Gold mineral exploration method, device and system and storage medium

Abstract

The invention provides a gold mineral exploration method, device and system and a storage medium. Relates to the technical field of mineral exploration. The gold mineral exploration method comprises the following steps: acquiring a backscattered electron image of a sample to be detected, and screening bright mineral phase particles based on a preset gray threshold; performing energy spectrum analysis to obtain an actually measured energy spectrum corresponding to each bright mineral phase particle; and determining the gold-bearing minerals in the to-be-detected sample according to the actually measured energy spectrum. According to the method provided by the invention, the gold-bearing mineral is determined by analyzing the actually measured energy spectrum, and the identification accuracy and the automation efficiency of the micro-fine gold are improved; the method can effectively solve the problem of missed judgment caused by energy spectrum signal mixing and low matching degree due to fine or wrapped particles in a traditional method, so that more accurate process mineralogy data is obtained, and a reliable basis is provided for formulating an efficient dressing and smelting recovery scheme.

Description

Technical Field

[0001] This invention relates to the field of mineral exploration technology, and more specifically, to a method, apparatus, system, and storage medium for gold mineral exploration. Background Technology

[0002] In the field of modern mineral resource development, process mineralogy research serves as a crucial bridge connecting geological exploration and metallurgical engineering. Through detailed analysis of the types, contents, grain size distribution, intergrowth relationships, and symbiotic combinations of minerals in ores, it provides a fundamental basis for developing scientific and efficient mineral processing and metal extraction schemes. For precious metal minerals such as gold, which are typically found in a highly dispersed state within ores, accurately identifying the occurrence state of gold minerals is crucial for the comprehensive evaluation and efficient recovery of mineral resources. Gold minerals often exist as micron- or even nanometer-sized particles, making their identification and statistical analysis extremely difficult. Therefore, developing rapid and accurate automated mineral analysis technologies, especially methods for detecting fine-grained gold minerals, has always been a key technological goal in this field.

[0003] Currently, the industry commonly uses automated mineral analysis systems (such as QEMSCAN or MLA) based on scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) for process mineralogy research. The workflow of this technique typically involves first acquiring a glossy image of the ore sample using backscattered electron (BSE) imaging. Since the average atomic number of a mineral is positively correlated with its grayscale value in the backscattered image, gold minerals with higher average atomic numbers will appear as bright, prominent particles. The operator sets a high grayscale threshold, for example, 200, and the system automatically filters out all bright mineral phase particles in the image with grayscale values ​​higher than this threshold as potential targets. Subsequently, the system automatically drives an electron beam to perform energy dispersive spectroscopy analysis on each of these selected bright mineral phase particles to obtain their chemical composition information. The acquired energy dispersive spectral data is then compared with the system's built-in standard mineral energy dispersive spectral database, and the mineral type of the particle is determined through matching and identification.

[0004] However, the aforementioned traditional automated detection methods have inherent technical limitations when processing fine-grained gold minerals. In nature, gold minerals are often encased within other minerals (such as pyrite and quartz). When the gold particles are very small (e.g., smaller than the excitation volume of an electron beam), the signal obtained from energy dispersive spectroscopy (EDS) is no longer a pure gold mineral signal. While the electron beam excites the target particle, it inevitably also excites signals from the surrounding matrix minerals, resulting in a mixed EDS spectrum containing both gold and matrix mineral elements. When the system compares this mixed spectrum with the pure standard gold mineral EDS spectrum in the database, it often fails to match due to low similarity, thus preventing the system from identifying the particle as a gold mineral.

[0005] Therefore, the fundamental problem with existing technologies lies in their over-reliance on full-spectrum matching for identification logic. This makes them severely inadequate in identifying fine-grained gold or complex gold minerals closely associated with other metals. This technological limitation directly leads to a large number of fine-grained gold particles being misidentified or missed during automated testing, resulting in significant deviations between the obtained key process mineralogical parameters, such as mineral type, content, and particle size distribution, and the actual situation. This deviation not only affects the accurate assessment of ore value but may also mislead the selection of subsequent beneficiation and smelting processes and the determination of process parameters, thereby adversely affecting gold recovery rates and creating a technological bottleneck.

[0006] In view of this, the present invention is hereby proposed. Summary of the Invention

[0007] The purpose of this invention is to provide a method, apparatus, system, and storage medium for gold mineral exploration. The gold mineral exploration method can accurately identify fine-grained gold that is missed by traditional methods due to signal mixing by analyzing measured energy spectra, thus providing more reliable data for beneficiation schemes.

