Intelligent prediction method and device for uranium metallogenic favorable zone based on multi-control ore factor fusion

Abstract

The application discloses a uranium ore-forming favorable zone intelligent prediction method and device based on ore-controlling factor fusion, and the method comprises the following steps: obtaining ore-controlling data of a target rock sample; determining a geological unit as an active uranium source; obtaining spatial distribution, uranium activation rate and uranium release flux of the active uranium source; taking the spatial distribution of the active uranium source as a material source boundary condition, coupling and solving a groundwater flow equation, a uranium ion migration equation and a redox reaction kinetics equation, simulating the whole process of uranium ore-forming fluid activation, migration and deposition from the active uranium source, and obtaining a three-dimensional groundwater flow field, uranium ion concentration space-time distribution and uranium deposition rate spatial distribution of a target region; based on the three-dimensional groundwater flow field, the uranium ion concentration space-time distribution and the uranium deposition rate spatial distribution, calculating uranium deposition probabilities of each geological unit; and based on the uranium deposition probabilities and a preset deposition threshold, determining a uranium ore-forming favorable zone. The application can improve the prediction accuracy and efficiency of the uranium ore-forming favorable zone.

Description

Technical Field

[0001] This invention belongs to the field of mineral exploration technology, and specifically relates to an intelligent prediction method and device for favorable uranium mineralization zones based on the integration of multiple ore-controlling factors. Background Technology

[0002] In recent years, the international uranium geology community has witnessed rapid development in 3D modeling and prediction technologies. Extensive predictive research has been conducted on deep geology and geophysical structures, successfully predicting and guiding the exploration and discovery of deep uranium mineralization bodies. With the development of information technology and GIS technology, mineral resource evaluation methods and tools are also continuously being improved and enhanced. In particular, the traditional methods for mineral resource evaluation have been improved, the prediction and evaluation process simplified, and scientific, convenient, efficient, and quantitative operations achieved in mineral resource evaluation.

[0003] Currently, predictions are typically based on statistical analysis of ore-controlling factors. Specifically, ore-controlling factors such as fracture density, stratigraphic thickness, uranium content, and the degree of oxide zone development can be extracted and weighted or probabilistically calculated using methods such as the weight of evidence method, information content method, and feature analysis to delineate favorable mineralization zones. This type of method, which uses the statistical characteristics of static ore-controlling factors as the basis for prediction, is the mainstream method for uranium ore prediction.

[0004] In the aforementioned uranium ore prediction process, various ore-controlling factors were treated as static variables and weighted together, neglecting the fact that uranium mineralization is essentially a dynamic process of "uranium source activation → fluid migration → redox precipitation." The complete chain of uranium element activation from source rock, entry into fluid, migration along a certain path, and precipitation and enrichment under suitable conditions cannot be characterized by the simple superposition of static factors, resulting in inaccurate and inefficient predictions. Summary of the Invention

[0005] This application provides an intelligent prediction method and apparatus for favorable uranium mineralization zones by integrating multiple ore-controlling factors, to solve one or more of the aforementioned problems in the prior art. This application can improve the prediction accuracy and efficiency of favorable uranium mineralization zones.

[0006] On the one hand, embodiments of this application provide an intelligent prediction method for favorable uranium mineralization zones based on the fusion of multiple ore-controlling factors, the method comprising: Obtain ore control data from the target rock sample; If the mineral control data meets the preset identification conditions, the geological unit of the target area is identified as an active uranium source; The spatial distribution, uranium activation rate, and uranium release flux of the active uranium source were obtained. In the preset three-dimensional geological-hydrological model, the spatial distribution of the active uranium source is used as the boundary condition of the material source. The groundwater flow equation, uranium ion migration equation and redox reaction kinetic equation are coupled and solved to simulate the whole process of uranium-bearing mineralizing fluid from activation, migration to precipitation of active uranium source, and to obtain the three-dimensional groundwater flow field, spatiotemporal distribution of uranium ion concentration and spatial distribution of uranium precipitation rate in the target area. Based on the three-dimensional groundwater flow field, the spatiotemporal distribution of uranium ion concentration, and the spatial distribution of uranium precipitation rate, the uranium precipitation probability of each geological unit is calculated. Based on the uranium precipitation probability and the preset precipitation threshold, favorable mineralization zones are determined.

[0007] Optionally, if the ore control data meets preset identification conditions, the geological units of the target area are identified as active uranium sources, including: The target rock sample was subjected to stepwise chemical extraction to separate the occurrence forms of uranium into exchangeable ion state, carbonate-bound state, iron-manganese oxide-bound state, sulfide and organic matter-bound state, and residue state; The active uranium content is determined by summing the contents of the four forms of uranium: exchangeable ion state, carbonate-bound state, iron-manganese oxide-bound state, and sulfide and organic matter-bound state. Based on the uranium content in the residue state, the inert uranium content is determined; Based on the active uranium content and the inert uranium content, the proportion of active uranium is determined; The active uranium source is determined based on the percentage of active uranium, the target total uranium content, and the preset identification conditions.

[0008] Optionally, before using the spatial distribution of the active uranium source as a material source boundary condition to couple and solve the groundwater flow equation, uranium ion migration equation, and redox reaction kinetic equation, the method further includes: Acquire geological and hydrological data for the target area; Based on the geological data, a three-dimensional geological model is constructed; Based on the aforementioned hydrological data, a hydrogeological model is constructed; The three-dimensional geological model and the hydrogeological model are integrated and modeled to obtain a preset three-dimensional geological-hydrological model.

[0009] Optionally, constructing a three-dimensional geological model based on the geological data includes: Based on the geological data, a geological evolution time-series framework for the target area is established, which includes sedimentary sequences, tectonic deformation sequences, and erosion event sequences. Based on the geological evolution time series framework, a stratigraphic superposition constraint rule set is generated, which includes the allowed superposition relationship and the prohibited superposition relationship of each geological body in three-dimensional space; Obtain borehole data and geological profile data of the target area, and perform three-dimensional spatial interpolation based on the borehole data and geological profile data to obtain an initial three-dimensional geological structure model; When the stratigraphic superposition constraint rule set is embedded into the three-dimensional spatial interpolation process, if the interpolation result violates any constraint rule in the stratigraphic superposition constraint rule set, geological correction is performed on the interpolation point that violates the constraint rule, and the stratigraphic affiliation of the interpolation point is adjusted to a position that satisfies all constraint rules. Output the three-dimensional geological model after the geological rationality correction.

[0010] Optionally, the geological data includes regional geological data, isotope dating data, and tectonic phase division results. The step of establishing a geological evolution timeline framework for the target region based on the geological data includes: Based on regional geological data, isotopic dating data and tectonic period division results of the target area, the absolute age or relative chronological order of each geological unit is determined. The sedimentary sequence is expressed as a stratigraphic chronology table, which records the sedimentary order of each stratigraphic unit from oldest to youngest. The tectonic deformation sequence is expressed as a tectonic event time series table, which records the chronological relationship of each tectonic activity and the stratigraphic range of its influence. The erosion event sequence is expressed as an erosion range-intensity spatiotemporal distribution table, which records the spatial range and erosion intensity of each erosion event. Based on the stratigraphic chronology table, stratigraphic chronology table, and spatiotemporal distribution table of erosion extent-intensity, a geological evolution timeline framework is constructed.

[0011] Optionally, after outputting the three-dimensional geological model after the geological rationality correction, the method further includes: Once new borehole data is acquired, it is added as hard data to the modeling dataset. While keeping the stratigraphic superposition constraint rule set unchanged, only the local areas within the influence range of the newly added borehole data are re-interpolated in three dimensions and geologically reasonable. Output the updated 3D geological model and generate a comparison report of the differences between the models before and after the update. The comparison report includes quantitative statistics on changes in stratigraphic interface morphology and stratigraphic attribution caused by the addition of borehole data.

[0012] Optionally, the step of constructing a hydrogeological model based on the hydrological data includes: The spatial variation of hydrological data in the target area is decomposed into three scale levels: regional facies scale, local intralayer scale, and microcementation scale. At the regional facies scale, based on the sedimentary facies distribution map of the target region, permeability coefficient benchmark values ​​at the facies level are assigned to different sedimentary facies units; At the local intralayer scale, within the same sedimentary facies unit, based on permeability test data from borehole cores, geostatistical methods are used to generate the spatial distribution of intralayer permeability coefficients. At the micro-cementation scale, a correction function between the degree of cementation and the permeability coefficient is established based on the type and content of cementing materials analyzed from core thin sections. The permeability coefficient baseline value, spatial distribution, and correction function are cascaded and superimposed to determine the final permeability coefficient at each spatial location; Based on the final permeability coefficient, a hydrogeological model integrating multi-scale heterogeneity is output.

