Iron ore exploration method

Abstract

The invention discloses an iron ore exploration method, and relates to the technical field of gravity and magnetic data processing. Comprising the steps of inputting gravity and magnetic anomaly measurement data; gridding the gravity and magnetic data, and determining a two-dimensional gravity anomaly field and total horizontal derivative anomaly; obtaining a gravity anomaly horizontal derivative based on the two-dimensional gravity anomaly field, and determining a gradient ratio function extreme point based on the gravity anomaly horizontal derivative; determining the geological depth corresponding to the gravity and magnetic anomaly measurement data based on the extreme point coordinates of the gradient ratio function; selecting a sliding window and calculating an average value and a mean square error of each small sub-domain; selecting the first three regions with smaller mean square error, calculating the mean square error of the total horizontal derivative of the three small sub-regions, and further determining a filtering result; solving a vertical derivative and a total horizontal derivative, further calculating to obtain an inclination angle of a sliding small sub-domain filtering result, and outputting a corresponding geological range; and outputting a corresponding exploration result based on the geological depth and the geological range. The depth information and the boundary information can be quickly and effectively identified.

Description

Technical Field

[0001] The present invention relates to the technical field of gravity and magnetic data processing, and particularly to an iron ore exploration method. Background Art

[0002] Gravity and magnetic anomalies are a comprehensive reflection of the uneven distribution of underground material density and magnetism, and have the advantage of high lateral resolution. Therefore, using the gravity and magnetic potential fields has a unique advantage in inferring the boundary position of geological target bodies. However, the common methods all determine the boundary of the target body by using the position of the anomaly maximum value or the zero value, which reduces the accuracy of edge recognition and has a weak ability to suppress noise. In addition, the depth of geological bodies is one of the conventional purposes of gravity and magnetic data interpretation. Calculating the depth of geological bodies includes using the fractal correspondence relationship between the anomaly function and the geological body; and establishing a linear inversion equation between the anomaly function and the position parameters of the geological body. However, most of them require calculating the second-order or even higher-order vertical derivatives of the anomaly, which will also increase the interference of noise and reduce the accuracy of the interpretation results.

[0003] Therefore, how to provide an iron ore exploration method to solve the difficulties existing in the prior art is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0004] In view of this, the present invention provides an iron ore exploration method, which can quickly and effectively identify depth information and boundary information, has high noise resistance, and the output results are more accurate and stable.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] An iron ore exploration method includes the following steps:

[0007] Input gravity and magnetic anomaly measurement data;

[0008] Grid the gravity and magnetic data to determine the two-dimensional gravity anomaly field and the total horizontal derivative anomaly;

[0009] Obtain the horizontal derivative of the gravity anomaly based on the two-dimensional gravity anomaly field, and determine the extreme value points of the gradient ratio function based on the horizontal derivative of the gravity anomaly;

[0010] Determine the geological depth corresponding to the gravity and magnetic anomaly measurement data based on the coordinates of the extreme value points of the gradient ratio function of the horizontal derivative of the gravity anomaly;

[0011] Select a sliding window and calculate the average value and the mean square deviation of each small sub-region;

[0012] Select the first three regions with smaller mean square deviations, calculate the mean square deviation of the total horizontal derivatives of the three small sub-regions, and output the mean value of the sub-region with the smallest mean square deviation to determine the filtering result;

[0013] Obtain the vertical derivative and the total horizontal derivative of the sliding small sub-domain filtering result, obtain the tilt angle of the sliding small sub-domain filtering result based on the vertical derivative and the total horizontal derivative, and output the corresponding geological range;

[0014] Output the corresponding exploration result based on the geological depth and the geological range.

[0015] For the above method, optionally, the gridded gravity and magnetic data includes using the Kriging method to grid the measured gravity and magnetic data to generate regular grid data.

[0016] For the above method, optionally, the expression of the horizontal derivative of the gravity anomaly is:

[0017]

[0018] where f is the gravity and magnetic anomaly, k is a parameter related to the density of the geological body and the gravitational constant, (x0, z0) is the central coordinate point, and N is the structural index;

[0019] Determining the extreme point of the gradient ratio function includes determining the extreme point based on the ratio of the horizontal derivative of the gravity anomaly to the two-dimensional gravity anomaly field.

[0020] For the above method, optionally, taking the sliding window includes selecting two sliding windows, which are divided into a large sliding window and a small sub-domain window. Control the size of the large sliding window to be n, and the internal node data is 5n. The size of the small sub-domain window is s, and the small sub-domain window slides point by point from left to right until it reaches the end of the large window, and then calculates the average value and the mean square deviation as the discrimination criteria.