[0008] In order to achieve the above-mentioned objectives of the present invention, the following technical solution is adopted: In a first aspect, the present invention provides a method for gold mineral exploration, comprising: Acquire backscattered electron images of the sample to be tested, and filter out bright mineral phase particles in the backscattered electron images based on a preset grayscale threshold; Energy dispersive spectroscopy (EDS) analysis was performed on the selected bright mineral phase particles to obtain the measured EDS for each bright mineral phase particle. The gold-bearing minerals in the sample to be tested are determined based on the measured energy spectrum.

[0009] In an optional implementation, the step of filtering out bright mineral phase particles in the backscattered electron image of the sample to be tested based on a preset grayscale threshold includes: The backscattered electron image is binarized. Phases with a gray level not less than the preset gray level threshold in the binarized backscattered electron image are selected as bright mineral phase particles.

[0010] In an optional implementation, the preset grayscale threshold is 200.

[0011] In an optional implementation, before filtering out bright mineral phase particles in the backscattered electron image based on a preset grayscale threshold, the method further includes: The backscattered electron image is standardized, including: The backscattered electron image is standardized by setting the brightness of the gold particles to 255 and the grayscale value of the epoxy resin background to 10.

[0012] In an optional implementation, determining the gold-bearing mineral in the sample based on the measured energy spectrum includes: The measured energy spectrum was compared with the energy spectrum of gold minerals in the theoretical synthesis spectrum of standard minerals to obtain the contrast result corresponding to the bright mineral phase particles. If the contrast result is higher than the preset energy spectrum threshold, the bright mineral phase particles are marked as gold-bearing minerals.

[0013] In an optional implementation, after comparing the measured energy spectrum with the energy spectrum of gold minerals in the theoretical synthesis spectrum of standard minerals to obtain the contrast result, the method further includes: If the contrast result is not higher than the preset energy spectrum threshold, then the signal intensity value of the gold characteristic peak in the gold element characteristic region in the measured energy spectrum of the bright mineral phase particles is obtained. If the signal strength value is greater than a preset strength threshold, the bright mineral phase particles are determined to be gold-containing particles.

[0014] In an optional embodiment, after obtaining the signal intensity value of the gold characteristic peak in the gold characteristic region of the measured energy spectrum of the bright mineral phase particles, the method further includes: If the signal strength value is not greater than the preset strength threshold, then the bright mineral phase particles are determined not to be gold-containing particles.

[0015] In a second aspect, the present invention provides a gold mineral exploration device, comprising: The screening module is used to screen out bright mineral phase particles in the backscattered electron image of the sample to be tested based on a preset grayscale threshold. The analysis module is used to perform energy dispersive spectroscopy analysis on the selected bright mineral phase particles to obtain the measured energy spectrum corresponding to each bright mineral phase particle. The measurement module is used to determine the gold-bearing minerals in the sample to be tested based on the measured energy spectrum.

[0016] Thirdly, the present invention provides a computer device including a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the gold mineral exploration method described in any of the foregoing embodiments.

[0017] Fourthly, the present invention provides a computer storage medium storing a computer program, which, when executed on a processor, implements the gold mineral exploration method according to any one of the foregoing embodiments.

[0018] This invention provides a method, apparatus, system, and storage medium for gold mineral exploration. The method involves acquiring backscattered electron images to screen bright mineral phase particles, performing energy dispersive spectroscopy (EDS) analysis on these particles, and finally determining the gold-bearing mineral based on the measured EDS, resulting in significant beneficial effects.

[0019] First, this method improves the accuracy of identifying gold minerals, especially fine-grained gold minerals. In traditional analysis, when gold particles are extremely fine and encapsulated by other minerals, the acquired energy dispersive spectral signals are severely interfered with by the surrounding matrix minerals, leading to a reduced match with the standard gold mineral energy dispersive spectral data and resulting in missed detections. This method, however, identifies gold-bearing minerals by analyzing measured energy dispersive spectral data. It can identify particles that exhibit only the characteristic signals of gold in the mixed energy dispersive spectral data, thus avoiding omissions caused by small particle size or encapsulation. This results in more accurate statistical results regarding parameters such as gold mineral content, particle size, and embedding characteristics, better representing the true condition of the sample.

[0020] Secondly, this method improves the overall efficiency of automated testing. By accurately and automatically identifying fine gold particles that are easily missed by traditional methods, it reduces reliance on manual review and searching. The entire testing process is smoother, shortening the time from sample preparation to data output, making rapid and accurate analysis of large numbers of samples possible.

