A method, device, medium and equipment for constructing gravity and magnetic section anomaly

Abstract

The present invention discloses a method, device, medium and equipment for constructing gravity and magnetic section anomalies, and relates to the field of geophysical exploration technology. The method includes: interpolating and expanding gravity and magnetic anomaly data to form gravity and magnetic plane grid data; using a grid data gradient filter method to filter and denoise the gravity and magnetic plane grid data while maintaining the gradient characteristics of the gravity and magnetic plane grid data; extending the gravity and magnetic plane grid data after filtering and denoising upward, and subtracting adjacent anomaly data at different heights to obtain plane anomalies corresponding to different extension heights; based on the local gradient of the sliding small subdomain filter method to adjust the filter strength, the plane anomaly is adaptively enhanced to obtain the filter enhanced anomaly; extracting the corresponding filter enhanced anomaly according to the required section position to obtain section anomaly data, integrating and interpolating the section anomaly data into a graph, and obtaining the gravity and magnetic section anomaly corresponding to the required section.

Description

Technical Field

[0001] The present invention relates to the field of geophysical exploration technology, and in particular to a method, device, medium and equipment for constructing gravity and magnetic section anomaly. Background Art

[0002] The gravity and magnetic anomaly field is composed of the gravity and magnetic effects of inhomogeneous geological bodies of different scales, shapes and burial depths from shallow to deep. Gravity anomalies reflect the density characteristics of geological bodies, and magnetic anomalies reflect the magnetic characteristics of geological bodies. Through the conversion, enhancement and separation of gravity and magnetic anomalies, the structure of geological bodies and the distribution of strata can be studied. Reconstructing gravity and magnetic anomalies is an important step in geophysical exploration data processing. By constructing gravity and magnetic cross-section anomalies through gravity and magnetic plane anomalies, and combining seismic and electrical profiles for anomaly analysis and interpretation, we can better study the distribution of faults and structural characteristics, and more comprehensively understand the development of shallow to deep geological bodies, provide multi-faceted data evidence for the study of spatial structural characteristics of geological bodies, provide a scientific basis for the exploration and development of mineral resources, and assist in the decision-making process.

[0003] At present, the method of constructing gravity and magnetic section anomalies is usually to denoise the gravity and magnetic anomaly data, and then grid the denoised gravity and magnetic anomaly data and extend it upward or downward. However, excessive smoothing may occur during the denoising process, resulting in suppression of information such as geological body edges, faults, and structural boundaries in the gravity and magnetic anomaly data, causing errors to be introduced in the subsequent extension process, ultimately affecting the accuracy of the constructed gravity and magnetic section anomalies. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, medium and equipment for constructing gravity and magnetic section anomalies in response to the above technical problems.

[0005] This manual adopts the following technical solutions:

[0006] This specification provides a method for constructing gravity and magnetic section anomalies, including:

[0007] Collect gravity and magnetic anomaly data, and interpolate and expand the gravity and magnetic anomaly data to form gravity and magnetic plane grid data;

[0008] The grid data preserving gradient filtering method is used to filter and denoise the gravity and magnetic plane grid data, while maintaining the gradient characteristics of the gravity and magnetic plane grid data to obtain the denoised gravity and magnetic plane grid data;

[0009] The denoised gravity and magnetic plane grid data are extended upward to obtain the performance of the gravity field and magnetic field at different heights, and the extended anomaly data at different heights are obtained; based on the difference in the extended anomaly data at adjacent heights, the gravity field and magnetic field anomalies at different heights are separated to obtain the gravity and magnetic local anomaly data corresponding to different heights;

[0010] The sliding small subdomain filtering method adaptively adjusts the filtering intensity according to the local gradient of the gravity and magnetic local anomaly data, and adaptively enhances the gravity and magnetic local anomaly data corresponding to different heights to obtain the filter enhanced anomaly data corresponding to different heights.

[0011] According to the set section coordinates, the corresponding filtered enhanced anomaly data are extracted from the filtered enhanced anomalies corresponding to different heights as section data; the section data are integrated and interpolated into a graph to obtain the gravity and magnetic section anomalies of the set section coordinates.

[0012] Further, the obtaining of local gravity and magnetic anomaly data corresponding to different altitudes specifically includes:

[0013] The extended anomaly data at different heights are respectively subtracted from the denoised gravity and magnetic plane grid data to obtain the gravity and magnetic residual anomaly data corresponding to different heights.

[0014] The gravity and magnetic residual anomaly data at adjacent heights are subtracted to separate the gravity and magnetic field anomalies at different heights and obtain the gravity and magnetic local anomaly data corresponding to different heights.

