Sliding small sub-domain enhanced equalization filtering method

Abstract

The invention belongs to the field of gravity and magnetic data processing, and particularly relates to a sliding small sub-domain enhanced equalization filtering method, which comprises the following steps of: 1, gridding gravity and magnetic data, and calculating total horizontal derivative anomaly of gravity and magnetic anomaly; 2, selecting the sizes of a sliding large window and a small sub-domain window; step 3, sliding the small sub-domain window for filtering, and outputting an average value corresponding to the small sub-domain with the minimum mean square error of the total horizontal derivative; 4, sliding the large window to the next point, repeating the step 3 until the calculation of the whole region is completed, and obtaining a result of sliding small subdomain filtering; 5, solving an enhanced equalization filter value of a sliding small subdomain filtering result by using a cosine function of different order derivative ratios; and step 6, according to the enhanced equalization filter value of the sliding small sub-domain filtering result, carrying out abnormal body boundary identification. The method is used for gravity and magnetic potential field boundary identification, the boundary of an abnormal body with a large burial depth can be highlighted, and the boundary of a deep abnormal geologic body can be rapidly and effectively identified.

Description

Technical Field

[0001] The invention belongs to the field of gravity and magnetic data processing, and in particular relates to a sliding small subdomain enhanced equalization filtering method. Background Art

[0002] Gravity and magnetic anomalies are a comprehensive reflection of the uneven density and magnetic distribution of underground materials. They have the advantage of high lateral resolution, so gravity and magnetic potential field data have unique advantages in inferring and extracting the boundary position of the target geological body. Before using gravity and magnetic data to extract the boundary position of the geological body, the gravity and magnetic data need to be processed and converted.

[0003] However, there are deficiencies in traditional interpretation methods and processing technologies. For example, conventional filtering methods will blur the boundaries between anomalies and reduce the filtering effect; conventional linear structure recognition methods will reduce the accuracy of edge recognition and have weak noise suppression capabilities. They cannot identify the boundaries of deeper geological bodies or the location of geological bodies accurately, and the identified boundaries are relatively divergent.

[0004] Therefore, it is urgent to propose a new recognition method for the recognition of weak information on the boundary of gravity and magnetic potential fields, which can improve the shortcomings of existing filters and enhance the results of boundary recognition. Summary of the invention

[0005] The purpose of the present invention is to provide a sliding small subdomain enhanced equalization filtering method, which is used for gravity and magnetic potential field boundary identification, can identify the boundary between deep and shallow anomalies, highlight the boundary information of geological bodies, make the output results more accurate and stable, and can quickly and effectively identify the boundaries of geological bodies.

[0006] The technical solution to achieve the purpose of the present invention is:

[0007] A sliding small sub-domain enhanced equalization filtering method, the method comprising:

[0008] Step 1: grid the gravity and magnetic data and calculate the total horizontal derivative anomaly of the gravity and magnetic anomalies;

[0009] Step 2: Select the size of the sliding large window and the small sub-domain window;

[0010] Step 3: Slide the small subdomain window for filtering, and output the average value corresponding to the small subdomain with the smallest mean square error of the total horizontal derivative;

[0011] Step 4: Slide the large window to the next point and repeat step 3 until the calculation of the entire area is completed to obtain the result of sliding small subdomain filtering;

[0012] Step 5, using cosine functions of different order derivative ratios to obtain the enhanced equalization filter value of the sliding small subdomain filtering result;

[0013] Step 6: According to the enhanced equalization filter value of the sliding small sub-domain filtering result, the boundary of the abnormal body is identified.

[0014] The calculation formula of the total horizontal derivative anomaly of gravity and magnetic anomalies in step 1 is:

[0015]

[0016] In the formula, f represents gravity and magnetic anomaly, f TD Represents the total horizontal derivative anomaly of gravity and magnetic anomalies.

[0017] In step 2, the size of the large sliding window is n, the large window node data is 5n, and the size of the internal small subdomain window is k=(n+1) / 2; the window of the small subdomain slides point by point from left to right and from top to bottom until it reaches the end of the large window.

[0018] The step 3 comprises:

[0019] Step 3.1, calculate the mean value and mean square error of each small subdomain;

[0020] Step 3.2, select three small sub-domains with smaller mean square errors as target areas;

[0021] Step 3.3, calculate the mean square error of the total horizontal derivatives of the three selected small subdomains;

[0022] Step 3.4: Select the average value corresponding to the small subdomain with the smallest mean square error of the total horizontal derivative as the output result.