[0021] Ultimately, by providing more accurate and comprehensive process mineralogical data, this method offers more reliable technical support for subsequent gold ore beneficiation process design and production optimization. Accurately identifying the occurrence state of fine-grained gold helps in developing more targeted recovery schemes, thus laying a solid foundation for improving the final gold recovery rate. Attached Figure Description

[0022] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of the hardware operating environment involved in an embodiment of the gold mineral exploration method of the present invention; Figure 2 This is a schematic flowchart of Embodiment 1 of the gold mineral exploration method of the present invention; Figure 3 This is a detailed flowchart of step S100 in Embodiment 2 of the gold mineral exploration method of the present invention; Figure 4This is a detailed flowchart of step S300 in Embodiment 3 of the gold mineral exploration method of the present invention; Figure 5 This is a schematic diagram of the module connections of the gold mineral exploration device of the present invention. Detailed Implementation

[0024] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0025] The components of the embodiments of this application described and illustrated in the accompanying drawings can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0026] In the following, the terms “comprising,” “having,” and their cognates, which may be used in various embodiments of this application, are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as excluding, firstly, the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more features, numbers, steps, operations, elements, components, or combinations thereof.

[0027] Furthermore, the terms "first," "second," and "third" are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0028] Unless otherwise specified, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of this application pertain. Terms (such as those defined in commonly used dictionaries) shall be interpreted as having the same meaning as in their contextual meaning in the relevant technical field and shall not be construed as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of this application.

[0029] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0030] like Figure 1 The diagram shown is a structural schematic of the hardware operating environment of the terminal involved in an embodiment of the present invention.

[0031] The gold mineral exploration system of this invention can be a PC, or a mobile terminal device such as a smartphone, tablet, or laptop. The system may include: a processor 1001 (e.g., a CPU), a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002. The communication bus 1002 is used to establish communication between these components. The user interface 1003 may include a display screen, an input unit such as a keyboard, or a remote control; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be a high-speed RAM or a stable memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the processor 1001. Optionally, the gold mineral exploration system may also include RF (Radio Frequency) circuitry, audio circuitry, a Wi-Fi module, etc.

[0032] Those skilled in the art will understand that Figure 1 The gold mineral exploration system shown is not intended to limit it and may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. Figure 1 As shown, the memory 1005, which is a computer-readable storage medium, may include an operating system, a data interface control program, a network connection program, and a gold mineral exploration program.

[0033] In summary, the method provided in this application identifies gold-bearing minerals by analyzing the measured energy dispersive spectral density (EDS) spectra of selected bright mineral phase particles, significantly improving the accuracy and automated testing efficiency of gold mineral identification, particularly for fine-grained gold. This method effectively identifies gold-bearing particles whose EDS signals are mixed due to their small size or encapsulation, overcoming the problem of missed detection caused by low EDS matching in traditional methods. This results in more accurate and representative process mineralogical parameters. This not only reduces reliance on manual review but also provides reliable data support for developing more targeted beneficiation and recovery schemes, laying the foundation for improving gold recovery rates.

[0034] Example 1 Reference Figure 2 This embodiment provides a method for gold mineral exploration, including: Step S100: Obtain the backscattered electron image of the sample to be tested, and filter out the bright mineral phase particles in the backscattered electron image based on a preset grayscale threshold.

[0035] This step is the initial target search phase. A special microscopy technique is used to "photograph" the mineral sample to be tested, and then, based on a "brightness" standard, the computer automatically circles all particles in the photograph that may be the target mineral.

[0036] The aforementioned sample to be tested can refer to a small piece of ore mined from a mine, whose internal composition needs to be analyzed. Furthermore, in order to be observed under an electron microscope, this ore cannot be a raw rock. It needs to be cut, embedded in a plastic-like epoxy resin, and then polished until it is as smooth and flat as a mirror, creating a "ore target" about the size of a coin. This prepared target is the "sample to be tested" placed into the instrument for analysis.

[0037] In this step, the polished surface of the ore sample is first scanned using a scanning electron microscope (SEM) and imaged using a backscattered electron (BSE) detector. In this imaging mode, minerals composed of elements with heavier average atomic numbers appear brighter. Since gold is an element with a very high atomic number, gold-bearing minerals and other heavy minerals (such as galena, chalcopyrite, etc.) will appear as bright dots or areas in the image.

[0038] Then, the image processing software analyzes this backscattered electron image. It examines the brightness value of each particle in the image and compares it to a preset value (i.e., a "preset grayscale threshold").

[0039] The result of this step is a set of filtered "bright mineral phase particles". All particles with brightness higher than or equal to a preset threshold are identified and marked as potential targets, while all particles with brightness lower than the threshold are ignored. This is equivalent to initially filtering out a list of bright mineral phase particles from the entire image.

