A prospecting method and system for characterizing tectonic stress based on rock fragmentation degree

Abstract

The present invention provides a prospecting method and system for characterizing tectonic stress based on the degree of rock fragmentation. This method relates to the field of geological exploration technology and aims to address the low accuracy of mineralization zone prediction in existing technologies. The method comprises: acquiring image data and sample data of the area to be predicted; constructing a first inversion model based on the sample data; and inverting the spectral index of target geological characteristic factors of the area to be predicted based on the first inversion model and hyperspectral imagery to obtain spectral indices of target geological characteristic factors of the area to be predicted; the target geological characteristic factors include crystallinity, alteration component concentration, and degree of weathering; fusing the fracture characteristics extracted from the high-spatial-resolution imagery with the spectral indices of the target geological characteristic factors to obtain a characteristic matrix; and inputting the characteristic matrix into a second inversion model to determine the target mineralization zone in the area to be predicted; the second inversion model is a pre-constructed tectonic stress field inversion model based on a graph convolutional neural network. This method can improve the accuracy of mineralization zone prediction.

Description

Technical Field

[0001] The present invention relates to the field of geological exploration technology, and in particular to a prospecting method and system for characterizing tectonic stress based on the degree of rock fragmentation. Background Art

[0002] In mineral resource exploration, the study of tectonic stress fields is crucial for uncovering mineralization patterns. Traditional mining inversion methods often rely on a single data source or qualitative analysis, making it difficult to accurately invert complex tectonic stress fields and quantitatively predict ore-enrichment locations.

[0003] In areas with complex tectonic settings and alteration zone characteristics, there is an urgent need for a high-precision mineralization prediction method that integrates multi-source data. Summary of the Invention

[0004] The purpose of the present invention is to provide a prospecting method and system based on the characterization of tectonic stress by the degree of rock fragmentation, so as to improve the prediction accuracy of mineralization areas.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] In a first aspect, the present invention provides a prospecting method for characterizing tectonic stress based on the degree of rock fragmentation, comprising: acquiring image data and sample data of a region to be predicted, the image data comprising high spatial resolution images and hyperspectral images, and the sample data being data corresponding to rock samples in a sample area of the region to be predicted;

[0007] Constructing a first inversion model based on the sample data, and performing inversion on the area to be predicted based on the first inversion model and the hyperspectral image to obtain a spectral index of a target geological characteristic factor of the area to be predicted; the target geological characteristic factor includes crystallinity, alteration component concentration, and weathering degree;

[0008] fusing the fracture characteristics extracted from the high spatial resolution image with the spectral index of the target geological characteristic factor to obtain a characteristic matrix;

[0009] The characteristic matrix is input into a second inversion model to determine the target mineralization area of the area to be predicted; the second inversion model is a tectonic stress field inversion model pre-constructed based on a graph convolutional neural network.

[0010] Optionally, the sample data includes X-ray data and spectral reflectance data obtained based on a short-wave infrared spectrometer;

[0011] Constructing a first inversion model based on the sample data, comprising:

[0012] constructing a spectral library based on the spectral reflectance data;

[0013] Calculating an index of the target geological characteristic factor based on the X-ray data;

[0014] Extracting a target waveband whose exponential correlation with the target geological characteristic factor is greater than or equal to a preset value from the spectral library, and constructing a spectral index of the target geological characteristic factor based on the target waveband;

[0015] The first inversion model is determined based on the spectral index of the target geological characteristic factor.

[0016] Optionally, the X-ray data at least includes X-ray diffraction data obtained based on an X-ray diffraction instrument;

[0017] Calculating the index of the target geological characteristic factor based on the X-ray data includes:

[0018] determining an X-ray diffraction pattern based on the X-ray diffraction data;

[0019] Based on the X-ray diffraction pattern, the formula is used:

[0020] ;

[0021] The crystallinity index is calculated; wherein, is the index of the crystallinity; is the area under a specific diffraction peak; is the total diffraction signal area; is the background signal; 1 is the starting angle; 2 is the end angle; is the angle of incidence; is the diffraction angle; is the lower limit of the integral; The upper limit of points.

[0022] Optionally, the X-ray data further includes X-ray fluorescence data obtained based on an X-ray fluorescence instrument;

[0023] Calculating the index of the target geological characteristic factor based on the X-ray data includes:

[0024] Based on the X-ray fluorescence data, an index of the alteration component concentration and an index of the weathering degree are calculated.

[0025] Optionally, determining the index of the alteration component concentration based on the X-ray fluorescence data includes:

[0026] Analyzing and obtaining target elements of alteration components in the rock sample; the alteration components include at least one or more of chlorite, muscovite, and pyrite; and the target elements include one or more of Fe, Mg, Al, and K;

[0027] extracting the concentration of the target element from the X-ray fluorescence data;

[0028] Using the formula:

[0029] ;

[0030] The index of the concentration of the alteration component is calculated; wherein, is an index of the concentration of the alteration component, is the concentration of the target element; is the background concentration of the target element.

[0031] Optionally, determining the weathering degree index based on the X-ray fluorescence data includes:

[0032] Extracting the content of target oxides from the X-ray fluorescence data; the target oxides include easily weathered oxides and stable oxides;

[0033] The ratio of the content of the easily weathered oxide to the content of the stable oxide is determined as the index of the weathering degree.

[0034] Optionally, inputting the characteristic matrix into a second inversion model to determine the target mineralization area of the area to be predicted includes:

[0035] Inputting the characteristic matrix into a second inversion model to obtain the tectonic stress field distribution of the area to be predicted;

[0036] Based on the tectonic stress field distribution and the preset mapping benchmark, a GIS tool is used to generate a mineralization potential mapping unit; the mineralization potential mapping unit includes a high potential area, a medium potential area, and a low potential area;

[0037] The high potential area is determined as the target mineralization area.

[0038] Optionally, the process of extracting crack features includes:

[0039] Processing the high spatial resolution image using a preset deformable convolution and a preset directional filter to obtain a crack feature map;

[0040] The crack characteristics are extracted from the crack characteristic map; the crack characteristics include crack density, crack direction and crack length.

[0041] Compared with the prior art, the present invention provides a prospecting method for characterizing tectonic stress based on the degree of rock fragmentation, obtains image data and sample data of the area to be predicted, constructs an inversion model using the sample data, and inverts the area to be predicted to obtain target indices corresponding to the crystallinity, concentration of alteration components and degree of weathering, thereby solving the problem of low data reliability caused by the use of only a single data source of image data in the prior art. Then, data fusion is performed on the crack characteristics extracted from the high spatial resolution image and the spectral index of the target geological characteristic factor to obtain a characteristic matrix. Finally, the characteristic matrix is input into a tectonic stress field inversion model based on a graph convolutional neural network to determine the target mineralization area of the area to be predicted. In this way, the output result of the tectonic stress field inversion model is more accurate, thereby improving the prediction accuracy of the mineralization area of the area to be predicted.

