Ion adsorption type rare earth ore deposit weathering crust thickness prediction method, device, equipment, medium and product

Abstract

The invention discloses an ion adsorption type rare earth ore deposit weathering crust thickness prediction method and device, equipment, a medium and a product, and relates to the technical field of exploration and prospecting. The method comprises the following steps: acquiring analog digital elevation data of a target ion adsorption type rare earth deposit research area; calculating landform factor data according to the analog digital elevation data; inputting the landform factor data into the weathering crust thickness prediction model to obtain a predicted weathering crust thickness; predicting the thickness of the weathering crust for prospecting, exploring and positioning so as to optimize the exploration layout; the weathering crust thickness prediction model is obtained by constructing a multiple linear regression equation based on landform factor data of a research area with known weathering crust thickness. The invention aims to improve the prospecting exploration efficiency.

Description

Technical Field

[0001] The present application relates to the technical field of prospecting and ore exploration, and in particular to a method, device, equipment, medium and product for predicting the thickness of weathering crust of ion adsorption type rare earth deposits. Background Art

[0002] As a typical exogenous deposit, the ion-adsorption rare earth ore is located in the weathering crust, and weathering is crucial to the formation of this type of deposit. Generally speaking, the stronger the weathering, the thicker the weathering crust, the higher the grade of the deposit, and the more likely it is to form an ion-adsorption rare earth deposit. Therefore, the thickness of the weathering crust is a key factor in the exploration of ion-adsorption rare earth deposits.

[0003] Topographic factors significantly control the formation and preservation of weathering crusts. The greater the slope and the deeper the cutting, the faster the erosion rate of the weathering crust, which makes the thickness of the weathering crust smaller, which is not conducive to the formation of ion-adsorption rare earth deposits. Therefore, evaluating the preservation of the weathering crust, especially the evaluation of the key indicator of weathering crust thickness, is crucial for ion-adsorption rare earth prospecting.

[0004] At present, in order to obtain the thickness of the weathering crust, the thickness of the weathering crust is mainly obtained indirectly or directly by field geological surveys and construction drilling projects. However, this method is labor-intensive and time-consuming, which is not conducive to the rapid evaluation of the development of the weathering crust. Summary of the invention

[0005] The purpose of this application is to provide a method, device, equipment, medium and product for predicting the thickness of the weathering crust of an ion adsorption type rare earth deposit, which can improve the efficiency of mineral exploration.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a method for predicting the thickness of the weathering crust of an ion adsorption type rare earth deposit, comprising:

[0008] Obtain simulated digital elevation data of the target ion adsorption type rare earth deposit research area;

[0009] Calculating topographical factor data according to the simulated digital elevation data;

[0010] The topographic and geomorphic factor data are input into a weathering crust thickness prediction model to obtain a predicted weathering crust thickness; the predicted weathering crust thickness is used for prospecting and exploration positioning to optimize the exploration layout; the weathering crust thickness prediction model is obtained by constructing a multivariate linear regression equation based on the topographic and geomorphic factor data of a research area with known weathering crust thickness.

[0011] Optionally, calculating the topographic factor data according to the simulated digital elevation data specifically includes:

[0012] Based on GIS spatial processing technology, topographic factor data is calculated according to the simulated digital elevation data.

[0013] Optionally, the terrain and geomorphic factor data include: elevation, elevation variation coefficient, surface cutting depth, slope, and terrain undulation.

[0014] Optionally, the elevation variation coefficient is obtained by dividing the standard deviation of the elevation data by the average value of the elevation data;

[0015] The surface cutting depth is obtained by subtracting the average value of the elevation data from the minimum value of the elevation data.

[0016] Optionally, the slope is the percentage of the quotient of the elevation data difference and the horizontal distance in the study area;

[0017] The terrain undulation degree is obtained by subtracting the maximum value of the elevation data from the minimum value of the elevation data.

[0018] Optionally, the expression corresponding to the weathering crust thickness prediction model is:

[0019]

[0020] Among them, H is the thickness of the weathering crust; CVE is the coefficient of elevation variation; DSD is the surface cutting depth; S is the slope; TP is the terrain undulation; Elev is the elevation.

[0021] In a second aspect, the present application provides an ion adsorption type rare earth deposit weathering crust thickness prediction device, comprising:

[0022] Data acquisition module, used to obtain simulated digital elevation data of the target ion adsorption type rare earth deposit research area;

[0023] A calculation module, used for calculating topographic factor data according to the simulated digital elevation data;

[0024] The prediction module is used to input the topographic and geomorphic factor data into the weathering crust thickness prediction model to obtain the predicted weathering crust thickness; the predicted weathering crust thickness is used for prospecting and exploration positioning to optimize the exploration layout; the weathering crust thickness prediction model is obtained by constructing a multivariate linear regression equation based on the topographic and geomorphic factor data of the study area with known weathering crust thickness.