[0040] The greatest advantage of this step is that it automates and increases the efficiency of target searching. Compared to manually searching for potential gold-bearing particles one by one under a microscope, this method can automatically scan the entire sample surface and delineate all potential targets in a very short time, greatly saving time and manpower, and avoiding omissions that may occur during manual searching.

[0041] The equipment used in this step can be a scanning electron microscope with a backscattered electron detector. After processing, all the white areas in the new image correspond to the selected "bright mineral phase grains".

[0042] Step S200: Perform energy dispersive spectroscopy (EDS) analysis on the selected bright mineral phase particles to obtain the measured EDS for each bright mineral phase particle.

[0043] This step involves the rapid identification of the chemical composition of each bright mineral phase particle selected in the first step.

[0044] The electron beam of an electron microscope can be controlled to precisely target and bombard each selected bright mineral phase particle. When the electron beam bombards the particle, it excites the atoms within the particle, causing them to release X-rays with the characteristics of their element. The energy dispersive X-ray detector (such as EDS / EDX) in the instrument collects these X-rays and classifies and counts them according to their energy levels.

[0045] For each bright mineral phase particle analyzed, a corresponding "measured energy spectrum" is obtained. This spectrum is essentially a graph or a set of data that shows the intensity distribution of various X-ray energies detected in the particle. The "peaks" on the spectrum represent specific chemical elements present in the particle.

[0046] The aforementioned energy dispersive spectroscopy (EDS) is a technique used to identify the chemical composition of a substance, thereby detecting which chemical elements it is composed of.

[0047] A measured energy spectrum is the specific result obtained after the "energy spectrum analysis" is completed, and it is usually a graph (a chart). The horizontal axis (X-axis) represents the energy level. The vertical axis (Y-axis) represents the number of X-rays detected at that energy point.

[0048] The advantage of this step is that it can quickly and non-destructively obtain the chemical composition "fingerprint" of each target particle. This chemical composition information is the fundamental basis for subsequent determination of whether the particle contains gold and what specific mineral it is.

[0049] The equipment used in this step can be an energy dispersive X-ray spectrometer (EDS / EDX) coupled with a scanning electron microscope.

[0050] It can be controlled by automated software. Based on the target particle coordinate list obtained in the first step, the software automatically controls the electron beam to move to each coordinate point in sequence, stays for a preset time (e.g., 20-60 milliseconds) to collect signals, and then binds and saves the acquired energy spectrum data with the coordinates, image and other information of the particle.

[0051] Step S300: Determine the gold-bearing minerals in the sample to be tested based on the measured energy spectrum.

[0052] In this step, the chemical composition "fingerprint" (measured energy spectrum) obtained in the previous step is interpreted to ultimately determine which particles are minerals that actually contain gold.

[0053] Each measured energy spectrum is analyzed using algorithms or programs. The core of the analysis is to check whether the energy spectrum contains characteristic signals representing the element "gold". The final result is a list of particles confirmed as "gold-bearing minerals". Thus, the exploration method completes the entire process of accurately locating gold-bearing minerals from massive amounts of information.

[0054] This step is crucial for ensuring the accuracy of the detection results. The method no longer relies solely on the indirect physical property of "brightness," but instead makes a final determination based on direct chemical elemental evidence. This allows the method to accurately locate gold, even when the gold signal is weak or mixed with signals from other elements.

[0055] Specifically, an algorithm based on characteristic peak identification can be used. The algorithm checks whether there is a significant "peak" (i.e., the signal count is much higher than the surrounding background signal) at the characteristic energy positions of these golds in the measured energy spectrum.

[0056] The gold mineral exploration method provided in this embodiment employs a two-step strategy. First, it automatically and efficiently filters all potential heavy mineral particles from ore samples using brightness differences in backscattered electron images. Next, it performs energy dispersive spectroscopy (EDS) analysis on each filtered particle to obtain its unique chemical composition "fingerprint." Most importantly, this method directly confirms the gold-bearing mineral based on these measured EDS spectra. The significant advantage of this approach is its ability to accurately identify gold-bearing particles whose EDS signals are mixed due to excessive fineness or encapsulation by other minerals. This effectively solves the problem of missed detection caused by low EDS matching in traditional methods, ultimately obtaining more accurate and representative process mineralogical data, providing reliable technical support for the subsequent development of efficient beneficiation and recovery schemes.

[0057] Example 2 Reference Figure 3 This embodiment provides a method for gold mineral exploration. Based on the aforementioned embodiment, step S100, which involves filtering bright mineral phase particles in the backscattered electron image of the sample to be tested based on a preset grayscale threshold, includes: Step S110: The backscattered electron image is binarized.

