Method for evaluating mineral content of geologic body based on mineral spectral data

Abstract

The invention discloses a geologic body mineral content evaluation method based on mineral spectral data, and relates to the technical field of mineral products. The method comprises the following steps: acquiring a peripheral constraint region of a first target region, acquiring spectral data of a plurality of mineral samples in the peripheral constraint region, and performing mineral matching degree analysis on the spectral data of the mineral samples in the peripheral constraint region and spectral data of at least one target mineral in a reference spectral database, and determining the mineral content of the first target area. The geologic body mineral-bearing property evaluation method based on mineral spectrum data is used for efficiently evaluating the mineral-bearing property of pegmatite vein in an aeolian sand coverage area.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of mineral resources, and particularly relates to a method for evaluating ore-bearing property of geological body based on mineral spectral data. BACKGROUND

[0002] Lepidolite-beryl deposit is an important strategic mineral resource. Traditional exploration of lepidolite-beryl deposit mainly relies on field geological survey, manual sampling and indoor analysis, which is not only low in work efficiency and high in cost, but also limited in exploration range due to the restrictions of topography, vegetation and artificial accessibility, and it is difficult to achieve large-area rapid survey.

[0003] At present, hyperspectral remote sensing technology can identify the characteristic spectrum of surface minerals, providing a new means for mineral exploration. However, in the area covered by aeolian sand, the traditional exploration of ore-bearing property of geological body often takes the entire covered area as the exploration object, and a large amount of manpower and material resources are needed to investigate the area without clear targets, resulting in serious waste of equipment, time and manpower resources. In addition, the aeolian sand coverage will form strong background interference, greatly reducing the signal-to-noise ratio of the exploration signal and affecting the accuracy of subsequent analysis. SUMMARY

[0004] The present disclosure is proposed in view of the above problems. The present disclosure provides a method for evaluating ore-bearing property of geological body based on mineral spectral data, which is used for efficient and accurate evaluation of ore-bearing property of lepidolite vein in aeolian sand covered area.

[0005] In order to achieve the above purpose, according to one aspect of the present disclosure, the following technical scheme is provided: acquire a peripheral constraint region of a first target region; acquire spectral data of a plurality of mineral samples of the peripheral constraint region; perform mineral matching degree analysis on the spectral data of the plurality of mineral samples of the peripheral constraint region and spectral data of at least one target mineral of a reference spectral database, and determine the ore-bearing property of the first target region.

[0006] Compared with the prior art, in the method for evaluating ore-bearing property of a geological body based on mineral spectral data provided by the disclosure, first, the peripheral constraint region of the first target region is obtained, so that the exploration range is limited to the periphery of the target geological body, thereby avoiding the resource waste caused by blind exploration of the entire aeolian sand covered area in the traditional method. Secondly, the spectral data of the plurality of mineral samples in the peripheral constraint region are obtained, and only the mineral samples of pegmatite veins or the mineral samples of weathered products thereof need to be collected in the limited peripheral region. The aeolian sand cover is usually thin or missing in the peripheral region, and the strong spectral interference of background rock and aeolian sand is greatly weakened. Therefore, the remote sensing spectral data of the minerals collected in the region can obtain purer spectral data, thereby improving the signal-to-noise ratio of the mineral matching degree analysis. Through the mineral matching degree analysis of the spectral data of the plurality of mineral samples in the peripheral constraint region and the spectral data of at least one target mineral in the reference spectral database, the ore-bearing property of the large-area first target region can be inferred through the mineral matching degree information of the peripheral constraint region. Not only the purity of the spectral data is ensured, but also the entire region covered by the sand does not need to be sampled, thereby greatly reducing the field work and improving the exploration efficiency. The disclosure improves the accuracy and reliability of the mineralization evaluation by sampling the peripheral constraint region and then performing the mineral matching degree analysis with the reference spectral database. The disclosure provides an efficient and economical solution for mineral exploration in complex topography, and has important practical value and popularization prospect.

[0007] According to another aspect of the disclosure, a device for evaluating ore-bearing property of a geological body based on mineral spectral data is provided, and the device comprises: a first obtaining module configured to obtain a peripheral constraint region of a first target region; a second obtaining module configured to obtain spectral data of a plurality of mineral samples in the peripheral constraint region; an obtaining module configured to perform mineral matching degree analysis on the spectral data of the plurality of mineral samples in the peripheral constraint region and spectral data of at least one target mineral in a reference spectral database, and obtain mineral matching degree information of the peripheral constraint region.

[0008] a determining module configured to determine the ore-bearing property of the first target region based on the mineral matching degree information.

[0009] Compared with the prior art, the device for evaluating ore-bearing property of a geological body based on mineral spectral data provided by the disclosure has the same beneficial effects as the method for evaluating ore-bearing property of a geological body based on mineral spectral data described in the above technical solution, and details are not repeated here.

[0010] According to another aspect of the disclosure, the disclosure further provides an electronic device, which comprises: a processor; and a memory storing a program; The program comprises instructions which, when executed by the processor, cause the processor to perform the method of the present disclosure.

[0011] Compared with the prior art, the electronic device provided by the present disclosure has the same beneficial effects as the method for evaluating the ore-bearing property of a geological body based on mineral spectral data, which will not be repeated here.

[0012] According to another aspect of the present disclosure, the present disclosure also provides a computer program product, which comprises a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to perform the method of the present disclosure.

[0013] Compared with the prior art, the computer program product provided by the present disclosure has the same beneficial effects as the method for evaluating the ore-bearing property of a geological body based on mineral spectral data, which will not be repeated here. BRIEF DESCRIPTION OF DRAWINGS