[0013] Optionally, the step of integrating the three-dimensional geological model and the hydrogeological model to obtain a preset three-dimensional geological-hydrological model includes: Based on the aforementioned three-dimensional geological model, the stratigraphic interfaces and lithological interfaces of the target area are determined; Based on the hydrogeological model, the spatial variation characteristics of the hydrological parameter field are determined, and the locations of abrupt change surfaces where permeability coefficient or porosity changes spatially are identified. The spatial deviation is obtained by spatially comparing the location of the abrupt change surface with the location of the formation interface. When the spatial deviation meets the correction conditions, the stratigraphic interface of the initial three-dimensional geological structure model is fine-tuned and corrected using the location of the abrupt change surface as the correction basis for the structural interface. The spatial assignment of hydrological parameters is reconstrained using the corrected three-dimensional geological structure model. The above steps are repeated until the spatial deviation between the structural interface position and the parameter abrupt change surface position is less than the preset threshold. Output an integrated three-dimensional geological-hydrological model refined through bidirectional constraint iteration.

[0014] Optionally, in the preset three-dimensional geological-hydrological model, the spatial distribution of the active uranium source is used as the boundary condition for the material source. The groundwater flow equation, uranium ion migration equation, and redox reaction kinetic equation are coupled and solved to simulate the entire process of uranium-bearing ore-forming fluid from activation, migration, and precipitation of the active uranium source. This yields the three-dimensional groundwater flow field, the spatiotemporal distribution of uranium ion concentration, and the spatial distribution of uranium precipitation rate in the target area, including: The simulation time is divided into macroscopic time steps and microscopic time steps, the length of the macroscopic time step is N times the length of the microscopic time step, and N≥10; At the beginning of each macroscopic time step, the groundwater flow equation is solved to obtain the three-dimensional groundwater flow field within that macroscopic time step, and the flow field is kept fixed within the current macroscopic time step; Within the current macroscopic time step, under the condition that the flow field is fixed, multiple microscopic time steps are divided, and within each microscopic time step, the following steps are executed sequentially: calculate the redox reaction rate based on the current uranium ion concentration field and update the reaction source / sink term; solve the uranium ion migration equation based on the updated reaction source / sink term and update the uranium ion concentration field; repeat the iteration until the uranium ion concentration field converges within the microscopic time step; Upon entering the next macroscopic time step, based on the updated uranium ion concentration field and uranium precipitation amount at the end of the previous macroscopic time step, the porosity and permeability coefficient at each spatial location are updated, and the groundwater flow equation is solved again to obtain the updated flow field. Repeat the above macro-micro alternating solution process until the preset total simulation time is reached to obtain the three-dimensional groundwater flow field, spatiotemporal distribution of uranium ion concentration, and spatial distribution of uranium precipitation rate in the target area.

[0015] On the other hand, embodiments of this application provide an intelligent prediction device for favorable uranium mineralization zones based on the fusion of multiple ore-controlling factors. The device includes: The first acquisition module is used to acquire ore control data of the target rock sample; The first determining module is used to determine the geological unit of the target area as an active uranium source when the ore control data meets the preset identification conditions; The second acquisition module is used to acquire the spatial distribution of the active uranium source, the uranium activation rate, and the uranium release flux. The simulation module is used to use the spatial distribution of the active uranium source as the material source boundary condition in a preset three-dimensional geological-hydrological model, and to couple and solve the groundwater flow equation, uranium ion migration equation and redox reaction kinetic equation to simulate the entire process of uranium-bearing mineralizing fluid from activation, migration to precipitation of active uranium source, and to obtain the three-dimensional groundwater flow field, spatiotemporal distribution of uranium ion concentration and spatial distribution of uranium precipitation rate in the target area. The calculation module is used to calculate the uranium precipitation probability of each geological unit based on the three-dimensional groundwater flow field, the spatiotemporal distribution of uranium ion concentration, and the spatial distribution of uranium precipitation rate. The second determining module is used to determine favorable mineralization zones based on the uranium precipitation probability and a preset precipitation threshold.

[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 intelligent prediction method for favorable uranium mineralization zones by integrating multiple mineralization factors 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 intelligent prediction method for favorable uranium mineralization zones by integrating multiple mineralization factors 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 intelligent prediction method for favorable uranium mineralization zones by integrating multiple mineralization factors as described in the first aspect.

[0019] Compared with existing technologies, the intelligent prediction method and device for uranium mineralization favorable zones based on the fusion of multiple ore-controlling factors in this application can perform geological identification through the ore-controlling data of the acquired target rock samples, determine different uranium sources for the geological units corresponding to the target areas of the target rock samples, and then dynamically simulate the entire process of uranium mineralization fluid from activation, migration to precipitation in a preset three-dimensional geological-hydrological model for active uranium sources. This overcomes the limitation of traditional static statistical methods that ignore the dynamic chain of uranium mineralization. By coupling the kinetic equations of groundwater flow, uranium ion migration and redox reaction, and solving them based on an integrated three-dimensional geological-hydrological model, the migration and enrichment patterns of uranium elements can be more accurately characterized, thereby achieving quantitative and high-precision prediction of uranium mineralization favorable zones and improving the scientificity and efficiency of uranium resource evaluation. 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 an embodiment of the intelligent prediction method for favorable uranium mineralization zones based on the fusion of multiple ore-controlling factors provided in this application. Figure 2 This is a schematic diagram of the structure of an intelligent prediction device for uranium mineralization favorable zones based on the fusion of multiple mineralization factors 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 intended to include the plural forms as well. 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 an intelligent prediction method and apparatus for favorable uranium mineralization zones by integrating multiple ore-controlling factors. In this embodiment, geological identification is performed using ore-controlling data from target rock samples. Different uranium sources are identified for the geological units corresponding to the target areas of the target rock samples. Then, for active uranium sources, the entire process of uranium mineralization fluid activation, migration, and precipitation from the active uranium source is dynamically simulated in a preset three-dimensional geological-hydrological model, overcoming the limitation of traditional static statistical methods that ignore the dynamic chain of uranium mineralization. By coupling the kinetic equations of groundwater flow, uranium ion migration, and redox reaction, and solving them based on an integrated three-dimensional geological-hydrological model, the migration and enrichment patterns of uranium elements can be more accurately characterized, thereby achieving quantitative and high-precision prediction of favorable uranium mineralization zones and improving the scientific rigor and efficiency of uranium resource evaluation.

[0025] The following section first introduces the intelligent prediction method for favorable uranium mineralization zones based on the fusion of multiple ore-controlling factors provided in the embodiments of this application.

[0026] Figure 1 This illustration shows a flowchart of an intelligent prediction method for favorable uranium mineralization zones based on the fusion of multiple ore-controlling factors, provided in one embodiment of this application. Figure 1 As shown, the intelligent prediction method for favorable uranium mineralization zones based on the fusion of multiple ore-controlling factors may include steps S101-S106: S101, Obtain ore control data for the target rock sample.

[0027] In this embodiment, ore-controlling data can be obtained through geological surveys, geochemical sampling and analysis, and geophysical exploration of the target area. For example, rock samples can be collected for laboratory analysis to obtain information such as uranium content, mineral composition, and redox state. As one implementation method, ore-controlling data can be manually entered or imported from existing geological databases.

[0028] S102, if the ore control data meets the preset identification conditions, the geological unit of the target area is identified as an active uranium source.

[0029] In this embodiment, the preset identification conditions can be set based on experience or statistical analysis. For example, when the total uranium content in a rock sample is higher than a certain threshold, it is initially identified as a potential uranium source. As one implementation method, a simple whole-rock analysis can be performed on the rock sample, and the active uranium source can be determined by comparing the analysis results with the preset threshold.

[0030] S103, to obtain the spatial distribution of active uranium sources, uranium activation rate and uranium release flux.

[0031] In the embodiments of this application, the uranium activation rate and uranium release flux can be estimated through laboratory simulation experiments, geochemical equilibrium calculations, or empirical formulas. For example, the average uranium activation rate and uranium release flux can be obtained by consulting relevant literature or experimental data based on different rock types and geochemical environments.

[0032] S104, in the preset three-dimensional geological-hydrological model, takes the spatial distribution of active uranium sources as the boundary condition of material sources, and couples and solves the groundwater flow equation, uranium ion migration equation and redox reaction kinetic equation to simulate the whole process of uranium-bearing ore-forming fluid from activation, migration to precipitation of active uranium sources, and obtains the three-dimensional groundwater flow field, spatiotemporal distribution of uranium ion concentration and spatial distribution of uranium precipitation rate in the target area.

[0033] In this embodiment of the application, the preset three-dimensional geological-hydrological model can be a simplified model constructed based on geological maps and borehole data. Its hydrological parameters can be assigned values ​​based on regional average values. It can be solved in a sequential manner, that is, first solve the groundwater flow equation, and then use the obtained flow field as input to solve the uranium ion migration equation and redox reaction kinetic equation.

