Sandstone type uranium ore mineralization prediction method, device, equipment and medium

Abstract

The present disclosure relates to the technical field of uranium resource exploration and provides a sandstone type uranium mineralization prediction method, device, equipment and medium, which comprises the following steps: obtaining remote sensing data and topographic data of a target operation area; determining geological information of the target operation area based on the data; determining whether the target operation area has lithology that can provide uranium ore based on the lithology information; if yes, determining uranium dispersion data based on the topographic data, lithology information and environmental information; simulating the uranium dispersion process according to the topographic data and the uranium dispersion data by using a fluid dynamics model to determine a uranium dispersion range map; determining a uranium precipitation area of the target operation area based on the environmental information; and determining the uranium distribution of the target operation area based on the uranium dispersion range map and the uranium precipitation area. The present embodiment realizes scientific prediction of the sandstone type uranium mineralization process by comprehensively using remote sensing technology, topographic data and a fluid dynamics model, and significantly improves the efficiency and accuracy of uranium exploration.

Description

Technical Field

[0001] This disclosure relates to the field of uranium resource exploration and prospecting technology, and more specifically, to a method, apparatus, equipment and medium for predicting the mineralization of sandstone-type uranium deposits. Background Technology

[0002] The application of remote sensing technology in uranium exploration has become an important research area in recent years. By acquiring images and data of the Earth's surface through satellites, aircraft, and other ground-based or aerial sensors, the potential distribution of uranium ore can be inferred through analysis of these images and data. Uranium ore typically emits specific radiation signals, causing its surrounding environment to differ from non-mining areas. Using remote sensing technology, professionals can quickly screen uranium-bearing areas by monitoring factors such as surface radiation levels, spectral characteristics, and topographic features. Remote sensing technology can cover vast areas and is highly efficient and non-invasive, thus possessing enormous application potential in uranium resource exploration.

[0003] However, the application of existing remote sensing technology in uranium exploration still faces some challenges. On the one hand, the spatial and spectral resolution of remote sensing technology may be insufficient to accurately detect small-scale mineralized areas or deeply buried ore bodies, limiting its accuracy. On the other hand, surface cover (such as vegetation and soil) and environmental factors (such as weather or human interference) may affect the interpretation of remote sensing data, obscuring characteristic signals of uranium deposits and increasing the complexity of data analysis. Summary of the Invention

[0004] This disclosure provides at least one method, apparatus, equipment, and medium for predicting the mineralization process of sandstone-type uranium deposits. By comprehensively utilizing remote sensing technology, topographic data, and fluid dynamics models, it achieves scientific prediction of the mineralization process of sandstone-type uranium deposits, significantly improving the efficiency and accuracy of uranium exploration.

[0005] This disclosure provides a method for predicting the mineralization of sandstone-type uranium deposits, including:

[0006] Acquire remote sensing data and topographic data of the target work area; and determine the geological information of the target work area based on the remote sensing data and the topographic data; wherein the geological information includes lithological information and environmental information;

[0007] Based on the lithological information, determine whether there are lithologies in the target work area that can provide uranium deposits; if so, determine the uranium dispersion data based on the topographic data, the lithological information, and the environmental information.

[0008] Using a fluid dynamics model, the uranium ore dispersion process is simulated based on the terrain data and the uranium ore dispersion data to determine the uranium ore dispersion range map of the target working area;

[0009] Based on the environmental information, the uranium ore precipitation zone of the target work area is determined; and based on the uranium ore dispersion range map and the uranium ore precipitation zone, the uranium ore distribution in the target work area is determined; wherein, the uranium ore distribution includes the uranium ore distribution probability.

[0010] In some possible embodiments, the remote sensing data includes UAV imagery data, satellite imagery data, and radar satellite imagery data. The UAV imagery data includes UAV multispectral imagery data and UAV hyperspectral imagery data, and the satellite imagery data includes satellite multispectral imagery data and satellite hyperspectral imagery data.

[0011] In some possible embodiments, the environmental information includes water system information, geological structure information, and alteration information; determining the geological information of the target work area based on the remote sensing data and the topographic data includes:

[0012] The water system information is determined based on the terrain data; and the geological structure information is determined based on the UAV multispectral image data, the satellite multispectral image data, and the radar satellite image data.

[0013] Based on the UAV image data and the satellite image data, the spectral characteristics of altered minerals in the target work area are determined, and the alteration information in the spectral characteristics of altered minerals is extracted based on the principal component analysis method and the spectral angle matching method to determine the alteration information;

[0014] The lithological information is determined based on the UAV hyperspectral image data and the satellite hyperspectral image data.

[0015] In some possible embodiments, determining uranium dispersion data based on the topographic data, the lithological information, and the environmental information includes:

[0016] The distribution area of ​​uranium ore in the target work area is determined based on the terrain data;

[0017] Based on the water system information and the geological structure information, multiple transport channels are determined in the uranium ore distribution area; and the multiple transport channels are screened according to the alteration information to obtain the target uranium ore transport channels; wherein, the target uranium ore transport channels include multiple target transport channels;

[0018] For each target transport route, the uranium content of the target transport route is determined based on the lithological information corresponding to the target transport route and the uranium content per unit fluid corresponding to the lithological information in the target transport route;

[0019] The uranium dispersion data is determined based on the amount of uranium ore corresponding to each target transport route.

[0020] In some possible embodiments, the simulation of the uranium dispersion process using a fluid dynamics model based on the terrain data and the uranium dispersion data includes:

[0021] The uranium ore dispersion point information is determined based on each target transport channel; wherein, the uranium ore dispersion point information includes the uranium ore dispersion start point and the uranium ore dispersion end point, the uranium ore dispersion start point is the location in each target transport channel where uranium ore can be provided by lithology, and the uranium ore dispersion end point is the corresponding end of each target transport channel;

[0022] Using a fluid dynamics model, the uranium dispersion process in the uranium distribution area is simulated based on the terrain data, the uranium dispersion data, and the uranium dispersion point information, to determine a uranium dispersion range map for the uranium distribution area.

[0023] In some possible embodiments, the geological structural information includes geological faults; determining the uranium deposit zone of the target working area based on the environmental information includes:

[0024] Based on the geological structure information and the alteration information, the oxidation zone and reduction zone of the uranium ore distribution area are determined respectively, and the boundary between the oxidation zone and reduction zone of the uranium ore distribution area is determined as the first uranium ore precipitation zone;

[0025] The geological faults corresponding to the uranium ore distribution area were identified as the second uranium ore precipitation zone.