[0014] The accompanying drawings, which are included to provide a further understanding of the present disclosure, constitute a part of the present disclosure, and the illustrative embodiments of the present disclosure and their description serve to explain the present disclosure, and do not constitute improper limitations on the present disclosure. In the drawings: Figure 1 A flow chart of the method for evaluating the ore-bearing property of a geological body based on mineral spectral data provided by the embodiments of the present disclosure is shown; Figure 2 A sampling point diagram of the Alken interrupt region provided by the embodiments of the present disclosure is shown; Figures 3A-3B A field photo of the spodumene-containing pegmatite sample of the Alken interrupt region provided by the illustrative embodiments of the present disclosure is shown; Figures 3C-3D A field photo of the spodumene-containing pegmatite sample of the Alken interrupt region provided by the illustrative embodiments of the present disclosure is shown; Figures 4A-4B A field photo of the spodumene-containing pegmatite sample of the Alken interrupt region provided by the illustrative embodiments of the present disclosure is shown; Figures 4C-4D A field photo of the spodumene-containing pegmatite sample of the Alken interrupt region provided by the illustrative embodiments of the present disclosure is shown; Figures 5A-5B A field photo of the spodumene-containing pegmatite sample of the Alken interrupt region provided by the illustrative embodiments of the present disclosure is shown; Figure 5C Figure 5D A field photo of the spodumene-containing pegmatite sample of the Alken interrupt region provided by the illustrative embodiments of the present disclosure is shown; A field photo of the spodumene-containing pegmatite sample of the Alken interrupt region provided by the illustrative embodiments of the present disclosure is shown;Figures 6A-6C A field photograph of sodium-bearing feldspar in an Altyn Disturbance region provided by an exemplary embodiment of this disclosure is shown; Figures 6D-6F Photographs of sodium-bearing pegmatite samples from the Altyn Disturbance region provided in exemplary embodiments of this disclosure are shown. Figure 7 The measured spectral image of the target mineral in the Altyn Discontinuity region according to an embodiment of this disclosure is shown; Figure 8 The diagram shows the interpretation results of the pegmatite dike in the middle section of the Altyn Tagh formation according to an embodiment of this disclosure; Figure 9 A schematic diagram of the sampling area of ​​the Shaliangxi pegmatite vein according to an embodiment of this disclosure is shown; Figure 10 Embodiments of this disclosure are shown. Figure 9 A schematic diagram of the spatial influence zone of the pulse body within the red box area; Figure 11 It shows Figure 10 Mineral mapping of the mid-space influence zone; Figure 12 The distribution map of the five regions where pegmatite veins are most concentrated according to embodiments of this disclosure is shown; Figure 13 This illustration shows a concentrated distribution map of another pegmatite vein delineated in the middle section of the Altyn Mountains according to an embodiment of this disclosure; Figure 14 Statistical diagrams of ore-bearing and ore-free veins in six pegmatite vein regions according to embodiments of this disclosure are shown; Figure 15 A field outcrop photograph of the pegmatite vein at point 5 in area III of this disclosure is shown; Figure 16 A photograph of a pegmatite vein sample collected at point 5 in area III of this embodiment is shown; Figure 17 A micrograph of the pegmatite vein sample collected at point 5 in area III of this disclosure is shown. Figure 18 A field outcrop photograph of the pegmatite vein at point 1 in zone V, according to an embodiment of this disclosure, is shown. Figure 19 A photograph of a pegmatite vein sample collected at point 1 in zone V, according to an embodiment of this disclosure, is shown. Figure 20 A micrograph of the pegmatite vein sample collected at point 1 in zone V according to an embodiment of this disclosure is shown. Figure 21 A schematic block diagram of the functional modules of a geological body mineralization evaluation device based on mineral spectral data according to an exemplary embodiment of the present disclosure is shown. Figure 22A schematic block diagram of a chip according to an exemplary embodiment of the present disclosure is shown; Figure 23 A structural block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure is shown. Detailed Implementation

[0015] To facilitate a clear description of the technical solutions in the embodiments of this disclosure, the terms "first" and "second" are used to distinguish identical or similar items with substantially the same function and effect. For example, the first threshold and the second threshold are merely used to distinguish different thresholds and do not limit their order. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0016] It should be noted that in this disclosure, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this disclosure should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0017] In this disclosure, "at least one" means one or more, and "more than one" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, a combination of a and b, a combination of a and c, a combination of b and c, or a, b, and c, where a, b, and c can be single or multiple.

[0018] As a key strategic mineral resource, the efficient exploration of pegmatite-type lithium-beryllium deposits has a significant impact on the new energy and high-end manufacturing industries. Traditional exploration methods mainly rely on field geological reconnaissance, manual sampling, and laboratory chemical analysis. These methods are not only inefficient and costly, but also severely limited by complex terrain, dense vegetation, and human accessibility, resulting in a limited exploration scope and making it difficult to meet the needs of rapid, large-scale general surveys.

[0019] In recent years, the rise of hyperspectral remote sensing technology has provided a new technological approach for mineral exploration. This technology can achieve detailed evaluation and classification of ground features based on mineral spectral characteristics, significantly improving the efficiency and accuracy of surface mineral mapping.

[0020] The following explanation uses the central section of the Altun Mountains in Xinjiang as an example. The central section of the Altun Mountains in Xinjiang is an important mineralization belt for rare metals such as lithium and beryllium in my country, and also a key target area for the current exploration of granite pegmatite-type rare metal deposits. After conducting field verification and remote sensing interpretation of typical pegmatite-type lithium and beryllium deposits in the Altun Mountains region, key ore-indicating minerals such as spodumene, lepidolite, beryl, muscovite, quartz, albite, and potassium feldspar are found in the surface ore-bearing pegmatite veins. Among these, the combination of spodumene, lepidolite, albite, muscovite, and quartz plays a crucial indicative role in the mineralization potential of lithium, while the combination of beryl, potassium feldspar, muscovite, and quartz provides a key indication of the occurrence of beryllium. The distribution characteristics of these minerals can reflect the mineralization environment and can also serve as a key basis for remote sensing interpretation and mineral exploration prediction.

[0021] However, the natural conditions in the central section of the Altun Mountains are extremely harsh. Traditional geological exploration methods struggle to effectively evaluate and delineate pegmatite dikes in geological environments characterized by widespread wind and sand cover and underdeveloped bedrock outcrops, severely hindering exploration progress. Pegmatite dikes often occur as narrow veins with complex morphologies and are easily obscured by aeolian sand, further increasing the difficulty of surface evaluation and spatial positioning. In such unique landscape areas, remote sensing technology demonstrates significant advantages due to its non-contact and wide-area coverage capabilities. High spatial resolution remote sensing data, in particular, can provide crucial information for morphological extraction and spatial distribution analysis of concealed dikes. However, current mineral exploration often targets the entire covered area, requiring substantial manpower and resources to search areas without clear objectives. This leads to a significant waste of equipment, time, and manpower resources and generates strong background interference, drastically reducing the signal-to-noise ratio of the exploration signal and affecting the accuracy of subsequent analysis.

[0022] To overcome the above problems, this disclosure provides a method for evaluating the mineralization of geological bodies based on mineral spectral data. This method is used to accurately evaluate the mineralization of pegmatite veins within a target area.

[0023] Figure 1 A flowchart illustrating an exemplary embodiment of this disclosure is shown. (Example: A method for evaluating the mineralization of geological bodies based on mineral spectral data is provided.) Figure 1 As shown in the embodiments of this disclosure, the method for evaluating the mineralization of geological bodies based on mineral spectral data includes: Step 101: Obtain the outer constraint region of the first target region.

[0024] It is understandable that the aforementioned first target area could be an outcrop of a pegmatite vein in the central section of the Altyn Tagh region of Xinjiang, or a distribution area of ​​concealed pegmatite veins that may exist based on geological inferences.

[0025] For example, the above-mentioned acquisition of the outer constraint region of the first target region specifically includes: first, acquiring the spatial distribution data of the first target region; second, determining the boundary of the first target region based on the spatial distribution data; and third, acquiring the outer constraint region surrounding the boundary based on the boundary of the first target region.

