Method and system for predicting scale of hydrothermal deposit

Abstract

The invention provides a hydrothermal deposit scale prediction method and system, and relates to the technical field of hydrothermal deposit scale analys.The hydrothermal deposit of a target mining area is subjected to three-dimensional modeling, the hydrothermal deposit is divided into a plurality of unit areas through a top view, and mineralization prediction variables of each unit area are defined; analyzing each unit area by integrating four metallogenic predictive variables including geological factors, geochemical factors, geophysical factors and environmental and climatic factors, and performing correlation analysis on the influence degrees of the weight factors and the metallogenic predictive variables on the unit areas; and generating a unit area metallogenic scale which reflects the metallogenic scale coefficient of each unit area, analyzing the cross influence between the metallogenic prediction variables, and finally generating a target mining area metallogenic scale evaluation coefficient which reflects the size of the metallogenic scale of the target mining area, thereby outputting the scale degree of the target mining area.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrothermal deposit scale analysis, and in particular to a hydrothermal deposit scale prediction method and system. Background Art

[0002] Hydrothermal deposits refer to deposits formed by the migration, precipitation and enrichment of high-temperature, high-pressure hydrothermal fluids in the earth's crust. Hydrothermal deposits occupy an important position in global mineral resources and are widely distributed in various geological environments, such as volcanic rock areas, tectonic activity areas and plate boundaries. The formation of hydrothermal deposits is mainly related to the activity of hydrothermal fluids inside the earth's crust. Hydrothermal fluids usually come from the following ways: magmatism, in which the fluids produced during the cooling of magma are released into the surrounding rocks to form hydrothermal deposits; metamorphism, in which volatiles and fluids in rocks are released during metamorphism to form hydrothermal fluids; groundwater circulation, in which groundwater is heated in the deep crust, rises and reacts with minerals to form hydrothermal fluids. There are several exploration methods for hydrothermal deposits: geophysical exploration methods: gravity exploration, magnetic exploration, electrical exploration and seismic exploration, etc., which can detect the physical properties and spatial distribution of underground ore bodies; geochemical exploration methods, soil and rock sample analysis, fluid inclusion research, isotope geochemical analysis, etc., which can identify the element distribution of the mining area and the source of the ore-forming fluid.

[0003] The Chinese invention patent with the publication number CN111709189A discloses a method and system for predicting the scale of a copper mine deposit, which includes obtaining the project data and information of the existing copper mine; determining the copper mine project database based on the project data and information; analyzing the key factors affecting the scale of the copper mine based on the copper mine project database, and determining the copper mine scale prediction index system; pre-processing each copper mine scale prediction index to determine the copper mine scale prediction data set; using the random forest model to determine the prediction model based on the copper mine scale prediction data set; and using the prediction model to predict the scale level of the copper mine deposit to be predicted. The method and system for predicting the scale of a copper mine deposit provided by it can realize the rapid prediction of the scale of a copper mine deposit. However, when it conducts comprehensive analysis and prediction on each indicator, it only processes it separately, without considering the cross-influence between the influencing factors of each deposit scale prediction variable, resulting in poor accuracy of the prediction results outputted by it.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for predicting the scale of hydrothermal deposits to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for predicting the scale of a hydrothermal deposit, comprising the following specific steps:

[0008] S1. Conduct three-dimensional modeling of the target mining area, divide it into C unit areas through a top view, and number them respectively;

[0009] S2. Define the pth mineralization prediction variable as x p , where p is 1, 2, 3 or 4, and the value of the p-th mineralization prediction variable in the i-th unit area is defined as x ip , and summarize the metallogenic prediction variables of each unit area into the prediction variable matrix X;

[0010] S3. Perform correlation analysis on the prediction variable matrix X to generate correlation coefficients and weight factor values, where the weight factor is used to reflect the weight of the mineralization prediction variable on the size of the hydrothermal deposit;

[0011] S4. Combine the mineralization prediction variables and weight factors to generate the mineralization scale of each unit area;

[0012] S5. Conduct correlation analysis on the unit area mineralization scale to generate a target mineralization scale evaluation coefficient, which is used to reflect the mineralization scale of the target mineralization area;

[0013] S6. Compare the target mining area's mineralization scale evaluation coefficient with the scale threshold, and output the hydrothermal mining area scale grade.

