Method, device and equipment for rapidly separating regional field from local field, medium and product

Abstract

The invention discloses a method, a device, equipment, a medium and a product for rapidly separating a regional field from a local field, and relates to the field of geophysical exploration data processing.The method comprises the steps that geophysical measurement data in a to-be-measured region is obtained, gridding processing is conducted on the data, an improved interpolation cutting method is adopted for conducting iteration cutting for multiple times, and a local field is obtained. And obtaining regional field data until a preset condition is met, and obtaining local field data through subtraction. According to the improved interpolation cutting method, a four-point circumferential average value is changed into an eight-point window average value, and a dynamic search threshold value is introduced. Through the improved interpolation cutting method, the separation precision of the regional field and the local field can be improved, distortion in data processing is reduced, good convergence is achieved, an accurate separation result can be rapidly obtained, the limitation of a traditional method when different depth level fields are divided is overcome, and the separation efficiency is improved. And more reliable data support is provided for geophysical exploration, and the exploration efficiency and effect are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of geophysical exploration data processing, and in particular to a method and device for quickly separating regional field and local field, equipment, medium and product. BACKGROUND

[0002] In geophysical exploration, data processing is one of the important links and is the basis and premise for geophysical exploration inversion and interpretation. The actual observed data is often a comprehensive reflection of various situations such as terrain undulation, non-uniformity of underground medium, and mutual overlap of various rocks. Accurately separating the anomalies generated by various anomaly sources from the observed actual data is an important way to achieve good geological effect of geophysical exploration methods. Therefore, it has theoretical significance and practical value to study the anomaly separation method of geophysical data.

[0003] Under normal circumstances, the measured data obtained by geophysical exploration is regarded as a combination of regional field and local field. However, how to accurately divide the regional field and the local field has always been a major challenge in this field. In related technologies, although there are various methods trying to solve this problem, so far there is no widely recognized general method. For example, the trend surface analysis method, the matching filter method and the upward continuation method all have certain limitations when trying to divide the local field and the regional field at different depth levels. Although the interpolation cutting method has certain advantages in distortion control, division accuracy and convergence, its separation effect of regional field and local field still needs to be further improved when facing high-precision geophysical exploration requirements, and it is difficult to fully meet the strict requirements of data separation accuracy in actual exploration.

[0004] Therefore, there is an urgent need for a method that can effectively overcome the limitations of the prior art and achieve rapid and accurate separation of regional field and local field. SUMMARY

[0005] The purpose of the present application is to provide a method, device, equipment, medium and product for quickly separating regional field and local field, which can effectively improve the efficiency and accuracy of geophysical exploration data processing and overcome the limitations of the prior art that the separation effect of regional field and local field is poor and difficult to meet the needs of high-precision exploration.

[0006] To achieve the above purpose, the present application provides the following solutions:

[0007] In a first aspect, the present application provides a method for quickly separating regional field and local field, comprising:

[0008] obtaining geophysical measurement data in a to-be-measured region; the geophysical measurement data includes regional field data and local field data;

[0009] grid the geophysical measurement data to obtain geophysical gridding data;

[0010] cut the geophysical gridding data multiple times by using the improved interpolation cutting method until the difference between the regional field data of the current cutting and the regional field data of the last cutting is less than a preset cutting difference, and obtain the regional field data in the region to be measured; the improved interpolation cutting method is obtained by changing the 4-point circular average of the interpolation cutting method to an 8-point window average and introducing a dynamic search threshold;

[0011] subtract the regional field data from the geophysical measurement data to obtain the local field data in the region to be measured.

[0012] In a second aspect, the present application provides a device for quickly separating regional field and local field, comprising:

[0013] a data acquisition module, configured to acquire geophysical measurement data in a region to be measured; the geophysical measurement data comprises regional field data and local field data;

[0014] a gridding processing module, configured to grid the geophysical measurement data to obtain geophysical gridding data;

[0015] a regional field data acquisition module, configured to cut the geophysical gridding data multiple times by using the improved interpolation cutting method until the difference between the regional field data of the current cutting and the regional field data of the last cutting is less than a preset cutting difference, and obtain the regional field data in the region to be measured; the improved interpolation cutting method is obtained by changing the 4-point circular average of the interpolation cutting method to an 8-point window average and introducing a dynamic search threshold;

[0016] a local field data acquisition module, configured to subtract the regional field data from the geophysical measurement data to obtain the local field data in the region to be measured.

[0017] In a third aspect, the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method for quickly separating regional field and local field according to any one of the above.