[0026] For example, the acquisition of the outer constraint area of ​​the first target area further includes: First, obtaining spatial distribution data of the first target area through high-resolution remote sensing image interpretation, geological map analysis, and field verification. This spatial distribution data includes the strike, dip angle, length, and width parameters of the pegmatite dikes in the first target area. Second, determining the boundary of the first target area based on the spatial distribution data. Next, using spatial proximity analysis, a spatial influence zone (e.g., setting a fixed radius along the boundary of the first target area) is generated around the pegmatite dikes in the first target area, based on the boundary of the first target area, to obtain the outer constraint area surrounding the boundary of the first target area. The width of the spatial influence zone is determined comprehensively based on the surface cover type, aeolian sand thickness, and the development range of mineralization halos, preferably 10 to 30 meters. This spatial proximity analysis method, by generating a spatial influence zone for the target object, collects indicator minerals related to mineralization only within this spatial influence zone. Then, by integrating multi-source information such as remote sensing images, geological data, and mineral spectra, it can systematically analyze the spatial correlation between the target and its surrounding environment, providing a basis for mineral resource exploration and evaluation. In geological prospecting applications, this technology can effectively mitigate the interference of complex landforms (such as aeolian sand and vegetation-covered areas) on remote sensing information extraction, improve mineral evaluation results, assist in optimizing prospecting models, thereby improving prediction accuracy and reducing missed and false predictions.

[0027] In one example, the generated spatial influence band can be subjected to topology optimization to remove discontinuous regions and outliers, ensuring the integrity and rationality of the spatial influence band, and ultimately forming the outer constraint region for subsequent spectral analysis. This process can employ existing publicly available techniques, which are not limited here.

[0028] Step 102: Obtain spectral data of multiple mineral samples from the outer constraint region.

[0029] For example, remote sensing image data of multiple mineral samples within the aforementioned peripheral constrained area are acquired to obtain spectral data of multiple representative mineral samples. The remote sensing image data can be obtained using existing methods; for example, the remote sensing image data can be satellite data acquired by WorldView-3, and the remote sensing image data includes spectral data.

[0030] Specifically, high spatial resolution satellites (such as WorldView-3) are used to image the surrounding constrained area, acquiring its multispectral or hyperspectral remote sensing images. Spectral reflectance data for each pixel within this area is extracted from the remote sensing images; this data constitutes the spectral data for the mineral samples used in the analysis.

[0031] Step 103: Perform mineral matching analysis on the spectral data of multiple mineral samples in the peripheral constrained area and the spectral data of at least one target mineral in the reference spectral database to determine the mineralization of the first target area.

[0032] It should be noted that the target minerals mentioned above include one or more of spodumene, lepidolite, beryl, muscovite, albite, and potassium feldspar. Preferably, the target minerals are spodumene, lepidolite, beryl, or other minerals that directly indicate lithium-beryllium mineralization.

[0033] For example, the above-mentioned mineral matching degree analysis using spectral matching technology specifically refers to the hybrid tuned matched filtering (MTMF) method. Mineral spectral matching technology compares and analyzes unknown spectral data with measured spectra of known minerals, standard mineral spectra, or image endmember spectra to identify the mineral types represented by the unknown spectra. This can be used to evaluate different mineral assemblages in pegmatite veins and determine the mineralization of the veins.

[0034] The aforementioned hybrid tuned matched filtering method is a hyperspectral remote sensing target detection and information extraction approach. It combines matched filtering (MF) and a hybrid tuning model to effectively separate specific target minerals from mixed pixels. The method first enhances the target signal using background statistical characteristics through a matched filtering algorithm, outputting an MF score reflecting the target's relative abundance. Then, hybrid tuning is used to calculate infeasibility values, assessing the reliability of the extraction results and effectively suppressing background interference and eliminating false positives. Finally, a two-dimensional scatter plot composed of the MF score and infeasibility values ​​is used to screen out high-abundance, low-infeasibility, high-confidence target pixels. The hybrid tuned matched filtering method requires only the target endmember spectrum and does not need complete background endmembers, exhibiting strong noise suppression capabilities and sub-pixel detection sensitivity.

[0035] In some examples, the aforementioned hybrid tuned matched filtering method specifically includes: matching the spectral data of each pixel in the remote sensing image of the peripheral constraint area with the endmember spectra of the target mineral in the reference spectral database to generate a matching score (MF score) and infeasibility value for each pixel relative to the target mineral. By setting a matching score threshold, high-confidence target mineral pixels are selected. Finally, the mineral matching degree information can be expressed as quantitative indicators such as the number of successfully matched pixels in the peripheral constraint area, the pixel proportion (abundance), the average matching score, or the spatial distribution density. This method only requires the target endmember spectrum and does not require predefined background endmembers. It is particularly suitable for extracting sub-pixel-level mineralization information in complex environments such as aeolian sand-covered areas, and can effectively improve the evaluation accuracy of mineral-bearing minerals (such as spodumene and beryl) in pegmatite veins.

[0036] In some embodiments, the above-described mineral matching degree analysis of the spectral data of multiple mineral samples in the peripheral constraint region and the spectral data of at least one target mineral in a reference spectral database to determine the mineralization of the first target region further includes: if the mineral matching degree between the spectral data of multiple mineral samples in the peripheral constraint region and the reference spectral data of at least one target mineral is greater than or equal to a preset mineral matching degree, the first target region is determined to be mineral-bearing; if the mineral matching degree between the spectral data of multiple mineral samples in the peripheral constraint region and the reference spectral data of at least one target mineral is less than the preset mineral matching degree, the first target region is determined to be mineral-free. It should be understood that the preset mineral matching degree includes a pixel abundance percentage threshold or an average matching score threshold. The pixel abundance percentage threshold can be set to 5%~10%, and the average matching score threshold can be set to 0.7~0.9, which can be set according to actual needs and is not limited here.

[0037] For example, when the pixel abundance ratio of the spectral data of multiple mineral samples in the peripheral constraint region to that of a target mineral is greater than or equal to a pixel abundance ratio threshold, the first target region is determined to contain minerals. When the pixel abundance ratio of the spectral data of multiple mineral samples in the peripheral constraint region to that of a target mineral is lower than the threshold, the first target region is determined to be non-mineralized. When the average matching score of the spectral data of multiple mineral samples in the peripheral constraint region to that of a target mineral is greater than or equal to an average matching score threshold, the first target region is determined to contain minerals. When the average matching score of the spectral data of multiple mineral samples in the peripheral constraint region to that of a target mineral is lower than the threshold, the first target region is determined to be non-mineralized.