[0015] Furthermore, the grid data preserving gradient filtering method is used to filter and denoise the gravity and magnetic plane grid data, specifically including:

[0016] The gravity and magnetic plane grid data are divided into four groups of different sub-domain pairs, and the absolute values ​​of the differences between the root mean square errors of the four groups of different sub-domain pairs are calculated respectively:

[0017] ΔRMSE 1 =|rmse 4 -rmse 0 |

[0018] ΔRMSE 2 =|rmse 5 -rmse 1 |

[0019] ΔRMSE 3 =|rmse 6 -rmse 2 |

[0020] ΔRMSE 4 =|rmse 7 -rmse 3 |

[0021] Among them, rmse 0 ~rmse 7 are the root mean square error values ​​of different subdomains;

[0022] The group with the largest absolute value of the difference between the root mean square error values ​​among the four different subdomain pairs is selected as the MAXΔRMSE in the main gradient direction. The corresponding subdomains are numbered x and x+4, where 0≤x≤4.

[0023] Take (mean(x)*rmse(x+4)+mean(x+4)*rmse(x)) / (rmse(x)+rmse(x+4)) as the output after filtering, where rmse n represents the mean square error of subdomain n, and mean(x) represents the mean of subdomain x.

[0024] Furthermore, the denoised gravity and magnetic plane grid data is extended upward by increasing the extension height in sequence from small to large with reference to multiples of the grid spacing.

[0025] Furthermore, the upward extension of the denoised gravity and magnetic plane grid data is to set the maximum height of the extension based on the comparison of abnormal morphological change characteristics represented by the denoised gravity and magnetic plane grid data during the upward extension.

[0026] Furthermore, the gravity and magnetic anomaly data include Bouguer gravity anomaly data, magnetic ΔT anomaly data and magnetic ΔT polarization anomaly data.

[0027] Furthermore, the interpolation and expansion of the gravity and magnetic anomaly data is to interpolate and expand the gravity and magnetic anomaly data using a radial basis function method to form regular grid data.

[0028] This specification provides a construction device for gravity and magnetic section anomaly, including:

[0029] The data acquisition module is used to collect gravity and magnetic anomaly data, and to form gravity and magnetic plane grid data after interpolation and expansion of the gravity and magnetic anomaly data;

[0030] A filtering and denoising module is used to filter and denoise the gravity and magnetic plane grid data by using a grid data preserving gradient filtering method, while maintaining the gradient characteristics of the gravity and magnetic plane grid data to obtain denoised gravity and magnetic plane grid data;

[0031] The extension module is used to extend the denoised gravity and magnetic plane grid data upward to obtain the performance of the gravity field and magnetic field at different heights, and obtain the extended anomaly data at different heights; based on the difference of the extended anomaly data at adjacent heights, the gravity field and magnetic field anomalies at different heights are separated to obtain the gravity and magnetic local anomaly data corresponding to different heights;

[0032] The enhancement module is used for the sliding small subdomain filtering method to adaptively adjust the filtering strength according to the local gradient of the gravity and magnetic local anomaly data, and adaptively enhance the gravity and magnetic local anomaly data corresponding to different heights to obtain the filter enhanced anomaly data corresponding to different heights;

[0033] A construction module is used to extract corresponding filtered enhanced anomaly data as profile data from the filtered enhanced anomalies corresponding to different heights according to the set profile coordinates; the profile data are integrated and interpolated into a graph to obtain the gravity and magnetic section anomalies of the set profile coordinates.

[0034] This specification provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned gravity and magnetic section anomaly construction method is implemented.

[0035] This specification provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned gravity and magnetic section anomaly construction method when executing the program.

[0036] At least one of the above technical solutions adopted in this specification can achieve the following beneficial effects:

[0037] In the gravity and magnetic section anomaly construction method provided in this specification, more accurate gravity and magnetic field anomaly data are obtained by denoising and enhancing before and after obtaining plane anomalies at different extension heights. Specifically, the grid data gradient filter method can effectively reduce the overall noise level of gravity and magnetic anomaly data and retain the main gradient anomaly feature information. The sliding small subdomain filter method can further enhance the gravity and magnetic anomaly data after extension according to the change of local gradient. This linkage filtering strategy before and after extension ensures that the error in the process of constructing gravity and magnetic section anomalies is minimized, so that more accurate section anomaly results can be obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0039] Figure 1 A schematic diagram of a method for constructing a gravity and magnetic section anomaly provided in this specification;

[0040] Figure 2 A detailed flow chart of the construction of gravity and magnetic section anomalies provided in this manual;

[0041] Figure 3 This is a schematic diagram of the data window and subdomain division of the 5-point × 5-point grid data-preserving gradient filtering method provided in this specification, where: Figure 3 (a) in the figure is the data window. Figure 3 (b) to (i) in the figure respectively divide the data window into different sub-domains;

