Potential resource estimation method and device

Abstract

An embodiment of the present invention provides a method and device for estimating potential resource quantities, wherein the method for estimating potential resource quantities includes: constructing a geoscience big data spatial database of a target work area; extracting necessary geological elements and abnormal characteristics reflecting the necessary geological elements from a variety of different types of data in the geoscience big data spatial database; performing a superposition operation based on the necessary geological elements and the abnormal characteristics reflecting the necessary geological elements to form geological units; selecting a model area from the geological unit; determining the metallogenic favorableness of the geological unit and the volume mineralization rate of the model area respectively; determining the potential resource quantity of each prospective prospective area based on the metallogenic favorableness of the geological unit and the volume mineralization rate of the model area, as well as the volume of the prospective prospective area in the geological unit. This application can effectively identify potential resource quantities and improve the accuracy of the potential resource quantities obtained.

Description

Technical Field

[0001] The present application belongs to the technical field of mineral resource estimation, and in particular relates to a method and device for estimating potential resource quantities. Background Art

[0002] Potential resources, also known as predicted resources, refer to resources that have not yet been discovered but may exist. The size of potential resources is an important indicator for measuring the prospecting prospects of a work area. It can serve as a basis for selecting prospecting areas within the target work area and also provide a reference for formulating a strategic mineral resource plan for the target work area. Potential resource estimation often uses already discovered deposits as a reference. Based on a comprehensive analysis of the relationship between discovered deposits in the target work area and geological, geophysical, geochemical, and remote sensing geoscience big data, based on the basic idea of ​​similarity and analogy, and characterized by extrapolation, a quantitative method is used to estimate the potential resources in prospecting areas. This work requires the processing and analysis of geoscience big data.

[0003] Currently, a variety of methods are used internationally for estimating potential resource quantities, including geological volumetric methods, surface metallurgy methods, geophysical inversion methods, the Delphi method, the grade-tonnage method, the regional value method, regression analysis, and the logical information method. In my country, geological volumetric methods are the most widely used, driven by the needs of prospecting and exploration, as well as to facilitate understanding among geologists.

[0004] However, the volume mineralization rate of prospective mineral exploration areas determined by the existing technology using the geological volume method is often based on the understanding that the mineralization favorableness of the most typical model area is the highest in the target working area, resulting in certain errors in the potential resource volume of prospective mineral exploration areas determined based on the geological volume method. Summary of the Invention

[0005] In view of this, an embodiment of the present invention provides a potential resource quantity estimation method and apparatus, which can effectively identify potential resource quantities and improve the accuracy of the obtained potential resource quantities.

[0006] An embodiment of the present invention provides a method for estimating potential resource volume, comprising:

[0007] Constructing a geoscience big data spatial database for a target work area, wherein the geoscience big data spatial database includes a variety of different types of data;

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

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

[0010] Selecting a model area from the geological unit, wherein the model area is a geological unit with a high degree of exploration and in which one or more mineral deposits have been discovered;

[0011] respectively determining the metallogenic favorableness of the geological unit and the volume mineralization rate of the model area;

[0012] The potential resource volume of each prospecting area is determined based on the metallogenic favorableness of the geological unit and the volume mineralization rate of the model area, as well as the volume of the prospecting area in the geological unit.

[0013] Optionally, the step of constructing a geoscience big data spatial database of the target workspace includes:

[0014] Acquire multiple different types of data in the target work area, including multi-source, heterogeneous, and massive geoscience big data;

[0015] Various types of data are converted into spatial coordinates to unify the coordinate systems of all types of data and generate the geoscience big data spatial database.

[0016] Optionally, the step of extracting necessary geological elements from a plurality of different types of data in the geoscience big data spatial database and reflecting abnormal characteristics of the necessary geological elements includes:

[0017] Guided by the theory of metallogenic systems, determine the necessary geological elements that reflect the target deposit type;

[0018] On the GIS platform, the necessary geological elements actually discovered during geological mapping can be directly extracted from the regional geological map;

[0019] On the geographic information system platform, geophysical, geochemical and remote sensing anomaly features reflecting necessary geological elements are extracted from a variety of different types of data through preset methods.

[0020] Optionally, the superposition operation is performed based on the necessary geological elements and the abnormal characteristics of the necessary geological elements to form geological units, which satisfies at least one or more of the following conditions:

[0021] Overlaying the areas corresponding to the first type of necessary geological elements and the first type of abnormal features to form the geological unit;

[0022] Overlaying the second type of necessary geological elements and the areas corresponding to the second type of abnormal features to form the geological unit;

[0023] Overlaying the areas corresponding to the third type of necessary geological elements and the third type of abnormal features to form a first overlay result, overlaying the areas corresponding to the fourth type of necessary geological elements and the fourth type of abnormal features to form a second overlay result, and overlaying the areas corresponding to the first overlay result and the second overlay result to form the geological unit;

[0024] The superposition 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 comprises:

[0026] determining a plurality of prediction elements of the geological unit;

[0027] determining a matching coefficient between any two prediction elements based on the number of the model areas in the geological unit and a plurality of the prediction elements;

[0028] Determine a weight coefficient of any prediction factor based on any prediction factor and the matching coefficient between the prediction factor and other prediction factors;

[0029] The metallogenic favorableness of the geological unit is determined based on the prediction factors and the weight coefficients of the prediction factors.