[0038] For example, when the pixel abundance ratio of spodumene in the outer constrained area exceeds a set pixel abundance ratio threshold, it indicates the presence of a significant lithium mineralization halo in that area. Based on this, the first target area (peggiform vein) within it can be inferred to be a ore-bearing body. Conversely, when the pixel abundance ratio of spodumene in the outer constrained area is less than the set pixel abundance ratio threshold, it indicates the presence of a significant lithium mineralization halo in that area. Based on this, the first target area within it can be inferred to be ore-free. The significant advantages of this method are: First, by analyzing the spectral characteristics of the mineralization halo to indirectly infer the main mineralization, it effectively solves the problem of weak mineralization signals and difficulty in direct detection in aeolian sand-covered areas. Second, the use of quantitative discrimination criteria significantly reduces the interference of subjective factors and improves the objectivity and repeatability of the evaluation results. Third, it only requires limited spectral sampling and analysis of the outer area, greatly reducing the workload in the field. It is particularly suitable for areas with harsh natural conditions and difficult manual exploration, providing reliable technical support for the rapid screening and target area selection of concealed-semi-concealed peggiform deposits. Therefore, this method can effectively separate target mineral signals in mixed pixels and suppress background ground object interference, thereby improving the accuracy of mineral evaluation and the reliability of spatial distribution.

[0039] As can be seen, the mineralization evaluation method for geological bodies based on mineral spectral data provided in this disclosure firstly limits the exploration scope to the vicinity of the target geological body by acquiring the outer constraint area of ​​the first target area, thus avoiding the resource waste of blindly exploring the entire aeolian sand-covered area in traditional methods. Secondly, by acquiring the spectral data of multiple mineral samples from the outer constraint area, it is only necessary to collect mineral samples of pegmatite veins or their weathering products in the limited outer area. The outer area is usually thinly or absently covered by aeolian sand, and the strong spectral interference from background rocks and aeolian sand is greatly reduced. By collecting remote sensing spectral data of minerals in this area, purer spectral data can be obtained, thereby improving the signal-to-noise ratio of mineral matching analysis. By performing mineral matching analysis on the spectral data of multiple mineral samples in the outer constraint area and the spectral data of at least one target mineral in the reference spectral database, the mineralization of the large-area first target area can be inferred from the mineral matching information of the outer constraint area. This not only ensures the purity of the spectral data but also eliminates the need to sample the entire aeolian sand-covered area, significantly reducing the amount of fieldwork and improving exploration efficiency.

[0040] The reflectance spectra of rocks and minerals are a crucial foundation for remote sensing information extraction, primarily influenced by their chemical composition, crystal structure, electronic transitions, and molecular vibrations. Characteristic spectral responses and corresponding spectral ranges of typical minerals and their functional groups include: in the visible-near-infrared band (400nm~1000nm), transition metals (such as Fe, Mn, Cr) and rare earth elements form characteristic absorption bands due to their electronic transitions; in the short-wave infrared band (1000nm~2200nm), hydroxyl-containing minerals (such as mica, chlorite), carbonates (such as calcite, dolomite), and sulfates exhibit diagnostic absorption characteristics due to molecular vibrations.

[0041] Currently, the acquisition of rock and mineral reflectance spectral data mainly relies on two types of data sources: standard spectral databases and field-measured spectra. Taking the USGS standard spectral library as an example, it measures high-purity mineral samples under strictly controlled laboratory conditions, such as lighting geometry, humidity, and temperature. This allows for precise characterization of the intrinsic spectral features of minerals, such as characteristic absorption peaks caused by electronic transitions and molecular vibrations. When field measurement conditions are limited, it can provide important references for preliminary identification of mineral types. However, remote sensing practice shows that due to regional differences in aeolian sand cover, vegetation, humidity, and the degree of weathering and roughness of rock and mineral surfaces, although the core characteristic absorption peaks of minerals (such as the absorption of hydroxyl groups near 2200 nm) are relatively stable, the spectral response in non-characteristic bands is easily interfered with by environmental noise, leading to spectral distortion. Therefore, compared to the universal standard spectral library, field-measured spectra conducted in the Altyn Tagh interruption region can more accurately capture the spectral response characteristics under local conditions, significantly improving the accuracy and reliability of mineral evaluation and providing crucial data support for high-precision remote sensing mineral mapping and quantitative inversion.

[0042] Based on this, in one example, the mineralization evaluation method for geological bodies based on mineral spectral data of this disclosure, before performing mineral matching analysis on the spectral data of multiple mineral samples in the peripheral constraint area and the spectral data of at least one target mineral in a reference spectral database, further includes: first, acquiring the spectral data of multiple ore samples in a second target area, wherein the first target area is located within the second target area. Then, based on the spectral data of the multiple ore samples in the second target area, determining a reference spectral database for the second target area, wherein the reference spectral database contains the spectral data of at least one target mineral. It should be understood that the second target area can be defined as the Altyn Discontinuity Region.

[0043] For example, the above-mentioned acquisition of spectral data of multiple ore samples from the second target region further includes: first, acquiring multiple ore samples from the second target region; then, performing spectral measurements on the multiple ore samples from the second target region in a laboratory environment to obtain spectral data.

[0044] For example, the above describes acquiring multiple ore samples from the second target region. Then, in a laboratory environment, spectral measurements are performed on these samples to obtain spectral data. Specifically, this includes: first, systematically collecting representative pegmatite ore samples from the Altyn Tagh interrupted region, covering both ore-bearing and non-ore-bearing types to ensure spatial distribution covers the main metallogenic belts; then, measuring the sample spectra using a high-precision spectrometer under standard laboratory conditions; and after noise filtering and continuum removal, extracting characteristic spectral curves of target minerals such as spodumene and beryl to establish a localized reference spectral database containing spectral data for at least one target mineral. Finally, the accuracy of the spectral data is verified using mineralogical methods such as XRD, forming a regionally specific spectral comparison benchmark.

[0045] Specifically, firstly, representative rock and mineral samples were systematically collected within the second target area. Secondly, reflectance spectroscopy tests were conducted using an SVC-HR-1024 portable hyperspectral analyzer in a controlled-light laboratory environment. A 100W halogen tungsten lamp was used as the illumination source, and signals were acquired through a 25° field-of-view fiber optic probe. A 3-second single scan duration was set, and automatic dark current correction was performed. After a 10-minute warm-up, the instrument was calibrated using a standard white board to ensure that the measurement error was controlled within 1%-3%. Next, dedicated software (such as the SVC-HR-1024 system) was used to remove sensor band overlap noise using the "Remove Overlapped Data" function. The data was then imported into computer analysis software, and the "Overlap Correction" tool was used to accurately correct overlapping bands. After manual verification of outliers, the "File Merge" tool was used to merge the processed data, generating a representative average spectrum for each sample, thereby constructing a high-precision spectral library of typical minerals in the study area. Therefore, through strict environmental control, instrument calibration, and data processing procedures, environmental interference and instrument errors were effectively eliminated, significantly improving the accuracy and reliability of spectral data.

[0046] It should be understood that the SVC-HR-1024 portable hyperspectral analyzer has a spectral range of 350nm to 2200nm and a total of 1024 channels; the resolution in the visible-near infrared band (350nm to 1000nm) is 3.5nm, and the resolutions in the short-wave infrared band are 9.5nm (1000nm to 1850nm) and 6.5nm (1850nm to 2200nm), respectively.

