CN112734076A - Large-scale positioning prediction method for hard rock type uranium ore resources - Google Patents
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Abstract
The invention belongs to the technical field of uranium ore geological research, and particularly relates to a large scale positioning prediction method for hard rock type uranium ore resources, which comprises the following steps: determining a prediction range and a scale of a prediction region; collecting geological information, geophysical prospecting information and chemical prospecting information of the prediction region, determining uranium ore control factors of the prediction region, establishing an ore forming mode of the uranium ore of the prediction region, and determining prediction factors of the uranium ore; compiling a basic drawing and an information drawing; extracting a prediction element according to the basic drawing and the information drawing; calculating the favorable degree of the finished ore and delimiting favorable areas of the finished ore; and comprehensively evaluating each favorable mining area by combining the geological conditions of the mining, the existing mineralization clues and the physicochemical exploration conditions, and screening out a target area which can be subjected to drilling verification. The method can comprehensively predict the hard rock type uranium mineralization favorable area by extracting the prediction elements through secondary development by utilizing the existing basic geological and physical and chemical exploration data.
Description
Technical Field
The invention belongs to the technical field of uranium ore geological research, and particularly relates to a large-scale positioning prediction method for hard rock type uranium ore resources.
Background
The hard rock type uranium ores include granite type uranium ores and volcanic type uranium ores, which are hydrothermal causative uranium ores. The scale of the hard rock type uranium ore is smaller than that of the sandstone type uranium ore, but the grade is high, and particularly, some large uranium-rich ores still have high development and utilization values. With the increase of the exploration degree of uranium ores, earth surface ores are completely explored, exploration work is carried out to the deep part, and the most direct and effective method is drilling and revealing. The drilling exposure cost is very high, and if the drilling cost is reduced and the drilling mineral finding rate is improved, the target area of the mineral finding needs to be accurately positioned.
On the basis, a positioning prediction method for hard rock type uranium ore resources is urgently needed to be established, regional geological data and large-scale exploration data are fully utilized, prediction elements are determined by analyzing uranium ore control factors, an ore deposit model comprehensive prediction method is utilized, an ore forming favorable area is defined, the ore finding range can be reduced, and a target area is provided for further exploration.
Disclosure of Invention
The invention aims to provide a large-scale positioning prediction method for hard rock type uranium ore resources, which can comprehensively predict a favorable hard rock type uranium ore forming area by extracting prediction elements through secondary development by utilizing existing basic geological and physicochemical exploration data.
The technical scheme for realizing the purpose of the invention is as follows: a large-scale positioning prediction method for hard rock type uranium ore resources comprises the following steps:
step (1), determining a prediction range and a scale of a prediction area;
step (2), collecting geological information, geophysical prospecting information and chemical prospecting information of the prediction region, determining uranium ore control factors of the prediction region, establishing an ore forming mode of the uranium ore of the prediction region, and determining uranium ore prediction elements;
step (3), compiling a basic drawing and an information drawing;
step (4), extracting a prediction element according to the basic graph and the information graph;
step (5), calculating the favorable degree of the finished ore and delimiting a favorable area of the finished ore;
and (6) comprehensively evaluating each favorable mining area by combining the geological conditions of the mining, the existing mineralization clues and the physicochemical exploration conditions, and screening out a target area which can be subjected to drilling investigation.
Further, the prediction range in the step (1) is a field-level range, and the prediction scale is 1: 25000-1: 10000.
Further, the step (2) includes:
step (2.1), collecting geological information, geophysical prospecting information and chemical prospecting information of the prediction area by a system, and determining uranium ore control factors of the prediction area;
step (2.2), selecting a typical ore deposit for research according to the type of the uranium ore in the prediction area, and establishing a uranium ore mineralization mode in the prediction area by combining the characteristics of regional uranium ore mineralization;
and (2.3) researching geophysical prospecting information and chemical prospecting information of the region where the prediction region is located in detail, comparing physical prospecting abnormity with the spatial distribution relation of the uranium ores, and determining prediction elements of the uranium ores.
Further, the geological information, the geophysical prospecting information and the chemical prospecting information of the prediction area in the step (2.1) comprise: geological mapping, geophysical prospecting measurements, and radiometric information consistent with the predicted scale.
Further, the ore controlling factors in the step (2.1) include: mineralizing geological body, ore control structure and hydrothermal alteration.
