the steps for synchronously acquiring multimodal mineral alteration data and generating synchronized mineral alteration data tuples are as follows:

[0020] The original dataset is preprocessed, and the preprocessed original datasets under the same timestamp are encapsulated to generate a time-series standardized data packet queue.

[0021] Based on a time-series standardized data packet queue, synchronized mineral alteration data tuples are generated through integrity verification, numerical rationality comparison, and logical consistency verification.

[0022] As a preferred embodiment of the scanning electron microscopy dynamic simulation method for the regional mineral alteration process described in this invention, the steps of analyzing the phase boundary interface position changes during the mineral alteration process, obtaining the instantaneous migration rate, and correlating it with synchronized mineral alteration data tuples to generate a quantitative relationship between environmental parameters and corrosion rate are as follows.

[0023] Based on synchronized mineral alteration data tuples, the phase boundary interface contours between regional mineral samples and alteration products are extracted through edge detection and image segmentation to generate a dataset of phase boundary interface position changes.

[0024] Based on the dataset of phase boundary interface position changes, information analysis was performed through alteration kinetics, and the instantaneous migration rate formula was calculated using the image calibration coefficients of scanning electron microscopy.

[0025] The influence of temperature and pH values ​​on instantaneous migration rate in the synchronized raw data tuples was analyzed to obtain a quantitative relationship between environmental parameters and corrosion rate.

[0026] As a preferred embodiment of the scanning electron microscopy dynamic simulation method for the regional mineral alteration process described in this invention, the steps of analyzing the morphological changes of the nanospeckled patterns on the mineral sample surface and generating a full-field strain distribution map based on the quantitative relationship between environmental parameters and corrosion rate are as follows.

[0027] Based on the quantitative relationship between environmental parameters and corrosion rate, the nanospeck pattern and backscattered electron image are combined, and the displacement offset of the nanospeck pattern is calculated by a gray-level cross-correlation matching algorithm.

[0028] Based on the displacement offset of the nanospeck pattern, the normal strain component and shear strain component at each time point are derived through spatial differential operation to generate full-field strain distribution data.

[0029] The full-field strain distribution data is mapped to the corresponding color, spatially superimposed and fused with the backscattered electron image, and then associated and labeled with temperature and pH values ​​to generate a full-field strain distribution map.

[0030] As a preferred embodiment of the scanning electron microscopy dynamic simulation method for regional mineral alteration processes described in this invention, the steps of basing the simulation on the full-field strain distribution map and correlating it with spatiotemporal data are as follows:

[0031] The full-field strain distribution map and the synchronized mineral alteration data tuples are time-stamp aligned and spatial coordinates are unified to generate a spatiotemporally registered multi-parameter fusion dataset of mineral alteration.

[0032] Extract the key parameter sequences of mineral alteration from the spatiotemporally registered multi-parameter fusion dataset of mineral alteration, calculate the correlation coefficient, and generate a multi-parameter correlation analysis dataset.

[0033] As a preferred embodiment of the scanning electron microscopy dynamic simulation method for regional mineral alteration processes described in this invention, the steps for analyzing multi-parameter evolution relationships and generating a mineral alteration process analysis report are as follows:

[0034] Based on the multi-parameter correlation analysis dataset, the key parameter sequence of mineral alteration is smoothed by the moving average algorithm, and the key turning point is identified by the change point detection algorithm to generate multi-parameter evolution relationship analysis results.

[0035] The results of multi-parameter evolution relationship analysis are combined with the full-field strain distribution map and backscattered electron image to generate a mineral alteration process analysis report.

[0036] Secondly, this invention provides a scanning electron microscope dynamic simulation system for regional mineral alteration processes, comprising,

[0037] The mineral processing module is used for micro- and nano-processing of mineral samples and data analysis through a scanning electron microscope environmental chamber to generate an initial spatiotemporal reference dataset.

[0038] The data acquisition module is used to synchronously acquire multimodal mineral alteration data based on the spatiotemporal reference initial dataset using a fully automatic time-series acquisition method, and generate synchronized mineral alteration data tuples.

[0039] The alteration analysis module is used to analyze the changes in the position of the phase boundary interface during the mineral alteration process, obtain the instantaneous migration rate, and correlate it with the synchronized mineral alteration data tuple to generate a quantitative relationship between environmental parameters and the corrosion rate.

[0040] The strain map generation module is used to analyze the morphological changes of nanospeck patterns on the surface of mineral samples based on the quantitative relationship between environmental parameters and corrosion rate, and to generate a full-field strain distribution map.

[0041] The report generation module is used to analyze the evolution relationship of multiple parameters based on the full-field strain distribution map through spatiotemporal registration and data correlation, and generate a mineral alteration process analysis report.