[0047] This disclosure improves the accuracy and reliability of mineral evaluation by pre-establishing a reference spectral library of typical mineralization indicator minerals in the Altyn Tagh region, such as spodumene, lepidolite, beryl, muscovite, albite, and potassium feldspar, and then performing a matching degree analysis between the mineral spectral data of the surrounding constrained area and the spectral information of the reference spectral library.

[0048] In one example, before performing mineral matching analysis on the spectral data of multiple mineral samples in the peripheral constraint area and the spectral data of at least one target mineral in the reference spectral database, the method further includes: resampling the mineral reflectance spectral curves in the constructed reference spectral library to be consistent with the band range and bandwidth of the remote sensing image (WorldView-3) used, so as to eliminate data scale differences.

[0049] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0050] Example Due to significant regional variations in remote sensing data, the main characteristic absorption peaks of similar minerals (such as the OH absorption characteristic of spodumene at 2220 nm) are generally relatively stable. However, the spectral responses in non-characteristic bands are easily affected by environmental factors (such as aeolian sand cover, surface weathering, and vegetation disturbance). Therefore, compared to standard spectral libraries such as the USGS, measured spectra can more effectively reflect the specific spectral characteristics of the target area, providing more targeted support for mineral evaluation.

[0051] Based on this, this embodiment first constructs a region-specific spectral database using measured spectral data, as follows: 1. Sample Collection: During the field investigation, in the pegmatite-type lithium-beryllium deposit exploration area of ​​the Altyn Tagh interruption region (i.e., the second target area), a systematic field geological survey and remote sensing interpretation identified 14 types of pegmatite veins, divided into 8 ore-bearing types and 6 non-ore-bearing types. The ore-bearing pegmatite veins are characterized by minerals such as spodumene, beryl, and lepidolite, while the non-ore-bearing pegmatite veins are mainly composed of potassium feldspar, quartz, and tourmaline. To construct a high-precision regional spectral database, this study adopted a multi-scale sampling strategy, collecting a total of 863 rock samples in the Altyn Tagh interruption region. Figure 2 A schematic diagram of the sampling points in the Altyn interruption region provided by an exemplary embodiment of this disclosure is shown. For example... Figure 2 As shown, the sampling points cover pegmatite veins with different lithologies, mineralization types and weathering degrees, ensuring the representativeness of the samples in terms of spatial distribution and mineral assemblage. Figures 3A-3B Field photographs of lithium-bearing spodumene in the Altyn Disturbance Region provided in exemplary embodiments of this disclosure are shown. Figures 3C-3D A photograph of a lithium-bearing pegmatite sample from the Altyn Disturbance Region provided in an exemplary embodiment of this disclosure is shown. Figures 4A-4B A field photograph of a beryl-bearing area in the Altyn Disturbance region provided in an exemplary embodiment of this disclosure is shown. Figures 4C-4D A photograph of a beryl-bearing pegmatite sample from the Altyn Disturbance Region provided in an exemplary embodiment of this disclosure is shown. Figures 5A-5BField photographs of potassium-bearing feldspar, quartz, and tourmaline in the Altyn Disturbance region provided in exemplary embodiments of this disclosure are shown. Figure 5C Figure 5D Photographs of pegmatite samples containing potassium feldspar, quartz, and tourmaline from the Altyn Disturbance region provided in an exemplary embodiment of this disclosure are shown. Figures 6A-6C A field photograph of sodium-bearing feldspar in an Altyn Disruption region provided by an exemplary embodiment of this disclosure is shown. Figures 6D-6F A photograph of a sodium-bearing pegmatite sample from the Altyn Disturbance region provided in an exemplary embodiment of this disclosure is shown.

[0052] 2. Spectral Measurement: The reflectance spectra of all 863 samples were measured using an SVC-HR-1024 ground object spectrometer (spectral range 350nm~2200nm, resolution 3.5nm-9.5nm) under standard laboratory conditions. The SVC-HR-1024 portable hyperspectral analyzer has a spectral range of 350nm~2200nm and a total of 1024 channels. The resolution in the visible-near infrared band (350nm~1000nm) is 3.5nm, and the resolutions in the short-wave infrared band are 9.5nm (1000nm~1850nm) and 6.5nm (1850nm~2200nm), respectively. The device integrates a high-precision GPS module, which can simultaneously record the spatial coordinates of the sampling points.

[0053] 3. Spectral Data Acquisition: A 100W halogen tungsten lamp was used as the illumination source for spectral data acquisition. This light source has excellent spectral stability. The spectrometer was connected to the computer via a USB interface. With the help of dedicated control software, a 25° field of view fiber optic probe was set for signal acquisition. The single scan time was 3 seconds, and automatic dark current correction was performed. After the instrument warmed up for 10 minutes, standard white board calibration was performed to ensure that the measurement error was controlled within 1%-3%.

[0054] 4. Spectral Data Processing: First, using dedicated software (such as the SVC-HR-1024 system), the "RemoveOverlapped Data" function is used to remove noise caused by overlapping sensor bands. Then, the data is imported into computer-based analysis software, and the "Overlap Correction" tool is used to accurately correct overlapping bands. Abnormal reflectance values ​​are manually checked and corrected. Finally, the "File Merge" tool is used to merge the processed data, ultimately generating a representative average spectrum for each sample and establishing a high-precision spectral database containing attributes such as spatial coordinates, lithology, mineralization type, and weathering degree.

[0055] A systematic spectral measurement and analysis was performed on 863 pegmatite samples. Figure 7 The measured spectral image of the target mineral in the Altyn Discontinuity region according to an embodiment of this disclosure is shown. For example...Figure 7 As shown, the characteristic absorption peak identification results were obtained based on the spectral characteristics of target minerals such as spodumene, lepidolite, beryl, albite, potassium feldspar, and mica: Spodumene exhibits significant absorption valleys at 1413 nm (Band 10), 1913 nm (Band 12), and 2220 nm (Band 14), corresponding to the vibrations of structural water molecules and the stretching vibrations of OH groups, respectively. The absorption depth at 2220 nm has a coefficient of variation of <10%, exhibiting the best stability, and the absorption at 2220 nm can be used as an identification marker.

[0056] Lithium mica: It exhibits unique Al-OH and metal-silicate group absorption characteristics at 2200nm~2220nm (Band 14) and 2350nm (Band 16), which are similar to the spectrum of spodumene, but the 2350nm absorption is its identifying feature.

[0057] Beryl: It exhibits weak absorption peaks at 2400 nm (Band 9), 1900 nm (Band 12) and 2200 nm (Band 14). The absorption at 2200 nm is related to the OH group, but the absorption depth is 30%~40% lower than that of spodumene.

[0058] Sodium feldspar and potassium feldspar: Sodium feldspar has characteristic absorption bands at 2320 nm (Band 15) and potassium feldspar has characteristic absorption bands at 2350 nm (Band 16). The two can be distinguished by the position of the absorption peaks.