[0014] Furthermore, the mineralization prediction variable x p According to the value of p, it can be divided into x 1 、x 2 、x 3 、x 4 , where x 1 represents geological factors, x 2 represents geochemical factors, x 3 represents geophysical factors, x 4 Indicates environmental and climatic factors.

[0015] Furthermore, the expression of the prediction variable matrix X is:

[0016]

[0017] Furthermore, a correlation analysis is performed on the predictor variable matrix X to generate the correlation coefficient Y kp , the formula used is:

[0018]

[0019] The correlation coefficient Y kp Used to reflect the correlation between the kth mineralization prediction variable and the pth mineralization prediction variable, where the value of k is 1, 2, 3 or 4.

[0020] Furthermore, for the correlation coefficient Y kp Perform correlation analysis and generate weight factor z p The value of is based on the formula:

[0021]

[0022] Weight factor z p It is used to reflect the weight of the pth mineralization prediction variable on the generation of the scale of the hydrothermal deposit.

[0023] Furthermore, for the weight factor z p and the value x of the pth mineralization prediction variable in the ith unit area ip Carry out correlation analysis to generate the mineralization scale f for each unit area i , the formula based on is:

[0024] f i =z 1 x i1 +z 2 x i2 +z 3 x i3 +z 4 x i4

[0025] The unit area mineralization scale f i Used to reflect the mineralization scale coefficient of the i-th unit area.

[0026] Furthermore, the correlation analysis of the unit area mineralization scale is carried out to generate the target mineralization scale evaluation coefficient CKX, based on the formula:

[0027]

[0028] Among them, the target mining area mineralization scale evaluation coefficient CKX is used to reflect the size of the target mining area mineralization scale.

[0029] Furthermore, the value of the scale threshold θ is 89.66. When CKX ≥ θ, the output hydrothermal deposit is of the first level, and the output hydrothermal deposit is of large scale. When the output hydrothermal ore area is of secondary scale, the output hydrothermal ore area deposit is of medium scale; when At that time, the scale level of the output hydrothermal mining area is level three, and the ore deposit in the output hydrothermal mining area is small-scale.

[0030] The present invention also provides a hydrothermal deposit scale prediction system, which is used to execute a hydrothermal deposit scale prediction method, comprising:

[0031] A three-dimensional modeling module, which is used to perform three-dimensional modeling on the target mining area, divide it into C unit areas through a top view, and number them respectively;

[0032] A metallogenic prediction variable definition module is used to define the pth metallogenic prediction variable as x p , and define the value of the pth mineralization prediction variable in the i-th unit area as x ip , and summarize the metallogenic prediction variables of each unit area into the prediction variable matrix X;

[0033] A variable matrix analysis module, which is used to perform correlation analysis on the prediction variable matrix X, generate correlation coefficients, and generate numerical values ​​of weight factors;

[0034] A variable analysis module, which is used to combine the mineralization prediction variables and weight factors to generate the mineralization scale of each unit area;

[0035] A mineralization scale analysis module, which is used to perform correlation analysis on the mineralization scale of the unit area and generate a mineralization scale evaluation coefficient for the target mining area;

[0036] A comparison module is used to compare the target mining area's mineralization scale evaluation coefficient with the scale threshold, and output the hydrothermal mining area scale grade.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The present invention performs three-dimensional modeling of the hydrothermal deposit in the target mining area, divides it into multiple unit areas through a bird's-eye view, defines the mineralization prediction variables for each unit area, analyzes each unit area through comprehensive geological factors, geochemical factors, geophysical factors, and environmental and climatic factors, performs correlation analysis on the weight factors and the degree of influence of the mineralization prediction variables on the unit area, generates a unit area mineralization scale reflecting the mineralization scale coefficient of each unit area, analyzes the cross-influence between the mineralization prediction variables, and finally generates a target mining area mineralization scale evaluation coefficient reflecting the size of the target mining area mineralization scale, thereby outputting the scale of the target mining area. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0040] Figure 2 It is a schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0041] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0042] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0043] Example:

[0044] See also Figure 1 , the present invention provides a technical solution:

[0045] A method for predicting the scale of a hydrothermal deposit, comprising the following specific steps:

[0046] Step 1: Carry out 3D modeling of the target mining area, divide it into C unit areas through a bird's-eye view, and number them respectively; use 3D software to carry out 3D modeling of the target mining area, the size of each unit area is 1mX1m, and the C unit areas are numbered according to 1, 2, 3, ..., i.