[0042] Figure 4 The constructed gravity and magnetic section anomalies, seismic / electrical sections and geological sections provided in this specification, wherein (a) is the magnetic section anomaly constructed based on the magnetic plane anomaly, (b) is the measured resistivity inversion section anomaly, (c) is the gravity section anomaly constructed based on the gravity plane anomaly, (d) is the measured two-dimensional seismic section, and (e) is the geological section comprehensively interpreted based on gravity, magnetic, electric and seismic data;

[0043] Figure 5 The constructed gravity and magnetic section anomalies, electrical sections and geological sections provided in this specification; among them, (a) is the magnetic section anomaly constructed based on the magnetic plane anomaly, (b) is the measured resistivity inversion section anomaly, (c) is the gravity section anomaly constructed based on the gravity plane anomaly, and (d) is the geological section comprehensively interpreted based on gravity, magnetic, electrical and other data. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solutions and advantages of this specification more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments in the specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] With the rapid development of oil, gas and helium resource survey and evaluation, how to quickly identify the underground geological structure and spatial variation characteristics of the working area has become increasingly important. At present, geophysical methods (gravity, magnetic method, electrical method, seismic) have good application effects in exploring basin structure, stratigraphic distribution, local structure, etc. Among them, seismic method is dominant and plays a decisive role. However, due to the influence of surface geological conditions and other factors, the degree of seismic exploration varies greatly, and the distribution of seismic data is extremely uneven; in addition, in areas with complex underground geological conditions, the signal-to-noise ratio of seismic data is low, and the effective reflection information is weak, so that the geological structure characteristics of the working area cannot be well obtained. Similar to seismic data, the working degree of electrical data also varies greatly. In comparison, gravity and magnetic data are widely distributed. The 1:1 million gravity and magnetic survey has basically covered the whole country. The main basins have basically completed the 1:200,000 to 1:500,000 gravity and magnetic surveys, and some areas have basically achieved 1:50,000 gravity and magnetic surveys. In addition, gravity and magnetic exploration is simple, fast, economical and wide-ranging. Therefore, using gravity and magnetic data to explore the underground distribution structure of the work area and combining seismic and electrical data to reveal the development characteristics of geological bodies has relatively important practical significance and application prospects.

[0046] The gravity and magnetic anomaly field is composed of the gravity and magnetic effects of inhomogeneous geological bodies of different scales, shapes and burial depths from shallow to deep. Gravity anomalies reflect the density characteristics of geological bodies, and magnetic anomalies reflect the magnetic characteristics of geological bodies. Through the conversion, enhancement and separation of gravity and magnetic anomalies, the structure of geological bodies and the distribution of strata can be studied. Gravity and magnetic anomalies have a high resolution on the plane. In the past, studies have mostly used gravity and magnetic anomalies to identify the plane distribution of underground geological bodies and geological structures. With the continuous deepening of exploration work, identifying the vertical development and spatial variation characteristics of geological bodies has become the focus of geologists. Seismic and electrical sections can better reveal the vertical changes of geological bodies, but in work areas where seismic / electrical data are sparsely distributed or lacking seismic / electrical data, how to achieve the joint interpretation of gravity and magnetic data with seismic / electrical data, or how to use gravity and magnetic data to more intuitively reveal the geological structure characteristics of the underground space in the work area, has long been a key issue of concern in geological and geophysical work.

[0047] Based on the needs of geological interpretation or structural understanding, predecessors have used a variety of methods to process and analyze gravity and magnetic anomalies and construct underground gravity and magnetic anomaly data bodies in order to study the spatial variation characteristics of geological bodies. The usual method is to denoise the gravity and magnetic data and then extend it upward or downward, while using multiple prior parameters for constraints, and finally obtain the required anomaly data. This method may be over-smoothed during the denoising process, resulting in the suppression of information such as geological body edges, faults, and structural boundaries in the gravity and magnetic anomaly data, causing errors to be introduced in the subsequent extension process, ultimately affecting the accuracy of the constructed gravity and magnetic section anomaly. Based on the above reasons, in order to construct more accurate section anomalies, so that gravity and magnetic data can be better used to study the change of vertical distribution of geological bodies, and there is no need to consider multiple parameters such as prior information, which is convenient for calculation and application; this manual uses the upward extension method to process the denoised gravity and magnetic anomaly data to obtain extension anomalies of different heights; then obtain the residual anomalies corresponding to different extension heights; then subtract the residual anomaly data with a larger extension height from the adjacent residual anomaly data with a smaller extension height, separate the corresponding anomaly differences between different residual anomalies, and obtain the gravity and magnetic local anomaly data corresponding to different extension heights (apparent depth); finally, according to the section coordinates to be constructed, the corresponding section data is extracted from the gravity and magnetic local anomaly data of different extension heights (apparent depths) and interpolated into a map to obtain the gravity and magnetic section anomaly of the section. It can be seen from the processing results that the section anomaly constructed by the method provided in this manual has a good correspondence with the electrical method and seismic section. The study of the spatial changes of geological bodies through gravity and magnetic section anomalies and seismic and electrical data can better reveal the fractures, structures and vertical distribution characteristics of geological bodies in the study area.