Further, the basic map in step (3) includes: uranium mine geological map, construction structure map, ground radioactive map, radioactive hydration map and chemical exploration map; the construction structure diagram is divided into a volcanic lithologic lithofacies structure diagram and an invaded magma structure diagram according to the types of the main rocks containing ores; the ground radioactivity diagram comprises a ground gamma total diagram, a ground energy spectrum,210Po and radon gas measurement diagrams; the radioactive hydration diagram comprises a uranium concentration diagram in water and a radon concentration diagram in water; the mapping comprises a water system sediment map and a component mapping map.
Further, the information map in step (3) includes: mineralizing element maps and predicting element maps.
Further, the prediction elements extracted in step (4) include: the mining structure beneficial area comprises a mine control structure buffer area, a structure intersection area and a structure activity dense area.
Further, the step (5) includes:
step (5.1), utilizing mineral resource evaluation software to grid the prediction area, loading the extracted prediction elements, selecting a proper ore deposit model through prediction variable purchase and screening, utilizing an analogy method or a classification method to calculate the mineralization profitability of each grid unit, generating an mineralization profitability color block diagram, and processing the mineralization profitability color block diagram into an equivalent diagram of the mineralization profitability;
step (5.2), combining the equivalent graph of the ore forming profitability and the distribution characteristics of ore control factors, and preliminarily delineating an ore forming profitable area;
and (5.3) calculating the mineralization probability of the preliminarily delineated favorable mineralization area by using the prediction elements again, setting a reasonable mineralization probability threshold value to optimize the preliminarily delineated favorable mineralization area, screening out favorable mineralization areas with low mineralization probability, and grading according to the mineralization probability.
Furthermore, the prediction elements extracted from the beneficial region preliminarily defined into the ore-forming in the step (5.1) are uranium ore-forming essential elements and key prediction elements closely related to the uranium ore-forming space.
Further, the analogy method or classification method in the step (5.1) comprises the following steps: feature analysis, evidence weight, fuzzy evidence weight.
Furthermore, in the step (5.3), the prediction elements utilized by the beneficial region of the uranium deposit are preferably all prediction elements having a certain spatial relationship with the uranium deposit.
The invention has the beneficial technical effects that:
1. according to the large-scale positioning prediction method for the hard rock type uranium ore resource, the existing geological data and ground physical exploration measurement data are fully utilized, the mineralization probability is calculated, the beneficial areas of different levels of mineralization are defined, a basis is provided for the next exploration and deployment, the period is short, and the cost is low;
2. according to the method for predicting the hard rock type uranium ore resource by large scale positioning, the reaction and distribution of deep geology and physical and chemical exploration elements on the earth surface are fully considered in the extraction process of the prediction elements, and the accuracy of deep prediction is improved.
Drawings
FIG. 1 is a plot of mineralizing favorable color patches in example 1 of the present invention;
FIG. 2 is a diagram of the ore-forming profitability equivalence in example 1 of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Example 1: large-scale positioning prediction of uranium ore resources in facies mountain ore field
And (1) determining a prediction range and a scale of the prediction area according to the work requirement and the investigation work degree.
The method comprises the following steps that (1) the mountain-in-the-sea mining fields are distributed in mountain-in-the-sea fire basin, and the whole volcanic basin is used as a prediction range; the geological, geophysical and chemical exploration work degree covering the whole basin reaches 1:25000, so that a 1:25000 prediction scale is adopted.
And (2) collecting geological information, geophysical prospecting information and chemical prospecting information of the prediction region, determining uranium ore control factors of the prediction region, establishing an ore forming mode of the uranium ore of the prediction region, and determining uranium ore prediction elements.
And (2.1) the research degree of the facies mountain ore field is higher, so that the research results of predecessors are fully utilized, geological information, geophysical prospecting information and chemical prospecting information of the facies mountain basin are collected by the system, and the uranium ore forming rule and ore control factors of the facies mountain ore field are determined. Uranium mineralization of the facies mountain mining field is controlled by factors such as fracture structure, hydrothermal alteration, intergroup interface and basal interface, late-stage dike vein and the like. The fracture structure is the most key ore control factor of the uranium ore in the facies mountain ore field, the northeast fracture structure and the secondary structure thereof control the spatial positioning of the uranium ore in the west of the basin, and the crossing composite part of the northeast fracture structure and the east-west granite belt (and the east-west fracture structure) controls the spatial positioning of the uranium ore in the north of the basin. The acidic alteration and alkaline alteration superposed ore control is a key ore control factor of uranium ores in facies mountain ore fields, alkaline alteration development is carried out on the upper and lower ore containing structures, and alkaline erosion becomes an important mark for predicting and positioning blind ores in the deep part of the structure zone of the area. The interfacial variation part between the goose lake ridge group and the top-beating group, the late granite porphyry development part of the substrate interface and the ore-controlling fracture structure are compounded to control the spatial positioning of uranium ores. The later rock vein (granite vein) has close spatial and temporal relationship with uranium mineralization and is also one of important mineral control factors of uranium mineralization.