[0018] In a fourth aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executable by a processor to implement the steps of the method for quickly separating regional field and local field according to any one of the above.

[0019] In a fifth aspect, the present application provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the method for quickly separating regional field and local field according to any one of the above.

[0020] According to the specific embodiments provided in the present application, the present application has the following technical effects:

[0021] The present application provides a method, device, equipment, medium and product for quickly separating regional field and local field. By obtaining geophysical measurement data in a to-be-measured region and performing grid processing on the geophysical measurement data, the present application solves the problem of irregular original data which is difficult to directly process, realizes the regularization and standardization of data, and provides a convenient condition for subsequent field separation operation. By using the improved interpolation cutting method to perform multiple iteration cutting on the geophysical grid data, the present application solves the problem of low separation precision and poor convergence of the traditional method, and realizes high-precision and fast separation of regional field and local field. The improved interpolation cutting method changes the traditional 4-point circular average to 8-point window average, and introduces a dynamic search threshold, which solves the problem of large distortion and insufficient adaptability of the traditional interpolation cutting method when processing complex data, realizes efficient processing of data with different complexity, and further improves the accuracy and reliability of separation. Finally, by subtracting the regional field data from the geophysical measurement data, the present application obtains local field data, solves the problem of difficult accurate extraction of local field data, realizes complete separation of regional field and local field, provides more accurate and valuable data support for inversion interpretation and other work in geophysical exploration, and helps to improve the efficiency and accuracy of geological exploration. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0023] Figure 1 FIG. 1 is a diagram of an application environment of a method for quickly separating regional field and local field according to an embodiment of the present application;

[0024] Figure 2 FIG. 2 is a flowchart of a method for quickly separating regional field and local field according to an embodiment of the present application;

[0025] Figure 3 FIG. 3 is a diagram of a geological model structure composed of two spheres according to an embodiment of the present application;

[0026] Figure 4 FIG. 4 is a diagram of a method for quickly separating regional field and local field according to an embodiment of the present application; Figure 3a gravity forward model of a geological model composed of two spheres; wherein, Figure 4 (a) an isogram representing a large sphere; Figure 4 (b) an isogram representing a small sphere; Figure 4 (c) an isogram representing a geological model composed of two spheres;

[0027] Figure 5 an isogram of an interpolation cutting method provided by an embodiment of the present application; wherein, Figure 5 (a) an isogram of a regional field after cutting using the interpolation cutting method; Figure 5 (b) an isogram of a local field after cutting using the interpolation cutting method;

[0028] Figure 6 an isogram of an improved interpolation cutting method provided by an embodiment of the present application; wherein, Figure 6 (a) an isogram of a regional field after cutting using the improved interpolation cutting method; Figure 6 (b) an isogram of a local field after cutting using the improved interpolation cutting method;

[0029] Figure 7 a functional module schematic diagram of a device for quickly separating a regional field and a local field provided by an embodiment of the present application;

[0030] Figure 8 a structural schematic diagram of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0032] In order to make the above purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0033] The method for quickly separating a regional field and a local field provided by the embodiments of the present application can be applied to, for example, Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the geophysical measurement data in the area to be measured to the server 104. After the server 104 receives the geophysical measurement data in the area to be measured, the server 104 grids the geophysical measurement data to obtain geophysical gridded data; the improved interpolation cutting method is used to iteratively cut the geophysical gridded data multiple times until the difference between the regional field data of the current cut and the regional field data of the previous cut is less than the preset cutting difference, and the iteration is ended to obtain the regional field data in the area to be measured; the improved interpolation cutting method is obtained by changing the 4-point circular average of the interpolation cutting method to the 8-point window average, and introducing a dynamic search threshold; the geophysical measurement data is subtracted from the regional field data to obtain the local field data in the area to be measured. The server 104 can feedback the obtained regional field data and local field data to the terminal 102. In addition, in some embodiments, the method of quickly separating regional fields from local fields can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly perform regional field and local field separation processing on the geophysical measurement data in the area to be measured, or the server 104 can obtain the geophysical measurement data in the area to be measured from the data storage system and perform regional field and local field separation processing on the geophysical measurement data in the area to be measured.

[0034] Terminal 102 may include, but is not limited to, various desktop computers, laptops, smartphones, tablet computers, IoT devices, and portable wearable devices. IoT devices may include smart speakers, smart TVs, smart air conditioners, and smart car devices. Portable wearable devices may include smart watches, smart bracelets, and head-mounted devices. Server 104 may be implemented as a standalone server or a server cluster consisting of multiple servers, or may be a cloud server.

