Prediction method and prediction device for favorable area of mineral resources

Abstract

The invention provides a prediction method and a prediction device for a favorable region of mineral resources, and the method comprises the steps: determining metallogenic modes and prospecting marks corresponding to different types of ore deposits according to the geographic information data of a research region in combination with expert knowledge, and constructing a prospecting prediction model; according to a target ore deposit type in the target area, screening out a plurality of target prospecting prediction elements to form a target prospecting prediction model; according to the geographic information data of the target area, determining an evidence factor corresponding to each target prospecting prediction element in the target area and a positive weight of each evidence factor; performing nesting on the evidence factors corresponding to each target prospecting prediction element in the target area, generating an evidence factor distribution map of the target area, and dividing the evidence factor distribution map into a plurality of prediction grid units; and for each prediction grid unit, according to the evidence factor distribution map and the positive weight of each evidence factor, determining a mineralization probability value of the prediction grid unit and delineating a mineral resource favorable area of the target mineral deposit type. Therefore, the accuracy and the prediction efficiency of the prediction result can be improved by fully combining geological knowledge.

Description

Technical Field

[0001] The present application relates to the field of geological prospecting technology, and in particular to a prediction method and a prediction device for a favorable mineral resource area. Background Art

[0002] Mineral resource favorable areas refer to areas with high mineral resource development potential and value under the comprehensive effect of geological, geographical, economic and other factors. Mineral resources are closely related to people's lives, are an important material basis for the development of human society, and play an indispensable and important role in the sustainable development of the national economy. Therefore, predicting mineral resource favorable areas is of great significance to maintaining the security of my country's mineral resources.

[0003] However, existing prospecting methods are often simple probabilistic mathematical methods that do not fully integrate geological knowledge. They often lead to unsatisfactory prediction results due to low levels of geological work, uneven levels of work, and few known mineral points. Summary of the invention

[0004] In view of this, the purpose of this application is to provide a prediction method and prediction device for favorable mineral resource areas, establish a prospecting prediction model based on expert knowledge and geographic information data, and determine the evidence factors required for prediction and the positive weight of each evidence factor according to the prospecting prediction model, and then calculate the metallogenic probability value of each prediction grid unit to predict favorable mineral resource areas. In this way, by fully combining geological knowledge to predict favorable mineral resource areas, the accuracy and efficiency of the prediction results can be improved.

[0005] The present application embodiment provides a method for predicting a favorable mineral resource area, the prediction method comprising:

[0006] According to the geographic information data of the study area, combined with expert knowledge, the metallogenic patterns and prospecting signs corresponding to different types of ore deposits are determined, and a prospecting prediction model is constructed; wherein the prospecting prediction model includes a variety of prospecting prediction elements corresponding to each type of ore deposit;

[0007] According to the target ore deposit type in the target area, a plurality of target ore prospecting prediction elements are selected from the ore prospecting prediction model, and a target ore prospecting prediction model is formed;

[0008] Determine, based on the geographic information data of the target area, the evidence factor corresponding to each target prospecting prediction element in the target prospecting prediction model in the target area and the positive weight of each evidence factor; wherein the evidence factor corresponding to each target prospecting prediction element is used to characterize whether the target area meets the characteristic description of the target prospecting prediction element;

[0009] Overlaying the evidence factors corresponding to each prospecting prediction element in the target area to generate an evidence factor distribution map of the target area, and dividing the evidence factor distribution map into a plurality of prediction grid units; wherein each evidence factor is represented as a layer in the evidence factor distribution map;

[0010] For each prediction grid unit, the metallogenic probability value of the prediction grid unit is determined according to the evidence factor distribution map and the positive weight of each evidence factor;

[0011] By integrating the mineralization probability value of each predicted grid unit, the favorable mineral resource area of ​​the target deposit type in the target area is delineated.

[0012] The present application also provides a device for predicting a favorable mineral resource area, the device comprising:

[0013] A model building module is used to determine the metallogenic patterns and prospecting signs corresponding to different types of ore deposits based on the geographic information data of the study area and in combination with expert knowledge, and to build a prospecting prediction model; wherein the prospecting prediction model includes a variety of prospecting prediction elements corresponding to each type of ore deposit;

[0014] A screening module is used to screen out a plurality of target mineral prospecting prediction elements from the mineral prospecting prediction model according to the target mineral deposit type in the target area, and form a target mineral prospecting prediction model;

[0015] The first determination module is used to determine the evidence factor corresponding to each target mineral prospecting prediction element in the target mineral prospecting prediction model in the target area and the positive weight of each evidence factor according to the geographic information data of the target area; wherein the evidence factor corresponding to each target mineral prospecting prediction element is used to characterize whether the target area meets the characteristic description of the target mineral prospecting prediction element;

[0016] A generation module is used to fit the evidence factors corresponding to each prospecting prediction element in the target area, generate an evidence factor distribution map of the target area, and divide the evidence factor distribution map into a plurality of prediction grid units; wherein each evidence factor is represented as a layer in the evidence factor distribution map;

[0017] A second determination module is used to determine the metallogenic probability value of each prediction grid unit according to the evidence factor distribution map and the positive weight of each evidence factor;

[0018] The delineation module is used to integrate the mineralization probability value of each predicted grid unit and delineate the favorable mineral resource area of ​​the target deposit type in the target area.

