Graphite ore boundary identification method based on thick covering layer area multiple horizontal derivative method

Abstract

The invention discloses a graphite ore boundary identification method based on a thick covering layer area multiple horizontal derivative method, and relates to the technical field of graphite ore boundary identification. The technical scheme is characterized by comprising the following steps: S1, collecting natural potential data in a work area; s2, correcting the natural potential data; s3, judging the reliability of the data, judging whether the data has relatively high quality, and if the quality does not meet the requirement or the interference is relatively strong, filtering the data; s4, gridding the filtered data to form regular grid natural potential data, and taking the regular grid natural potential data as input data delta u (x, y, 0) of boundary identification; s5, first-order derivatives of the input data delta u (x, y, 0) in the X direction and the Y direction are calculated; s6, calculating a model of a first-order horizontal derivative of the input data; s7, solving X-direction and Y-direction derivatives for the model of the first-order horizontal derivative, and calculating the derivative model of the first-order horizontal derivative; and S8, drawing a horizontal derivative contour map for multiple times, and identifying the boundary of the graphite ore body according to the maximum value. According to the method, boundary delineation of the hidden graphite ore body in the strong coverage area is achieved, the calculated amount is small, the exploration efficiency is improved, and the boundary recognition result is reliable.

Description

Technical Field

[0001] The invention relates to the field of geophysics, and more particularly to a graphite mine boundary recognition method using a multiple horizontal derivative method in a thick overburden region. Background Art

[0002] With the development of the national economy, the demand for basic resources is increasing, and the technical requirements for mineral exploration are becoming more and more stringent. In addition, with the mineral exploration and evaluation activities in the past few decades, the work level of graphite mine prospecting areas with direct clues on the surface has been very high. How to find the area with thick overburden and the location of hidden graphite mines is the subsequent development direction. The geological body boundary identification technology is widely used in the field of gravity and magnetic exploration. By transforming and calculating the data, it plays an important role in understanding the boundaries of geological bodies at different scales. Based on past research, there are three main types of geological body boundary identification in geophysical calculation methods: (1) by calculating derivatives of different orders, (2) by the ratio of derivatives of different orders, and (3) by filtering and potential field separation calculation. Among them, the calculation of derivatives of different orders and the ratio of derivatives of different orders are the most widely used, such as horizontal derivatives, vertical derivatives, and total horizontal derivatives in different directions. The tilt angle method, enhanced tilt angle method, and boundary identification methods developed on this basis have the characteristics of uneven response results for field sources with different scale characteristics. At present, boundary recognition technology is developing rapidly, but most of the boundary recognition technologies are only used in the field of gravity and magnetic exploration. There are few studies on boundary recognition of ore bodies in the field of graphite mine exploration. At present, the boundary delineation of graphite mines mainly relies on two-dimensional and three-dimensional inversion with large subsequent calculation amount to locate deep graphite ore bodies. There is a practical problem of low exploration efficiency, and it is impossible to guide the next step of engineering construction layout immediately after data collection. In view of this, the inventors propose a graphite mine boundary recognition method based on multiple horizontal derivative method in thick overburden area. Summary of the invention

[0003] The purpose of the present invention is to provide a graphite ore boundary recognition method using a multiple horizontal derivative method in a thick overburden area, which can improve boundary recognition capability and has less calculation amount than two-dimensional and three-dimensional inversion calculation amount.

[0004] The above technical purpose of the present invention is achieved through the following technical solution: a graphite mine boundary identification method using multiple horizontal derivative method in thick overburden area, comprising the following steps:

[0005] S1. Collect natural potential data in the work area;

[0006] S2. Correcting the collected natural potential data;

[0007] S3. Determine the reliability of the collected natural electric field data and whether it has high quality. If the quality does not meet the requirements or the interference is strong, filter it;

[0008] S4. The filtered data is gridded using the Kriging interpolation method to form regular grid spontaneous potential data as input data Δu(x, y, 0) for boundary identification;

[0009] S5. Calculate the first-order derivative ΔUx in the X direction and the first-order derivative ΔUy in the Y direction of the input data Δu(x,y,0);

[0010] S6. Calculate the modulus of the first-order horizontal derivative of the input data. The specific formula is:

[0011]

[0012] Where: G(x,y,0) is the modulus of the first-order horizontal derivative, ΔU x is the first-order derivative in the X direction, ΔU y is the first-order derivative in the Y direction;

[0013] S7. Obtain the derivatives in the X and Y directions according to step S5 for the modulus of the calculated first-order horizontal derivative, and calculate the derivative modulus according to the following formula:

[0014]

[0015] Among them: STHD is the multiple horizontal derivative mode, is the horizontal derivative modulo the partial derivative in the x direction, is the horizontal derivative modulo the partial derivative in the y direction;

[0016] S8. Draw multiple horizontal derivative contour maps and identify the boundaries of the graphite ore body based on the maximum value.