[0058] In this step, the original microscope image undergoes a "simplification" process. The original backscattered electron image is a grayscale image containing various shades of gray (typically 256 levels) ranging from pure black to pure white. "Binarization" transforms this complex grayscale image into a very simple black-and-white image containing only pure black and pure white. An image processing algorithm compares each pixel in the image to a preset brightness standard (i.e., a "preset grayscale threshold").

[0059] The process yields a new, clear black-and-white image. In this new image, all pixels with original brightness levels equal to or higher than the brightness standard are transformed into pure white, while all pixels with brightness levels lower than the standard are transformed into pure black. This significantly enhances the contrast between the target and the background, eliminating interference from mid-tone grayscale. This allows the computer to easily and quickly identify the white areas representing "bright mineral phase grains," laying the foundation for further screening and localization.

[0060] Specifically, the thresholding algorithm from image processing can be used.

[0061] For example, let the original grayscale image be I, and the grayscale value of any pixel (x, y) in the image be I(x, y), with a preset grayscale threshold of T. The new image generated after binarization is B, and the value of its pixel B(x, y) is determined by the following formula: B(x,y)={1 (or 255, representing white), if I(x,y)≥T; {0 (represents black), if I(x,y)} <T。

[0062] The above formula means that for each point in the image, if its brightness value is not less than the threshold T, it is turned into white; otherwise, it is turned into black.

[0063] Step S120: Select phases in the backscattered electron image after binarization that have a gray level not less than the preset gray level threshold as the bright mineral phase particles.

[0064] This step involves formally selecting all white areas (i.e., grains) from the black and white image generated in the previous step.

[0065] By scanning black-and-white images, all connected white pixel regions are identified and segmented. Each individual white region is considered a "bright mineral phase grain." This process yields a list containing information on the location, size, and shape of all bright mineral phase grains. These grains are the final targets for the next step of energy dispersive spectroscopy (EDS) analysis.

[0066] This step completes the transformation from image to data, converting visual "highlights" into a list of targets with specific coordinates and geometric parameters that can be processed by a computer, thus achieving full automation of the filtering process.

[0067] For example, a connected-component labeling algorithm can be used. This algorithm scans the binarized image, finds all adjacent white pixels, and groups them into a single group (an object or a "component"). The algorithm assigns a unique label to each individual white region and calculates its centroid coordinates, area, and other information.

[0068] For example, in a black and white image, there are three independent white areas. After the algorithm runs, it will output: "Found 3 targets. Target 1, coordinates (x1, y1), area A1; Target 2, coordinates (x2, y2), area A2; Target 3, coordinates (x3, y3), area A3." Furthermore, the preset grayscale threshold is 200.

[0069] For example, a gold-bearing particle has an average grayscale value of 210 in the original image. Because 210 ≥ 200, this particle will become white after binarization and will be selected for subsequent analysis. Another pyrite particle has an average grayscale value of 195. Because 195 < 200, this particle will become black after binarization and will be ignored in this step.

[0070] In some embodiments, before step S100, which filters out bright mineral phase particles in the backscattered electron image based on a preset grayscale threshold, the method further includes: Step S130: Standardize the backscattered electron image; This step is equivalent to calibrating the image itself and the environment being measured before measuring the object. Its core purpose is to ensure that every measurement is performed under a uniform, stable, and comparable "brightness scale," eliminating errors that may be caused by differences in instrument status or operating settings.

[0071] Standardization processes include: The backscattered electron image is standardized by setting the brightness of the gold particles to 255 and the grayscale value of the epoxy resin background to 10.

[0072] In the calibration procedure provided in this step, the operator needs to find two "reference points" to define the highest and lowest points of the entire brightness range: (1) Define the brightest point: Find a pure gold particle on the sample, and then adjust the brightness / contrast setting of the instrument so that the brightness value (gray value) of this gold particle on the backscattered electron image is accurately displayed as 255 (i.e. pure white in the digital image).

[0073] (2) Define the darkest point: Find the flat area of ​​epoxy resin used to embed the mineral in the sample (this area does not contain any minerals), and adjust the instrument settings so that the brightness value of this area is accurately displayed as 10 (a value that is close to pure black but not absolutely black).

[0074] After processing, a standardized backscattered electron image is obtained. In this image, brightness values ​​are no longer relative concepts, but absolute benchmarks with clear physical meaning: the brightest gold is 255, the darkest background is 10, and the brightness values ​​of all other minerals are fixed within this range. This is crucial to ensuring the reliability and repeatability of the entire method.

