Potential resource quantity estimation method and device

Abstract

The embodiment of the invention provides a potential resource quantity estimation method and device, and the method comprises the steps: constructing a geoscience big data spatial database of a target working area; extracting necessary geological elements and abnormal features reflecting the necessary geological elements from various different types of data information of the geoscience big data spatial database; on the basis of the necessary geological elements and the abnormal features reflecting the necessary geological elements, superposition operation is executed, and geological units are formed; selecting a model area from the geological unit; respectively determining the metallogenic favorable degree of the geological unit and the volume ore content of the model area; and according to the metallogenic favorable degree of the geological unit, the volume ore-bearing rate of the model area and the volume of the prospecting prospecting area in the geological unit, determining the potential resource quantity of each prospecting prospecting area. According to the method, the potential resource quantity can be effectively identified, and the accuracy of the obtained potential resource quantity is improved.

Description

Technical Field

[0001] This application belongs to the technical field of mineral resource estimation, and particularly relates to a method and device for estimating potential resources. Background Art

[0002] Potential resources, also known as predicted resources, refer to resources that have not been discovered but may exist. The size of potential resources is an important indicator for measuring the prospecting potential of a working area. It can be used as a basis for selecting prospecting target areas in the target working area and also provide a reference for formulating the strategic plan for mineral resources in the target working area. The estimation of potential resources often takes the discovered deposits as reference objects. Based on the comprehensive analysis of the relationship between the discovered deposits in the target working area and geological, geophysical, geochemical, and remote sensing geoscience big data, and based on the basic idea of similar analogy, characterized by extrapolation, quantitative methods are used to estimate the potential resources in the prospecting target area. In this work, it is necessary to process and analyze geoscience big data.

[0003] Currently, there are various methods for estimating potential resources internationally, such as: geological volume method, surface metal amount method, geophysical inversion method, Delphi method, grade-tonnage method, regional value method, regression analysis method, and logic information method, etc. In China, for the need of prospecting exploration and for the convenience of geological personnel to understand more easily, the geological volume method is the most widely used.

[0004] However, the ore-bearing rate of the volume of the prospecting target area determined by the existing geological volume method is often based on the understanding that the ore-forming favorability of the most typical model area is the highest in the target working area, resulting in certain errors in the potential resources of the prospecting target area determined based on the geological volume method. Summary of the Invention

[0005] In view of this, the embodiments of the present invention provide a method and device for estimating potential resources, which can effectively identify potential resources and improve the accuracy of the obtained potential resources.

[0006] The embodiments of the present invention provide a method for estimating potential resources, including:

[0007] Construct a geoscience big data spatial database for the target working area, where the geoscience big data spatial database contains various types of data materials;

[0008] Extract necessary geological elements and abnormal characteristics reflecting the necessary geological elements from various types of data materials in the geoscience big data spatial database;

[0009] Based on the necessary geological elements and abnormal characteristics reflecting the necessary geological elements, perform a superposition operation to form geological units;

[0010] Select a model area from the geological unit, where the model area is a geological unit with a relatively high exploration degree in the geological unit and where one or more ore deposits have been discovered;

[0011] Respectively determine the metallogenic favorability of the geological unit and the volume ore-bearing rate of the model area;

[0012] Based on the metallogenic favorability of the geological unit, the volume ore-bearing rate of the model area, and the volume of the prospective ore area in the geological unit, determine the potential resources of each prospective ore area.

[0013] Optionally, the steps of constructing the geoscience big data spatial database for the target working area include:

[0014] Obtain multiple different types of data materials for the target working area, including multi-source, heterogeneous, and massive geoscience big data materials;

[0015] Perform spatial coordinate conversion processing on various types of data materials, unify the coordinate systems of all types of data materials, and generate the geoscience big data spatial database.

[0016] Optionally, the steps of extracting necessary geological elements and the anomaly characteristics reflecting the necessary geological elements from various different types of data materials in the geoscience big data spatial database include:

[0017] Guided by the metallogenic system theory, determine the necessary geological elements reflecting the target ore deposit type;

[0018] On the geographic information system platform, directly extract the necessary geological elements actually discovered during the geological mapping process from the regional geological map;

[0019] On the geographic information system platform, extract the geophysical, geochemical, and remote sensing anomaly characteristics reflecting the necessary geological elements from various different types of data materials through a preset method.

[0020] Optionally, based on the necessary geological elements and the anomaly characteristics reflecting the necessary geological elements, perform an overlay operation to form a geological unit that satisfies at least one or more of the following:

[0021] Perform an overlay operation on the area corresponding to the first type of necessary geological elements and the first type of anomaly characteristics to form the geological unit;

[0022] Perform an overlay operation on the area corresponding to the second type of necessary geological elements and the second type of anomaly characteristics to form the geological unit;

[0023] Perform an overlay operation on the regions corresponding to the third type of necessary geological elements and the third type of abnormal characteristics to form a first overlay result, perform an overlay operation on the regions corresponding to the fourth type of necessary geological elements and the fourth type of abnormal characteristics to form a second overlay result, and perform an overlay operation on the regions corresponding to the first overlay result and the second overlay result to form the geological unit;

[0024] Wherein, the overlay operation includes at least one of a merge operation, an intersection operation, or a subtraction operation.

[0025] Optionally, the step of determining the metallogenic favorability of the geological unit includes:

[0026] Determine multiple prediction elements of the geological unit;

[0027] According to the number of model areas in the geological unit and the multiple prediction elements, determine the matching coefficient between any two prediction elements;

[0028] According to any one prediction element and the matching coefficient between this prediction element and other prediction elements, determine the weight coefficient of any one prediction element;

[0029] According to the prediction elements and the weight coefficients of the prediction elements, determine the metallogenic favorability of the geological unit.