[0047] Step 2: Define the pth mineralization prediction variable as x p , where p is 1, 2, 3 or 4, and the value of the p-th mineralization prediction variable in the i-th unit area is defined as x ip , and summarize the metallogenic prediction variables of each unit area into the prediction variable matrix X; the metallogenic prediction variable x p According to the value of p, it can be divided into x 1 、x 2 、x 3 、x 4 , where x 1 represents geological factors, x 2 represents geochemical factors, x 3 represents geophysical factors, x4 Indicates environmental and climatic factors.

[0048] Geological factors include the influence of rock type and stratum. Different rock types and stratum structures directly affect the formation and distribution of ores; the influence of tectonic activities. Tectonic activities can provide channels for the migration of ore fluids and form ore bodies. The influence of geological evolution history. The geological evolution history of a region determines its mineralization potential. Geochemical factors include the influence of element abundance and distribution. The initial abundance and distribution of ore-forming elements have an important influence on the formation and scale of ore bodies; the influence of hydrothermal activity. Hydrothermal activity is an important mechanism for the formation of many mineral deposits. The ore-forming elements in hydrothermal fluids precipitate under suitable physical and chemical conditions to form mineral deposits; the influence of chemical deposition. Chemical deposition is an important process in the formation of mineral deposits. Dissolved substances in water bodies precipitate when chemical conditions change to form mineral deposits. Geophysical factors include the influence of gravity anomalies. Gravity anomalies can reveal the distribution and scale of underground ore bodies; the influence of magnetic anomalies. Magnetic anomalies reflect the magnetic differences of underground rocks; the influence of resistivity differences. The resistivity differences between ore bodies and surrounding rocks can be used for geophysical exploration. Low resistivity areas often indicate ore bodies with good conductivity. Environmental and climatic factors include the impact of climate change. Long-term climate change can affect the formation and preservation of mineral deposits; the impact of surface water and groundwater activities. Surface water and groundwater activities can promote the migration and precipitation of minerals and affect the formation and distribution of ore bodies; the impact of topography and landforms. Topography and landform conditions can affect the exposure and preservation of mineral deposits. There is a certain degree of connection between geological factors, geochemical factors, geophysical factors, environmental and climatic factors, and each factor will have a cross-influence. By integrating at least ten geological experts to evaluate and score each unit area of ​​the target mining area according to different mineralization prediction variables and taking the average value, the value x of the pth mineralization prediction variable in the i-th unit area is generated. ip .

[0049] The expression of the prediction variable matrix X is:

[0050]

[0051] Step 3: Conduct correlation analysis on the prediction variable matrix X to generate correlation coefficients and the value of weight factors. The weight factors are used to reflect the weight of the mineralization prediction variables on the size of the hydrothermal deposits.

[0052] Step 4: Combine the mineralization prediction variables and weight factors to generate the mineralization scale of each unit area;

[0053] For the weight factor z p and the value x of the pth mineralization prediction variable in the ith unit area ip Carry out correlation analysis to generate the mineralization scale f for each unit area i, the formula based on is:

[0054] f i =z 1 x i1 +z 2 x i2 +z 3 x i3 +z 4 x i4

[0055] The unit area mineralization scale f i Used to reflect the mineralization scale coefficient of the i-th unit area.

