Reconstruction and tracing method and device for uranium-bearing rock series multi-evidence source system

Abstract

The application discloses a uranium-containing rock series multi-evidence source system reconstruction and tracing method and device, belongs to the technical field of uranium exploration, and comprises the following steps: obtaining lattice alpha radiation damage cumulative information and mechanical behavior response information of a target uranium-containing rock series sample; determining the lattice alpha radiation damage cumulative information as a first physical fingerprint; determining the mechanical behavior response information as a second physical fingerprint; collecting the first physical fingerprint and the second physical fingerprint into a physical intrinsic property double-mode fingerprint file of the target uranium-containing rock series sample; performing physical atlas comparison on the physical intrinsic property double-mode fingerprint file and standard double-mode fingerprints in a standard fingerprint library of a potential source area to obtain a comparison result; and determining the source attribution of the uranium-containing rock series sample according to the comparison result. The application can reduce the possibility of distortion caused by multi-stage transmission of tracing information, and improve the accuracy and efficiency.

Description

Technical Field

[0001] This invention belongs to the field of uranium exploration technology, and specifically relates to a method and apparatus for reconstructing and tracing a multi-evidence source system of uranium-bearing rock formations. Background Technology

[0002] The provenance region refers to the source area or parent region of clastic material in a basin. Provenance analysis infers the petrological characteristics of the parent rocks in the clastic provenance region, as well as the tectonic setting and climatic conditions during sedimentation, based on the final products of sedimentation. Provenance analysis is of great significance in determining the location and nature of sediment sources, sediment transport pathways, and even the sedimentary processes and tectonic evolution of the entire basin. Currently, commonly used provenance analysis methods include thin section analysis, grain size analysis, heavy mineral assemblage analysis, rare earth element analysis, and zircon U-Pb dating.

[0003] Currently, heavy mineral assemblage analysis is commonly used to trace the provenance of uranium-bearing rock series. For example, by statistically analyzing the types and contents of stable heavy minerals in samples and comparing them with potential provenance areas, the provenance direction can be inferred. Alternatively, provenance can be determined using features such as the Sc-La-Th discriminant function, rare earth element distribution patterns, and Sm-Nd isotopes.

[0004] In the aforementioned technologies, whether it is heavy minerals or trace elements, external tracers are required to indirectly infer the source of the uranium mineralization itself. The tracer information is transmitted through multiple levels, resulting in information attenuation and distortion, which leads to low accuracy and low efficiency in uranium-bearing rock source tracing. Summary of the Invention

[0005] This application provides a method and apparatus for reconstructing and tracing a multi-evidence source system of uranium-bearing rock formations, aiming to solve one or more of the aforementioned problems in the prior art. This application can reduce the possibility of distortion caused by multi-level transmission of tracer information, thereby improving accuracy and efficiency.

[0006] On the one hand, embodiments of this application provide a method for reconstructing and tracing a multi-evidence source system of uranium-bearing rock formations, the method comprising: The cumulative information of lattice α radiation damage and the mechanical behavior response information of the target uranium-bearing rock series sample are obtained. The target uranium-bearing rock series sample includes uranium minerals and non-uranium minerals. The cumulative information of lattice α radiation damage is used to characterize the age and thermal evolution history of the uranium minerals since crystallization. The mechanical behavior response information is used to characterize the inherent structure and mechanical properties of the rocks in the source area of ​​the non-uranium mineral detrital particles. The accumulated information of lattice α-radiation damage is determined as the first physical fingerprint; The mechanical behavior response information is determined as the second physical fingerprint; The first physical fingerprint and the second physical fingerprint are combined into a dual-mode fingerprint profile of the intrinsic physical properties of the target uranium-bearing rock series sample; The physical intrinsic attribute dual-mode fingerprint file is compared with the standard dual-mode fingerprint in the standard fingerprint library of potential source regions using physical maps to obtain the comparison results. The provenance of the uranium-bearing rock series samples was determined based on the comparison results.

[0007] Optionally, information on the cumulative lattice α-radiation damage of the target uranium-bearing rock series sample is obtained, including: Uranium mineral grains were separated from the target uranium-bearing rock samples; A standardized indentation load is applied to a designated micro-region on the surface of the uranium mineral single mineral grain, and a load-displacement curve is obtained; Calculate the microhardness and elastic modulus of the micro-region based on the load-displacement curve. The microhardness value and the elastic modulus value are used as information on the cumulative lattice α-radiation damage.

[0008] Optionally, applying a standardized indentation load to a designated micro-region on the surface of the uranium mineral single-mineral grain to obtain a load-displacement curve includes: A dynamic indentation load is applied to the specified micro-region using a continuous stiffness measurement mode, and the contact stiffness value corresponding to each depth is obtained in real time as the indentation depth increases continuously. Based on the trend of the contact stiffness value with the indentation depth, the lattice radiation damage depth distribution gradient of the uranium mineral particles from the surface to the interior is determined; Based on the gradient of the depth distribution of lattice radiation damage, load-displacement curves were plotted.

[0009] Optionally, obtaining the lattice α-radiation damage accumulation information of the target uranium-bearing rock sample further includes: For the same micro-region of the single mineral grain of the uranium mineral, the full width at half maximum (FWHM), peak intensity, and peak position offset of the characteristic diffraction peaks in the X-ray diffraction pattern are obtained; Obtain the full width at half maximum (FWHM), peak position shift, and fluorescence background intensity of the characteristic vibrational peak of UO in micro Raman spectra; To obtain the differences in luminescence intensity and the presence, number, and width of the ring structure in the scanning electron microscope cathodoluminescence image; The X-ray diffraction pattern parameters, the micro Raman spectral parameters, and the cathodoluminescence image structural features are summarized into the lattice α-radiation damage accumulation information.

[0010] Optionally, obtaining the mechanical behavior response information of the target uranium-bearing rock sample includes: Detrital particles were selected from the non-uranium mineral detrital particles remaining after uranium mineral sorting of the target uranium-bearing rock series sample. The debris particles are subjected to standardized single-impact crushing with constant impact energy; Obtain the fracture timing characteristics of the debris particles during the impact crushing process; Obtain the particle size distribution curve of the crushed product after impact crushing and sieving. Obtain statistical results of the frequency of occurrence of various fracture surfaces in the cross-sectional micromorphology of particles with a particle size larger than a preset particle size in the crushed product; The fracture timing characteristics, the fragmentation particle size distribution curve, and the statistical results of the cross-sectional microstructure are summarized as the mechanical behavior response information.

[0011] Optionally, determining the accumulated lattice α-radiation damage information as the first physical fingerprint includes: The accumulated information of lattice α-radiation damage is divided into a long-range ordered information subset, a short-range ordered information subset, and a spatial distribution information subset of lattice damage according to the information dimension. The long-range ordered information subset includes X-ray diffraction peak full width at half maximum (FWHM), diffraction peak intensity, and diffraction peak position shift information. The short-range ordered information subset includes Raman characteristic peak FWHM, characteristic peak position shift, and fluorescence background intensity information. The spatial distribution information subset of lattice damage includes cathodic emission ring structure information, microhardness value, and elastic modulus value. The long-range ordered information subset of the lattice, the short-range ordered information subset of the lattice, and the spatial distribution information subset of the lattice damage are organized in a hierarchical structure to construct the first physical fingerprint with a multi-dimensional hierarchical structure.

[0012] Optionally, the step of comparing the physical intrinsic attribute dual-mode fingerprint file with the standard dual-mode fingerprint in the potential source region standard fingerprint database to obtain the comparison result includes: When the radiation damage fingerprint group feature map of the first physical fingerprint in the dual-mode fingerprint file of the physical intrinsic attributes presents multiple feature peak groups, each of the feature peak groups is matched with the standard feature peak groups of each source region in the standard fingerprint library. Based on the matching results of each characteristic peak group, the existence of each source region in the target uranium-bearing rock sample is determined; Based on the peak area ratio of each characteristic peak group in the radiation damage fingerprint group feature spectrum, the relative contribution ratio of each source region is estimated. The existence of each source region and the relative contribution ratio are used as the comparison results.

[0013] Optionally, estimating the relative contribution ratio of each source region based on the peak area ratio of each of the characteristic peak groups in the radiation damage fingerprint group feature map includes: Based on the peak area ratio of each characteristic peak group in the radiation damage fingerprint group feature map, calculate the initial relative contribution ratio of each source region; Based on the second physical fingerprint, the mechanical fragmentation response fingerprint group characteristics of non-uranium mineral detrital particles in the target uranium-bearing rock sample are obtained. The mechanical fracture response fingerprint group features are compared with the standard mechanical response fingerprints of each potential source region to determine the mechanical response matching degree of each source region. Using the mechanical response matching degree of each source region as a weighting coefficient, the initial relative contribution ratio is weighted and corrected to obtain the corrected relative contribution ratio.