[0030] Optionally, the potential resource volume estimation method satisfies the following:

[0031] Use the following formula to determine the matching coefficient:

[0032]

[0033] Where r jk is the matching coefficient between the jth prediction element and the kth prediction element, x jj is the jth element value of the ith model area, x ik is the kth element value of the ith model area, m is the number of prediction elements, and n is the number of model areas;

[0034] Use the following formula to determine the weight coefficient:

[0035]

[0036] Where a j is the weight coefficient of the jth element;

[0037] The step of determining the metallogenic favorability of the geological unit according to the prediction factors and the weight coefficients of the prediction factors includes: calculating the sum of the products of each prediction factor and its corresponding weight coefficient respectively; taking the ratio between the sum of the products of each prediction factor and the sum of the weight coefficients of each prediction factor as the metallogenic favorability of the geological unit, where the calculation formula is:

[0038]

[0039] where f is the metallogenic favorability of each geological unit in the target working area, x1, x2,......, x m are the prediction factor values of each geological unit, and the prediction factor range covers all necessary geological elements, important geological elements and minor geological elements, as well as geophysical, geochemical and remote sensing anomaly characteristics reflecting the corresponding prediction factors; a1, a2,......, a m are the weight coefficients of the prediction factors of each geological unit.

[0040] Optionally, the step of determining the volume ore-bearing rate of each model area includes:

[0041] Obtaining the cumulative identified resource amount of each ore deposit in each model area;

[0042] Determining the cumulative identified resource amount of each model area according to the cumulative identified resource amounts of all ore deposits in each model area;

[0043] Determining the volume of each model area according to the area of each model area and its corresponding maximum exploration depth;

[0044] Determining the volume ore-bearing rate of each model area according to the cumulative identified resource amount of each model area and its volume.

[0045] Optionally, the formula for determining the volume ore-bearing rate of each model area is:

[0046]

[0047] where k i 模型区 is the volume ore-bearing rate of the i-th model area, Q i 模型区 is the cumulative identified resource amount of all ore deposits in the i-th model area, V i 模型区 is the volume of the i-th model area, q ig is the cumulative identified resource amount of the g-th ore deposit in the i-th model area, S i 模型区 is the area of the i-th model area, h i 模型区is the maximum exploration depth of the i-th model area, and d is the number of ore occurrences in the i-th model area.

[0048] Optionally, the step of determining the potential resource amount of each prospecting area according to the metallogenic favorability of the geological unit, the volume ore-bearing rate of the model area, and the volume of the prospecting area in the geological unit includes:

[0049] Determining the weighted average value of the volume ore-bearing rate of the model area according to the volume ore-bearing rate and metallogenic favorability of all model areas, and taking it as the comprehensive volume ore-bearing rate of the model area, including:

[0050]

[0051] where K 模型区 is the comprehensive volume ore-bearing rate after weighted average of the model area, n is the number of model areas, and k i 模型区 is the volume ore-bearing rate of the i-th model area, and f i 模型区 is the metallogenic favorability of the i-th model area;

[0052] Determining the volume ore-bearing rate of each prospecting area according to the comprehensive volume ore-bearing rate of the model area and the metallogenic favorability of the prospecting area;

[0053] Determining the potential resource amount of each prospecting area according to the volume ore-bearing rate of each prospecting area and its corresponding volume.

[0054] An embodiment of the present invention further provides a potential resource amount estimation device, including:

[0055] A construction unit, configured to construct a geoscience big data spatial database of the target working area, and the geoscience big data spatial database contains various types of data;

[0056] An extraction unit, configured to extract necessary geological elements and abnormal characteristics reflecting the necessary geological elements from various types of data in the geoscience big data spatial database;

[0057] A generation unit, configured to perform a superposition operation based on the necessary geological elements and abnormal characteristics reflecting the necessary geological elements to form a geological unit;

[0058] A selection unit, configured to select a model area from the geological unit, and the model area is a geological unit with a higher exploration degree and one or more ore occurrences found in the geological unit;

[0059] A processing unit is configured to respectively determine the metallogenic favorability of the geological unit and the volume ore-bearing rate of the model area; and determine the potential resource amount of each prospecting target area according to the metallogenic favorability of the geological unit, the volume ore-bearing rate of the model area, and the volume of the prospecting target area in the geological unit.

[0060] Compared with the prior art, the technical solution of the embodiment of the present invention has the following advantages:

[0061] By using the potential resource amount estimation method provided by the embodiment of the present invention, through establishing a geoscience big data spatial database, various types of data materials for characterizing the ore-bearing amount of the target working area can be obtained. Furthermore, the necessary geological elements indispensable in the process of ore deposit formation and the abnormal characteristics corresponding to the necessary geological elements can be determined from various types of data materials. By performing a superposition operation on the necessary geological elements and the abnormal characteristics corresponding to the necessary geological elements, the formed geological unit has a clearer geological meaning, and the necessary geological elements are considered, so that the estimated accuracy of the potential resource amount of the prospecting target area predicted based on the geological unit is higher. Moreover, by pre-selecting a model area from the geological unit and determining the potential resource amount of each prospecting target area based on the metallogenic favorability of the geological unit, the volume ore-bearing rate of the model area, and the volume of the prospecting target area, both the material basis and the enrichment and emplacement conditions are taken into account, so that the potential resource amount can be effectively identified and the accuracy of the obtained potential resource amount can be improved. Description of the Drawings

[0062] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0063] Figure 1 Shows a flowchart of a potential resource amount estimation method in an embodiment of the present invention;

[0064] Figure 2 Shows a schematic diagram of the principle of the superposition operation in an embodiment of the present invention;

[0065] Figure 3 Shows a schematic diagram of the regional division of a target working area in an embodiment of the present invention;

[0066] Figure 4 Shows a flowchart for determining the metallogenic favorability of a geological unit in an embodiment of the present invention;

[0067] Figure 5The flowchart for determining the volume ore-bearing rate of the model area in an embodiment of the present invention is shown;

[0068] Figure 6 The structural schematic diagram of a potential resource volume estimation device in an embodiment of the present invention is shown. Detailed implementation manners

[0069] As described in the background art, there are certain errors in the potential resource volume determined based on the geological volume method, because:

[0070] When estimating the potential resource volume of the prospecting target area using the geological volume method, most use a mathematical model similar to the following formula, that is: Q 潜在 = K 潜在 × V 潜在 . Wherein, Q 潜在 refers to the potential resource volume of the prospecting target area, V 潜在 refers to the volume of the prospecting target area, and K 潜在 refers to the volume ore-bearing rate of the prospecting target area.