[0042] In a second aspect, the present invention further provides a prospecting system for characterizing tectonic stress based on the degree of rock fragmentation, comprising: a data acquisition module and a data processor; the data acquisition module is in communication with the data processor;

[0043] The data acquisition module includes a drone; the drone is equipped with a high-definition camera and a hyperspectral camera; the high-definition camera is used to collect high spatial resolution images of the area to be predicted, and the hyperspectral camera is used to collect hyperspectral images of the area to be predicted;

[0044] The data processor is used to obtain image data and sample data of the area to be predicted, wherein the image data includes a hyperspectral image and a high spatial resolution image, and the sample data is data corresponding to rock samples in a sample area in the area to be predicted;

[0045] Constructing a first inversion model based on the sample data, and performing inversion on the area to be predicted based on the first inversion model and the hyperspectral image to obtain a spectral index of a target geological characteristic factor of the area to be predicted; the target geological characteristic factor includes crystallinity, alteration component concentration, and weathering degree;

[0046] fusing the fracture characteristics extracted from the high spatial resolution image with the spectral index of the target geological characteristic factor to obtain a characteristic matrix;

[0047] The characteristic matrix is input into a second inversion model to determine the target mineralization area of the area to be predicted; the second inversion model is a tectonic stress field inversion model pre-constructed based on a graph convolutional neural network.

[0048] Optionally, the data acquisition module further includes a short-wave infrared spectrometer, an X-ray diffraction instrument, and an X-ray fluorescence instrument;

[0049] The short-wave infrared spectrometer is used to analyze and obtain spectral reflectance data of rock samples in the sample area;

[0050] The X-ray diffraction instrument is used to collect X-ray diffraction data of the rock sample;

[0051] The X-ray fluorescence instrument is used to collect X-ray fluorescence data of the rock sample. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0053] Figure 1 One of the flow diagrams of a prospecting method for characterizing tectonic stress based on the degree of rock fragmentation provided by one embodiment of the present invention;

[0054] Figure 2 A schematic diagram of rock samples with different degrees of fragmentation provided by one embodiment of the present invention;

[0055] Figure 3 A second flow chart of a prospecting method for characterizing tectonic stress based on the degree of rock fragmentation provided by an embodiment of the present invention;

[0056] Figure 4 An indication map of a target mineralization area provided in one embodiment of the present invention;

[0057] Figure 5 One of the structural schematic diagrams of a prospecting system for characterizing tectonic stress based on the degree of rock fragmentation provided by one embodiment of the present invention;

[0058] Figure 6 The second structural schematic diagram of a prospecting system for characterizing tectonic stress based on the degree of rock fragmentation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0059] To facilitate a clear description of the technical solutions of the embodiments of the present invention, the words "first" and "second" are used in the embodiments of the present invention to distinguish between identical or similar items with substantially the same functions and effects. For example, the first threshold and the second threshold are merely used to distinguish between different thresholds and do not limit their order. Those skilled in the art will understand that the words "first" and "second" do not limit the quantity or execution order, and the words "first" and "second" do not necessarily mean different.

[0060] It should be noted that, in the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the present invention should not be interpreted as being more preferred or advantageous than other embodiments or design schemes. To be precise, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way. In the present invention, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships can exist.

[0061] like Figure 1 As shown, an embodiment of the present invention provides a prospecting method for characterizing tectonic stress based on the degree of rock fragmentation, which may include:

[0062] Step 110: Obtain image data and sample data of the area to be predicted, where the image data includes high spatial resolution images and hyperspectral images, and the sample data is data corresponding to rock samples in the sample area of the area to be predicted.

[0063] It is understandable that before obtaining sample data and image data, the following steps need to be performed:

[0064] (1) Based on the degree of rock fragmentation in the rock sample, provide clear classification standards for the degree of fragmentation: for example, high degree of fragmentation (close to the fault zone layer), medium degree of fragmentation and low degree of fragmentation (for example, intact surrounding rock).

[0065] Based on actual surveys, several small areas are selected in the area to be predicted as sample areas for collecting rock samples. Rock samples include but are not limited to: ① Broken rock samples: including severely broken to slightly broken rocks. ② Surrounding rock samples: select intact surrounding rocks that are not significantly affected by crushing. ③ Altered mineral samples: collected from the center of the alteration zone or fracture zone, closely related to hydrothermal activity such as chlorite, muscovite, etc. ④ Weathered mineral samples: collected from the surface of the rock mass or weathering crust, directly exposed to the atmospheric environment such as limonite, etc. ⑤ Non-mineral samples. Rock samples need to cover at least the alteration zone and fracture zone. It is understandable that the collection of non-mineral samples can ensure that the samples are representative.

[0066] Combine Figure 2 Sorting out the classification and characteristics of rock samples:

[0067] High fragmentation: crack density is high and the directions are diverse.

[0068] Moderate fragmentation: The cracks are sparse and have a certain consistency in direction.

[0069] Low chipping: Few or almost no cracks.

[0070] After collecting rock samples, the samples are numbered, classified, and geological background information is recorded to establish mapping units. The classification standards for mapping units are as follows:

[0071] High potential mapping units:

[0072] Stress field characteristics: high stress concentration, manifested as densely distributed fractures, diverse crack directions, and the formation of obvious stress concentration areas.

[0073] Degree of fragmentation: The rock is significantly broken, the cracks in the fragmentation unit are dense and wide, the fillings are enriched with mineralization characteristics, and alteration minerals (such as chlorite and muscovite) are widely distributed.

[0074] Mineralization anomaly: The mineral concentration is significantly high, the alteration component is highly enriched, and the chemical weathering index is low (high degree of weathering), showing strong mineralization indications.

[0075] Medium potential mapping unit:

[0076] Stress field characteristics: moderate stress distribution, moderate crack density, and relatively consistent crack direction, indicating a moderate stress field response.

[0077] Degree of fragmentation: The rock is moderately fragmented, with limited but visible cracks within the fragmentation unit, local fillings and mineralization, and sparse distribution of alteration minerals.

[0078] Mineralization anomaly: The mineral concentration and alteration component enrichment are moderate, the chemical weathering index is in a moderate range (showing certain weathering characteristics), and the mineralization potential is indicative to a certain extent.

[0079] Low potential mapping units:

[0080] Stress field characteristics: The stress field distribution is weak, the cracks are sparse and have a single direction, and the stress concentration effect within the mapping unit is not significant.

[0081] Degree of fragmentation: The rock is basically intact, with only a few cracks in the fragmented units, no obvious filling features, and few or absent alteration minerals.

[0082] Mineralization anomaly: low mineral concentration, poor alteration components, high chemical weathering index (low degree of weathering), and no obvious signs of mineralization.

[0083] (2) Use drones to collect image data. First, the drone is equipped with a high-definition camera to collect high-spatial resolution images of the area to be predicted; second, the drone is equipped with a hyperspectral camera to collect hyperspectral images of the area to be predicted.

[0084] Step 120: Construct a first inversion model based on the sample data, and perform inversion on the area to be predicted based on the first inversion model and the hyperspectral image to obtain spectral indices of target geological characteristic factors of the area to be predicted; the target geological characteristic factors include crystallinity, alteration component concentration, and weathering degree.

[0085] Specifically, crystallinity affects spectral characteristics: Materials with high crystallinity have regular atomic arrangements and relatively regular lattice vibrations, typically exhibiting sharp, distinct absorption or emission peaks in the spectrum. In contrast, materials with low crystallinity have more defects and disorder in their atomic arrangements, resulting in relatively broad and weak spectral peaks, and some characteristic peaks may even become obscured.

[0086] After obtaining the first inversion model, quantitative analysis of the hyperspectral imagery can yield information about the crystallinity of the material. For example, in X-ray diffraction spectra, samples with high crystallinity exhibit sharp, intense diffraction peaks with narrow half-widths. Based on parameters such as the intensity and width of the diffraction peaks, the ratio of the diffraction peak area to the integrated area of the entire diffraction pattern is used to quantify the mineral's crystallinity.