[0023] The calculation formulas for the mean value and mean square error of the small subdomain in step 3.1 are:

[0024]

[0025] In the formula, f represents gravity and magnetic anomaly, represents the average value of the small subdomain, δ represents the mean square error of the small subdomain, k represents the size of the small subdomain window, i represents the row, j represents the column, represents the average value of the small subdomain in the i-th row, f i(j) Represents the gravity and magnetic anomaly value of the i-th row and j-th column.

[0026] The calculation formula of the mean square error of the total horizontal derivative of the small subdomain in step 3.3 is:

[0027]

[0028] In the formula, f TD The total horizontal derivative anomaly represents the gravity and magnetic anomalies. represents the average value of the total horizontal derivative in the small subdomain, δ TDRepresents the mean square error of the total horizontal derivative in the small subdomain.

[0029] In step 4, the large window slides from left to right and from top to bottom to the next point until it reaches the end of the entire area, completing the calculation of the entire area, and the average value of each small sub-domain in the entire area is added and averaged to obtain the result MSSf of the sliding small sub-domain filtering.

[0030] The calculation formula of the enhanced equalization filter value of the sliding small sub-domain filtering result in step 5 is:

[0031]

[0032] In the formula, MSSEC TD represents the value of the sliding subdomain enhanced equalization filter, R represents the real part of the calculation result, MSSf represents the gravity and magnetic anomaly after sliding subdomain filtering, is the amplitude of different order derivatives, and n represents the order of the derivative.

[0033] The step 6 is specifically as follows: the position of the field source body boundary can be identified by using the position of the maximum value of the enhanced equalization filter of the sliding small subdomain filtering result; the position of the maximum value of the enhanced equalization filter of the sliding small subdomain filtering result corresponds to the position of the field source body boundary.

[0034] The beneficial technical effects of the present invention are:

[0035] The present invention provides a sliding small subdomain enhanced equalization filtering method, which can highlight weak anomalies with large burial depth and small physical property difference, balance the amplitude of anomalies at different depths, have a better enhancement effect on boundaries in gravity and magnetic fields, can enhance and amplify the recognition and extraction of weak information, and can better highlight the boundary information of geological bodies, making the output results more accurate and stable, and quickly and effectively identifying the boundaries of geological bodies. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A flow chart of a sliding small sub-domain enhanced equalization filtering method provided by the present invention;

[0037] Figure 2 It is a partial schematic diagram of a sliding large window and a small sub-domain window in a sliding small sub-domain enhanced equalization filtering method provided by the present invention;

[0038] Figure 3 This is a sliding decomposition diagram of a sliding sub-domain in an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0040] like Figure 1As shown, the present invention provides a sliding small sub-domain enhanced equalization filtering method, which specifically includes the following steps:

[0041] Step 1: Grid the gravity and magnetic data and calculate the total horizontal derivative anomaly of the gravity and magnetic anomaly

[0042] The measured gravity and magnetic data are gridded using the Kriging method to generate regular grid data. The magnetic data must first be polarized, and then the total horizontal derivative anomaly of the gravity and magnetic anomaly is calculated according to formula (1);

[0043]

[0044] In the formula, f represents gravity and magnetic anomaly, f TD Represents the total horizontal derivative anomaly of gravity and magnetic anomalies.

[0045] Step 2: Select the size of the sliding large window and the small sub-domain window

[0046] Two sliding windows, the size of the large sliding window is n, and the large window node data is 5n ( Figure 2 The size of the inner small sub-domain window is k = (n+1) / 2 ( Figure 2 The red window in the middle), and then the window of the small subdomain slides point by point from left to right and from top to bottom until it reaches the end of the large window. The sliding method is as follows Figure 2 As shown;

[0047] Step 3: Slide the small subdomain window for filtering and output the average value corresponding to the small subdomain with the smallest mean square error of the total horizontal derivative.

[0048] Step 3.1: Calculate the mean and mean square error of each subdomain

[0049]

[0050] In the formula, f represents gravity and magnetic anomaly, represents the average value of the small subdomain, δ represents the mean square error of the small subdomain, k represents the size of the small subdomain window, i represents the row, j represents the column, represents the average value of the small subdomain in the i-th row, f i(j) Represents the gravity and magnetic anomaly value of the i-th row and j-th column.

[0051] Step 3.2: Select three small sub-regions with smaller mean square error as the target area

[0052] In the layout of k 2 Select the first three sub-regions with the smallest mean square error of data as the target area;

[0053] Step 3.3: Calculate the mean square error of the total horizontal derivatives of the three selected small subdomains

[0054] Calculate the mean square error of the total horizontal derivatives of the selected three small subdomains;

[0055]

[0056] In the formula, f TD The total horizontal derivative anomaly represents the gravity and magnetic anomalies. represents the average value of the total horizontal derivative in the small subdomain, δ TD Represents the mean square error of the total horizontal derivative in the small subdomain.