[0019] An embodiment of the present application also provides an electronic device, including: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps of the method for predicting favorable areas of mineral resources as described above are performed.

[0020] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for predicting favorable mineral resource areas as described above are executed.

[0021] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 A flow chart showing a method for predicting a favorable mineral resource area provided in an embodiment of the present application is shown;

[0024] Figure 2 A mineralization probability color block diagram provided in an embodiment of the present application is shown;

[0025] Figure 3 A schematic diagram showing a favorable mineral resource area provided in an embodiment of the present application;

[0026] Figure 4 A schematic diagram showing the structure of a device for predicting favorable mineral resource areas provided in an embodiment of the present application is shown;

[0027] Figure 5 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0028] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application usually described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, each other embodiment obtained by those skilled in the art without making creative work belongs to the scope of protection of the present application.

[0029] Research has found that favorable mineral resource areas refer to areas with high potential and value for mineral resource development under the combined effects of geological, geographical, economic and other factors. Mineral resources are closely related to people's lives, are an important material basis for the development of human society, and play an indispensable and important role in the sustainable development of the national economy. Therefore, predicting favorable mineral resource areas is of great significance to maintaining the security of my country's mineral resources.

[0030] However, existing prospecting methods are often simple probabilistic mathematical methods that do not fully integrate geological knowledge. They often lead to unsatisfactory prediction results due to low levels of geological work, uneven levels of work, and few known mineral points.

[0031] Based on this, an embodiment of the present application provides a method for predicting favorable areas of mineral resources to improve the accuracy and efficiency of prediction results.

[0032] See also Figure 1 , Figure 1 This is a flow chart of a method for predicting a favorable mineral resource area provided in an embodiment of the present application. Figure 1 As shown in, the prediction method provided in the embodiment of the present application includes:

[0033] S101. Based on the geographic information data of the study area and combined with expert knowledge, the mineralization patterns and prospecting signs corresponding to different types of ore deposits are determined to obtain a prospecting prediction model.

[0034] Among them, the prospecting prediction model includes a variety of prospecting prediction elements corresponding to each type of ore deposit, and also includes a characteristic description corresponding to each prospecting prediction element. The characteristic description refers to the geological characteristics that the prospecting prediction elements should meet if a certain type of ore deposit exists. Exemplarily, the prospecting prediction elements may include at least one of the following: tectonic position, mineralization zone, mineralization age, ore-bearing formation, mineralization structure, magmatic activity, ore body characteristics, ore mineral composition, ore structure, ore structure, ore-controlling conditions, alteration, reserves, ore deposit scale, geochemical anomalies, aeromagnetic anomalies, gravity anomalies, etc.

[0035] In a specific implementation, step S101 may include:

[0036] S1011. According to the geographic information data of the study area, the amount of prospecting information corresponding to each prospecting prediction element in the preset prospecting prediction element library is determined by the following formula:

[0037]

[0038] In the formula, A is the element of prospecting prediction j The amount of information indicating the existence of type B deposits; N is the total number of ore-bearing units in the study area; N j Type B ore deposits and prospecting prediction factors A in the study area j The number of prospecting units that appear at the same time; S is the total number of prospecting units in the study area; S j A is the mineral prospecting prediction factor in the study area. j The number of prospecting units that appear.

[0039] In this step, a prospecting prediction factor library is pre-set, a research area is selected and divided into multiple prospecting units, and the above parameter S is obtained. For any type of ore deposit (such as type B ore deposit), any prospecting prediction factor A is determined based on the geographic information data of the research area. j The corresponding parameter N j , N and S h , and calculate the prospecting information through the above formula Among them, the amount of prospecting information is used to measure the prospecting prediction factor A j Its indicative significance for the search for B-type mineral deposits.

[0040] S1012. Filter out a plurality of candidate mineral prospecting prediction elements from the preset mineral prospecting prediction element library according to the mineral prospecting information amount corresponding to each mineral prospecting prediction element and a preset information amount threshold.

[0041] For example, the prospecting prediction elements with prospecting information greater than the preset information threshold of 0.1 can be selected as candidate prospecting elements. In this way, the information can provide an objective data basis for the construction of the prospecting prediction model.

[0042] S1013. Determine the mineralization patterns and prospecting indicators corresponding to different types of ore deposits in combination with expert knowledge, and match the mineralization patterns and prospecting indicators corresponding to different types of ore deposits with the multiple candidate prospecting prediction factors.

[0043] S1014, selecting candidate prospecting prediction elements matched to each ore deposit type to construct the prospecting prediction model.