[0059] Mica: A significant absorption valley exists at 2200 nm (Band 14), which is related to the vibration of OH groups. Its absorption intensity is positively correlated with the content of muscovite.

[0060] It should be noted that Band9, Band10, Band11, Band12, Band13, Band14, Band15, and Band16 refer to the shortwave infrared bands of the WorldView-3 satellite.

[0061] As can be seen from the above, the absorption feature at 2220 nm (Band 14) corresponds to the inherent vibrational mode of the OH group in the mineral lattice, is less affected by environmental interference, and has a coefficient of variation of <10% in different lithium-bearing pegmatite samples, making it a reliable diagnostic marker. In contrast, the spectral responses of the 1413 nm (Band 10) and 1913 nm (Band 12) bands are closely related to the physicochemical effects of surface moisture and weathering products, and are easily affected by the degree of surface weathering and changes in the microenvironment, resulting in coefficients of variation as high as 12%~30%. Weathering correction and multi-feature fusion are necessary to enhance their application value. This difference highlights the importance of prioritizing highly stable feature bands with strong anti-interference capabilities in the exploration of aeolian sand-covered areas.

[0062] The spectral characteristics of vein-like ore bodies (width < 0.5m) are easily affected by the surrounding rocks. Therefore, the success rate of identifying the above-mentioned target minerals in WorldView-3 imagery is only 61.3%. However, the accuracy of the reference spectral library in this embodiment for identifying the above-mentioned main minerals is as follows: spodumene: 82.4% (high absorption stability at 2220nm), lepidolite: 78.6% (specific absorption characteristics at 2350nm), beryl: 71.2% (weak absorption characteristics, easily affected by mixed pixels), and mica: 85.0% (significant and stable absorption at 2200nm).

[0063] To improve the accuracy of the evaluation, the mineral reflectance spectral curves in the reference spectral library constructed in this embodiment are resampled to match the band range and bandwidth of the remote sensing image (WorldView-3) used, so as to eliminate data scale differences.

[0064] Figure 8 An interpretation diagram of the pegmatite dike in the middle section of the Altyn Tagh formation, according to an embodiment of this disclosure, is shown. For example... Figure 8 As shown, the pegmatite dikes in the middle section of the Altyn Tagh exhibit a distinct zonal cluster distribution in space, mainly controlled by regional fault structures. The dikes trend mainly in the northeast-east and north-northwest directions, with lengths ranging from 50m to 1200m and widths mostly concentrated between 1m and 5m.

[0065] Figure 9 A schematic diagram of the sampling area of ​​the Shaliangxi pegmatite dike, according to an embodiment of this disclosure, is shown. Figure 9 As shown, the pegmatite veins interpreted in this disclosure are clearly marked with different levels of mineralization. Red pegmatite veins contain spodumene, lepidolite, or beryl, while yellow pegmatite veins do not contain these key minerals and have weak or virtually no lithium or beryllium minerals. Blue boxes indicate favorable areas of the pegmatite veins, and red boxes indicate favorable areas for spodumene.

[0066] then, Figure 10 Embodiments of this disclosure are shown. Figure 9 A schematic diagram of the spatial influence zone of the pulse body within the red box area. Figure 10 As shown, according to the method of this embodiment, firstly... Figure 9 The red boxes enclose each vein of the ore-bearing pegmatite vein, and from these veins, a 10m-15m outer constraint zone (spatial influence zone) is demarcated. Figure 10 (Middle purple band), then to Figure 10 Mineral mapping was performed on the purple band in the diagram to verify the effectiveness of this disclosure in determining the mineralization of the first target area using the outer constraint region. Figure 11 It shows Figure 10 Mineral mapping of the mid-space influence zone. For example... Figure 11 It is evident, it is evident Figure 10The presence of spodumene (red), albite (green), and muscovite (blue) in the vein indicates that the mineral evaluation method of this embodiment is effective in evaluating the mineralization of pegmatite veins.

[0067] This embodiment also delineates five areas in the Altyn Tagh interruption region where pegmatite dikes are most concentrated. Figure 12 The diagram shows the distribution of five regions where pegmatite veins are most concentrated, according to embodiments of this disclosure. For example... Figure 12 As shown, the five pegmatite veins are named as Zone I, Zone II, Zone III, Zone IV and Zone V, respectively. Zone I is the South Zone of Washi Gorge, Zone II is the South Zone of Washi Gorge, Zone III is the River Zone of Washi Gorge, Zone IV is the Ayage Zone and Zone V is the Tugeman Zone. The density of pegmatite veins in these areas is significantly higher than that in the surrounding areas, and they are clearly spatially coupled with the regional fault structures. Figure 13 This disclosure shows a distribution map of another concentrated area of ​​pegmatite dikes delineated in the middle section of the Altyn Tagh Mountains, as illustrated in this embodiment. Figure 13 As shown, another area with the highest concentration of pegmatite dikes is delineated, named Area VI, which is the western part of Shaliang. Using WorldView-3 remote sensing technology, a total of 612 pegmatite dikes were identified in the six pegmatite dike areas. According to the method of this embodiment, a 10m-15m outer constraint area is first delineated around each pegmatite dike. Then, remote sensing image data of the outer constraint area of ​​each pegmatite dike is acquired. Spectral data is then obtained based on the remote sensing image data. Next, the acquired spectral data is compared with the spectral data in the reference database of this embodiment to determine the mineralization of each dike in the six pegmatite dike areas of this embodiment. Figure 14 The following diagrams illustrate statistical figures of ore-bearing and ore-free veins in six pegmatite vein regions according to embodiments of this disclosure: Figure 14 As shown, of the 612 pegmatite veins identified in the six pegmatite vein regions, 186 (30.4%) are ore-bearing veins, and 426 (69.6%) are non-ore-bearing veins. Mineralization classification and geological characteristic correlation analysis were performed on the 612 pegmatite veins identified in the six regions to obtain data on the scale, occurrence, and mineral assemblage characteristics of the pegmatite veins in each region. See Table 1 below: Table 1: Statistical Table of Geological Characteristics of Six Pegmatite Dike Regions

[0068] This embodiment also conducted field verification in Zones III, IV, and V of the aforementioned Altun Mountains central section. First, using tools such as a spectrometer, GPS equipment, digital camera, geological hammer, and compass, a detailed survey was carried out at 158 ​​locations. Representative locations were then selected and marked on the map. For example... Figure 12 and Figure 13As shown, the blue dots mark representative locations. It can be seen that different types of pegmatite vein samples were collected from the target area to ensure sample diversity. These pegmatite vein samples cover both lithium-beryllium ore-bearing and lithium-beryllium ore-free pegmatites. Simultaneously, comparative samples such as surrounding rocks were collected to analyze the differences between remote sensing and actual mineralization characteristics. On-site spectral measurements were performed on the collected samples, focusing on the absorption characteristics in the SWIR band. The measured spectra were then compared with the spectral database collected in this embodiment to verify whether they match the spectral characteristics of lithium-beryllium ore.