[0042] The beneficial effects of this invention are as follows: by fully automatic time-series acquisition and multimodal data synchronization processing, high-precision time alignment of morphology, composition and environmental parameters is achieved, solving the problem of data asynchrony and improving the reliability of dynamic correlation analysis; by combining nanospeckled speckle with gray-scale cross-correlation algorithm, quantitative visualization of micro-area strain field is achieved, revealing the mechanical response mechanism in the alteration process and enhancing the ability to predict fracture precursors. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart of a scanning electron microscope (SEM) dynamic simulation method for regional mineral alteration processes.

[0045] Figure 2 This is a schematic diagram of a scanning electron microscope dynamic simulation system for regional mineral alteration processes.

[0046] Figure 3 This is a flowchart for fully automated timing data acquisition.

[0047] Figure 4 A flowchart for generating quantitative relationships and strain distribution diagrams. Detailed Implementation

[0048] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0049] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0050] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0051] Reference Figures 1-4 As one embodiment of the present invention, this embodiment provides a scanning electron microscope dynamic simulation method for regional mineral alteration processes, comprising the following steps:

[0052] S1: Micro-nano processing of mineral samples and data analysis using a scanning electron microscope environmental chamber to generate an initial spatiotemporal reference dataset;

[0053] S1.1: Three-stage precision polishing and micro / nano processing of regional mineral samples are performed by focused ion beam vapor deposition, and nano-speck patterns are obtained on the surface of regional mineral samples through deposition process to generate functionalized mineral samples.

[0054] Furthermore, regional mineral samples were prepared into standard cubic block specimens, and the observation surface was polished in three stages using a high-precision polisher with diamond suspension to obtain a mirror-like observation plane. Micro-nano processing was performed on the polished observation plane using focused ion beam vapor deposition. First, micro-groove structures were sculpted under high acceleration voltage and beam current conditions, and then the conditions were switched to low beam current conditions. Gold particles were deposited in the area around the grooves through vapor deposition and processed to form a nanoscale speckle pattern array. A functionalized mineral sample with surface integrated micro-groove structure and nanoscale speckle pattern array was generated.

[0055] It should be noted that beam current conditions refer to the ion beam current intensity parameters used in focused ion beam processing. High beam current conditions (e.g., 30 kV accelerating voltage combined with 10 nanoamp beam current) are used to achieve high-speed material milling to sculpt microgroove structures, while low beam current conditions (e.g., 1 nanoamp) are used to achieve fine material deposition processing and form nanoscale speckle pattern arrays in specific areas through vapor deposition processes.

[0056] S1.2: Functionalized mineral samples are positioned in a vacuum isolation environment via a vacuum sample transfer node and installed in a scanning electron microscope environment chamber to generate a parameter-responsive mineral sample assembly;

[0057] Furthermore, the functionalized mineral samples are transferred from the atmospheric environment and precisely placed into the vacuum isolation environment through the vacuum sample transfer node, ensuring that the transfer process is completed under vacuum conditions to avoid contamination; in the vacuum isolation environment, the functionalized mineral samples are installed on the sample stage of the scanning electron microscope environmental chamber, and silver wires are connected to realize the electrical connection between the sensor and the dedicated interface of the scanning electron microscope environmental chamber; thus generating a parameter-response mineral sample assembly integrated in the scanning electron microscope environmental chamber and possessing multi-parameter response capabilities.

[0058] S1.3: Based on the parameter response mineral sample assembly, high-resolution backscattered electron images and energy spectrum elemental distribution data are acquired, and environmental pH and temperature reference values ​​are obtained to generate an initial spatiotemporal reference dataset.

[0059] Furthermore, based on the parameter-response mineral sample assembly, the scanning electron microscope (SEM) vacuum pumping subnode is activated to pump the environmental chamber to a high vacuum condition. The SEM electron optical imaging subnode is initialized, and the electron beam current is set to a low current condition (e.g., 100 pA) and the accelerating voltage is set to a medium voltage condition (e.g., 15 kV). The sample stage position is adjusted so that the observation area is located at the electron beam focusing center. The initial observation area coordinates are recorded, and a high-resolution backscattered electron image is acquired. Simultaneously, the energy-dispersive X-ray spectrometer is activated to perform elemental surface distribution scanning in the same area to obtain initial distribution data for multiple elements. The reference readings of the iridium oxide pH sensor and the thin-film thermocouple sensor are read and recorded as pH reference values ​​and temperature reference values, respectively. A spatiotemporal reference initial dataset containing backscattered electron images, energy-dispersive elemental distribution data, temperature reference values, and pH reference values ​​is generated.