[0087] Alternatively, the Scherrer equation can be used to estimate the crystal grain size and thus infer the degree of crystallinity. It is understood that in infrared spectroscopy and Raman spectroscopy, the crystallinity of a substance can also be qualitatively or semi-quantitatively analyzed by the shape and intensity ratio of characteristic peaks.

[0088] The concentration of alteration components changes spectral characteristics: Different alteration components have specific spectral absorption or emission characteristics. When the concentration of an alteration component changes, the intensity of the corresponding spectral signal also changes. For example, iron oxides are a common alteration component in rocks. As the concentration of iron oxides increases, the absorption valley of the rock spectrum in the visible-near-infrared band deepens and the reflectivity decreases, especially at wavelengths associated with iron ion electronic transitions, such as 0.45μm, 0.52μm, and 0.63μm.

[0089] Establishing a quantitative relationship model between spectral characteristic parameters and alteration component concentrations, i.e., the first inversion model described above (e.g., the multivariate linear regression method), can invert the alteration component concentrations. In this embodiment, by acquiring hyperspectral data, the distribution range and relative concentration of altered minerals can be effectively identified and delineated.

[0090] The degree of weathering causes spectral changes: As weathering progresses, the composition and structure of rocks or minerals change, leading to shifts in spectral characteristics. In the early stages of weathering, mineral particles on the rock surface begin to break down and decompose, which may manifest as a decrease in the intensity or shift in the position of some characteristic peaks in the spectrum. In later stages of weathering, the rock may form a large number of secondary minerals, such as clay minerals, and new absorption features associated with these secondary minerals will appear in the spectrum. For example, when clay minerals such as kaolin are formed after weathering of granite, absorption peaks associated with the hydroxyl groups in kaolin will appear in the near-infrared spectrum.

[0091] Spectral analysis of weathering degree: The degree of weathering can be analyzed and determined based on spectral characteristics. By extracting characteristic parameters from the spectrum, such as spectral slope, absorption peak depth, and reflectivity ratio, and comparing and statistically analyzing them with indicators related to weathering degree, a relationship between the two can be established. For example, the relative intensity changes in the absorption characteristics of iron ions and clay minerals in the visible-near-infrared spectrum can be used to classify the weathering grade of a rock.

[0092] From the above, it can be seen that the purpose of this embodiment is to construct a first inversion model using sample data from the sample area, and then substitute the hyperspectral image into the first inversion model to invert the crystallinity, alteration component concentration and weathering degree of the entire predicted area.

[0093] Existing technologies often only obtain spectral images and directly use existing inversion models to invert and then obtain mining area prediction results. That is to say, for the sake of convenience, existing technologies often do not collect and analyze new sample data. This has the following shortcomings: First, historical inversion data may not be collected from the area to be predicted but sample data from other areas. Such a prediction process will definitely make the mining area prediction results inaccurate and the mineralization area prediction accuracy low because it is not applicable to the new area to be predicted. Second, the existing inversion model is definitely not as accurate as the inversion constructed using new sample data. Therefore, it is definitely not applicable to the new area to be predicted, which will make the mining area prediction results inaccurate and the mineralization area prediction accuracy low.

[0094] The new sample data used in this embodiment to construct the inversion model and predict the mining area has accurate data and high data precision, which makes the final mining area prediction result more accurate.

[0095] Step 130: Fusing the fracture features extracted from the high spatial resolution image with the spectral index of the target geological characteristic factor to obtain a characteristic matrix.

[0096] It is understandable that fracture characteristics and the degree of rock fragmentation are related.

[0097] Step 140: Input the characteristic matrix into the second inversion model to determine the target mineralization area of the area to be predicted; the second inversion model is a tectonic stress field inversion model pre-built based on a graph convolutional neural network.

[0098] In existing technologies, once fracture characteristics are acquired, they are directly used to construct the subsequent tectonic stress field inversion model. This considers a relatively limited number of factors, resulting in low inversion accuracy. This embodiment associates three geological characteristic factors—crystallinity, alteration component concentration, and weathering degree—with fracture characteristics. This allows the graph neural network to learn the relationship between fracture characteristics and a wider range of associated factors (crystallinity, alteration component concentration, and weathering degree). This makes the data basis for mining area predictions more reliable and achieves higher prediction accuracy.

[0099] From the above content, it can be seen that the prospecting method for characterizing tectonic stress based on the degree of rock fragmentation provided by the embodiment of the present invention obtains image data and sample data of the area to be predicted, constructs an inversion model using the sample data, and inverts the area to be predicted to obtain target indices corresponding to the crystallinity, alteration component concentration and weathering degree, thereby solving the problem of low data reliability caused by the use of only a single data source of image data in the prior art. Then, the crack characteristics extracted from the high spatial resolution image and the spectral index of the target geological characteristic factor are fused to obtain a characteristic matrix. Finally, the characteristic matrix is input into the tectonic stress field inversion model based on the graph convolutional neural network to determine the target mineralization area of the area to be predicted. In this way, the output result of the tectonic stress field inversion model is more accurate, which improves the prediction accuracy of the mineralization area of the area to be predicted.

[0100] Optionally, the sample data includes X-ray data and spectral reflectance data obtained based on a short-wave infrared spectrometer; see Figure 3 The step 120 of constructing a first inversion model based on the sample data may specifically include:

[0101] Step 121: constructing a spectral library based on the spectral reflectance data;

[0102] Step 122: Calculate the index of the target geological characteristic factor based on the X-ray data;

[0103] Step 123: extracting a target band whose index correlation with the target geological characteristic factor is greater than or equal to a preset value from the spectral library, and constructing a spectral index of the target geological characteristic factor based on the target band;

[0104] Step 124: Determine a first inversion model based on the spectral index of the target geological characteristic factor.

[0105] Before performing step 121, the spectral reflectance acquisition process is described as follows:

[0106] The first step is to set the spectral range: using the visible light (VIR) - short wave infrared spectrum (SWIR) range: 400nm-2500nm.

[0107] Analyze the characteristic bands of key minerals:

[0108] Visible light band (400nm-700nm): Analyze mineral color characteristics and signs of surface oxidation.

[0109] Near-infrared band (700nm-1000nm): detects the presence of iron ions and their coordination environment.

[0110] Short-wave infrared band (1000nm-2500nm): Extract mineral characteristic absorption peaks (such as OH-, , Al-OH, Mg-OH groups). OH-group absorption: 1400nm-1450nm (related to water); Group absorption: around 2300nm (related to carbonates); Al-OH, Mg-OH group absorption: 2200nm-2350nm (related to alteration minerals such as muscovite and chlorite); Fe-O group absorption: 1000nm-1300nm (related to iron minerals).

[0111] The second step is to prepare the acquisition tool: the acquisition tool can be a shortwave infrared spectrometer (ASDFieldSpec).

[0112] Before collection, rock samples must be dried and crushed into uniform particles to reduce surface effects.

[0113] Finally, the following collection process is adopted:

[0114] 1) Set the integration time and optimize the acquisition conditions to ensure the signal-to-noise ratio. For low-reflectivity samples (such as dark rocks and weathered minerals), set a longer time (such as 100ms-500ms); for high-reflectivity samples (such as muscovite and quartz), set a shorter time (such as 10ms-50ms).

[0115] 2) Collect multiple points for each sample and take the average value to reduce local errors.

[0116] 3) Use ENVI software to calibrate the spectral data (subtract background noise and standardize the spectral curve).

[0117] After the collection is completed, step 121 is performed, which may specifically include:

[0118] Data preprocessing steps include radiometric correction to remove instrumental noise, background subtraction, and normalization of spectra using blackbody reference data. Smoothing (e.g., Savitzky-Golay filtering) is also performed to reduce noise.