[0071] Generally speaking, it is correct to estimate the potential resource volume using this formula. However, when predecessors calculated the volume ore-bearing rate of the prospecting target area, they often simply used K 潜在 = α × K 模型区 to calculate the volume ore-bearing rate of the prospecting target area, where α is the similarity coefficient. However, this approach is not rigorous, because determining the volume ore-bearing rate of the target area in this way is often based on the understanding that the ore-forming favorability of the most typical model area is the highest in the target working area. In fact, this is not entirely the case. It is entirely possible to find a prospecting target area in the working area with a higher ore-forming favorability than the most typical model area, because the most typical model area may not necessarily have all the ore-forming favorable factors. The ore-forming favorable factors are the result of summarizing and generalizing the geoscience big data of multiple model areas, rather than the summary of the ore-forming favorable factors of the most typical model area.

[0072] To solve the above technical problems, an embodiment of the present invention provides a potential resource volume estimation scheme. By establishing a geoscience big data spatial database, various types of data materials for characterizing the ore content in the target working area can be obtained. Furthermore, from various types of data materials, the necessary geological elements indispensable in the process of deposit formation and the abnormal characteristics corresponding to the necessary geological elements can be determined. By performing a superposition operation on the necessary geological elements and the abnormal characteristics corresponding to the necessary geological elements, the formed geological unit has a clearer geological significance, and the necessary geological elements are considered, so that the estimated accuracy of the potential resource volume of the prospecting target area predicted based on the geological unit is higher. Moreover, by pre-selecting a model area from the geological units and determining the potential resource volume of each prospecting target area based on the ore-forming favorability of the geological unit, the volume ore content rate of the model area, and the volume of the prospecting target area, both the material basis and the enrichment and emplacement conditions are taken into account, so that potential resource volume can be effectively identified and the accuracy of the obtained potential resource volume can be improved.

[0073] To enable those skilled in the art to better understand the inventive concept, working principle, and advantages of the embodiments of the present invention, the potential resource volume estimation scheme in the embodiments of the present invention is described in detail below.

[0074] Refer to Figure 1 The flowchart of a potential resource volume estimation method in an embodiment of the present invention shown in Figure 1 As shown, the following steps can be performed:

[0075] S10, construct a geoscience big data spatial database for the target working area, where the geoscience big data spatial database contains various types of data materials.

[0076] In this embodiment, based on typical deposits and regional metallogenic laws (such as time, space, genesis, and ore types), and combined with existing regional geological and mineral resources data, an ore-forming favorable area is selected as the target working area, which can increase the possibility of detecting potential resource volume.

[0077] In some examples, the selection criteria for the target working area can be satisfied: 1) The target working area is located within the scope of a known large metallogenic belt or ore concentration area; 2) There is a known deposit (such as a magmatic copper-nickel sulfide deposit) in the target working area.

[0078] It should be noted that the selection criteria listed in the above examples are only for illustrative purposes and are used to represent the requirements satisfied by the target working area, and should not be construed as a limitation to the present invention.

[0079] In this embodiment, the multiple different types of data refer to the parameters for characterizing different parameter information of the target working area. By obtaining multiple different types of data, the topography and geological parameters of the target working area can be more truly reflected.

[0080] In this embodiment, step S10 may include: obtaining multiple different types of data of the target working area, including multi-source, heterogeneous, and massive geoscience big data; performing spatial coordinate transformation processing on various types of data to unify the coordinate systems of all types of data and generating the geoscience big data spatial database.

[0081] Among them, the multiple different types of data may include at least two of geological data, geophysical exploration data, geochemical exploration data, remote sensing data, and ore deposit data, and the ore deposit data is necessary.

[0082] More specifically, the geological data mainly relates to the regional geological map.

[0083] In some embodiments, the regional geological map is mainly stored in vector files, and some old data exists in paper media and needs to be vectorized first.

[0084] The geophysical exploration data mainly relates to the regional gravity, magnetic method, and radioactive measurement data obtained from geophysical exploration surveys.

[0085] In some embodiments, the geophysical exploration data is mainly stored in table files or vector files, and some old data exists in paper media and needs to be vectorized first.

[0086] The collection of geochemical exploration data mainly relates to the regional stream sediment, rock chip, and soil measurement data obtained from geochemical exploration surveys. Among them, the media are different for different scales and different geochemical landscapes.

[0087] In some embodiments, the geochemical exploration data is mainly stored in table files or vector files, and some old data exists in paper media and needs to be vectorized first.

[0088] There are many types of remote sensing data, which need to be determined according to the target ore deposit type and target task. Among them, the remote sensing data can be roughly divided into remote sensing data for extracting alteration information and remote sensing data for extracting ore-controlling structures and ore-bearing geological bodies.

[0089] In some embodiments, the remote sensing data is mainly stored in raster files.

[0090] It should be particularly noted that, generally, for the convenience of similar analogy, the collection of regional geological, geophysical, geochemical and remote sensing data in the target working area should adhere to the principle of scale equivalence, that is, the scales of the data in the target working area should be consistent, and it is best to select data with a larger scale.

[0091] The ore deposit data mainly involve the discovered ore deposits in the target working area, including information such as coordinates, cumulative identified resource amounts, and exploration depths.

[0092] In some embodiments, the ore deposit data are mainly stored in vector or table files.

[0093] It should be noted that the multiple different types of data materials in the above examples are only for illustrative purposes, used to represent the acquisition of multiple data materials, and should not be construed as a limitation of the present invention. In some other embodiments, the data in the target working area may further include: mask data.

[0094] In this embodiment, since the above-mentioned geological, mineral, geophysical, geochemical and remote sensing data collected are of different types, ages and sources, there are also significant differences in the geographic and projection coordinate systems. Among them, the common geographic coordinate systems corresponding to the projection coordinate systems include Beijing 54 Geographic Coordinate System, Xi'an 80 Geographic Coordinate System, CGCS2000 Geographic Coordinate System and WGS84 Geographic Coordinate System; the projection coordinate systems include Gauss-Kruger (3-degree zone or 6-degree zone), Universal Mercator and Lambert projection.