[0035] In an exemplary embodiment, Figure 2 As shown, a method for quickly separating regional fields from local fields is provided. The method is executed by a computer device, specifically a computer device such as a terminal or a server, or a terminal and a server. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the process, including the following steps 201 to 204.

[0036] Step 201: Acquire geophysical measurement data within the area to be measured; the geophysical measurement data includes regional field data and local field data.

[0037] Step 202, the geophysical measurement data is gridded to obtain geophysical gridded data.

[0038] Step 203, the geophysical gridded data is iteratively cut by using an improved interpolation cutting method until the difference between the regional field data of the current cutting and the regional field data of the last cutting is less than a preset cutting difference, and the regional field data in the region to be measured is obtained; the improved interpolation cutting method is obtained by changing the 4-point circular average value of the interpolation cutting method to an 8-point window average value and introducing a dynamic search threshold.

[0039] Step 204, the geophysical measurement data is subtracted by the regional field data to obtain the local field data in the region to be measured.

[0040] By implementing the above steps 201 to 204, the application can further suppress the regional field oscillation effect, accelerate the convergence, and improve the processing speed, so as to realize the rapid and accurate separation of the regional field and the local field in the geophysical measurement data, provide more accurate and valuable data support for the inversion interpretation and other work in geophysical exploration, and help to improve the efficiency and accuracy of geological exploration and promote the development of geophysical exploration technology.

[0041] In another exemplary embodiment of the application, step 203 specifically comprises:

[0042] The geophysical gridded data is taken as initial grid data.

[0043] The dynamic search threshold corresponding to each grid point data in the initial grid data is calculated respectively.

[0044] Each grid point data is traversed, and if the average value of the difference between the current grid data and a preset number of adjacent grid data is greater than the current dynamic search threshold, the current grid data is taken as local abnormal region data, until all grid points are traversed, and a plurality of local abnormal region data is obtained.

[0045] Each local abnormal region data is traversed, the 8-point interpolation cutting operator of the current local abnormal region data is calculated, and the current local abnormal region data is replaced by the corresponding 8-point interpolation cutting operator, until all local abnormal region data is replaced, and the replaced grid point data is obtained.

[0046] The initial grid data and the replaced grid point data are added by using a weighting method to obtain the regional field data of the current cutting.

[0047] The region field data of the current cutting is taken as initial grid data, and the step of calculating the dynamic searching threshold corresponding to each grid point data in the initial grid data is returned until the difference between the region field data of the current cutting and the region field data of the last cutting is less than a preset cutting difference, and the iteration is ended, and the region field data is obtained.

[0048] In another example embodiment of the present application, the calculation formula of the dynamic searching threshold is:

[0049]

[0050] wherein E represents the dynamic searching threshold; k represents the searching threshold coefficient; (i, j) represents the grid point in the i-th row and the j-th column; G represents the grid point set; wherein σ represents the maximum residual value of all grid points obtained by performing 8-point interpolation cutting for the first time; σ represents the mean square error of all grid points.

[0051] In another example embodiment of the present application, the replaced grid point data is:

[0052]

[0053] wherein A(x, y) represents the replaced grid point data corresponding to the coordinates (x, y); G(·, ·) represents the initial grid data at the coordinates corresponding to the grid point data of the distance cutting radius of the current local abnormal region data; and r represents the cutting radius.

[0054] In another example embodiment of the present application, the initial grid data and the replaced grid point data are added by a weighting method to obtain the region field data of the current cutting, and the method specifically comprises:

[0055] R(x, y) = aA(x, y) + bG(x, y).

[0056] wherein R(x, y) represents the region field data corresponding to the coordinates (x, y) of the current cutting; A(x, y) represents the replaced grid point data corresponding to the coordinates (x, y); G(x, y) represents the initial grid data corresponding to the coordinates (x, y); a and b represent the first weighting coefficient and the second weighting coefficient respectively; a≥0; b≥0; and a+b=1.

[0057] In another example embodiment of the present application, the calculation formula of the first weighting coefficient and the second weighting coefficient is:

[0058]

[0059] g = c + d + e + f (0≤g≤4).

[0060] Wherein, g represents the comprehensive weight coefficient; c represents the weight coefficient of x direction data change influence; d represents the weight coefficient of y direction data change influence; e represents the weight coefficient of diagonal direction data change influence along x direction increasing and y direction increasing; f represents the weight coefficient of diagonal direction data change influence along x direction decreasing and y direction increasing.