[0048] Figure 1 The following is a flow chart of the method for constructing the gravity and magnetic section anomaly of this embodiment. Figure 1The method for constructing the gravity and magnetic section anomaly is described in detail, and specifically includes the following steps:

[0049] S1. Collect gravity and magnetic anomaly data, and interpolate and expand the gravity and magnetic anomaly data to form gravity and magnetic plane grid data. Specifically:

[0050] Gravity and magnetic anomaly data refers to the general term for gravity data and magnetic data obtained by measuring the earth's gravity field and magnetic field in geophysical exploration, which are used to study and infer the internal structure and geological characteristics of the earth. Gravity data is obtained through gravity measurement and reflects the strength and distribution of the earth's gravity field. Magnetic data is obtained through magnetic measurement and reflects the strength and distribution of the earth's magnetic field. Bouguer gravity anomaly is an anomaly that uses gravity measurement to study the internal structure of the earth. It reflects the difference between the earth's gravity field and the theoretical gravity field of the reference ellipsoid. This difference is caused by the uneven distribution of materials inside the earth. The calculation steps of Bouguer gravity anomaly are usually as follows: first, a gravimeter is used to measure on the surface of the earth to obtain the measured gravity value of the point. Then the normal gravity value of the point under the reference ellipsoid theory is calculated. Then the measured gravity value is corrected, including the influence of factors such as terrain, altitude, and the change of the earth's internal mass with altitude. Finally, the normal gravity value and other correction items are subtracted from the measured gravity value to obtain the Bouguer gravity anomaly value.

[0051] First, collect or load the gravity and magnetic data of the collection work area, mainly Bouguer gravity anomaly data, magnetic ΔT anomaly or magnetic ΔT polar anomaly data, sort the gravity and magnetic data, and finally obtain the data of Bouguer gravity anomaly (G1) and magnetic ΔT polar anomaly (M1). Then, use the radial basis function method to interpolate and expand the above gravity and magnetic anomaly data (G1, M1), and finally form the gravity and magnetic plane grid data (G2, M2).

[0052] S2. Use the grid data preserving gradient filtering method to filter and denoise the gravity and magnetic plane grid data, while maintaining the gradient characteristics of the gravity and magnetic plane grid data to obtain the denoised gravity and magnetic plane grid data.

[0053] The grid data preserving gradient filtering method is used to process the gravity and magnetic plane grid data (G2, M2) to remove random interference and other noise, and obtain the denoised gravity and magnetic plane grid data (G3, M3). The grid data preserving gradient filtering method is also called the symmetrical small subdomain filtering method. Taking a 5-point × 5-point data window as an example, its subdomain division method is as follows: Figure 3 As shown in the figure, this division method considers the subdomains that are symmetrical about the center in the window, and calculates the data average of each individual subdomain separately. In order to examine the numerical distribution or gradient change of the data in the filtering window and find the main gradient direction of the data change in the window, the root mean square error and average gradient size discrimination indicators are used to measure, and this discrimination indicator is used instead of the data change smoothness coefficient discrimination indicator.

[0054] The calculation steps are as follows:

[0055] ① Divide the gravity and magnetic plane grid data into four different subdomain pairs, such as Figure 3 As shown in (b) to (i) in the figure, the absolute values ​​of the differences between the root mean square errors of the four different subdomain pairs are calculated, which are:

[0056] ΔRMSE 1 =|rmse 4 -rmse 0 |

[0057] ΔRMSE 2 =|rmse 5 -rmse 1 |

[0058] ΔRMSE 3 =|rmse 6 -rmse 2 |

[0059] ΔRMSE 4 =|rmse 7 -rmse 3 |

[0060] Among them, rmse 0 ~rmse 7 are the root mean square errors of different subdomains.

[0061] ② Select the group with the largest absolute value of the difference between the root mean square error values ​​of the four different sub-domain pairs as the main gradient direction MAXΔRMSE, and the corresponding sub-domains are numbered x and x+4, where 0≤x≤4.

[0062] ③The final output after filtering is:

[0063] (mean(x)*rmse(x+4)+mean(x+4)*rmse(x)) / (rmse(x)+rmse(x+4))

[0064] Among them, rmse n represents the mean square error of subdomain n, and mean(x) represents the mean of subdomain x.