[0026] The uranium ore precipitation zone is determined based on the first uranium ore precipitation zone and the second uranium ore precipitation zone.

[0027] In some possible embodiments, determining the uranium ore distribution in the target working area based on the uranium ore dispersion map and the uranium ore precipitation zone includes:

[0028] Obtain the uranium precipitation ratio corresponding to each of the uranium ore precipitation zones;

[0029] The probability of uranium distribution in the uranium distribution area is determined based on the uranium dispersion range map, the uranium precipitation zone, and the uranium precipitation ratio corresponding to each uranium precipitation zone.

[0030] This disclosure provides a sandstone-type uranium deposit mineralization prediction device, comprising:

[0031] The data acquisition module is used to acquire remote sensing data and terrain data of the target work area; and to determine the geological information of the target work area based on the remote sensing data and terrain data; wherein the geological information includes lithological information and environmental information;

[0032] The data determination module is used to determine whether there are lithologies in the target work area that can provide uranium deposits based on the lithological information; if so, it determines uranium ore dispersion data based on the topographic data, the lithological information, and the environmental information.

[0033] The data simulation module is used to simulate the uranium ore dispersion process based on the terrain data and the uranium ore dispersion data using a fluid dynamics model, and to determine the uranium ore dispersion range map of the target working area;

[0034] The uranium ore determination module is used to determine the uranium ore precipitation zone of the target work area based on the environmental information; and to determine the uranium ore distribution in the target work area based on the uranium ore dispersion range map and the uranium ore precipitation zone; wherein the uranium ore distribution includes the uranium ore distribution probability.

[0035] In some possible embodiments, the remote sensing data includes UAV imagery data, satellite imagery data, and radar satellite imagery data. The UAV imagery data includes UAV multispectral imagery data and UAV hyperspectral imagery data, and the satellite imagery data includes satellite multispectral imagery data and satellite hyperspectral imagery data.

[0036] In some possible embodiments, the environmental information includes water system information, geological structure information, and alteration information; the data acquisition module is specifically used for:

[0037] The water system information is determined based on the terrain data; and the geological structure information is determined based on the UAV multispectral image data, the satellite multispectral image data, and the radar satellite image data.

[0038] Based on the UAV image data and the satellite image data, the spectral characteristics of altered minerals in the target work area are determined, and the alteration information in the spectral characteristics of altered minerals is extracted based on the principal component analysis method and the spectral angle matching method to determine the alteration information;

[0039] The lithological information is determined based on the UAV hyperspectral image data and the satellite hyperspectral image data.

[0040] In some possible embodiments, the data determination module is specifically used for:

[0041] The distribution area of ​​uranium ore in the target work area is determined based on the terrain data;

[0042] Based on the water system information and the geological structure information, multiple transport channels are determined in the uranium ore distribution area; and the multiple transport channels are screened according to the alteration information to obtain the target uranium ore transport channels; wherein, the target uranium ore transport channels include multiple target transport channels;

[0043] For each target transport route, the uranium content of the target transport route is determined based on the lithological information corresponding to the target transport route and the uranium content per unit fluid corresponding to the lithological information in the target transport route.

[0044] The uranium dispersion data is determined based on the amount of uranium ore corresponding to each target transport route.

[0045] In some possible embodiments, the data simulation module is specifically used for:

[0046] The uranium ore dispersion point information is determined based on each target transport channel; wherein, the uranium ore dispersion point information includes the uranium ore dispersion start point and the uranium ore dispersion end point, the uranium ore dispersion start point is the location in each target transport channel where uranium ore can be provided by lithology, and the uranium ore dispersion end point is the corresponding end of each target transport channel;

[0047] Using a fluid dynamics model, the uranium dispersion process in the uranium distribution area is simulated based on the terrain data, the uranium dispersion data, and the uranium dispersion point information, to determine a uranium dispersion range map for the uranium distribution area.

[0048] In some possible embodiments, the geological structural information includes geological faults; the uranium ore determination module is specifically used for:

[0049] Based on the geological structure information and the alteration information, the oxidation zone and reduction zone of the uranium ore distribution area are determined respectively, and the boundary between the oxidation zone and reduction zone of the uranium ore distribution area is determined as the first uranium ore precipitation zone;

[0050] The geological faults corresponding to the uranium ore distribution area were identified as the second uranium ore precipitation zone.

[0051] The uranium ore precipitation zone is determined based on the first uranium ore precipitation zone and the second uranium ore precipitation zone.

[0052] In some possible embodiments, the uranium ore determination module is specifically used for:

[0053] Obtain the uranium precipitation ratio corresponding to each of the uranium ore precipitation zones;

[0054] The probability of uranium distribution in the uranium distribution area is determined based on the uranium dispersion range map, the uranium precipitation zone, and the uranium precipitation ratio corresponding to each uranium precipitation zone.

[0055] This disclosure provides a computer device including a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, they perform the sandstone-type uranium deposit mineralization prediction method as described in any of the above possible embodiments.

[0056] This disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the sandstone-type uranium deposit mineralization prediction method as described in any of the above possible embodiments.

[0057] The sandstone-type uranium deposit mineralization prediction method, apparatus, equipment, and medium provided in this disclosure comprehensively analyze remote sensing data, topographic data, lithological information, and environmental information. Simultaneously, by simulating the uranium dispersion process using a fluid dynamics model, it can efficiently and accurately predict the uranium distribution in the target working area, providing a scientific basis for uranium exploration. Furthermore, based on the determination of the uranium distribution area and probability, the allocation of exploration resources can be optimized, reducing the time and cost of ineffective exploration and improving the development efficiency of uranium resources.

[0058] To make the above-mentioned objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0059] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings referenced in the embodiments will be briefly described below. These drawings are incorporated in and constitute a part of this specification. They illustrate embodiments conforming to this disclosure and, together with the specification, serve to explain the technical solutions of this disclosure. It should be understood that the following drawings only show some embodiments of this disclosure and should not be considered as limiting the scope. Those skilled in the art can obtain other related drawings based on these drawings without creative effort.

[0060] Figure 1 A flowchart of a sandstone-type uranium deposit mineralization prediction method provided in this disclosure is shown.

[0061] Figure 2 A flowchart of a geological information determination method provided by an embodiment of this disclosure is shown;

[0062] Figure 3 A flowchart of a method for determining uranium ore dispersion data provided in an embodiment of this disclosure is shown;

[0063] Figure 4A flowchart of a method for determining uranium ore precipitation zones provided in an embodiment of this disclosure is shown;

[0064] Figure 5 This diagram illustrates the structure of a sandstone-type uranium deposit mineralization prediction device provided in an embodiment of this disclosure.