[0025] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned method for predicting the thickness of the weathering crust of an ion-adsorption type rare earth deposit.

[0026] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for predicting the thickness of the weathering crust of an ion-adsorption type rare earth deposit.

[0027] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned method for predicting the thickness of the weathering crust of an ion-adsorption rare earth deposit.

[0028] According to the specific embodiments provided in this application, this application has the following technical effects:

[0029] The present application provides a method, device, equipment, medium and product for predicting the thickness of the weathering crust of an ion adsorption type rare earth deposit, which obtains simulated digital elevation data of a target rare earth deposit research area; calculates topographic and geomorphic factor data based on the simulated digital elevation data; inputs the topographic and geomorphic factor data into a weathering crust thickness prediction model to obtain a predicted weathering crust thickness; the predicted weathering crust thickness is used for prospecting and exploration positioning to optimize the exploration layout; since the weathering crust thickness prediction model is obtained by constructing a multivariate linear regression equation based on the topographic and geomorphic factor data of a research area with known weathering crust thickness, it can improve the efficiency of prospecting and exploration. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0031] Figure 1 A flow chart of the method for predicting the thickness of weathering crust of rare earth deposits;

[0032] Figure 2 The flow chart of the design idea of ​​the method for predicting the thickness of weathering crust of rare earth deposits;

[0033] Figure 3 The figure is a schematic diagram of the verification results. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0035] The application of digital elevation model (DEM) in basic geomorphological analysis has become increasingly mature. Geomorphological element analysis based on DEM can quickly and accurately extract topographic and geomorphological features, and then evaluate the development of weathering crust and predict the thickness of weathering crust, which can greatly reduce the cost of rare earth mineral exploration and improve exploration efficiency. Therefore, this application can economically and efficiently predict the thickness of weathering crust of ion adsorption type rare earth deposits by simulating digital elevation data, and can quickly locate areas with thick weathering crust in prospecting and exploration, which is conducive to the layout of subsequent exploration work and thus improves the efficiency of prospecting and exploration.

[0036] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0037] In an exemplary embodiment, Figure 1 As shown, a method for predicting the thickness of the weathering crust of an ion adsorption type rare earth deposit is provided, the method comprising:

[0038] Step 100: Acquire simulated digital elevation data of the target ion adsorption type rare earth deposit study area.

[0039] Step 200: Calculate terrain factor data based on simulated digital elevation data.

[0040] Calculating terrain and geomorphic factor data according to simulated digital elevation data, specifically including: calculating terrain and geomorphic factor data according to simulated digital elevation data based on GIS spatial processing technology.

[0041] The topographic and geomorphic factor data include: elevation, elevation variation coefficient, surface cutting depth, slope, and terrain undulation.

[0042] The elevation variation coefficient is obtained by dividing the standard deviation of the elevation data by the average value of the elevation data; the surface cutting depth is obtained by dividing the average value of the elevation data by the minimum value of the elevation data.

[0043] The slope is the percentage of the quotient of the elevation data difference and the horizontal distance in the study area; the terrain undulation is obtained by subtracting the maximum value of the elevation data from the minimum value of the elevation data.

[0044] Step 300: Input the topographic and geomorphic factor data into the weathering crust thickness prediction model to obtain the predicted weathering crust thickness. The predicted weathering crust thickness is used for prospecting and exploration positioning to optimize the exploration layout; the weathering crust thickness prediction model is obtained by constructing a multivariate linear regression equation based on the topographic and geomorphic factor data of the study area with known weathering crust thickness.

[0045] The corresponding expression of the weathering crust thickness prediction model is:

[0046]

[0047] Among them, H is the thickness of the weathering crust; CVE is the coefficient of elevation variation; DSD is the surface cutting depth; S is the slope; TP is the terrain undulation; Elev is the elevation.

[0048] In practical applications, the design ideas of the method mentioned in this application are as follows: Figure 2 As shown, the following steps are included:

[0049] S1. Obtain simulated digital elevation data of the study area and statistically analyze topographic and geomorphic factor data.

[0050] S2. Collect rare earth exploration data and count the construction depth of drilling projects in the weathering crust. Rare earth exploration data comes from ion adsorption rare earth deposits with granite as the mineralization parent rock. The drilling project in the weathering crust should be an effective project to expose the weathering crust. That is, the weathering crust thickness data is the construction depth of the drilling project to expose the weathering crust.

[0051] S3. Construct a weathering crust thickness prediction model based on topographic and geomorphic factor data and drilling project depth.