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

[0031] The matching coefficient is determined using the following formula:

[0032]

[0033] Among them, r jk is the matching coefficient between the j-th prediction factor and the k-th prediction factor, x jj is the jth element value of the i-th model area, x ik is the kth element value of the ith model area, m is the number of predicted elements, and n is the number of model areas;

[0034] The following formula is used to determine the weight coefficient:

[0035]

[0036] Among them, a j is the weight coefficient of the jth factor;

[0037] The step of determining the metallogenic favorableness of the geological unit based on the prediction factors and the weight coefficients of the prediction factors includes: respectively calculating the sum of products between each prediction factor and its corresponding weight coefficient; and 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 favorableness of the geological unit, wherein the calculation formula is:

[0038]

[0039] Among them, f is the metallogenic favorable degree of each geological unit in the target working area, x1, x2, ..., x m is the predicted element value of each geological unit. The predicted element range covers all necessary geological elements, important geological elements and minor geological elements as well as the geophysical, geochemical and remote sensing abnormal characteristics reflecting the corresponding predicted elements; a1, a2, ..., a m is the weight coefficient of the prediction factor of each geological unit.

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

[0041] Obtain the cumulative proven resources of each mineral deposit within each model area;

[0042] Determine the cumulative proven resources of each model area based on the cumulative proven resources of all mineral deposits within each model area;

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

[0044] The volumetric ore content of each model area is determined based on the cumulative ascertained resources and the respective volumes of each model area.

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

[0046]

[0047] Among them, k i 模型区 is the volume mineralization rate of the i-th model area, Q i 模型区 is the cumulative proven resources of all mineral 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 proven resources of the g-th mineral deposit in the i-th model area, S i 模型区 is the area of ​​the ith model region, h i 模型区is the maximum exploration depth of the ith model area, and d is the number of mineral deposits in the ith model area.

[0048] Optionally, the step of determining the potential resource volume of each prospective ore area according to the metallogenic favorableness of the geological unit and the volume mineralization rate of the model area, as well as the volume of the prospective ore area in the geological unit, comprises:

[0049] Based on the volume mineralization rates and mineralization favorableness of all model areas, a weighted average of the volume mineralization rates of the model areas is determined and used as the comprehensive volume mineralization rate of the model areas, including:

[0050]

[0051] Among them, K 模型区 is the comprehensive volume mineralization rate after weighted average of the model area, n is the number of model areas, k i 模型区 is the volume mineralization rate of the i-th model area, f i 模型区 is the metallogenic favorableness of the i-th model area;

[0052] Determining the volume ore content of each prospecting area based on the comprehensive volume ore content of the model area and the metallogenic favorability of the prospecting area;

[0053] The potential resource volume of each prospecting area is determined based on the volume mineralization rate of each prospecting area and its corresponding volume.

[0054] An embodiment of the present invention further provides a device for estimating potential resource volume, comprising:

[0055] A construction unit, configured to construct a geoscience big data spatial database for a target work area, wherein the geoscience big data spatial database includes a plurality of different types of data;

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

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

[0058] A selection unit is used to select a model area from the geological unit, wherein the model area is a geological unit with a high degree of exploration and in which one or more mineral deposits have been discovered;

[0059] A processing unit is used to respectively determine the metallogenic favorableness of the geological unit and the volume mineral content of the model area; and to determine the potential resource amount of each prospective mineral area according to the metallogenic favorableness of the geological unit and the volume mineral content of the model area and the volume of the prospective mineral 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 adopting the potential resource estimation method provided by the embodiment of the present invention, by establishing a geological big data spatial database, it is possible to obtain various types of data for characterizing the mineral content of the target working area, and then determine the necessary geological elements that are indispensable in the formation process of the ore deposit, and the abnormal features corresponding to the necessary geological elements from the various types of data. By performing a superposition operation on the necessary geological elements and the abnormal features corresponding to the necessary geological elements, the formed geological unit has a clearer geological significance, and taking into account the necessary geological elements, the potential resource volume of the prospecting area predicted based on the geological unit has a higher estimation accuracy. In addition, by selecting a model area from the geological unit in advance, and based on the metallogenic favorable degree of the geological unit and the volume mineral content of the model area and the volume of the prospecting area, when determining the potential resource volume of each prospecting area, both the material basis and the enrichment in place conditions are taken into account, thereby effectively identifying the potential resource volume and improving the accuracy of the potential resource volume obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0063] Figure 1 A flow chart of a potential resource estimation method according to an embodiment of the present invention is shown;

[0064] Figure 2 A schematic diagram showing the principle of superposition operation in one embodiment of the present invention is shown;

[0065] Figure 3 A schematic diagram of area division of a target working area in one embodiment of the present invention is shown;

[0066] Figure 4 A flow chart for determining the metallogenic favorableness of a geological unit according to an embodiment of the present invention is shown;

[0067] Figure 5A flow chart for determining the volume ore content of a model area in one embodiment of the present invention is shown;

[0068] Figure 6 A schematic structural diagram of a potential resource estimation device in one embodiment of the present invention is shown. DETAILED DESCRIPTION

[0069] As mentioned in the background, the potential resource volume determined based on the geological volumetric method has certain errors. This is because:

[0070] When using the geological volume method to estimate the potential resource volume in prospecting areas, most people use a mathematical model similar to the following formula, namely: Q 潜在 =K 潜在 ×V 潜在 Among them, Q 潜在 Refers to the potential resources in the prospecting area, V 潜在 Refers to the volume of the prospecting area, K 潜在 It refers to the volume mineral content in the prospecting area.