[0065] Figure 6 A schematic diagram of the structure of a computer device provided in an embodiment of this disclosure is shown. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. The components of the embodiments of this disclosure described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed disclosure, but merely represents selected embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0067] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0068] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0069] The application of remote sensing technology in uranium exploration has become an important research area in recent years. Researching and applying remote sensing technology to achieve large-scale element extraction and mineralization location determination is of great significance for high efficiency and sustainable development. The application of remote sensing technology in uranium exploration has become one of the main technological directions. First, remote sensing technology can identify potential uranium ore areas by acquiring surface radiation data. Uranium ore typically exhibits specific radioactive reactions, so remote sensing technology can indirectly detect radiation levels based on these radioactive reactions to identify potential ore areas. Second, remote sensing technology can analyze the chemical properties of uranium ore by acquiring surface spectral data. Uranium ore has specific spectral characteristics, such as the absorption and emission spectra of radioactive elements. By analyzing spectral data in different bands of remote sensing images, specific chemical elements and compounds can be detected, thereby inferring the distribution area of ​​potential uranium ore. Furthermore, remote sensing technology can also infer the distribution of uranium ore by acquiring surface morphology data. Surface cover and topographic features have a significant impact on the distribution of mineral resources. Radar and laser altimeters carried by satellites and aircraft can provide high-precision surface topography data for analyzing surface topography and underground structures, thereby locating potential uranium ore deposits.

[0070] Research has revealed several challenges in applying existing remote sensing technologies to uranium exploration. Firstly, the spatial and spectral resolution of remote sensing may be insufficient for accurately detecting small-scale mineralized areas or deeply buried ore bodies, limiting its accuracy. Secondly, surface cover (such as vegetation and soil) and environmental factors (such as weather or human interference) can affect the interpretation of remote sensing data, obscuring characteristic signals of uranium deposits and increasing the complexity of data analysis.

[0071] Based on the above research, this disclosure provides a method, apparatus, equipment, and medium for predicting sandstone-type uranium deposits, including: acquiring remote sensing data and topographic data of a target working area; determining the geological information of the target working area based on this data; determining whether there are lithologies in the target working area that can provide uranium deposits based on lithological information; if so, determining uranium dispersion data based on topographic data, lithological information, and environmental information; simulating the uranium dispersion process using a fluid dynamics model based on the topographic data and uranium dispersion data to determine a uranium dispersion range map; determining the uranium precipitation zone in the target working area based on environmental information; and determining the uranium distribution in the target working area based on the uranium dispersion range map and the uranium precipitation zone.

[0072] In this embodiment, by combining remote sensing data, topographic data, lithological information, and environmental information for comprehensive analysis, and by simulating the uranium ore dispersion process using a fluid dynamics model, the distribution of uranium ore in the target working area can be predicted efficiently and accurately, providing a scientific basis for uranium ore exploration. Furthermore, based on the determination of the distribution area and probability of uranium ore, the allocation of exploration resources can be optimized, reducing the time and cost of ineffective exploration and improving the development efficiency of uranium resources.

[0073] To facilitate understanding of this embodiment, the executing entity of the sandstone-type uranium deposit mineralization prediction method provided in this disclosure will first be described in detail. The executing entity of the sandstone-type uranium deposit mineralization prediction method provided in this disclosure is a computer device. This computer device can be a server. Specifically, the server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, big data, and artificial intelligence platforms.

[0074] The method for predicting the mineralization of sandstone-type uranium deposits provided in this application will be described in detail below with reference to the accompanying drawings. See also Figure 1 The diagram shows a flowchart of a sandstone-type uranium deposit mineralization prediction method provided in this disclosure, which includes the following steps S101-S104:

[0075] 101. Acquire remote sensing data and terrain data of the target work area; and determine the geological information of the target work area based on the remote sensing data and terrain data.

[0076] Understandably, remote sensing data is acquired through sensors and provides objective information about the Earth's surface and its changes. Remote sensing technology primarily relies on the reflection of electromagnetic waves of different wavelengths to analyze the characteristics of ground objects. Here, remote sensing data can include UAV imagery data, satellite imagery data, and radar satellite imagery data. UAV imagery data, captured by high-precision cameras mounted on UAVs, has high resolution and can include multispectral and hyperspectral UAV imagery data. Multispectral imagery data includes information from multiple bands, such as infrared and visible light bands, used to analyze different physical characteristics of the Earth's surface; hyperspectral imagery data contains even more bands, providing more detailed ground information and enabling more precise analysis of vegetation types, mineral composition, etc. Satellite imagery data is imagery data acquired by satellite sensors and can include satellite multispectral and hyperspectral imagery data. Similar to UAV imagery data, satellite imagery data provides a large area of ​​geographic information and helps analyze geological features through images in different bands. The satellite multispectral imagery used in this disclosure primarily includes Landsat-8, Sentinel-2, Gaofen-2, and ASTER satellite imagery; the satellite hyperspectral imagery mainly uses Gaofen-5 satellite imagery. Radar satellite imagery data, acquired using Synthetic Aperture Radar (SAR) technology, can penetrate adverse conditions such as clouds and rain / snow, providing stronger penetration and resolution, and is commonly used for monitoring terrain and environmental changes. The radar satellite imagery data used in this disclosure primarily originates from Sentinel-1 and Gaofen-3 satellite imagery.

[0077] Understandably, terrain data refers to information describing the shape of the Earth's surface, primarily used to analyze physical characteristics such as elevation, slope, and aspect. For example, a Digital Elevation Model (DEM) is a commonly used type of terrain data; it accurately displays the three-dimensional shape of the Earth's surface by measuring the elevation of different points on the ground. Here, terrain data can be collected by satellites or drones.

[0078] Specifically, after acquiring remote sensing and topographic data of the target work area, geological information of the target work area can be determined based on this data. Geological information describes the geological characteristics of the target work area and can include lithological and environmental information. Environmental information can include drainage system information, geological structure information, and alteration information. Lithological information refers to the lithological classification information of the target work area, which can include rock type, mineral composition, and strata characteristics. Drainage system information refers to the flow direction, runoff accumulation, flow length, and river network of the surface water flow model of the target work area. Geological structure information refers to the crustal structural features of the target work area, which can include geological faults, folds, fault zones, and other geological structural units. Alteration information refers to the alteration mineral information of the target work area, which can include alteration mineral assemblage information and the distribution range of alteration minerals.