[0034] S105, based on the three-dimensional groundwater flow field, the spatiotemporal distribution of uranium ion concentration, and the spatial distribution of uranium precipitation rate, calculates the uranium precipitation probability of each geological unit.

[0035] In this embodiment, the uranium precipitation probability can be calculated by normalizing the relative magnitude of the uranium precipitation rate and using it as a direct indicator of the uranium precipitation probability. Alternatively, a comprehensive indicator can be obtained by linearly combining the uranium ion concentration and the precipitation rate.

[0036] S106, based on the uranium precipitation probability and the preset precipitation threshold, determines the favorable mineralization zone.

[0037] In the embodiments of this application, all areas with a uranium precipitation probability higher than a certain fixed percentage can be designated as favorable zones. For example, simulation results can be manually interpreted, and the range of favorable zones can be determined based on experience.

[0038] In this embodiment, geological identification is performed using ore-controlling data of the acquired target rock samples. Different uranium sources are identified for the geological units corresponding to the target areas. Then, for active uranium sources, the entire process of uranium ore-forming fluid activation, migration, and precipitation is dynamically simulated in a pre-defined three-dimensional geological-hydrological model. This overcomes the limitation of traditional static statistical methods that ignore the dynamic chain of uranium ore formation. By coupling groundwater flow, uranium ion migration, and redox reaction kinetic equations, and solving them based on an integrated three-dimensional geological-hydrological model, the migration and enrichment patterns of uranium elements can be more accurately characterized. This enables quantitative and high-precision prediction of favorable uranium ore-forming zones, improving the scientific rigor and efficiency of uranium resource evaluation.

[0039] In some other embodiments, S102 may include: The target rock sample was subjected to stepwise chemical extraction to separate the occurrence forms of uranium into exchangeable ion state, carbonate-bound state, iron-manganese oxide-bound state, sulfide and organic matter-bound state, and residue state; The active uranium content is determined by summing the contents of four forms of uranium: exchangeable ion state, carbonate-bound state, iron-manganese oxide-bound state, and sulfide and organic matter-bound state. The content of inert uranium is determined based on the content of uranium in the residue state; The proportion of active uranium is determined based on the content of active uranium and inert uranium. The active uranium source is determined based on the proportion of active uranium, the target total uranium content, and the preset identification conditions.

[0040] In this embodiment, in actual uranium mineralization prediction, how to accurately and quantitatively assess the geochemical activity of uranium in rock samples and identify the truly active uranium sources that can provide uranium material is a key issue affecting the accuracy of subsequent ore-forming fluid simulations. If the method for identifying active uranium sources is not precise enough, it may lead to deviations in the simulation results of uranium activation, migration, and precipitation processes, thereby reducing the reliability of predicting favorable mineralization zones. Therefore, the contents of four relatively easily activated and migrated uranium forms obtained through stepwise extraction can be summarized to quantify the total amount of geochemically active uranium in the rock sample. In nature, especially under the action of ore-forming fluids, these forms of uranium are more likely to be released from the solid phase into the liquid phase, thereby participating in the activation, migration, and precipitation processes of uranium. Therefore, summing the contents of these forms of uranium can directly reflect the potential contribution capacity of the geological unit as a source of uranium material.

[0041] When defining uranium content later, the residual uranium content remaining after stepwise extraction can be defined as the inert uranium content. Residual uranium typically refers to uranium tightly bound to stable minerals (such as zircon, monazite, and silicate minerals), which is difficult to activate and migrate under conventional geochemical processes. Therefore, the inert uranium content represents the portion of uranium in the rock sample that contributes very little to the mineralization process. Distinguishing it from active uranium helps to more accurately assess the effectiveness of the uranium source.

[0042] The activity of uranium is then evaluated by calculating the proportion of active uranium to total uranium content. This proportion considers not only the absolute uranium content but, more importantly, reflects the "availability" or "mobilizability" of uranium. For example, a rock sample with a high total uranium content may have a lower potential as an active uranium source if its active uranium proportion is low. This proportion provides a more comprehensive assessment of a geological unit's ability to provide uranium resources.

[0043] When finally determining the active uranium source, the relative activity (proportion of active uranium) and absolute abundance (target total uranium content) of uranium are comprehensively considered, along with preset identification conditions. This multi-parameter comprehensive judgment mechanism, combining the proportion of active uranium, absolute abundance, and preset identification conditions, avoids misjudgments that may arise from a single indicator, ensuring the accuracy and reliability of active uranium source identification. The target total uranium content can be obtained by adding the active uranium content and the inert uranium content, or by performing full uranium analysis on the original rock sample.

[0044] By performing stepwise chemical extraction on target rock samples, the occurrence forms of uranium can be effectively separated, thus distinguishing between active uranium, which is easily activated and migrates, and inert uranium, which is difficult to participate in the mineralization process. Then, based on the calculation of active uranium content, inert uranium content, and active uranium ratio, combined with the target total uranium content and preset identification conditions, it is possible to more accurately and objectively determine whether a geological unit has the potential to serve as a source of uranium-forming materials. This significantly improves the accuracy of simulation results of three-dimensional groundwater flow field, spatiotemporal distribution of uranium ion concentration, and spatial distribution of uranium precipitation rate, thereby improving the reliability and effectiveness of predicting favorable uranium mineralization zones.

[0045] In some other embodiments, S104 may include: Acquire geological and hydrological data for the target area; Construct a three-dimensional geological model based on geological data; Based on hydrological data, construct a hydrogeological model; The three-dimensional geological model and the hydrogeological model are integrated to form a preset three-dimensional geological-hydrological model.

[0046] In this embodiment, geological data can be the basis for describing information such as the underground geological structure, lithological composition, and structural characteristics of the target area. It can be obtained through regional geological survey reports, borehole core records, geophysical exploration data (such as seismic reflection profiles, gravity anomalies, and magnetic anomaly data), remote sensing image interpretation results, and various rock sample analysis data (such as lithological identification, mineral composition analysis, and isotope dating data).

[0047] Hydrological data is used to characterize the occurrence, movement, and hydrochemical features of the groundwater system in the target area. Its sources may include hydrogeological borehole data, long-term water level observation data, pumping test data, water quality analysis data, hydrogeophysical exploration data, and rainfall, evaporation, and runoff data from surface hydrological stations.

[0048] When constructing a three-dimensional geological model, professional geological modeling software can be used to perform spatial interpolation, surface fitting, and volume element division on borehole data, geological profile data, and geophysical interpretation results to establish stratigraphic interfaces, faults, folds, and other structural surfaces, and assign lithological attributes to different geological units based on lithological data.

[0049] Then, hydrological data (such as pumping test results, well water level changes, and water quality data) are combined with the lithology and structural characteristics of geological units. Hydrogeological parameters for each spatial location are determined and assigned using geostatistical methods (such as Kriging interpolation and co-Kriging interpolation) or parameter inversion techniques to construct a hydrogeological model.

[0050] After constructing the 3D geological model and the hydrogeological model, they can be integrated to form a pre-defined 3D geological-hydrological model, ensuring consistency and mutual constraints between geological structures and hydrological parameters. For example, hydrogeological parameters (such as permeability coefficient and porosity) can be accurately mapped onto the corresponding geological units or grids of the 3D geological model, and the control effect of geological structures (such as stratigraphic interfaces and faults) on the spatial distribution of hydrogeological parameters can be considered.

[0051] For example, stratigraphic interfaces are often where permeability coefficients change abruptly, and faults may form barriers to water flow or water-conducting channels. Through this integration, a pre-defined three-dimensional geological-hydrological model can be obtained that contains both detailed geological structural information and accurate spatial distribution of hydrogeological parameters, providing a unified and reliable computational domain for subsequent coupled simulations.

[0052] In this embodiment, comprehensive geological and hydrological data of the target area are first acquired, providing a solid data foundation for model construction. Secondly, a three-dimensional geological model and a hydrogeological model are constructed separately, allowing for independent and detailed characterization of geological structures and hydrological parameters. Based on this, the two models are integrated to ensure consistency and mutual constraints between geological structures and hydrological parameters. For example, structural features such as stratigraphic interfaces and faults effectively constrain the spatial distribution of hydrological parameters, avoiding mismatches between the geological and hydrological models. This integrated modeling approach enables the final pre-defined three-dimensional geological-hydrogeological model to more accurately and reliably reflect the underground geological structure and hydrogeological conditions of the target area.

[0053] In some embodiments, a three-dimensional geological model is constructed based on geological data, including: Based on geological data, a geological evolution time-series framework for the target area is established, which includes sedimentary sequences, tectonic deformation sequences, and erosion event sequences. Based on the geological evolution time series framework, a stratigraphic superposition constraint rule set is generated, which includes the allowed superposition relationships and prohibited superposition relationships of each geological body in three-dimensional space; Obtain borehole data and geological profile data of the target area, and perform three-dimensional spatial interpolation based on the borehole data and geological profile data to obtain an initial three-dimensional geological structure model; When the stratigraphic superposition constraint rule set is embedded into the three-dimensional spatial interpolation process, if the interpolation result violates any constraint rule in the stratigraphic superposition constraint rule set, geological correction is performed on the interpolation point that violates the constraint rule, and the stratigraphic affiliation of the interpolation point is adjusted to a position that satisfies all constraint rules. Output a three-dimensional geological model after geological rationality correction.