[0075] Different scanning electron microscopes, or even the same instrument at different times (e.g., after filament aging), may produce images with varying brightness and contrast. Without standardization, for example, the grayscale value of the same mineral might be 210 today, but 190 tomorrow.

[0076] Standardization ensures that the grayscale threshold "200" used in subsequent steps always has the same filtering capability. Without standardization, when the overall image is dark, gold-bearing minerals with a grayscale value of 210 might be misclassified as 190 and missed; conversely, when the overall image is bright, non-target minerals with a grayscale value of 190 might be misclassified as 210 and incorrectly included. Through standardization, the grayscale value of a specific mineral is stabilized around a fixed value, thus guaranteeing the accuracy of the filtering.

[0077] Example 3 Reference Figure 4 This embodiment provides a method for gold mineral exploration. Based on the aforementioned embodiment, step S300, determining the gold-bearing minerals in the sample to be tested based on the measured energy dispersive spectroscopy, includes: Step S310: Compare the measured energy spectrum with the energy spectrum of gold minerals in the theoretical synthesis spectrum of standard minerals to obtain the contrast result corresponding to the bright mineral phase particles.

[0078] This step is equivalent to performing a "fingerprint comparison". The system will compare the chemical composition "fingerprint" (i.e., measured energy spectrum) obtained on-site from unknown particles with a database pre-stored in the computer that contains "fingerprints" of various known standard minerals.

[0079] In this method, the "standard mineral theoretical synthesis spectrum" is essentially an authoritative energy spectrum database pre-installed in the analysis software. This database stores the ideal energy spectra of hundreds or thousands of standard minerals, as well as the pure energy spectra of various gold minerals (such as native gold, silver-gold ore, etc.).

[0080] A mathematical algorithm is used to calculate the similarity between the newly acquired "measured energy spectrum" and each "gold mineral energy spectrum" in the database. This algorithm comprehensively compares the overall shape of the two spectra, the positions of all peaks, and the height ratio between peaks.

[0081] For each bright mineral phase grain, this comparison process outputs a quantified value, known as the "contrast result." This result can be understood as a "similarity score," usually expressed as a percentage. For example, comparing the measured energy spectrum of a grain with the standard spectrum of "native gold" in a database might yield a 98% similarity score.

[0082] This full-spectrum comparison method is highly reliable and accurate for identifying "standard" mineral particles that are relatively pure and large enough. It can provide a very clear identification conclusion based on the overall chemical composition.

[0083] Specifically, statistical algorithms such as least squares fitting or chi-squared test can be used. These algorithms essentially involve "point-by-point difference." The algorithm compares the difference in count values ​​at each energy channel between the measured and standard energy spectra, and then sums all the differences using a mathematical formula (e.g., summing the squares). The smaller the total difference, the more similar the two spectra are, and the higher the "contrast result" or "similarity score."

[0084] This can be simplified to a similarity formula: Similarity score = 1 - (sum of differences between the two spectra at all energy points / total signal intensity of the spectra). When the sum of differences approaches 0, the similarity score approaches 1 (or 100%).

[0085] Step S320: If the contrast result is higher than the preset energy spectrum threshold, then the bright mineral phase particles are marked as gold-bearing minerals.

[0086] This step is a "decision" step based on the score from the previous step. The system will determine whether the "similarity score" has reached a pre-set "passing grade" (i.e., a "preset energy spectrum threshold").

[0087] This step involves a simple numerical comparison. The "contrast result" obtained in the previous step (e.g., 98%) is compared with the "preset energy spectrum threshold" (e.g., possibly set to 90% in the software). A clear identification conclusion is then obtained.

[0088] If the contrast result is higher than the threshold, the system considers the fingerprint match successful and officially identifies the particle as the corresponding gold mineral. If the contrast result is lower than the threshold, the particle's identity cannot be confirmed at this step.

[0089] By setting a clear threshold, the confidence level of the identification results can be guaranteed. Labeling is only performed when the similarity is sufficiently high, which avoids incorrectly labeling particles with similar compositions but not being the target mineral as gold minerals, thus ensuring the accuracy of the identification results.

[0090] For example, in scenario A (successful identification), the system analyzes a bright mineral phase particle, and its "measured energy spectrum" is compared with the standard spectrum of "native gold" in the database, resulting in a "contrast result" of 97%. Assume the "preset energy spectrum threshold" is 90%. Because 97% > 90%, the system will immediately label this particle as "native gold".