[0071] In general, it is correct to use this formula to estimate the potential resource volume. However, the predecessors often simply used K when calculating the volume mineralization rate of prospecting areas. 潜在 =α×K 模型区 The volumetric mineralization rate of the prospective area is calculated using the formula α, where α refers to the similarity coefficient. However, this approach is not rigorous, as it often assumes that the most typical model area has the highest metallogenic favorableness within the target working area. In fact, this is not entirely true. It is entirely possible to find prospective areas within the working area with higher metallogenic favorableness than the most typical model area, as the most typical model area does not necessarily possess all the favorable metallogenic factors. The favorable metallogenic factors are the result of summarizing and inducing geological big data from multiple model areas, rather than summarizing the favorable metallogenic factors of the most typical model area.

[0072] In order to solve the above technical problems, an embodiment of the present invention provides a potential resource estimation solution. By establishing a geological big data spatial database, it is possible to obtain a variety of different types of data for characterizing the mineral content of the target working area, and then determine the necessary geological elements that are indispensable in the formation process of the ore deposit, and the abnormal features corresponding to the necessary geological elements from the various types of data. By performing a superposition operation on the necessary geological elements and the abnormal features corresponding to the necessary geological elements, the formed geological units have a clearer geological significance, and taking into account the necessary geological elements, the potential resource volume of the prospecting area predicted based on the geological units has a higher estimation accuracy. In addition, by selecting a model area from the geological unit in advance, and based on the metallogenic favorable degree of the geological unit and the volume mineral content of the model area and the volume of the prospecting area, when determining the potential resource volume of each prospecting area, both the material basis and the enrichment in place conditions are taken into account, thereby effectively identifying the potential resource volume and improving the accuracy of the potential resource volume obtained.

[0073] In order 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 estimation scheme in the embodiments of the present invention is described in detail below.

[0074] See also Figure 1 The flowchart of a potential resource estimation method in one embodiment of the present invention is shown in FIG. Figure 1 As shown, you can perform the following steps:

[0075] S10, constructing a geoscience big data spatial database of the target workspace, wherein the geoscience big data spatial database includes a variety of different types of data.

[0076] In this embodiment, based on typical mineral deposits and regional mineralization laws (for example, time, space, genesis, and mineral types, etc.), and combined with existing regional geological and mineral data, areas with favorable mineralization are selected as target working areas, which can increase the possibility of exploring potential resources.

[0077] In some examples, the selection criteria for the target working area may meet the following requirements: 1) the target working area is located within a known large-scale mineralization belt or mineral cluster; 2) there are known mineral deposits (e.g., magmatic copper-nickel sulfide deposits) within the target working area.

[0078] It should be noted that the selection criteria listed in the above examples are merely illustrative and are used to indicate the requirements to be met by the target workspace, and should not be construed as limiting the present invention.

[0079] In this embodiment, the multiple different types of data refer to parameters used to characterize different parameter information of the target working area. By obtaining the multiple different types of data, the morphology and geological parameters of the target working area can be more realistically reflected.

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

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

[0082] More specifically, geological data primarily relates to regional geological maps.

[0083] In some embodiments, regional geological maps are mainly stored as vector files, and some old data exist in the form of paper media and need to be vectorized first.

[0084] Geophysical data mainly involve regional gravity, magnetic and radioactivity measurement data obtained from geophysical scanning.

[0085] In some embodiments, geophysical data are mainly stored in table files or vector files, and some old data exist in paper media and need to be vectorized first.

[0086] The collection of geochemical data mainly involves the measurement data of regional stream sediments, rock chips and soil obtained from geochemical scanning. The media vary at different scales and in different geochemical landscapes.

[0087] In some embodiments, geochemical exploration data are mainly stored in table files or vector files, and some old data exist in paper media and need to be vectorized first.

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

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

[0090] It is particularly important to note that, in general, in order to facilitate similar analogies, the collection of regional geological, geophysical, geochemical and remote sensing data within the target work area should adhere to the principle of scale equivalence, that is, the scale of the data within the target work area needs to be consistent, and it is best to choose data with a larger scale.

[0091] Mineral deposit data mainly involves discovered mineral deposits within the target work area, including coordinates, cumulative proven resources, exploration depth and other information.

[0092] In some embodiments, mineral site data is stored primarily in vector or tabular files.

[0093] It should be noted that the multiple different types of data in the above examples are merely illustrative and are used to illustrate the acquisition of multiple data, and should not be construed as limiting the present invention. In some other embodiments, the data of the target working area may further include mask data.