[0054] In this embodiment, firstly, based on geological data, a geological evolution time-series framework for the target area is established. This framework includes sedimentary sequences, tectonic deformation sequences, and erosion event sequences. Specifically, the geological evolution time-series framework refers to the systematic review and chronological ordering of geological history events in the target area. The geological evolution time-series framework can include sedimentary sequences, tectonic deformation sequences, and erosion event sequences. The sedimentary sequences describe the order of deposition and spatial distribution of different stratigraphic units. The tectonic deformation sequences record the phases, patterns, and extent of influence of tectonic activities such as folding and faulting that occurred within the region. The erosion event sequences characterize the timing, intensity, and spatial distribution of surface erosion.

[0055] Subsequently, based on the geological evolution timeline framework, a stratigraphic superposition constraint rule set is generated. This set includes the permissible and prohibited superposition relationships of various geological bodies in three-dimensional space. The stratigraphic superposition constraint rule set is a series of logical rules concerning the spatial relationships of geological bodies, extracted from the geological evolution timeline framework. For example, younger strata generally cannot lie beneath older strata (unless there is an overturned structure, which requires special rule handling), faults can cut strata but cannot be cut by strata (unless they are syn-sedimentary faults), and intrusive bodies can penetrate surrounding rocks, etc.

[0056] Next, borehole data and geological profile data of the target area are acquired. Borehole data provides discrete point information such as stratigraphic sequence, lithology, and depth at specific underground locations, which is the most direct hard data for constructing a three-dimensional geological model. Geological profile data provides two-dimensional information on the stratigraphic distribution and structural morphology along a specific direction. Then, using these discrete borehole data and two-dimensional profile data, the continuous distribution of various geological bodies in the three-dimensional space of the entire target area is inferred through geostatistical methods (such as Kriging interpolation, inverse distance weighting, etc.) or deterministic interpolation methods, thereby generating a preliminary three-dimensional geological structure model.

[0057] Furthermore, by embedding a set of stratigraphic overlay constraint rules into the 3D spatial interpolation process, when the interpolation result violates any constraint rule in the set, geological correction is performed on the interpolation point that violates the constraint rule, adjusting the stratigraphic assignment of the interpolation point to a position that satisfies all constraint rules. Specifically, these rules can be directly integrated into the interpolation algorithm, making the interpolation process itself tend to generate results that conform to geological laws; alternatively, after the initial interpolation result is generated, the system can automatically detect whether there are points or regions in the model that violate preset constraint rules. Once a violation is detected (for example, an interpolation point is incorrectly assigned to a stratigraphic type with a prohibited overlay relationship with its adjacent strata), geological correction is performed on the interpolation point to make it conform to all stratigraphic overlay constraint rules, thereby eliminating geological inconsistencies. As an example, correction methods may include resampling, local reinterpolation, or rule-based logical judgment and correction.

[0058] Finally, the three-dimensional geological model after geological correction conforms to the preset geological evolution time frame and stratigraphic superposition constraint rules in terms of its internal stratigraphic structure, contact relationships and spatial distribution.

[0059] When constructing a 3D geological model, a geological evolution timeline framework for the target region can be established, and a set of stratigraphic overlay constraint rules can be generated based on this framework. Embedding these geological constraint rules into the spatial interpolation and correction processes effectively identifies and corrects potentially unreasonable geological structures that may arise during interpolation, such as stratigraphic inversions or unrealistic stratigraphic contact relationships. By performing geological rationality correction on the model, it is ensured that the final output 3D geological model highly conforms to the regional geological evolution patterns in both geometry and geological logic, significantly improving the model's reliability and accuracy.

[0060] In other embodiments, if the construction process of the geological evolution timeline framework lacks a refined and structured representation of key geological events, it may lead to uncertainties or inaccuracies in the subsequent three-dimensional geological model's reflection of the true geological history and stratigraphic relationships, thereby affecting the model's reliability. Therefore, based on geological data, a geological evolution timeline framework for the target area is established, including: Based on regional geological data, isotopic dating data and tectonic period division results of the target area, the absolute age or relative chronological order of each geological unit is determined. The sedimentary sequence is expressed as a stratigraphic chronology table, which records the sedimentary order of each stratigraphic unit from oldest to youngest. The tectonic deformation sequence is expressed as a tectonic event time series table, which records the chronological relationship of each tectonic activity and the stratigraphic extent of its influence. The erosion event sequence is expressed as a spatiotemporal distribution table of erosion range-intensity, which records the spatial range and erosion intensity of each erosion event. Based on the stratigraphic chronology table, the stratigraphic chronology table, and the spatiotemporal distribution table of erosion extent and intensity, a geological evolution timeline framework is constructed.

[0061] In this embodiment, the geological data includes regional geological data, isotope dating data, and tectonic period division results. The regional geological data can be existing geological maps, geological reports, exploration results, and literature of the target area, providing basic information such as macroscopic stratigraphic and lithological distribution, tectonic structure, and sedimentary environment. It can be obtained through data collection and digitization.

[0062] Isotope dating data are absolute age information obtained by radiometric decay dating of rock or mineral samples. This data provides precise time constraints for geological events (such as magmatic intrusions, metamorphism, and sedimentary events), and is crucial for calibrating relative chronological sequences and establishing accurate geological time scales. It typically relies on specialized laboratory analysis to obtain this data.

[0063] The results of tectonic phase division are the results of dividing the tectonic movements that occur in the target area into stages. They clarify the occurrence time, duration, deformation patterns and the range of influence on strata and rock masses of different tectonic events. These results are usually based on regional tectonic analysis, field geological surveys and geophysical data interpretation, and are comprehensively interpreted and summarized by geological experts.

[0064] Based on the above geological data, we first comprehensively utilize the relative relationships provided by regional geological data, the absolute ages provided by isotopic dating data, and the event sequences provided by the tectonic period division results to sort all geological units (such as strata, intrusive rocks, metamorphic rocks, etc.) in the target area in time, and clarify their chronological relationship of formation or occurrence.

[0065] Subsequently, the sedimentary sequence is expressed as a stratigraphic chronology table, which systematically records the depositional order of various stratigraphic units within the target area. This table can include fields such as the name of the stratigraphic unit, lithological characteristics, depositional environment, and absolute or relative age information, and is arranged in chronological order from oldest to youngest.

[0066] This table provides a clear understanding of the superposition relationships between strata and their sedimentary evolution history.

[0067] Meanwhile, the tectonic deformation sequence is expressed as a tectonic event time series table, which is used to record detailed information about each phase of tectonic activity in the target area. It can include the name of the tectonic event, the time of occurrence (absolute or relative), the duration, the main deformation type (such as folding, faulting, cleaving, etc.), the affected strata or rock mass range, and its impact on the regional stress field.

[0068] The establishment of this event timeline helps to understand the control of tectonic movements on geological structures and fluid migration paths.

[0069] Furthermore, the erosion event sequence is expressed as a spatiotemporal distribution table of erosion range-intensity. This table is used to quantify and record the spatial distribution, occurrence time, and erosion intensity of erosion events in each period within the target area. The erosion intensity can be measured by indicators such as erosion thickness and erosion rate.

[0070] This spatiotemporal distribution table of erosion extent and intensity is of great significance for assessing stratigraphic gaps, paleogeographic reconstruction, and the evolution of ore-forming fluid migration pathways. Its construction may involve methods such as paleogeographic reconstruction, sedimentary provenance analysis, and thermal history simulation.

[0071] Finally, the structured stratigraphic chronology table, tectonic event time series table, and erosion extent-intensity spatiotemporal distribution table are integrated to form a comprehensive, logically rigorous, and computable geological evolution time series framework. This framework not only includes the temporal sequence of geological events but also the spatial extent and intensity information of these events, providing a solid foundation for the generation of stratigraphic overlay constraint rule sets in subsequent 3D geological modeling.

[0072] In some other embodiments, after outputting the geologically plausible 3D geological model, the method further includes: Once new borehole data is acquired, it is added as hard data to the modeling dataset. While keeping the stratigraphic superposition constraint rule set unchanged, only the local area within the influence range of the newly added borehole data is re-interpolated in three dimensions and geologically reasonable is corrected. Output the updated 3D geological model and generate a comparison report of the differences between the model before and after the update. The comparison report includes quantitative statistics on changes in stratigraphic interface morphology and stratigraphic attribution caused by the addition of borehole data.

