A method and system for predicting the occurrence depth of a weathered crust type rare earth ore body

Abstract

The application discloses a method and system for predicting the occurrence depth of an ore body of a weathered crust type rare earth deposit, and relates to the technical field of geological exploration. The method comprises the following steps: performing on-site testing on the weathered crust by using a high-density electrical method; calculating and inverting the testing results to obtain apparent resistivity data of the weathered crust at different depths; correcting the distortion values of the apparent resistivity data with the terrain fluctuation according to the terrain of the weathered crust, obtaining the apparent resistivity data of the weathered crust at different depths after the terrain correction by using a ratio method, and further obtaining a first derivative curve of the apparent resistivity changing with the depth, and determining the occurrence depth of the rare earth ore body in combination with a logistic regression model parameter. The application uses the variation law of the apparent resistivity of the weathered crust at different depths to find out the groundwater structure of the weathered crust, further uses the logistic regression model parameter to determine the position of the ore body of the weathered crust type rare earth deposit, so as to accurately predict the occurrence depth of the rich ore layer of the weathered crust type rare earth deposit, and efficiently and accurately explore the rare earth ore body in the weathered crust.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geological exploration, more particularly to a method and system for predicting the occurrence depth of ore body of weathering crust type rare earth deposit. BACKGROUND

[0002] As "industrial vitamins", rare earth elements are widely used in new energy technology, new materials, aerospace, military industry and other fields. With the increasing demand for rare earth, the exploration and development of rare earth deposit become particularly important.

[0003] Weathering crust type rare earth deposit is mainly distributed in the weathering crust of granitic rock, with high content of medium and heavy rare earth, full distribution, low radioactivity and easy mining; groundwater structure can affect the migration and enrichment of rare earth in weathering crust type rare earth deposit, and the increase of water content in weathering crust leads to the decrease of weathering crust permeability, which is conducive to the adsorption of clay minerals to rare earth ions and the formation of ore body; the buried depth of weathering crust type rare earth ore body is generally 1-10m, and part of it can reach 20m. However, the existing use of geochemical exploration to delineate ore body often leads to the neglect of part of the ore body, abnormality of ore body judgment, and inability to accurately confirm the occurrence depth of rich ore layer in weathering crust type rare earth ore body.

[0004] Therefore, how to accurately confirm the occurrence depth of rich ore layer in weathering crust type rare earth ore body is a problem to be solved by those skilled in the art. SUMMARY

[0005] Therefore, the present application provides a method and system for predicting the occurrence depth of ore body of weathering crust type rare earth deposit, which uses a logistic regression model parameter to determine the position of ore body of weathering crust type rare earth deposit, and further improves the delineation accuracy of the occurrence depth of rich ore layer of weathering crust type rare earth deposit.

[0006] In order to achieve the above purpose, the present application provides the following technical scheme:

[0007] The present application discloses a method for predicting the occurrence depth of ore body of weathering crust type rare earth deposit, and the specific steps are as follows:

[0008] High-density electrical method is used to test the weathering crust on site to obtain test results;

[0009] The test results are calculated and inverted to obtain apparent resistivity data of different depths of the weathering crust;

[0010] According to the topography of the weathering crust, the distortion value fluctuating with the topography in the apparent resistivity data is corrected to obtain topography corrected apparent resistivity data;

[0011] Based on the topography corrected apparent resistivity data, a first derivative curve of apparent resistivity changing with depth is obtained, and the occurrence depth of rare earth ore body is determined in combination with a logistic regression model parameter.

[0012] Further, the field test comprises: taking the ridge top of the weathering crust as the midpoint, using the Wenner device with a set of pole distance and channel number, and longitudinally sampling the resistivity along the profile of the measuring line to obtain the observed resistivity of the measuring point.

[0013] Further, the test result is calculated and inverted by using the SODI method, and the apparent resistivity of the weathering crust is obtained through iterative calculation, and the iterative formula is:

[0014]

[0015] In the formula, (l, n) represents a small unit of the lth row and the nth column, ρ i and ρ i+1 respectively represent the resistivity values obtained in the i and i+1 iterations, ρ0 represents the observed apparent resistivity value, and ρ ci represents the apparent resistivity value calculated by using the finite element method.