[0119] Feature extraction step: Automatically detect the absorption peak position, depth and width. Determine the key characteristic bands of the mineral and verify the characteristic peaks.

[0120] To build a spectral library: Import the processed spectral data into the ENVI Spectral Library module. Assign labels to each rock sample, such as "Fractured Zone_Severely Fractured" or "Altered_Chlorite." Use ENVI's built-in spectral matching tools (e.g., Spectral AngleMapper, SAM) to verify spectral consistency. Finally, save the spectral data by classification (e.g., .sli or .hdr format) to ensure a structured and scalable spectral library.

[0121] 4) Spectral Library Verification: Verify the spectral library by comparing it with known mineral standard spectra (such as those from the USGS or the ENVI built-in library). Supplement missing spectral data with representative mineral samples. Remove anomalous spectra to ensure data quality.

[0122] In step 122 , in calculating the index of the target geological characteristic factor based on the X-ray data, specifically, the index of each target geological characteristic factor is calculated.

[0123] For example, for crystallinity, the X-ray data is X-ray diffraction data obtained based on an X-ray diffraction instrument; step 122 may specifically include:

[0124] Step 1: Determine the X-ray diffraction pattern based on X-ray diffraction data;

[0125] Specifically, X-ray diffraction (XRD) instruments are used to analyze the crystal structure of collected samples. Characteristic diffraction peaks of minerals are recorded and, combined with parameters such as peak position, intensity, and full width at half maximum (FWHM), the main mineral species and their distribution characteristics are identified, ultimately generating an X-ray diffraction pattern.

[0126] Step 2: Based on the X-ray diffraction pattern, use the formula:

[0127] (1)

[0128] The crystallinity index is calculated; wherein, is the index of the crystallinity; is the area under a specific diffraction peak; is the total diffraction signal area; is the background signal; 1 is the starting angle; 2 is the end angle; is the angle of incidence; is the diffraction angle; is the lower limit of integration for calculating the entire diffraction pattern; is the upper limit of the integral for calculating the entire diffraction pattern.

[0129] Regarding the alteration component concentration and weathering degree, step 122 may specifically include: calculating an index of the alteration component concentration and an index of the weathering degree based on X-ray fluorescence data, wherein the X-ray fluorescence data is data obtained by an X-ray fluorescence instrument.

[0130] For example, with respect to the alteration component concentration, step 122 may specifically include:

[0131] First, analyzing the target elements of the alteration components in the rock sample; the alteration components include at least one or more of chlorite, muscovite and pyrite; the target elements include one or more of Fe, Mg, Al and K;

[0132] In this embodiment, XRF instrument is used to perform full element scanning analysis on rock samples and non-mineral samples to determine their main chemical components (such as 、 、 、 The mineralization potential and alteration degree of the sample can be preliminarily determined based on the element distribution characteristics.

[0133] By comparing the chemical composition of ore samples and surrounding rock samples, the enrichment of corresponding elements in altered minerals (such as chlorite, muscovite, pyrite, etc.) is analyzed in detail.

[0134] Second, the concentration of the target element is extracted from the X-ray fluorescence data;

[0135] Third, use the formula:

[0136] (2)

[0137] The index of alteration component concentration is calculated; where, is an index of the concentration of alteration components, is the concentration of the target element in the ore sample; is the background concentration of the target element in the surrounding rock sample.

[0138] For example, regarding the degree of weathering, step 122 may specifically include:

[0139] Extracting the content of target oxides from X-ray fluorescence data; target oxides include easily weathered oxides and stable oxides;

[0140] The ratio of the content of easily weathered oxides to the content of stable oxides was determined as an index of weathering degree.

[0141] In the first embodiment, the calculation of the weathering degree index is exemplified as follows:

[0142] Based on the content of main element oxides measured by XRF (such as 、 、 、 、 、 etc.), calculate the chemical weathering index (WPI, Weathering Potential Index)

[0143] (3)

[0144] : Represents the total amount of easily weathered alkali metal oxides.

[0145] : Indicates the total amount of stable components and other non-weathering oxides.

[0146] Index range: >1: fresh rock (low weathering degree); <0.5 indicates high weathering (significantly weathered rock or soil).

[0147] As for step 123: extracting a target band whose index correlation with the target geological characteristic factor is greater than or equal to a preset value from the spectral library, and constructing a spectral index of the target geological characteristic factor based on the target band, the following specific steps are included:

[0148] The first sub-step: using correlation analysis, calculate the correlation coefficient between the reflectivity of each band and the index of the target geological characteristic factor to determine the target band corresponding to each target geological characteristic factor. The target band refers to the band whose correlation is greater than or equal to the preset value.

[0149] The target bands determined are as follows:

[0150] Target wavelengths for crystallinity: 2200 nm (Al-OH groups) and 2350 nm (Mg-OH groups).

[0151] Target bands for alteration mineral concentration index: Al-OH-MI (aluminum-hydroxy mineral index) is used to reflect the concentration of hydroxy minerals (such as muscovite and kaolinite, etc., and 2200nm and 2160nm are selected. FeI-SWIR (iron oxide index) is used to reflect the enrichment of iron-stained minerals (such as limonite and hematite), and 2200nm, 1660nm, 850nm and 650nm are selected.

[0152] Target band of weathering degree: alkali metal oxides ( 、 ) related short-wave infrared bands have weak iron absorption and reflection characteristics related to easily weathered minerals at 1000nm, while 1750nm reflects the absorption characteristics of alkali metal minerals. These bands reflect the characteristics of hydration and weakly bound alkali metals in rocks. Silicate minerals (such as 、 ) The absorption characteristic band is not affected by water vapor absorption, and 2200nm and 2350nm are selected. 2200nm reflects the Al-OH absorption characteristics of stable minerals such as muscovite and kaolinite, and 2350nm reflects the Mg-OH absorption characteristics of stable minerals such as chlorite.

[0153] The second sub-step: Use the feature extraction algorithm (principal component analysis PCA) to compress the spectral dimension, use PCA to screen out the bands that are most sensitive to the target geological characteristic factors, reduce the interference of non-related bands, and improve the analysis efficiency of spectral data.

[0154] Based on the screening bands, the spectral index of crystallinity (CI-SWIR), the spectral index of alteration component concentration (ACI-SWIR) and the spectral index of weathering degree (WPI-SWIR) are constructed, so that the spectral indices and geological properties have clear physical meanings.

[0155] The specific process of the second sub-step is described in detail below:

[0156] 1) Spectral index of crystallinity (CI-SWIR)

[0157] As partially explained above, the spectral index of crystallinity is mainly based on the 2200nm and 2350nm bands to construct the crystallinity index of cataclasite. Its theoretical concept is as follows:

[0158] Absorption depth: Absorption depth reflects the absorption intensity of a mineral to light of a specific wavelength. In the mineral spectrum, it is the depth of the lowest reflectivity point (absorption center) of a specific band relative to the spectral baseline.

[0159] Characteristics of high crystallinity: deeper absorption peak (high absorption depth), sharper absorption band.

[0160] The relationship between absorption depth and crystallinity is primarily reflected in how a mineral's spectral absorption characteristics reflect the degree of crystal structure order. Absorption depth is positively correlated with mineral crystallinity, and this correlation exhibits the following characteristics: Crystal structure order enhances spectral absorption: In highly crystalline minerals, atoms are arranged in an orderly and stable manner, resulting in stronger absorption bands for specific chemical groups (such as Al-OH and Mg-OH). This ordered structure enhances the interaction between light and atoms within the mineral, leading to deeper absorption bands.