[0069] Specifically, two of the most representative locations will be selected for introduction: Figure 15 A field outcrop photograph of a pegmatite vein at point 5 in area III, according to an embodiment of this disclosure, is shown. Figure 15 As shown, point 5 in Zone III: This point is located in the Mesoproterozoic Altun Group, with lithology mainly consisting of two-mica schist. A north-northeast trending fault also crosses this location. Macroscopically, three mineral-free pegmatite dikes were discovered at this point, trending at 80°, with widths of 2-4 meters and extending approximately 1 km. Figure 16 A photograph of a pegmatite vein sample collected at point 5 in area III, according to an embodiment of this disclosure, is shown. Figure 16 As shown, the collected vein sample contains potassium feldspar, tourmaline, and quartz minerals. Potassium feldspar is typically grayish-white to light pinkish-red, occurring as tabular or columnar crystals, generally 5mm to 30mm in size, and is characterized by a mass-like distribution with a vitreous luster. Tourmaline is black, often appearing as needle-like or radial aggregates, approximately 2mm to 10mm in length. Quartz is mainly smoky gray or colorless, existing as mass-like aggregates with a greasy luster, and generally 2mm to 8mm in size. Figure 17 A micrograph of the pegmatite dike sample collected at point 5 in area III of this disclosure is shown. Figure 17 As shown, potassium feldspar, tourmaline, and quartz minerals were also identified under a microscope. It can be seen that potassium feldspar accounts for 35% to 50% of the entire thin section, tourmaline accounts for 10% to 15%, and quartz accounts for 30% to 35%.

[0070] Figure 18 A field outcrop photograph of the pegmatite vein at point 1 in zone V, according to an embodiment of this disclosure, is shown. Figure 18 As shown, this point is located within the Tugman pluton, and the lithology is mainly two-mica granite. Macroscopically, two spodumene-bearing pegmatite veins were discovered at this point, with a strike of 75°, a width of 0.2m to 1.5m, and an extension of approximately 750m. Figure 19 A photograph of a pegmatite vein sample collected at point 1 in zone V, according to an embodiment of this disclosure, is shown. Figure 19As shown, the collected vein sample reveals spodumene, albite, and quartz minerals. The spodumene in the hand specimen is grayish-white, exhibiting platy and columnar morphologies, ranging from 3 cm to 13 cm in length. The quartz is colorless and transparent, forming granules with a grain size between 2 cm and 6 cm. The albite is gray, exhibiting subhedral platy-columnar morphologies, ranging from 2 cm to 8 cm in length. Figure 20 A micrograph of the pegmatite dike sample collected at point 1 in zone V, according to an embodiment of this disclosure, is shown. Figure 20 As shown, spodumene and albite minerals were also identified under a microscope. Spodumene accounts for 12%–30% of the entire thin section, and albite accounts for 20%–32%.

[0071] Based on this, this embodiment demonstrates that the pegmatite veins in the Altyn Tagh interrupted region contain abundant lithium, primarily found in spodumene and lepidolite, with a typical mineral assemblage consisting of spodumene, albite, muscovite, and quartz. The pegmatite veins are distributed in a banded pattern, with a width of 1.2m–3.5m and an extension length of 50m–1200m, highly consistent with WorldView-3 remote sensing interpretation results. Therefore, this indicates that the pegmatite veins in this region have a relatively long extension on the surface and a relatively stable mineral assemblage. The spectral and geological attribute data of the rock samples collected in this embodiment have high accuracy and representativeness, providing important evidence for mineral exploration.

[0072] This embodiment also conducted engineering verification for Zone VI. Among the 88 newly identified pegmatite veins in Zone VI (Shaliang West), after delineating the outer constraint region according to the method of this embodiment, spectral data of the outer constraint region was collected and then analyzed for matching degree with the reference spectral library of this embodiment. The analysis results showed that 28 veins in Zone VI were predicted to be ore-bearing pegmatite veins. After verification through drilling engineering, 25 lithium-bearing pegmatite veins were actually delineated, indicating a prediction accuracy of 89%.

[0073] The foregoing primarily describes the solutions provided by the embodiments of this disclosure from the perspective of the server. It is understood that, in order to implement the above functions, the server includes the corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this disclosure can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0074] This disclosure embodiment can divide the server into functional units according to the above method example. For example, it can divide each function into a separate functional module, or it can integrate two or more functions into one processing module. The integrated module can be implemented in hardware or as a software functional module. It should be noted that the module division in this disclosure embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0075] In the case of dividing each functional module according to its corresponding functions, this disclosure also provides a geological body mineralization evaluation device based on mineral spectral data. This geological body mineralization evaluation device based on mineral spectral data can be a server or a chip applied to a server. Figure 21 A schematic block diagram of the functional modules of a geological body mineralization evaluation apparatus based on mineral spectral data according to an exemplary embodiment of this disclosure is shown. Figure 21 As shown, the mineralization evaluation device 2100 based on mineral spectral data includes: The first acquisition module 2101 is used to acquire the outer constraint region of the first target region; The second acquisition module 2102 is used to acquire spectral data of multiple mineral samples in the peripheral constrained area; The determination module 2103 is used to perform mineral matching degree analysis on the spectral data of multiple mineral samples in the peripheral constraint area and the spectral data of at least one target mineral in the reference spectral database to determine the mineralization of the first target area.

[0076] As one possible implementation, the above-mentioned mineral matching degree analysis method employs a hybrid tuned matched filtering method.

[0077] In some alternative embodiments, the determining module 2103 is further configured to determine that the first target region contains minerals if the mineral matching degree between the spectral data of multiple mineral samples in the peripheral constraint region and the reference spectral data of at least one target mineral is greater than or equal to a preset mineral matching degree; and to determine that the first target region does not contain minerals if the mineral matching degree between the spectral data of multiple mineral samples in the peripheral constraint region and the reference spectral data of at least one target mineral is less than a preset mineral matching degree.

[0078] In some alternative methods, the first acquisition module 2101 is further configured to acquire spatial distribution data of the first target area, determine the boundary of the first target area based on the spatial distribution data, and acquire the peripheral constraint area surrounding the boundary based on the boundary of the first target area.

[0079] In some alternative approaches, the target minerals mentioned above include one or more of spodumene, lepidolite, and beryl.

[0080] Figure 22 A schematic block diagram of a chip according to an exemplary embodiment of the present disclosure is shown. Figure 22 As shown, the chip 2200 includes one or more processors 2201 and a communication interface 2202. The communication interface 2202 can support the server in performing the data transmission and reception steps in the above-described image processing method, and the processor 2201 can support the server in performing the data processing steps in the above-described image processing method.

[0081] Optional, such as Figure 22 As shown, the chip 2200 also includes a memory 2203, which may include read-only memory and random access memory, and provides operation instructions and data to the processor. A portion of the memory may also include non-volatile random access memory (NVRAM).

