Gravity potential field boundary identification method and device

Abstract

The invention discloses a gravitational potential field boundary identification method and device. Firstly, boundary enhancement is carried out on gravity anomaly grid data to extract a boundary line; and then performing boundary enhancement on the gravity anomaly grid data again by adopting different methods to obtain a maximum value point of the enhanced boundary, and calibrating the extracted boundary line by utilizing the maximum value point. According to the gravity potential field boundary identification method, the accuracy of boundary identification can be ensured, and the problems of false anomaly and boundary discontinuity in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the field of geophysical exploration technology, and more particularly to a method and device for identifying gravity potential field boundaries. Background Art

[0002] Gravity potential field boundary recognition technology has important application value in the field of geological exploration, especially in determining the horizontal position of underground faults, contact zones and other geological body edges, providing a key basis for geological structure interpretation.

[0003] Traditional gravity potential field boundary identification techniques primarily involve boundary enhancement and boundary picking. Despite progress in addressing the influence of factors such as geological depth and oblique magnetization, several technical challenges remain. These include the fact that existing boundary enhancement methods are prone to generating false anomalies or erroneous boundaries and exhibit low boundary resolution, while boundary picking methods often suffer from poor continuity.

[0004] In addition, the boundary vector results generated by many new methods are point data rather than continuous lines, which is not conducive to the subsequent work.

[0005] Therefore, the existing gravity potential field boundary identification method urgently needs to be improved to overcome the above problems. Summary of the Invention

[0006] In view of this, in order to at least partially solve the above technical problems, the present invention provides a method and device for identifying the boundary of a gravitational potential field. To achieve the above objectives, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present application provides a method for identifying gravity field boundaries, comprising:

[0008] S1. Use method 1 to perform boundary enhancement on gravity anomaly grid data;

[0009] S2. Extracting boundary lines based on the enhanced network data, the steps include:

[0010] According to the spatial distance of the enhanced network data, a connection restriction matrix is ​​established to obtain multiple data clusters.

[0011] Preset the number of clusters and cluster multiple data clusters based on the minimum information loss.

[0012] Generate boundary lines based on clustered grid points;

[0013] S3. Use method 2 to enhance the boundary of the gravity anomaly grid data and pick the maximum point of the enhanced boundary;

[0014] S4. Calibrate the boundary line using the maximum value point.

[0015] As a preferred method, the first method is the derivative analysis method, and the second method is the mathematical statistics method; wherein,

[0016] The derivative analysis method includes the vertical derivative method, the total horizontal derivative method, the oblique derivative method and the Theta graph method; the mathematical statistics method includes the small subdomain method and the normalized standard deviation method.

[0017] Preferably, in step S2, the information loss amount is obtained by the following formula:

[0018]

[0019] Where n is the number of sample points after clustering, s k is the value of sample point k.

[0020] Preferably, in step S2, classification label values ​​are set for the clustered grid points, and boundary lines are generated based on the classification label values.

[0021] Preferably, in step S3, the formula for picking the maximum point of the enhanced boundary is:

[0022]

[0023] Where, f i,j is the signal value at grid point i,j.

[0024] Preferably, in step S4, calibrating the boundary line using the maximum point includes:

[0025] Determine the boundary line passing through the maximum point or the boundary line closest to the maximum point, and treat the uncalibrated boundary lines as false boundary lines and eliminate them.

[0026] In a second aspect, the present application provides a gravitational potential field boundary recognition device, which applies the gravitational potential field boundary recognition method described above, including:

[0027] A first boundary enhancement unit is used to perform boundary enhancement on gravity anomaly grid data using method one;

[0028] The boundary line extraction unit is used to extract the boundary line based on the enhanced network data, and the steps include:

[0029] According to the spatial distance of the enhanced network data, a connection restriction matrix is ​​established to obtain multiple data clusters.

[0030] Preset the number of clusters and cluster multiple data clusters based on the minimum information loss.

[0031] Generate boundary lines based on clustered grid points;

[0032] The second boundary enhancement unit is used to enhance the boundary of the gravity anomaly grid data using the second method and pick the maximum value point of the enhanced boundary;

[0033] A boundary line calibration unit is used to calibrate the boundary line using the maximum value point.

[0034] As can be seen from the above technical solution, compared with the existing technology, the present invention provides a method and device for identifying gravitational potential field boundaries. First, by establishing a connection restriction matrix and clustering data, a smooth and continuous boundary line is generated, solving the problem of weak boundary continuity in traditional methods. Then, the boundary line is calibrated using the maximum value points picked, ensuring the accuracy of the boundary.