[0014] Optionally, determining the provenance of the uranium-bearing rock series sample based on the comparison results includes: Obtain the distribution curve of the spectral matching degree between the physical intrinsic attribute dual-mode fingerprint file contained in the comparison result and each standard dual-mode fingerprint in the standard fingerprint library of the potential source region on the physical parameter axis; Identify the morphological characteristics of the distribution curve, including the trend of the matching degree changing with physical parameters, the location of extreme values, and the fluctuation amplitude; Based on the matching of the morphological characteristics with the standard morphological characteristics of each standard source region, the source attribution of the target uranium-bearing rock series sample is determined.

[0015] On the other hand, embodiments of this application provide a device for reconstructing and tracing a multi-evidence source system of uranium-bearing rock formations, the device comprising: The acquisition module is used to acquire the lattice α radiation damage accumulation information and mechanical behavior response information of the target uranium-bearing rock series sample. The target uranium-bearing rock series sample includes uranium minerals and non-uranium minerals. The lattice α radiation damage accumulation information is used to characterize the age and thermal evolution history of the uranium minerals since crystallization. The mechanical behavior response information is used to characterize the inherent structure and mechanical properties of the rocks in the source area of ​​the non-uranium mineral detrital particles. The first determining module is used to determine the accumulated information of lattice α radiation damage as the first physical fingerprint; The second determining module is used to determine the mechanical behavior response information as a second physical fingerprint; The aggregation module is used to aggregate the first physical fingerprint and the second physical fingerprint into a dual-mode fingerprint file of the physical intrinsic properties of the target uranium-bearing rock series sample; The comparison module is used to perform physical map comparison between the physical intrinsic attribute dual-mode fingerprint file and the standard dual-mode fingerprint in the potential source region standard fingerprint database to obtain the comparison result; The third determining module is used to determine the provenance of the uranium-bearing rock series sample based on the comparison results.

[0016] In another aspect, embodiments of this application provide an electronic device, the device comprising: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the method for reconstructing and tracing uranium-bearing rock series multi-evidence source systems as described in the first aspect.

[0017] In another aspect, embodiments of this application provide a computer storage medium storing computer program instructions, which, when executed by a processor, implement the method for reconstructing and tracing a multi-evidence source system of uranium-bearing rock formations as described in the first aspect.

[0018] In another aspect, embodiments of this application provide a computer program product in which the instructions are executed by the processor of an electronic device, causing the electronic device to perform the uranium-bearing rock series multi-evidence source system reconstruction and tracing method as described in the first aspect.

[0019] The method and apparatus for reconstructing and tracing uranium-bearing rock strata using multiple evidence sources, as described in this application, can directly acquire the lattice α-radiation damage accumulation information and mechanical behavior response information of uranium minerals and non-uranium minerals in uranium-bearing rock strata samples, and integrate them into a dual-mode fingerprint archive of intrinsic physical properties. This avoids the information attenuation and distortion problems caused by external tracers in traditional methods. Therefore, this method can provide more direct and reliable provenance evidence, effectively improving the accuracy and efficiency of uranium-bearing rock strata provenance tracing, and providing a new solution for sediment provenance analysis based on the sample's intrinsic properties. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the embodiments of this specification. For those skilled in the art, other drawings can be obtained based on these drawings. Figure 1 This is a flowchart illustrating a method for reconstructing and tracing a multi-evidence source system of uranium-bearing rock formations according to an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a multi-evidence source system reconstruction and tracing device for uranium-bearing rock formations provided in another embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in another embodiment of this application. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] To facilitate understanding of the embodiments of this application, further explanation and description will be provided below with reference to the accompanying drawings and specific embodiments. These embodiments do not constitute a limitation on the embodiments of this application. In the drawings, the dimensions and relative dimensions of components may be exaggerated for clarity and / or descriptive purposes. When exemplary embodiments can be implemented differently, a specific process sequence may be performed in a different order than that described. For example, two consecutively described processes may be performed substantially simultaneously or in the reverse order of their description. Furthermore, the same reference numerals denote the same components.

[0023] The terminology used herein is for the purpose of describing particular embodiments and is not intended to be limiting. As used herein, unless the context clearly indicates otherwise, the singular forms “a” and “(the)” are also intended to include the plural forms. Furthermore, when the terms “comprising” and / or “including” and variations thereof are used in this specification, it indicates the presence of the stated features, integrals, steps, operations, parts, components, and / or groups thereof, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, parts, components, and / or groups thereof. It should also be noted that, as used herein, the terms “substantially,” “about,” and other similar terms are used as approximate terms rather than as terms of degree, thus explaining the inherent biases in measurements, calculated values, and / or provided values ​​that would be recognized by one of ordinary skill in the art.

[0024] To address the problems of existing technologies, this application provides a method and apparatus for reconstructing and tracing multi-evidence provenance systems of uranium-bearing rock formations. In this application, by directly acquiring the lattice α-radiation damage accumulation information and mechanical behavior response information of uranium and non-uranium minerals in uranium-bearing rock samples, and integrating them into a dual-mode fingerprint profile of intrinsic physical properties, the information attenuation and distortion problems caused by external tracers in traditional methods are avoided. Therefore, this method can provide more direct and reliable provenance evidence, effectively improving the accuracy and efficiency of provenance tracing of uranium-bearing rock formations, and providing a new solution for sediment provenance analysis based on the sample's intrinsic properties.

[0025] The following section first introduces the method for reconstructing and tracing the multi-evidence source system of uranium-bearing rock formations provided in the embodiments of this application.

[0026] Figure 1 This illustration shows a flowchart of a method for reconstructing and tracing a multi-evidence source system of uranium-bearing rock formations according to an embodiment of this application. Figure 1 As shown, the method for reconstructing and tracing multi-evidence source systems of uranium-bearing rock formations may include steps S101-S106: S101, acquire lattice α-radiation damage accumulation information and mechanical behavior response information of the target uranium-bearing rock series sample.

[0027] In this embodiment, the target uranium-bearing rock sample typically contains uranium minerals and non-uranium minerals. Accumulated lattice alpha radiation damage information is used to characterize the age and thermal evolution history of the uranium minerals since crystallization. For example, the degree of lattice damage can be indirectly reflected by recording the surface morphology of the uranium minerals through simple microscopic observation, or by measuring their overall hardness. Mechanical behavior response information is used to characterize the inherent structure and mechanical properties of the rocks in the source region of the non-uranium mineral detrital particles. For example, preliminary information on the mechanical response can be obtained by recording the average force required for crushing the non-uranium mineral detrital particles through simple crushing tests, or by visually observing the roughness of their fracture surfaces.

[0028] S102, the accumulated information of lattice α radiation damage is determined as the first physical fingerprint.

[0029] In the embodiments of this application, the morphological features observed under a microscope or the measured hardness value can be directly used as components of the first physical fingerprint to form a simple feature vector.

[0030] S103, the mechanical behavior response information is determined as the second physical fingerprint.

[0031] In this embodiment of the application, the average force value or fracture surface roughness information recorded in the crushing test can be directly used as a component of the second physical fingerprint to form another simple feature vector.

[0032] S104, combine the first physical fingerprint and the second physical fingerprint into a dual-mode fingerprint profile of the intrinsic physical properties of the target uranium-bearing rock series sample.

[0033] In this embodiment of the application, two independent feature vectors can be simply concatenated together to form a longer comprehensive feature vector, or they can be stored in the same data structure for subsequent unified management and comparison.

[0034] S105, compare the physical intrinsic attribute dual-mode fingerprint file with the standard dual-mode fingerprint in the potential source region standard fingerprint database to obtain the comparison result.

[0035] In this embodiment of the application, when performing physical fingerprint comparison, a simple distance calculation method, such as Euclidean distance or cosine similarity, can be used to quantify the similarity between the target sample fingerprint file and each standard fingerprint in the database. The comparison result can be expressed as a series of similarity scores or matching values.

[0036] S106, the provenance of the uranium-bearing rock series samples was determined based on the comparison results.

[0037] In this embodiment of the application, the source region corresponding to the standard fingerprint with the highest similarity score or the largest matching value to the fingerprint file of the target sample can be selected as the source attribution of the target uranium-bearing rock series sample.