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

[0095] To analyze data within a unified spatial coordinate system, the geographic coordinate system will be the CGCS2000 geographic coordinate system, currently used in my country, based on centroidal coordinates. For smaller areas, the projected coordinate system will use the Gauss-Kriging (3-degree or 6-degree zone) projection, while for larger areas, the Lambert projection will be used. Conversion of different spatial coordinate systems to a unified coordinate system can be performed within GIS software using three-parameter or seven-parameter projection transformations, ultimately establishing a unified spatial database for geoscience data.

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

[0097] S20, extracting necessary geological elements and abnormal characteristics reflecting the necessary geological elements from a variety of different types of data in the geoscience big data spatial database.

[0098] Among them, the selection of necessary geological elements is guided by the theory of metallogenic system and carried out with the type of ore deposit as the core, that is, the formation of ore deposits consists of three processes of "source-transport-storage". The selection of necessary geological elements must fully reflect these three necessary geological processes of ore deposit formation, in order to express the material basis and enrichment conditions required for mineralization.

[0099] More specifically, the necessary geological elements mainly refer to parameters such as mineral-bearing geological bodies (for example, ore-bearing strata or mineral-bearing rock bodies) and mineralization-controlling structures (folds, faults).

[0100] It should be noted that due to the complexity of mineral deposit formation, the necessary geological elements for different types of mineral deposits vary greatly, and specific issues require specific analysis.

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

[0102] For example, if the target deposit type is sedimentary bauxite among exogenous mineral resources, the necessary geological elements are the ore-bearing strata and ore-controlling synclines, as well as the geophysical, geochemical, and remote sensing anomaly characteristics that indirectly express these necessary geological elements. Among them, the ore-bearing strata are the material basis for mineralization, and the ore-controlling synclines provide the enrichment and emplacement conditions for the formation of the ore deposit.

[0103] It should be pointed out that these necessary geological elements and the abnormal 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: determining the necessary geological elements reflecting the target mineral deposit type under the guidance of the mineralization system theory; directly extracting the necessary geological elements actually discovered in the geological mapping process from the regional geological map on the geographic information system (GIS) platform; extracting the geophysical, geochemical and remote sensing anomaly characteristics reflecting the necessary geological elements from a variety of different types of data (for example, geophysical, geochemical and remote sensing data) on the geographic information system platform through preset methods (for example, 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 abnormal characteristics reflecting the necessary geological elements are determined based on geological parameters, the subsequently formed geological units have clear geological significance, which makes the estimation of potential resources more accurate.

[0106] S30: Based on the necessary geological elements and the abnormal characteristics reflecting the necessary geological elements, a superposition operation is performed to form a geological unit.

[0107] In this embodiment, the necessary geological elements and anomaly characteristics are selected from a large-scale spatial database of geoscience data and represent parameters that characterize the actual geological characteristics and morphology of the target work area from different aspects. In this way, when delineating geological units based on the necessary geological elements and performing an overlay operation, the delineated geological units are often irregular, constrained by the necessary elements, and have clear geological significance. They represent the current development of the metallogenic system in which the target deposit type exists and serve as the basic unit for prospecting prediction and evaluation.

[0108] In this embodiment, Figure 2 FIG. 1 is a schematic diagram showing the principle of superposition operation in one embodiment of the present invention, as shown in FIG. Figure 2 As shown, the overlay operation includes at least one of a merge operation, an intersection operation, or a subtraction operation. By performing overlay analysis on the parameters of the necessary geological elements (including the necessary geological elements themselves and the abnormal characteristics reflecting the necessary geological elements), geological units under various overlay 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; and by performing a subtraction operation on the geological element parameters, a third type of geological unit C3 can be obtained.

[0110] It should be pointed out that, first, in actual application, a single superposition method may be used, or a combination of several superposition methods may be used, depending on the complexity and correlation of the necessary geological elements; second, Figure 2 The geological units delineated after superposition analysis can be considered as the product of the development of the metallogenic system of the target deposit type, and are the basic units for prospecting prediction and evaluation; thirdly, Figure 2 There does not necessarily have to be overlapping areas between the necessary elements. Generally speaking, intersection and subtraction overlays require overlapping areas, while merging overlays do not necessarily require overlapping areas. Fourth, Figure 2 Only two of the necessary geological element parameters are illustrated. In actual applications, the number of necessary geological element parameters may be two or more. The present invention does not limit the specific number of necessary geological element parameters.

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

[0112] A superposition operation (ie, at least one of a merging operation, an intersecting operation, or a subtracting operation) is performed 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] A superposition operation (ie, at least one of a merging operation, an intersecting operation, or a subtracting operation) is performed on the areas corresponding to the second type of necessary geological elements and the second type of abnormal features to form the geological unit.

[0114] A superposition operation (i.e., at least one of a merging operation, an intersecting operation, or a subtracting operation) is performed on the areas corresponding to the third type of necessary geological elements and the third type of abnormal features to form a first superposition result; a superposition operation is performed on the areas corresponding to the fourth type of necessary geological elements and the fourth type of abnormal features to form a second superposition result (i.e., at least one of a merging operation, an intersecting operation, or a subtracting operation), and a superposition operation (i.e., at least one of a merging operation, an intersecting operation, or a subtracting operation) is performed on the areas corresponding to the first superposition result and the second superposition result to form the geological unit.