[0035] This application uses two different boundary enhancement methods to effectively reduce the occurrence of false anomalies or erroneous boundaries, thereby improving the accuracy of boundary identification. In the field of geological exploration, the gravity potential field boundary identification technology has higher accuracy, resolution, and continuity, providing more reliable technical support for geological structure interpretation. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0037] Figure 1 This is a flow chart of the gravity potential field boundary identification method of the present invention;

[0038] Figure 2 This is a flow chart of an example of the gravity potential field boundary identification method of the present invention;

[0039] Figure 3 This is a simulation diagram of the gravity field of the present invention;

[0040] Figure 4 This is the result of identifying the abnormal boundary of the simulated gravity potential field according to the present invention. DETAILED DESCRIPTION

[0041] 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.

[0042] In order to solve the problems of false boundaries and boundary discontinuity in existing boundary recognition methods, an embodiment of the present invention discloses a gravity potential field boundary recognition method based on the concept of combinatorial optimization.

[0043] Specifically, if Figure 1 As shown, the method includes the following steps:

[0044] S1. Use method 1 to perform boundary enhancement on gravity anomaly grid data;

[0045] S2. Extracting boundary lines based on the enhanced network data, the steps include:

[0046] According to the spatial distance of the enhanced network data, a connection restriction matrix is ​​established to obtain multiple data clusters.

[0047] Preset the number of clusters and cluster multiple data clusters based on the minimum information loss.

[0048] Generate boundary lines based on clustered grid points;

[0049] S3. Use method 2 to enhance the boundary of the gravity anomaly grid data and pick the maximum point of the enhanced boundary;

[0050] S4. Calibrate the boundary line using the maximum value point.

[0051] In one embodiment, to ensure the optimization effect, method one and method two are different boundary enhancement methods. Since the derivative analysis method can provide information about the rate of change of the data, it is more suitable for generating continuous boundary lines; while the results of the mathematical statistics method are extreme points, which may not be continuous and cannot be used directly to form boundary lines. Extreme points indicate more of the center of the abnormal area rather than the complete outline of the boundary. Therefore, preferably, in this application, method one is the derivative analysis method, and method two is the mathematical statistics method; and the derivative analysis method includes the vertical derivative method, the total horizontal derivative method, the oblique derivative method and the Theta graph method; the mathematical statistics method includes the small subdomain method and the normalized standard deviation method.

[0052] Although existing methods that only use derivative analysis can make boundaries more prominent and clear, the high-order derivative operations in the derivative analysis method are easily interfered by noise, resulting in false anomalies or erroneous boundaries; or when only mathematical statistics methods are used, the identified boundary resolution is low.

[0053] The present invention optimizes by combining different boundary enhancement methods, which can effectively solve the problem of false boundaries existing in the derivative analysis method. At the same time, calibration based on the boundary lines extracted by this application can obtain smooth and continuous boundary lines, thereby solving the problem of weak continuity of picked boundaries in traditional methods (point position extraction or maximum value extraction) and the problem of low resolution of identified boundaries generated by only using mathematical statistics methods.

[0054] In one embodiment, step S2 includes:

[0055] S21. Based on the spatial distance of the enhanced network data, a connection restriction matrix is ​​established to obtain multiple data clusters. The spatial distance calculation formula is as follows:

[0056]

[0057] Among them, x, y are the spatial coordinate values ​​of the grid point;

[0058] S22. Preset the number of clusters and cluster the multiple data clusters based on the minimum information loss, wherein the information loss is obtained by the following formula:

[0059]

[0060] Where n is the number of sample points after clustering, s k is the value of sample point k. Each time cluster merging is performed, the information loss is required to be minimized, that is, the sum of all clusters increases the least compared to the value before merging.

[0061] S23. Setting classification label values ​​for the clustered grid points, and generating boundary lines based on the classification label values.

[0062] In one embodiment, the formula for picking the maximum point of the enhanced boundary in step S3 is:

[0063]

[0064] Where, f i,j is the signal value at grid point i,j.

[0065] In one embodiment, step S4 calibrates the boundary line using the maximum point, including:

[0066] Determine the boundary line passing through the maximum point or the boundary line closest to the maximum point, and treat the uncalibrated boundary lines as false boundary lines and eliminate them.

[0067] The present invention will be further described below through specific examples.

[0068] In an exemplary embodiment, Figure 2 As shown in FIG, the gravity potential field boundary identification method includes:

[0069] Step 1: Oblique derivative boundary enhancement, that is, using the oblique derivative method to enhance the boundary of the gravity anomaly grid data to highlight the signal energy at the boundary and obtain the grid data after boundary enhancement. Input the gravity anomaly grid data, such as Figure 3 , where the lines represent the edges of the geological bodies, and the oblique derivative method formula is as follows:

[0070]

[0071] Where Δg is the gravity anomaly, and x, y, and z are the three components in the horizontal and vertical directions;

[0072] Step 2: Boundary line extraction. Input the grid data generated in step 1 and use the spatial distance of the grid point coordinates to establish the nearest neighbor connection restriction matrix. The calculation formula of the spatial distance d is:

[0073]

[0074] Among them, x and y are the spatial coordinate values ​​of the grid point.