[0095] In order to perform data analysis in a unified spatial coordinate system, the geographic coordinate system will uniformly adopt the CGCS2000 geographic coordinate system with the centroid coordinate type as the core in China. For a smaller range, the Gauss-Kruger (3-degree zone or 6-degree zone) projection is adopted for the projection coordinate system, and for a very large range, the Lambert projection is adopted. Different spatial coordinate systems can be converted into a unified coordinate system by using three-parameter or seven-parameter projection transformation methods in GIS software, and finally a unified geoscience big data spatial database is established.

[0096] In other words, by converting the spatial coordinates of various types of data materials to the same spatial coordinate system, the format consistency between various types of data materials can be improved, which is conducive to reducing the difficulty of subsequent data processing in the geoscience big data spatial database.

[0097] S20. Extract necessary geological elements and abnormal characteristics reflecting the necessary geological elements from various different types of data materials in the geoscience big data spatial database.

[0098] Among them, the selection of necessary geological elements is carried out under the guidance of the metallogenic system theory with the deposit type as the core. That is, the formation of a deposit consists of three processes: "source - transportation - storage". The selection of necessary geological elements should fully reflect these three necessary geological processes of deposit formation to express the material basis and enrichment and emplacement conditions required for mineralization.

[0099] More specifically, necessary geological elements mainly refer to parameters such as ore - bearing geological bodies (e.g., ore - hosting strata or ore - bearing rock masses) and ore - forming and ore - controlling structures (folds, faults), etc.

[0100] It should be noted that due to the complexity of deposit formation, for different types of deposits, the necessary geological elements vary greatly, and specific problems need to be analyzed specifically.

[0101] For example, if the target deposit type is magmatic copper - nickel sulfide deposit, the necessary geological elements are basic - ultrabasic rock masses and deep - large fault zones, as well as geophysical, geochemical, and remote - sensing anomaly characteristics that indirectly express these necessary geological elements. Among them, the basic - ultrabasic rock mass is the material basis for mineralization, and the deep - large fault zone provides the enrichment and emplacement conditions for the formation of the deposit.

[0102] Also, for example, if the target deposit type is sedimentary bauxite among exogenous minerals, the necessary geological elements are ore - bearing horizons and ore - controlling syncline structures, as well as geophysical, geochemical, and remote - sensing anomaly characteristics that indirectly express these necessary geological elements. Among them, the ore - bearing horizon is the material basis for mineralization, and the ore - controlling syncline structure provides the enrichment and emplacement conditions for the formation of the deposit.

[0103] It should be pointed out that these necessary geological elements and the anomaly characteristics reflecting these necessary geological elements can be extracted from the aforementioned geoscience big - data spatial database.

[0104] In this embodiment, step S20 may include: under the guidance of the metallogenic system theory, determining the necessary geological elements reflecting the target deposit type; on the Geographic Information System (GIS) platform, directly extracting the necessary geological elements actually discovered during the geological mapping process from the regional geological map; on the Geographic Information System platform, extracting geophysical, geochemical, and remote - sensing anomaly characteristics reflecting the necessary geological elements from various different types of data (e.g., geophysical, geochemical, and remote - sensing data) through preset methods (e.g., continuation analysis, PCA analysis, C - A method, and visual interpretation, etc.).

[0105] It should be pointed out that since the necessary geological elements and the anomaly characteristics reflecting the necessary geological elements are both determined based on geological parameters, the subsequent formed geological units have clear geological meanings, which makes the estimation of potential resource amounts more accurate.

[0106] S30. Based on the necessary geological elements and the abnormal features reflecting the necessary geological elements, perform a superposition operation to form geological units.

[0107] In this embodiment, both the necessary geological elements and the abnormal features are selected from the geoscience big data spatial database and are parameters characterizing the actual geological features and landforms of the target working area from different aspects. In this way, when delineating geological units based on the necessary geological elements and through performing a superposition operation, the delineated geological units are often irregular, restricted by the necessary elements, have relatively clear geological significance, represent the products of the development of the metallogenic system where the target ore deposit type is located up to now, and are the basic units for prospecting prediction and evaluation.

[0108] In this embodiment, as Figure 2 shown in the schematic diagram of the principle of the superposition operation in an embodiment of the present invention, as Figure 2 shown, the superposition operation includes at least one of: a merging operation, an intersection operation, or a subtraction operation. By performing a superposition analysis on the necessary geological element parameters (including the necessary geological elements themselves and the abnormal features reflecting the necessary geological elements), various types of geological units under different superposition operation types can be obtained.

[0109] More specifically, by performing an intersection operation on the geological element parameters, a first type of geological unit C1 can be obtained; by performing a merging operation on the geological element parameters, a second type of geological unit C2 can be obtained; by performing a subtraction operation on the geological element parameters, a third type of geological unit C3 can be obtained.

[0110] It should be noted that, first, in actual application, a single superposition method may be adopted, or a combination of several superposition methods may be used, which depends on the complexity and correlation of the necessary geological elements; second, Figure 2 shown, the geological units delineated after superposition analysis can be considered as the products of the development of the metallogenic system where the target ore deposit type is located up to now and are the basic units for prospecting prediction and evaluation; third, Figure 2 there is not necessarily an overlapping area between each necessary element. Generally, overlapping areas are required for intersection and subtraction superpositions, while overlapping areas are not necessarily required for merging superposition. Fourth, Figure 2 only two necessary geological element parameters are schematically shown. In actual application, the number of necessary geological element parameters can be two or more. The present invention does not limit the specific number of necessary geological element parameters.

[0111] In this embodiment, step S30 can meet at least one or more of the following:

[0112] Perform a superposition operation (i.e., at least one of a merging operation, an intersection operation, or a subtraction operation) on the areas corresponding to the first type of necessary geological elements and the first type of abnormal features to form the geological unit.