[0038] In this embodiment, by directly acquiring the lattice α-radiation damage accumulation information and mechanical behavior response information of uranium minerals and non-uranium minerals in uranium-bearing rock samples, and integrating them into a dual-mode fingerprint profile of intrinsic physical properties, the information attenuation and distortion problems caused by exogenous tracers in traditional methods are avoided. Therefore, this method can provide more direct and reliable provenance evidence, effectively improving the accuracy and efficiency of provenance tracing of uranium-bearing rock formations, and providing a new solution for sediment provenance analysis based on the sample's intrinsic properties.

[0039] In some other embodiments, S101 may include: Uranium mineral grains were separated from the target uranium-bearing rock samples; A standardized indentation load was applied to a designated micro-region on the surface of a single uranium mineral grain, and the load-displacement curve was obtained. Calculate the microhardness and elastic modulus of the micro-region based on the load-displacement curve; Microhardness and elastic modulus values ​​are used as information on lattice α-radiation damage accumulation.

[0040] In this embodiment, to reduce interference from other minerals or impurities, single uranium mineral particles can be separated from the target uranium-bearing rock sample. This involves separating the target mineral (i.e., uranium mineral) from the complex rock matrix to ensure that subsequent measurements of lattice α-radiation damage accumulation are applied to the pure uranium mineral itself. The separation process may include crushing and grinding the rock sample, and then utilizing the differences in physical properties (such as density, magnetism, and surface properties) between minerals. Various methods, such as heavy liquid separation, magnetic separation, flotation, or manual sorting, are used to gradually enrich and ultimately obtain high-purity uranium mineral particles. For example, for common uranium minerals such as zircon or monazite, their high density can be used for initial enrichment through heavy liquid separation, combined with magnetic separation and manual sorting under a microscope to obtain single mineral particles that meet experimental requirements.

[0041] Subsequently, micro-nano indentation technology can be used to probe the mechanical response of uranium mineral lattices. Specifically, during operation, a representative micro-region without cracks or obvious defects on the surface of a single uranium mineral grain is selected as the "designated micro-region." Using a high-precision indenter, an indenter with a specific geometry (e.g., Berkovich or Vickers type) is pressed into this micro-region at a preset loading rate, maximum load, and unloading rate. Throughout the loading and unloading process, the instrument continuously records the relationship between the load borne by the indenter and the displacement (depth) of the indenter into the sample surface in real time, thus plotting a complete load-displacement curve.

[0042] It is worth noting that standardized indentation load can be achieved by using uniform indentation parameters and operating procedures in experiments to ensure the comparability of measurement results between different samples or different micro-regions.

[0043] Next, since the load-displacement curve contains information on the elastic and plastic deformation of the material during the indentation process, the contact stiffness of the material can be calculated based on classical indentation mechanics models such as the Oliver-Pharr curve by analyzing the curve, especially the initial part of the unloading curve. Combined with the geometry of the indenter and the contact depth, the contact area of ​​the indentation can then be calculated.

[0044] In this embodiment, the microhardness value is usually defined as the maximum indentation load divided by the contact area of ​​the indentation, reflecting the material's ability to resist plastic deformation; the elastic modulus value is calculated using parameters such as contact stiffness, contact area, and the material's Poisson's ratio, characterizing the material's ability to resist elastic deformation.

[0045] When uranium in uranium minerals undergoes alpha decay, it releases alpha particles and recoil nuclei. These high-energy particles collide with lattice atoms as they move within the lattice, leading to point defects such as atomic displacement, vacancies, and interstitial atoms. These defects gradually form defect clusters and can even cause the lattice to become amorphous. Therefore, microhardness and elastic modulus values ​​can be used as information on the cumulative damage caused by alpha radiation in the lattice.

[0046] These changes in crystal structure significantly affect the mechanical properties of minerals, typically manifesting as a decrease in hardness and elastic modulus. Therefore, by accurately measuring the microhardness and elastic modulus values ​​of individual micro-regions of uranium mineral grains, the degree of accumulation of alpha radiation damage within the crystal lattice can be directly and quantitatively reflected, thus serving as effective information for characterizing the age and thermal evolution history of uranium minerals since crystallization.

[0047] In this embodiment, pure uranium mineral single-mineral particles are first obtained through precise sorting to ensure the accuracy of subsequent measurement results. Then, by applying standardized indentation loads to designated micro-regions of these particles and obtaining load-displacement curves, the changes in mechanical properties caused by cumulative damage to the uranium mineral lattice due to alpha radiation can be directly and locally detected. The microhardness and elastic modulus values ​​calculated based on the load-displacement curves can quantitatively characterize the degree of damage inside the lattice. This not only reduces the errors that may be caused by traditional indirect or macroscopic measurements, but also provides radiation damage information with high spatial resolution and high sensitivity. Thus, it provides a reliable and detailed data foundation for subsequently determining the cumulative alpha radiation damage information of the lattice as the first physical fingerprint, greatly improving the accuracy and reliability of the source tracing.

[0048] In other embodiments, a standardized indentation load is applied to a designated micro-region on the surface of a single uranium mineral grain to obtain a load-displacement curve, including: A dynamic indentation load is applied to a specified micro-area using a continuous stiffness measurement mode, and the contact stiffness value corresponding to each depth is obtained in real time as the indentation depth increases continuously. Based on the trend of contact stiffness value with indentation depth, the gradient of lattice radiation damage depth distribution of uranium mineral particles from the surface to the interior was determined. Load-displacement curves were plotted based on the gradient of the depth distribution of lattice radiation damage.

[0049] In this embodiment, advanced nanoindentation technology can be employed. When the indenter presses into a designated micro-region of a uranium mineral single-mineral particle, in addition to applying the main indentation load, a small, high-frequency dynamic oscillation load is superimposed. By synchronously monitoring the response of the indenter displacement to the dynamic load, the system can continuously measure the contact stiffness of the sample at different indentation depths in real time during the continuous indentation of the sample. This dynamic indentation load is typically in the form of a sine wave, and its frequency and amplitude can be precisely controlled, ensuring the stability and accuracy of the measurement process. By applying the dynamic indentation load, continuous profile information of the material's mechanical properties (such as elastic modulus and hardness) as a function of depth can be obtained, rather than just at a specific depth or average value.

[0050] After obtaining the continuous correlation between contact stiffness values ​​and indentation depth, these data were analyzed. Since lattice alpha radiation damage can lead to lattice structure defects and amorphization, thus affecting the mechanical properties of the material, the variation trend of contact stiffness values ​​(or the hardness and elastic modulus derived from it) with indentation depth can directly reflect the variation of the degree of radiation damage within uranium mineral particles from the surface to the interior. By plotting the curve of contact stiffness value versus depth, the gradient distribution of damage degree can be clearly identified. For example, surface damage may be more severe or have undergone repair, while internal damage reflects a more primitive cumulative state.

[0051] In continuous stiffness measurement mode, the indentation device still records the total load and indentation depth data of the indenter during the indentation and unloading processes. Based on this total load and indentation depth data, a load-displacement curve reflecting the overall indentation process can be plotted.

[0052] Furthermore, by gaining a deeper understanding of the gradient of the depth distribution of lattice radiation damage, the plotted load-displacement curves can be refined or corrected, enabling them to more accurately reflect the non-uniform mechanical response within uranium mineral particles. This provides a more reliable basis for subsequent calculations of microhardness and elastic modulus values.

[0053] Through the above technical solution, this application employs a continuous stiffness measurement mode to apply dynamic indentation loads to single uranium mineral grains, enabling real-time acquisition of contact stiffness values ​​corresponding to each depth as the indentation depth continuously increases. Based on these depth-resolved contact stiffness values, the depth distribution gradient of lattice radiation damage from the surface to the interior of the uranium mineral grains can be accurately determined. By plotting load-displacement curves based on the depth distribution gradient, the age and thermal evolution history of uranium minerals since crystallization can be more accurately reflected, thus providing more reliable and comprehensive information on the accumulation of lattice α radiation damage for the refined reconstruction and tracing of uranium-bearing rock sources, significantly improving the accuracy and resolution of source tracing.

[0054] In some other embodiments, S101 may further include: For the same micro-region of a single uranium mineral grain, the full width at half maximum (FWHM), peak intensity, and peak position shift of the characteristic diffraction peaks in the X-ray diffraction pattern are obtained. Obtain the full width at half maximum (FWHM), peak position shift, and fluorescence background intensity of the characteristic vibrational peak of UO in micro Raman spectra; To obtain the differences in luminescence intensity and the presence, number, and width of the ring structure in the scanning electron microscope cathodoluminescence image; The X-ray diffraction pattern parameters, micro Raman spectral parameters, and cathodoluminescence image structural features are summarized into lattice α-radiation damage accumulation information.