[0079] Specifically, refer to Figure 2 As shown, when determining the geological information of the target work area based on remote sensing data and terrain data, the following steps S201~S203 may be included:

[0080] S201, determine the water system information based on the terrain data; and determine the geological structure information based on the UAV multispectral image data, the satellite multispectral image data, and the radar satellite image data.

[0081] Understandably, since water flow facilitates the dissolution, transport, and deposition of uranium, determining river system information is crucial for determining the direction of uranium movement. Because water flow distribution is often influenced by geological structures such as faults and folds, high-precision topographic data (usually presented as a digital elevation model (DEM)) can accurately depict the flow direction, confluence points, and river lengths of river systems, thus providing river system information. By analyzing the flow direction, confluence points, and their relationship with geological structures, prospectors can deduce uranium migration paths and potential depositional areas, effectively guiding uranium discovery and mining.

[0082] For example, multispectral imagery data from unmanned aerial vehicles (UAVs), satellites, and radar satellites can be used in remote sensing technology to extract geological structural information. By processing the image data, such as adjusting tone, texture, and shadow color, the spatial distribution of geological structures can be extracted from the images, and potential faults or crustal movements can be inferred. Radar satellite imagery data, in particular, can penetrate cloud cover, making it suitable for geological surveys under adverse weather conditions.

[0083] S202, based on the UAV image data and the satellite image data, determine the spectral characteristics of altered minerals in the target work area, and extract the alteration information from the spectral characteristics of the altered minerals based on the principal component analysis method and the spectral angle matching method to determine the alteration information.

[0084] Specifically, alteration minerals are minerals formed during geological processes due to temperature, pressure, or chemical effects. Their characteristic spectra can reflect the geological background of the region. For example, some minerals exhibit significant absorption characteristics in specific wavelengths, which can be captured using multispectral and hyperspectral imagery data from satellites and drones. Principal component analysis (PCA) and spectral angle matching (SAM) methods can effectively extract the spectral characteristics of specific alteration minerals. Specifically, PCA can transform high-dimensional spectral data into low-dimensional data, simplifying complex spectral information and highlighting features associated with alteration minerals. Spectral angle matching calculates the angular differences between the sample spectrum and the spectra of known minerals to determine the presence of specific alteration minerals in the area. This information, combined with the geological background, can be used to delineate the distribution areas of alteration minerals and classify them according to their anomalies, thereby identifying anomalous areas with high mineral exploration value.

[0085] In some other embodiments, the determination of alteration information can also be achieved through other technologies or methods, such as machine learning algorithms (e.g., support vector machines, random forests, etc.), deep learning models (e.g., convolutional neural networks), or geological interpretation methods based on expert systems. These can be selected based on specific operational requirements, data characteristics, and processing efficiency, and are not specifically limited here.

[0086] In a specific example of this disclosure, iron-stained alteration minerals are used as the research object. These minerals exhibit specific reflection and absorption characteristics in the visible and near-infrared bands, particularly showing strong absorption in the 0.6-0.9 μm wavelength range, with the absorption valley located precisely at 0.9 μm. This characteristic can be clearly reflected in UAV and satellite imagery data. Based on the reflection peaks and absorption valleys in the bands, principal component analysis can effectively remove redundant information between different bands, thereby highlighting the spectral characteristics of the iron-stained alteration minerals. Subsequently, according to the anomaly judgment principle of the Crosta method, iron-stained alteration was extracted, and the iron-stained anomaly type was subdivided into first-level, second-level, and third-level anomalies, thereby identifying anomalous areas with high mineral exploration value.

[0087] S203, determine the lithological information based on the UAV hyperspectral image data and the satellite hyperspectral image data.

[0088] Here, hyperspectral image data can be used to obtain the response characteristics of rocks in different spectral bands, which typically reflect the rock composition. This disclosure uses a combination of principal component analysis (PCA) and support vector machine (SVM) methods for lithology classification. PCA converts raw spectral data into principal components, extracting features that are lithologically distinctive; SVM is a supervised learning algorithm used to classify the extracted features and identify different types of rock units. This method not only accurately identifies different lithologies but also allows for verification using field measurement data and geological maps, thereby improving classification accuracy. Through these steps, a detailed understanding of the distribution of different lithologies within the target area can be obtained, leading to inferences about geological structural characteristics and providing a reliable basis for mineral resource exploration.

[0089] S102, based on the lithological information, determine whether there are lithologies in the target work area that can provide uranium ore; if so, determine the uranium ore dispersion data based on the topographic data, the lithological information, and the environmental information.

[0090] Understandably, the most widely accepted theory for the mineralization of interlayered redox sandstone-type uranium deposits is that weathering of uranium-bearing magmatic rocks causes uranium to migrate from the rocks and subsequently enter the underground environment along specific geological structural zones with the help of water flow. Under the influence of gravity, uranium-bearing water flows along sandstone layers with a mud-sand-mud structure in sloping or gentle slope zones. During this process, uranium is gradually reduced and enriched into minerals by reducing substances in the sandstone layers. Based on this, and considering the complex and diverse tectonic settings in China, geologists have continuously introduced and developed more advanced mineralization theories that can more reasonably explain complex mineralization results in practice. However, whether it is the "multi-cycle deep-circulation endogenous-exogenous integrated" mineralization mechanism, the "water-formed uranium deposit" source-transport-reservoir process, or the "full-situ" uranium mineralization model, they all follow the three basic principles of "conservation of matter, conservation of energy, and infinite but ordered spacetime." Therefore, based on the above theory, this disclosure proposes to analyze the mineralization prediction of sandstone-type uranium deposits according to three elements: source-basin margin ore-forming material, transport-ore-forming fluid field, and reservoir-foreland mineralization space.

[0091] In some other embodiments, in order to improve the efficiency of uranium ore prediction, the three elements proposed in this disclosure, namely source-basin margin ore-forming material, transport-ore-forming fluid field, and reservoir-foreland mineralization space, can be used for preliminary judgment. The task of predicting the distribution of uranium ore in the target working area is only carried out under the premise that the three elements of source-basin margin ore-forming material, transport-ore-forming fluid field, and reservoir-foreland mineralization space exist in the target working area. The relevant contents of "source", "transport" and "reservoir" will be elaborated in detail below.

[0092] Specifically, uranium deposits are typically enriched in specific lithologies, primarily including sandstone, limestone, and certain metamorphic rocks in sedimentary formations. These lithologies possess unique mineral compositions and physicochemical properties, often favorable factors for uranium deposit occurrence. For example, sandstone-type uranium deposits are secondary ore bodies formed by the transport of hexavalent U within sandstone bodies by fluids to interlayer oxidation zones, where they accumulate and become enriched through adsorption and reduction processes. Abundant uranium-bearing parent rocks (granite, metamorphic rocks, etc.) at basin margins are usually the main uranium sources for the target area. Therefore, when exploring for uranium deposits, combining metallogenic theories with the regional geological background and analyzing the lithological information of the target exploration area can help determine whether the area has the potential to become a source of uranium deposits.