[0060] S2: Based on the initial spatiotemporal reference dataset, synchronous acquisition of multimodal mineral alteration data is performed using a fully automated time-series acquisition method to generate synchronized mineral alteration data tuples;

[0061] S2.1: Based on the initial dataset of the spatiotemporal reference, create a time series acquisition task, set the acquisition task parameters, and compile the configured acquisition task parameters into a machine-executable instruction set;

[0062] Specifically, based on the initial observation area coordinates, scanning electron microscope electron optical imaging sub-node parameters, and sensor reference values ​​recorded in the spatiotemporal reference initial dataset, a fully automated time-series acquisition task is created. The acquisition time interval is set to a fixed period (example value 60s, based on the required time resolution to capture the dynamic changes of the mineral alteration process), and the total duration is set to a preset duration (example value 10h, based on the complete cycle of the mineral alteration reaction covered by the experimental plan). The backscattered electron image acquisition mode, energy dispersive X-ray spectrometer point scan mode, iridium oxide pH sensor reading mode, and thin-film thermocouple sensor reading mode are selected. The backscattered electron image acquisition parameters are configured as high resolution mode, low electron beam current condition, and medium accelerating voltage condition. The energy dispersive X-ray spectrometer point scan integration time is configured as a short period, and the sensor data acquisition mode is configured as high-frequency sampling and averaging mode. The configuration parameters are then compiled into a set of low-level machine instructions that can be directly recognized and executed. The instruction set configured for the time-series acquisition task is used as the input for subsequent steps.

[0063] S2.2: Execute the machine-executable instruction set, acquire backscattered electron images and energy spectrum element characteristic peak intensities through three acquisition channels, bind them with timestamp markers, and generate a raw data set with time stamps;

[0064] Specifically, the machine-executable instruction set is executed to generate a hardware synchronization trigger signal, which is simultaneously sent to three acquisition channels via the BNC interface: the first trigger signal drives the scanning electron microscope electron optical imaging sub-node to acquire a single frame of high-resolution backscattered electron image; the second trigger signal activates the energy dispersive X-ray spectrometer to perform fixed-point energy spectrum acquisition at the electron beam focusing center point and generate an array of elemental characteristic peak intensities; the third trigger signal drives the environmental chamber data acquisition card to synchronously read the instantaneous analog signals from the iridium oxide pH sensor and the thin-film thermocouple sensor and convert them into digital values; after receiving the synchronization trigger signal, the three acquisition channels complete data acquisition and add a unified timestamp to the data packet at each acquisition moment; and generate a raw data set of time-stamped backscattered electron image data packets, energy spectrum elemental characteristic peak intensity data packets, and sensor value data packets arranged in time sequence.

[0065] S2.3: Preprocess the original dataset and encapsulate the preprocessed original datasets under the same timestamp to generate a time-series standardized data packet queue;

[0066] Specifically, the raw dataset is preprocessed by extracting timestamps, backscattered electron image matrices, energy-dispersive X-ray spectrometer (EDX-ray) elemental characteristic peak intensity arrays, iridium oxide pH sensor voltage values, and thin-film thermocouple sensor voltage values ​​from each data packet. The iridium oxide pH sensor voltage values ​​are converted to pH values ​​using a pH calibration curve, and the thin-film thermocouple sensor voltage values ​​are converted to temperature values ​​using a temperature calibration curve. The EDX-ray spectrometer elemental characteristic peak intensity arrays are converted to standard elemental characteristic peak intensity arrays using a peak recognition algorithm. Backscattered electron image data, standard elemental characteristic peak intensity arrays, pH values, and temperature values ​​at the same timestamp are encapsulated into structured data nodes in a standardized format. Each structured data node contains a timestamp field, an image data field, an EDX-ray spectrometer data field, a pH data field, and a temperature data field. A standardized data packet queue arranged in time sequence is then generated.

[0067] S2.4: Based on the time-series standardized data packet queue, synchronized mineral alteration data tuples are generated through integrity verification, numerical rationality comparison and logical consistency verification.

[0068] Specifically, based on the time-series standardized data packet queue, an integrity verification process is executed to check whether each standardized data packet completely contains the timestamp field, backscattered electron image data field, energy dispersive spectroscopy (EDS) elemental characteristic peak intensity array field, pH data field, and temperature data field, and to verify whether any field contains null values ​​or abnormal interruptions; a numerical reasonableness comparison is performed: the pH data field value is compared with the theoretical pH range (e.g., 0-14), and the temperature data field value is verified with the experimental temperature range (e.g., room temperature to 500 degrees Celsius) to ensure that all values ​​are within a reasonable range; a logical consistency verification process is implemented to check the continuity and monotonically increasing characteristics of the timestamp field, verify the spatial correspondence between the backscattered electron image data field and the EDS elemental characteristic peak intensity array field, and analyze the physical reasonableness of the changes in the pH data field and the temperature data field; the standardized data packets that pass the verification are reorganized into structured data containing the timestamp field, image data field, EDS data field, pH data field, and temperature data field, generating a complete time-series synchronized mineral alteration data tuple.

[0069] S3: Analyze the changes in the position of the phase boundary interface during the mineral alteration process, obtain the instantaneous migration rate, and correlate it with the synchronized mineral alteration data tuple to generate a quantitative relationship between environmental parameters and corrosion rate.