[0016] Further, the distortion value of the apparent resistivity data with the terrain fluctuation is corrected, including:

[0017] The elevation of the measuring point is collected, and the terrain angle domain slope of the weathering crust is obtained according to the elevation of each measuring point.

[0018] The apparent resistivity distortion value of each point is obtained by using the product superposition formula.

[0019]

[0020] In the formula, ρ s / ρ is the apparent resistivity distortion value of a point on the continuous finite terrain, (ρ s / ρ )1…(ρ s / ρ) m is the apparent resistivity distortion value of each angle domain at the corresponding point, β1…β m is the slope of each angle domain at the corresponding point.

[0021] The apparent resistivity ρ ci is compared with the apparent resistivity distortion value ρ s / ρ , and the terrain corrected apparent resistivity data ρ d is obtained.

[0022] Further, the apparent resistivity of adjacent points in the longitudinal direction of the profile of the measuring line is averaged in the terrain corrected apparent resistivity data, and then an encrypted longitudinal apparent resistivity curve is fitted, wherein the encryption means that the apparent resistivity curve is fitted by the original data and the average data of adjacent points; the longitudinal apparent resistivity curve is derived to obtain the first derivative curve of the apparent resistivity with respect to the depth.

[0023] The formula of the logistic regression model is:

[0024] y=0.10984649*Depth-0.00107128*ρ d -0.00172985*FirstDerivative+0.00158755

[0025]

[0026] Wherein, y represents an intercept, P represents a logistic regression output coefficient, Depth represents a depth, and FirstDerivative represents a first derivative; in combination with the depth and the corrected apparent resistivity value, the position of the ore body is determined by using the logistic regression model parameters, and the ore body is determined as being present when P is greater than 0.5.

[0027] The application further discloses a system for predicting the occurrence depth of an ore body of a weathered crust type rare earth deposit.

[0028] The data acquisition module obtains a test result by performing in-situ testing on the weathered crust by using a high-density electrical method.

[0029] The inversion module calculates and inverts the test result to obtain apparent resistivity data of the weathered crust at different depths.

[0030] The distortion correction module corrects the distortion value fluctuating with the terrain in the apparent resistivity data according to the terrain of the weathered crust, and obtains terrain corrected apparent resistivity data in combination with a ratio method.

[0031] The occurrence depth confirmation module obtains a first derivative curve of the apparent resistivity changing with the depth based on the terrain corrected apparent resistivity data, and determines the occurrence depth of the rare earth ore body in combination with logistic regression model parameters.

[0032] Compared with the prior art, the method and system for predicting the occurrence depth of an ore body of a weathered crust type rare earth deposit are provided, the depth of the weathered crust, the terrain corrected apparent resistivity and the first derivative of the terrain corrected apparent resistivity are used, and the occurrence depth of the ore body of the weathered crust type rare earth deposit is determined in combination with a P value, the correctness of the depth of the ore body can reach 0.83, the ore body can be quickly delineated in-situ and non-destructively by using a geophysical method at a low cost, the demand for quickly and accurately delineating the ore body in the geological exploration work of the weathered crust type rare earth deposit is met, a large number of measuring lines can be set to avoid missing the rare earth ore body, the depth information of the regional ore body can be accurately obtained, and the rare earth resources can be accurately evaluated and efficiently and reasonably utilized. BRIEF DESCRIPTION OF DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only represent some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative effort based on the provided drawings.

[0034] Figure 1 It is a whole flowchart of the embodiment of the present application.

[0035] Figure 2 It is a contrast diagram of terrain-corrected apparent resistivity of weathering crust with electrode spacing of 1m, 2m and 5m.

[0036] Figure 3 It is a diagram of the relationship between the ore body indicated by the rare earth element content and P value of four weathering crusts.

[0037] Figure 4 It is a distribution diagram of terrain-corrected apparent resistivity and ore body probability (P value) of the cross section of the weathering crust. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments only represent some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort belong to the scope of protection of the present application.