[0082] In some implementations, such as Figure 22 As shown, processor 2201 executes corresponding operations by calling operation instructions stored in memory (which may be stored in the operating system). Processor 2201 controls the processing operations of any terminal device; processor can also be called a central processing unit (CPU). Memory 2203 may include read-only memory and random access memory, and provides instructions and data to processor 2201. A portion of memory 2203 may also include NVRAM. For example, in applications, memory, communication interfaces, and other components are coupled together via a bus system, which may include, in addition to a data bus, a power bus, a control bus, and a status signal bus, etc. However, for clarity, in... Figure 22 The general designated all buses as Bus System 2204.

[0083] The methods disclosed in the embodiments of this disclosure can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above methods can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, a digital signal processor (DSP), an ASIC, a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this disclosure. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this disclosure can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above methods.

[0084] Exemplary embodiments of this disclosure also provide an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, which, when executed by the at least one processor, causes the electronic device to perform a method according to an embodiment of this disclosure.

[0085] Exemplary embodiments of this disclosure also provide a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform a method according to embodiments of this disclosure.

[0086] Exemplary embodiments of this disclosure also provide a computer program product, including a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform a method according to an embodiment of this disclosure.

[0087] refer to Figure 23The present invention describes a structural block diagram of an electronic device 2300 that can serve as a server or client of the present disclosure, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0088] like Figure 23 As shown, the electronic device 2300 includes a computing unit 2301, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 2302 or a computer program loaded from a storage unit 2308 into a random access memory (RAM) 2303. The RAM 2303 may also store various programs and data required for the operation of the electronic device 2300. The computing unit 2301, ROM 2302, and RAM 2303 are interconnected via a bus 2304. An input / output (I / O) interface 2305 is also connected to the bus 2304.

[0089] Multiple components in electronic device 2300 are connected to I / O interface 2305, including: input unit 2306, output unit 2307, storage unit 2308, and communication unit 2309. Input unit 2306 can be any type of device capable of inputting information to electronic device 2300. Input unit 2306 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of electronic device. Output unit 2307 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 2308 may include, but is not limited to, disk and optical disk. Communication unit 2309 allows electronic device 2300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth™ devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0090] The computing unit 2301 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 2301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 2301 performs the various methods and processes described above. For example, in some embodiments, the methods of exemplary embodiments of this disclosure can be implemented as computer software programs tangibly contained in a machine-readable medium, such as storage unit 2308. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 2300 via ROM 2302 and / or communication unit 2309. In some embodiments, the computing unit 2301 can be configured to perform the methods of exemplary embodiments of this disclosure by any other suitable means (e.g., by means of firmware).

[0091] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0092] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0093] As used in this disclosure, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0094] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0095] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0096] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other.

[0097] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this disclosure are performed entirely or partially. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a terminal, a user equipment, or other programmable device. The computer program or instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; it can also be an optical medium, such as a digital video disc (DVD); or it can be a semiconductor medium, such as a solid-state drive (SSD).

[0098] Although this disclosure has been described in conjunction with specific features and embodiments, it will be apparent that various modifications and combinations can be made therein without departing from the spirit and scope of this disclosure. Accordingly, this specification and drawings are merely exemplary illustrations of the disclosure as defined by the appended claims and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this disclosure. It is obvious that those skilled in the art can make various alterations and modifications to this disclosure without departing from its spirit and scope. Thus, this disclosure is also intended to include any such modifications and modifications that fall within the scope of the claims of this disclosure and their equivalents.

Claims

1. A method for evaluating ore potential of a geological body based on mineral spectral data, characterized by, The method comprises the following steps: acquiring a peripheral constraint region of a first target region; acquiring spectral data of a plurality of mineral samples in the peripheral constraint region; performing mineral matching degree analysis on the spectral data of the plurality of mineral samples in the peripheral constraint region and spectral data of at least one target mineral in a reference spectral database to determine the ore-bearing property of the first target region.

2. The method for evaluating the ore potential of a geological body based on mineral spectral data according to claim 1, characterized in that, The mineral matching degree analysis method adopts a hybrid tuning matching filter method.

3. The method for evaluating the ore potential of a geological body based on mineral spectral data according to claim 2, characterized in that, The mineral matching degree analysis on the spectral data of the plurality of mineral samples in the peripheral constraint region and the spectral data of at least one target mineral in the reference spectral database to determine the ore-bearing property of the first target region comprises: if the mineral matching degree between the spectral data of the plurality of mineral samples in the peripheral constraint region and the reference spectral data of at least one target mineral is greater than or equal to a preset mineral matching degree, it is determined that the first target region is ore-bearing; if the mineral matching degree between the spectral data of the plurality of mineral samples in the peripheral constraint region and the reference spectral data of at least one target mineral is less than the preset mineral matching degree, it is determined that the first target region is not ore-bearing.

4. The method for evaluating the ore potential of a geological body based on mineral spectral data according to claim 1, characterized in that, The acquisition of the peripheral constraint region of the first target region comprises: acquiring spatial distribution data of the first target region; determining the boundary of the first target region based on the spatial distribution data; acquiring a peripheral constraint region surrounding the boundary of the first target region based on the boundary of the first target region.

5. The method for evaluating the ore potential of a geological body based on mineral spectral data according to claim 1, characterized in that, Before the mineral matching degree analysis on the spectral data of the plurality of mineral samples in the peripheral constraint region and the spectral data of at least one target mineral in the reference spectral database, the method further comprises: acquiring spectral data of a plurality of ore samples of a second target region, the first target region being located in the second target region; determining a reference spectral database of the second target region based on the spectral data of the plurality of ore samples of the second target region, the reference spectral database containing spectral data of at least one target mineral.

6. The method for evaluating the ore potential of a geological body based on mineral spectral data according to claim 5, characterized in that, The acquisition of the spectral data of the plurality of ore samples of the second target region comprises: acquiring a plurality of ore samples of the second target region; performing spectral measurement on the plurality of ore samples of the second target region in a laboratory environment to obtain spectral data.

7. The method for ore potential assessment of a geological body based on mineral spectral data according to any one of claims 1 to 6, characterized in that, The target mineral comprises one or more of spodumene, lepidolite, and amazonite.

8. A device for evaluating ore potential of a geological body based on mineral spectral data, characterized by, The device comprises: a first acquisition module configured to acquire a peripheral constraint region of a first target region; a second acquisition module configured to acquire spectral data of a plurality of mineral samples in the peripheral constraint region; a determination module configured to perform mineral matching degree analysis on the spectral data of the plurality of mineral samples in the peripheral constraint region and spectral data of at least one target mineral in a reference spectral database to determine the ore-bearing property of the first target region.

9. An electronic device, comprising: The device comprises: a processor; and a memory storing a program; wherein the program comprises instructions that, when executed by the processor, cause the processor to perform the method according to any one of claims 1-7.

10. A computer program product, characterised in that, The computer program comprises a computer program which, when executed by a processor of a computer, is adapted to cause the computer to carry out the method according to any one of claims 1-7.

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