[0044] For the above steps S1013 and 1014, expert knowledge in the field of geological exploration is obtained as prior knowledge, the mineralization patterns and prospecting signs corresponding to different types of ore deposits are determined in advance, and matched with candidate prospecting prediction elements, and a prospecting prediction model is constructed by successfully matching the candidate prospecting prediction elements corresponding to each type of ore deposit.

[0045] Alternatively, expert knowledge in the field of geological exploration may be obtained as prior knowledge to predetermine the metallogenic patterns and prospecting signs corresponding to different types of ore deposits, and form a preset prospecting prediction factor library. Afterwards, the prospecting information amount corresponding to each prospecting prediction factor in the preset prospecting prediction factor library is determined by the method in S1011, and then the prospecting prediction factors whose prospecting information amount is greater than the preset information amount threshold are screened out to form a prospecting prediction model.

[0046] S102. According to the target ore deposit type in the target area, a plurality of target ore prospecting prediction elements are selected from the ore prospecting prediction model, and a target ore prospecting prediction model is formed.

[0047] In one example, the target area is the Hadamengou area in Inner Mongolia, and the target deposit type is gold. Geological knowledge shows that the ore-bearing strata of the Hadamengou gold deposit are the Archean Wulashan Group, and the first and second lithological sections of the first rock group and the first lithological section of the second rock group have obvious mineralization specificity. The ore veins discovered so far in the Hadamengou mining section are mainly located in the first lithological section of the first rock group (Ar2wl1-1) and the second lithological section of the first rock group (Ar2wl1-2), and the veins 313 and 314 of the Liubagou mining section are located in the first lithological section of the second rock group (Ar2wl2-1). And the ore veins are mostly developed in the garnet-bearing biotite plagioclase gneiss in these lithological sections. Therefore, the garnet-bearing biotite plagioclase gneiss in the first and second lithological sections of the first rock group and the first lithological section of the second rock group in the metamorphic rocks of the Wulashan Group is one of the important stratum signs for prospecting in the ore field.

[0048] The ore bodies in the mining area are strictly controlled by near EW-trending faults. The near EW-trending brittle fractures superimposed on the early ductile deformation often constitute the roof or floor of the ore body and are one of the most important prospecting signs in the mining area.

[0049] The vein rock development area is accompanied by the mineralization development, especially the dense area of ​​diabase porphyry veins and granite pegmatite veins is favorable for mineralization and is the main location of gold ore bodies in the ore field. Therefore, the densely developed area of ​​granite pegmatite veins is also an important prospecting mark.

[0050] Regional aeromagnetic and gravity data show that the Wulashan Group usually has a higher density (2.74-2.76g / cm3) and higher magnetism (2×104-5×104CGSM), while granite shows low density (2.60g / cm3) and weak magnetism. Gold mineralization is mostly related to potassium feldsparization and silicification, which makes the gold veins weakly magnetic relative to the surrounding rocks, non-magnetic and low-density characteristics, and generally causes "negative" anomalies on the surface. In addition, potassium feldsparization and silicification in gold mines usually make gold veins have higher resistivity, and polymetallic sulfide mineralization makes gold veins have higher polarizability and amplitude frequency. Therefore, the ore body usually shows high resistivity anomalies, high polarizability and high amplitude frequency anomalies on the surface. Therefore, the combination of low gravity, low magnetism and high resistivity, high polarizability and high amplitude frequency geophysical anomalies is also an important prospecting mark in the mining area.

[0051] In the Hadamengou mining area, Au, Pb, W, and Mn show strong positive anomalies near the surface ore bodies; As and Sb appear higher on both sides of the ore bodies and relatively lower in the ore body; Cu, Co, Ni, and Cr migrate outward and show negative anomalies. The combination of three different types of elemental geochemical anomalies constitutes an important geochemical prospecting mark on the surface of the mining area.

[0052] Based on this, a variety of target prospecting prediction elements can be screened out from the prospecting prediction model, and the target prospecting prediction element model of Hadamengou gold mine is formed as shown in Table 1 below.

[0053] Table 1 Target prospecting prediction factor model of Hadamengou gold mine

[0054]

[0055]

[0056]

[0057] As shown in Table 1 above, the target prospecting prediction factor model includes the name and characteristic description of each target prospecting prediction factor, and classifies each factor into three categories according to its importance: necessary, important, and secondary.

[0058] S103. Determine, based on the geographic information data of the target area, the evidence factor corresponding to each prospecting prediction element in the target area and the positive weight of each evidence factor.