[0161] Amorphous characteristics of low-crystallinity minerals: The atoms of low-crystallinity or amorphous minerals are disordered, the absorption of light of specific wavelengths is weakened, the absorption band is shallower and the shape is wider.

[0162] Catacladic rocks often occur in areas that have experienced intense hydrothermal alteration, often resulting in the coexistence of absorption peaks at 2200nm and 2350nm. The 2200nm wavelength reflects the presence of muscovite or kaolinite, while the 2350nm wavelength reflects the presence of chlorite or other magnesian minerals. Both Al-OH and Mg-OH groups may coexist in catacladic rocks. To more comprehensively characterize catacladic rocks, the absorption signatures at both 2200nm and 2350nm can be combined through an index.

[0163] The absorption characteristic at 2200nm (Al-OH groups) is typically produced by muscovite, kaolinite, and other Al-OH-containing minerals. This absorption peak is associated with the vibration of the Al-OH groups within the minerals. Muscovite or kaolinite in cataclasites often form through hydrothermal alteration or alteration under tectonic stress. It can characterize the following characteristics of cataclasites: First, the intensity of alteration; a high absorption depth generally corresponds to stronger alteration. Second, changes in crystallinity; the shift and depth of the absorption peak can reflect the degree of order in the crystal structure of the Al-OH groups. This method is suitable for characterizing cataclasites primarily composed of muscovite and kaolinite, particularly within alteration zones.

[0164] The absorption characteristic at 2350nm (Mg-OH group) is produced by chlorite, serpentine, and other Mg-OH-containing minerals. This absorption peak is associated with the vibration of the Mg-OH group. Mg-containing minerals in cataclasites (such as chlorite and serpentine) are often associated with alteration and tectonic stress transformation. The following characteristics of cataclasites can be characterized: ① Hydrothermal alteration: The depth and shape of the Mg-OH group absorption peak reflect the intensity of hydrothermal activity. ② Changes in mineral composition: The Mg-OH absorption characteristic can identify the compositional evolution of minerals under tectonic stress. This method is more suitable for cataclasites containing chlorite or serpentine, especially in areas with significant hydrothermal transformation.

[0165] Spectral index of crystallinity ( ) is a spectral index based on the shortwave infrared (SWIR) band, which is used to characterize the degree of order of mineral crystals. Based on relevant mineral characteristics such as Al-OH groups and Mg-OH groups, the formula is constructed as follows:

[0166] = (4)

[0167] Parameter Description:

[0168] in: . Depbaseline: Determined by the overall spectral baseline of the fractured rock.

[0169] The calculation formula for absorption depth is:

[0170] (4-1)

[0171] In formula (4-1): is the wavelength, such as 2200nm, 2350nm; is the actual reflectivity of the target band; is the baseline reflectivity of the target band.

[0172] The calculation formula is as follows:

[0173] (4-2)

[0174] In formula (4-2): (4-3)

[0175] In formula (4-3) is the baseline reflectivity of the 2200nm absorption band. is the starting wavelength, for example, the starting wavelength of the 2200nm absorption band is 2150nm; is the end wavelength, for example, the end wavelength of the 2200nm absorption band is 2250nm; the end wavelength of the 2350nm absorption band is 2400nm. is the wavelength The actual reflectivity, is the wavelength The actual reflectivity.

[0176] 2) Spectral index of alteration component concentration (ACI-SWIR)

[0177] Calculate the Al-OH-MI spectral index:

[0178] Al-OH-MI (aluminum-hydroxy mineral spectral index) is used to reflect the enrichment of hydroxy minerals (such as muscovite, kaolinite, etc.). Its calculation formula is:

[0179] (5)

[0180] R2200: Reflectivity in the 2200nm band, reflecting the Al-OH absorption characteristics.

[0181] R2160: Reflectance at 2160nm, used as a baseline reference.

[0182] Calculate the Iron Oxide Index (FeI-SWIR):

[0183] FeI-SWIR (spectral index of iron oxides) is used to reflect the enrichment of iron-stained minerals (such as limonite and hematite). Its calculation formula is:

[0184] (6)

[0185] R2200: Reflectance in the 2200nm band, used to indicate the response of iron-dyed minerals to the shortwave infrared band.

[0186] R1660: 1660nm band reflectance, used for baseline correction.

[0187] R850 and R650: Reflectances at 850nm and 650nm, respectively, representing the characteristic absorption of iron-dyed minerals in the visible light range.

[0188] Comprehensive alteration component concentration index (ACI-SWIR):

[0189] The spectral index ACI-SWIR of comprehensive alteration component concentration is constructed by combining the two sub-indices of Al-OH-MI and FeI-SWIR:

[0190] ACI-SWIR=w1*Al-OH-MI+w2*FeI-SWIR (7)

[0191] w 1、 w2 is the weight factor, and both can be adjusted and optimized according to the mineral composition and alteration characteristics of the area to be predicted.

[0192] 3) Spectral index of weathering severity (WPI-SWIR)

[0193] The total amount of easily weathered alkali metal oxides: mainly including Na2O and K2O. These components are easily affected by chemical weathering and show absorption characteristics in specific bands in the spectrum.

[0194] Stable components and the total amount of non-weathering oxides: mainly include SiO2, Al2O3 and other components. These components are more stable during the weathering process and also have significant characteristics in the spectrum.

[0195] The weathering index (WPI) indicates the ratio of easily weathered components to stable components. The higher the value, the greater the weathering potential of the rock.

[0196] (8)

[0197] With respect to step 124: determining the first inversion model based on the spectral index of the target geological characteristic factor, the spectral index of each of the above target geological characteristic factors is part of the first inversion model, that is, the spectral index of all the target geological characteristic factors constitute the first inversion model. The function of the first inversion model is to invert the hyperspectral image of the area to be predicted collected by the hyperspectral camera to obtain the mineral composition and chemical composition of the area to be predicted (for example, the target index of crystallinity, the target index of alteration component concentration and the target index of weathering degree all reveal the content of their respective corresponding mineral compositions and chemical compositions).

[0198] For example, when using drones to collect high-spatial-resolution images, the drones, equipped with high-definition cameras, follow a pre-set flight path, covering the area to be predicted. The flight altitude is controlled at 30-50 meters, and the image overlap rate is maintained at above 80%. This data is used to extract the spatial distribution of crack features, including the direction, length, and density of linear features.

[0199] For example, when using drones to collect hyperspectral images, the drone's onboard hyperspectral camera captures the image, collecting spectral data in the 400-2500nm range. This data is used for mineral inversion, including crystallinity, concentration, and degree of weathering of altered minerals.

[0200] High spatial resolution images are geometrically corrected and seamlessly mosaicked to generate complete crack distribution images.

[0201] Hyperspectral images use the FLAASH model or QUAC model to eliminate atmospheric effects and use geographic coordinates to align images to ensure consistency between hyperspectral image data and other data sources.

[0202] After acquiring high spatial resolution images, hyperspectral images, crack images, X-ray diffraction data, and X-ray fluorescence data, these multi-source data can be cleaned and standardized, for example using the Min-Max normalization method.

[0203] Exemplarily, the process of extracting crack features includes:

[0204] The high spatial resolution image is processed using a preset deformable convolution and a preset directional filter to obtain a crack feature map;

[0205] The crack characteristics are extracted from the crack characteristic map; the crack characteristics include crack density, crack direction and crack length.