[0070] S3.1: Based on the synchronized mineral alteration data tuples, the phase boundary interface contour between regional mineral samples and alteration products is extracted through edge detection and image segmentation to generate a dataset of phase boundary interface position changes.

[0071] Furthermore, based on the synchronized mineral alteration data tuples, time-series backscattered electron image data fields are extracted and reassembled sequentially using timestamps to form an image sequence. Subsequent images are then registered with subpixel precision using the initial time point image as a spatial reference. The Canny edge detection algorithm combined with the region growth image segmentation algorithm is used to identify and extract the phase boundary interface contours between the regional mineral sample phases and alteration product phases in the backscattered electron images frame by frame. The contour pixel coordinates are converted into actual physical coordinates using scanning electron microscope image calibration coefficients, and the ratio of the displacement of the phase boundary interface contour at adjacent time points to the acquisition time interval is obtained to obtain the interface migration rate. The phase boundary interface contour coordinates, displacement, and migration rate at each time point are bound to the corresponding timestamp to generate a dataset of phase boundary interface position changes.

[0072] It should be noted that the phase boundary interface position refers to the set of spatial contour coordinates at the interface between the mineral sample phase and the alteration product phase in the characterization region, which is accurately identified and extracted from the backscattered electron image through image processing algorithms. These coordinates are converted into actual physical scales using scanning electron microscope image calibration coefficients, and the coordinate changes, displacement directions, and migration rates are recorded over time to quantify and analyze the dynamic interface evolution behavior of the mineral alteration process.

[0073] S3.2: Based on the dataset of phase boundary interface position changes, information analysis is performed through alteration kinetics, and the instantaneous migration rate is calculated using scanning electron microscopy image calibration coefficients. The formula is as follows:

[0074] ;

[0075] in, Represents the instantaneous migration rate at a given time point. The rate of movement of the temporal boundary interface, This represents the image calibration coefficients of a scanning electron microscope. Indicates a point in time The pixel of the centroid of the phase boundary interface contour coordinate, Indicates a point in time The pixel of the centroid of the phase boundary interface contour coordinate, Indicates a point in time The pixel of the centroid of the phase boundary interface contour coordinate, Indicates a point in time The pixel of the centroid of the phase boundary interface contour coordinate, It represents the time difference between adjacent time points.

[0076] Furthermore, based on the phase boundary interface position change dataset, the phase boundary interface contour coordinate data at each time point is extracted using image processing algorithms. The pixel units of the contour coordinates are converted into actual physical distance units using the image calibration coefficient of scanning electron microscopy. The Euclidean distance between the centroids of the phase boundary interface contours at adjacent time points is obtained as the interface migration distance. The instantaneous migration rate at each time point is obtained by calculating the ratio of the interface migration distance to the acquisition time interval. The instantaneous migration rate is then matched with the timestamps in the phase boundary interface position change dataset.

[0077] It should be noted that the image calibration coefficient of a scanning electron microscope refers to the conversion parameter determined by calibrating a standard sample. It represents the actual physical size corresponding to each pixel in the image and is used to convert the pixel distance measured in the image into the physical distance in the real world. For example, when the calibration coefficient is 0.1 micrometers per pixel, the displacement of 10 pixels in the image corresponds to an actual physical displacement of 1 micrometer.

[0078] S3.3: Analyze the influence of temperature and pH values ​​on instantaneous migration rate in the synchronized original data tuples to obtain a quantitative relationship between environmental parameters and corrosion rate.

[0079] Furthermore, based on the synchronized original data tuples, temperature, pH, and instantaneous migration rate sequences are extracted. A multiple linear regression analysis method is used to establish quantitative relationship nodes with temperature and pH values ​​as independent variables and instantaneous migration rate as the dependent variable. Through the least squares parameter estimation algorithm, the influence coefficients of temperature and pH values ​​on instantaneous migration rate are obtained, generating a quantitative relationship between environmental parameters and corrosion rate.

[0080] It should be noted that the quantitative relationship between environmental parameters and corrosion rate was obtained by fitting the data using the least squares method with temperature and pH values ​​as independent variables and instantaneous migration rate as the dependent variable. This quantitative analysis reveals the synergistic control mechanism of temperature-accelerated chemical reaction kinetics and pH-enhanced solution corrosivity on mineral alteration rate.

[0081] Temperature is positively correlated with instantaneous migration rate; an increase in temperature leads to a higher instantaneous migration rate by accelerating chemical reaction kinetics and mass diffusion processes. pH is negatively correlated with instantaneous migration rate; a decrease in pH leads to a higher instantaneous migration rate by enhancing solution corrosivity and promoting mineral dissolution. Temperature and pH affect instantaneous migration rate synergistically; changes in temperature alter the sensitivity of pH to reaction rate, while changes in pH modulate the intensity of the effect of temperature on the activation energy of the reaction.