[0065] S3. Extend the denoised gravity and magnetic plane grid data upward to obtain the performance of the gravity field and magnetic field at different heights, and obtain the extended anomaly data at different heights; based on the difference of the extended anomaly data at adjacent heights, separate the gravity field and magnetic field anomalies at different heights, and obtain the gravity and magnetic local anomaly data corresponding to different heights. Specifically:

[0066] The denoised gravity and magnetic plane grid data is extended upward. According to the principle of gradually increasing from low to high, the extension height is increased from small to large with reference to the multiples of the grid spacing. If the grid spacing is set to d, the anomalies after upward extension are G4 -1 (G3 is extended upward by 1 times the grid spacing) -2 (G3 is extended upwards by 2 times the grid spacing) ... and M4 -1 (Anomaly after M3 is extended upward by 1 times the grid spacing), M4 -2 (M3 anomaly after upward extension of 2 times the grid spacing)... Or the maximum extension height can be set based on the comparison of the morphological changes of the anomaly represented by the denoised gravity and magnetic plane grid data during upward extension.

[0067] The process of calculating the outlier value on the plane higher than the outlier value on the observation plane is called upward continuation. Since the gravity field value is inversely proportional to the square of the distance from the field source to the measuring point, for two field source bodies with a large difference in depth, the speed of their respective anomalies weakening or increasing is different when the same height is extended. When performing upward extension calculations, the "high-frequency" anomaly with a small range and relatively sharp caused by the shallow field source body decays faster with increasing height; while the "low-frequency" anomaly with a large range and gentle caused by the deep field source body decays more slowly with increasing height. Therefore, upward continuation is conducive to relatively highlighting the deep anomaly characteristics. From the comparison of the changing characteristics of the upward continuation anomaly, it can be seen that when the upward continuation height changes from small to large, the "high-frequency" anomaly characteristics caused by the shallow field source body in the original anomaly gradually decay and disappear, while the "low-frequency" anomaly characteristics caused by the deep field source body gradually become prominent. In this embodiment, when the upward extension height is 15km, the local anomaly basically disappears, and the anomaly is generally smooth and gentle, highlighting the gravity anomaly characteristics of the deep area; when the upward extension height is greater than 15km, the obtained anomaly morphology is basically the same and there is not much change. Therefore, this time, 2km is added to the extension height of 15km, and the maximum upward extension height is set to 17km.

[0068] The denoised gravity and magnetic plane grid data are used to subtract the extended anomaly data at different heights to obtain the gravity and magnetic residual anomaly data corresponding to different heights (G5 -1 =G3 minus G4 -1 , G5 -2 =G3 minus G4 -2 ...M5 -1 =M3 minus M4 -1 , M5 -2 =M3 minus M4 -2 ……).

[0069] Subtract the gravity and magnetic residual anomaly data at adjacent heights to separate the gravity and magnetic field anomalies at different heights and obtain the gravity and magnetic local anomaly data corresponding to different heights. That is, the gravity and magnetic residual anomaly data with a larger extension height is subtracted from the adjacent gravity and magnetic residual anomaly data with a smaller extension height. The corresponding anomaly differences between different residual anomalies are separated by the mutual difference of adjacent gravity and magnetic residual anomaly data to obtain the gravity and magnetic local anomaly data corresponding to different extension heights (apparent depths) (G6 -1 =G5 -2 Minus G5 -1 , G6 -2 =G5 -3 Minus G5 -2 ...M6 -1 =M5 -2 Minus M5 -1 , M6 -2 =M5 -3 Minus M5 -2 ……).

[0070] S4. The sliding small subdomain filtering method adaptively adjusts the filtering strength according to the local gradient of the gravity and magnetic local anomaly data, and adaptively enhances the gravity and magnetic local anomaly data corresponding to different heights to obtain the filter enhanced anomaly data corresponding to different heights. Specifically:

[0071] The sliding subdomain filtering method is a nonlinear filtering denoising method, which was first used in image processing. It combines local adaptive filtering and subdomain (sub-block) division strategy. The key idea of ​​this method is that the edges and details in the image are usually associated with larger gradient changes, while smooth areas are associated with smaller gradient changes. Its working principle is: 1. Subdomain division: Divide a series of subdomains. 2. Local gradient calculation: For each subdomain, calculate the gradient size. 3. Filter strength adjustment: Adjust the filter strength according to the size of the local gradient. 4. Filter application: Use the adjusted filter to filter each subdomain. 5. Sliding and iteration: The filter slides to process the subdomains one by one.

[0072] The sliding subdomain filtering method is used to filter G6 -1 , G6 -2 ...and M6 -1 , M6 -2 ...processing, and obtain the filter enhancement anomaly (G7 -1 , G7 -2 ...and M7 -1 , M7 -2 ……).