[0021] The expression for calculating the average value is:

[0022]

[0023] The expression for calculating the mean square deviation is:

[0024]

[0025] where i = 1, 2, 3,..., s 2 。

[0026] For the above method, optionally, selecting the top three regions with smaller mean square deviations includes: selecting the mean value of the sub-domain with the smallest mean square deviation of the horizontal derivative in the selected 3 small sub-domains as the final output result; the large window slides from left to right to the next point until the calculation of the whole area is completed, and the result of the sliding small sub-domain filtering is obtained.

[0027] For the above method, optionally, the expression for determining the tilt angle is:

[0028]

[0029] Among them, f VZ represents the vertical derivative, and f TD represents the total horizontal derivative.

[0030] For the above method, optionally, obtaining the corresponding exploration results includes: judging the iron ore boundary line corresponding to the data based on the inclination angle, determining the corresponding ordinate based on the extreme point, and further determining the iron ore depth corresponding to the data, and determining the exploration result based on the iron ore boundary line and the iron ore depth.

[0031] As can be seen from the above technical solutions, compared with the prior art, the present invention provides an iron ore exploration method, which has the following beneficial effects: 1) The present invention does not require complex operations, and the depth information can be directly, quickly, and accurately obtained through the extreme point, and has high anti-noise performance; 2) The present invention can enhance and amplify the recognition and extraction of weak information, and can better highlight the boundary information, making the output result more accurate and stable, and can quickly and effectively identify the boundary. Description of the Drawings

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the provided drawings.

[0033] Figure 1 It is a flow chart of an iron ore exploration method disclosed by the present invention. Detailed Embodiments

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0035] Referring to Figure 1 as shown, the present invention discloses an iron ore exploration method, including the following steps:

[0036] Input the gravity and magnetic anomaly measurement data;

[0037] Grid the gravity and magnetic data to determine the two-dimensional gravity anomaly field and the total horizontal derivative anomaly;

[0038] Obtain the horizontal derivative of the gravity anomaly based on the two-dimensional gravity anomaly field, and determine the extreme points of the gradient ratio function based on the horizontal derivative of the gravity anomaly;

[0039] Determine the geological depth corresponding to the gravity and magnetic anomaly measurement data based on the coordinates of the extreme points of the gradient ratio function of the horizontal derivative of the gravity anomaly;

[0040] Select a sliding window and calculate the mean and mean square deviation of each small sub-region;

[0041] Select the first three regions with smaller mean square deviations, calculate the mean square deviation of the total horizontal derivative of the three small sub-regions, and output the mean value of the sub-region with the smallest mean square deviation to determine the filtering result;

[0042] Obtain the vertical derivative and total horizontal derivative of the filtering result of the sliding small sub-region, calculate the inclination angle of the filtering result of the sliding small sub-region based on the vertical derivative and total horizontal derivative, and output the corresponding geological range;

[0043] Output the corresponding exploration result based on the geological depth and geological range.

[0044] Furthermore, the gridded gravity and magnetic data includes gridding the measured gravity and magnetic data using the Kriging method to generate regular grid data.

[0045] Specifically, the gravity and magnetic anomaly measurement data is usually discretely distributed. For the convenience of subsequent mathematical calculations and analyses, it needs to be gridded. The Kriging method is an optimal unbiased interpolation method based on the regionalized variable theory and variogram. It fully considers the spatial correlation of the data. By processing the measured gravity and magnetic data, it can generate regular grid data, transforming the actual exploration area into a two-dimensional data matrix form that can be processed by a computer. Based on the gridded gravity and magnetic data, the two-dimensional gravity anomaly field is further determined, and at the same time, the total horizontal derivative anomaly is calculated, providing basic data for subsequent feature analyses.

[0046] Furthermore, the processing method of the Kriging method includes: inputting discrete gravity and magnetic data containing coordinates and anomaly values;

[0047] Calculate the variogram based on the discrete gravity and magnetic data, and fit to generate a theoretical model;

[0048] Interpolate the regular grid nodes based on the theoretical model to generate a two-dimensional data matrix, and then construct a two-dimensional gravity anomaly field.

[0049] Furthermore, the expression of the horizontal derivative of the gravity anomaly is:

[0050]

[0051] where f is the gravity and magnetic anomaly, k is a parameter related to the density of the geological body and the gravitational constant, (x0, z0) is the central coordinate point, and N is the structural index;

[0052] Determining the extreme points of the gradient ratio function includes determining the extreme points based on the ratio of the horizontal derivative of the gravity anomaly to the two-dimensional gravity anomaly field.

[0053] Specifically, the expression of the gradient ratio function is as follows:

[0054]

[0055] The horizontal coordinate of the extreme point of the gradient ratio function is independent of the structural index, and the horizontal distance between the two extreme points is equal to 2z0. That is, the midpoint of the two extreme points corresponds to the horizontal position of the centroid of the geological body. Therefore, the position parameters of the geological body can be obtained without prior information.