[0056] The correlation analysis of the predictor variable matrix X is performed by the sum of squares method. The stronger the correlation between a variable and other variables, the more important the variable is, and the correlation coefficient Y is generated. kp and use the correlation coefficient Y kp As a calibration indicator of the correlation between variables, the formula used is:

[0057]

[0058] The correlation coefficient Y kp Used to reflect the correlation between the kth mineralization prediction variable and the pth mineralization prediction variable, where the value range of k is 1, 2, 3 or 4.

[0059] Correlation coefficient Y kp Perform correlation analysis and generate weight factor z p The value of is based on the formula:

[0060]

[0061] Weight factor z p It is used to reflect the weight of each mineralization prediction variable on the favorable effect of each mineralization prediction variable on the scale of deposits in hydrothermal mining areas.

[0062] Step 5: Conduct correlation analysis on the unit area mineralization scale to generate the target mineralization scale evaluation coefficient, which is used to reflect the mineralization scale of the target mineralization area;

[0063] The correlation analysis of the unit area mineralization scale is carried out to generate the target mineralization scale evaluation coefficient CKX, based on the formula:

[0064]

[0065] Among them, the target mining area mineralization scale evaluation coefficient CKX is used to reflect the size of the target mining area mineralization scale.

[0066] Step 6: Compare the target mining area's mineralization scale evaluation coefficient with the scale threshold, and output the hydrothermal mining area scale grade.

[0067] Specifically, the value of the scale threshold θ is 89.66. When CKX ≥ θ, the output hydrothermal mining area scale level is level 1, and the output hydrothermal mining area deposit is large-scale; when When the output hydrothermal ore area is of secondary scale, the output hydrothermal ore area deposit is of medium scale; when At that time, the scale level of the output hydrothermal mining area is level three, and the ore deposit in the output hydrothermal mining area is small-scale.

[0068] See also Figure 2 The present invention also provides a hydrothermal deposit scale prediction system for executing a hydrothermal deposit scale prediction method, comprising:

[0069] A three-dimensional modeling module, which is used to perform three-dimensional modeling on the target mining area, divide it into C unit areas through a top view, and number them respectively;

[0070] A metallogenic prediction variable definition module is used to define the pth metallogenic prediction variable as x p , and define the value of the pth mineralization prediction variable in the i-th unit area as x ip , and summarize the metallogenic prediction variables of each unit area into the prediction variable matrix X;

[0071] A variable matrix analysis module, which is used to perform correlation analysis on the prediction variable matrix X, generate correlation coefficients, and generate numerical values ​​of weight factors;

[0072] A variable analysis module, which is used to combine the mineralization prediction variables and weight factors to generate the mineralization scale of each unit area;

[0073] A mineralization scale analysis module, which is used to perform correlation analysis on the mineralization scale of the unit area and generate a mineralization scale evaluation coefficient for the target mining area;

[0074] A comparison module is used to compare the target mining area's mineralization scale evaluation coefficient with the scale threshold, and output the hydrothermal mining area scale grade.

[0075] Through three-dimensional modeling of the hydrothermal deposits in the target mining area and dividing it into multiple unit areas through a bird's-eye view, the mineralization prediction variables of each unit area are defined, and each unit area is analyzed by integrating the four mineralization prediction variables of geological factors, geochemical factors, geophysical factors, and environmental and climatic factors. The correlation analysis is carried out on the weight factors and the degree of influence of the mineralization prediction variables on the unit area, and the unit area mineralization scale reflecting the mineralization scale coefficient of each unit area is generated. The cross-influence of the mineralization prediction variables is analyzed, and finally the target mining area mineralization scale evaluation coefficient reflecting the size of the target mining area is generated, thereby outputting the scale of the target mining area.

[0076] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0077] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0078] 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, and 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.