[0073] S5. According to the set or required section coordinates, extract the corresponding filtered enhanced anomaly data from the corresponding filtered enhanced anomalies at different heights as section data; integrate and interpolate the section data into a graph to obtain the gravity and magnetic section anomaly of the set section coordinates. Specifically:

[0074] Determine the section position according to the research needs, and use the section coordinates in (G7 -1 , G7 -2 ...and M7 -1 , M7 -2 ……) to extract the profile abnormal data and obtain G8 -1 , G8 -2 ...and the M8 -1 , M8 -2 ...profile anomaly. The extracted profile anomaly data are integrated and interpolated to form a profile anomaly map. During interpolation, the point spacing (or point number) of the profile anomaly data is set as the horizontal coordinate, and the upward extension height is set as the vertical coordinate (increasing downward).

[0075] This specification is intended to protect a method for constructing gravity and magnetic cross-section anomalies, which constructs cross-section anomalies at any spatial position based on the residual anomaly step-by-step difference data body of the anomaly extended upward at different heights, and reveals the abnormal changes of different underground geological bodies. The purpose is to use a simpler and easier to understand anomaly separation and enhancement filtering method to quickly construct gravity and magnetic cross-section anomalies, and combine seismic / electrical data to reveal the spatial distribution characteristics of underground geological bodies.

[0076] The characteristics of the method provided in this specification are fast and efficient. It does not require control parameters such as known prior information of the working area, nor does it require the calculation of other constraint coefficients. Second, the technical process is easy to understand and implement. The upward extension method, grid data retention gradient filtering method, and sliding subdomain filtering method are all relatively mature processing methods. The combined use of methods can meet the needs. Without considering prior parameters such as related physical properties, it is possible to quickly construct the gravity and magnetic section anomaly of the working area, explore the underground geological structure of the working area, and understand the spatial changes of the geological body. In the working area where seismic or electrical data are scarce, it can assist in the joint interpretation of data; in the working area where seismic and electrical data are scarce, it can be well used to study the distribution of underground geological structures and vertical spatial change characteristics. On the one hand, the gradient filtering processing of grid data retention can maximize the gradient change characteristics of the original abnormal field while filtering out noise interference; on the other hand, the use of different heights to continuously extend the anomaly upward to obtain the remaining anomaly, and then construct the gravity and magnetic section anomaly of any section according to the enhanced filtering results of the residual anomaly mutual difference, which can better reflect the abnormal development of the underground geological body in the working area and reveal the characteristics of the continuous spatial change of the geological body.

[0077] When applying the method provided in this manual, you can Figure 2The steps shown are executed in sequence. Step 1. Collect the previous gravity and magnetic data of the study area or collect gravity and magnetic data on site; Step 2. Interpolate and expand the data in step 1 to form gravity and magnetic plane grid data; Step 3. Use the grid data preserving gradient filtering method to process the grid data of step 2; Step 4. Extend the data processed in step 3 upward, and the extension height increases from small to large according to the multiples of the grid spacing. The maximum extension height is set according to the morphological change characteristics of the upward extension anomaly; Step 5. Subtract the upward extension anomalies of different heights in step 4 from the data processed in step 3 to obtain the residual anomalies corresponding to different extension heights; Step 6. Perform mutual difference on the adjacent residual anomalies obtained in step 5 to obtain the local anomalies corresponding to different extension heights (apparent depth); Step 7. Use the sliding small subdomain filtering method to filter and enhance the results of step 6; Step 8. Set the position of the profile to be constructed as needed, and extract the profile anomaly data from the results of step 7 according to the profile coordinates; Integrate and interpolate the extracted profile anomaly data into a map to form a cross-sectional anomaly.

[0078] This manual also provides Figure 1 The example verification of the method shown has achieved good application effect in the comprehensive research of geological and geophysical data in the Yin'e Basin.

[0079] The Yin'e Basin is located in the Ejin Banner-Yingen area in the western part of the Inner Mongolia Autonomous Region in the central part of northern China. The area has gravity and magnetic data covering the entire basin. The scale of gravity data in most areas is 1:200,000, and the scale of magnetic data is 1:500,000. Due to various reasons such as surface geological conditions, the degree of seismic and electrical data work in this area varies greatly: there are more seismic data in some local areas, but the distribution range is small; electrical data are relatively sparse and the workload is less; in order to conduct a systematic study of the overall structure of the basin, based on the joint interpretation of gravity, magnetic, electric and seismic data and the need for structural analysis of the entire basin, key sections were selected at the same coordinate position as the seismic or electrical sections in the working area, and gravity and magnetic section anomalies were constructed based on the technical methods provided in this manual, such as Figure 4 , Figure 5 shown.