[0113] Perform an overlay operation (i.e., at least one of a merging operation, an intersection operation, or a subtraction operation) on the regions corresponding to the second type of necessary geological elements and the second type of abnormal features to form the geological unit.

[0114] Perform an overlay operation (i.e., at least one of a merging operation, an intersection operation, or a subtraction operation) on the regions corresponding to the third type of necessary geological elements and the third type of abnormal features to form a first overlay result; perform an overlay operation (i.e., at least one of a merging operation, an intersection operation, or a subtraction operation) on the regions corresponding to the fourth type of necessary geological elements and the fourth type of abnormal features to form a second overlay result, and perform an overlay operation (i.e., at least one of a merging operation, an intersection operation, or a subtraction operation) on the regions corresponding to the first overlay result and the second overlay result to form the geological unit.

[0115] As a specific example, if the ore deposit type in the target working area is a magmatic copper-nickel sulfide deposit, the basic-ultrabasic rock mass can be merged with the abnormal features reflecting the basic-ultrabasic rock mass to form a comprehensive basic-ultrabasic rock mass, and the deep major fault zone can be merged with the abnormal features reflecting the deep major fault zone to form a comprehensive deep major fault zone, and then an intersection process is performed on the comprehensive basic-ultrabasic rock mass and the comprehensive deep major fault zone to form a geological unit.

[0116] As another specific example, if the ore deposit type in the target working area is a sedimentary bauxite deposit, a specific ore-bearing horizon can be merged with the abnormal features reflecting the ore-bearing horizon to form a comprehensive ore-bearing horizon, and the ore-controlling syncline structure can be merged with the abnormal features reflecting the ore-controlling syncline structure to form a comprehensive ore-controlling syncline structure, and then the two are intersected to form a geological unit.

[0117] S40, select a model area from the geological unit, where the model area is a geological unit in the geological unit with a relatively high exploration degree and where one or more ore occurrences have been discovered.

[0118] In this embodiment, when conducting prospecting prediction for a geological unit, a batch of geological units with a relatively high exploration degree and where one or more ore occurrences have been discovered can be selected from the geological unit as model areas, so that the model areas can be used as a reference basis to estimate the potential resource volume of the prospecting prospective area.

[0119] In other words, after determining the geological unit, multiple model areas can be selected from the geological unit, and thus, based on the model areas, the volume ore-bearing rate of the prospecting prospective area can be determined.

[0120] See Figure 3 In the schematic diagram of the regional division of a target working area in an embodiment of the present invention shown as Figure 3 shown, the target working area includes a model area and a prospecting prospective area.

[0121] In other words, the geological units delineated by the above-mentioned necessary geological element superposition method are divided into model areas and prospecting target areas based on the exploration degree and the occurrence of mineral deposits. Among them, the model area refers to the geological unit with a relatively high exploration degree and the occurrence of mineral deposits. Other geological units outside the model area are prospecting target areas, which are also the objects for estimating potential resource amounts.

[0122] It should be noted in particular that the sizes and quantities of the existing mineral deposits in the model area can vary. For example, Figure 3 in the two model areas shown schematically, one model area has two mineral deposits with different sizes, while the other model area has only one mineral deposit.

[0123] S50. Determine the metallogenic favorability of the geological unit and the volume ore-bearing rate of the model area respectively.

[0124] Among them, the metallogenic favorability of the geological unit (including the model area and the prospecting target area) is an important factor affecting the volume ore-bearing rate of the prospecting target area, and it directly determines the potential resource amount in the prospecting target area.

[0125] More specifically, the metallogenic favorability is a comprehensive index indicating the possibility of mineral formation and enrichment in the geological unit of the target working area.

[0126] The volume ore-bearing rate of the model area is used to represent the ore content per unit volume, which reflects the distribution density of the minerals in the model area. Among them, the larger the volume ore-bearing rate, the greater the distribution density of the minerals; conversely, the smaller the distribution density of the minerals.

[0127] In this embodiment, referring to Figure 4 the flowchart for determining the metallogenic favorability of the geological unit in an embodiment of the present invention shown in Figure 4 shown, the following steps can be executed:

[0128] A1. Determine multiple prediction elements of the geological unit.

[0129] In this embodiment, the prediction elements refer to the necessary geological elements, important geological elements, secondary geological elements related to mineralization, and the corresponding geophysical, geochemical, and remote sensing anomaly characteristics.

[0130] Among them, on the GIS platform, the necessary geological elements, important geological elements, and secondary geological elements actually discovered during the geological mapping process can be extracted from the regional geological map; on the GIS platform, from the geophysical, geochemical, and remote sensing data, the geophysical, geochemical, and remote sensing anomaly characteristics corresponding to the necessary geological elements, important geological elements, and secondary geological elements can be extracted through preset methods (such as continuation analysis, PCA analysis, C - A method, and visual interpretation, etc.).

[0131] A2. Determine the matching coefficient between any two prediction elements according to the number of the model areas in the geological unit and the multiple prediction elements.

[0132] In this embodiment, the matching coefficient between the prediction elements is used as a metric for the correlation between the prediction elements, that is, the correlation between any two prediction elements.

[0133] In this embodiment, the prediction elements have a corresponding relationship with the model areas. One model area may include multiple prediction elements, and the model area is an area where the minerals are known. Based on the number of the model areas and the prediction elements included in the model areas, the matching coefficient between any two prediction elements in any one model area can be confirmed.

[0134] In a specific embodiment, assume that there are n model areas and m prediction elements in the target working area. Then, the matching coefficient between each prediction element and other elements can be determined. For example, the calculation formula for the matching coefficient between the jth prediction element and the kth prediction element is as follows:

[0135]

[0136] where r jk is the matching coefficient between the jth prediction element and the kth prediction element, x ij is the value of the jth element in the ith model area, x jk is the value of the kth element in the ith model area, m is the number of the prediction elements, and n is the number of the model areas.

[0137] It should be noted that the two-classification method can be used to determine the value of the prediction element. For example, take 1 when the prediction element exists and take 0 when the prediction element does not exist.

[0138] Specifically, whether the prediction element exists or not can be directly read on the GIS platform.