[0115] As a specific example, if the deposit type in the target working area is a magmatic copper-nickel sulfide deposit, the basic-ultramafic rock mass and the abnormal characteristics reflecting the basic-ultramafic rock mass can be merged to form a comprehensive basic-ultramafic rock mass, and the deep and large fault zone and the abnormal characteristics reflecting the deep and large fault zone can be merged to form a comprehensive deep and large fault zone, and then the comprehensive basic-ultramafic rock mass and the comprehensive deep and large fault zone can be intersected to form a geological unit.

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

[0117] S40, selecting a model area from the geological units, wherein the model area is a geological unit with a high degree of exploration and in which one or more mineral deposits have been discovered.

[0118] In this embodiment, when conducting prospecting prediction for geological units, a group of geological units with a high degree of exploration and in which one or more mineral deposits have been discovered can be selected from the geological units as model areas, so that the model areas can be used as a reference basis to estimate the potential resource volume of the prospecting area.

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

[0120] See also Figure 3 A schematic diagram of the area division of a target working area in one embodiment of the present invention is shown in FIG. Figure 3 As shown, the target working area includes the model area and the prospecting area.

[0121] In other words, the geological units delineated by the aforementioned overlay method of necessary geological elements are further divided into model areas and prospecting areas based on the degree of exploration and mineral deposits. The model area refers to the geological units with a high degree of exploration and mineral deposits, while the geological units outside the model area are prospecting areas and are also the subject of potential resource estimation.

[0122] It should be noted that the scale and number of existing mineral deposits in the model area may vary. For example, Figure 3 Of the two model areas shown, one model area has two mineral deposits of different sizes, while the other model area has only one mineral deposit.

[0123] S50, respectively determining the metallogenic favorableness of the geological unit and the volume mineralization rate of the model area.

[0124] Among them, the metallogenic favorableness of the geological unit (including the model area and the prospecting area) is an important factor affecting the volume mineralization rate of the prospecting area, which directly determines the potential resource volume in the prospecting area.

[0125] More specifically, mineralization favorability refers to a comprehensive indicator of the possibility of mineral formation and enrichment within the geological unit of the target working area.

[0126] The volume mineralization rate of the model area is used to indicate the mineral content per unit volume, which reflects the distribution density of minerals in the model area. The larger the volume mineralization rate, the greater the distribution density of the minerals; conversely, the smaller the distribution density of the minerals.

[0127] In this embodiment, see Figure 4 The flowchart for determining the metallogenic favorableness of a geological unit in one embodiment of the present invention is shown in FIG. Figure 4 As shown, you can perform the following steps:

[0128] A1, determining a plurality of prediction elements of the geological unit.

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

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

[0131] A2: determining a matching coefficient between any two prediction elements according to the number of the model areas in the geological unit and a plurality of the prediction elements.

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

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

[0134] In a specific embodiment, assuming that there are n model areas and m prediction elements in the target work area, the matching coefficient between each prediction element and other elements can be determined. For example, the matching coefficient between the j-th prediction element and the k-th prediction element is calculated as follows:

[0135]

[0136] Among them, r jk is the matching coefficient between the j-th prediction factor and the k-th prediction factor, x ij is the jth element value of the i-th model area, x jk is the kth element value of the ith model area, m is the number of predicted elements, and n is the number of model areas.

[0137] It should be noted that a binary classification method can be used to determine the value of the prediction factor. For example, if the prediction factor exists, the value is 1, and if the prediction factor does not exist, the value is 0.

[0138] Specifically, whether the predicted elements exist can be directly read on the GIS platform.

[0139] A3, determining a weight coefficient of any prediction factor based on any prediction factor and a matching coefficient between the prediction factor and other prediction factors.

[0140] In this embodiment, different model areas contain different prediction elements, so that different prediction elements have different weight coefficients, and the weight coefficients determine the importance of the prediction elements.

[0141] In this embodiment, the square sum method can be used to calculate the weight coefficient of the prediction factor, wherein the idea of ​​the square sum method is that the stronger the correlation between the prediction factor and other prediction factors, the more important the prediction factor is and the greater the impact on mineralization, which is consistent with the characteristics of geological mineralization.

[0142] For example, for tectonic alteration rock type gold deposits, fault structure is a very important predictive factor because it controls the migration and placement of ore-bearing hydrothermal fluids, affects hydrothermal alteration and the spatial distribution of ore bodies, and is closely related to various mineralization control factors, and its corresponding weight coefficient is also very large.

[0143] In a specific embodiment, the weight coefficient is determined using the following formula:

[0144]

[0145] Among them, a j is the weight coefficient of the jth factor.

[0146] A4. Determine the metallogenic favorableness of the geological unit based on the prediction factors and the weight coefficients of the prediction factors.

[0147] In this embodiment, the prediction factor represents a factor that is favorable to mineralization. When it exists, it is favorable to mineralization, and when it does not exist, it is unfavorable to mineralization. The weight coefficient of the prediction factor represents the importance of the prediction factor in the geological unit. Therefore, based on the above two parameters, the accuracy of the obtained mineralization favorableness can be improved.

[0148] In this embodiment, step A4 may include: A41, respectively calculating the sum of products of each prediction factor and its corresponding weight coefficient; and A42, using the ratio of the sum of products of each prediction factor to the sum of weight coefficients of each prediction factor as the metallogenic favorableness of the geological unit.