[0080] Figure 4 It is a gravity and magnetic section anomaly constructed based on known seismic and electrical sections at the same coordinate position. Figure 4 (a) to (e) in the figure are: constructed magnetic section anomaly, resistivity inversion section anomaly, constructed gravity section anomaly, 2D seismic section and comprehensive interpretation geological section. By comparing the anomaly characteristics, it can be seen that the depression area revealed by the seismic section shows low gravity on the gravity section, and the non-depression area on the seismic section shows high gravity on the gravity section; the magnetic variation structural characteristics shown by the magnetic section anomaly are similar to the high and low variation zoning structural characteristics of the resistivity anomaly reflected by the electrical section.

[0081] Figure 5 The gravity and magnetic section anomaly is constructed based on the same electrical section coordinates. Figure 5 (a) to (d) in the figure are: constructed magnetic section anomaly, resistivity inversion section anomaly, constructed gravity section anomaly and comprehensive interpretation geological section. From the overall characteristics of the high and low changes in the anomaly values, it can be seen that the correspondence between the gravity and magnetic section anomalies and the electrical section anomalies is obvious, the structural boundary positions of the anomalies of different geological bodies are clear, and the change trend of the gravity and magnetic section anomalies reveals the vertical changes and distribution characteristics of the geological bodies.

[0082] Depend on Figure 4 and Figure 5 Data comparison shows that the gravity and magnetic section anomaly constructed by the method provided in this specification has a good correspondence with the seismic and electrical section data. Seismic data can better reveal the distribution, development and structural characteristics of underground geological bodies. In comparison, the vertical distribution of underground geological bodies reflected by gravity, magnetic and electrical data is not as clear as seismic data, but to a certain extent, it can also reveal the distribution and structural characteristics of geological bodies. Therefore, in the work area lacking seismic data, the gravity and magnetic section anomaly constructed by this method can provide a strong data basis and provide help for the study of the overall structure of the work area.

[0083] The method provided in this manual can quickly construct the gravity and magnetic section anomaly of any section in the working area. This section can be combined with the seismic and electrical sections to obtain better interpretation results, providing researchers with a multi-dimensional perspective to understand the various geophysical anomaly characteristics of different geological anomalies. This method has important practical significance for the rapid study and evaluation of the geological structure of the working area.

[0084] The above is a gravity and magnetic section anomaly construction method provided by one or more embodiments of this specification. Based on the same idea, this specification also provides a corresponding gravity and magnetic section anomaly construction device, including:

[0085] The data acquisition module is used to collect gravity and magnetic anomaly data, and to form gravity and magnetic plane grid data after interpolation and expansion of the gravity and magnetic anomaly data.

[0086] The filtering and denoising module is used to filter and denoise the gravity and magnetic plane grid data using the grid data preserving gradient filtering method, while maintaining the gradient characteristics of the gravity and magnetic plane grid data to obtain the denoised gravity and magnetic plane grid data.

[0087] The extension module is used to extend the denoised gravity and magnetic plane grid data upward to obtain the performance of the gravity field and magnetic field at different heights, and obtain the extended anomaly data at different heights; based on the differences in the extended anomaly data at adjacent heights, the gravity field and magnetic field anomalies at different heights are separated to obtain the gravity and magnetic local anomaly data corresponding to different heights.

[0088] The enhancement module is used for the sliding small subdomain filtering method to adaptively adjust the filtering strength according to the local gradient of the gravity and magnetic local anomaly data, and adaptively enhance the gravity and magnetic local anomaly data corresponding to different heights to obtain the filtered enhanced anomaly data corresponding to different heights.

[0089] A construction module is used to extract corresponding filtered enhanced anomaly data as profile data from the filtered enhanced anomalies corresponding to different heights according to the set profile coordinates; the profile data are integrated and interpolated into a graph to obtain the gravity and magnetic section anomalies of the set profile coordinates.

[0090] For the specific definition of the gravity and magnetic section anomaly construction device, please refer to the definition of the gravity and magnetic section anomaly construction method above, which will not be repeated here. Each module in the above gravity and magnetic section anomaly construction device can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0091] This specification also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 A method for constructing gravity and magnetic section anomalies is provided.

[0092] This specification also provides a computer device. At the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, it may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 A method for constructing gravity and magnetic section anomalies is provided.

[0093] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this specification can include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

Claims

1. A method for constructing gravity and magnetic section anomaly, characterized in that: include: Collect gravity and magnetic anomaly data, and interpolate and expand the gravity and magnetic anomaly data to form gravity and magnetic plane grid data; The grid data preserving gradient filtering method is used to filter and denoise the gravity and magnetic plane grid data, while maintaining the gradient characteristics of the gravity and magnetic plane grid data to obtain the denoised gravity and magnetic plane grid data; The denoised gravity and magnetic plane grid data are extended upward to obtain the performance of the gravity field and magnetic field at different heights, and the extended anomaly data at different heights are obtained; based on the difference in the extended anomaly data at adjacent heights, the gravity field and magnetic field anomalies at different heights are separated to obtain the gravity and magnetic local anomaly data corresponding to different heights; The sliding small subdomain filtering method adaptively adjusts the filtering intensity according to the local gradient of the gravity and magnetic local anomaly data, and adaptively enhances the gravity and magnetic local anomaly data corresponding to different heights to obtain the filter enhanced anomaly data corresponding to different heights. According to the set section coordinates, the corresponding filtered enhanced anomaly data are extracted from the filtered enhanced anomalies corresponding to different heights as section data; the section data are integrated and interpolated into a graph to obtain the gravity and magnetic section anomalies of the set section coordinates.