[0073] In this embodiment, after acquiring new borehole data, this new borehole data is added to the modeling dataset as hard data. This allows the latest measured information to be incorporated into the model building process, thereby improving the model's accuracy and reliability. During implementation, after receiving this new borehole data, the system parses it and stores it in a format compatible with the existing modeling dataset (e.g., including coordinates, stratigraphic attribution, lithology, etc.). These new data points will be treated as known, unchangeable constraints in subsequent modeling updates.

[0074] Based on this, while keeping the stratigraphic superposition constraint rule set unchanged, only the local area within the influence range of the newly added borehole data is re-interpolated in three dimensions and geologically reasonable is corrected. By re-interpolating and correcting only the local area, the huge computational burden brought about by global remodeling can be effectively avoided, while ensuring that the impact of the new data can be accurately and efficiently reflected in the model.

[0075] Specifically, the influence range of newly added borehole data can be determined in several ways. For example, a preset radius centered on the new borehole can be set, and the area within this radius can be defined as the influence range; or the influence area can be delineated based on the boundary of the geological unit where the new borehole is located; or a sensitivity analysis can be performed on the three-dimensional spatial interpolation algorithm used (such as Kriging interpolation, inverse distance weighting, etc.) to assess the actual influence range of the new data points on the interpolation results of the surrounding area. After determining the influence range, the system performs three-dimensional spatial interpolation only on the grid points or voxels within this local area, using all relevant data, including the new borehole data. After the interpolation is completed, the interpolation results within the local area are again subjected to geological rationality correction by applying the stratigraphic overlay constraint rule set to ensure that the updated local model still conforms to geological laws. This may involve adjusting the stratigraphic attribution of the interpolation points within the local area to satisfy all constraint rules.

[0076] Finally, the updated 3D geological model is output, and a comparison report of the differences between the model before and after the update is generated. The comparison report can include quantitative statistics on changes in stratigraphic interface morphology and stratigraphic attribution caused by the new borehole data. By quantifying the specific impact of the new data on the model structure, the comparison report helps users clearly understand the reasons and extent of the model changes, thereby assessing the effectiveness of the update and providing a more accurate basis for subsequent exploration decisions.

[0077] When generating a difference comparison report, morphological changes can be quantified by comparing the geometry of the same stratigraphic interfaces in the model before and after the update (e.g., calculating the average displacement, volume change, and surface area change of interface grid points). Simultaneously, the report identifies which spatially located voxels or grid points have changed their stratigraphic attribution before and after the update, and calculates the number of changed voxels or the percentage of changed volume to quantify the change in stratigraphic attribution. The report can be presented in text, charts, or interactive 3D views, clearly showing the changed areas, types of changes, and quantitative indicators.

[0078] Through the above technical solution, when new borehole data is acquired, the system can efficiently integrate it into the existing 3D geological model, avoiding time-consuming and resource-intensive global reconstruction of the entire model. By re-interpolating and geologically accurately correcting only local areas within the influence range of the newly added borehole data, computational complexity and time costs are significantly reduced, improving the efficiency and response speed of model updates. Simultaneously, the generated difference comparison report clearly and quantitatively demonstrates the specific impact of the new data on stratigraphic interface morphology and stratigraphic attribution, making the model update process transparent and traceable. This facilitates geologists' rapid understanding of model changes, evaluation of update effects, and provides more accurate and timely geological evidence for subsequent exploration decisions.

[0079] In other embodiments, to ensure that the three-dimensional geological-hydrological model is more realistic, the hydrogeological model is constructed based on hydrological data, including: The spatial variation of hydrological data in the target area is decomposed into three scale levels: regional facies scale, local intralayer scale, and microcementation scale. At the regional facies scale, based on the sedimentary facies distribution map of the target area, permeability coefficient benchmark values ​​at the facies level are assigned to different sedimentary facies units; At the local intralayer scale, within the same sedimentary facies unit, based on permeability test data from borehole cores, geostatistical methods are used to generate the spatial distribution of intralayer permeability coefficients. At the micro-cementation scale, a correction function between the degree of cementation and the permeability coefficient is established based on the type and content of cementing materials analyzed from core thin sections. The permeability coefficient baseline value, spatial distribution, and correction function are cascaded and superimposed to determine the final permeability coefficient at each spatial location; Based on the final permeability coefficient, a hydrogeological model integrating multi-scale heterogeneity is output.

[0080] In this embodiment, the spatial variation of hydrological data in the target area is first decomposed into three scale levels: regional facies scale, local intralayer scale, and microcementation scale. The regional facies scale focuses on the differences between large-scale geological units, the local intralayer scale focuses on the subtle changes within the same geological unit, and the microcementation scale focuses on the influence of rock pore structure and cement on permeability.

[0081] Specifically, at the regional facies scale, based on the sedimentary facies distribution map of the target area, permeability benchmark values ​​at the facies level can be assigned to different sedimentary facies units. The sedimentary facies distribution map provides spatial distribution information of different sedimentary facies units (such as fluvial, lacustrine, deltaic, and marine facies) within the target area. Since different sedimentary facies have their typical lithological assemblages, grain size characteristics, and pore structures, their permeability coefficients also differ significantly. By assigning a representative permeability benchmark value to each sedimentary facies unit, the permeability differences between different sedimentary facies zones can be macroscopically captured, providing macroscopic background constraints for subsequent more refined modeling. These benchmark values ​​are typically determined based on regional hydrogeological surveys, empirical data, or the results of large-scale pumping tests.

[0082] At the local intra-layer scale, within the same sedimentary facies unit, the spatial distribution of permeability coefficients is generated using geostatistical methods based on permeability test data from borehole cores. The local intra-layer scale focuses on the subtle variations in permeability coefficients within the same sedimentary facies unit caused by factors such as sedimentary microfacies and diagenesis.

[0083] It is worth noting that the permeability test data from the borehole core provides highly accurate point-like permeability information.

[0084] At the microscopic cementation scale, a correction function between the degree of cementation and permeability is established based on the type and content of cementing materials analyzed in core thin sections. Cementation is an important component of diagenesis, and different types (such as carbonate cement, siliceous cement, and clay cement) and contents of cementing materials significantly alter the porosity and pore-throat structure of rocks, thereby affecting the permeability coefficient. Core thin section analysis can provide detailed microscopic information such as cementing material type, content, and distribution characteristics.

[0085] By analyzing the relationship between these microscopic features and the permeability coefficient, a correction function can be established. This correction function can finely adjust the permeability coefficients obtained at the macroscopic and mesoscopic scales according to the degree of cementation at a specific location, making them more consistent with the actual microscopic permeability characteristics.

[0086] Subsequently, based on the baseline value of permeability coefficient at the regional facies scale, the permeability coefficient is refined within each facies zone using a spatial distribution generated by geostatistical methods at the local intra-layer scale. Finally, the local permeability coefficient is fine-tuned according to a correction function at the micro-cementation scale, ensuring that heterogeneity information at different scales can be systematically integrated, thereby obtaining a final permeability coefficient spatial field that comprehensively reflects multi-scale heterogeneity and is closer to the actual stratigraphic situation.

[0087] Finally, based on the final permeability coefficient, a hydrogeological model integrating multi-scale heterogeneity is output. This model can more realistically reflect the flow path and velocity of groundwater in complex strata, providing a reliable physical basis for subsequent uranium ion migration simulations.

[0088] Through the above technical solution, this application effectively addresses the shortcomings of traditional hydrogeological models in handling multi-scale heterogeneity of formation media. By decomposing the spatial variability of hydrological data into regional facies scale, local intra-layer scale, and microcementation scale, and employing different modeling strategies for each scale, a refined characterization of permeability coefficients from macroscopic to microscopic levels is achieved. The baseline value at the regional facies scale provides the macroscopic background, the geostatistical method at the local intra-layer scale captures intra-layer variability, and the correction function at the microcementation scale further refines the influence of micropore structure, enabling a more realistic reflection of groundwater flow fields and uranium ion migration processes. This provides a more solid and accurate physical basis for subsequent prediction of favorable uranium mineralization zones, thereby improving the accuracy and reliability of the entire prediction method.

[0089] In other embodiments, the three-dimensional geological model and the hydrogeological model are integrated to obtain a preset three-dimensional geological-hydrological model, including: Based on a three-dimensional geological model, the stratigraphic and lithological interfaces of the target area are determined; Based on the hydrogeological model, the spatial variation characteristics of the hydrological parameter field are determined, and the location of abrupt change surfaces where permeability coefficient or porosity changes spatially is identified. Spatial deviation is obtained by spatially comparing the location of the abrupt change surface with the location of the formation interface; When the spatial deviation meets the correction conditions, the location of the abrupt change surface is used as the basis for the correction of the structural interface, and the stratigraphic interface of the initial three-dimensional geological structure model is finely adjusted and corrected. The spatial assignment of hydrological parameters is reconstrained using the corrected three-dimensional geological structure model. The above steps are repeated until the spatial deviation between the structural interface position and the parameter abrupt change surface position is less than the preset threshold. Output an integrated three-dimensional geological-hydrological model refined through bidirectional constraint iteration.