[0057] Step 3.4: Select the average value corresponding to the small subdomain with the smallest mean square error of the total horizontal derivative as the output result

[0058] Select the average value corresponding to the sub-domain with the smallest mean square error of the total horizontal derivative among the selected three small sub-domains As the final output result, that is, Figure 2 The value of the center point of the orange window;

[0059] Step 4: Slide the large window to the next point and repeat step 3 until the calculation of the entire area is completed to obtain the result of sliding small subdomain filtering.

[0060] The large window slides to the next point (from left to right, from top to bottom) until it reaches the end of the entire area, completing the calculation of the entire area. The average value of each small sub-domain in the entire area is added and averaged to obtain the result of sliding small sub-domain filtering (MSSf);

[0061] Step 5, using cosine functions of different order derivative ratios to obtain the enhanced equalization filter value (MSSECF) of the sliding small subdomain filtering result;

[0062]

[0063] In the formula, MSSEC TD represents the value of the sliding subdomain enhanced equalization filter, R represents the real part of the calculation result, MSSf represents the gravity and magnetic anomaly after sliding subdomain filtering, is the amplitude of different order derivatives, and n represents the order of the derivative.

[0064] Step 6: According to the enhanced equalization filter value of the sliding small subdomain filtering result, the boundary of the abnormal body is identified.

[0065] The position of the boundary of the field source body can be identified by using the position of the maximum value of the enhanced equalization filter of the sliding small subdomain filtering result. The position of the maximum value of the enhanced equalization filter of the sliding small subdomain filtering result corresponds to the position of the boundary of the field source body.

[0066] Example

[0067] Taking the ground Bouguer gravity anomaly data of a certain area in Xiangshan Basin as an example, the present invention provides a sliding small subdomain enhanced equalization filtering method, which specifically includes the following steps:

[0068] Step 1: Use Kriging method to grid the two-dimensional discrete Bouguer gravity anomaly data in Surfer (the gridding parameters are grid spacing of 50m and search radius of 500m) to generate 141×139 grid data. Then, the total horizontal derivative anomaly of the Bouguer gravity anomaly data is obtained according to formula (1):

[0069]

[0070] Where f represents the Bouguer gravity anomaly, f TD Total horizontal derivative anomaly representing the Bouguer gravity anomaly.

[0071] Step 2: Select the size of the sliding large window and the small sub-domain window. The size of the large sliding window is n (n=5), the large window node data is 25, and the size of the internal small sub-domain window is k=(n+1) / 2 (k=3). Then the small sub-domain window slides point by point from left to right and from top to bottom. Figure 3 As shown;

[0072] Step 3: Slide the small subdomain window for filtering and output the average value corresponding to the small subdomain with the smallest mean square error of the total horizontal derivative.

[0073] Step 3.1, use the following formula (2) and formula (3) to calculate the mean square error of the 9 small sub-domains;

[0074]

[0075] Where f represents the Bouguer gravity anomaly data, represents the average value of the Bouguer gravity anomaly data of the nine small subdomains, and δ represents the mean square error of the Bouguer gravity anomaly data of the nine small subdomains.

[0076] Step 3.2, in the layout k 2 (9) Select the first three sub-regions with the smallest mean square error of data as the target area;

[0077] Step 3.3, use formula (2) and formula (4) to calculate the mean and mean square error of the total horizontal derivatives of the Bouguer gravity data of the three small sub-domains in step 2;

[0078]

[0079] In the formula, f TD represents the total horizontal derivative of the Bouguer gravity anomaly data, represents the average value of the total horizontal derivative in the small subdomain, δ TDRepresents the mean square error of the total horizontal derivative in the small subdomain.

[0080] Step 3.4: Select the sub-domain with the smallest mean square error of the total horizontal derivative from the three selected sub-domains, and calculate the average value corresponding to this sub-domain As the final output result, that is, the value of the center point of the large window;

[0081] Step 4: The large window (the window size is still 5) slides to the next point (from left to right, from top to bottom) until the calculation of the entire area is completed, and the result of the sliding small subdomain filtering (MSSf) is obtained;

[0082] Step 5, using the cosine function of different order derivative ratios of formula (5) to obtain the enhanced equalization filter value (MSSECF) of the sliding small subdomain filtering result;

[0083]

[0084] In the formula, MSSEC TD represents the value of the sliding subdomain enhanced equalization filter, R represents the real part of the calculation result, MSSf represents the gravity and magnetic anomaly after sliding subdomain filtering, is the amplitude of different order derivatives, and n represents the order of the derivative. Step 6: The position of the maximum value of the enhanced equalization filter of the sliding small subdomain filtering result can be used to identify the position of the boundary of the field source body. The position of the maximum value of the enhanced equalization filter of the sliding small subdomain filtering result corresponds to the position of the boundary of the field source body.