[0039] The embodiment of the present application discloses a method for predicting the occurrence depth of the ore body of the weathering crust type rare earth deposit, as shown in the figure, and the specific steps are as follows: Figure 1

[0040] The high-density electrical method is used to obtain the test results of the weathering crust in the field test;

[0041] The test results are calculated and inverted to obtain the apparent resistivity data of the weathering crust at different depths;

[0042] According to the terrain of the weathering crust, the distortion value fluctuating with the terrain in the apparent resistivity data is corrected to obtain the terrain-corrected apparent resistivity data;

[0043] Based on the terrain-corrected apparent resistivity data, the first derivative curve of the apparent resistivity changing with the depth is obtained, and the occurrence depth of the rare earth ore body is determined in combination with the parameters of the logistic regression model.

[0044] In one specific embodiment, the field test includes: taking the ridge top of the weathering crust as the midpoint, using the Wenner device with a set electrode spacing and channel number, and performing resistance sampling along the longitudinal profile of the test line section to obtain the observed resistivity of the test point.​

[0045] Specifically, the ore body of the weathering crust type rare earth ore deposit is usually 1-30m in depth, and the thickness gradually thins from the top of the mountain to the foot of the mountain, so the test line is selected by taking the top of the mountain ridge as the midpoint and extending to both sides of the mountain ridge, and the test points are arranged at different intervals along the test line direction. Figure 2 As shown in Table 1, the test resolution of the high-density electrical method with a 2m electrode spacing is close to 1m electrode spacing, and is much better than 5m electrode spacing. At the same time, using 2m electrode spacing can effectively reduce the calculation amount during data processing, so 2m electrode spacing and 120-channel Wenner device are used for field test in this embodiment.

[0046] Table 1 Depth- electrode spacing- resistivity relationship table

[0047]

[0048]

[0049] In one specific embodiment, the test results are calculated and inverted by using the SODI method, and the apparent resistivity of the weathering crust is obtained by iterative calculation, and the iterative formula is as follows:

[0050]

[0051] In the formula, (l, n) represents the small unit of the lth row and the nth column, ρ i and ρ i+1 respectively represent the resistivity values obtained by the i and i+1 iterations, ρ0 represents the observed resistivity value, and ρ ci represents the apparent resistivity value calculated by the finite element method.

[0052] Specifically, the initial test results are taken as the initial model, and are divided into small units of a certain size corresponding to the test points. The initial model is forward calculated by using the finite element simulation method to obtain the theoretical apparent resistivity value of each unit. The calculated theoretical apparent resistivity value is compared with the observed resistivity value, and the root mean square error of all units is calculated. When the error is greater than the set range, the resistivity value of each small unit is updated according to the iterative formula, and the iteration is stopped until the error is less than the set range. The resistivity value of each small unit obtained by the last iteration update is used to obtain the apparent resistivity data of the weathering crust.

[0053] Since the observed resistivity values of each measuring point in the field test are obtained by detecting the current size of the electrode, the current of the measuring point small unit contains not only the current that can pass through the measuring point small unit itself, but also the partial current passing through the adjacent small unit. The observed resistivity value calculated directly according to the current and voltage of the measuring point small unit is affected by the adjacent small unit and cannot accurately represent the actual resistance of the ore body at the measuring point position. Therefore, the apparent resistivity of each measuring point is further calculated by combining the SODI method with the finite element simulation method.

[0054] In a specific embodiment, the distortion value of the apparent resistivity data fluctuating with the terrain is corrected, including:

[0055] The elevations of the measuring points are collected, and the terrain angular domain slope of the weathering crust is obtained according to the elevations of the measuring points;

[0056] The product superposition formula is used to obtain the apparent resistivity distortion value of each point;

[0057] In a specific embodiment, the product superposition formula is:

[0058]

[0059] In the formula, ρ s / ρ is the apparent resistivity distortion value of a point on the continuous finite terrain, (ρ s / ρ )1…(ρ s / ρ) m is the apparent resistivity distortion value of each angular domain at the corresponding point, β1…β m is the slope of each angular domain at the corresponding point.

[0060] The apparent resistivity ρ ci is compared with the apparent resistivity distortion value ρ s / ρ , and the terrain corrected apparent resistivity data ρ d is obtained.