And (2.2) dividing the uranium ore types of the facies mountain ore field into a sub-volcanic rock sub-type and a dense fissure zone sub-type, respectively selecting a horizontal mountain and a Zhoushan typical ore deposit for research, and determining the uranium ore forming mode of the prediction region by combining regional uranium ore forming characteristics.
And (2.3) researching various geophysical prospecting information and chemical prospecting information of the facies mountain region in detail, comparing physical prospecting abnormity with the spatial distribution relation of the uranium ores, and determining prediction elements of the uranium ores. The prediction elements comprise mineralizing geological elements, physical exploration and radioactive comprehensive information. The mineralizing geological elements comprise uranium mineralizing information, uranium-rich volcanic rock, ore control structure, mineralizing favorable space, alkaline alteration, acidic alteration and the like. The comprehensive information of the physical exploration and the radioactivity comprises ground radioactivity characteristics, soil radon gas characteristics and radioactive hydrogeological characteristics. The prediction elements are direct or indirect evidence of uranium mineralization development of the prediction region and are important auxiliary evidence for delineating the target region in the prediction region.
And (3) compiling a basic drawing and an information drawing.
On the basis of collecting information, determining uranium ore control factors of the prediction region, establishing a uranium ore mineralization mode of the prediction region and determining prediction elements of the uranium ore, compiling a geologic map of the uranium ore of the facies mountain mining field, a volcanic lithologic lithofacies structure map, an mineralization element map, a prediction element map, a ground energy spectrum uranium content equivalent map, a soil radon gas concentration abnormal map, a radioactive hydration abnormal map and the like, setting necessary map part hierarchical structures and attribute structures of all the map layers, and filling necessary attributes according to the attribute structures.
And (4) extracting a prediction element according to the basic graph and the information graph.
Broken zebra rock, zebra run rock and sub granite zebra rock in the facies mountain field are favorable to forming the mineral geologic body and are used as prediction elements of the mineral geologic body.
The fracture structure is the most key mine control factor in the mountain region, and the ground surface range for constructing the favorable region needs to be provided with a buffering direction and a buffering distance according to the deep extension condition of the fracture structure. The screened ore formation structure prediction elements are processed and extracted as follows: on the premise of containing uranium mineralization information in the zone to the maximum extent, the main fracture, the secondary fracture and the fracture structure in the zone are buffered to different degrees, and the buffering to different degrees is carried out to two sides according to fracture tendency. The ore-forming fracture of the prediction area is mostly steep, the dip angle is mostly between 70 degrees and 85 degrees, and the projection of the deep fracture part on the ground surface tends to deviate. Therefore, the fracture damping tendency is shifted in accordance with the fracture inclination angle. The buffered fracture structure region is one of the prediction factors.
The acid alteration and alkaline alteration superposed ore control is one of key ore control factors of uranium ores in facies mountain ore fields. The alkaline alteration and the acidic alteration are closely associated in space, the alkaline alteration and the acidic alteration are mostly developed in the same fracture structure zone, the scale of the alkaline alteration is larger than that of the acidic alteration, the alkaline alteration is arranged at the upper part of the same fracture structure alteration zone, and the acidic alteration is arranged at the lower part of the same fracture structure alteration zone; the center of the mineralization-rich altered zone is an acid altered zone, and the two sides of the mineralization-rich altered zone are alkaline altered zones. The acidic alteration and the alkaline alteration are separated and compounded, and the acid and alkaline alteration compound part is more beneficial to forming rich and large ores. Therefore, the development regions of acidic and basic alterations, respectively, are evaluated as predictive factors.
According to the research on ore control factors of the facies mountain areas, the interface (variation part) between the facies mountain basin top-beating group and the goose lake ridge group controls the spatial distribution of uranium ores on the upper part of the firing mountain basin, and the part for intensively producing the uranium-rich ore bodies is near the interface between the facies mountain basins. And calculating the line density of the contour lines by utilizing the contour map of the interfaces between the groups through secondary development, reflecting the inclination degree of the interfaces between the groups, and extracting the parts with relatively steep inclination variation. The substrate interface controls the later granite vein of the facies mountain fire basin and controls the spatial distribution and positioning of uranium ores at the lower part of the basin. The substrate interface is an important mineral forming space in the deep part of the area. The granite controlled by the substrate interface has close space-time relationship with the uranium ore, a broken zone is constructed in the granite and a contact zone, and a fissure zone and a flaking zone in the volcanic rock near the granite vein become a uranium ore favorable ore holding space. Therefore, intergroup interface variation parts and granite development areas are combined into a mineral-forming favorable space combination to participate in mineral-forming prediction.