[0017] The present invention is further configured as follows: in step S2, the correction is diurnal variation correction and terrain correction.

[0018] The present invention is further configured as follows: in step S3, the judgment criteria are: if the data of each measuring point of each measuring line changes linearly without obvious mutation points, and the difference between two measurements of the same point of the measured data is not greater than ±0.2mv, then the data is considered to be reliable; the measured data is checked at different times, by different technicians, and by different instruments, and the inspection ratio is 3% of the total measuring points, and the arithmetic mean absolute error is not greater than ±5mv, and the calculation formula is:

[0019]

[0020] Where: △ is the arithmetic mean data error, n is the number of checkpoints, △U i The potential difference measured at point i, △U' i Check the potential difference for point i.

[0021] The present invention is further configured as follows: in step S5, the first-order derivative ΔU in the X direction of Δu(x,y,0) is calculated x and the first-order derivative in the Y direction ΔU y , as follows:

[0022] S5.1 Use Fourier transform to calculate the spectrum ΔU from the measured spontaneous potential anomaly ΔU(x,y,0) a (u,v,z);

[0023] S5.2 Spectrum of measured spontaneous potential anomaly ΔU a (u, v, z) is multiplied by the corresponding transformed weight function spectrum 2πiu and 2πiv to obtain the transformed abnormal spectrum ΔU of the corresponding first-order derivative b (u,v,z);

[0024] S5.3 Using the inverse Fourier transform, the abnormal spectrum ΔU after transformation b (u, v, z) to calculate the converted natural potential anomaly ΔU b (x,y,0).

[0025] In summary, the present invention has the following beneficial effects: the present invention brings into play the characteristic of multiple derivatives highlighting local features by calculating and fusing multiple derivatives of measured data, and does not require tedious two-dimensional and three-dimensional inversion in the later stage of the laboratory, so that more convergent boundary results can be obtained, and the boundary delineation of hidden graphite ore bodies in thick coverage areas can be quickly solved. The amount of calculation is small, and the exploration efficiency can be effectively improved, and subsequent engineering construction can be guided. The boundary identification result is reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a flow chart of a graphite mine boundary identification method using a multiple horizontal derivative method in a thick overburden region in an embodiment of the present invention;

[0027] Figure 2 It is a comparison diagram of the effects in the embodiments of the present invention (where A is the original plane diagram, B is the vertical second-order derivative, C is the total derivative modulus, D is the total horizontal derivative modulus, E is the normalized total horizontal derivative modulus, and F is the multiple total horizontal derivative modulus). DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] Embodiment 1:

[0030] A graphite mine boundary identification method based on multiple horizontal derivatives in thick overburden areas, such as Figure 1 As shown, the following steps are included:

[0031] S1. Collect natural potential data in the work area.

[0032] S2. Perform diurnal variation correction and terrain correction on the collected natural potential data.

[0033] S3. Determine the reliability of the collected natural electric field data and whether it has high quality. If the quality does not meet the requirements or the interference is strong, filter it. The filter can adopt the existing filter processing means, which will not be described here.

[0034] The judgment criteria are as follows: the data of each measuring point on each measuring line changes linearly without obvious mutation points, and the difference between two measurements of the same point of the measured data is no more than ±0.2mv, then the data is considered reliable; the measured data is checked at different times, by different technicians, and with different instruments, and the inspection ratio is 3% of the total measuring points. If the arithmetic mean absolute error is no more than ±5mv, then the data is considered to have high quality, and the calculation formula is:

[0035]

[0036] Where: △ is the arithmetic mean data error, n is the number of checkpoints, △U i The potential difference measured at point i, △U' i Check the potential difference for point i.

[0037] S4. The filtered data is gridded using the Kriging interpolation method to form regular grid natural potential data as input data Δu(x, y, 0) for boundary identification.

[0038] S5. Calculate the first-order derivative ΔU in the X direction of the input data Δu(x,y,0) x , the first-order derivative in the Y direction ΔU y .

[0039] (1) Using Fourier transform, the spectrum ΔUa(u,v,z) is calculated from the measured spontaneous potential anomaly ΔU(x,y,0);

[0040] (2) The abnormal spectrum ΔUa(u, v, z) of the measured natural potential anomaly is multiplied by the spectrum of the corresponding transformed weight function 2πiu, 2πiv to obtain the abnormal spectrum ΔU of the corresponding first-order derivative after transformation. b (u,v,z);

[0041] (3) Using the inverse Fourier transform, the abnormal spectrum ΔU after transformation b (u, v, z) to calculate the converted natural potential anomaly ΔUb (x,y,0);

[0042] S6. Calculate the modulus of the first-order horizontal derivative of the input data. The specific formula is:

[0043]

[0044] Where: G(x,y,0) is the modulus of the first-order horizontal derivative, ΔU x is the first-order derivative in the X direction, ΔU y is the first-order derivative in the Y direction;

[0045] S7. Obtain the derivatives in the X and Y directions according to step 5 for the modulus of the calculated first-order horizontal derivative, and calculate the derivative modulus according to the following formula:

[0046]

[0047] Among them: STHD is the multiple horizontal derivative mode, is the horizontal derivative modulo the partial derivative in the x direction, is the horizontal derivative modulo the partial derivative in the y direction;

[0048] S8. Draw multiple horizontal derivative modulus contour maps and identify the graphite ore body boundary based on the maximum value.