[0206] Specifically, the preset deformable convolution can enhance the adaptability of the deep learning network model to complex morphological cracks, and the preset directional filter can be combined to highlight the linear crack features.

[0207] The core concept of deformable convolution: Standard convolution uses a fixed-size sampling window (e.g., 3×3) in space. Deformable convolution introduces an offset, allowing the convolution kernel to dynamically adjust the sampling position, better adapting to the irregular shape of cracks.

[0208] Convolutional feature extraction: The initial convolution layer in the deformable convolution algorithm is used to extract edge, texture, and grayscale features of high-spatial-resolution images. The deformable convolution layer dynamically adjusts the position of the sampling window to enhance its adaptability to cracks.

[0209] Loss function: Optimize the identification of crack regions using a specific loss for edge detection.

[0210] The design of the directional filter is introduced below.

[0211] Function of the filter: Cracks usually have linear or strip-like features. Directional filters can enhance the linear structure along a specific direction in the image and suppress background interference.

[0212] Exemplarily, a Fourier directional filter is used, and the filter kernel function is as follows;

[0213] (9)

[0214] is the filter kernel function; : The direction of the filter; σ: Gaussian kernel width, which controls the scale of the filter; : Center frequency, determines the sensitive frequency of the filter.

[0215] against =0 degrees, 15 degrees, 30 degrees, 45 degrees, 60 degrees, 75 degrees, 90 degrees, 105 degrees, 120 degrees, 135 degrees, 150 degrees, 165 degrees directions, filtering is performed successively.

[0216] Parameter setting: 0 degrees ;

[0217] 15 degrees ;

[0218] 30 degrees ;

[0219] 45 degrees ;

[0220] 60 degrees ;

[0221] 75 degrees ;

[0222] 90 degrees ;

[0223] 105 degrees ;

[0224] 120 degrees ;

[0225] 135 degrees ;

[0226] 150 degrees ;

[0227] 165 degrees ;

[0228] The filtering results in all directions are combined to generate the enhanced crack feature map.

[0229] After obtaining the crack feature map, crack extraction and post-processing are performed:

[0230] 1) Binarization processing:

[0231] (10)

[0232] is a binary image; This is the crack feature map after directional filtering.

[0233] The filtered feature image is converted into a binary image, and the threshold T is set. The value of T is determined by the Otsu method.

[0234] 2) Based on the degree of rock fragmentation and the direction, length, and density of the cracks, the stress response of different regions is inferred from high-spatial-resolution image data: Highly fragmented regions exhibit concentrated stress, high crack density, and diverse orientations. Moderately fragmented regions exhibit weaker stress and more consistent crack orientations. Lowly fragmented regions exhibit the weakest stress and sparse cracks.

[0235] The fused data includes fracture characteristics (length, direction, density) and mineral spectral characteristics (crystallinity, concentration of alteration components and degree of weathering, etc.). The above multi-source data generate a unified feature matrix to construct the sample space.

[0236] Characteristic matrix form:

[0237] (11)

[0238] : The first target index to the nth target index of crystallinity. : The first target index to the nth target index of alteration component concentration. : The first target index to the nth target index of weathering degree. : The length of the first crack to the length of the nth crack. : The first crack density to the nth crack density. : The direction of the first crack to the direction of the nth crack.

[0239] Fracture characteristics (length, direction, and density) reveal the degree of rock fragmentation, while mineral crystallinity and concentration provide information on the stress field's response to rock mineral distribution. Weathering severity reflects the chemical stability of the rock, further enhancing our understanding of the stress field. Fracture characteristics, mineral crystallinity, alteration component concentration, and a target index for weathering severity are fused. Based on a fused feature matrix, sample data from the sample area and image data from the target area are used to invert the tectonic stress field and predict ore-rich areas in the target area. Fracture characteristics, mineral distribution characteristics, and weathering indices are ultimately unified into a single feature matrix, enabling the establishment of a quantitative relationship between the tectonic stress field and multiple variables. This data fusion not only improves the model's prediction accuracy but also enhances the interpretation of geological phenomena.

[0240] After obtaining the above-mentioned feature matrix, part of the feature matrix is used as a training set and the other part as a test set to train and verify the pre-constructed graph convolutional neural network to obtain the second inversion model, namely the tectonic stress field inversion model.

[0241] The pre-built graph convolutional neural network can learn the relationship between stress fields, fractures, and mineral distribution. It can automatically extract nonlinear features in high-dimensional data and is suitable for complex multi-source data fusion analysis.

[0242] The tectonic stress field inversion model obtained after training and validation can be combined with geological spatial relationships to simulate stress field distribution. Simulating geological spatial relationships (such as the spatial correlation between fracture distribution and mineral concentration) can more accurately predict the spatial variation of the stress field.

[0243] When training a graph convolutional neural network using a training set, the fused feature matrix (crack features + mineral features + weathering index) is first input. The graph convolutional neural network is then trained to minimize the error between the predicted stress field and the actual stress distribution. The training process is as follows:

[0244] Batch Size: Set to 8.

[0245] Optimization algorithm: Synchronous stochastic gradient descent (SGD) is used with weight decay set to 0.0001 and momentum set to 0.9.

[0246] Loss function: Binary Cross-Entropy Loss is used to optimize the crack segmentation results.

[0247] Iterations: Train the model until the loss on the validation set stabilizes or is minimized.

[0248] After obtaining the tectonic stress field inversion model, data preprocessing, model cleaning and input data standardization are carried out; fracture extraction and the first inversion model are processed in parallel; the data fusion model generates a unified feature matrix; and the tectonic stress field inversion model outputs the final results.

[0249] After obtaining the final results of the stress field inversion model output, mineralization potential mapping is carried out to finely divide the mineralization potential area and calibrate the prospecting direction.

[0250] Optionally, prospecting targets can be optimized based on comprehensive indicators such as stress field, fracture characteristics, mineral concentration and weathering potential.

[0251] When mapping mineralization potential, GIS tools are used to generate mineralization potential mapping units based on stress field inversion results and mineralization potential indicators, and the mineralization potential is evaluated in a hierarchical manner:

[0252] High potential area: The stress concentration in the mapping unit is high, the fracture density is large, the mineral concentration is significant, the alteration components are enriched, the crystallinity is high, the weathering potential index shows strong stability, and it presents strong mineralization indications.

[0253] Medium potential area: The stress characteristics within the mapping unit are moderate, the fracture direction is relatively consistent, the mineral concentration and alteration components are at a medium level, the weathering potential index is moderate, and it has a certain mineralization possibility.

[0254] Low potential area: The stress field effect within the mapping unit is not significant, the cracks are sparse, the mineral concentration is low, the weathering potential index is high, and there is no significant sign of mineralization.

[0255] Through mineralization potential mapping and zoning, high potential areas are clearly marked to form the preliminary scope of the target mineralization area, that is, the prospecting target area. Figure 4 .

[0256] For example, after determining the target mineralization area, the prospecting target area can be optimized by combining the historical mineralization belt and tectonic background. The mineralization potential mapping results are superimposed and analyzed with the historical mineralization belt data, the distribution of major fault zones and the geological structure background in the area. See the following steps:

[0257] Structural trend overlay analysis: Based on the trends of major fault zones and fracture zones, the correlation between high-potential mapping units and fault systems is verified.

[0258] Verification of historical mineralized zones: Analyze whether high-potential mapped units coincide with known mineralized areas as a basis for model verification.

[0259] Comprehensive analysis of geological background: combining stratum lithology, tectonic intensity and altered mineral distribution characteristics to further optimize the boundaries of prospecting target areas.