The uranium deposit and the uranium mineralization point are known to be important clues for finding ores, and the extraction work of uranium mineralization information based on the uranium deposit and the uranium mineralization point is a basis and reference basis for carrying out ore finding on ores, improving the scale of the uranium ores and expanding the total amount of resources. Uranium deposit and mineralization points are buffered to a certain degree according to mineralization scale, and a face document is formed to serve as one of prediction elements.
Radioactivity measurement is a direct and effective exploration means for the most uranium ores, and ground gamma energy spectrum measurement abnormity is extracted to serve as one of prediction elements.
The radon gas measurement can reflect deep mineralization information, and a radon gas concentration abnormal region is extracted to be used as an important prediction element for deep uranium ore prediction.
The distribution of the radioactive water anomaly has a certain spatial relationship with the regional structure, lithology and mineralization, and the existence and the distribution direction of the water anomaly can indicate the ore-containing part and possible ore-containing perspective of the region. Extracting the high-altitude uranium field in the water in the facies mountain area as one of the prediction elements.
And (5) calculating the favorable degree of the finished ore and delimiting the favorable area of the finished ore.
Step (5.1), utilizing mineral resource evaluation software MRAS to grid the predicted area of the mountain mineral field by 100m x 100m, loading the geological body of the mineral, the favorable area of the mineral formation structure, the favorable area of the corrosion change and the prediction elements of the existing mineralization buffer area, and utilizing the prediction variable purchasing and screening to calculate the mineralization profitability of each grid unit by utilizing a characteristic analysis method by taking 10 mineral beds of district dwelling carina, Chinese mountains, plum ridge, cave, transverse mountain, Gongshang, Hongdong, lake field, sandbank and Yunjian as models to generate a colour block diagram of the mineralization profitability, as shown in figure 1, and processing the colour block diagram into an isocratic diagram of the mineralization profitability, as shown in figure 2.
And (5.2) preliminarily defining 36 ore-forming favorable areas by combining an ore-forming favorable degree equivalent graph and ore control element distribution characteristics in a man-machine interaction mode.
And (5.3) loading the preliminarily delineated 36 favorable areas serving as geological units into MRAS software, and calculating the correlation between all extracted relevant prediction elements and the model deposit, wherein the correlation coefficient of each element is greater than 0.6, which shows that each element has better correlation with uranium mineralization. Calculating the ore forming probability of the favorable area, screening out 4 primary selection areas with lower ore forming probability through preferential classification, and finally dividing 15 favorable areas of class A, 8 favorable areas of class B and 9 favorable areas of class C.
And (6) comprehensively evaluating each favorable mining area by combining the geological conditions of the mining, the existing mineralization clues and the physicochemical exploration conditions, and screening out target areas capable of being drilled and verified according to the working degree. In the A-type favorable areas, 4 areas have no uranium deposit yet found, and the areas are key deployment areas for next exploration work.
In this embodiment, the drawing, the extraction of the predicted elements and the delineation of the favorable mining area are all performed on a maptis platform, and the purchasing and screening of the predicted elements and the calculation of the favorable mining degree adopt mineral resource evaluation software (MRAS, MORPAS, etc., applicable to maptis format).
The present invention has been described in detail with reference to the drawings and examples, but the present invention is not limited to the examples, and various changes can be made within the knowledge of those skilled in the art without departing from the spirit of the present invention. The prior art can be adopted in the content which is not described in detail in the invention.
Claims (12)
1. A large-scale positioning prediction method for hard rock type uranium ore resources is characterized by comprising the following steps:
step (1), determining a prediction range and a scale of a prediction area;
step (2), collecting geological information, geophysical prospecting information and chemical prospecting information of the prediction region, determining uranium ore control factors of the prediction region, establishing an ore forming mode of the uranium ore of the prediction region, and determining uranium ore prediction elements;
step (3), compiling a basic drawing and an information drawing;
step (4), extracting a prediction element according to the basic graph and the information graph;
step (5), calculating the favorable degree of the finished ore and delimiting a favorable area of the finished ore;
and (6) comprehensively evaluating each favorable mining area by combining the geological conditions of the mining, the existing mineralization clues and the physicochemical exploration conditions, and screening out a target area which can be subjected to drilling investigation.