[0056] Furthermore, the selection of the sliding window includes two selections of the sliding window, which are divided into a large sliding window and a small sub-domain window. The size of the large sliding window is controlled to be n, and the internal node data is 5n. The size of the small sub-domain window is s. The small sub-domain window slides point by point from left to right until it reaches the end of the large window. Then, the average value and the mean square deviation are calculated as the discrimination criteria.

[0057] The expression for calculating the average value is:

[0058]

[0059] The expression for calculating the mean square deviation is:

[0060]

[0061] where i = 1, 2, 3, …, s 2 .

[0062] Specifically, The average value reflects the overall level of the data in the small sub-domain, and the mean square deviation measures the degree of dispersion of the data. The two are used as the discrimination criteria for subsequent area screening.

[0063] Furthermore, the selection of the first three regions with smaller mean square deviations includes: selecting the mean value of the sub-domain with the smallest mean square deviation of the horizontal derivative in the selected 3 small sub-domains as the final output result; the large window slides from left to right to the next point until the calculation of the whole area is completed, and the result of the sliding small sub-domain filtering is obtained.

[0064] Furthermore, the expression for calculating the mean square deviation of the total horizontal derivative is:

[0065]

[0066] where δ TDi represents the mean square deviation of the total horizontal derivative in the i-th small sub-domain, represents the average value of the total horizontal derivative in the i-th small sub-domain, and f TD represents the total horizontal derivative anomaly.

[0067] Furthermore, the expression for determining the inclination angle is:

[0068]

[0069] Among them, f VZ is expressed as the vertical derivative, and the expression is: f TD is expressed as the total horizontal derivative anomaly.

[0070] Furthermore, obtaining the corresponding exploration results includes: judging the iron ore boundary line corresponding to the data based on the inclination angle, determining the corresponding ordinate based on the extreme point, and then determining the iron ore depth corresponding to the data, and determining the exploration results based on the iron ore boundary line and the iron ore depth.

[0071] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious 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 invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An iron ore exploration method, characterized in that, It includes the following steps: Input the measured gravity and magnetic anomaly data; Grid the gravity and magnetic data to determine the two-dimensional gravity anomaly field and the total horizontal derivative anomaly; Obtain the horizontal derivative of the gravity anomaly based on the two-dimensional gravity anomaly field, and determine the extreme points of the gradient ratio function based on the horizontal derivative of the gravity anomaly; Determine the geological depth corresponding to the measured gravity and magnetic anomaly data based on the coordinates of the extreme points of the gradient ratio function of the horizontal derivative of the gravity anomaly; Select a sliding window and calculate the mean and variance of each small sub-domain; Select the first three regions with smaller variances, calculate the variance of the total horizontal derivatives of the three small sub-domains, and output the mean of the sub-domain with the smallest variance to determine the filtering result; Obtain the vertical derivative and the total horizontal derivative of the filtering result of the sliding small sub-domain, calculate the tilt angle of the filtering result of the sliding small sub-domain based on the vertical derivative and the total horizontal derivative, and output the corresponding geological range; Output the corresponding exploration result based on the geological depth and the geological range.

2. The iron ore exploration method according to claim 1, wherein Gridding the gravity and magnetic data includes using the Kriging method to grid the measured gravity and magnetic data to generate regular grid data.

3. The iron ore exploration method according to claim 1, wherein The expression for the horizontal derivative of gravity anomaly is as follows: where f is the gravity and magnetic anomaly, k is a parameter related to the density of the geological body and the gravitational constant, (x0, z0) is the central coordinate point, and N is the structural index; Determining the extreme points of the gradient ratio function includes determining the extreme points based on the ratio of the horizontal derivative of the gravity anomaly to the two-dimensional gravity anomaly field.

4. The iron ore exploration method according to claim 1, wherein Selecting the sliding window includes selecting the sliding window twice, which are divided into a large sliding window and a small sub-domain window. Control the size of the large sliding window as n, and the internal node data is 5n. The size of the small sub-domain window is s, and the small sub-domain window slides point by point from left to right until it reaches the end of the large window, and then calculate the mean and variance as the discrimination criteria, The expression for calculating the mean is: The expression for calculating the variance is: where i = 1, 2, 3, …, s 2 .

5. The iron ore exploration method according to claim 1, wherein Selecting the first three regions with smaller variances includes: selecting the mean of the sub-domain with the smallest horizontal derivative variance in the selected 3 small sub-domains as the final output result; the large window slides from left to right to the next point until the calculation of the whole area is completed to obtain the result of the sliding small sub-domain filtering.

6. The iron ore exploration method according to claim 1, wherein The expression for determining the tilt angle is: where f VZ represents the vertical derivative, and f TD represents the total horizontal derivative.

7. The iron ore exploration method according to claim 1, wherein Obtaining the corresponding exploration result includes: judging the iron ore boundary line corresponding to the data based on the tilt angle, determining the corresponding ordinate based on the extreme points, and then determining the iron ore depth corresponding to the data, and determining the exploration result based on the iron ore boundary line and the iron ore depth.

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