[0260] For example, after determining the target mineralization area, the results can be visualized and a mineralization potential distribution map can be generated, as shown in the following steps:

[0261] Generate intuitive mineralization potential maps: Based on the mineralization potential zoning, use GIS tools to generate mineralization potential distribution maps, marking the scope and priority of high-potential target areas.

[0262] Integrated Multi-Dimensional Data Display: The mineralization potential map combines information on fault zones, fracture zones, historical mineral deposits, and geological structures to form a multi-dimensional overlay map to assist in prospecting decisions. The significance of each zone includes: Prioritize detailed geological exploration and sample collection in high-potential areas. Conduct supplementary surveys in medium-potential areas to verify the reliability of model predictions. Reduce resource investment in low-potential areas, retaining them only as secondary targets.

[0263] As can be seen from the above, the distribution of tectonic stress fields and ore body enrichment is not only influenced by fracture characteristics but also by mineral distribution and weathering degree. A single data source cannot fully reflect the complexity of the stress field. Therefore, existing methods for identifying target mineralization areas using a single data source are inaccurate, and the resulting mineral distribution maps lack accuracy. This example simultaneously utilizes shortwave infrared (SWIR) spectrometers, X-ray diffractometers (XRD), and X-ray fluorescence (XRF) spectrometers to collect multi-source data, including spectral characteristics, mineral crystallinity, and chemical composition, from rock samples within the sample area. This data is then extracted for crystallinity, alteration component concentration, weathering degree, and fracture characteristics (length, direction, and density). Using a drone equipped with a high-definition camera and a hyperspectral camera, the collected data is preprocessed and feature extracted to generate a unified multimodal feature matrix. A graph convolutional neural network (GCN) is used to construct a stress field inversion model to simulate fracture distribution and stress field characteristics. Based on the model output, a mineralization potential distribution map for the study area is generated, high-potential areas are identified, and prospecting targets are optimized. The present invention realizes for the first time an integrated technical solution from rock fragmentation degree mapping to tectonic stress field inversion and ore body enrichment area prediction. It is efficient, accurate and applicable, and is particularly suitable for mineral exploration under complex geological backgrounds.

[0264] like Figure 5 As shown, the present invention also provides a prospecting system for characterizing tectonic stress based on the degree of rock fragmentation, comprising: a data acquisition module 510 and a data processor 520; the data acquisition module 510 is in communication with the data processor 520;

[0265] The data acquisition module 510 includes a drone; the drone is equipped with a high-definition camera and a hyperspectral camera; the high-definition camera is used to collect high spatial resolution images of the area to be predicted, and the hyperspectral camera is used to collect hyperspectral images of the area to be predicted;

[0266] Data processors are used to:

[0267] Obtaining image data and sample data of the area to be predicted, wherein the image data includes hyperspectral images and high spatial resolution images, and the sample data is data corresponding to rock samples in the sample area of the area to be predicted;

[0268] A first inversion model is constructed based on the sample data, and the prediction area is inverted based on the first inversion model and the hyperspectral image to obtain the spectral index of the target geological characteristic factors of the prediction area; the target geological characteristic factors include crystallinity, alteration component concentration, and weathering degree;

[0269] The crack characteristics extracted from the high spatial resolution image and the spectral index of the target geological characteristic factors are fused to obtain a characteristic matrix;

[0270] The characteristic matrix is input into the second inversion model to determine the target mineralization area in the predicted area; the second inversion model is a tectonic stress field inversion model pre-built based on a graph convolutional neural network.

[0271] Optionally, the data acquisition module 510 may also include a short-wave infrared spectrometer, an X-ray diffraction instrument, and an X-ray fluorescence instrument;

[0272] The short-wave infrared spectrometer is used to analyze and obtain the spectral reflectance data of the rock samples in the sample area;

[0273] X-ray diffraction instruments are used to collect X-ray diffraction data of rock samples;

[0274] X-ray fluorescence instruments are used to collect X-ray fluorescence data of rock samples.

[0275] See also Figure 6 For example, a prospecting system for characterizing tectonic stress based on rock fragmentation may include three components: an integrated multimodal data collector, a data processor, and a visualization and operation platform. The integrated multimodal data collector is in communication with the data processor, which in turn is in communication with the visualization and operation platform.

[0276] The integrated multimodal data collector is used to collect multi-source data from mining environments, providing foundational information for subsequent analysis and modeling. Specifically, the integrated multimodal data collector collects multi-source data: fracture distribution information, mineralogical properties, spectral characteristics, and chemical composition of geological rock samples, while enabling automated and portable operation, making it suitable for complex field environments.

[0277] The integrated multimodal data collector may specifically include:

[0278] The rock fragmentation degree recorder takes high-resolution images of the mining area, extracts the length, direction and density characteristics of the cracks, and automatically compares the degree of rock fragmentation in the sample area.

[0279] Unmanned aerial vehicle system: Equipped with a high-definition camera and a hyperspectral camera. The HD camera is used to acquire data at high spatial resolution. GPS and a flight control system are configured to ensure image coverage and data accuracy. The hyperspectral camera, mounted on the drone, captures hyperspectral images in the 400-2500nm range, enabling rapid acquisition of spectral signature data across a wide range of mining areas.

[0280] Shortwave infrared spectrometer (SWIR): measures the shortwave infrared spectrum of rock samples and extracts parameters such as absorption depth, peak position and peak area.

[0281] XRD analysis equipment (X-ray diffractometer): Determines the crystallinity of minerals in rock samples and provides crystal structure information. It can also identify the position, full width at half maximum (FWHM), and intensity of characteristic diffraction peaks of minerals.

[0282] XRF spectrometer (X-ray fluorescence spectrometer): Rapidly determine the chemical composition of rock samples (such as oxide content) and assess alteration components and weathering potential.

[0283] The data processor is used to clean, process, fuse and model the collected multi-source data, and ultimately output stress field distribution and ore body prediction results.

[0284] Data processors specifically include:

[0285] High-performance computing unit: Equipped with a GPU acceleration module for deep learning model training and prediction. Supporting high parallel computing capabilities, it can handle multi-source data fusion and large-scale stress field simulation.

[0286] The ENVI software in the analysis software tools is used for radiometric correction, band screening, and feature extraction of hyperspectral data. The ArcGIS software in the analysis software tools is used for data visualization and spatial analysis.

[0287] Deep Learning Framework: Use TensorFlow or PyTorch to build graph convolutional neural network (GCN) models.

[0288] Data processing unit: The standardization tool is used to unify the dimensions of multi-source data. The feature extraction module is used to extract key feature parameters from spectral data, image data, and chemical composition.

[0289] Model Training and Prediction Unit: Apply deep learning algorithms to perform stress field inversion. Demarcate mineralization potential areas based on fracture characteristics and mineral properties.

[0290] The visualization and operation platform provides a result display and user interface, presenting prediction results and assisting in mining exploration decisions. It also visualizes tectonic stress fields and mineralization potential regional distribution maps. It provides intuitive mining exploration data support and assists in field operation planning.

[0291] The visualization and operation platform specifically includes the following components:

[0292] GIS system: used to generate tectonic stress field distribution maps and mineralization potential area prediction maps.

[0293] Result overlay tool: used to combine fault zones, fracture zones, and mineral feature distribution to generate multi-dimensional prediction maps.

[0294] User terminal: used for user parameter settings, etc. Portable display device within the user terminal: with touch functionality, used for viewing analysis results in real time in the field. Interactive interface within the user terminal: allows operators to select input data ranges, adjust analysis parameters, and generate prediction maps in real time.