[0090] In this embodiment, firstly, based on a three-dimensional geological model, the stratigraphic interfaces and lithological interfaces of the target area are determined. The stratigraphic interfaces and lithological interfaces are key structural elements in the three-dimensional geological model. They define the spatial boundaries between different stratigraphic or lithological units and have a decisive influence on the flow path of underground fluids and the distribution of hydrogeological parameters. For example, for models constructed based on voxels or triangular meshes, the interface can be represented as a shared surface between adjacent different geological unit voxels, or it can be determined by extracting geometric elements such as the top and bottom surfaces of strata, faults, and unconformities.

[0091] Secondly, since hydrological parameters (such as permeability and porosity) are not uniformly distributed in space, but change significantly with the changes in geological units, identifying the interfaces where these parameters change abruptly can accurately characterize groundwater flow and solute transport.

[0092] Abrupt surfaces can be identified by performing spatial gradient analysis or edge detection algorithms on parameters such as permeability or porosity in hydrogeological models. For example, the spatial derivatives of the parameter field in various directions can be calculated, and when the derivatives exceed a preset threshold, an abrupt change is considered to exist.

[0093] Next, the location of the abrupt change surface is spatially compared with the location of the stratigraphic interface to obtain the spatial deviation. This can quantify the spatial inconsistency between the geological structure interface and the hydrological parameter abrupt change surface, reflecting the difference between the two independent models when describing the same physical boundary. Therefore, spatial comparison is required to achieve spatial alignment.

[0094] As an example, spatial comparison can be accomplished by calculating the distance, overlap, or geometric differences between two three-dimensional curved or planar regions.

[0095] Subsequently, when a significant deviation is found between the geological interface and the abrupt change surface of hydrological parameters, the geological interface needs to be adjusted to better align with the actual changing trends of the hydrological parameters. Since it directly reflects changes in fluid properties, the abrupt change surface is chosen as the basis for correction.

[0096] Furthermore, the spatial assignment of hydrological parameters is reconstrained using the corrected 3D geological structure model, and the above steps are repeated until the spatial deviation between the structural interface location and the parameter abrupt change surface location is less than a preset threshold. In each iteration, the hydrological parameters are first spatially reassigned based on the corrected geological structure model, and then a new abrupt change surface is identified again based on the updated hydrological parameter field and compared with the current geological interface to calculate the new spatial deviation, until the spatial deviation between the geological interface and the hydrological parameter abrupt change surface converges to an acceptable preset threshold.

[0097] Finally, an integrated 3D geological-hydrological model, refined through bidirectional constraint iteration, is output. This 3D geological-hydrological model not only includes accurate geological structures but also a hydrological parameter field highly coordinated with the geological structures, providing a reliable foundation for subsequent simulations of groundwater flow and solute transport.

[0098] The above technical solution systematically identifies and quantifies the deviations between geological and hydrogeological models in interface description. By using the hydrogeological parameter abrupt change surface as a correction basis, the geological structure interface is fine-tuned, and conversely, the corrected geological structure is used to reconstrain the hydrogeological parameters, achieving mutual correction and optimization between the geological structure and the hydrogeological parameter field. This iterative process ensures a high degree of spatial agreement between the geological structure interface and the hydrogeological parameter abrupt change surface, thereby constructing an integrated three-dimensional geological-hydrological model with stronger internal consistency and clearer physical meaning. This significantly improves the model's simulation accuracy and prediction reliability for groundwater flow fields, the spatiotemporal distribution of uranium ion concentration, and the spatial distribution of uranium precipitation rate, providing a more solid foundation for intelligent prediction of favorable uranium mineralization zones.

[0099] In some other embodiments, S104 may include: The simulation time is divided into macroscopic time steps and microscopic time steps. The length of the macroscopic time step is N times the length of the microscopic time step, where N≥10. At the beginning of each macroscopic time step, the groundwater flow equation is solved to obtain the three-dimensional groundwater flow field within that macroscopic time step, and the flow field is kept fixed within the current macroscopic time step; Within the current macroscopic time step, under the condition of a fixed flow field, multiple microscopic time steps are divided. Within each microscopic time step, the following steps are executed sequentially: calculate the redox reaction rate based on the current uranium ion concentration field and update the reaction source / sink term; solve the uranium ion migration equation based on the updated reaction source / sink term and update the uranium ion concentration field; repeat the iteration until the uranium ion concentration field converges within the microscopic time step. Upon entering the next macroscopic time step, based on the updated uranium ion concentration field and uranium precipitation amount at the end of the previous macroscopic time step, the porosity and permeability coefficient at each spatial location are updated, and the groundwater flow equation is solved again to obtain the updated flow field. Repeat the above macro-micro alternating solution process until the preset total simulation time is reached to obtain the three-dimensional groundwater flow field, spatiotemporal distribution of uranium ion concentration, and spatial distribution of uranium precipitation rate in the target area.

[0100] In this embodiment, the simulation time is first divided into macroscopic time steps and microscopic time steps, where the length of the macroscopic time step is N times the length of the microscopic time step, and N ≥ 10. The macroscopic time step is used to capture slower-changing physical processes, such as groundwater flow fields, while the microscopic time step is used to finely simulate faster-changing chemical processes, such as uranium ion migration and redox reactions. Before the simulation begins, the specific lengths of the macroscopic and microscopic time steps can be determined based on the actual geological and hydrological conditions of the study area, the characteristic timescales of uranium ore-forming fluid migration and reactions, and acceptable computational resources and accuracy requirements. For example, the macroscopic time step can be set to several years, decades, or even centuries to reflect changes in the flow field over geological timescales; the microscopic time step can be set to several days or months to capture the rapid dynamics of uranium ion migration and reactions.

[0101] Secondly, by solving the groundwater flow equation once at the beginning of the macroscopic time step, a relatively stable flow field can be obtained within that macroscopic time step. Fixing this flow field can simplify the simulation of uranium ion migration and reaction in subsequent microscopic time steps, avoiding the need to resolve the flow field at each microscopic time step, thereby significantly improving computational efficiency.

[0102] As an example, numerical methods such as the finite difference method, finite element method, or finite volume method can be used to solve the groundwater flow equation, for instance, Darcy's law combined with the mass conservation equation. During the solution process, the boundary conditions and initial conditions of the current macroscopic time step, as well as the hydrological parameters from the geological-hydrological model, need to be input. The boundary conditions can include hydraulic head and flow rate, while the hydrological parameters can include permeability coefficient and porosity, to obtain the hydraulic head or pressure distribution at each grid point within the target area. This allows for the calculation of the three-dimensional groundwater flow field, i.e., the Darcy velocity vector.

[0103] Furthermore, within the current macroscopic time step, under the condition of a fixed flow field, multiple microscopic time steps are defined, and the following operations are performed sequentially within each microscopic time step: calculate the redox reaction rate based on the current uranium ion concentration field and update the reaction source / sink terms; solve the uranium ion migration equation based on the updated reaction source / sink terms and update the uranium ion concentration field; repeat the iteration until the uranium ion concentration field converges within the microscopic time step. Under a fixed flow field background, the migration of uranium ions and complex redox reaction processes can be accurately simulated through iterative solutions over multiple microscopic time steps.

[0104] Specifically, based on the uranium ion concentration field and the concentration fields of other relevant components (such as oxidants and reductants) at the beginning of the current micro-time step, the activation, precipitation, or reactivation rates of uranium are calculated using a pre-defined redox reaction kinetic model (such as based on the law of mass action or empirical formulas). These rates are converted into source terms (activation, dissolution) or sink terms (precipitation, adsorption) in the uranium ion migration equation. Subsequently, the convection-diffusion-reaction equation is used to describe the uranium ion migration process. This equation considers convection (driven by groundwater flow), diffusion (molecular diffusion and hydrodynamic dispersion), and reaction (source / sink terms), and can be solved using numerical methods such as finite difference, finite element, or finite volume methods. Within each micro-time step, methods such as Picard iteration or Newton-Raphson iteration can be used to repeatedly calculate reaction rates, update source / sink terms, and solve the migration equation until the difference between the uranium ion concentration fields obtained from two consecutive iterations is less than a pre-defined convergence threshold, indicating that the simulation results within that micro-time step have reached a stable state.

[0105] Subsequently, at the next macroscopic time step, based on the updated uranium ion concentration field and uranium precipitation amount at the end of the previous macroscopic time step, the porosity and permeability coefficient at each spatial location are updated, and the groundwater flow equation is resolved to obtain the updated flow field. Uranium precipitation or dissolution alters the pore structure of the rock, thus affecting porosity and permeability, ultimately changing the groundwater flow field. By updating these hydrological parameters at the end of each macroscopic time step and resolving the groundwater flow equation, the reaction of uranium mineralization processes to the hydrogeological environment can be captured, making the simulation results more consistent with reality.