[0139] A3. Determine the weight coefficient of any one prediction element according to any one prediction element and the matching coefficient between this prediction element and other prediction elements.

[0140] In this embodiment, for different model areas, the included prediction elements are different, so that different prediction elements have different weight coefficients, and the weight coefficient determines the importance degree of the prediction element.

[0141] In this embodiment, the weight coefficient of the prediction element can be calculated by the sum-of-squares method. Among them, the ideological basis of the sum-of-squares method is that the stronger the correlation between the prediction element and other prediction elements, the more important the prediction element is, and the greater the impact on mineralization is, which conforms to the characteristics of geological mineralization.

[0142] For example, in the case of tectonic altered rock type gold deposits, fault structures are very important prediction elements because they control the migration and emplacement of ore-bearing hydrothermal fluids, affect hydrothermal alteration and the spatial distribution of ore bodies, have a very close relationship with various ore-forming control factors, and their corresponding weight coefficients are also very large.

[0143] In a specific embodiment, the following formula is used to determine the weight coefficient:

[0144]

[0145] where a j is the weight coefficient of the jth element.

[0146] A4. According to the prediction elements and the weight coefficients of the prediction elements, determine the metallogenic favorability of the geological unit.

[0147] In this embodiment, the prediction elements represent factors favorable for ore formation. When they exist, they are favorable for ore formation, and when they do not exist, they are unfavorable for ore formation. The weight coefficients of the prediction elements represent the importance of the prediction elements in the geological unit. Therefore, based on these two parameters, the accuracy of the obtained metallogenic favorability can be improved.

[0148] In this embodiment, step A4 may include: A41. Calculate the sum of the products of each prediction element and its corresponding weight coefficient respectively. A42. Use the ratio between the sum of the products of each prediction element and the sum of the weight coefficients of each prediction element as the metallogenic favorability of the geological unit.

[0149] More specifically, the following formula is used to determine the metallogenic favorability of the geological unit:

[0150]

[0151] where f is the metallogenic favorability of each geological unit in the target working area, x1, x2,..., x m are the prediction element values of each geological unit. The prediction element range can cover all necessary geological elements, important geological elements, and minor geological elements, as well as geophysical, geochemical, and remote sensing anomaly characteristics reflecting the corresponding prediction elements; a1, a2,..., a m are the weight coefficients of the prediction elements of each geological unit.

[0152] It should be noted that the above example lists one of the ways to determine the metallogenic favorability. In actual operation, if other methods are used, the metallogenic favorability can be determined according to the own principles and formulas of the methods adopted. For example, the weight of evidence method, the information content method, the logistic regression method, etc.

[0153] In this embodiment, refer toFigure 5 Flowchart for determining the volume ore-bearing rate of the model area in an embodiment of the present invention, as shown in Figure 5 shown, the following steps may be performed:

[0154] B1. Obtain the cumulative identified resource amount of each ore-producing area in each model area.

[0155] In this embodiment, for the same model area, the number of ore-producing areas it contains may be multiple, and there are differences among the ore-producing areas. Therefore, the cumulative identified resource amounts of the ore-producing areas in the model area can be obtained separately.

[0156] B2. Determine the cumulative identified resource amount of each model area according to the cumulative identified resource amounts of all ore-producing areas in each model area.

[0157] In this embodiment, for the same model area, the cumulative identified resource amounts of all ore-producing areas can be added up to determine the cumulative identified resource amount of each model area.

[0158] B3. Determine the volume of each model area according to the area of each model area and its corresponding maximum exploration depth.

[0159] In this embodiment, the product of the area of the model area and its corresponding maximum exploration depth can be used as the volume of the model area.

[0160] B4. Determine the volume ore-bearing rate of each model area according to the cumulative identified resource amount of each model area and its volume.

[0161] In this embodiment, the cumulative identified resource amount of the model area represents the mineral resource content in the model area, and the volume of the model area represents the size of the model area. Based on these two parameters, the ore production per unit volume can be determined.

[0162] In a specific embodiment, the formula for determining the volume ore-bearing rate of each model area is:

[0163]

[0164] where k i 模型区 is the volume ore-bearing rate of the i-th model area, Q i 模型区 is the cumulative identified resource amount of all ore-producing areas of the i-th model area, V i 模型区 is the volume of the i-th model area, q ig is the cumulative identified resource amount of the g-th ore-producing area in the i-th model area, S i 模型区 is the area of the i-th model area, hi 模型区 is the maximum exploration depth of the i-th model area, and d is the number of ore deposits in the i-th model area.

[0165] Among them, the cumulative identified resource volume of ore deposits in the model area and the maximum exploration depth in the model area can be obtained through relevant exploration reports, and the area of the model area can be directly read through the GIS platform.

[0166] Thus, by adopting the above solution, the metallogenic favorability of all geological units in the target working area and the volume ore-bearing rate of each model area can be determined respectively. Furthermore, according to the above two parameters, the potential resource volume of each prospecting area in the target working area can be determined.

[0167] It should be noted that, first, each model area does not necessarily contain all elements. In other words, the model area is not necessarily the area with the best metallogenic conditions. Second, the prospecting area may contain all metallogenic geological elements or only a part of the metallogenic geological elements.

[0168] That is, step S60, according to the metallogenic favorability of the geological unit, the volume ore-bearing rate of the model area, and the volume of the prospecting area in the geological unit, determine the potential resource volume of each prospecting area.

[0169] In a specific embodiment, step S60 may include:

[0170] S61, according to the volume ore-bearing rate and metallogenic favorability of all model areas, determine the weighted average value of the volume ore-bearing rate of the model area and use it as the comprehensive volume ore-bearing rate of the model area.

[0171] In a specific embodiment, the following method may be adopted to determine the comprehensive volume ore-bearing rate of the model area:

[0172]

[0173] where K 模型区 is the comprehensive volume ore-bearing rate after weighted average of the model area, n is the number of model areas, k i 模型区 is the volume ore-bearing rate of the i-th model area, and f i 模型区 is the metallogenic favorability of the i-th model area.