[0059] In this step, the corresponding evidence factors can be collected from the geographic information data of the target area according to each prospecting prediction element in the target area. The evidence factor is the specific data corresponding to the prospecting prediction element, specifically referring to whether the target area meets the characteristic description of the target prospecting prediction element, such as 1 for compliance (i.e., the evidence factor exists), and 0 for non-compliance (i.e., the evidence factor does not exist). In the specific implementation, the geological, geophysical, geochemical, remote sensing, and ore deposit (point) data in the study area can be obtained as evidence factors. The geological data mainly includes the geological map of the working area. Each element in the geological map (such as the underlying structure) needs to have relevant attributes to facilitate the extraction of each element as an evidence factor; the geophysical data mainly includes the scanning data of the working area such as gravity and magnetic method, as well as various geological elements such as faults and concealed rock bodies interpreted through the development of geophysical data; the geochemical data mainly includes the data of various elements related to gold such as area-based water-washed sediments and soil measurements; the remote sensing data includes the extracted alteration information such as hydroxyl and iron staining, as well as the fault structure and annular structure interpreted by remote sensing.

[0060] The importance of each prospecting prediction factor is determined in combination with the actual geographic information data of the target area, and the positive weight of the corresponding evidence factor is assigned according to the importance. The positive weight of the corresponding evidence factor can also be determined by combining the prior classification of the importance of each prospecting prediction factor in the prospecting prediction model and the actual geographic information data of the target area.

[0061] More specifically, the weighting rules for each evidence factor are as follows: Generally, the absolute value of the positive and negative weights of the evidence factors is between 0 and 0.5, which is generally indicative; between 0.5 and 1, which is moderately indicative; between 1 and 2, which is strongly indicative; and when it is >2, which is extremely indicative. According to this principle, when applying the evidence weight method, the embodiments of this application make the following agreements when assigning positive weight values ​​to the evidence factors: the evidence factor with the best prospecting indicative value is set to have a positive weight of about 2.5; the evidence factor with better indicative value is set to have a positive weight of about 1.5; the evidence factor with average indicative value is set to have a positive weight of about 0.75; the evidence factor with poor indicative value is set to have a positive weight of about 0.25.

[0062] Furthermore, the formula of S1011 above can be referred to to determine the amount of prospecting information of each target prospecting prediction factor in the target area, and the positive weight of the evidence factor corresponding to each prospecting prediction factor is given; and the amount of prospecting information is positively correlated with the positive weight. Exemplarily, according to the numerical value of the prospecting information and the positive weight range, the positive weight of the evidence factor corresponding to the prospecting information is determined through a pre-calibrated mapping relationship.

[0063] Corresponding to the above example, based on the study of typical ore deposits in Hadamengou, the weight values ​​of various evidence factors in the prediction area of ​​Hadamengou are comprehensively determined:

[0064] (1) The surface distribution of the ore body is the most direct evidence, and its positive weight value is determined to be 2.5.

[0065] (2) Archean Wulashan Group. The Hadamengou gold deposit has stratigraphic specificity, and the positive weight is determined to be 2.5. However, due to the large proportion of stratigraphic area, its positive weight is determined to be the maximum adjustable value.

[0066] (3) The east-west fault is the main ore-controlling fault in the area, with a positive weight value of 1.5. The northeast and northwest faults have a certain control effect on mineralization, and some ore bodies are developed there, with a positive weight value of 0.75.

[0067] (4) The east-west oriented vein rocks form a structural weak zone, which provides space for magmatic hydrothermal mineralization filling, and the positive weight value is determined to be 1.5. The positive weight value of the northeast and northwest oriented vein rocks is determined to be 0.75.

[0068] (5) The Au element has a high anomaly intensity and an obvious concentration center, which has a strong indicative effect on mineralization. The positive weight value is determined to be 1.5. Since the Au anomaly range (2.47) in the prediction area is large and the stratigraphic elements are the same, its positive weight value is determined to be the adjustable maximum value.

[0069] (6) Aeromagnetic and gravity anomalies have a certain indicative effect on mineralization, and the weight value is determined to be 0.25.

[0070] S104, matching the evidence factors corresponding to each target prospecting prediction element in the target area to generate an evidence factor distribution map of the target area, and dividing the evidence factor distribution map into a plurality of prediction grid units.

[0071] Each evidence factor is represented as a layer in the evidence factor layout diagram.

[0072] In this step, firstly, geographic information mapping software can be used to extract the evidence factors corresponding to each target prospecting prediction element in the target area and produce a single-element distribution map; then, the single-element distribution map is coordinate corrected, the corresponding map parameters are assigned and a topological check is performed, for example, a topological check is performed on the distribution maps of elements such as areas and lines; the single-element distribution maps obtained after coordinate correction, parameter assignment and topological check are fitted to obtain the evidence factor distribution map.

[0073] Specifically, ArcGIS software can be used to organize the layers of each single element distribution map, calibrate the coordinates so that they can fit together, and organize their related attributes for subsequent extraction and prediction, and divide the prediction grid units according to the prediction scale and prediction accuracy. In the above example, the geological and physical and chemical remote sensing data collected are mostly at a scale of 1:50,000, so a 500m×500m grid is selected as the prediction grid unit.