[0093] Here, when the presence of lithology suitable for uranium ore is confirmed in the target work area, uranium dispersion data can be determined by integrating topographic data, lithological information, and environmental data. Uranium dispersion data refers to the migration and distribution of uranium ore in the geological environment, including the areas where uranium ore exists, its distribution routes, and transportation pathways.

[0094] It is understandable that the more uranium source material received at a point within the target work area and introduced underground through oxidation by oxygen-rich water, the higher the probability of mineralization. Based on this, this disclosure uses the amount of uranium-bearing groundwater received at any point within the study area as the evaluation standard for mineralization probability or mineralization quality. Specifically, refer to... Figure 3 As shown, when determining uranium ore dispersion data based on topographic data, lithological information, and environmental information, the following steps S301~S304 may be included:

[0095] S301, Determine the uranium ore distribution area in the target work area based on the terrain data.

[0096] Understandably, topographic data, encompassing natural geographical features such as elevation changes, mountain range orientations, and the distribution of rivers and lakes, forms the basis for understanding surface water flow paths, sediment accumulation patterns, and their impact on uranium deposit distribution. For example, in geological exploration, elevation models and topographic maps can identify potential groundwater flow paths, particularly those along tectonic slopes flowing from uranium source areas (erosion zones) to sedimentary basins. Paleochannels along these slopes serve as primary groundwater conduits, and their flow direction is crucial for tracing the migration direction of uranium sources and locating mineralization areas. In tectonic slopes where gravitational potential energy dominates water flow direction, long-term stable valley morphology becomes a reliable indicator of paleochannel location, allowing analysis of water flow directions throughout geological history and, consequently, inferences about uranium deposit distribution areas.

[0097] S302, Based on the water system information and the geological structure information, determine multiple transportation channels in the uranium ore distribution area; and filter the multiple transportation channels according to the alteration information to obtain the target uranium ore transportation channel.

[0098] Specifically, after determining the approximate distribution area of ​​uranium deposits, multiple potential transport channels can be precisely delineated based on hydrological and geological information. These channels represent possible paths for groundwater from its source to the sedimentary area. By further analyzing alteration information, such as changes in rock color and mineral composition, the "target uranium transport channels" most likely to carry and enrich uranium minerals can be identified. These target uranium transport channels include multiple target transport channels.

[0099] S303, for each target transport channel, the uranium content of the target transport channel is determined based on the lithological information corresponding to the target transport channel and the uranium content per unit fluid corresponding to the lithological information in the target transport channel.

[0100] To accurately assess the uranium content of different rock types, this disclosure assigns different uranium content grades to various igneous rocks based on parameters such as roughness, weathering degree, and original uranium content obtained from relevant literature. After determining the uranium content grades, the uranium content per unit fluid is calculated based on lithological information such as porosity and permeability, and the influence of these parameters on the migration and content of uranium in groundwater. Porosity determines the size of the space within the rock available for groundwater flow, while permeability reflects the flow velocity of groundwater within the rock. Rocks with high porosity and permeability tend to facilitate rapid groundwater flow and sufficient contact with the rock, thereby increasing the migration and enrichment opportunities of uranium in groundwater. Here, the uranium content per unit fluid refers to the amount of uranium contained in a unit volume of groundwater, directly reflecting the groundwater's ability to enrich uranium when flowing through a specific rock type. Therefore, for each selected target transport channel, the amount of uranium ore it carries needs to be estimated based on its lithological information and the corresponding uranium content per unit fluid.

[0101] Specifically, in estimating the uranium ore quantity of the target transport corridor, it is necessary to first determine the lithological information of the upstream of the corridor, and then calculate the total lithological area that can provide uranium sources for the entire target uranium transport corridor based on the lithological information corresponding to each target transport corridor. Then, by combining the proportion of the area of ​​each target transport corridor's lithological type to the total lithological area and the uranium content per unit fluid corresponding to these lithologies, the amount of uranium source provided by each target transport corridor to the mineralization area can be further calculated.

[0102] S304, determine the uranium dispersion data based on the amount of uranium ore corresponding to each target transport channel.

[0103] Specifically, by combining the uranium ore quantities along each target transport route, uranium dispersion data for the uranium ore distribution area can be constructed. This dispersion data not only reflects the spatial distribution characteristics of uranium ore but also provides quantitative information about the degree of uranium enrichment.

[0104] In this embodiment, by combining topographic data, drainage information, geological structure information, and alteration information, the distribution and enrichment areas of uranium ore can be accurately assessed, thereby improving the efficiency and accuracy of uranium exploration. Simultaneously, by analyzing the impact of different lithologies on uranium enrichment, potential uranium transport routes and enrichment areas can be better identified, providing important data for subsequent uranium mining. Furthermore, based on the assessment of uranium reserves, accurate uranium dispersion data can be calculated, further optimizing the assessment of mineralization probability, improving mineral resource utilization, and reducing unnecessary resource waste.

[0105] S103, using a fluid dynamics model to simulate the uranium ore dispersion process based on the terrain data and the uranium ore dispersion data, and determining the uranium ore dispersion range map of the target work area.

[0106] Among them, the fluid dynamics model is a numerical model that can accurately calculate the velocity field, pressure distribution, and diffusion range of water flow. It also incorporates processes such as sediment transport and deposition to comprehensively characterize the dispersion pattern of uranium-bearing water. Especially in areas with complex topography where parameters are difficult to measure directly, numerical simulation can use topographic data and uranium dispersion data as input to generate spatially resolved dispersion range maps. Therefore, numerical simulation can not only quantitatively analyze the fluid movement range in alluvial fan regions but also predict potential accumulation areas of uranium-bearing water. Through the fluid dynamics model, uranium dispersion range maps can be simulated and determined.

[0107] Here, the mass and momentum equations of the fluid dynamics model can be expressed as:

[0108] ;

[0109] ;

[0110] ;

[0111] In the formula, ρ is the fluid density; h is the fluid thickness; g is the gravitational constant; u is the depth integral velocity in the x-direction; v is the depth integral velocity in the y-direction; z is the fluid elevation. This is the coefficient of lateral earth pressure; For the base friction stress; and Let be the components of the base friction stress in the x and y directions, respectively.