[0055] In this embodiment, when obtaining the full width at half maximum (FWHM), peak intensity, and peak position shift of characteristic diffraction peaks in an X-ray diffraction pattern, X-ray diffraction (XRD) is a technique used to analyze crystal structures. When uranium minerals are damaged by alpha radiation, their crystal structure changes, such as the formation of point defects, dislocations, lattice distortions, or even amorphization. These changes are directly reflected in the X-ray diffraction pattern.

[0056] Specifically, an increase in the full width at half maximum (FWHM) of characteristic diffraction peaks typically indicates an increase in lattice disorder, a decrease in grain size, or an increase in microstrain; a decrease in diffraction peak intensity may mean a reduction in crystal volume or a decrease in crystallinity; while the shift in diffraction peak position reflects changes in lattice parameters, such as lattice expansion or contraction. These parameters can be obtained by scanning a specified micro-region of a single uranium mineral grain using a high-precision X-ray diffractometer, thereby quantifying changes in long-range lattice order.

[0057] Micro Raman spectroscopy is a technique for probing molecular and lattice vibrational modes in materials, highly sensitive to changes in short-range ordered structures and chemical bonding states. It is used to obtain the full width at half maximum (FWHM), peak shifts, and fluorescence background intensity of the characteristic vibrational peaks of uranium oxide (UO) in micro Raman spectroscopy. The characteristic vibrational peaks of UO in uranium minerals correspond to specific vibrational modes of the uranium-oxygen polyhedron. Alpha radiation damage leads to UO bond breakage, changes in bond length and bond angle, and disturbances in the local coordination environment. These microstructural changes cause an increase in the FWHM of the characteristic vibrational peaks (indicating increased local disorder), a peak shift (reflecting changes in bonding strength or local stress), and an enhancement in fluorescence background intensity (possibly caused by radiation-induced defects acting as luminescent centers).

[0058] Scanning electron microscopy (SEM) cathodoluminescence (CL) techniques can reveal the distribution of trace elements, growth zoning structures, and defect information within minerals when acquiring differences in luminescence intensity and the presence, quantity, and width of zoning structures in cathodoluminescence images. Alpha radiation damage can affect luminescent centers in minerals, leading to differences in luminescence intensity. Simultaneously, radiation damage may cause localized changes in crystal structure, thereby affecting or destroying the original growth zoning structure. By observing the distribution of luminescence intensity differences and the presence, quantity, and width of zoning structures in cathodoluminescence images, the spatial distribution characteristics and evolutionary history of damage can be obtained.

[0059] Finally, the parameters and structural features obtained from X-ray diffraction patterns, micro Raman spectroscopy, and scanning electron microscopy cathodoluminescence images are integrated and summarized into lattice α-radiation damage accumulation information. By comprehensively analyzing this information from different detection scales, the degree, type, and spatial distribution of uranium mineral lattice damage can be characterized more accurately, providing a solid data foundation for the subsequent construction of the first physical fingerprint.

[0060] When acquiring information on the cumulative lattice α-radiation damage of target uranium-bearing rock samples, not only were macroscopic mechanical responses considered, but also a variety of microscopic characterization techniques such as X-ray diffraction, micro Raman spectroscopy, and scanning electron microscopy cathodoluminescence were introduced. X-ray diffraction parameters can accurately reflect changes in long-range lattice order, such as lattice distortion and amorphization; micro Raman spectroscopy parameters can reveal subtle changes in the UO bonding environment and short-range ordered structure; and the structural features of scanning electron microscopy cathodoluminescence images provide information on the spatial distribution and evolution of damage, enabling the determined first physical fingerprint to more accurately reflect the age and thermal evolution history of uranium minerals since crystallization, significantly improving the accuracy and reliability of source system reconstruction and tracing.

[0061] In some other embodiments, S101 may further include: Detrital particles were selected from the non-uranium mineral detrital particles remaining after uranium mineral sorting of the target uranium-bearing rock series sample. Standardized single-impact crushing of debris particles using constant impact energy; Obtain the fracture timing characteristics of debris particles during impact crushing; Obtain the particle size distribution curve of the crushed product after impact crushing and sieving. Statistical results of the frequency of occurrence of various fracture surfaces in the cross-sectional micromorphology of particles with a diameter larger than a preset diameter in the crushed product. The fracture timing characteristics, fragmentation particle size distribution curves, and cross-sectional microstructure statistical results are summarized into mechanical behavior response information.

[0062] In this embodiment, to reduce interference from uranium minerals, firstly, detrital particles are selected from the non-uranium mineral detrital particles remaining after uranium mineral sorting of the target uranium-bearing rock sample. This ensures that the focus of subsequent mechanical behavior analysis is on the non-uranium mineral components, which can directly reflect the inherent structure and mechanical properties of the source rock. The selected detrital particles should be representative, and their particle size, shape, and other characteristics should meet the requirements of subsequent impact crushing experiments to ensure the validity and comparability of the experimental results.

[0063] Subsequently, the debris particles were subjected to standardized single-impact crushing with constant impact energy. This process ensures the consistency of conditions for each crushing experiment by precisely controlling the impact energy, thereby making the mechanical responses between different samples comparable. The standardized single-impact crushing can be achieved using equipment such as a drop hammer impact testing machine. By adjusting the impact height and the mass of the impacting material, the impact energy can be precisely controlled to simulate the mechanical stress experienced by the debris particles during geological processes and induce their inherent fracture behavior.

[0064] During impact crushing, technologies such as high-speed cameras and acoustic emission sensors can be used to monitor and record the entire dynamic process of particles from impact to complete crushing in real time. The fracture timing characteristics can include the time sequence of crack initiation, propagation path and final fracture. The fracture timing characteristics can reveal the structural weaknesses inside the particles, crystal anisotropy and the strength differences of different mineral phase interfaces, providing dynamic information for a deeper understanding of their mechanical behavior.

[0065] After impact crushing, the particle size distribution curve of the crushed products after sieving is obtained. The crushed products are classified by particle size using a series of standard sieves, and the mass or quantity percentage of each particle size range is statistically analyzed to plot the particle size distribution curve. This curve visually reflects the degree of particle crushing under constant impact energy and the characteristic particle size of the crushed products, serving as an important macroscopic indicator for measuring the toughness, brittleness, and impact resistance of materials.

[0066] Furthermore, high-resolution imaging techniques such as scanning electron microscopy (SEM) can typically be used to obtain statistical results on the frequency of various fracture surfaces of particles larger than a predetermined size in the fractured product. This allows for detailed examination of the fracture surfaces of larger particles with sufficient observation area. Different types of fracture morphologies (such as cleavage fracture, intergranular fracture, and transgranular fracture) reflect the microscopic mechanisms followed by the material during fracture. For example, cleavage fracture may indicate specific weak planes in the crystal structure, while intergranular fracture may be related to grain boundary strength or grain boundary defects. Quantitative statistics on these microscopic features can provide detailed information on the influence of the source rock's microstructure and mineralogical composition on mechanical behavior.

[0067] Finally, the fracture timing characteristics, fragmentation particle size distribution curves, and cross-sectional micromorphology statistical results are summarized into mechanical behavior response information, which can ensure a comprehensive characterization of the mechanical behavior of non-uranium mineral detrital particles, thereby constructing a second physical fingerprint that can accurately reflect the inherent structure and mechanical properties of rocks in the source area.

[0068] Through the aforementioned technical solution, this application can comprehensively and systematically obtain the mechanical behavior response information of non-uranium mineral detrital particles in target uranium-bearing rock samples. By standardizing impact crushing of the detrital particles and conducting in-depth analysis from three levels—dynamic process (fracture timing characteristics), macroscopic results (fracture particle size distribution curve), and microscopic mechanism (statistical results of cross-sectional micromorphology)—the obtained mechanical behavior response information not only includes the overall impact resistance of the particles but also reveals deeper characteristics such as internal structural defects, crystal anisotropy, and microscopic fracture mechanisms. This multi-dimensional and refined mechanical behavior characterization enables the second physical fingerprint to more accurately and comprehensively reflect the inherent structure and mechanical properties of the rocks in the source area of ​​non-uranium mineral detrital particles. When this second physical fingerprint, together with the first physical fingerprint (lattice α-radiation damage accumulation information), constitutes a dual-mode fingerprint archive of intrinsic physical properties, it can significantly improve the distinguishability and reliability of provenance tracing, effectively solving the problem of provenance identification ambiguity that may be caused by relying on only a single information source, thereby providing more solid technical support for the provenance attribution of uranium-bearing rock systems.