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

[0150]

[0151] Among them, f is the metallogenic favorable degree of each geological unit in the target working area, x1, x2, ..., x m is the predicted element value of each geological unit. The predicted element range can cover all necessary geological elements, important geological elements and minor geological elements as well as the geophysical, geochemical and remote sensing abnormal characteristics reflecting the corresponding predicted elements; a1, a2, ..., a m is the weight coefficient of the prediction factor of each geological unit.

[0152] It should be noted that the above example only lists one method for determining metallogenic favorableness. In actual operation, if other methods are used, the metallogenic favorableness can be determined based on the principles and formulas of the method. For example, the weight of evidence method, the information content method, logistic regression, etc.

[0153] In this embodiment, see Figure 5 The flowchart for determining the volume ore content of the model area in one embodiment of the present invention is shown in FIG. Figure 5 As shown, you can perform the following steps:

[0154] B1, obtain the cumulative proven resources of each mineral deposit in each model area.

[0155] In this embodiment, the same model area may contain multiple mineral deposits, each of which is different. Therefore, the cumulative proven resources of each mineral deposit in the model area can be obtained separately.

[0156] B2. Determine the cumulative proven resources of each model area based on the cumulative proven resources of all mineral deposits within each model area.

[0157] In this embodiment, for the same model area, the cumulative ascertained resources of all mineral deposits may be accumulated to determine the cumulative ascertained resources of each model area.

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

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

[0160] B4. Determine the volumetric ore content of each model area based on the cumulative identified resources and the respective volumes of each model area.

[0161] In this embodiment, the cumulative ascertained resources of the model area represent the mineral resource content within the model area, and the volume of the model area represents the size of the model area. Based on these two parameters, the mineral production per unit volume can be determined.

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

[0163]

[0164] Among them, k i 模型区 is the volume mineralization rate of the i-th model area, Q i 模型区 is the cumulative proven resources of all mineral 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 proven resources of the g-th mineral deposit in the i-th model area, S i 模型区 is the area of ​​the ith model region, hi 模型区 is the maximum exploration depth of the ith model area, and d is the number of mineral deposits in the ith model area.

[0165] Among them, the cumulative proven resources of mineral 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] Therefore, by adopting the above scheme, the mineralization favorableness of all geological units in the target working area and the volume mineralization rate of each model area can be determined respectively, and then the potential resource volume of each prospecting prospect area in the target working area can be determined based on the above two parameters.

[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 mineralization conditions. Second, the prospecting prospect area may contain all mineralization geological elements or only a part of the mineralization geological elements.

[0168] That is, step S60, based on the metallogenic favorableness of the geological unit and the volume mineralization rate of the model area, as well as the volume of the prospective mineralization area in the geological unit, the potential resource volume of each prospective mineralization area is determined.

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

[0170] S61, determining a weighted average of the volume mineralization rates of all model areas according to the volume mineralization rates and mineralization favorableness of all model areas, and taking the weighted average of the volume mineralization rates of the model areas as the comprehensive volume mineralization rate of the model areas.

[0171] In a specific embodiment, the comprehensive volume mineralization rate of the model area can be determined in the following manner:

[0172]

[0173] Among them, K 模型区 is the comprehensive volume mineralization rate after weighted average of the model area, n is the number of model areas, k i 模型区 is the volume mineralization rate of the i-th model area, f i 模型区 is the metallogenic favorableness of the i-th model area.

[0174] S62, determining the volume ore content of each prospective ore area according to the comprehensive volume ore content of the model area and the metallogenic favorability of the prospective ore area.

[0175] The metallogenic favorableness of a prospective area refers to the metallogenic favorableness of the geological unit that serves as the prospective area. In other words, this scheme uses the metallogenic favorableness of the prospective area and the comprehensive volume mineralization of the model area to determine the volume mineralization of the prospective area.

[0176] In a specific embodiment, the volume ore content of the prospecting area can be determined in the following manner:

[0177] K 潜在 =f*K 模型区

[0178] Where f is the metallogenic favorableness of the prospecting area.

[0179] S63, determining the potential resource volume of each prospective prospective area based on the volumetric ore content of each prospective prospective area and the corresponding volume of each prospective prospective area.

[0180] In this embodiment, the product of the volume mineralization rate and the volume of the prospective mineral area can be used as the potential resource volume of the prospective mineral area.

[0181] In a specific embodiment, the potential resource volume Q of the prospecting area can be determined in the following manner: 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 ​​prospecting area, h 潜在 The depth of the prospecting area.

[0184] It is important to note that, first, the depth of the prospective area can generally be taken as the maximum exploration depth of the model area, or, based on current economic and technical conditions, the maximum mineable depth of the target working area. If the maximum exploration depth of the model area is taken, it is considered to represent the maximum mineable depth under current economic and technical conditions. Second, since a geological unit itself is composed of the model area and the prospective area, the metallogenic favorability of the model area and the prospective area can be considered the metallogenic favorability of the geological unit itself. Therefore, using the potential resource estimation method in the above example, when using geological units as the basic units of prediction, the necessary elements superposition method is used to delineate the geological units, which makes the geological units more geologically meaningful. Based on the volumetric mineralization and metallogenic favorability of all model areas, a weighted average of the volumetric mineralization of all model areas is calculated. This resulting comprehensive volumetric mineralization may select prospective areas with higher volumetric mineralization than the model area. This method is not only more easily accepted by geologists than traditional geological volumetric methods, but also facilitates the discovery of larger-scale deposits. Moreover, this plan closely revolves around the material basis (geological volume) and enrichment in situ conditions (volume mineralization rate) to improve the estimation method of potential resources, better depicts the basic elements of mineral deposit formation, and is a potential resource estimation method with a solid mineralization theoretical foundation.