[0112] Specifically, when using fluid dynamics models to simulate the uranium dispersion process, a suitable model framework must first be established based on topographic data and uranium dispersion data. Simultaneously, to accurately simulate the uranium dispersion process, especially under complex terrain and varying geological conditions, it is also necessary to clearly define the specific location and associated information of each target transport route, i.e., uranium dispersion point information, which can include the starting and ending points of uranium dispersion. The starting point of uranium dispersion is typically located in the lithological location within each target transport route that provides uranium ore, i.e., the source of the uranium ore. These sources are usually underground uranium ore reservoirs or uranium-bearing rock layers, providing uranium resources and serving as the starting point of the dispersion process. The ending point of uranium dispersion corresponds to the end of the target transport route, usually the end of the transport path or the final destination area. These endpoints may be uranium deposition areas or areas influenced by topographic, fluid dynamic, and other factors.

[0113] Once the uranium ore dispersion point information is determined, it can be input into the fluid dynamics model. By simulating the fluid motion under different conditions and combining topographic data, uranium ore dispersion data, and uranium ore dispersion point information, the fluid dynamics model can accurately predict the uranium ore dispersion process. Through calculations of the fluid velocity field, pressure field, and mineral transport characteristics, the model can reveal how uranium ore gradually diffuses to more distant areas with the flow of fluid, thus forming a uranium ore dispersion range map.

[0114] Thus, by applying the fluid dynamics model, the formation conditions and "transport" elements of uranium deposits were clarified. The fluid dynamics model was then used to further interpret the uranium source migration process. Through simulation analysis of the migration paths and concentration mechanisms of uranium sources under different geological conditions, a scientific basis was provided for the exploration of uranium resources.

[0115] S104, Based on the environmental information, determine the uranium ore precipitation zone of the target work area; and based on the uranium ore dispersion range map and the uranium ore precipitation zone, determine the uranium ore distribution in the target work area.

[0116] Understandably, in order to achieve the "reservoir" process of uranium ore, it is necessary to first identify the uranium ore precipitation zone. Specifically, the redox environment is a very important factor for uranium mineralization. The oxidizing environment controls the migration process of uranium, while the reducing environment determines the precipitation conditions of uranium. Identifying and tracking the distribution of the redox interface in the groundwater system is one of the core contents of sandstone-type uranium deposit exploration.

[0117] For example, refer to Figure 4 As shown, determining the uranium ore precipitation zone may include the following steps S401~S403:

[0118] S401, Based on the geological structure information and the alteration information, the oxidation zone and reduction zone of the uranium ore distribution area are determined respectively, and the boundary between the oxidation zone and reduction zone of the uranium ore distribution area is determined as the first uranium ore precipitation zone.

[0119] Here, the boundary between oxidation and reduction zones is typically extracted from remote sensing images using different alteration features. For example, the formation of iron oxide is a typical alteration phenomenon in an oxidizing environment. This disclosure utilizes a ratio method and a redox index model to efficiently pinpoint the locations of oxidation and reduction zones. The ratio method highlights alteration information by comparing the ratios of reflectance across different spectral bands, while the redox index model integrates multiple spectral features to construct a quantitative index reflecting the redox state.

[0120] Specifically, because uranium exhibits different migration and precipitation behaviors under varying redox conditions, it tends to precipitate and accumulate in the fluid, typically in the transition zone between the oxidation and reduction zones, due to the abrupt change in redox conditions. Therefore, the first uranium ore precipitation zone can be determined based on the clear demarcation between the oxidation and reduction zones.

[0121] S402, the geological fault corresponding to the uranium ore distribution area is identified as the second uranium ore precipitation zone.

[0122] For example, geological faults are fracture surfaces in underground rock strata and are typically closely related to groundwater flow and mineral migration. During uranium precipitation, fault zones provide a low-resistance flow path for uranium-bearing fluids, promoting uranium precipitation. When uranium-bearing groundwater flows through faults or fracture zones, its redox environment changes significantly, further promoting uranium precipitation. Furthermore, the high permeability of fault zones provides favorable conditions for the accumulation of ore-forming fluids and the formation of uranium precipitation. Moreover, the presence of reducing substances such as organic matter or sulfides in fault zones also provides an ideal chemical environment for uranium precipitation.

[0123] Specifically, fault identification is crucial for determining the distribution and scale of uranium deposits. Particularly in sandstone-type uranium exploration, water-conducting faults within the basin can serve as key factors in the groundwater mineralization system. Therefore, remote sensing imagery can effectively identify faults in the study area, especially drainage faults, thus clarifying the extent of uranium deposits, i.e., the secondary uranium deposit zone.

[0124] S403, the uranium ore precipitation zone is determined based on the first uranium ore precipitation zone and the second uranium ore precipitation zone.

[0125] It is understandable that after obtaining the first and second uranium ore precipitation zones, the first uranium ore precipitation zone (delineated by the redox zone) and the second uranium ore precipitation zone (determined by geological faults) can be integrated to obtain the overall uranium ore precipitation zone, which represents the potential area for uranium ore precipitation.

[0126] Specifically, after obtaining the uranium dispersion map and uranium precipitation zones of the target operating area, the distribution of uranium ore can be further analyzed based on this information. Here, the uranium ore distribution includes the uranium ore distribution area and the uranium ore distribution probability. The calculation of the uranium ore distribution probability needs to consider the uranium ore precipitation ratio, which typically reflects the concentration and precipitation characteristics of uranium ore within each precipitation zone. Based on the uranium dispersion map, uranium ore precipitation zones, and the precipitation ratio corresponding to each precipitation zone, spatial data analysis methods can be used to obtain the uranium ore distribution probability at each location, thereby determining the uranium ore distribution in the target operating area.

[0127] The sandstone-type uranium deposit mineralization prediction method, apparatus, equipment, and medium provided in this disclosure comprehensively analyze remote sensing data, topographic data, lithological information, and environmental information. Simultaneously, by simulating the uranium dispersion process using a fluid dynamics model, it can efficiently and accurately predict the uranium distribution in the target working area, providing a scientific basis for uranium exploration. Furthermore, based on the determination of the uranium distribution area and probability, the allocation of exploration resources can be optimized, reducing the time and cost of ineffective exploration and improving the development efficiency of uranium resources.