[0069] In other embodiments, since the accumulated information of lattice α-radiation damage often contains data with multiple dimensions and properties in practical applications, without effective organization and structuring, the characterization ability of the first physical fingerprint may be insufficient, making it difficult to accurately reflect the age and thermal evolution history of uranium minerals since crystallization, thereby affecting the accuracy and reliability of provenance tracing. Therefore, S102 may include: The accumulated information of lattice α radiation damage is divided into three subsets according to information dimensions: long-range ordered information subset, short-range ordered information subset, and spatial distribution information subset of lattice damage. The long-range ordered information subset includes X-ray diffraction peak full width at half maximum (FWHM), diffraction peak intensity, and diffraction peak position shift information. The short-range ordered information subset includes Raman characteristic peak FWHM, characteristic peak position shift, and fluorescence background intensity information. The spatial distribution information subset of lattice damage includes cathodic emission ring structure information, microhardness value, and elastic modulus value. The long-range ordered information subset, the short-range ordered information subset, and the spatial distribution information subset of lattice damage are organized in a hierarchical structure to construct a first physical fingerprint with a multi-dimensional hierarchical structure.

[0070] In this embodiment, the lattice α-radiation damage accumulation information is systematically classified according to the lattice structure characteristics and spatial distribution characteristics of the damage it reflects; among them, the long-range order information subset of the lattice is mainly obtained through techniques such as X-ray diffraction (XRD) to reflect the integrity and order of the crystal structure on a larger scale. For example, the full width at half maximum (FWHM) of X-ray diffraction peaks can characterize the degree of crystal defects and microstrain; the intensity of diffraction peaks is related to the integrity and crystallinity of the crystal structure; and the shift of diffraction peak positions indicates changes in the lattice constant, which may be caused by cell expansion or contraction due to α-radiation damage. The subset of short-range lattice order information is mainly obtained through techniques such as micro Raman spectroscopy, and is used to reflect the local orderliness of the crystal structure at the atomic bonding scale. For example, the FWHM of Raman characteristic peaks can reflect the local structural disorder or defect density; the peak position shift information indicates changes in bond length and bond angle or local stress; and the fluorescence background intensity information may be related to defect states or impurities caused by radiation damage. The subset of spatial distribution information of lattice damage is mainly obtained through techniques such as cathodoluminescence (CL) imaging and nanoindentation, and is used to reflect the distribution characteristics of radiation damage inside the crystal. For example, the information on the annular structure of the cathode luminescence reveals the periodic or regional distribution of radiation damage during crystal growth, while the microhardness and elastic modulus values ​​reflect the changes in the mechanical properties of different micro-regions through nanoindentation technology. These changes are closely related to the degree of accumulation of α-radiation damage and can reveal the depth distribution or regional differences of the damage.

[0071] Based on this, the aforementioned subsets of long-range lattice ordered information, short-range lattice ordered information, and spatial distribution information of lattice damage are integrated to form a first physical fingerprint with a sense of hierarchy and multiple dimensions. For example, long-range ordered information can serve as macroscopic features, while short-range ordered information and spatial distribution information provide more refined local details.

[0072] During the construction process, data fusion techniques can be employed to standardize data from different information subsets and then integrate them using methods such as weighted averaging, principal component analysis (PCA), and factor analysis. Alternatively, a tree-like or graph-like data structure can be constructed, with different information subsets serving as nodes or branches, and defining the relationships between them. For example, the top-level node could be "lattice alpha radiation damage accumulation information," the next level could be three subsets: "long-range order," "short-range order," and "spatial distribution," and the next level could be specific parameters such as full width at half maximum (FWHM) and peak offset. In this way, different weights can be assigned based on the importance of different information subsets to the source tracing, thus highlighting key features.

[0073] Through the aforementioned technical solution, this application is able to systematically classify and organize complex lattice alpha radiation damage accumulation information, and construct a first physical fingerprint with a multi-dimensional hierarchical structure. This structured fingerprint not only reflects the age and thermal evolution history of uranium minerals since crystallization in greater detail, but also significantly enhances the characterization ability and distinguishability of the first physical fingerprint through the complementarity and verification of information from different dimensions. Ultimately, this enables more accurate and reliable identification and differentiation of uranium minerals from different source regions in subsequent physical map comparisons, thereby greatly improving the accuracy and reliability of uranium-bearing rock source tracing.

[0074] In other embodiments, the physical intrinsic property dual-mode fingerprint profile of the target uranium-bearing rock sample is compared with the standard dual-mode fingerprint in the standard fingerprint library of potential source areas, and the relative contribution ratio of each source area is estimated based on the peak area ratio of each characteristic peak group in the radiation damage fingerprint group feature spectrum. However, relying solely on the radiation damage fingerprint group feature spectrum formed by the lattice α radiation damage accumulation information to estimate the source contribution ratio may not fully reflect the overall characteristics of the source area rocks, especially when different source areas have similar radiation damage characteristics or selective enrichment or loss occurs during deposition and transport. This may limit the accuracy of the source contribution ratio estimation. Therefore, S105 may include: When the radiation damage fingerprint group feature spectrum of the first physical fingerprint in the dual-mode fingerprint archive of physical intrinsic attributes presents multiple feature peak groups, each feature peak group is matched with the standard feature peak groups of each source region in the standard fingerprint database. Based on the matching results of each characteristic peak group, the existence of each source region in the target uranium-bearing rock series sample is determined; Based on the peak area ratio of each characteristic peak group in the radiation damage fingerprint population feature spectrum, the relative contribution ratio of each source region is estimated. The existence and relative contribution ratio of each source region were used as the comparison results.

[0075] In this embodiment, when the radiation damage fingerprint cluster feature spectrum of the first physical fingerprint in the dual-mode physical property fingerprint file exhibits multiple characteristic peak groups, it indicates that the target uranium-bearing rock sample may be composed of a mixture of uranium minerals from different source regions. The first physical fingerprint, i.e., the cumulative information of lattice α radiation damage, can form a radiation damage fingerprint cluster feature spectrum after data processing and statistical analysis. If the spectrum shows multiple discrete, statistically significant characteristic peak groups, then each peak group represents a subgroup of uranium minerals with similar radiation damage characteristics, possibly corresponding to a specific source. For example, groups with similar characteristics can be identified by performing multidimensional statistical analysis or cluster analysis on microhardness values, elastic modulus values, X-ray diffraction peak parameters, Raman spectral parameters, etc., and these groups are represented as characteristic peak groups in the spectrum.

[0076] Based on this, various statistical or pattern recognition methods can be employed. For example, the distance between each characteristic peak group in the target sample and the standard characteristic peak group of each source region in the standard fingerprint database (such as Euclidean distance, Mahalanobis distance, cosine similarity, etc.) can be calculated; the smaller the distance, the higher the matching degree. Alternatively, clustering algorithms, classification algorithms, etc., can be used to classify and identify the characteristic peak groups, assigning them to the most matching standard source region. The standard characteristic peak groups in the standard fingerprint database should contain sufficiently representative data and have undergone rigorous source identification.

[0077] Subsequently, if a target feature peak group successfully matches a feature peak group of a standard source region, and the matching degree reaches a preset threshold or confidence level, then the source region can be determined to exist in the target sample. For example, if peak group A in the target sample has the highest matching degree with the standard peak group of source region X and exceeds the threshold, then source region X is considered to be a source of the target sample.

[0078] Furthermore, in the radiation damage fingerprint ensemble feature map, the peak area of ​​each characteristic peak group can intuitively reflect the relative abundance of that group in the entire sample. For example, if the spectrum is plotted based on frequency or density distribution, the peak area can be obtained by integration or summation. If the peak groups are obtained through cluster analysis, the proportion of particles in each cluster to the total number of particles can also be used as an estimate of its relative contribution. In practical applications, it may be necessary to calibrate the peak area to eliminate the influence of differences in particle size, density, or analytical methods from different source minerals.

[0079] Finally, the presence and relative contribution ratio of each source region are used as the comparison results. The comparison results are presented in the form of structured data, which clearly shows the multi-source characteristics of the target sample and its quantitative information. For example, it can be a list or table containing the name of each identified source region, its presence (yes / no) in the target sample, and its estimated relative contribution ratio (percentage).