[0106] Specifically, based on the accumulated uranium precipitation (or dissolution) in the previous macroscopic time step, an empirical relationship or physical model is established between porosity and precipitation amount (e.g., precipitation filling pores leads to a decrease in porosity). Empirical relationships such as the Kozeny-Carman relationship typically exist between permeability and porosity, and the permeability can be updated accordingly. Then, using the updated porosity and permeability as input parameters, the groundwater flow equation is solved again to obtain a new three-dimensional groundwater flow field. This new flow field will serve as the background flow field for uranium ion migration and reaction simulations in the next macroscopic time step.

[0107] Finally, the above macro-micro alternating solution process is repeated until the preset total simulation time is reached, and the three-dimensional groundwater flow field, spatiotemporal distribution of uranium ion concentration, and spatial distribution of uranium precipitation rate in the target area are obtained.

[0108] Specifically, a total simulation duration, such as tens or hundreds of thousands of years, can be set to cover the entire geological history of uranium mineralization. The process of iteratively solving for both macroscopic and microscopic time steps is then repeated until the cumulative simulation time reaches the total duration. This ensures a complete and accurate simulation of the entire uranium mineralization fluid evolution process over a sufficiently long timescale. During the simulation, key parameters, such as the three-dimensional groundwater flow field, the spatiotemporal distribution of uranium ion concentration, the spatial distribution of uranium precipitation rate, and the cumulative precipitation amount, can be periodically output or stored for subsequent analysis and visualization.

[0109] By employing the aforementioned technical solution, the simulation time is divided into macroscopic and microscopic time steps. Within the macroscopic time step, the flow field is fixed, significantly reducing the number of solutions to the groundwater flow equations and substantially improving computational efficiency. Simultaneously, within the microscopic time step, precise iterative solutions are performed on uranium ion migration and reactions, ensuring accurate capture of rapidly changing chemical processes and avoiding numerical oscillations or error accumulation caused by excessively large time steps. Furthermore, by updating porosity and permeability coefficients between macroscopic time steps to reflect the impact of uranium precipitation on the flow field, the entire simulation process dynamically reflects the interaction between uranium-forming fluids and the geological environment. This results in a more realistic and accurate three-dimensional groundwater flow field, spatiotemporal distribution of uranium ion concentration, and spatial distribution of uranium precipitation rate, providing a more reliable simulation basis for intelligent prediction of favorable uranium mineralization zones.

[0110] In this embodiment, when determining the uranium precipitation probability, the initial uranium precipitation probability value P of each grid cell can be calculated based on the simulated three-dimensional groundwater flow field, uranium ion concentration field, and uranium precipitation rate field. raw(x,y,z) ; For all grid cells within the study area, P raw A variogram analysis was performed on the values ​​to calculate the spatial variogram γ(h) of the uranium precipitation probability, where γ(h) characterizes the variation of the probability value with spatial distance h. Based on the variogram analysis results, the spatial continuity parameters of the uranium precipitation probability field (range a, sill value C, nugget value C0) were determined. The P values ​​of each grid cell were then used as the basis for further analysis. raw Using conditional data and a variogram model as structural parameters, Kriging interpolation or sequential Gaussian simulation is employed to spatially smooth and reconstruct the probability field, yielding a spatially continuous adaptive uranium precipitation probability field P. adapt(x,y,z) ;P adapt(x,y,z) While maintaining the original data trend, local random fluctuations were eliminated, making high-probability areas exhibit spatially continuous geological body morphology.

[0111] In other embodiments, when determining favorable mineralization zones, a three-dimensional connected domain analysis algorithm, such as the 3D region growing method, can be used based on the uranium precipitation probability field to cluster all grid cells above a threshold into several spatial connected components; for each connected component, its three-dimensional spatial morphological parameters are calculated: Volume V: Total number of mesh cells × Cell volume; Elongation R ext The ratio of the longest axis length to the shortest axis length reflects whether the ore body is plate-shaped, lenticular, or isometric. Compactness C comp Volume / equivalent sphere volume, reflecting the regularity of the shape; Spatial distance D from known mineral deposits known ; Based on the above morphological parameters, a favorable zone screening discriminant function is constructed: S zone =f(V,R ext, C comp D known ), Only S zone Only connected components that exceed the screening threshold are considered valid advantageous zones; For the selected effective advantageous zones, morphological optimization algorithms (such as opening and closing operations) are used to smooth the zone boundaries, eliminating irregular boundaries such as "islands" and "peninsulas". Output the three-dimensional favorable zone after morphological filtering and boundary optimization.

[0112] Based on the intelligent prediction method for favorable uranium mineralization zones fused with multiple ore-controlling factors provided in the above embodiments, this application also provides a specific implementation of an intelligent prediction device for favorable uranium mineralization zones fused with multiple ore-controlling factors. Please refer to the following embodiments.

[0113] First see Figure 2 The intelligent prediction device 200 for uranium mineralization favorable zones based on the fusion of multiple mineralization factors provided in this application embodiment may include: The first acquisition module 201 is used to acquire ore control data of the target rock sample; The first determining module 202 is used to determine the geological unit of the target area as an active uranium source when the ore control data meets the preset identification conditions; The second acquisition module 203 is used to acquire the spatial distribution of the active uranium source, the uranium activation rate, and the uranium release flux. The simulation module 204 is used to use the spatial distribution of active uranium sources as the material source boundary condition in a preset three-dimensional geological-hydrological model, and to couple and solve the groundwater flow equation, uranium ion migration equation and redox reaction kinetic equation to simulate the entire process of uranium-bearing mineralizing fluid from activation, migration to precipitation of active uranium sources, and to obtain the three-dimensional groundwater flow field, spatiotemporal distribution of uranium ion concentration and spatial distribution of uranium precipitation rate in the target area. Calculation module 205 is used to calculate the uranium precipitation probability of each geological unit based on the three-dimensional groundwater flow field, the spatiotemporal distribution of uranium ion concentration and the spatial distribution of uranium precipitation rate; The second determining module 206 is used to determine favorable mineralization zones based on uranium precipitation probability and preset precipitation threshold.

[0114] Figure 3 A schematic diagram of the hardware structure of the electronic device provided in an embodiment of this application is shown.

[0115] An electronic device may include a processor 301 and a memory 302 storing computer program instructions.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Memory 302 may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical / tangible memory storage devices. Therefore, typically, 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 intelligent prediction method for uranium mineralization favorable zones based on the fusion of multiple mineralization factors according to the first aspect of this disclosure.

[0120] The processor 301 reads and executes computer program instructions stored in the memory 302 to achieve... Figure 1 The embodiment shown is an intelligent prediction method for favorable uranium mineralization zones by integrating multiple ore-controlling factors.

[0121] 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.

[0122] The communication interface 303 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0123] 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.

[0124] This electronic device can execute the intelligent prediction method for favorable uranium mineralization zones based on the fusion of multiple mineralization factors as described in the embodiments of this application, thereby achieving a combination of... Figures 1-2 The invention describes a method and apparatus for intelligent prediction of favorable uranium mineralization zones by integrating multiple ore-controlling factors.

[0125] Furthermore, in conjunction with the intelligent prediction method for favorable uranium mineralization zones based on the fusion of multiple ore-controlling factors 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 one of the intelligent prediction methods for favorable uranium mineralization zones based on the fusion of multiple ore-controlling factors in the above embodiments.

[0126] In an optional embodiment, in conjunction with the intelligent prediction method for uranium mineralization favorable zones based on the fusion of multiple mineralization factors described in the above embodiments, this application embodiment can provide a computer program product for implementation. The instructions in the computer program product are executed by the processor of an electronic device, enabling the electronic device to implement any of the intelligent prediction methods for uranium mineralization favorable zones based on the fusion of multiple mineralization factors described in the above embodiments.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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 smart prediction method for favorable uranium mineralization zones based on the fusion of multiple ore-controlling factors, characterized in that, include: Obtain ore-controlling data from the target rock sample; If the mineral control data meets the preset identification conditions, the geological unit of the target area is identified as an active uranium source; The spatial distribution, uranium activation rate, and uranium release flux of the active uranium source were obtained. In the preset three-dimensional geological-hydrological model, the spatial distribution of the active uranium source is used as the boundary condition of the material source. The groundwater flow equation, uranium ion migration equation and redox reaction kinetic equation are coupled and solved to simulate the whole process of uranium-bearing mineralizing fluid from activation, migration to precipitation of active uranium source, and to obtain the three-dimensional groundwater flow field, spatiotemporal distribution of uranium ion concentration and spatial distribution of uranium precipitation rate in the target area. Based on the three-dimensional groundwater flow field, the spatiotemporal distribution of uranium ion concentration, and the spatial distribution of uranium precipitation rate, the uranium precipitation probability of each geological unit is calculated. Based on the uranium precipitation probability and the preset precipitation threshold, favorable mineralization zones are determined.