[0061] In another exemplary embodiment of the present application, a method for quickly separating regional field and local field is provided, which specifically comprises:

[0062] Step 1: obtaining the geophysical measurement data in the region to be measured by the geophysical exploration instrument in the field measurement mode, and regarding the data as the combination of regional field and local field.

[0063] Supposing that the geophysical measurement data G(x, y) in the region to be measured D is composed of regional field data R(x, y) and local field data L(x, y), i.e. G(x, y) = R(x, y) + L(x, y).

[0064] Step 2: based on the above data, firstly performing grid processing, and then using the improved dynamic search 8-point interpolation cutting operator to perform multiple iteration cutting on the grid data, and separating to obtain the regional field R(x, y) result in the region D.

[0065] Subtracting the measured data G(x, y) from the regional field data R(x, y) as the local field anomaly L(x, y).

[0066] Specifically, the dynamic search 8-point interpolation cutting operator is divided into two parts, one part is the search threshold setting, and the other part is the 8-point interpolation cutting operator.

[0067] The essence of the interpolation cutting method is that the average value of the data of the surrounding points of the anomaly point is used to replace the data of the point, the change trend of the surrounding is kept unchanged, and the influence of the anomaly point is eliminated. Thus, the search threshold is set first, and only the limited region centered on the local anomaly is processed, which can reduce the influence of the interpolation cutting operator on the regional field R(x, y), reduce the error, and also can reduce the time for data processing and improve the processing speed.

[0068] As an optional implementation, the search threshold is selected according to the characteristics of the local anomaly data and the error causes, and a formula for determining the data range for separation processing is selected by setting the algorithm, i.e.

[0069]

[0070] The above formula is composed of two terms, the first term is the estimation of the upper limit of the false residual generated by the nonlinearity of the regional field R(x, y), wherein The first term is the residual maximum value obtained by first 8-point interpolation cutting of the measured data, and the threshold coefficient k is taken in the interval [0.1, 0.001]; the second term is the transfer error of the 8-point average fitting area field formula, and σ is the mean square error of the data.

[0071] As an optional implementation, the 8-point interpolation cutting operator is: let A(x, y) represent the average value of some 8-point data at a distance r from the point (x, y), that is:

[0072]

[0073] In the formula, r is the cutting radius. The area field R(x, y) is the weighted sum of G(x, y) and A(x, y), that is

[0074] R(x, y) = aA(x, y) + bG(x, y).

[0075] In the formula, a and b are the first and second weighting coefficients respectively, a ≥ 0, b ≥ 0, and a + b = 1.

[0076] Among them, for the determination of the first and second weighting coefficients, this implementation is improved by the following operators:

[0077]

[0078] ΔB x = G(x + r, y) - G(x - r, y).

[0079] ΔB y = G(x, y + r) - G(x, y - r).

[0080] ΔB xy = G(x + r, y + r) - G(x - r, y - r).

[0081] ΔB yx = G(x - r, y + r) - G(x + r, y - r).

[0082] (0 ≤ c ≤ 1; when , then c = 1).

[0083] (0 ≤ d ≤ 1; when , then d = 1).

[0084] (0 ≤ e ≤ 1; when , then e = 1).

[0085] (0 ≤ f ≤ 1; when Then f = 1.

[0086] g = c + d + e + f (0 ≤ g ≤ 4).

[0087] Take

[0088] Wherein, g represents the comprehensive weight coefficient; c represents the x-direction data change influence weight coefficient; d represents the y-direction data change influence weight coefficient; e represents the diagonal direction data change influence weight coefficient along the x-direction increasing and the y-direction increasing; f represents the diagonal direction data change influence weight coefficient along the x-direction decreasing and the y-direction increasing; ΔB x represents the data change amount in the x-direction, reflecting the difference of the grid point data at the coordinate (x, y) in the x-direction with the adjacent grid data; represents the reference value of ΔB x ; ΔB y represents the data change amount in the y-direction, reflecting the difference of the grid point data at the coordinate (x, y) in the y-direction with the adjacent grid data; represents the reference value of ΔB y ; ΔB xy represents the data change amount in the diagonal direction along the x-direction increasing and the y-direction increasing, reflecting the difference of the grid point data at the coordinate (x, y) in the diagonal direction with the adjacent grid data; represents the reference value of ΔB xy ; ΔB yx represents the data change amount in the diagonal direction along the x-direction decreasing and the y-direction increasing, reflecting the difference of the grid point data at the coordinate (x, y) in the diagonal direction with the adjacent grid data; represents the reference value of ΔB yx .