[0074] S105. For each prediction grid unit, determine the metallogenic probability value of the prediction grid unit according to the evidence factor distribution map and the positive weight of each evidence factor.

[0075] In a possible implementation, step S105 may include:

[0076] S1051. For each evidence factor, determine the degree of correlation between the evidence factor and the target mineral deposit type and the third number of predicted grid cells with minerals in the evidence factor distribution map according to the evidence factor distribution map.

[0077] S1052. For each prediction grid unit, determine the mineralization probability value of the prediction grid unit based on the third number of prediction grid units with minerals in the evidence factor distribution map, the total number of prediction grid units, and the degree of correlation between each evidence factor included in the prediction grid unit and the target ore deposit type.

[0078] In specific implementation, S1051 may include:

[0079] For each evidence factor, determine a first number of predicted grid cells in the area where the evidence factor exists and a second number of predicted grid cells in the area where the evidence factor does not exist in the evidence factor distribution map; determine a third number of predicted grid cells with minerals in the evidence factor distribution map and a negative weight of the evidence factor based on the first number, the second number and the positive weight; determine the degree of correlation between the evidence factor and the target mineral deposit type based on the positive weight and the negative weight of the evidence factor.

[0080] In specific implementation, according to the evidence weight method, it is assumed that the target area is divided into T units of equal area (that is, the prediction grid units are divided, and the size of the prediction grid units is determined according to the accuracy of the prediction area data. Generally, for 1:50,000 scale data, the prediction unit is 500 meters × 500 meters), of which D units are mineralized units. For any evidence factor, its weight is defined as:

[0081] W + =ln[P(B / D) / P(E / D - )]

[0082] W - =ln[P(B - / D) / P(E - / D - )]

[0083] Among them, W + and W -are the positive weight (i.e., the weight value of the area where the evidence factor exists) and the negative weight (i.e., the weight value of the area where the evidence factor does not exist) of the evidence factor, respectively. The weight value of the area where the original data is missing is 0; P represents the proportion; B represents the number of units where both the evidence factor and the target type of deposit exist; B - is the number of units containing the target type of deposits where the evidence factor does not exist; D is the total number of units with mineralization; D - is the total number of units without mineralization; E is the number of units where the evidence factor exists but does not contain the target type of mineral deposit; E - The six parameters in the formula are interrelated, so the negative weights can be solved from the positive weights.

[0084] The positive weight of the evidence factor is actually the natural logarithm of the point "normalized density" defined by Elliot et al. (1992), namely ND, where: "ND = the percentage of the number of deposits (points) in the evidence factor to the total number of deposits (points) / the percentage of the area where the evidence factor exists to the total area of ​​the study area". The assigned positive weight value is approached by adjusting the percentage of the number of deposits (points) in the evidence factor to the total number of deposits (points).

[0085] The specific operation method is to define virtual deposits (points) as training points to control the weight values ​​of each evidence factor. Because the weight values ​​of each evidence factor cannot be set directly, they need to be defined by artificially controlling the percentage of virtual deposits (points) in the area where each evidence factor exists. The virtual deposits (points) or virtual training points are not actual deposits (points) and have no specific geographical location. They are only used to set the weight of the evidence factor. Specifically, the setting method is to control the positive weight of the evidence factor by adjusting the percentage of virtual training points in the area where each evidence factor exists, that is, adjusting the size of P (B / D). In this way, the percentage of virtual training points in the area where the evidence factor does not exist is determined, that is, its negative weight is also determined immediately. Correspondingly, the third number of predicted grid cells with mines, that is, parameter D, is also solved. However, since the positive weight is only approximately the natural logarithm of ND, the positive weights of each evidence factor finally calculated are actually only close to the above-mentioned determined values. In the actual calculation process, if the area ratio of a certain element is large and it is impossible to give a larger weight value by adjusting the proportion of mineral points, then the virtual mineral points are taken to exist in the element at 99% to calculate its weight value. Then, the negative weights are calculated.

[0086] Afterwards, the correlation between this evidence factor and the target deposit type is determined as:

[0087] C=W + -W -

[0088] Among them, C represents the degree of correlation.

[0089] In specific implementation, S1052 may include:

[0090] Determine the prior probability ratio of mineralization of the prediction grid cell according to the third number of prediction grid cells with minerals in the evidence factor distribution map and the total number of prediction grid cells; determine the posterior probability ratio of mineralization of the prediction grid cell according to the prior probability ratio of mineralization of the prediction grid cell and the degree of correlation between each evidence factor included in the prediction grid cell and the target ore deposit type; determine the mineralization probability value of the prediction grid cell according to the posterior probability ratio of mineralization of the prediction grid cell.

[0091] Posterior probability ratio O:

[0092]

[0093] In the formula, is the correlation between the jth evidence factor and the target deposit type, and base is the logarithm of the prior probability ratio.

[0094] base=ln(D / (TD))

[0095] Where D is the third number of predicted grid cells with mineral deposits in the evidence factor distribution diagram; T is the total number of predicted grid cells.