[0128] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0129] Based on the same inventive concept, this disclosure also provides a sandstone-type uranium deposit mineralization prediction device corresponding to the sandstone-type uranium deposit mineralization prediction method. Since the principle of the device in this disclosure for solving the problem is similar to the sandstone-type uranium deposit mineralization prediction method described above, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0130] Reference Figure 5 The diagram shown is a schematic representation of a sandstone-type uranium deposit mineralization prediction device 500 provided in an embodiment of this disclosure. The device includes:

[0131] The data acquisition module 501 is used to acquire remote sensing data and topographic data of the target work area; and to determine the geological information of the target work area based on the remote sensing data and the topographic data; wherein the geological information includes lithological information and environmental information;

[0132] The data determination module 502 is used to determine whether there are lithologies in the target work area that can provide uranium ore based on the lithological information; if so, it determines uranium ore dispersion data based on the topographic data, the lithological information, and the environmental information.

[0133] Data simulation module 503 is used to simulate the uranium ore dispersion process based on the terrain data and the uranium ore dispersion data using a fluid dynamics model, and to determine the uranium ore dispersion range map of the target working area;

[0134] The uranium ore determination module 504 is used to determine the uranium ore precipitation zone of the target work area based on the environmental information; and to determine the uranium ore distribution in the target work area based on the uranium ore dispersion range map and the uranium ore precipitation zone; wherein the uranium ore distribution includes the uranium ore distribution probability.

[0135] In some possible embodiments, the remote sensing data includes UAV imagery data, satellite imagery data, and radar satellite imagery data. The UAV imagery data includes UAV multispectral imagery data and UAV hyperspectral imagery data, and the satellite imagery data includes satellite multispectral imagery data and satellite hyperspectral imagery data.

[0136] In some possible embodiments, the environmental information includes water system information, geological structure information, and alteration information; the data acquisition module 501 is specifically used for:

[0137] The water system information is determined based on the terrain data; and the geological structure information is determined based on the UAV multispectral image data, the satellite multispectral image data, and the radar satellite image data.

[0138] Based on the UAV image data and the satellite image data, the spectral characteristics of altered minerals in the target work area are determined, and the alteration information in the spectral characteristics of altered minerals is extracted based on the principal component analysis method and the spectral angle matching method to determine the alteration information;

[0139] The lithological information is determined based on the UAV hyperspectral image data and the satellite hyperspectral image data.

[0140] In some possible embodiments, the data determination module 502 is specifically used for:

[0141] The distribution area of ​​uranium ore in the target work area is determined based on the terrain data;

[0142] Based on the water system information and the geological structure information, multiple transport channels are determined in the uranium ore distribution area; and the multiple transport channels are screened according to the alteration information to obtain the target uranium ore transport channels; wherein, the target uranium ore transport channels include multiple target transport channels;

[0143] For each target transport route, the uranium content of the target transport route is determined based on the lithological information corresponding to the target transport route and the uranium content per unit fluid corresponding to the lithological information in the target transport route.

[0144] The uranium dispersion data is determined based on the amount of uranium ore corresponding to each target transport route.

[0145] In some possible embodiments, the data simulation module 503 is specifically used for:

[0146] The uranium ore dispersion point information is determined based on each target transport channel; wherein, the uranium ore dispersion point information includes the uranium ore dispersion start point and the uranium ore dispersion end point, the uranium ore dispersion start point is the location in each target transport channel where uranium ore can be provided by lithology, and the uranium ore dispersion end point is the corresponding end of each target transport channel;

[0147] Using a fluid dynamics model, the uranium dispersion process in the uranium distribution area is simulated based on the terrain data, the uranium dispersion data, and the uranium dispersion point information, to determine a uranium dispersion range map for the uranium distribution area.

[0148] In some possible embodiments, the geological structure information includes geological faults; the uranium ore determination module 504 is specifically used for:

[0149] Based on the geological structure information and the alteration information, the oxidation zone and reduction zone of the uranium ore distribution area are determined respectively, and the boundary between the oxidation zone and reduction zone of the uranium ore distribution area is determined as the first uranium ore precipitation zone;

[0150] The geological faults corresponding to the uranium ore distribution area were identified as the second uranium ore precipitation zone.

[0151] The uranium ore precipitation zone is determined based on the first uranium ore precipitation zone and the second uranium ore precipitation zone.

[0152] In some possible embodiments, the uranium ore determination module 504 is specifically used for:

[0153] Obtain the uranium precipitation ratio corresponding to each of the uranium ore precipitation zones;

[0154] The probability of uranium distribution in the uranium distribution area is determined based on the uranium dispersion range map, the uranium precipitation zone, and the uranium precipitation ratio corresponding to each uranium precipitation zone.

[0155] Based on the same technical concept, this disclosure also provides a computer device. (See also...) Figure 6 The diagram shows the structure of a computer device 600 provided in this embodiment of the present disclosure, including a processor 601, a memory 602, and a bus 603. The memory 602 stores execution instructions and includes a main memory 6021 and an external memory 6022. The main memory 6021, also called internal memory, is used to temporarily store computational data in the processor 601 and data exchanged with external memory 6022 such as a hard disk. The processor 601 exchanges data with the external memory 6022 through the main memory 6021.

[0156] In this embodiment, the memory 602 is specifically used to store application code that executes the solution of this application, and its execution is controlled by the processor 601. That is, when the computer device 600 is running, the processor 601 communicates with the memory 602 through the bus 603, so that the processor 601 executes the application code stored in the memory 602, and then executes the method described in any of the foregoing embodiments.

[0157] The memory 602 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0158] Processor 601 may be an integrated circuit chip with signal processing capabilities. The aforementioned processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor.

[0159] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the computer device 600. In other embodiments of this application, the computer device 600 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0160] This disclosure also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the sandstone-type uranium deposit mineralization prediction method described in the above-described method embodiments. The storage medium can be a volatile or non-volatile computer-readable storage medium.

[0161] This disclosure also provides a computer program product carrying program code. The program code includes instructions that can be used to execute the steps of the sandstone-type uranium deposit mineralization prediction method described in the above method embodiments. For details, please refer to the above method embodiments, which will not be repeated here.

[0162] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium; in another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0163] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. In the several embodiments provided in this disclosure, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0164] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0165] In addition, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0166] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0167] Finally, it should be noted that the above-described embodiments are merely specific implementations of this disclosure, used to illustrate the technical solutions of this disclosure, and not to limit it. The protection scope of this disclosure is not limited thereto. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this disclosure. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be covered within the protection scope of this disclosure. Therefore, the protection scope of this disclosure should be determined by the protection scope of the claims.

