A method for identifying graphite ore boundaries using multiple horizontal derivatives in thick overburden regions.

Abstract

This invention discloses a method for identifying graphite ore boundaries in thick overburden areas using multiple horizontal derivatives, belonging to the field of graphite ore boundary identification technology. The key technical points are: S1. Collecting spontaneous potential data in the work area; S2. Correcting the spontaneous potential data; S3. Determining the reliability of the data and its quality; if the quality is insufficient or interference is strong, filtering is performed; S4. Gridding the filtered data to form a regular grid of spontaneous potential data, which serves as the input data Δu(x,y,0) for boundary identification; S5. Calculating the first derivatives of the input data Δu(x,y,0) in the X and Y directions; S6. Calculating the modulus of the first horizontal derivative of the input data; S7. Obtaining the X and Y derivatives from the modulus of the first horizontal derivative and calculating their derivative modulus; S8. Drawing contour maps of multiple horizontal derivatives and identifying the graphite ore body boundary based on the maximum value. This invention solves the problem of delineating the boundaries of concealed graphite ore bodies in heavily overburdened areas, with less computation, improved exploration efficiency, and reliable boundary identification results.

Description

Technical Field

[0001] This invention relates to the field of geophysics, and more specifically, to a method for identifying graphite mineral boundaries using the multiple horizontal derivative method in thick overburden regions. Background Technology

[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 of the past few decades, the work in the graphite prospecting areas with direct clues on the surface has reached a high level. How to discover areas with thick overburden and the location of hidden graphite deposits is the direction of future development. Geological body boundary identification technology is widely used in the fields of gravity and magnetic exploration. By transforming and calculating data, it plays an important role in understanding the boundary of geological bodies at different scales. Based on past research, the geological body boundary identification of geophysical calculation methods mainly falls into three categories: (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, total horizontal derivatives in different directions, tilt angle method, enhanced tilt angle method, and boundary identification methods developed on this basis. These methods have the characteristic of uneven response results for field sources with different scale characteristics. Boundary identification technology is developing rapidly, but most current technologies are only applicable to gravity and magnetic exploration. Research on ore body boundary identification in graphite exploration is limited. Currently, graphite ore body boundary delineation mainly relies on computationally intensive two-dimensional and three-dimensional inversion for locating deep ore bodies, resulting in low exploration efficiency and an inability to immediately guide subsequent engineering construction after data acquisition. Therefore, the inventors propose a graphite ore boundary identification method based on multiple horizontal derivatives in thick overburden regions. Summary of the Invention

[0003] The purpose of this invention is to provide a graphite ore boundary identification method based on multiple horizontal derivatives in thick overburden regions, which can improve boundary identification capability and requires less computation than two-dimensional and three-dimensional inversion.

[0004] The above-mentioned technical objective of the present invention is achieved through the following technical solution: a method for identifying graphite ore boundaries using the multiple horizontal derivative method in thick overburden regions, comprising the following steps:

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

[0006] S2. Correct 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, then filter it.

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

[0009] S5. Calculate the first derivatives ΔUx and ΔUy in the X and Y directions 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 as follows:

[0011]

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

[0013] S7. Obtain the X and Y direction derivatives of the calculated first-order horizontal derivative according to step S5, and calculate its derivative magnitude according to the following formula:

[0014]

[0015] Where STHD is the modulus of the multiple horizontal derivatives. The horizontal derivative modulus and the partial derivative in the x-direction are the partial derivatives. The horizontal derivative is the partial derivative in the y-direction.

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

[0017] The present invention is further configured such that, 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: the data of each measuring line and measuring point show linear changes without obvious abrupt changes, and the difference between two measurements of the same point is not greater than ±0.2mv, then the data is considered reliable; the measured data are checked at different times, by different technicians, and with different instruments, with the check ratio being 3% of the total measuring points, and the arithmetic mean absolute error is not greater than ±5mv, calculated using the following formula:

[0019]

[0020] Where: △ is the arithmetic mean error, n is the number of checkpoints, and △U i Measure the potential difference ΔU' at point i. i Check the potential difference at point i.

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

[0022] S5.1 Using the forward Fourier transform, the spectrum ΔU is obtained from the measured spontaneous potential anomaly ΔU(x,y,0). a (u,v,z);

[0023] S5.2 The spectrum ΔU of the measured spontaneous potential anomaly a Multiplying (u,v,z) by the spectrum of the corresponding transformation weight function 2πiu and 2πiv yields the abnormal spectrum ΔU of the corresponding first derivative after transformation. b (u,v,z);

[0024] S5.3 uses the inverse Fourier transform to obtain the spectrum ΔU of the transformed anomaly. b The converted natural potential anomaly ΔU is calculated from (u,v,z). b (x,y,0).