[0096] The posterior probability P, that is, the probability value of mineralization:

[0097] P=O / (1+O)

[0098] S106. Based on the metallogenic probability value of each prediction grid unit, a favorable mineral resource area of ​​the target deposit type in the target area is delineated.

[0099] In specific implementation, step S106 may include:

[0100] According to the mineralization probability value and probability-level mapping relationship of each prediction grid unit, the mineralization probability level of each prediction grid unit is determined; according to the mineralization probability level and corresponding color of each prediction grid unit, a mineralization probability color block map of the target ore deposit type in the target area is formed; the center point coordinates of each prediction grid unit are taken to generate a discrete point data set with geographic location information; wherein the attribute value of each point in the discrete point data set includes the mineralization probability value corresponding to the point; according to the attribute value of each point in the discrete point data set, the discrete point data set is divided into multiple discrete point subsets; a contour map corresponding to each discrete point subset is generated, and the area of ​​the area circled in each contour map is calculated; according to the ratio of the area of ​​the area circled in each contour map to the area of ​​the target area, the favorable area of ​​mineral resources of the target ore deposit type in the target area is determined.

[0101] See also Figure 2 and Figure 3 , Figure 2 A mineralization probability color block diagram provided in an embodiment of the present application; Figure 3 A schematic diagram of a favorable mineral resource area provided in an embodiment of the present application.

[0102] In a possible implementation, Figure 2 As shown in the figure, the probability value of mineralization probability is divided into 4 levels to form a mineralization probability color block map. In the geographic information software, various prediction factors such as gold geochemical anomalies and strata are combined.

[0103] Furthermore, based on the a posteriori probability color block map, the coordinates of the center point of each prediction grid unit are taken to generate a discrete point data set with geographic location information. The discrete point data set is divided into 10 levels according to the mineralization probability value and contour maps are generated respectively; the area of ​​the area circled by each contour value is calculated, and the contour value curve with an area close to 1 / 3 of the target area is selected as the favorable area for gold resources. Figure 3 As shown in the figure, based on the posterior probability value of each favorable area and combined with the formation conditions and distribution of important mineral (chemical) point types and the characteristics of physical and chemical remote anomalies, the favorable areas for prospecting are divided into three levels: A, B, and C. Among them, level A is a favorable area with very high probability of mineralization, very good fit of physical and chemical anomalies, and existing mineral points have been discovered in the area; level B is an area with high probability of mineralization and good physical and chemical anomalies; level C is an area with high probability of mineralization and physical and chemical anomalies.

[0104] Corresponding to the above example, Class A: There are known mineral deposits (points) in the predicted area, with good ore-controlling structures, hosted in the Ulashan rock group, with aeromagnetic anomalies > 250nT, residual gravity > 5×10-5m / s2, and Au single element anomaly value > 1.5×10-9.

[0105] Class B: There are good ore-controlling structures, which are located in the Wulashan rock group. The Au single element anomaly value is greater than 1.5×10-9 or the aeromagnetic anomaly is greater than 250nT, or the residual gravity is greater than 5×10-5m / s2.

[0106] Class C: There are good ore-controlling structures + it is found in the Wulashan rock group.

[0107] The embodiment of the present application provides a method for predicting favorable areas of mineral resources, which studies and dissects typical mineral deposits, establishes a prospecting prediction model based on expert knowledge and geographic information data, and specifically determines the evidence factors required for prediction and the positive weight of each evidence factor according to the prospecting prediction model, and then calculates the mineralization probability value of each prediction grid unit to predict favorable areas of mineral resources. In this way, by fully combining geological knowledge to predict favorable areas of mineral resources, the accuracy and efficiency of the prediction results can be improved.

[0108] See also Figure 4 , Figure 4 This is a schematic diagram of the structure of a device for predicting favorable mineral resource areas provided in an embodiment of the present application. Figure 4 As shown in , the prediction device 400 includes:

[0109] The model building module 410 is used to determine the metallogenic patterns and prospecting signs corresponding to different types of ore deposits based on the geographic information data of the study area and in combination with expert knowledge, and to build a prospecting prediction model; wherein the prospecting prediction model includes a plurality of prospecting prediction elements corresponding to each type of ore deposit;

[0110] A screening module 420 is used to screen out a plurality of target mineral prospecting prediction elements from the mineral prospecting prediction model according to the target mineral deposit type in the target area, and form a target mineral prospecting prediction model;

[0111] The first determination module 430 is used to determine the evidence factor corresponding to each target mineral prospecting prediction element in the target mineral prospecting prediction model in the target area and the positive weight of each evidence factor according to the geographic information data of the target area; wherein the evidence factor corresponding to each target mineral prospecting prediction element is used to characterize whether the target area meets the characteristic description of the target mineral prospecting prediction element;

[0112] A generation module 440 is used to fit the evidence factors corresponding to each target prospecting prediction element in the target area, generate an evidence factor distribution map of the target area, and divide the evidence factor distribution map into a plurality of prediction grid units; wherein each evidence factor is represented as a layer in the evidence factor distribution map;

[0113] A second determination module 450 is used to determine the metallogenic probability value of each prediction grid unit according to the evidence factor distribution map and the positive weight of each evidence factor;

[0114] The delineation module 460 is used to integrate the mineralization probability value of each prediction grid unit and delineate the favorable area of ​​mineral resources of the target deposit type in the target area.