[0089] The regional field obtained by the above method is called the first cut regional field, denoted as R1(x, y); the above method is repeatedly used on R1(x, y) to obtain the second cut regional field R2(x, y); and the iteration is sequentially carried out to finally have Thus, there is The regional field R n (x, y) is called the regional field with the cutting radius r. Generally, in actual calculation, R is changed into the following inequality:

[0090]

[0091] In the inequality, ε is the cutting precision, that is, the preset cutting interpolation; ε is taken as a very small positive number. When the inequality is established, the iteration operation is ended, and it is considered that the regional field R n (x, y) is obtained. The measured data G(x, y) and the regional field Rn (x, y) subtraction, the local field is obtained:

[0092] L(x, y) = G(x, y) - R n (x, y).

[0093] According to the 8-point interpolation cutting operator, in order to make the grid data in the to-be-tested region D complete interpolation cutting calculation, the data boundary area needs to be expanded by a cutting radius r before interpolation cutting.

[0094] In addition, there are inevitably some observation errors in the measured data, especially random errors. Therefore, error propagation will occur in interpolation cutting. According to the error propagation formula, the mean square error propagation relationship of the 8-point interpolation cutting operator is:

[0095]

[0096] Since the mean square errors of the points are the same, let them be δ0, and the error propagation coefficient K n = (δ / δ0) n , where n is the number of error propagation. Then, after n times of interpolation cutting, the error propagation coefficient is Through analysis, it can be concluded that the improved algorithm will not amplify random errors, and can accelerate convergence and improve data processing speed.

[0097] Step 3: Based on the above results, the separation of the regional field and the local field is realized.

[0098] In another exemplary embodiment of the present application, a geological model composed of two spheres is established, and the model structure is shown in Figure 3 . The densities of the two spheres are both 2 kg / m 3 , the burial depth of the large sphere H = 500 m, the sphere radius of the large sphere R = 200 m, the burial depth of the small sphere h = 10 m, the sphere radius of the small sphere r = 8 m, and the corresponding contour lines are shown in Figure 4 , Figure 4 . Figure 3 The gravity forward graph of the geological model composed of the two spheres is shown in Figure 4 , where Figure 4 (a) represents the contour graph of the large sphere; Figure 4 (b) represents the contour graph of the small sphere; and (c) represents the contour graph of the geological model composed of the two spheres. Different cutting radii r and cutting precisions ε are selected, and the interpolation cutting method and the improved interpolation cutting method are used to separate and process the geological model data, and the threshold coefficient k is first selected as 0.01. The errors corresponding to different cutting radii r and cutting precisions ε of the geological model are counted, and the details are shown in Table 1 below. The marked part is the cutting radius and cutting precision corresponding to the minimum cutting error.

[0099] Error statistics of interpolation cutting method and improved interpolation cutting method

[0100]

[0101] As can be seen from Table 1, the improved interpolation cutting method has the same cutting radius error. This is because the search threshold coefficient k is determined, which locks the range of interpolation cutting, so that the edge data outside the range cannot be separated and processed, and the maximum error is also generated. Therefore, it is very important to determine the range of interpolation cutting. The more the actual local field range is consistent, the smaller the error of the separated data is.

[0102] Under the condition of minimum error, the interpolation cutting method is used to process the geological model data, and the program is iterated 126 times, which takes 0.811 seconds. The improved interpolation cutting method is used to process the geological model data, and the program is iterated 94 times, which takes 0.62 seconds. The processing results are shown in Figs. 1 and 2. Figure 5 、 Figure 6 Figure 5 Fig. 1 is a contour map of the interpolation cutting method; wherein, Figure 5 (a) represents the regional field contour map after cutting using the interpolation cutting method; Figure 5 (b) represents the local field contour map cut using the interpolation cutting method. Figure 6 Fig. 2 is a contour map of the improved interpolation cutting method; wherein, Figure 6 (a) represents the regional field contour map after cutting using the improved interpolation cutting method; Figure 6 (b) represents the local field contour map cut using the improved interpolation cutting method. It can be seen that the improved algorithm restores the regional field almost, and only in the region where the local field exists, there are some defects (marked by a blue square), and the separated local field is closer to the initial geological model.

[0103] In summary, the present application can accelerate the convergence of the operator and obtain better separation effect. The divided regional field has smaller distortion, the local field is closer to the initial model, and the processing speed is also improved.