[0082] S4: Based on the quantitative relationship between environmental parameters and corrosion rate, analyze the morphological changes of nanospeck patterns on the surface of mineral samples and generate a full-field strain distribution map.

[0083] S4.1: Based on the quantitative relationship between environmental parameters and corrosion rate, the formula for calculating the displacement offset of the nanospeckle pattern by combining the nanospeckle pattern and backscattered electron image using a gray-level cross-correlation matching algorithm is as follows:

[0084] ;

[0085] in, This indicates the displacement of the nanospeck pattern. Represents the image calibration coefficients. Indicates the nanospeck pattern in coordinates grayscale value at that location Represents the backscattered electron image in coordinates grayscale value at that location This represents the average grayscale value of the nanospeck pattern. This represents the average grayscale value of the backscattered electron image. Indicates in The offset to be attempted in the direction, Indicates in The offset to be attempted in the direction.

[0086] Furthermore, based on the quantitative relationship between environmental parameters and corrosion rate, temperature and pH values ​​are extracted from the synchronized raw data tuple as input parameters. The nanospeckle pattern is spatially aligned with the backscattered electron image at the corresponding time point to ensure that they are in the same observation area and scale. A gray-level cross-correlation matching algorithm is applied, and by setting the same size calculation window on the nanospeckle pattern and the backscattered electron image, the maximum cross-correlation coefficient of the gray-level distribution in the two images is calculated window by window. The displacement offset of the nanospeckle pattern is determined according to the peak position of the cross-correlation coefficient. The displacement offset of the nanospeckle pattern is dynamically corrected by combining the temperature and pH values.

[0087] S4.2: Based on the displacement offset of the nanospeck pattern, the normal strain component and shear strain component at each time point are derived through spatial differential operation to generate full-field strain distribution data;

[0088] Furthermore, based on the displacement offset of the nanospeckled pattern, the displacement offset data at each time point is organized into a two-dimensional displacement field. Then, spatial differential operation is applied, and the displacement gradient is obtained using the central difference method. For example, for the normal strain component in the x-direction, it is obtained by obtaining the ratio of the difference in displacement of adjacent points in the x-direction to the grid spacing; for the normal strain component in the y-direction, it is obtained by obtaining the ratio of the difference in displacement of adjacent points in the y-direction to the grid spacing; for the shear strain component, it is obtained by obtaining the sum of the rate of change of displacement in the x-direction in the y-direction and the rate of change of displacement in the y-direction in the x-direction. The normal strain component and shear strain component at each time point are integrated into full-field strain distribution data containing strain data of all points.

[0089] It should be noted that organizing the displacement offset data at each time point into a two-dimensional displacement field is achieved by spatial grid mapping, discretizing the sample surface into a regular grid lattice, and arranging the displacement offset at each grid node into a two-dimensional matrix according to its spatial coordinate position on the mineral sample surface, thereby constructing a complete two-dimensional displacement field.

[0090] Full-field strain distribution data refers to a structured dataset that fully characterizes the strain state of each point on the sample surface at a specific time point, generated after processing the displacement offset of the nanospeckled pattern through spatial differential operations. It includes the normal strain and shear strain components of all calculated points on the sample surface at each time point, records the strain values ​​at all spatial locations through a gridded organization, and is strictly bound to timestamps, forming four-dimensional data (three spatial dimensions + one temporal dimension) that can describe the evolution of the strain field over time. This data is used to reveal the spatial heterogeneity and dynamic evolution of the mechanical response during mineral alteration.

[0091] S4.3: Map the full-field strain distribution data to the corresponding color, spatially overlay and fuse it with the backscattered electron image, and associate and label it with the temperature and pH values ​​to generate a full-field strain distribution map.

[0092] Furthermore, the normal strain component and shear strain component values ​​in the full-field strain distribution data are converted into corresponding color values ​​using a pseudo-color encoding method (e.g., using a rainbow color system to map low strain values ​​to blue and high strain values ​​to red), generating a color strain distribution map. The color strain distribution map is then spatially registered with the backscattered electron image at the corresponding time point, and a semi-transparent overlay method is used to achieve the fusion display of morphology and strain field. Temperature and pH values ​​at the corresponding time points are extracted from the synchronized mineral alteration process analysis data tuple, and environmental parameter annotation boxes are created in a specified area (e.g., the upper right corner) of the fused image using an annotation generator. The output is a full-field strain distribution map that simultaneously includes strain color mapping, backscattered electron image background, temperature value annotation, and pH value annotation.

[0093] S5: Based on the full-field strain distribution map, through spatiotemporal registration and data correlation, analyze the evolution relationship of multiple parameters and generate a mineral alteration process analysis report.

[0094] S5.1: The full-field strain distribution map and the synchronized mineral alteration data tuple are time-stamp aligned and spatial coordinates are unified to generate a spatiotemporally registered multi-parameter fusion dataset of mineral alteration.