[0295] Cloud storage and remote collaboration: Cloud servers can store analysis results and support remote data access. The collaborative platform allows multiple users to share real-time data, improving mining exploration efficiency.

[0296] Although the present invention has been described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art may understand and implement other variations of the disclosed embodiments by reviewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple situations. A single processor or other unit may implement several functions listed in the claims. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0297] Although the present invention has been described with reference to specific features and embodiments thereof, it will be apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the invention. Accordingly, this specification and drawings are merely illustrative of the invention as defined by the appended claims and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the invention. It will be apparent that various modifications and variations may be made to the present invention by those skilled in the art without departing from the spirit and scope of the invention. Thus, the present invention is intended to include such modifications and variations as fall within the scope of the claims of the present invention and their equivalents.

Claims

1. A prospecting method based on characterizing tectonic stress by the degree of rock fragmentation, characterized in that: include: Acquire image data and sample data of the area to be predicted, wherein the image data includes a hyperspectral image and a high spatial resolution image, and the sample data is data corresponding to rock samples in a sample area of the area to be predicted; Constructing a first inversion model based on the sample data, and performing inversion on the area to be predicted based on the first inversion model and the hyperspectral image to obtain a spectral index of a target geological characteristic factor of the area to be predicted; the target geological characteristic factor includes crystallinity, alteration component concentration, and weathering degree; fusing the fracture characteristics extracted from the high spatial resolution image with the spectral index of the target geological characteristic factor to obtain a characteristic matrix; Inputting the characteristic matrix into a second inversion model to determine the target mineralization area of the area to be predicted; the second inversion model is a tectonic stress field inversion model pre-constructed based on a graph convolutional neural network; Inputting the characteristic matrix into the second inversion model to determine the target metallogenic area of the area to be predicted includes: inputting the characteristic matrix into the second inversion model to obtain the tectonic stress field distribution of the area to be predicted; Based on the tectonic stress field distribution and the preset mapping benchmark, a mineralization potential mapping unit is generated using a GIS tool; the mineralization potential mapping unit includes a high potential area, a medium potential area and a low potential area; the high potential area is determined as the target mineralization area.

2. The mineral prospecting method based on characterizing tectonic stress by the degree of rock fragmentation according to claim 1, characterized in that: The sample data includes spectral reflectance data and X-ray data obtained based on a short-wave infrared spectrometer; Constructing a first inversion model based on the sample data includes: constructing a spectral library based on the spectral reflectance data; Calculating an index of the target geological characteristic factor based on the X-ray data; Extracting a target waveband whose exponential correlation with the target geological characteristic factor is greater than or equal to a preset value from the spectral library, and constructing a spectral index of the target geological characteristic factor based on the target waveband; The first inversion model is determined based on the spectral index of the target geological characteristic factor.

3. The mineral prospecting method based on characterizing tectonic stress by the degree of rock fragmentation according to claim 2, characterized in that: The X-ray data at least includes X-ray diffraction data obtained based on an X-ray diffraction instrument; Calculating the index of the target geological characteristic factor based on the X-ray data includes: determining an X-ray diffraction pattern based on the X-ray diffraction data; Based on the X-ray diffraction pattern, the formula is used: Calculating the crystallinity index; wherein CI is the crystallinity index; is the area under a specific diffraction peak; is the total diffraction signal area; I(2θ) is the diffraction intensity; I background (2θ) is the background signal; θ1 is the starting angle; θ2 is the ending angle; θ is the incident angle; 2θ is the diffraction angle; θ min is the lower limit of integration; θ max The upper limit of points.

4. The mineral prospecting method based on characterizing tectonic stress by the degree of rock fragmentation according to claim 3, characterized in that: The X-ray data also includes X-ray fluorescence data obtained based on an X-ray fluorescence instrument; Calculating the index of the target geological characteristic factor based on the X-ray data includes: Based on the X-ray fluorescence data, an index of the alteration component concentration and an index of the weathering degree are calculated.

5. The mineral prospecting method based on characterizing tectonic stress by the degree of rock fragmentation according to claim 4, characterized in that: Determining the alteration component concentration index based on the X-ray fluorescence data includes: Analyzing and obtaining target elements of alteration components in the rock sample; the alteration components include at least one or more of chlorite, muscovite, and pyrite; and the target elements include one or more of Fe, Mg, Al, and K; extracting the concentration of the target element from the X-ray fluorescence data; Using the formula: The index of the alteration component concentration is calculated; wherein ACI is the index of the alteration component concentration, C mineral is the concentration of the target element; C background is the background concentration of the target element.

6. The mineral prospecting method based on characterizing tectonic stress by the degree of rock fragmentation according to claim 5, characterized in that: Determining the weathering degree index based on the X-ray fluorescence data includes: Extracting the content of target oxides from the X-ray fluorescence data; the target oxides include easily weathered oxides and stable oxides; The ratio of the content of the easily weathered oxide to the content of the stable oxide is determined as the index of the weathering degree.

7. The mineral prospecting method based on characterizing tectonic stress by the degree of rock fragmentation according to claim 1, characterized in that: The process of extracting the crack characteristics includes: Processing the high spatial resolution image using a preset deformable convolution and a preset directional filter to obtain a crack feature map; The crack characteristics are extracted from the crack characteristic map; the crack characteristics include crack density, crack direction and crack length.

8. A prospecting system based on the characterization of tectonic stress by the degree of rock fragmentation, characterized by: include: Data acquisition module and data processor; The data acquisition module is in communication with the data processor; The data acquisition module includes a drone; the drone is equipped with a high-definition camera and a hyperspectral camera; the high-definition camera is used to collect high spatial resolution images of the area to be predicted, and the hyperspectral camera is used to collect hyperspectral images of the area to be predicted; The data processor is used to obtain image data and sample data of the area to be predicted, wherein the image data includes a hyperspectral image and a high spatial resolution image, and the sample data is data corresponding to rock samples in a sample area in the area to be predicted; Constructing a first inversion model based on the sample data, and performing inversion on the area to be predicted based on the first inversion model and the hyperspectral image to obtain a spectral index of a target geological characteristic factor of the area to be predicted; the target geological characteristic factor includes crystallinity, alteration component concentration, and weathering degree; fusing the fracture characteristics extracted from the high spatial resolution image with the spectral index of the target geological characteristic factor to obtain a characteristic matrix; Inputting the characteristic matrix into a second inversion model to determine the target metallogenic area of the area to be predicted; the second inversion model is a tectonic stress field inversion model pre-constructed based on a graph convolutional neural network; inputting the characteristic matrix into the second inversion model to determine the target metallogenic area of the area to be predicted, comprising: inputting the characteristic matrix into the second inversion model to obtain the tectonic stress field distribution of the area to be predicted; Based on the tectonic stress field distribution and the preset mapping benchmark, a mineralization potential mapping unit is generated using a GIS tool; the mineralization potential mapping unit includes a high potential area, a medium potential area and a low potential area; the high potential area is determined as the target mineralization area.

9. The mineral prospecting system based on characterizing tectonic stress by rock fragmentation degree according to claim 8, characterized in that: The data acquisition module also includes a short-wave infrared spectrometer, an X-ray diffraction instrument and an X-ray fluorescence instrument; The short-wave infrared spectrometer is used to analyze and obtain hyperspectral data of rock samples in the sample area; The X-ray diffraction instrument is used to collect X-ray diffraction data of the rock sample; The X-ray fluorescence instrument is used to collect X-ray fluorescence data of the rock sample.

Images