[0185] The embodiment of the present invention also provides a device corresponding to the potential resource estimation method, such as Figure 6 The schematic diagram of the structure of a potential resource estimation device in one embodiment of the present invention is shown in FIG. Figure 6 As shown, a potential resource estimate 100 may include:

[0186] A construction unit 110 is used to construct a geoscience big data spatial database of a target work area, wherein the geoscience big data spatial database includes a plurality of different types of data;

[0187] The extraction unit 120 is used to extract necessary geological elements and abnormal characteristics reflecting the necessary geological elements from a variety of different types of data in the geoscience big data spatial database;

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

[0189] A selection unit 140 is configured to select a model area from the geological units, wherein the model area is a geological unit with a high degree of exploration and in which one or more mineral deposits have been discovered;

[0190] The processing unit 150 is used to respectively determine the metallogenic favorableness of the geological unit and the volume mineral content of the model area; and to determine the potential resource amount of each prospective mineral area according to the metallogenic favorableness of the geological unit and the volume mineral content of the model area and the volume of the prospective mineral 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 be found in the relevant description of the above examples.

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

[0193] It should be noted that “one embodiment” or “embodiment” referred to in the present invention refers to a specific feature, structure or characteristic that may be included in at least one implementation method of the present invention. And in the description of the present invention, terms such as “first” and “second” are used only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, features defined by terms such as “first” and “second” may explicitly or implicitly include one or more of the features. Moreover, terms such as “first” and “second” are used to distinguish similar objects, and are not necessarily used to describe a specific order or to express importance. It is understood that the terms used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than that illustrated or described.

[0194] Although the embodiments of the present invention are disclosed 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 scope of protection of the present invention should be based on the scope defined by the claims.

Claims

1. A method for estimating potential resource volume, characterized in that: include: Constructing a geoscience big data spatial database for a target work area, wherein the geoscience big data spatial database includes a variety of different types of data; Extracting necessary geological elements and abnormal characteristics reflecting the necessary geological elements from various types of data in the geoscience big data spatial database; Based on the necessary geological elements and the abnormal characteristics reflecting the necessary geological elements, performing a superposition operation to form a geological unit; Selecting a model area from the geological unit, wherein the model area is a geological unit with a high degree of exploration and in which one or more mineral deposits have been discovered; The metallogenic favorable degree of the geological unit and the volume mineralization rate of the model area are determined respectively; the step of determining the metallogenic favorable degree of the geological unit comprises: determining a plurality of prediction elements of the geological unit; determining a matching coefficient between any two prediction elements according to the number of the model areas in the geological unit and the plurality of prediction elements; determining a weight coefficient of any prediction element according to any prediction element and the matching coefficient between the prediction element and other prediction elements; determining the metallogenic favorable degree of the geological unit according to the prediction element and the weight coefficient of the prediction element; The matching coefficient is determined using the following formula: Among them, r jk is the matching coefficient between the j-th prediction factor and the k-th prediction factor, x ij is the jth element value of the i-th model area, x ik is the kth element value of the ith model area, m is the number of predicted elements, and n is the number of model areas; The following formula is used to determine the weight coefficient: Among them, a j is the weight coefficient of the jth factor; The step of determining the metallogenic favorableness of the geological unit based on the prediction factors and the weight coefficients of the prediction factors includes: respectively calculating the sum of products between each prediction factor and its corresponding weight coefficient; and 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 favorableness of the geological unit, wherein the calculation formula is: Among them, f is the metallogenic favorable degree of each geological unit in the target working area, x1, x2, ..., x m is the predicted element value of each geological unit. The predicted element range covers all necessary geological elements, important geological elements and minor geological elements as well as the geophysical, geochemical and remote sensing abnormal characteristics reflecting the corresponding predicted elements; a1, a2, ..., a m is the weight coefficient of the prediction factor of each geological unit; The potential resource volume of each prospecting area is determined based on the metallogenic favorableness of the geological unit and the volume mineralization rate of the model area, as well as the volume of the prospecting area in the geological unit.

2. The method for estimating potential resources according to claim 1, characterized in that: The steps of constructing a geoscience big data spatial database of the target workspace include: Acquire multi-source, heterogeneous, massive geoscience big data in the target work area; Various types of data are converted into spatial coordinates to unify the coordinate systems of all types of data and generate the geoscience big data spatial database.

3. The method for estimating potential resources according to claim 1, characterized in that: The steps of extracting necessary geological elements from a plurality of different types of data in the geoscience big data spatial database and reflecting abnormal characteristics of the necessary geological elements include: Guided by the theory of metallogenic systems, determine the necessary geological elements that reflect the target deposit type; On the GIS platform, the necessary geological elements actually discovered during geological mapping can be directly extracted from the regional geological map; On the geographic information system platform, geophysical, geochemical and remote sensing anomaly features reflecting necessary geological elements are extracted from a variety of different types of data through preset methods.