2. The method for constructing gravity and magnetic section anomaly according to claim 1, characterized in that: The obtaining of local gravity and magnetic anomaly data corresponding to different altitudes specifically includes: The extended anomaly data at different heights are respectively subtracted from the denoised gravity and magnetic plane grid data to obtain the gravity and magnetic residual anomaly data corresponding to different heights. The gravity and magnetic residual anomaly data at adjacent heights are subtracted to separate the gravity and magnetic field anomalies at different heights and obtain the gravity and magnetic local anomaly data corresponding to different heights.

3. The method for constructing gravity and magnetic section anomaly according to claim 1, characterized in that: The method of filtering and denoising the gravity and magnetic plane grid data by using the grid data preserving gradient filtering method specifically includes: The gravity and magnetic plane grid data are divided into four groups of different sub-domain pairs, and the absolute values ​​of the differences between the root mean square errors of the four groups of different sub-domain pairs are calculated respectively: ΔRMSE1=|rmse4-rmse0| ΔRMSE2=|rmse5-rmse1| ΔRMSE3=|rmse6-rmse2| ΔRMSE4=|rmse7-rmse3| Among them, rmse0~rmse7 are the root mean square error values ​​of different subdomains; The group with the largest absolute value of the difference between the root mean square error values ​​among the four different subdomain pairs is selected as the MAXΔRMSE in the main gradient direction. The corresponding subdomains are numbered x and x+4, where 0≤x≤4. Take (mean(x)*rmse(x+4)+mean(x+4)*rmse(x)) / (rmse(x)+rmse(x+4)) as the output after filtering, where rmse n represents the mean square error of subdomain n, and mean(x) represents the mean of subdomain x.

4. The method for constructing gravity and magnetic section anomaly according to claim 1, characterized in that: The upward extension of the denoised gravity and magnetic plane grid data is to increase the extension height in sequence from small to large with reference to multiples of the grid spacing.

5. The method for constructing gravity and magnetic section anomaly according to claim 1, characterized in that: The upward extension of the denoised gravity and magnetic plane grid data is to set the maximum height of the extension according to the comparison of the abnormal morphological change characteristics represented by the denoised gravity and magnetic plane grid data during the upward extension.

6. The method for constructing gravity and magnetic section anomaly according to claim 1, characterized in that: The gravity and magnetic anomaly data include Bouguer gravity anomaly data, magnetic ΔT anomaly data and magnetic ΔT polarization anomaly data.

7. The method for constructing gravity and magnetic section anomaly according to claim 1, characterized in that: The interpolation and expansion of gravity and magnetic anomaly data is to interpolate and expand the gravity and magnetic anomaly data using a radial basis function method to form regular grid data.

8. A device for constructing gravity and magnetic section anomalies, characterized in that: include: The data acquisition module is used to collect gravity and magnetic anomaly data, and to form gravity and magnetic plane grid data after interpolation and expansion of the gravity and magnetic anomaly data; A filtering and denoising module is used to filter and denoise the gravity and magnetic plane grid data by using a grid data preserving gradient filtering method, while maintaining the gradient characteristics of the gravity and magnetic plane grid data to obtain denoised gravity and magnetic plane grid data; The extension module is used to extend the denoised gravity and magnetic plane grid data upward to obtain the performance of the gravity field and magnetic field at different heights, and obtain the extended anomaly data at different heights; based on the difference of the extended anomaly data at adjacent heights, the gravity field and magnetic field anomalies at different heights are separated to obtain the gravity and magnetic local anomaly data corresponding to different heights; The enhancement module is used for the sliding small subdomain filtering method to adaptively adjust the filtering strength according to the local gradient of the gravity and magnetic local anomaly data, and adaptively enhance the gravity and magnetic local anomaly data corresponding to different heights to obtain the filter enhanced anomaly data corresponding to different heights; A construction module is used to extract corresponding filtered enhanced anomaly data as profile data from the filtered enhanced anomalies corresponding to different heights according to the set profile coordinates; the profile data are integrated and interpolated into a graph to obtain the gravity and magnetic section anomalies of the set profile coordinates.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in any one of claims 1 to 7 is implemented.

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