[0079] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for predicting the scale of a hydrothermal deposit, characterized in that: The specific steps include: S1. Conduct three-dimensional modeling of the target mining area, divide it into C unit areas through a top view, and number them respectively; S2. Define the pth mineralization prediction variable as x p , where p is 1, 2, 3 or 4, and the value of the p-th mineralization prediction variable in the i-th unit area is defined as x ip , and summarize the metallogenic prediction variables of each unit area into the prediction variable matrix X; S3. Perform correlation analysis on the prediction variable matrix X to generate correlation coefficients and weight factor values, where the weight factor is used to reflect the weight of the mineralization prediction variable on the size of the hydrothermal deposit; S4. Combine the mineralization prediction variables and weight factors to generate the mineralization scale of each unit area; S5. Conduct correlation analysis on the unit area mineralization scale to generate a mineralization scale evaluation coefficient for the target mineralization area. The mineralization scale evaluation coefficient for the target mineralization area is used to reflect the mineralization scale of the target mineralization area; S6. Compare the target mining area's mineralization scale evaluation coefficient with the scale threshold, and output the hydrothermal mining area scale grade.

2. A method for predicting the scale of a hydrothermal deposit according to claim 1, characterized in that: The mineralization prediction variable x p According to the value of p, it can be divided into x1, x2, x3, and x4, where x1 represents geological factors, x2 represents geochemical factors, x3 represents geophysical factors, and x4 represents environmental and climate factors.

3. A method for predicting the scale of a hydrothermal deposit according to claim 2, characterized in that: The expression of the prediction variable matrix X is:

4. A method for predicting the scale of a hydrothermal deposit according to claim 3, characterized in that: Perform correlation analysis on the predictor variable matrix X and generate the correlation coefficient Y kp , the formula used is: The correlation coefficient Y kp Used to reflect the correlation between the kth mineralization prediction variable and the pth mineralization prediction variable, where the value of k is 1, 2, 3 or 4.

5. A method for predicting the scale of a hydrothermal deposit according to claim 4, characterized in that: Correlation coefficient Y kp Perform correlation analysis and generate weight factor z p The value of is based on the formula: Weight factor z p It is used to reflect the weight of the pth mineralization prediction variable on the generation of the scale of the hydrothermal deposit.

6. A method for predicting the scale of a hydrothermal deposit according to claim 5, characterized in that: For the weight factor z p and the value x of the pth mineralization prediction variable in the ith unit area ip Carry out correlation analysis to generate the mineralization scale f for each unit area i , the formula based on is: f i =z1x i1 +z2x i2 +z3x i3 +z4x i4 The unit area mineralization scale f i Used to reflect the mineralization scale coefficient of the i-th unit area.

7. A method for predicting the scale of a hydrothermal deposit according to claim 6, characterized in that: The correlation analysis of the unit area mineralization scale is carried out to generate the target mineralization scale evaluation coefficient CKX, based on the formula: Among them, the target mining area mineralization scale evaluation coefficient CKX is used to reflect the size of the target mining area mineralization scale.

8. A method for predicting the scale of a hydrothermal deposit according to claim 7, characterized in that: The value of the scale threshold θ is 89.

66. When CKX ≥ θ, the scale level of the output hydrothermal mining area is level one, and the output hydrothermal mining area deposit is large-scale; when When the output hydrothermal ore area is of secondary scale, the output hydrothermal ore area deposit is of medium scale; when At that time, the scale level of the output hydrothermal mining area is level three, and the ore deposit in the output hydrothermal mining area is small-scale.

9. A hydrothermal deposit scale prediction system, used to execute a hydrothermal deposit scale prediction method according to claim 1, characterized in that: include: A three-dimensional modeling module, which is used to perform three-dimensional modeling on the target mining area, divide it into C unit areas through a top view, and number them respectively; A metallogenic prediction variable definition module is used to define the pth metallogenic prediction variable as x p , and define the value of the pth mineralization prediction variable in the i-th unit area as x ip , and summarize the metallogenic prediction variables of each unit area into the prediction variable matrix X; A variable matrix analysis module, which is used to perform correlation analysis on the prediction variable matrix X, generate correlation coefficients, and generate numerical values ​​of weight factors; A variable analysis module, which is used to combine the mineralization prediction variables and weight factors to generate the mineralization scale of each unit area; A mineralization scale analysis module, which is used to perform correlation analysis on the mineralization scale of the unit area and generate a mineralization scale evaluation coefficient for the target mining area; A comparison module is used to compare the target mining area's mineralization scale evaluation coefficient with the scale threshold, and output the hydrothermal mining area scale grade.

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