[0049] The effect diagram of this embodiment and the comparison effect with the prior art are as follows Figure 2 As shown, among them: Figure A is the measured original data; Figure B is the measured original data for the vertical second-order derivative, and the result is displayed as a beaded shape, and the linear characteristics are not obvious; Figure C is the measured original data for the total derivative model, and the result has a low lateral resolution; Figure D is the measured original data for the total horizontal derivative model, and the result shows a certain linear feature, but still has the characteristic of low lateral resolution; Figure E is the measured original data for the normalized total horizontal derivative model, although the lateral resolution is improved to a certain extent, the boundary is still fuzzy; Figure F is the calculation of the measured original data by the method of the present invention.

[0050] Depend on Figure 2 It can be seen that the present invention solves the boundary delineation of the hidden graphite ore body in the thick coverage area, and does not need to go through the cumbersome two-dimensional and three-dimensional inversion in the later stage, that is, a more convergent boundary result can be obtained, and the horizontal resolution of the boundary is significantly improved. The boundary delineation of the hidden graphite ore body in the thick coverage area can be quickly solved, the calculation amount is small, the exploration efficiency can be effectively improved, the subsequent engineering construction can be guided, and the boundary recognition result is reliable. The calculation amount is small, the exploration efficiency can be effectively improved, the subsequent engineering construction can be guided, and the boundary recognition result is reliable.

[0051] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A graphite mine boundary identification method using multiple horizontal derivative method in thick overburden area, characterized by: The steps include: S1. Collect natural potential data in the work area; S2. Correcting the collected natural potential data; S3. Determine the reliability of the collected natural electric field data and whether it has high quality. If the quality does not meet the requirements or the interference is strong, filter it; S4. The filtered data is gridded using the Kriging interpolation method to form regular grid spontaneous potential data as input data Δu(x, y, 0) for boundary identification; S5. Calculate the first-order derivative ΔUx in the X direction and the first-order derivative ΔUy in the Y direction of the input data Δu(x,y,0); S6. Calculate the modulus of the horizontal derivative of the input data. The specific formula is: Where: G(x,y,0) is the modulus of the first-order horizontal derivative, ΔU x is the first-order derivative in the X direction, ΔU y is the first-order derivative in the Y direction; S7. Obtain the X and Y direction derivatives of the calculated horizontal derivative according to step S5, and calculate the derivative modulus according to the following formula: Among them: STHD is the multiple horizontal derivative mode, is the horizontal derivative modulo the partial derivative in the x direction, is the horizontal derivative modulo the partial derivative in the y direction; S8. Draw multiple horizontal derivative contour maps and identify the boundaries of the graphite ore body based on the maximum value.

2. The graphite mine boundary identification method of the thick overburden region using the multiple horizontal derivative method according to claim 1 is characterized in that: In step S2, the correction is diurnal variation correction and terrain correction.

3. The graphite ore boundary identification method of a thick overburden region using multiple horizontal derivative method according to claim 1 is characterized in that: In step S3, the judgment criteria are: if the data of each measuring point on each measuring line changes linearly without obvious mutation points, and the difference between two measurements of the same point of the measured data is no more than ±0.2mv, the data is considered to be reliable; if the measured data is checked at different times, by different technicians, and by different instruments, and the inspection ratio is 3% of the total measuring points, and the arithmetic mean absolute error is no more than ±5mv, the data is considered to have high quality, and the calculation formula is: Where: △ is the arithmetic mean data error, n is the number of checkpoints, △U i The potential difference measured at point i, △U' i Check the potential difference for point i.

4. The graphite ore boundary identification method of a thick overburden region using multiple horizontal derivative method according to claim 1 is characterized in that: In step S5, the first-order derivative ΔU in the X direction of Δu(x,y,0) is calculated x and the first-order derivative in the Y direction ΔU y , as follows: S5.1 Use Fourier transform to calculate the spectrum ΔU from the measured spontaneous potential anomaly ΔU(x,y,0) a (u,v,z); S5.2 Spectrum of measured spontaneous potential anomaly ΔU a (u, v, z) is multiplied by the corresponding transformed weight function spectrum 2πiu and 2πiv to obtain the transformed abnormal spectrum ΔU of the corresponding first-order derivative b (u,v,z); S5.3 Using the inverse Fourier transform, the abnormal spectrum ΔU after transformation b (u, v, z) to calculate the converted natural potential anomaly ΔU b (x,y,0).

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