2. The method according to claim 1, characterized in that, When the mineral control data meets preset identification conditions, the geological units of the target area are identified as active uranium sources, including: The target rock sample was subjected to stepwise chemical extraction to separate the occurrence forms of uranium into exchangeable ion state, carbonate-bound state, iron-manganese oxide-bound state, sulfide and organic matter-bound state, and residue state; The active uranium content is determined by summing the contents of the four forms of uranium: exchangeable ion state, carbonate-bound state, iron-manganese oxide-bound state, and sulfide and organic matter-bound state. Based on the uranium content in the residue state, the inert uranium content is determined; Based on the active uranium content and the inert uranium content, the proportion of active uranium is determined; The active uranium source is determined based on the percentage of active uranium, the target total uranium content, and the preset identification conditions.

3. The method according to claim 1, characterized in that, Before using the spatial distribution of the active uranium source as a material source boundary condition to couple and solve the groundwater flow equation, uranium ion migration equation, and redox reaction kinetic equation, the method further includes: Acquire geological and hydrological data for the target area; Based on the geological data, a three-dimensional geological model is constructed; Based on the aforementioned hydrological data, a hydrogeological model is constructed; The three-dimensional geological model and the hydrogeological model are integrated and modeled to obtain a preset three-dimensional geological-hydrological model.

4. The method according to claim 3, characterized in that, The construction of a three-dimensional geological model based on the geological data includes: Based on the geological data, a geological evolution time-series framework for the target area is established, which includes sedimentary sequences, tectonic deformation sequences, and erosion event sequences. Based on the geological evolution time series framework, a stratigraphic superposition constraint rule set is generated, which includes the allowed superposition relationship and the prohibited superposition relationship of each geological body in three-dimensional space; Obtain borehole data and geological profile data of the target area, and perform three-dimensional spatial interpolation based on the borehole data and geological profile data to obtain an initial three-dimensional geological structure model; When the stratigraphic superposition constraint rule set is embedded into the three-dimensional spatial interpolation process, if the interpolation result violates any constraint rule in the stratigraphic superposition constraint rule set, geological correction is performed on the interpolation point that violates the constraint rule, and the stratigraphic affiliation of the interpolation point is adjusted to a position that satisfies all constraint rules. Output the three-dimensional geological model after the geological rationality correction.

5. The method according to claim 4, characterized in that, The geological data includes regional geological data, isotope dating data, and tectonic phase classification results. Based on the geological data, establishing a geological evolution timeline framework for the target region includes: Based on regional geological data, isotopic dating data and tectonic period division results of the target area, the absolute age or relative chronological order of each geological unit is determined. The sedimentary sequence is expressed as a stratigraphic chronology table, which records the sedimentary order of each stratigraphic unit from oldest to youngest. The tectonic deformation sequence is expressed as a tectonic event time series table, which records the chronological relationship of each tectonic activity and the stratigraphic range of its influence. The erosion event sequence is expressed as an erosion range-intensity spatiotemporal distribution table, which records the spatial range and erosion intensity of each erosion event. Based on the stratigraphic chronology table, stratigraphic chronology table, and spatiotemporal distribution table of erosion extent-intensity, a geological evolution timeline framework is constructed.

6. The method according to claim 4, characterized in that, After outputting the three-dimensional geological model that has undergone the geological rationality correction, the method further includes: Once new borehole data is acquired, it is added as hard data to the modeling dataset. While keeping the stratigraphic superposition constraint rule set unchanged, only the local areas within the influence range of the newly added borehole data are re-interpolated in three dimensions and geologically reasonable corrected. Output the updated 3D geological model and generate a comparison report of the differences between the models before and after the update. The comparison report includes quantitative statistics on changes in stratigraphic interface morphology and stratigraphic attribution caused by the addition of borehole data.

7. The method according to claim 3, characterized in that, The construction of a hydrogeological model based on the hydrological data includes: The spatial variation of hydrological data in the target area is decomposed into three scale levels: regional facies scale, local intralayer scale, and microcementation scale. At the regional facies scale, based on the sedimentary facies distribution map of the target region, permeability coefficient benchmark values ​​at the facies level are assigned to different sedimentary facies units; At the local intralayer scale, within the same sedimentary facies unit, based on permeability test data from borehole cores, geostatistical methods are used to generate the spatial distribution of intralayer permeability coefficients. At the micro-cementation scale, a correction function between the degree of cementation and the permeability coefficient is established based on the type and content of cementing materials analyzed from core thin sections. The permeability coefficient baseline value, spatial distribution, and correction function are cascaded and superimposed to determine the final permeability coefficient at each spatial location; Based on the final permeability coefficient, a hydrogeological model integrating multi-scale heterogeneity is output.

8. The method according to claim 3, characterized in that, The process of integrating the three-dimensional geological model and the hydrogeological model to obtain a preset three-dimensional geological-hydrological model includes: Based on the aforementioned three-dimensional geological model, the stratigraphic interfaces and lithological interfaces of the target area are determined; Based on the hydrogeological model, the spatial variation characteristics of the hydrological parameter field are determined, and the locations of abrupt change surfaces where permeability coefficient or porosity changes spatially are identified. The spatial deviation is obtained by spatially comparing the location of the abrupt change surface with the location of the formation interface. When the spatial deviation meets the correction conditions, the stratigraphic interface of the initial three-dimensional geological structure model is fine-tuned and corrected using the location of the abrupt change surface as the correction basis for the structural interface. The spatial assignment of hydrological parameters is reconstrained using the corrected three-dimensional geological structure model. The above steps are repeated until the spatial deviation between the structural interface position and the parameter abrupt change surface position is less than the preset threshold. Output an integrated three-dimensional geological-hydrological model refined through bidirectional constraint iteration.

9. The method according to claim 1, characterized in that, In the pre-defined three-dimensional geological-hydrological model, the spatial distribution of the active uranium source is used as the boundary condition for the material source. The groundwater flow equation, uranium ion migration equation, and redox reaction kinetic equation are coupled and solved to simulate the entire process of uranium-bearing ore-forming fluid from activation, migration, and precipitation of the active uranium source. This yields the three-dimensional groundwater flow field, the spatiotemporal distribution of uranium ion concentration, and the spatial distribution of uranium precipitation rate in the target area, including: The simulation time is divided into macroscopic time steps and microscopic time steps, the length of the macroscopic time step is N times the length of the microscopic time step, and N≥10; At the beginning of each macroscopic time step, the groundwater flow equation is solved to obtain the three-dimensional groundwater flow field within that macroscopic time step, and the flow field is kept fixed within the current macroscopic time step; Within the current macroscopic time step, under the condition that the flow field is fixed, multiple microscopic time steps are divided, and within each microscopic time step, the following steps are executed sequentially: calculate the redox reaction rate based on the current uranium ion concentration field and update the reaction source / sink term; solve the uranium ion migration equation based on the updated reaction source / sink term and update the uranium ion concentration field; repeat the iteration until the uranium ion concentration field converges within the microscopic time step; Upon entering the next macroscopic time step, based on the updated uranium ion concentration field and uranium precipitation amount at the end of the previous macroscopic time step, the porosity and permeability coefficient at each spatial location are updated, and the groundwater flow equation is solved again to obtain the updated flow field. Repeat the above macro-micro alternating solution process until the preset total simulation time is reached to obtain the three-dimensional groundwater flow field, spatiotemporal distribution of uranium ion concentration, and spatial distribution of uranium precipitation rate in the target area.

10. A smart prediction device for favorable uranium mineralization zones based on the fusion of multiple ore-controlling factors, characterized in that, The device includes: The first acquisition module is used to acquire ore control data of the target rock sample; The first determining module is used to determine the geological unit of the target area as an active uranium source when the ore control data meets the preset identification conditions; The second acquisition module is used to acquire the spatial distribution of the active uranium source, the uranium activation rate, and the uranium release flux. The simulation module is used to use the spatial distribution of the active uranium source as the material source boundary condition in a preset three-dimensional geological-hydrological model, and to couple and solve the groundwater flow equation, uranium ion migration equation and redox reaction kinetic equation to simulate the entire process of uranium-bearing mineralizing fluid from activation, migration to precipitation of active uranium source, and to obtain the three-dimensional groundwater flow field, spatiotemporal distribution of uranium ion concentration and spatial distribution of uranium precipitation rate in the target area. The calculation module is used to calculate the uranium precipitation probability of each geological unit based on the three-dimensional groundwater flow field, the spatiotemporal distribution of uranium ion concentration, and the spatial distribution of uranium precipitation rate. The second determining module is used to determine favorable mineralization zones based on the uranium precipitation probability and a preset precipitation threshold.

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