[0095] Furthermore, the timestamp field of the full-field strain distribution map is precisely matched and aligned with the timestamp field of the synchronized mineral alteration data tuple to ensure that each data point has a completely consistent time identifier. The spatial coordinate nodes of the full-field strain distribution map and the pixel coordinates of the backscattered electron image data field in the synchronized mineral alteration data tuple are unified. An affine transformation algorithm is used to map the spatial grid coordinates of the strain distribution map to the pixel coordinate system of the backscattered electron image to achieve spatial correspondence. The matched full-field strain distribution data, backscattered electron image data, energy spectrum element characteristic peak intensity array, temperature value, and pH value are integrated into a structured dataset according to the time series. Each structured dataset contains a unified timestamp, unified spatial coordinates, and complete multi-parameter data fields. A spatiotemporal registered mineral alteration multi-parameter fusion dataset containing time dimension, spatial dimension, and parameter dimension is generated.

[0096] S5.2: Extract the key parameter sequences of mineral alteration from the spatiotemporally registered multi-parameter fusion dataset of mineral alteration, calculate the correlation coefficient, and generate a multi-parameter correlation analysis dataset;

[0097] Furthermore, key mineral alteration parameter sequences were extracted from the spatiotemporally registered multi-parameter fusion dataset of mineral alteration, including the maximum principal strain value sequence, average strain value sequence, temperature value sequence, pH value sequence, and specific element intensity sequences from the energy dispersive spectroscopy (EDS) element characteristic peak intensity array. The Pearson correlation coefficient calculation method was used to calculate the correlation coefficients between key parameter sequences pairwise, including strain-temperature correlation, strain-pH correlation, temperature-pH correlation, strain-iron element intensity correlation, and strain-magnesium element intensity correlation. All the calculated correlation coefficients and corresponding parameter pair information were integrated into a structured data table to generate a multi-parameter correlation analysis dataset containing parameter names, correlation coefficient values, and significance indicators.

[0098] The formula for calculating the correlation coefficient is:

[0099] ;

[0100] in, Represents the correlation coefficient. Represents the first variable's first... One observation value, The second variable represents the first One observation value, This represents the arithmetic mean of all observations of the first variable. This represents the arithmetic mean of all observations of the second variable. This indicates the total number of data points involved in the calculation.

[0101] It should be noted that the corresponding parameters refer to the five sets of parameter pairings specified in the process of generating the multi-parameter correlation analysis dataset: including the pairing of the maximum principal strain value sequence / average strain value sequence with the temperature value sequence, the pairing of the maximum principal strain value sequence / average strain value sequence with the pH value sequence, the pairing of the temperature value sequence with the pH value sequence, the pairing of the maximum principal strain value sequence / average strain value sequence with the characteristic peak intensity sequence of iron, and the pairing of the maximum principal strain value sequence / average strain value sequence with the characteristic peak intensity sequence of magnesium.

[0102] S5.3: Based on the multi-parameter correlation analysis dataset, the key parameter sequence of mineral alteration is smoothed by the moving average algorithm, and the key turning point is identified by the change point detection algorithm to generate multi-parameter evolution relationship analysis results.

[0103] Furthermore, based on the multi-parameter correlation analysis dataset, key mineral alteration parameter sequences (including maximum principal strain value sequence, average strain value sequence, temperature value sequence, and pH value sequence) are extracted. A moving average algorithm is used to smooth the key parameter sequences, setting the sliding window width and obtaining the arithmetic mean of the data within the window to generate a smoothed parameter sequence. A change point detection algorithm is applied to analyze the smoothed parameter sequence, and statistical tests are used to identify key inflection points in the parameter evolution process. The location coordinates and change magnitude of each inflection point are recorded, generating a multi-parameter evolution relationship analysis result containing the smoothed parameter sequence, inflection point location markers, and descriptions of change characteristics.

[0104] It should be noted that the results of the multi-parameter evolution relationship analysis are comprehensive data generated by smoothing the key parameter sequences in the mineral alteration process through moving average and detecting and analyzing change points. The results include the smoothed parameter evolution curves, the identified key inflection point locations and variation characteristics, as well as the co-evolution laws among parameters. This data is used to reveal the dynamic coupling mechanism between temperature and pH environmental parameters and mineral strain response, and to provide a quantitative basis for understanding the environmental control factors of the alteration process.

[0105] S5.4: Integrate the results of multi-parameter evolution relationship analysis with the full-field strain distribution map and backscattered electron image to generate a mineral alteration process analysis report.

[0106] Furthermore, the key evolution charts in the multi-parameter evolution relationship analysis results are time-stamped and spatially registered with the full-field strain distribution map sequence and backscattered electron image sequence to generate multi-modal data. The multi-parameter evolution relationship charts, full-field strain distribution maps and backscattered electron images are then integrated according to logical correlation and formatted to generate a mineral alteration process analysis report.