[0080] Using the above technical solution, when the target uranium-bearing rock sample comes from multiple source regions, it is possible to effectively identify multiple characteristic peak groups in the radiation damage fingerprint group feature spectrum of the first physical fingerprint, and accurately match them with the standard characteristic peak groups in the standard fingerprint library. This not only qualitatively determines the existence of each source region in the target sample, but also quantitatively estimates the relative contribution ratio of each source region by the peak area ratio.

[0081] In other embodiments, the relative contribution ratio of each source region is estimated based on the peak area ratio of each characteristic peak group in the radiation damage fingerprint ensemble feature map, including: The initial relative contribution ratio of each source region is calculated based on the peak area ratio of each characteristic peak group in the radiation damage fingerprint population feature map. Based on the second physical fingerprint, the mechanical fragmentation response fingerprint group characteristics of non-uranium mineral detrital particles in the target uranium-bearing rock series sample are obtained; The mechanical fracture response fingerprint group characteristics are compared with the standard mechanical response fingerprints of each potential source region to determine the mechanical response matching degree of each source region. Using the mechanical response matching degree of each source region as a weighting coefficient, the initial relative contribution ratio is weighted and corrected to obtain the corrected relative contribution ratio.

[0082] In this embodiment, each characteristic peak group in the radiation damage fingerprint cluster feature map first represents a potential source region. By performing curve fitting or integral calculation on these characteristic peak groups, the peak area of ​​each peak group can be obtained. The ratio of the peak area to the total area of ​​all characteristic peak groups is the initial relative contribution ratio of that source region. For example, Gaussian fitting, Lorentz fitting, or other suitable peak shape fitting methods can be used to accurately determine the area of ​​each peak group. This initial ratio reflects the preliminary assessment of the source contribution based on the radiation damage characteristics of uranium minerals.

[0083] Furthermore, based on the second physical fingerprint, the mechanical fracture response fingerprint group characteristics of non-uranium mineral detrital particles in the target uranium-bearing rock series sample are obtained. The mechanical fracture response fingerprint group characteristics refer to the set of fracture timing characteristics, particle size distribution curve of fracture products, and cross-sectional micromorphological statistics of particles of specific sizes in the fracture products exhibited by non-uranium mineral detrital particles separated from the target uranium-bearing rock series sample during a standardized single impact crushing process. These characteristics comprehensively reflect the overall mechanical behavior of non-uranium mineral detrital particles, such as their toughness, brittleness, and cleavage characteristics. These characteristics are closely related to the lithology, structure, and metamorphic degree of the source rocks.

[0084] Subsequently, various statistical or pattern recognition methods can be used to compare the group characteristics of the mechanical fracture response fingerprint with the standard mechanical response fingerprint of each potential source region to determine the mechanical response matching degree of each source region.

[0085] It is worth noting that the standard mechanical response fingerprint is a pre-established reference data representing the mechanical behavior of non-uranium mineral detrital particles from various potential source regions. Various statistical or pattern recognition methods can be employed, including calculating correlation coefficients, Euclidean distances, Mahalanobis distances, or using machine learning algorithms for classification and similarity assessment. A higher match indicates a greater similarity between the mechanical behavior of the non-uranium mineral detrital particles in the target sample and the standard mechanical behavior of that source region, thus suggesting a greater likelihood of that source region contributing to the target sample.

[0086] Finally, the mechanical response matching degree of each source region is used as a weighting coefficient to weight and correct the initial relative contribution ratio, resulting in a corrected relative contribution ratio. For example, if the initial contribution ratio of a source region is high but its mechanical response matching degree is low, it indicates that the actual contribution of that source may be overestimated and needs to be adjusted downwards using a lower weighting coefficient. Conversely, if the initial contribution ratio is low but the mechanical response matching degree is high, it may need to be adjusted upwards using a higher weighting coefficient. The corrected relative contribution ratio can more comprehensively and accurately reflect the actual contribution of each source region to the target uranium-bearing rock series sample.

[0087] Through the aforementioned technical solution, this application, based on the preliminary estimation of the source contribution ratio, further introduces the mechanical behavior response information of non-uranium mineral detrital particles for weighted correction. This correction mechanism effectively compensates for the potential limitations of a single source tracing index. For example, when different source regions have similar radiation damage characteristics, the mechanical behavior response information can provide an independent basis for discrimination. By organically combining the lattice α radiation damage accumulation information of uranium minerals (reflecting age and thermal evolution history) with the mechanical behavior response information of non-uranium minerals (reflecting inherent structure and mechanical properties), and using the mechanical response matching degree as a weighting coefficient to correct the initial contribution ratio, the estimation of the source contribution ratio becomes more comprehensive and accurate. This not only improves the reliability and accuracy of source tracing but also provides more solid multi-evidence support for the reconstruction of the source system of uranium-bearing rock series, thereby enabling a more accurate revelation of the formation and evolution process of uranium-bearing rock series.

[0088] In this embodiment of the application, S106 may include: Obtain the distribution curve of the spectral matching degree between the physical intrinsic attribute dual-mode fingerprint file contained in the comparison result and each standard dual-mode fingerprint in the standard fingerprint library of potential source areas on the physical parameter axis; Identify the morphological characteristics of the distribution curve, including the trend of the matching degree changing with physical parameters, the location of extreme values, and the amplitude of fluctuations; Based on the matching of morphological characteristics with the standard morphological characteristics of each standard source region, the source attribution of the target uranium-bearing rock series sample is determined.

[0089] In this embodiment, after obtaining the alignment results, these results need to be further processed to form a distribution curve. The alignment results may include a matching metric between the target sample and each standard source fingerprint, such as a similarity score, correlation coefficient, or distance metric.

[0090] These matching metrics can be associated with specific physical parameters. For example, if fingerprint analysis involves debris particles of different sizes, the particle size can be used as a physical parameter axis. Alternatively, if the fingerprint is multidimensional, the matching degree can be projected onto a physically meaningful axis using dimensionality reduction techniques such as principal component analysis (PCA), or a physical quantity that can represent the core features of the fingerprint can be selected as the axis.

[0091] Distribution curves can be generated by interpolating, smoothing, or directly plotting discrete matching values ​​on the physical parameter axis, thus visually demonstrating the changes in the degree of matching between the target sample and various standard sources across different physical parameter dimensions. For example, a curve can be plotted where the horizontal axis represents a key physical parameter (such as the degree of radiation damage, the proportion of mineral components, a specific mechanical response threshold, etc.), and the vertical axis represents the degree of matching between the target sample and a standard source along that parameter dimension.

[0092] After obtaining the distribution curve, we can analyze the trend of the matching degree of the distribution curve with the change of physical parameters. The "trend of matching degree with the change of physical parameters" can be the overall direction of the curve, such as whether it is monotonically rising or falling, or showing a peak structure of rising first and then falling, or remaining relatively stable.

[0093] It is worth noting that extreme values ​​can represent the best or worst match between the target sample and a specific source on certain key physical parameters. Extreme values ​​can be identified by finding the points where the first derivative of the curve is zero or changes sign. "Variation amplitude" can be the degree of fluctuation or dispersion of the curve on the physical parameter axis. A large fluctuation amplitude may indicate that the match between the target sample and the source is significantly different on different physical parameters, or it may reflect the complexity or mixing of the source. The fluctuation amplitude can be quantified by calculating statistics such as the standard deviation of the curve, the peak-to-trough difference, or the average deviation from the trend line.

[0094] Finally, the morphological characteristics of the distribution curves of the identified target uranium-bearing rock series samples are compared with the standard morphological characteristics of each pre-established standard source region. Each standard source region should have its own unique morphological characteristic template of the distribution curve with a high degree of spectral matching, validated by a large amount of data. For example, if the distribution curve of the target sample shows a trend of having a significant peak at a certain physical parameter, and the position and amplitude of this peak highly match the characteristics of a certain standard source region, then that standard source region is considered to be the main source of the target sample. By comprehensively evaluating the matching of all potential source regions, the final source attribution of the target uranium-bearing rock series samples can be determined, including the determination of a single source, the identification of multiple sources, and a preliminary judgment of their relative contributions.