[0115] See also Figure 5 , Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 5 As shown in , the electronic device 500 includes a processor 510 , a memory 520 and a bus 530 .

[0116] The memory 520 stores machine-readable instructions executable by the processor 510. When the electronic device 500 is running, the processor 510 communicates with the memory 520 via the bus 530. When the machine-readable instructions are executed by the processor 510, the above-mentioned Figure 1 The steps of the method for predicting favorable areas of mineral resources in the method embodiment shown, and the specific implementation method can be found in the method embodiment, which will not be repeated here.

[0117] The present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the computer program can execute the above-mentioned Figure 1 The steps of the method for predicting favorable areas of mineral resources in the method embodiment shown, and the specific implementation method can be found in the method embodiment, which will not be repeated here.

[0118] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0119] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0120] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0121] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0122] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application can essentially be embodied in the form of a software product, or in other words, the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0123] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The protection scope of the present application is not limited thereto. Although the present application is described in detail with reference to the above-mentioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-mentioned embodiments within the technical scope disclosed in the present application, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for predicting favorable mineral resource areas, characterized in that: The prediction method comprises: According to the geographic information data of the study area, combined with expert knowledge, the metallogenic patterns and prospecting signs corresponding to different types of ore deposits are determined, and a prospecting prediction model is constructed; wherein the prospecting prediction model includes a variety of prospecting prediction elements corresponding to each type of ore deposit; According to the target ore deposit type in the target area, a plurality of target ore prospecting prediction elements are selected from the ore prospecting prediction model, and a target ore prospecting prediction model is formed; Determine, based on the geographic information data of the target area, the evidence factor corresponding to each target prospecting prediction element in the target prospecting prediction model in the target area and the positive weight of each evidence factor; wherein the evidence factor corresponding to each target prospecting prediction element is used to characterize whether the target area meets the characteristic description of the target prospecting prediction element; Matching the evidence factors corresponding to each target prospecting prediction element in the target area to generate an evidence factor distribution map of the target area, and dividing the evidence factor distribution map into a plurality of prediction grid units; wherein each evidence factor is represented as a layer in the evidence factor distribution map; For each prediction grid unit, the metallogenic probability value of the prediction grid unit is determined according to the evidence factor distribution map and the positive weight of each evidence factor; By integrating the mineralization probability value of each predicted grid unit, the favorable mineral resource area of ​​the target deposit type in the target area is delineated.

2. The prediction method according to claim 1, characterized in that: Based on the geographic information data of the study area, combined with expert knowledge, the metallogenic patterns and prospecting signs corresponding to different types of mineral deposits are determined, and a prospecting prediction model is constructed, including: According to the geographic information data of the study area, the amount of prospecting information corresponding to each prospecting prediction element in the preset prospecting prediction element library is determined by the following formula: In the formula, A is the element of prospecting prediction j The amount of information indicating the existence of type B deposits; N is the total number of ore-bearing units in the study area; N j Type B ore deposits and prospecting prediction factors A in the study area j The number of prospecting units that appear at the same time; S is the total number of prospecting units in the study area; S j A is the mineral prospecting prediction factor in the study area. j The number of prospecting units present; According to the prospecting information amount corresponding to each prospecting prediction factor and the preset information amount threshold, a plurality of candidate prospecting prediction factors are screened out from the preset prospecting prediction factor library; Determine the metallogenic patterns and prospecting signs corresponding to different types of ore deposits in combination with expert knowledge, and match the metallogenic patterns and prospecting signs corresponding to different types of ore deposits with the plurality of candidate prospecting prediction elements; The candidate prospecting prediction elements matched to each ore deposit type are selected to construct the prospecting prediction model.

3. The prediction method according to claim 1, characterized in that: The evidence factors corresponding to each target prospecting prediction element in the target area are matched to generate an evidence factor distribution map of the target area, including: Using geographic information mapping software, extract the evidence factors corresponding to each target prospecting prediction element in the target area and make a single element distribution map; Correct the coordinates of the single-element distribution map, assign corresponding map parameters and perform topological checks; The single factor distribution diagrams are fitted to obtain the evidence factor distribution diagram.