[0174] S62, according to the comprehensive volume ore-bearing rate of the model area and the metallogenic favorability of the prospecting area, determine the volume ore-bearing rate of each prospecting area.

[0175] Among them, the metallogenic favorability of a prospective ore area refers to the metallogenic favorability of the geological unit that serves as a prospective ore area in the geological unit. In other words, this solution uses the metallogenic favorability of the prospective ore area and the comprehensive volume ore-bearing rate of the model area to determine the volume ore-bearing rate of the prospective ore area.

[0176] In a specific embodiment, the following method can be used to determine the volume ore-bearing rate of the prospective ore area:

[0177] K 潜在 = f * K 模型区

[0178] In the formula, f is the metallogenic favorability of the prospective ore area.

[0179] S63. According to the volume ore-bearing rate of each prospective ore area and its corresponding volume, determine the potential resource amount of each prospective ore area.

[0180] In this embodiment, the product of the volume ore-bearing rate of the prospective ore area and the volume can be used as the potential resource amount of the prospective ore area.

[0181] In a specific embodiment, the following method can be used to determine the potential resource amount Q of the prospective ore area 潜在 :

[0182] Q 潜在 = K 潜在 * V 潜在 = K 潜在 * S 潜在 * h 潜在

[0183] Among them, S 潜在 is the area of the prospective ore area, and h 潜在 is the depth of the prospective ore area.

[0184] It should be noted that, first, the depth of the prospecting area can generally be taken as the maximum exploration depth of the model area, or it can also be taken as the maximum recoverable depth of the target working area according to the current economic and technical conditions. If the maximum exploration depth of the model area is taken, it is considered to represent the maximum recoverable depth under the current economic and technical conditions. Second, since the geological unit itself is composed of two parts, namely the model area and the prospecting area, the ore-forming favorability of the model area and the prospecting area can be considered as the ore-forming favorability of the geological unit. Thus, when using the potential resource volume estimation method in the above example and taking the geological unit as the basic unit for prediction, the geological unit is delineated by using the necessary factor superposition method, which makes the geological unit more clearly defined geologically; according to the volume ore-bearing rate and ore-forming favorability of all model areas, the weighted average of the volume ore-bearing rates of all model areas is calculated, and the comprehensive volume ore-bearing rate obtained may select a prospecting area with a higher volume ore-bearing rate than that of the model area. In this way, compared with the traditional geological volume method, it is not only easy to be recognized by geologists, but also helps to further discover larger-scale ore deposits. Moreover, this solution focuses on the material basis (geological volume) and the enrichment and emplacement conditions (volume ore-bearing rate) to improve the potential resource volume estimation method, and better depicts the basic elements of ore deposit formation, which is a potential resource volume estimation method with a solid ore-forming theoretical basis.

[0185] The embodiment of the present invention also provides a device corresponding to the potential resource volume estimation method, as Figure 6 shown in the structural schematic diagram of a potential resource volume estimation device in an embodiment of the present invention, as Figure 6 shown, the potential resource volume estimation device 100 may include:

[0186] A construction unit 110, configured to construct a geoscience big data spatial database of the target working area, where the geoscience big data spatial database contains various types of data materials;

[0187] An extraction unit 120, configured to extract necessary geological elements and abnormal characteristics reflecting the necessary geological elements from various types of data materials in the geoscience big data spatial database;

[0188] A generation unit 130, configured to perform a superposition operation based on the necessary geological elements and the abnormal characteristics reflecting the necessary geological elements to form a geological unit;

[0189] A selection unit 140, configured to select a model area from the geological units, where the model area is a geological unit with a relatively high exploration degree and one or more ore-producing areas have been discovered in the geological unit;

[0190] A processing unit 150 is configured to respectively determine the metallogenic favorability of the geological unit and the volume ore-bearing rate of the model area; and determine the potential resource amount of each prospective ore area according to the metallogenic favorability of the geological unit, the volume ore-bearing rate of the model area, and the volume of the prospective ore area in the geological unit.

[0191] Among them, the specific working processes and principles of the construction unit 110, the extraction unit 120, the generation unit 130, the selection unit 140, and the processing unit 150 can refer to the relevant descriptions in the foregoing examples.

[0192] It can be understood that the division of the above units is only a division of logical functions. In actual implementation, they can be fully or partially integrated into a physical entity, or physically separated. In addition, the above units can be implemented in the form of a processor invoking software.

[0193] It should be noted that the so-called "one embodiment" or "embodiment" of the present invention refers to specific features, structures or characteristics that can be included in at least one implementation manner of the present invention. And in the description of the present invention, terms such as "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, features defined with terms such as "first" and "second" may explicitly or implicitly include one or more of such features. Moreover, terms such as "first" and "second" are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or indicate importance. It can be understood that such terms can be interchanged under appropriate circumstances, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described.

[0194] Although the embodiments of the present invention are disclosed as above, the present invention is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention should be subject to the scope defined by the claims.

Claims

1. A method for estimating potential resource quantity, characterized in that, Including: Constructing a geoscience big data spatial database for the target working area, where the geoscience big data spatial database contains various types of data materials; Extracting necessary geological elements and abnormal characteristics reflecting the necessary geological elements from various types of data materials in the geoscience big data spatial database; Performing a superposition operation based on the necessary geological elements and abnormal characteristics reflecting the necessary geological elements to form geological units; Selecting model areas from the geological units, where the model areas are geological units with a relatively high exploration degree and where one or more ore deposits have been discovered in the geological units; Respectively determining the metallogenic favorability of the geological units and the volume ore-bearing rate of the model areas; Determining the potential resource amounts of each prospecting area according to the metallogenic favorability of the geological units, the volume ore-bearing rate of the model areas, and the volume of the prospecting areas in the geological units.

2. The potential resource volume estimation method according to claim 1, wherein The steps of constructing the geoscience big data spatial database for the target working area include: Obtaining multi-source, heterogeneous, and massive geoscience big data materials for the target working area; Performing spatial coordinate conversion processing on various types of data materials to unify the coordinate systems of all types of data materials and generating the geoscience big data spatial database.