[0107] This embodiment also provides a scanning electron microscope dynamic simulation system for regional mineral alteration processes, including:

[0108] The mineral processing module is used for micro- and nano-processing of mineral samples and data analysis through a scanning electron microscope environmental chamber to generate an initial spatiotemporal reference dataset.

[0109] The data acquisition module is used to synchronously acquire multimodal mineral alteration data based on the spatiotemporal reference initial dataset using a fully automatic time-series acquisition method, and generate synchronized mineral alteration data tuples.

[0110] The alteration analysis module is used to analyze the changes in the position of the phase boundary interface during the mineral alteration process, obtain the instantaneous migration rate, and correlate it with the synchronized mineral alteration data tuple to generate a quantitative relationship between environmental parameters and the corrosion rate.

[0111] The strain map generation module is used to analyze the morphological changes of nanospeck patterns on the surface of mineral samples based on the quantitative relationship between environmental parameters and corrosion rate, and to generate a full-field strain distribution map.

[0112] The report generation module is used to analyze the evolution relationship of multiple parameters based on the full-field strain distribution map through spatiotemporal registration and data correlation, and generate a mineral alteration process analysis report.

[0113] This embodiment also provides a computer device applicable to the scanning electron microscope dynamic simulation method for regional mineral alteration processes, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the scanning electron microscope dynamic simulation method for regional mineral alteration processes as proposed in the above embodiment.

[0114] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0115] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the scanning electron microscope dynamic simulation method for realizing regional mineral alteration processes as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0116] In summary, this invention achieves high-precision time alignment of morphology, composition, and environmental parameters through fully automated time-series acquisition and multimodal data synchronization processing, solving the problem of data asynchrony and improving the reliability of dynamic correlation analysis; and through nanospeckled speckle combined with gray-scale cross-correlation algorithm, it realizes quantitative visualization of micro-region strain field, reveals the mechanical response mechanism in the alteration process, and enhances the predictive ability of fracture precursors.

[0117] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A scanning electron microscopy dynamic simulation method for regional mineral alteration processes, characterized in that: include, Mineral samples were micro- and nano-fabricated, and data analysis was performed using a scanning electron microscope environmental chamber to generate an initial spatiotemporal reference dataset. Based on the initial spatiotemporal reference dataset, a fully automated time-series acquisition method is used to synchronously acquire multimodal mineral alteration data and generate synchronized mineral alteration data tuples. The changes in the position of the phase boundary interface during the mineral alteration process are analyzed to obtain the instantaneous migration rate, which is then correlated with the synchronized mineral alteration data tuple to generate a quantitative relationship between environmental parameters and corrosion rate. Based on the quantitative relationship between environmental parameters and corrosion rate, the morphological changes of nanospeck patterns on the surface of mineral samples are analyzed to generate a full-field strain distribution map. Based on the full-field strain distribution map, through spatiotemporal registration and data correlation, the evolution relationship of multiple parameters is analyzed, and a mineral alteration process analysis report is generated; The following steps describe the quantitative relationship between environmental parameters and corrosion rate, analyzing the morphological changes of nanospeckled patterns on the surface of mineral samples, and generating a full-field strain distribution map. Based on the quantitative relationship between environmental parameters and corrosion rate, the nanospeck pattern and backscattered electron image are combined, and the displacement offset of the nanospeck pattern is calculated by a gray-level cross-correlation matching algorithm. Based on the displacement offset of the nanospeck pattern, the normal strain component and shear strain component at each time point are derived through spatial differential operation to generate full-field strain distribution data. The full-field strain distribution data is mapped to the corresponding color, spatially superimposed and fused with the backscattered electron image, and then associated and labeled with temperature and pH values ​​to generate a full-field strain distribution map.

2. The scanning electron microscopy dynamic simulation method for regional mineral alteration processes as described in claim 1, characterized in that: The aforementioned micro-nano processing of mineral samples refers to performing three-stage precision polishing and micro-nano processing on regional mineral samples using focused ion beam vapor deposition, and obtaining nano-speck patterns on the surface of regional mineral samples through deposition processes to generate functionalized mineral samples.

3. The scanning electron microscopy dynamic simulation method for regional mineral alteration processes as described in claim 2, characterized in that: The steps for generating an initial spatiotemporal reference dataset through data analysis using a scanning electron microscope environmental chamber are as follows. Functionalized mineral samples are positioned in a vacuum isolation environment via a vacuum sample transfer node and installed in a scanning electron microscope environmental chamber to generate a parameter-responsive mineral sample assembly. Based on the parameter response mineral sample assembly, high-resolution backscattered electron images and energy spectrum elemental distribution data were acquired, and environmental pH and temperature baseline values ​​were obtained to generate an initial spatiotemporal baseline dataset.