[0061] Specifically, due to the fluctuation of the weathering crust terrain, the distribution of the underground electric field changes, and the apparent resistivity is distorted, thereby covering the useful geological information. The broken line representing the terrain fluctuation can be obtained through the coordinates and elevations of the measuring points, and the angular domain affecting the distortion of the apparent resistivity is determined according to the broken line. The apparent resistivity value obtained under the original terrain condition is divided by the apparent resistivity value obtained after mapping the measuring line to the same plane to obtain the apparent resistivity distortion value ρ s / ρ. The terrain anomaly value of a measuring point on the continuous terrain can be calculated by the terrain anomaly values of the adjacent m angular domain points of the measuring point through the superposition formula, and then the apparent resistivity ρ ci is compared with the apparent resistivity distortion value ρ s / ρ The apparent resistivity of each measuring point is obtained by dividing the apparent resistivity by the apparent resistivity distortion value d .

[0062] In one specific embodiment, comprising:

[0063] The apparent resistivity of adjacent points in the terrain-corrected apparent resistivity data in the longitudinal direction of the profile is averaged, and a longitudinal apparent resistivity curve is obtained by fitting. The first derivative of the longitudinal apparent resistivity curve is obtained, which is the first derivative curve of the apparent resistivity with respect to depth, and the occurrence depth of the ore body is obtained by using the logistic regression algorithm.

[0064] The specific method is: the depth, apparent resistivity and first derivative are selected as characteristic variables, and whether it is a mineral body is selected as a label variable, wherein 1 represents a mineral body and 0 represents a non-mineral body.

[0065] The logistic regression model is constructed using the characteristic variables and the label variables, and the model is trained to find the optimal parameters by the maximum likelihood estimation method. After training, the coefficients and intercepts of the logistic regression model are obtained. Through the model parameters obtained by training:

[0066] y = 0.10984649 x Depth - 0.00107128 x p d -0.00172985 x FirstDerivative + 0.00158755

[0067] and y is the logistic regression output value

[0068]

[0069] Wherein, y represents the intercept, P represents the logistic regression output coefficient, Depth represents the depth, and FirstDerivative represents the first derivative; the occurrence depth of the mineral body is determined, and when P is greater than 0.5, it is determined as the position of the mineral body.

[0070] In one specific embodiment, the weathering crust samples (R1, R2, R3, R4) of four weathering crust type rare earth ore deposits are tested, and the high-density electrical method with a 2m electrode spacing and 120 channels of the Wenner device is used to test the weathering crust in the field. The measured apparent resistivity is calculated and inverted using the Sodi method, and the inversion results are terrain-corrected to obtain terrain-corrected inversion apparent resistivity. Then, the terrain-corrected inversion apparent resistivity of every two adjacent points in the longitudinal direction is averaged, and the first derivative of the terrain-corrected inversion apparent resistivity is obtained in the Origin software (as shown in Table 2). According to the logistic regression model parameters, the P value is obtained, and P>0.5 indicates the occurrence position of the mineral body in the weathering crust (as shown in Table 3). Figure 3The first derivative curve of apparent resistivity changing with depth is obtained based on the topographic correction apparent resistivity data, and the occurrence depth of the rare earth ore body is determined by combining the logistic regression model parameters. Figure 4 The first derivative curve of apparent resistivity changing with depth is obtained based on the topographic correction apparent resistivity data, and the occurrence depth of the rare earth ore body is determined by combining the logistic regression model parameters.

[0071] Table 2: Relationship table of weathering crust depth-rare earth element content-resistivity-first derivative-P value

[0072]

[0073]

[0074]

[0075] The embodiment of the present application also discloses a system for predicting the occurrence depth of a weathering crust type rare earth ore body, comprising:

[0076] The data acquisition module: obtains the test results by using the high-density electrical method to perform field test on the weathering crust;

[0077] The inversion module: calculates and inverts the test results to obtain the apparent resistivity data of the weathering crust;

[0078] The distortion correction module: corrects the distortion values fluctuating with the terrain in the apparent resistivity data according to the terrain of the weathering crust to obtain the topographic correction apparent resistivity data;

[0079] The occurrence depth confirmation module: obtains the first derivative curve of the apparent resistivity changing with depth based on the topographic correction apparent resistivity data, and determines the occurrence depth of the rare earth ore body by combining the logistic regression model parameters.

[0080] The embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and the same or similar parts of each embodiment can be referred to each other. For the device disclosed by the embodiments, since it corresponds to the method disclosed by the embodiments, the description is relatively simple, and the related parts can be referred to the method part.