[0052] S4. Predict the thickness of the weathering crust based on the established weathering crust thickness prediction model.

[0053] The weathering crust thickness prediction model was constructed based on the drilling engineering construction depth and topographic and geomorphic factor data. The specific equation establishment method is as follows.

[0054] (1) Obtain the topographic and geomorphic factor data of the drilling project. The topographic and geomorphic factor data mainly include elevation, elevation variation coefficient, surface cutting depth, slope, and terrain undulation.

[0055] (2) Obtain the latitude and longitude coordinates and construction depth data of the drilling project, which represents the weathering crust thickness data.

[0056] (3) Taking the weathering crust thickness data as the dependent variable and the topographic factor data as the independent variable, the weathering crust thickness prediction equation is established. The corresponding expression is as follows:

[0057] H=-1415.603*CVE+1.444*DSD-0.061*S+5.969*TP+0.139*Elev-51.112.

[0058] This application inputs the topographic factors obtained by simulating digital elevation data into the prediction equation, predicts the thickness of the weathering crust, quickly locates the areas with thick weathering crust, replaces part of the field geological survey work, and assists in the exploration of rare earth deposits, thereby achieving the purpose of improving exploration efficiency and reducing exploration costs. Compared with the existing methods based on field route surveys and drilling engineering construction, this method has the advantages of low data acquisition cost, short time cycle, and low safety risk.

[0059] Taking a mine in a certain area as an example, the lithology is medium-fine (coarse) grained biotite granite. After field geological survey and drilling engineering verification, it was determined to be an ion adsorption type rare earth deposit.

[0060] S1. Obtain the simulated digital elevation data of the study area of ​​the mine and collect statistics on topographic and geomorphic factor data.

[0061] Among them, the simulated digital elevation data is obtained using the digital elevation model (DEM), which is the ASTER GDEM 30M resolution digital elevation data. Based on the spatial processing technology of the geographic information system (GIS), the simulated digital elevation data is used to calculate the topographic and geomorphic factor data. The topographic and geomorphic factor data mainly include five types: elevation, elevation variation coefficient, surface cutting depth, slope, and terrain undulation. The calculation method of topographic and geomorphic factor data is as follows:

[0062] Elevation: This data is extracted directly from the simulated digital elevation data.

[0063] The coefficient of variation of elevation is the standard deviation of elevation data / average elevation within the study area.

[0064] The surface cutting depth is the average value of the elevation data minus the minimum value of the elevation data within the study area.

[0065] The slope is the difference in elevation data within the study area / horizontal distance)*100%.

[0066] Terrain undulation = maximum value of elevation data - minimum value of elevation data.

[0067] S2. Collect rare earth exploration data and count the construction depth of drilling projects in the weathering crust.

[0068] The weathering crust depth is the drilling engineering construction depth in the "XX Single Engineering Calculation Table". The geological exploration data of this embodiment is derived from the "XX Single Engineering Calculation Table" in the "XX Rare Earth Mine Exploration in XX County, XX Province" report.

[0069] S3. Construct a weathering crust thickness prediction model based on topographic and geomorphic factor data and drilling project construction depth.

[0070] The multiple linear regression equation of the weathering crust thickness prediction model is constructed. The specific method is as follows:

[0071] (1) Obtain the topographic and geomorphic factor data of the drilling project. The topographic and geomorphic factors mainly include elevation, elevation variation coefficient, surface cutting depth, slope, and terrain undulation.

[0072] (2) Obtain the latitude and longitude coordinates and construction depth data of the drilling project. The construction depth represents the weathering crust thickness data.

[0073] (3) Taking the weathering crust thickness data as the dependent variable and the topographic factors data as the independent variables, the weathering crust thickness prediction equation is established. The prediction equation is as follows:

[0074] H=-1415.603*CVE+1.444*DSD-0.061*S+5.969*TP+0.139*Elev-51.112.

[0075] Among them, H is the depth of weathering crust, CVE is the coefficient of elevation variation, DSD is the surface cutting depth, S is the slope, TP is the terrain undulation, and Elev is the elevation.

[0076] S4. Predict the thickness of the weathering crust based on the established prediction model.

[0077] The topographic and geomorphic factor data of the study area are brought into the prediction equation to obtain the predicted weathering crust thickness. The present application example compares the weathering crust depth verified by 40 drilling projects with the weathering crust depth predicted by the model to verify the reliability of the model. The schematic diagram of the verification result is shown in Figure 3 ,The verification results are shown in Table 1.