[0025] In summary, the present invention has the following beneficial effects: by calculating and fusing multiple derivatives of measured data, the present invention leverages the characteristic of multiple derivatives to highlight local features, eliminating the need for cumbersome two-dimensional and three-dimensional inversion in the later stages, and can obtain more convergent boundary results. It quickly solves the boundary delineation of concealed graphite ore bodies in thick-covered areas, with a small amount of calculation, which can effectively improve exploration efficiency, guide subsequent engineering construction, and provide reliable boundary identification results. Attached Figure Description

[0026] Figure 1 This is a flowchart of the graphite ore boundary identification method using the multiple horizontal derivative method in the thick overburden region according to an embodiment of the present invention;

[0027] Figure 2 This is a comparison diagram of the effects in the embodiments of the present invention (where A is the original planar diagram, B is the vertical second 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 Implementation

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0029] Example 1:

[0030] A method for identifying graphite ore boundaries using multiple horizontal derivatives in thick overburden regions, such as... Figure 1 As shown, it includes the following steps:

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

[0032] S2. Perform diurnal variation correction and topographic 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, then filter it. Existing filtering methods can be used, which will not be elaborated here.

[0034] The judgment criteria are as follows: if the data from each measuring point along the measuring line shows a linear change with no obvious abrupt changes, and the difference between two measurements of the same point is no greater than ±0.2mV, then the data is considered reliable; if the measured data is checked at 3% of the total measuring points using different times, different technicians, and different instruments, and the arithmetic mean absolute error is no greater than ±5mV, then the data is considered to have high quality. The calculation formula is as follows:

[0035]

[0036] Where: △ is the arithmetic mean error, n is the number of checkpoints, and △U i Measure the potential difference ΔU' at point i. i Check the potential difference at point i.

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

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

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

[0040] (2) The spectrum of the anomaly after transformation, ΔU, is obtained by multiplying the measured spectrum of the spontaneous potential anomaly ΔUa(u,v,z) by the spectrum of the corresponding transformed weight function 2πiu and 2πiv. b (u,v,z);

[0041] (3) Using the inverse Fourier transform, the transformed anomalous spectrum ΔU b The converted natural potential anomaly ΔU is calculated from (u,v,z).b (x,y,0);

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

[0043]

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

[0045] S7. Obtain the X and Y direction derivatives of the calculated first-order horizontal derivative according to step 5, and calculate its derivative magnitude according to the following formula:

[0046]

[0047] Where STHD is the modulus of the multiple horizontal derivatives. The horizontal derivative modulus and the partial derivative in the x-direction are the partial derivatives. The horizontal derivative is the partial derivative in the y-direction.

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

[0049] The effect diagram of this embodiment and its comparison with the prior art are as follows: Figure 2 As shown, Figure A represents the original measurement data; Figure B shows the vertical second derivative of the original measurement data, which appears as a beaded pattern with weak linearity; Figure C shows the total derivative modulus of the original measurement data, which has low lateral resolution; Figure D shows the total horizontal derivative modulus of the original measurement data, which shows some linearity but still has low lateral resolution; Figure E shows the normalized total horizontal derivative modulus of the original measurement data, which improves the lateral resolution to some extent, but the boundaries remain blurred; Figure F shows the calculations performed on the original measurement data by the method of this invention.

[0050] Depend on Figure 2 This invention solves the problem of delineating the boundaries of concealed graphite ore bodies in thick-covered areas. It eliminates the need for cumbersome subsequent 2D and 3D inversions, achieving more convergent boundary results and significantly improving the horizontal resolution of the boundaries. It can quickly delineate the boundaries of concealed graphite ore bodies in thick-covered areas with minimal computational cost, effectively improving exploration efficiency, guiding subsequent engineering construction, and providing reliable boundary identification results.

[0051] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for identifying graphite ore boundaries using multiple horizontal derivatives in thick overburden regions, characterized by: The steps include: S1. Collect natural potential data in the work area; S2. Correct 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, then filter it. S4. The filtered data is gridded using the Kriging interpolation method to form regular gridded natural potential data, which is used as the input data Δu(x,y,0) for boundary identification; S5. Calculate the first derivatives ΔUx and ΔUy in the X and Y directions of the input data Δu(x,y,0); S6. Calculate the modulus of the horizontal derivative of the input data, using the following formula: Where: G(x,y,0) is the magnitude of the first-order horizontal derivative, ΔU x Let ΔU be the first derivative in the X direction. y The first derivative in the Y direction; S7. Obtain the X and Y direction derivatives from the calculated horizontal derivative according to step S5, and calculate its magnitude using the following formula: Where STHD is the modulus of the multiple horizontal derivatives. The horizontal derivative modulus and the partial derivative in the x-direction are the partial derivatives. The horizontal derivative is the partial derivative in the y-direction. S8. Draw multiple horizontal derivative contour maps and identify the graphite ore body boundary based on the maximum value.

2. The graphite ore boundary identification method based on multiple horizontal derivatives in a thick overburden region according to claim 1, characterized in that, In step S2, the correction is diurnal variation correction and terrain correction.

3. The graphite ore boundary identification method based on multiple horizontal derivatives in a thick overburden region according to claim 1, characterized in that: in In step S3, the judgment criteria are as follows: if the data from each measurement point along the measurement line shows a linear change without obvious abrupt changes, and the difference between two measurements of the same point is no greater than ±0.2mV, then the data is considered reliable; if the measured data is checked at different times, by different technicians, and with different instruments, and the check ratio is 3% of the total measurement points, and the arithmetic mean absolute error is no greater than ±5mV, then the data is considered to have high quality. The calculation formula is: Where: △ is the arithmetic mean error, n is the number of checkpoints, and △U i Measure the potential difference ΔU' at point i. i Check the potential difference at point i.

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

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