[0095] By employing the aforementioned technical solution, the comparison results are transformed into a spectral matching degree distribution curve on the physical parameter axis. This provides richer and more continuous information than a single matching degree, overcoming the limitations of directly relying on comparison results for provenance attribution. The morphological characteristics of this distribution curve are identified, including the trend of matching degree changes with physical parameters, extreme value locations, and fluctuation amplitudes. This leads to a deeper and more detailed understanding of provenance characteristics. It can effectively distinguish provenances with similar overall matching degrees but different characteristic patterns, especially in cases involving multiple provenance mixtures or complex provenance characteristics, providing more accurate and reliable provenance attribution judgments. Through detailed analysis of the distribution curve's morphological characteristics and matching it with standard morphological characteristics, this application can more accurately reveal the intrinsic connections between the target uranium-bearing rock samples and various potential provenance areas, thereby significantly improving the accuracy and refinement of uranium-bearing rock provenance tracing and providing stronger technical support for geological research and resource exploration.

[0096] Based on the method for reconstructing and tracing the multi-evidence source system of uranium-bearing rock formations provided in the above embodiments, this application also provides specific implementation methods for the device for reconstructing and tracing the multi-evidence source system of uranium-bearing rock formations. Please refer to the following embodiments.

[0097] First see Figure 2 The uranium-bearing rock series multi-evidence source system reconstruction and tracing device 200 provided in this application embodiment may include: The acquisition module 201 is used to acquire the lattice α radiation damage accumulation information and mechanical behavior response information of the target uranium-bearing rock series sample. The target uranium-bearing rock series sample includes uranium minerals and non-uranium minerals. The lattice α radiation damage accumulation information is used to characterize the age and thermal evolution history of uranium minerals since crystallization, and the mechanical behavior response information is used to characterize the inherent structure and mechanical properties of rocks in the source area of ​​non-uranium mineral detrital particles. The first determining module 202 is used to determine the accumulated information of lattice α radiation damage as the first physical fingerprint; The second determining module 203 is used to determine the mechanical behavior response information as the second physical fingerprint; The summary module 204 is used to summarize the first physical fingerprint and the second physical fingerprint into a dual-mode fingerprint file of the physical intrinsic properties of the target uranium-bearing rock series sample. The comparison module 205 is used to perform physical map comparison between the physical intrinsic attribute dual-mode fingerprint file and the standard dual-mode fingerprint in the potential source region standard fingerprint library to obtain the comparison result; The third determination module 206 is used to determine the provenance of uranium-bearing rock series samples based on the comparison results.

[0098] As an alternative implementation, the acquisition module 201 can also be used for: Uranium mineral grains were separated from the target uranium-bearing rock samples; A standardized indentation load was applied to a designated micro-region on the surface of a single uranium mineral grain, and the load-displacement curve was obtained. Calculate the microhardness and elastic modulus of the micro-region based on the load-displacement curve; Microhardness and elastic modulus values ​​are used as information on lattice α-radiation damage accumulation.

[0099] As an alternative implementation, the acquisition module 201 can also be used for: A dynamic indentation load is applied to a specified micro-area using a continuous stiffness measurement mode, and the contact stiffness value corresponding to each depth is obtained in real time as the indentation depth increases continuously. Based on the trend of contact stiffness value with indentation depth, the gradient of lattice radiation damage depth distribution of uranium mineral particles from the surface to the interior was determined. Load-displacement curves were plotted based on the gradient of the depth distribution of lattice radiation damage.

[0100] As an alternative implementation, the acquisition module 201 can also be used for: For the same micro-region of a single uranium mineral grain, the full width at half maximum (FWHM), peak intensity, and peak position shift of the characteristic diffraction peaks in the X-ray diffraction pattern are obtained. Obtain the full width at half maximum (FWHM), peak position shift, and fluorescence background intensity of the characteristic vibrational peak of UO in micro Raman spectra; To obtain the differences in luminescence intensity and the presence, number, and width of the ring structure in the scanning electron microscope cathodoluminescence image; The X-ray diffraction pattern parameters, micro Raman spectral parameters, and cathodoluminescence image structural features are summarized into lattice α-radiation damage accumulation information.

[0101] As an alternative implementation, the acquisition module 201 can also be used for: Detrital particles were selected from the non-uranium mineral detrital particles remaining after uranium mineral sorting of the target uranium-bearing rock series sample. Standardized single-impact crushing of debris particles using constant impact energy; Obtain the fracture timing characteristics of debris particles during impact crushing; Obtain the particle size distribution curve of the crushed product after impact crushing and sieving. Statistical results of the frequency of occurrence of various fracture surfaces in the cross-sectional micromorphology of particles with a diameter larger than a preset diameter in the crushed product. The fracture timing characteristics, fragmentation particle size distribution curves, and cross-sectional microstructure statistical results are summarized into mechanical behavior response information.

[0102] As an alternative implementation, the first determining module 202 can also be used for: The accumulated information of lattice α radiation damage is divided into three subsets according to information dimensions: long-range ordered information subset, short-range ordered information subset, and spatial distribution information subset of lattice damage. The long-range ordered information subset includes X-ray diffraction peak full width at half maximum (FWHM), diffraction peak intensity, and diffraction peak position shift information. The short-range ordered information subset includes Raman characteristic peak FWHM, characteristic peak position shift, and fluorescence background intensity information. The spatial distribution information subset of lattice damage includes cathodic emission ring structure information, microhardness value, and elastic modulus value. The long-range ordered information subset, the short-range ordered information subset, and the spatial distribution information subset of lattice damage are organized in a hierarchical structure to construct a first physical fingerprint with a multi-dimensional hierarchical structure.

[0103] As an alternative implementation, the comparison module 205 can also be used for: When the radiation damage fingerprint group feature spectrum of the first physical fingerprint in the dual-mode fingerprint archive of physical intrinsic attributes presents multiple feature peak groups, each feature peak group is matched with the standard feature peak groups of each source region in the standard fingerprint database. Based on the matching results of each characteristic peak group, the existence of each source region in the target uranium-bearing rock series sample is determined; Based on the peak area ratio of each characteristic peak group in the radiation damage fingerprint population feature spectrum, the relative contribution ratio of each source region is estimated. The existence and relative contribution ratio of each source region were used as the comparison results.

[0104] As an alternative implementation, the comparison module 205 can also be used for: The initial relative contribution ratio of each source region is calculated based on the peak area ratio of each characteristic peak group in the radiation damage fingerprint population feature map. Based on the second physical fingerprint, the mechanical fragmentation response fingerprint group characteristics of non-uranium mineral detrital particles in the target uranium-bearing rock series sample are obtained; The mechanical fracture response fingerprint group characteristics are compared with the standard mechanical response fingerprints of each potential source region to determine the mechanical response matching degree of each source region. Using the mechanical response matching degree of each source region as a weighting coefficient, the initial relative contribution ratio is weighted and corrected to obtain the corrected relative contribution ratio.

[0105] As an alternative implementation, the third determining module 206 can also be used for: Obtain the distribution curve of the spectral matching degree between the physical intrinsic attribute dual-mode fingerprint file contained in the comparison result and each standard dual-mode fingerprint in the standard fingerprint library of potential source areas on the physical parameter axis; Identify the morphological characteristics of the distribution curve, including the trend of the matching degree changing with physical parameters, the location of extreme values, and the amplitude of fluctuations; Based on the matching of morphological characteristics with the standard morphological characteristics of each standard source region, the source attribution of the target uranium-bearing rock series sample is determined.

[0106] Figure 3 A schematic diagram of the hardware structure of the electronic device provided in an embodiment of this application is shown.

[0107] An electronic device may include a processor 301 and a memory 302 storing computer program instructions.

[0108] Specifically, the processor 301 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0109] Memory 302 may include mass storage for data or instructions. For example, and not limitingly, memory 302 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. In one instance, memory 302 may include removable or non-removable (or fixed) media, or memory 302 may be a non-volatile solid-state memory. Memory 302 may be internal or external to an electronic device.

[0110] In one instance, memory 302 may be read-only memory (ROM). In one instance, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0111] Memory 302 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Therefore, generally, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the video query method according to the first aspect of this disclosure.

[0112] The processor 301 reads and executes computer program instructions stored in the memory 302 to achieve... Figure 1 A video query method is shown in the embodiment.

[0113] In one example, the electronic device may also include a communication interface 303 and a bus 304. For example, Figure 3 As shown, the processor 301, memory 302, and communication interface 303 are connected through bus 304 and complete communication with each other.

[0114] The communication interface 303 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0115] Bus 304 includes hardware, software, or both, that couples components of an electronic device together. For example, and not as a limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 304 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0116] The electronic device can execute the video query method in the embodiments of this application, thereby achieving a combination Figures 1-2 The video query method and apparatus described.