4. The method for estimating potential resources according to claim 1, characterized in that: The superposition operation is performed based on the necessary geological elements and reflects the abnormal characteristics of the necessary geological elements to form geological units, which meet at least one or more of the following requirements: Overlaying the areas corresponding to the first type of necessary geological elements and the first type of abnormal features to form the geological unit; Overlaying the second type of necessary geological elements and the areas corresponding to the second type of abnormal features to form the geological unit; Overlaying the areas corresponding to the third type of necessary geological elements and the third type of abnormal features to form a first overlay result, overlaying the areas corresponding to the fourth type of necessary geological elements and the fourth type of abnormal features to form a second overlay result, and overlaying the areas corresponding to the first overlay result and the second overlay result to form the geological unit; The superposition operation includes at least one of a merge operation, an intersection operation or a subtraction operation.

5. The method for estimating potential resources according to claim 1, characterized in that: The step of determining the volume ore content of each model area comprises: Obtain the cumulative proven resources of each mineral deposit within each model area; Determine the cumulative proven resources of each model area based on the cumulative proven resources of all mineral deposits within each model area; Determining the volume of each model area according to the area of ​​each model area and the corresponding maximum exploration depth; The volumetric ore content of each model area is determined based on the cumulative ascertained resources and the respective volumes of each model area.

6. The method for estimating potential resources according to claim 5, characterized in that: The formula for determining the volume ore content of each model area is: Among them, k i 模型区 is the volume mineralization rate of the i-th model area, Q i 模型区 is the cumulative proven resources of all mineral 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 proven resources of the g-th mineral deposit in the i-th model area, S i 模型区 is the area of ​​the ith model region, h i 模型区 is the maximum exploration depth of the ith model area, and d is the number of mineral deposits in the ith model area.

7. The method for estimating potential resources according to claim 1, characterized in that: The step of determining the potential resource volume of each prospective ore area according to the metallogenic favorableness of the geological unit and the volume mineralization rate of the model area, as well as the volume of the prospective ore area in the geological unit, comprises: Based on the volume mineralization rates and mineralization favorableness of all model areas, a weighted average of the volume mineralization rates of the model areas is determined and used as the comprehensive volume mineralization rate of the model areas, including: Among them, K 模型区 is the comprehensive volume mineralization rate after weighted average of the model area, n is the number of model areas, k i 模型区 is the volume mineralization rate of the i-th model area, f i 模型区 is the metallogenic favorableness of the i-th model area; Determining the volume ore content of each prospecting area based on the comprehensive volume ore content of the model area and the metallogenic favorability of the prospecting area; The potential resource volume of each prospecting area is determined based on the volume mineralization rate of each prospecting area and its corresponding volume.

8. A device for estimating potential resource volume, characterized in that: include: A construction unit, configured to construct a geoscience big data spatial database for a target work area, wherein the geoscience big data spatial database includes a plurality of different types of data; An extraction unit, configured to extract necessary geological elements and abnormal characteristics reflecting the necessary geological elements from a plurality of different types of data in the geoscience big data spatial database; A generating unit, configured to perform a superposition operation based on the necessary geological elements and the abnormal characteristics reflecting the necessary geological elements to form geological units; A selection unit is used to select a model area from the geological unit, wherein the model area is a geological unit with a high degree of exploration and in which one or more mineral deposits have been discovered; a processing unit for respectively determining the metallogenic favorableness of the geological unit and the volume mineralization rate of the model area; and determining the potential resource amount of each prospective mineralization area based on the metallogenic favorableness of the geological unit, the volume mineralization rate of the model area, and the volume of the prospective mineralization area in the geological unit; The step of determining the metallogenic favorableness of the geological unit includes: determining a plurality of prediction elements of the geological unit; determining a matching coefficient between any two prediction elements based on the number of the model areas in the geological unit and the plurality of prediction elements; determining a weight coefficient of any prediction element based on any prediction element and the matching coefficient between the prediction element and other prediction elements; and determining the metallogenic favorableness of the geological unit based on the prediction element and the weight coefficient of the prediction element; The matching coefficient is determined using the following formula: Among them, r jk is the matching coefficient between the j-th prediction factor and the k-th prediction factor, x ij is the jth element value of the i-th model area, x ik is the kth element value of the ith model area, m is the number of predicted elements, and n is the number of model areas; The following formula is used to determine the weight coefficient: Among them, a j is the weight coefficient of the jth factor; The step of determining the metallogenic favorableness of the geological unit based on the prediction factors and the weight coefficients of the prediction factors includes: respectively calculating the sum of products between each prediction factor and its corresponding weight coefficient; and 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 favorableness of the geological unit, wherein the calculation formula is: Among them, f is the metallogenic favorable degree of each geological unit in the target working area, x1, x2, ..., x m The predicted element values ​​for each geological unit include all necessary geological elements, important geological elements, and minor geological elements, as well as geophysical, geochemical, and remote sensing anomaly characteristics that reflect the corresponding predicted elements. a1, a2, ..., a m is the weight coefficient of the prediction factor of each geological unit.

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