Claims

1. A method for predicting the formation of sandstone-type uranium deposits, characterized by, The method comprises the following steps: acquiring remote sensing data and topographic data of a target operation area; wherein the remote sensing data comprises unmanned aerial vehicle image data, satellite image data, and radar satellite image data; the unmanned aerial vehicle image data comprises unmanned aerial vehicle multispectral image data and unmanned aerial vehicle hyperspectral image data; the satellite image data comprises satellite multispectral image data and satellite hyperspectral image data; determining geological information of the target operation area based on the remote sensing data and the topographic data; wherein the geological information comprises lithological information and environmental information; the environmental information comprises water system information, geological structure information, and alteration information; judging whether the target operation area has lithology that can provide uranium ore based on the lithological information; if so, determining uranium ore dispersion data based on the topographic data, the lithological information, and the environmental information; simulating a uranium ore dispersion process according to the topographic data and the uranium ore dispersion data by using a fluid dynamics model, and determining a uranium ore dispersion range map of the target operation area; determining a uranium ore precipitation area of the target operation area based on the environmental information; and determining a uranium ore distribution condition of the target operation area based on the uranium ore dispersion range map and the uranium ore precipitation area; wherein the uranium ore distribution condition comprises a uranium ore distribution probability; wherein the determination of the geological information of the target operation area based on the remote sensing data and the topographic data comprises: determining the water system information based on the topographic data; and determining the geological structure information based on the unmanned aerial vehicle multispectral image data, the satellite multispectral image data, and the radar satellite image data; determining spectral characteristics of altered minerals in the target operation area based on the unmanned aerial vehicle image data and the satellite image data, and extracting alteration information in the spectral characteristics of altered minerals based on a principal component analysis method and a spectral angle matching method, to determine the alteration information; determining the lithological information based on the unmanned aerial vehicle hyperspectral image data and the satellite hyperspectral image data; wherein the determination of the uranium ore dispersion data based on the topographic data, the lithological information, and the environmental information comprises: determining a uranium ore distribution region in the target operation area according to the topographic data; determining a plurality of transport channels in the uranium ore distribution region based on the water system information and the geological structure information; and screening the plurality of transport channels according to the alteration information to obtain target uranium ore transport channels; wherein the target uranium ore transport channels comprise a plurality of target transport channels; for each target transport channel, determining a uranium ore amount of the target transport channel based on lithological information corresponding to the target transport channel and a unit fluid uranium content corresponding to the lithological information in the target transport channel; determining the uranium ore dispersion data based on the uranium ore amount corresponding to each target transport channel.

2. The method of claim 1, wherein, the simulation of the uranium ore dispersion process according to the topographic data and the uranium ore dispersion data by using the fluid dynamics model comprises: determine uranium mine dispersion point information based on each target transport channel; wherein the uranium mine dispersion point information includes a uranium mine dispersion starting point and a uranium mine dispersion ending point, the uranium mine dispersion starting point is the position of the lithology that can provide uranium mine in each target transport channel, and the uranium mine dispersion ending point is the corresponding end of each target transport channel; simulate a uranium mine dispersion process of the uranium mine distribution area according to the terrain data, the uranium mine dispersion data and the uranium mine dispersion point information by using a fluid dynamics model, and determine a uranium mine dispersion range map of the uranium mine distribution area.

3. The method of claim 2, wherein, The geological structure information includes geological faults; and the uranium mine precipitation area of the target operation area is determined based on the environmental information, including: determine the oxidation zone and the reduction zone of the uranium mine distribution area based on the geological structure information and the alteration information respectively, and determine the boundary of the oxidation zone and the reduction zone of the uranium mine distribution area as a first uranium mine precipitation area; determine the geological faults corresponding to the uranium mine distribution area as a second uranium mine precipitation area; determine the uranium mine precipitation area according to the first uranium mine precipitation area and the second uranium mine precipitation area.

4. The method of claim 3, wherein, The uranium mine distribution situation of the target operation area is determined based on the uranium mine dispersion range map and the uranium mine precipitation area, including: obtain a uranium mine precipitation ratio corresponding to each uranium mine precipitation area; determine a uranium mine distribution probability of the uranium mine distribution area based on the uranium mine dispersion range map, the uranium mine precipitation area and the uranium mine precipitation ratio corresponding to each uranium mine precipitation area.

5. A device for predicting the formation of sandstone-type uranium deposits, characterized by, including: a data acquisition module configured to acquire remote sensing data and terrain data of a target operation area; wherein the remote sensing data includes unmanned aerial vehicle image data, satellite image data and radar satellite image data, the unmanned aerial vehicle image data includes unmanned aerial vehicle multi-spectral image data and unmanned aerial vehicle hyperspectral image data, and the satellite image data includes satellite multi-spectral image data and satellite hyperspectral image data; an information determination module configured to determine geological information of the target operation area based on the remote sensing data and the terrain data; wherein the geological information includes lithological information and environmental information, and the environmental information includes water system information, geological structure information and alteration information; a data determination module configured to determine uranium mine dispersion data based on the lithological information if the target operation area has lithology that can provide uranium mine; a data simulation module configured to simulate a uranium mine dispersion process by using a fluid dynamics model according to the terrain data and the uranium mine dispersion data, and determine a uranium mine dispersion range map of the target operation area; a uranium mine determination module configured to determine a uranium mine precipitation area of the target operation area based on the environmental information, and determine a uranium mine distribution situation of the target operation area based on the uranium mine dispersion range map and the uranium mine precipitation area; wherein the uranium mine distribution situation includes a uranium mine distribution probability; wherein the information determination module is specifically configured to: determine the water system information based on the terrain data, and determine the geological structure information based on the unmanned aerial vehicle multi-spectral image data, the satellite multi-spectral image data, and the radar satellite image data; determine the altered mineral spectral features of the target operation area based on the unmanned aerial vehicle image data and the satellite image data, extract altered information in the altered mineral spectral features based on a principal component analysis method and a spectral angle matching method, and determine the altered information; determine the lithology information based on the unmanned aerial vehicle hyperspectral image data and the satellite hyperspectral image data; The data determination module is specifically configured to: determine a uranium distribution area in the target operation area according to the terrain data; determine a plurality of transport channels in the uranium distribution area based on the water system information and the geological structure information, and screen the plurality of transport channels according to the altered information to obtain a target uranium transport channel; wherein the target uranium transport channel includes a plurality of target transport channels; for each target transport channel, determine a uranium amount of the target transport channel based on the lithology information corresponding to the target transport channel and a unit fluid uranium content corresponding to the lithology information in the target transport channel; determine the uranium dispersion data based on the uranium amount corresponding to each target transport channel.

6. A storage medium having stored thereon a computer program, characterized in that The computer program, when executed by a processor, implements the method of any one of claims 1 to 4.

7. A computer device comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, The processor, when executing the computer program, implements the method of any one of claims 1 to 4.

Images