[0075] Select Euclidean distance as the connection metric, Ward method as the connection method, set the number of clusters to 4, perform clustering, and obtain the boundary line.

[0076] Step 3: Input gravity anomaly grid data ( Figure 3 ), the boundary is enhanced by normalized standard deviation, that is, the boundary of gravity anomaly grid data is enhanced by using normalized standard deviation. The normalized standard deviation formula is:

[0077]

[0078] Where σ is the standard deviation within the sliding window, Δg is the gravity anomaly, and x, y, and z are the three components in the horizontal and vertical directions;

[0079] Step 4: Boundary point picking: Pick boundary points of the grid data generated in step 3 to obtain the boundary maximum value point.

[0080] Step 5: Boundary line calibration. Use the boundary points obtained in step 4 to calibrate the boundary line obtained in step 2 to obtain the optimized boundary line. Figure 4 , the blue line is the edge of the geological body, and the red line is the boundary recognition result.

[0081] In another embodiment, the present invention discloses a gravitational potential field boundary recognition device, which applies the gravitational potential field boundary recognition method described above, including:

[0082] A first boundary enhancement unit is used to perform boundary enhancement on gravity anomaly grid data using method one;

[0083] The boundary line extraction unit is used to extract the boundary line based on the enhanced network data, and the steps include:

[0084] According to the spatial distance of the enhanced network data, a connection restriction matrix is ​​established to obtain multiple data clusters.

[0085] Preset the number of clusters and cluster multiple data clusters based on the minimum information loss.

[0086] Generate boundary lines based on clustered grid points;

[0087] The second boundary enhancement unit is used to enhance the boundary of the gravity anomaly grid data using the second method and pick the maximum value point of the enhanced boundary;

[0088] A boundary line calibration unit is used to calibrate the boundary line using the maximum value point.

[0089] Since the content of the gravitational potential field boundary identification device of the present application is consistent with the above-mentioned gravitational potential field boundary identification method, it will not be repeated here.

[0090] The present invention effectively combines different boundary enhancement methods, solves the problems of false boundaries and boundary discontinuity through a combined optimization method, and obtains accurate and continuous boundary recognition results. This application is conducive to the development of the next step of interpretation research work.

[0091] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0092] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for identifying the boundary of a gravity potential field, characterized in that: S1. Use method 1 to perform boundary enhancement on gravity anomaly grid data; S2, extracting boundary lines based on enhanced network data, the steps include: According to the spatial distance of the enhanced network data, a connection restriction matrix is ​​established to obtain multiple data clusters. Preset the number of clusters and cluster multiple data clusters based on the minimum information loss. Generate boundary lines based on clustered grid points; S3, using method 2 to enhance the boundary of the gravity anomaly grid data, and picking up the maximum value point of the enhanced boundary; S4. Calibrate the boundary line using the maximum value point.

2. A gravity potential field boundary identification method according to claim 1, characterized in that: Method one is the derivative analysis method, and method two is the mathematical statistics method.

3. A gravity potential field boundary identification method according to claim 1, characterized in that: In step S2, the information loss amount is obtained by the following formula: In the formula, n is the number of sample points after clustering, s k is the value of sample point k.

4. A gravity potential field boundary identification method according to claim 1, characterized in that: In step S2, classification label values ​​are set for the clustered grid points, and boundary lines are generated based on the classification label values.

5. A method for identifying gravity potential field boundaries according to claim 1, characterized in that: In step S3, the formula for picking the maximum value point of the enhanced boundary is: In the formula, f i,j is the signal value at the grid point (i, j).

6. A method for identifying gravity potential field boundaries according to claim 1, characterized in that: In step S4, the boundary line is calibrated using the maximum value point, including: Determine the boundary line that passes through the maximum point or the boundary line closest to the maximum point, and treat the uncalibrated boundary lines as false boundary lines and remove them.

7. A gravitational potential field boundary recognition device, characterized in that: The method for identifying the boundary of a gravity potential field according to any one of claims 1 to 6 comprises: A first boundary enhancement unit is used to perform boundary enhancement on the gravity anomaly grid data using method one; The boundary line extraction unit is used to extract the boundary line based on the enhanced network data, and the steps include: According to the spatial distance of the enhanced network data, a connection restriction matrix is ​​established to obtain multiple data clusters. Preset the number of clusters and cluster multiple data clusters based on the minimum information loss. Generate boundary lines based on clustered grid points; The second boundary enhancement unit is used to enhance the boundary of the gravity anomaly grid data using the second method and pick up the maximum value point of the enhanced boundary; A boundary line calibration unit is used to calibrate the boundary line using the maximum value point.

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