[0078] Table 1 Verification results

[0079]

[0080] Based on the same inventive concept, the embodiment of the present application also provides an ion adsorption type rare earth ore deposit weathering crust thickness prediction device for realizing the above-mentioned ion adsorption type rare earth ore deposit weathering crust thickness prediction method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of one or more ion adsorption type rare earth ore deposit weathering crust thickness prediction devices provided below can refer to the limitations of the ion adsorption type rare earth ore deposit weathering crust thickness prediction method above, and will not be repeated here.

[0081] In an exemplary embodiment, a device for predicting the thickness of a weathering crust of an ion adsorption type rare earth deposit is provided, comprising:

[0082] The data acquisition module is used to obtain the simulated digital elevation data of the target ion adsorption type rare earth deposit research area.

[0083] The calculation module is used to calculate the terrain and geomorphic factor data based on the simulated digital elevation data.

[0084] The prediction module is used to input the topographic and geomorphic factor data into the weathering crust thickness prediction model to obtain the predicted weathering crust thickness; the predicted weathering crust thickness is used for prospecting and exploration positioning to optimize the exploration layout; the weathering crust thickness prediction model is obtained by constructing a multivariate linear regression equation based on the topographic and geomorphic factor data of the study area with known weathering crust thickness.

[0085] In an exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for predicting the thickness of the weathering crust of an ion adsorption type rare earth deposit is implemented.

[0086] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0087] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0088] In this application, all actions to obtain signals, information or data are carried out in compliance with the relevant data protection laws and policies of the country where they are located and with the authorization of the corresponding device owner. It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0089] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0090] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for predicting the thickness of weathering crust of ion adsorption type rare earth deposits, characterized in that: The method for predicting the thickness of the weathering crust of an ion adsorption type rare earth deposit comprises: Obtain simulated digital elevation data of the target ion adsorption type rare earth deposit research area; Calculating topographical factor data according to the simulated digital elevation data; The topographic and geomorphic factor data are input into a weathering crust thickness prediction model to obtain a predicted weathering crust thickness; the predicted weathering crust thickness is used for prospecting and exploration positioning to optimize the exploration layout; the weathering crust thickness prediction model is obtained by constructing a multivariate linear regression equation based on the topographic and geomorphic factor data of a research area with known weathering crust thickness.

2. The method for predicting the thickness of the weathering crust of an ion adsorption type rare earth deposit according to claim 1, characterized in that: Calculating topographical factor data according to the simulated digital elevation data specifically includes: Based on GIS spatial processing technology, topographic factor data is calculated according to the simulated digital elevation data.

3. The method for predicting the thickness of the weathering crust of an ion adsorption type rare earth deposit according to claim 1, characterized in that: The terrain and geomorphic factor data include: elevation, elevation variation coefficient, surface cutting depth, slope, and terrain undulation.

4. The method for predicting the thickness of the weathering crust of an ion adsorption type rare earth deposit according to claim 3, characterized in that: The elevation variation coefficient is obtained by dividing the standard deviation of the elevation data by the average value of the elevation data; The surface cutting depth is obtained by subtracting the average value of the elevation data from the minimum value of the elevation data.

5. The method for predicting the thickness of the weathering crust of an ion adsorption type rare earth deposit according to claim 3, characterized in that: The slope is the percentage of the difference between the elevation data and the horizontal distance in the study area; The terrain undulation degree is obtained by subtracting the maximum value of the elevation data from the minimum value of the elevation data.

6. The method for predicting the thickness of the weathering crust of an ion adsorption type rare earth deposit according to claim 3, characterized in that: The expression corresponding to the weathering crust thickness prediction model is: Among them, H is the thickness of the weathering crust; CVE is the coefficient of variation of elevation; DSD is the depth of surface cutting; S is the slope; TP is the degree of terrain undulation; Elev is the elevation.

7. An ion adsorption type rare earth deposit weathering crust thickness prediction device, characterized in that: The device for predicting the thickness of the weathering crust of an ion adsorption type rare earth deposit comprises: Data acquisition module, used to obtain simulated digital elevation data of the target ion adsorption type rare earth deposit research area; A calculation module, used for calculating topographic factor data according to the simulated digital elevation data; The prediction module is used to input the topographic and geomorphic factor data into the weathering crust thickness prediction model to obtain the predicted weathering crust thickness; the predicted weathering crust thickness is used for prospecting and exploration positioning to optimize the exploration layout; the weathering crust thickness prediction model is obtained by constructing a multivariate linear regression equation based on the topographic and geomorphic factor data of the study area with known weathering crust thickness.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for predicting the thickness of the weathering crust of an ion-adsorption rare earth deposit as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for predicting the thickness of the weathering crust of an ion-adsorption rare earth deposit described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for predicting the thickness of the weathering crust of an ion-adsorption rare earth deposit described in any one of claims 1 to 6 is implemented.

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