4. The scanning electron microscopy dynamic simulation method for regional mineral alteration processes as described in claim 3, characterized in that: The initial dataset based on the spatiotemporal reference is acquired using a fully automated time-series acquisition method, with the following steps. Based on the initial dataset of the spatiotemporal reference, a time series acquisition task is created, acquisition task parameters are set, and the configured acquisition task parameters are compiled into a machine-executable instruction set; The machine executes a set of machine-executable instructions, acquires backscattered electron images and energy spectrum elemental characteristic peak intensities through three acquisition channels, and binds them with timestamp markers to generate a raw data set with timestamps.

5. The scanning electron microscopy dynamic simulation method for regional mineral alteration processes as described in claim 4, characterized in that: The steps for synchronously acquiring multimodal mineral alteration data and generating synchronized mineral alteration data tuples are as follows. The original dataset is preprocessed, and the preprocessed original datasets under the same timestamp are encapsulated to generate a time-series standardized data packet queue. Based on a time-series standardized data packet queue, synchronized mineral alteration data tuples are generated through integrity verification, numerical rationality comparison, and logical consistency verification.

6. The scanning electron microscope dynamic simulation method for regional mineral alteration processes as described in claim 5, characterized in that: The steps for analyzing the phase boundary interface position changes during the mineral alteration process, obtaining the instantaneous migration rate, and correlating it with synchronized mineral alteration data tuples to generate a quantitative relationship between environmental parameters and corrosion rate are as follows. Based on synchronized mineral alteration data tuples, the phase boundary interface contours between regional mineral samples and alteration products are extracted through edge detection and image segmentation to generate a dataset of phase boundary interface position changes. Based on the dataset of phase boundary interface position changes, information analysis was performed through alteration kinetics, and the instantaneous migration rate formula was calculated using the image calibration coefficients of scanning electron microscopy. The influence of temperature and pH values ​​on instantaneous migration rate in the synchronized raw data tuples was analyzed to obtain a quantitative relationship between environmental parameters and corrosion rate.

7. The scanning electron microscopy dynamic simulation method for regional mineral alteration processes as described in claim 1, characterized in that: The steps for establishing a data association based on the full-field strain distribution map through spatiotemporal registration are as follows: The full-field strain distribution map and the synchronized mineral alteration data tuples are time-stamp aligned and spatial coordinates are unified to generate a spatiotemporally registered multi-parameter fusion dataset of mineral alteration. Extract the key parameter sequences of mineral alteration from the spatiotemporally registered multi-parameter fusion dataset of mineral alteration, calculate the correlation coefficient, and generate a multi-parameter correlation analysis dataset.

8. The scanning electron microscopy dynamic simulation method for regional mineral alteration processes as described in claim 7, characterized in that: The steps for analyzing multi-parameter evolution relationships and generating a mineral alteration process analysis report are as follows. Based on the multi-parameter correlation analysis dataset, the key parameter sequence of mineral alteration is smoothed by the moving average algorithm, and the key turning point is identified by the change point detection algorithm to generate multi-parameter evolution relationship analysis results. The results of multi-parameter evolution relationship analysis are combined with the full-field strain distribution map and backscattered electron image to generate a mineral alteration process analysis report.

9. A scanning electron microscope (SEM) dynamic simulation system for regional mineral alteration processes, based on the SEM dynamic simulation method for regional mineral alteration processes according to any one of claims 1 to 8, characterized in that: include, The mineral processing module is used for micro- and nano-processing of mineral samples and data analysis through a scanning electron microscope environmental chamber to generate an initial spatiotemporal reference dataset. The data acquisition module is used to synchronously acquire multimodal mineral alteration data based on the spatiotemporal reference initial dataset using a fully automatic time-series acquisition method, and generate synchronized mineral alteration data tuples. The alteration analysis module is used to analyze the changes in the position of the phase boundary interface during the mineral alteration process, obtain the instantaneous migration rate, and correlate it with the synchronized mineral alteration data tuple to generate a quantitative relationship between environmental parameters and the corrosion rate. The strain map generation module is used to analyze the morphological changes of nanospeck patterns on the surface of mineral samples based on the quantitative relationship between environmental parameters and corrosion rate, and to generate a full-field strain distribution map. The report generation module is used to analyze the evolution relationship of multiple parameters based on the full-field strain distribution map through spatiotemporal registration and data correlation, and generate a mineral alteration process analysis report. The following steps describe the quantitative relationship between environmental parameters and corrosion rate, analyzing the morphological changes of nanospeckled patterns on the surface of mineral samples, and generating a full-field strain distribution map. Based on the quantitative relationship between environmental parameters and corrosion rate, the nanospeck pattern and backscattered electron image are combined, and the displacement offset of the nanospeck pattern is calculated by a gray-level cross-correlation matching algorithm. Based on the displacement offset of the nanospeck pattern, the normal strain component and shear strain component at each time point are derived through spatial differential operation to generate full-field strain distribution data. The full-field strain distribution data is mapped to the corresponding color, spatially superimposed and fused with the backscattered electron image, and then associated and labeled with temperature and pH values ​​to generate a full-field strain distribution map.

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