4. The prediction method according to claim 1, characterized in that: For each prediction grid unit, the metallogenic probability value of the prediction grid unit is determined according to the evidence factor distribution map and the positive weight of each evidence factor, including: For each evidence factor, determining, according to the evidence factor distribution map, the degree of correlation between the evidence factor and the target mineral deposit type and a third number of predicted grid cells with mineral deposits in the evidence factor distribution map; For each prediction grid unit, the mineralization probability value of the prediction grid unit is determined based on the third number of prediction grid units with mines in the evidence factor distribution map, the total number of prediction grid units, and the degree of correlation between each evidence factor included in the prediction grid unit and the target ore deposit type.

5. The prediction method according to claim 4, characterized in that: For each evidence factor, determining the correlation between the evidence factor and the target mineral deposit type and the third number of predicted grid cells with mineral deposits in the evidence factor distribution map according to the evidence factor distribution map, including: For each evidence factor, determining a first number of predicted grid units in a region where the evidence factor exists and a second number of predicted grid units in a region where the evidence factor does not exist in the evidence factor distribution diagram; Determine, according to the first number, the second number and the positive weight, a third number of predicted grid cells with mineral deposits in the evidence factor distribution diagram and a negative weight of the evidence factor; According to the positive weight and negative weight of the evidence factor, the degree of correlation between the evidence factor and the target mineral deposit type is determined.

6. The prediction method according to claim 5, characterized in that: For each prediction grid unit, according to the third number of prediction grid units with minerals in the evidence factor distribution map, the total number of prediction grid units, and the degree of correlation between each evidence factor included in the prediction grid unit and the target ore deposit type, the metallogenic probability value of the prediction grid unit is determined, including: Determining a priori probability ratio of mineralization of the prediction grid cell according to the third number of prediction grid cells with minerals in the evidence factor distribution diagram and the total number of prediction grid cells; Determine the posterior probability ratio of the mineralization of the prediction grid unit according to the prior probability ratio of the mineralization of the prediction grid unit and the degree of correlation between each evidence factor included in the prediction grid unit and the target ore deposit type; According to the posterior probability ratio of the mineralization of the prediction grid unit, the mineralization probability value of the prediction grid unit is determined.

7. The prediction method according to claim 1, characterized in that: Based on the mineralization probability value of each predicted grid unit, the favorable mineral resource areas of the target deposit type in the target area are delineated, including: Determine the mineralization probability level of each prediction grid unit according to the mineralization probability value of each prediction grid unit and the probability-level mapping relationship; According to the metallogenic probability level and corresponding color of each predicted grid unit, a metallogenic probability color block map of the target ore deposit type in the target area is formed; Taking the center point coordinates of each prediction grid unit, generating a discrete point data set with geographic location information; wherein the attribute value of each point in the discrete point data set includes the metallogenic probability value corresponding to the point; According to the attribute value of each point in the discrete point data set, the discrete point data set is divided into a plurality of discrete point subsets; Generate a contour map corresponding to each discrete point subset, and calculate the area of ​​the circled region in each contour map; Based on the ratio of the area circled in each contour map to the area of ​​the target area, the favorable area of ​​mineral resources of the target deposit type in the target area is determined.

8. The prediction method according to claim 1, characterized in that: The mineral exploration prediction elements include at least one of the following: tectonic position, mineralization zone, mineralization age, mineralization-bearing formations, mineralization structures, magmatic activity, ore body characteristics, ore mineral composition, ore structure, ore structure, ore-controlling conditions, alteration, reserves, ore deposit scale, geochemical anomalies, aeromagnetic anomalies, and gravity anomalies.

9. A device for predicting favorable areas of mineral resources, characterized in that: The prediction device comprises: A model building module is used to determine the metallogenic patterns and prospecting signs corresponding to different types of ore deposits based on the geographic information data of the study area and in combination with expert knowledge, and to build a prospecting prediction model; wherein the prospecting prediction model includes a variety of prospecting prediction elements corresponding to each type of ore deposit; A screening module is used to screen out a plurality of target mineral prospecting prediction elements from the mineral prospecting prediction model according to the target mineral deposit type in the target area, and form a target mineral prospecting prediction model; The first determination module is used to determine the evidence factor corresponding to each target mineral prospecting prediction element in the target mineral prospecting prediction model in the target area and the positive weight of each evidence factor according to the geographic information data of the target area; wherein the evidence factor corresponding to each target mineral prospecting prediction element is used to characterize whether the target area meets the characteristic description of the target mineral prospecting prediction element; A generation module is used to fit the evidence factors corresponding to each target prospecting prediction element in the target area, generate an evidence factor distribution map of the target area, and divide the evidence factor distribution map into a plurality of prediction grid units; wherein each evidence factor is represented as a layer in the evidence factor distribution map; A second determination module is used to determine the metallogenic probability value of each prediction grid unit according to the evidence factor distribution map and the positive weight of each evidence factor; The delineation module is used to integrate the mineralization probability value of each predicted grid unit and delineate the favorable mineral resource area of ​​the target deposit type in the target area.

10. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are run by the processor, the steps of the method for predicting favorable areas of mineral resources as described in any one of claims 1 to 8 are executed.

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