[0085] The present invention is described in detail above with reference to the accompanying drawings and embodiments, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of ordinary technicians in the field without departing from the purpose of the present invention. The contents not described in detail in the present invention can adopt the existing technology.

Claims

1. A sliding small sub-domain enhanced equalization filtering method, characterized in that: The method comprises: Step 1: grid the gravity and magnetic data and calculate the total horizontal derivative anomaly of the gravity and magnetic anomalies; Step 2: Select the size of the sliding large window and the small sub-domain window; Step 3: Slide the small subdomain window for filtering, and output the average value corresponding to the small subdomain with the smallest mean square error of the total horizontal derivative; Step 4: Slide the large window to the next point and repeat step 3 until the calculation of the entire area is completed to obtain the result of sliding small subdomain filtering; Step 5, using cosine functions of different order derivative ratios to obtain the enhanced equalization filter value of the sliding small subdomain filtering result; Step 6: According to the enhanced equalization filter value of the sliding small sub-domain filtering result, the boundary of the abnormal body is identified.

2. The sliding small sub-domain enhanced equalization filtering method according to claim 1, characterized in that: The calculation formula of the total horizontal derivative anomaly of gravity and magnetic anomalies in step 1 is: In the formula, f represents gravity and magnetic anomaly, f TD Represents the total horizontal derivative anomaly of gravity and magnetic anomalies.

3. The sliding small sub-domain enhanced equalization filtering method according to claim 1, characterized in that: In step 2, the size of the large sliding window is n, the large window node data is 5n, and the size of the internal small subdomain window is k=(n+1) / 2; the window of the small subdomain slides point by point from left to right and from top to bottom until it reaches the end of the large window.

4. The sliding small sub-domain enhanced equalization filtering method according to claim 1, characterized in that: The step 3 comprises: Step 3.1, calculate the mean value and mean square error of each small subdomain; Step 3.2, select three small sub-domains with smaller mean square errors as target areas; Step 3.3, calculate the mean square error of the total horizontal derivatives of the three selected small subdomains; Step 3.4: Select the average value corresponding to the small subdomain with the smallest mean square error of the total horizontal derivative as the output result.

5. The sliding small sub-domain enhanced equalization filtering method according to claim 1, characterized in that: The calculation formulas for the mean value and mean square error of the small subdomain in step 3.1 are: In the formula, f represents gravity and magnetic anomaly, represents the average value of the small subdomain, δ represents the mean square error of the small subdomain, k represents the size of the small subdomain window, i represents the row, j represents the column, represents the average value of the small subdomain in the i-th row, f i(j) Represents the gravity and magnetic anomaly value of the i-th row and j-th column.

6. The sliding small sub-domain enhanced equalization filtering method according to claim 1, characterized in that: The calculation formula of the mean square error of the total horizontal derivative of the small subdomain in step 3.3 is: In the formula, f TD The total horizontal derivative anomaly represents the gravity and magnetic anomalies. represents the average value of the total horizontal derivative in the small subdomain, δ TD Represents the mean square error of the total horizontal derivative in the small subdomain.

7. The sliding small sub-domain enhanced equalization filtering method according to claim 1, characterized in that: In step 4, the large window slides from left to right and from top to bottom to the next point until it reaches the end of the entire area, completing the calculation of the entire area, and the average value of each small sub-domain in the entire area is added and averaged to obtain the result MSSf of the sliding small sub-domain filtering.

8. The sliding small sub-domain enhanced equalization filtering method according to claim 1, characterized in that: The calculation formula of the enhanced equalization filter value of the sliding small sub-domain filtering result in step 5 is: In the formula, MSSEC TD represents the value of the sliding subdomain enhanced equalization filter, R represents the real part of the calculation result, MSSf represents the gravity and magnetic anomaly after sliding subdomain filtering, is the amplitude of different order derivatives, and n represents the order of the derivative.

9. The sliding small sub-domain enhanced equalization filtering method according to claim 1, characterized in that: The step 6 is specifically as follows: the position of the field source body boundary can be identified by using the position of the maximum value of the enhanced equalization filter of the sliding small subdomain filtering result; the position of the maximum value of the enhanced equalization filter of the sliding small subdomain filtering result corresponds to the position of the field source body boundary.

Images