2. The large-scale positioning prediction method for the hard rock type uranium ore resources according to claim 1, wherein the prediction range in the step (1) is a field-level range, and the prediction scale is 1: 25000-1: 10000.
3. The method for predicting the large-scale positioning of the uranium ore resources in hard rock type according to claim 1, wherein the step (2) comprises:
step (2.1), collecting geological information, geophysical prospecting information and chemical prospecting information of the prediction area by a system, and determining uranium ore control factors of the prediction area;
step (2.2), selecting a typical ore deposit for research according to the type of the uranium ore in the prediction area, and establishing a uranium ore mineralization mode in the prediction area by combining the characteristics of regional uranium ore mineralization;
and (2.3) researching geophysical prospecting information and chemical prospecting information of the region where the prediction region is located in detail, comparing physical prospecting abnormity with the spatial distribution relation of the uranium ores, and determining prediction elements of the uranium ores.
4. The method for predicting the large-scale positioning of the hard rock type uranium ore resource according to claim 3, wherein the geological information, the geophysical prospecting information and the chemical prospecting information of the prediction area in the step (2.1) comprise: geological mapping, geophysical prospecting measurements, and radiometric information consistent with the predicted scale.
5. The method for predicting large-scale positioning of a hard rock type uranium ore resource according to claim 3, wherein the ore control factor in the step (2.1) comprises: mineralizing geological body, ore control structure and hydrothermal alteration.
6. The method for predicting the large-scale positioning of the hard rock type uranium ore resource according to claim 1, wherein the basic map in the step (3) comprises: uranium mine geological map, construction structure map, ground radioactive map, radioactive hydration map and chemical exploration map; the construction structure diagram is divided into a volcanic lithologic lithofacies structure diagram and an invasion magma according to the types of the main rocks containing oresA construction diagram; the ground radioactivity map comprises a ground gamma total map, a ground energy spectrogram,210Po diagram and radon gas measurement diagram; the radioactive hydration diagram comprises a uranium concentration diagram in water and a radon concentration diagram in water; the mapping comprises a water system sediment map and a component mapping map.
7. The method for large-scale positioning and predicting of uranium mining resources of hard rock type according to claim 1, wherein the information map in the step (3) includes: mineralizing element maps and predicting element maps.
8. The method for predicting the large-scale positioning of the hard rock type uranium ore resource according to claim 1, wherein the prediction elements extracted in the step (4) comprise: the mining structure beneficial area comprises a mine control structure buffer area, a structure intersection area and a structure activity dense area.
9. The method for predicting the large-scale positioning of the uranium ore resources of hard rock type according to claim 1, wherein the step (5) comprises:
step (5.1), utilizing mineral resource evaluation software to grid the prediction area, loading the extracted prediction elements, selecting a proper ore deposit model through prediction variable purchase and screening, utilizing an analogy method or a classification method to calculate the mineralization profitability of each grid unit, generating an mineralization profitability color block diagram, and processing the mineralization profitability color block diagram into an equivalent diagram of the mineralization profitability;
step (5.2), combining the equivalent graph of the ore forming profitability and the distribution characteristics of ore control factors, and preliminarily delineating an ore forming profitable area;
and (5.3) calculating the mineralization probability of the preliminarily delineated favorable mineralization area by using the prediction elements again, setting a reasonable mineralization probability threshold value to optimize the preliminarily delineated favorable mineralization area, screening out favorable mineralization areas with low mineralization probability, and grading according to the mineralization probability.
10. The large-scale positioning prediction method for hard rock type uranium ore resources according to claim 9, wherein prediction elements extracted from the preliminary delineation of beneficial regions of mineralization in step (5.1) are uranium mineralization essential elements and key prediction elements closely related to uranium mineralization space.
11. The method for predicting the large-scale positioning of the uranium ore resources in hard rock type according to claim 9, wherein the analogy method or the classification method in the step (5.1) comprises: feature analysis, evidence weight, fuzzy evidence weight.
12. The large-scale positioning prediction method for the hard rock type uranium ore resource according to claim 9, wherein the prediction elements preferably used in the beneficial ore forming area in the step (5.3) are all prediction elements having a certain spatial relationship with the uranium ore.
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| CN116912534A (en) * | 2023-09-14 | 2023-10-20 | 中国地质大学（武汉） | Method for identifying spatial structure of hydrothermal deposit ore-forming system by self-adaptive search matching |
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