[0091] In scenario B (which cannot be identified in this step), the system analyzed another very fine bright mineral phase grain encased in pyrite. Its measured energy dispersive spectroscopy (EDS) is a mixture of gold and pyrite. When compared to the standard spectrum of pure native gold in the database, the difference is too large, resulting in a contrast ratio of only 65%. Since 65% < 90%, the system cannot confirm it as a gold-bearing mineral through this step.

[0092] In some embodiments, after comparing the measured energy spectrum with the energy spectrum of gold minerals in the theoretical synthesis spectrum of standard minerals to obtain the contrast result, step S310 further includes: Step S330: If the contrast result is not higher than the preset energy spectrum critical value, then obtain the signal intensity value of the gold characteristic peak in the gold element characteristic region in the measured energy spectrum of the bright mineral phase particles.

[0093] This step addresses particles that failed to be identified in the first round of full-spectrum comparison due to their similarity score (i.e., contrast result) not meeting the preset energy spectrum threshold. For these "pending" particles, the system will no longer focus on the overall morphology of their energy spectra, but instead will focus on the most crucial local evidence in the energy spectrum that proves the presence of gold. The processed objects are the measured energy spectra of particles that were not successfully labeled in the previous steps.

[0094] Specifically, the algorithm locates the specific range of characteristic X-ray energies of gold on the horizontal axis (energy axis) of the measured energy spectrum, i.e., the "gold characteristic region". For example, the characteristic X-ray energy of the Lα series of gold is located around 9.71 keV. In this embodiment, the gold characteristic region can be defined as 9.71 ± 0.16.

[0095] Within the identified characteristic region, the signal intensity of the characteristic peak representing gold is measured. This "signal intensity value" typically refers to the X-ray photon count detected by the detector at that energy point.

[0096] For each bright mineral phase particle to be identified, the output of this step is a specific numerical value: "signal intensity value". This value directly reflects the strength of the gold element signal in that particle.

[0097] The advantage of this step lies in its targeted and sensitive identification. It ignores the interference of the overall energy spectrum morphology caused by factors such as matrix minerals, and directly seeks the fundamental evidence proving the presence of gold. Even if the gold content is extremely low, resulting in a small proportion of the entire energy spectrum, this method can still identify it as long as the signal intensity of its characteristic peaks can be effectively detected.

[0098] This can be achieved using Region of Interest (ROI) analysis or peak intensity measurement algorithms. The algorithm first defines one or more energy windows (ROIs) corresponding to the characteristic peaks of gold. Then, the algorithm calculates the total X-ray count falling within the window and subtracts the background count caused by factors such as background radiation, thus obtaining the net peak intensity value. Alternatively, the maximum count value within the energy region can be directly identified as the peak height intensity. The signal intensity value is calculated as: total count within the ROI - estimated background count.

[0099] Step S340: If the signal strength value is greater than a preset strength threshold, then the bright mineral phase particles are determined to be gold-containing particles.

[0100] This step is a decision-making step based on the signal strength value obtained in the previous step. A final judgment is made by comparing the measured signal strength with a preset minimum effective signal standard (i.e., the "preset strength threshold").

[0101] A numerical comparison is performed to determine whether the "signal strength value" obtained in the previous step is greater than the preset "intensity threshold." A clear judgment is then made. If the signal strength value is greater than the threshold, the particle is determined to be a "gold-containing particle," even if it fails the full-spectrum comparison.

[0102] This step is crucial to ensuring that fine gold grains are not missed, providing a solution for identifying complex situations where energy dispersive spectral signals are mixed due to excessively fine grains, encapsulation, or association with other minerals. By setting a reasonable intensity threshold, weak gold signals can be effectively identified while filtering out instrument noise, thereby greatly improving the accuracy and detection rate.

[0103] For example, a fine gold particle encased in pyrite (the sample to be tested) has a contrast ratio of only 50% in its full spectrum comparison, which is below the 90% threshold and therefore cannot be identified. Subsequently, the system executes step S330, measuring a signal intensity value of 60 counts in the 9.71 keV region of its energy spectrum. Assuming a preset intensity threshold of 25 counts, because 60 > 25, the system ultimately classifies this particle as a "gold-containing particle."

[0104] In some embodiments, after obtaining the signal intensity value of the gold characteristic peak in the gold characteristic region of the measured energy spectrum of the bright mineral phase particles in step S330, the method further includes: Step S350: If the signal strength value is not greater than the preset strength threshold, then it is determined that the bright mineral phase particles are not gold-containing particles.

[0105] This step clarifies that if a particle fails to meet either of the criteria for being identified as gold after passing the full spectrum comparison and characteristic peak intensity detection, it should be ultimately classified as a non-gold particle and ignored.