3. The potential resource volume estimation method according to claim 1, characterized in that The steps of extracting necessary geological elements and abnormal characteristics reflecting the necessary geological elements from various types of data materials in the geoscience big data spatial database include: Guided by the metallogenic system theory, determining the necessary geological elements reflecting the target ore deposit type; Directly extracting the necessary geological elements actually discovered during the geological mapping process from the regional geological map on the geographic information system platform; On the geographic information system platform, extracting geophysical, geochemical, and remote sensing abnormal characteristics reflecting the necessary geological elements from various types of data materials through a preset method.

4. The method for estimating potential resource volume according to claim 1, wherein The performing of the superposition operation based on the necessary geological elements and abnormal characteristics reflecting the necessary geological elements to form geological units satisfies at least one or more of the following: Performing a superposition operation on the areas corresponding to the first type of necessary geological elements and the first type of abnormal characteristics to form the geological units; Performing a superposition operation on the areas corresponding to the second type of necessary geological elements and the second type of abnormal characteristics to form the geological units; Performing a superposition operation on the areas corresponding to the third type of necessary geological elements and the third type of abnormal characteristics to form a first superposition result, performing a superposition operation on the areas corresponding to the fourth type of necessary geological elements and the fourth type of abnormal characteristics to form a second superposition result, and performing a superposition operation on the areas corresponding to the first superposition result and the second superposition result to form the geological units; Wherein, the superposition operation includes at least one of a merge operation, an intersection operation, or a subtraction operation.

5. The potential resource volume estimation method according to claim 1, characterized in that The steps of determining the metallogenic favorability of the geological units include: Determining multiple prediction elements of the geological units; According to the number of the model areas in the geological units and the multiple prediction elements, determining the matching coefficient between any two prediction elements; Determine the weight coefficient of any prediction factor based on any prediction factor and the matching coefficient between this prediction factor and other prediction factors; Determine the metallogenic favorability of the geological unit based on the prediction factor and the weight coefficient of the prediction factor.

6. The potential resource volume estimation method according to claim 5, characterized in that: Use the following formula to determine the matching coefficient: where r jk is the matching coefficient between the j-th prediction factor and the k-th prediction factor, x ij is the value of the j-th factor in the i-th model area, x ik is the value of the k-th factor in the i-th model area, m is the number of prediction factors, and n is the number of model areas; Use the following formula to determine the weight coefficient: where a j is the weight coefficient of the j-th element; The step of determining the metallogenic favorability of the geological unit based on the prediction factor and the weight coefficient of the prediction factor includes: respectively calculating the sum of the products between each prediction factor and its corresponding weight coefficient; taking the ratio between the sum of the products of each prediction factor and the sum of the weight coefficients of each prediction factor as the metallogenic favorability of the geological unit, where the calculation formula is: Among them, f is the metallogenic favorability of each geological unit in the target working area, and x1, x2,......, x m are the predicted element values of each geological unit. The prediction element range covers all necessary geological elements, important geological elements, and secondary geological elements, as well as geophysical, geochemical, and remote sensing anomaly characteristics reflecting the corresponding predicted elements; a1, a2,......, a m are the weight coefficients of the predicted elements of each geological unit.

7. The method for estimating potential resource quantity according to claim 1, wherein The step of determining the volume ore-bearing rate of each model area includes: Obtain the cumulative identified resource volume of each ore occurrence in each model area; Determine the cumulative identified resource volume of each model area based on the cumulative identified resource volume of all ore occurrences in each model area; Determine the volume of each model area based on the area of each model area and its corresponding maximum exploration depth; Determine the volume ore-bearing rate of each model area based on the cumulative identified resource volume of each model area and its volume.

8. The method for estimating potential resource quantity according to claim 7, wherein The formula for determining the volume ore-bearing rate of each model area is: Among them, k i 模型区 is the volume ore-bearing rate of the i-th model area, Q i 模型区 is the cumulative identified resource amount of all ore occurrences in the i-th model area, V i 模型区 is the volume of the i-th model area, q ig is the cumulative identified resource amount of the g-th ore occurrence in the i-th model area, S i 模型区 is the area of the i-th model area, h i 模型区 is the maximum exploration depth of the i-th model area, and d is the number of ore occurrences in the i-th model area.

9. The method for estimating potential resource quantity according to claim 1, wherein The step of determining the potential resource volume of each prospecting target area based on the metallogenic favorability of the geological unit, the volume ore-bearing rate of the model area, and the volume of the prospecting target area in the geological unit includes: Determine the weighted average value of the volume ore-bearing rate of the model area based on the volume ore-bearing rate and metallogenic favorability of all model areas, and use it as the comprehensive volume ore-bearing rate of the model area, including: Among them, K 模型区 is the comprehensive volume ore-bearing rate after weighted average in the model area, n is the number of model areas, and k i 模型区 is the volume ore-bearing rate of the i-th model area, and f i 模型区 is the favorable degree of mineralization of the i-th model area; Determine the volume ore-bearing rate of each prospecting target area based on the comprehensive volume ore-bearing rate of the model area and the metallogenic favorability of the prospecting target area; Determine the potential resource volume of each prospecting target area based on the volume ore-bearing rate of each prospecting target area and its corresponding volume.

10. A potential resource quantity estimation device, characterized in that, Include: A construction unit for constructing a geoscience big data spatial database of the target working area, where the geoscience big data spatial database contains various types of data; An extraction unit for extracting necessary geological elements and the abnormal characteristics reflecting the necessary geological elements from various types of data in the geoscience big data spatial database; A generation unit for performing a superposition operation based on the necessary geological elements and the abnormal characteristics reflecting the necessary geological elements to form a geological unit; A selection unit for selecting a model area from the geological unit, where the model area is a geological unit with a relatively high exploration degree and one or more ore occurrences discovered in the geological unit; A processing unit for respectively determining the metallogenic favorability of the geological unit and the volume ore-bearing rate of the model area; and determining the potential resource volume of each prospecting target area based on the metallogenic favorability of the geological unit, the volume ore-bearing rate of the model area, and the volume of the prospecting target area in the geological unit.

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