[0117] Furthermore, in conjunction with the video query methods described in the above embodiments, this application embodiment can provide a computer storage medium for implementation. This computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the video query methods described in the above embodiments.

[0118] In an optional embodiment, in conjunction with the video query methods in the above embodiments, this application embodiment can provide a computer program product to implement the method. The instructions in the computer program product are executed by the processor of the electronic device, enabling the electronic device to implement any of the video query methods in the above embodiments.

[0119] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0120] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0121] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0122] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0123] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method for reconstructing and tracing a multi-evidence provenance system of uranium-bearing rock formations, characterized in that, include: The cumulative information of lattice α radiation damage and the mechanical behavior response information of the target uranium-bearing rock series sample are obtained. The target uranium-bearing rock series sample includes uranium minerals and non-uranium minerals. The cumulative information of lattice α radiation damage is used to characterize the age and thermal evolution history of the uranium minerals since crystallization. The mechanical behavior response information is used to characterize the inherent structure and mechanical properties of the rocks in the source area of ​​the non-uranium mineral detrital particles. The accumulated information of lattice α-radiation damage is determined as the first physical fingerprint; The mechanical behavior response information is determined as the second physical fingerprint; The first physical fingerprint and the second physical fingerprint are combined into a dual-mode fingerprint profile of the intrinsic physical properties of the target uranium-bearing rock series sample; The physical intrinsic attribute dual-mode fingerprint file is compared with the standard dual-mode fingerprint in the standard fingerprint library of potential source regions using physical maps to obtain the comparison results. The provenance of the uranium-bearing rock series samples was determined based on the comparison results.

2. The method according to claim 1, characterized in that, Obtain information on the cumulative lattice α-radiation damage of the target uranium-bearing rock sample, including: Uranium mineral grains were separated from the target uranium-bearing rock samples; A standardized indentation load is applied to a designated micro-region on the surface of the uranium mineral single mineral grain, and a load-displacement curve is obtained; Calculate the microhardness and elastic modulus of the micro-region based on the load-displacement curve. The microhardness value and the elastic modulus value are used as information on the cumulative lattice α-radiation damage.

3. The method according to claim 2, characterized in that, The step of applying a standardized indentation load to a designated micro-region on the surface of the uranium mineral single-mineral grain and obtaining a load-displacement curve includes: A dynamic indentation load is applied to the specified micro-region using a continuous stiffness measurement mode, and the contact stiffness value corresponding to each depth is obtained in real time as the indentation depth increases continuously. Based on the trend of the contact stiffness value with the indentation depth, the lattice radiation damage depth distribution gradient of the uranium mineral particles from the surface to the interior is determined; Based on the gradient of the depth distribution of lattice radiation damage, load-displacement curves were plotted.

4. The method according to claim 2, characterized in that, The acquisition of lattice α-radiation damage accumulation information of the target uranium-bearing rock series sample also includes: For the same micro-region of the single mineral grain of the uranium mineral, the full width at half maximum (FWHM), peak intensity, and peak position offset of the characteristic diffraction peaks in the X-ray diffraction pattern are obtained; Obtain the full width at half maximum (FWHM), peak position shift, and fluorescence background intensity of the characteristic vibrational peak of UO in micro Raman spectra; To obtain the differences in luminescence intensity and the presence, number, and width of the ring structure in the scanning electron microscope cathodoluminescence image; The X-ray diffraction pattern parameters, the micro Raman spectral parameters, and the cathodoluminescence image structural features are summarized into the lattice α-radiation damage accumulation information.

5. The method according to claim 1, characterized in that, The acquisition of mechanical behavior response information of the target uranium-bearing rock series sample includes: Detrital particles were selected from the non-uranium mineral detrital particles remaining after uranium mineral sorting of the target uranium-bearing rock series sample. The debris particles are subjected to standardized single-impact crushing with constant impact energy; Obtain the fracture timing characteristics of the debris particles during the impact crushing process; Obtain the particle size distribution curve of the crushed product after impact crushing and sieving. Obtain statistical results of the frequency of occurrence of various fracture surfaces in the cross-sectional micromorphology of particles with a particle size larger than a preset particle size in the crushed product; The fracture timing characteristics, the fragmentation particle size distribution curve, and the statistical results of the cross-sectional microstructure are summarized as the mechanical behavior response information.

6. The method according to claim 1, characterized in that, The step of determining the accumulated information of lattice α-radiation damage as the first physical fingerprint includes: The accumulated information of lattice α-radiation damage is divided into a long-range ordered information subset, a short-range ordered information subset, and a spatial distribution information subset of lattice damage according to the information dimension. The long-range ordered information subset includes X-ray diffraction peak full width at half maximum (FWHM), diffraction peak intensity, and diffraction peak position shift information. The short-range ordered information subset includes Raman characteristic peak FWHM, characteristic peak position shift, and fluorescence background intensity information. The spatial distribution information subset of lattice damage includes cathodic emission ring structure information, microhardness value, and elastic modulus value. The long-range ordered information subset of the lattice, the short-range ordered information subset of the lattice, and the spatial distribution information subset of the lattice damage are organized in a hierarchical structure to construct the first physical fingerprint with a multi-dimensional hierarchical structure.

7. The method according to claim 1, characterized in that, The step of comparing the physical intrinsic attribute dual-mode fingerprint file with the standard dual-mode fingerprint in the potential source region standard fingerprint database to obtain the comparison result includes: When the radiation damage fingerprint group feature map of the first physical fingerprint in the dual-mode fingerprint file of the physical intrinsic attributes presents multiple feature peak groups, each of the feature peak groups is matched with the standard feature peak groups of each source region in the standard fingerprint library. Based on the matching results of each characteristic peak group, the existence of each source region in the target uranium-bearing rock sample is determined; Based on the peak area ratio of each characteristic peak group in the radiation damage fingerprint group feature spectrum, the relative contribution ratio of each source region is estimated. The existence of each source region and the relative contribution ratio are used as the comparison results.

8. The method according to claim 7, characterized in that, The step of estimating the relative contribution ratio of each source region based on the peak area ratio of each characteristic peak group in the radiation damage fingerprint group feature map includes: Based on the peak area ratio of each characteristic peak group in the radiation damage fingerprint group feature map, calculate the initial relative contribution ratio of each source region; Based on the second physical fingerprint, the mechanical fragmentation response fingerprint group characteristics of non-uranium mineral detrital particles in the target uranium-bearing rock sample are obtained. The mechanical fracture response fingerprint group features are compared with the standard mechanical response fingerprints of each potential source region to determine the mechanical response matching degree of each source region. Using the mechanical response matching degree of each source region as a weighting coefficient, the initial relative contribution ratio is weighted and corrected to obtain the corrected relative contribution ratio.

9. The method according to claim 1, characterized in that, The determination of the provenance of the uranium-bearing rock series sample based on the comparison results includes: Obtain the distribution curve of the spectral matching degree between the physical intrinsic attribute dual-mode fingerprint file contained in the comparison result and each standard dual-mode fingerprint in the standard fingerprint library of the potential source region on the physical parameter axis; Identify the morphological characteristics of the distribution curve, including the trend of the matching degree changing with physical parameters, the location of extreme values, and the fluctuation amplitude; Based on the matching of the morphological characteristics with the standard morphological characteristics of each standard source region, the source attribution of the target uranium-bearing rock series sample is determined.

10. A device for reconstructing and tracing a multi-evidence source system of uranium-bearing rock formations, characterized in that, The device includes: The acquisition module is used to acquire the lattice α radiation damage accumulation information and mechanical behavior response information of the target uranium-bearing rock series sample. The target uranium-bearing rock series sample includes uranium minerals and non-uranium minerals. The lattice α radiation damage accumulation information is used to characterize the age and thermal evolution history of the uranium minerals since crystallization. The mechanical behavior response information is used to characterize the inherent structure and mechanical properties of the rocks in the source area of ​​the non-uranium mineral detrital particles. The first determining module is used to determine the accumulated information of lattice α radiation damage as the first physical fingerprint; The second determining module is used to determine the mechanical behavior response information as a second physical fingerprint; The aggregation module is used to aggregate the first physical fingerprint and the second physical fingerprint into a dual-mode fingerprint file of the physical intrinsic properties of the target uranium-bearing rock series sample; The comparison module is used to perform physical map comparison between the physical intrinsic attribute dual-mode fingerprint file and the standard dual-mode fingerprint in the potential source region standard fingerprint database to obtain the comparison result; The third determining module is used to determine the provenance of the uranium-bearing rock series sample based on the comparison results.

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