[0106] refer to Figure 5 This application provides a gold mineral exploration device, comprising: The screening module 10 is used to screen out bright mineral phase particles in the backscattered electron image of the sample to be tested based on a preset grayscale threshold. Analysis module 20 is used to perform energy dispersive spectroscopy analysis on the selected bright mineral phase particles to obtain the measured energy spectrum corresponding to each bright mineral phase particle; The measurement module 30 is used to determine the gold-bearing minerals in the sample to be tested based on the measured energy spectrum.

[0107] This application provides a computer device, which includes a processor and a memory. The memory stores a computer program, and the processor executes the computer program to implement the gold mineral exploration method as described above.

[0108] The processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including at least one of a Central Processing Unit (CPU), Graphics Processing Unit (GPU), Network Processor (NP), Digital Signal Processor (DSP), Application-Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0109] The memory can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The memory is used to store computer programs, and the processor can execute the computer programs accordingly after receiving execution instructions.

[0110] This application provides a computer storage medium storing a computer program, which, when executed on a processor, implements the gold mineral exploration method described above.

[0111] For example, the computer storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0112] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that, in alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0113] In addition, the functional modules or units in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0114] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0115] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method of mineral exploration for gold, characterised by, The method comprises the following steps: acquiring a backscattered electron image of a sample to be measured, and screening out bright mineral phase particles in the backscattered electron image based on a preset gray threshold; performing energy spectrum analysis on the screened-out bright mineral phase particles to acquire a measured energy spectrum corresponding to each of the bright mineral phase particles; determining gold-containing minerals in the sample to be measured according to the measured energy spectrum.

2. The method of claim 1, wherein the gold mineral prospecting method is characterized by, The step of screening out bright mineral phase particles in the backscattered electron image of the sample to be measured based on a preset gray threshold comprises the following steps: performing binaryzation processing on the backscattered electron image; screening out phases with a gray value not less than the preset gray threshold in the backscattered electron image after the binaryzation processing as the bright mineral phase particles.

3. The gold mineral exploration method as described in claim 2, characterized in that, The preset gray threshold is 200.

4. The gold mineral exploration method as described in claim 1, characterized in that, Before the step of screening out bright mineral phase particles in the backscattered electron image based on a preset gray threshold, the method further comprises the following steps: performing standardization processing on the backscattered electron image, comprising: standardizing the backscattered electron image by taking the brightness of gold element particles as 255 and the gray value of the epoxy resin background as 10.

5. The gold mineral exploration method as described in claim 1, characterized in that, The step of determining gold-containing minerals in the sample to be measured according to the measured energy spectrum comprises the following steps: comparing the measured energy spectrum with the gold mineral energy spectrum in the standard mineral theoretical synthesis spectrum to obtain a contrast result corresponding to the bright mineral phase particles; if the contrast result is higher than a preset energy spectrum critical value, marking the bright mineral phase particles as gold-containing minerals.

6. The gold mineral exploration method as described in claim 5, characterized in that, After the step of comparing the measured energy spectrum with the gold mineral energy spectrum in the standard mineral theoretical synthesis spectrum to obtain a contrast result, the method further comprises the following steps: if the contrast result is not higher than the preset energy spectrum critical value, acquiring a signal intensity value of a gold characteristic peak in a gold element characteristic region in the measured energy spectrum of the bright mineral phase particles; if the signal intensity value is greater than a preset intensity threshold, determining that the bright mineral phase particles are gold-containing particles.

7. The gold mineral exploration method as described in claim 6, characterized in that, After the step of acquiring a signal intensity value of a gold characteristic peak in a gold element characteristic region in the measured energy spectrum of the bright mineral phase particles, the method further comprises the following steps: if the signal intensity value is not greater than the preset intensity threshold, determining that the bright mineral phase particles are not gold-containing particles.

8. A gold mineral exploration apparatus, characterised in that, The method comprises the following steps: a screening module, configured to screen out bright mineral phase particles in a backscattered electron image of a sample to be measured based on a preset gray threshold; an analysis module, configured to perform energy spectrum analysis on the screened-out bright mineral phase particles to acquire a measured energy spectrum corresponding to each of the bright mineral phase particles; a determination module, configured to determine gold-containing minerals in the sample to be measured according to the measured energy spectrum.

9. A computer device, comprising: The computer device comprises a processor and a memory, the memory stores a computer program, and the processor is configured to execute the computer program to implement the gold mineral exploration method in any one of claims 1-7.

10. A computer storage medium, characterized in that, The computer program is stored in the memory and is executed on the processor to implement the gold mineral exploration method in any one of claims 1-7.

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