[0081] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting the depth of occurrence of ore bodies of weathered crust type rare earth deposits, characterized in that, The specific steps are as follows: The test results are obtained by using the high-density electrical method to perform field testing on the weathering crust; The apparent resistivity data of the weathering crust at different depths are obtained by calculating and inverting the test results; The distortion values in the apparent resistivity data that fluctuate with the terrain are corrected according to the terrain of the weathering crust, and terrain-corrected apparent resistivity data are obtained; Based on the terrain-corrected apparent resistivity data, a first derivative curve of the apparent resistivity changing with depth is obtained, and the occurrence depth of the rare earth ore body is determined in combination with a logistic regression model parameter; The apparent resistivities of adjacent points in the terrain-corrected apparent resistivity data in the longitudinal direction of the line profile are averaged, and then an encrypted longitudinal apparent resistivity curve is fitted; and the first derivative curve of the apparent resistivity changing with depth is obtained by differentiating the longitudinal apparent resistivity curve; The formula of the logistic regression model is: wherein, y represents an intercept, P represents a logistic regression output coefficient, represents a depth, represents a terrain-corrected apparent resistivity data, represents a first derivative; in combination with the depth and the corrected apparent resistivity value, the position of the ore body is determined using the logistic regression model parameters, when P is greater than 0.5, the ore body is determined.

2. The method for predicting the occurrence depth of the ore body of the weathered crust type rare earth ore deposit according to claim 1, characterized in that, The field testing includes: taking the ridge top point of the weathering crust as a midpoint, using a Wenner device with a set of electrode spacing and channel numbers to perform resistance sampling along the longitudinal direction of the line profile, and obtaining the observed resistivity values of the test points.

3. The method for predicting the occurrence depth of the ore body of the weathered crust type rare earth ore deposit according to claim 1, characterized in that, The apparent resistivity of the weathering crust is obtained by iterative calculation through Zohdy method for calculating and inverting the test results, and the iterative formula is: ； wherein (l,n ) represents the first l row, the first n column of small cells, The distortion values in the apparent resistivity data that fluctuate with the terrain are corrected, including: i and The elevations of the test points are collected, and the terrain angle domain slope of the weathering crust is obtained according to the elevations of the test points; i+1 respectively represent the first i and the second i resistivity values obtained at the first The apparent resistivity distortion values of the points are obtained by using the product superposition formula; 0 denotes the observed apparent resistivity values, The formula of the logistic regression model is: ci denotes the apparent resistivity values calculated with finite elements.

4. The method for predicting the occurrence depth of the ore body of the weathered crust type rare earth ore deposit according to claim 3, characterized in that, A system for predicting the occurrence depth of a weathering crust type rare earth ore body is provided, including: A data acquisition module: test results are obtained by using the high-density electrical method to perform field testing on the weathering crust; An inversion module; ； wherein The apparent resistivity data of the weathering crust at different depths are obtained by calculating and inverting the test results; s / A distortion correction module: the distortion values in the apparent resistivity data that fluctuate with the terrain are corrected according to the terrain of the weathering crust, and terrain-corrected apparent resistivity data are obtained in combination with the ratio method; is the apparent resistivity distortion value at a point on the continuous finite terrain, An occurrence depth confirmation module: based on the terrain-corrected apparent resistivity data, a first derivative curve of the apparent resistivity changing with depth is obtained, and the occurrence depth of the rare earth ore body is determined in combination with a logistic regression model parameter; s / ρ ) 1 The apparent resistivities of adjacent points in the terrain-corrected apparent resistivity data in the longitudinal direction of the line profile are averaged, and then an encrypted longitudinal apparent resistivity curve is fitted; and the first derivative curve of the apparent resistivity changing with depth is obtained by differentiating the longitudinal apparent resistivity curve; s The formula of the logistic regression model is: m is the apparent resistivity distortion value at a point on the continuous finite terrain, β 1 …β m is the slope of each angular domain at the corresponding point; The terrain-corrected apparent resistivity data are obtained by using the ratio method ​ d . ​ ​ ​ ​ ​ ​ ​ ​ wherein, y represents an intercept, P represents a logistic regression output coefficient, represents a depth, represents a terrain-corrected apparent resistivity data, represents a first derivative; in combination with the depth and the corrected apparent resistivity value, the position of the ore body is determined using the logistic regression model parameters, when P is greater than 0.5, the ore body is determined.

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