[0104] ​The application further provides an application scenario of the method for quickly separating regional fields and local fields. Specifically, the method for quickly separating regional fields and local fields provided in this embodiment can be applied in a mineral resource exploration scenario. The mineral resource exploration scenario includes a field exploration link, a data processing link and a resource evaluation link. Exploration data enters the data processing link from the field exploration link, is processed by the method for quickly separating regional fields and local fields, and then regional field data and local field data after separation are obtained and enter the resource evaluation link. The method for quickly separating regional fields and local fields provided in this embodiment belongs to the data processing link in mineral resource exploration. Specifically, the method can efficiently separate regional fields and local fields in the data processing link, provide more clear and accurate geophysical field information for the subsequent resource evaluation link, and thus help exploration personnel more accurately identify the distribution range and reserves of mineral resources, improve the overall efficiency and accuracy of mineral resource exploration, and reduce exploration cost and risk.

[0105] Based on the same inventive concept, the embodiment of the application further provides a device for implementing the method for quickly separating regional fields and local fields. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, and therefore the specific limitations in one or more device embodiments for quickly separating regional fields and local fields provided below can refer to the limitations of the method for quickly separating regional fields and local fields described above, and will not be repeated here.

[0106] In one exemplary embodiment, as shown in Figure 7 a device for quickly separating regional fields and local fields is provided, which includes:

[0107] The data acquisition module 301 is configured to acquire geophysical measurement data in a to-be-measured region. The geophysical measurement data includes regional field data and local field data.

[0108] The gridding processing module 302 is configured to perform gridding processing on the geophysical measurement data to obtain geophysical gridded data.

[0109] The regional field data acquisition module 303 is configured to perform multiple iterations of cutting on the geophysical gridded data by using an improved interpolation cutting method until the difference between the regional field data of the current iteration and the regional field data of the previous iteration is less than a preset cutting difference, and then the iteration is ended to obtain the regional field data in the to-be-measured region. The improved interpolation cutting method is obtained by changing the 4-point circular average value of the interpolation cutting method to an 8-point window average value and introducing a dynamic search threshold.

[0110] The local field data acquisition module 304 is configured to subtract the regional field data from the geophysical measurement data to obtain the local field data in the to-be-measured region.

[0111] The beneficial effects of the present application are:

[0112] The local anomaly separation method provided by the present application is based on effective inversion result data, further extracts and analyzes local anomaly field information by effectively suppressing regional background field. For complex geological structure area, anomaly separation makes the final interpretation result more accurate and reliable, and also supports improving the accuracy of inversion imaging.

[0113] The method for quickly separating regional field and local field provided by the present application can be used as a supplementary means of conventional geophysical data processing and interpretation, can more fully mine the geological information implied therein, can effectively improve the interpretation speed, accuracy and reliability of stratum structure and hidden geological body, and form a method and technical system suitable for identifying hidden geological body under complex geological conditions. The technology has important supporting role for national deep geological prospecting and ore body identification in covered area, and has wide application prospect.

[0114] In an exemplary embodiment, a computer device is provided, which can be a server or a terminal, and an internal structure diagram thereof can be as shown in Figure 8 The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is used to store regional field and local field processing data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through network connection. The computer program is executed by the processor to implement a method for quickly separating regional field and local field.

[0115] Those skilled in the art can understand that Figure 8 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement. In an exemplary embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in each method embodiment described above.

[0116] In an exemplary embodiment, a computer readable storage medium storing a computer program is provided, the computer program, when executed by a processor, implements the steps in the above method embodiments.

[0117] In an exemplary embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the steps in the above method embodiments.

[0118] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0119] Those skilled in the art can understand that all or part of the processes in the above method embodiments can be completed by a computer program instructing related hardware. 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 above method embodiments. Any reference to memory, database or other medium used in the embodiments provided by the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ReadOnly Memory, ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (Resistive Random Access Memory, ReRAM), magnetoresistive random access memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric memory (Ferroelectric Random Access Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc.

[0120] The database involved in each of the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, and the like, without being limited thereto. The processor involved in each of the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, and the like, without being limited thereto.

[0121] The technical features of the above embodiments can be combined in any manner. To make the description concise, all possible combinations of the technical features in the above embodiments are not described, but it should be considered that any combination of the technical features is within the scope of the present disclosure, as long as there is no contradiction.

[0122] The principles and implementation manners of the present application are described by using specific examples herein, and the above embodiments are only used to help understand the method of the present application and its core idea. Meanwhile, for those skilled in the art, the specific implementation manners and application ranges can be changed according to the idea of the present application. In summary, the content of the present description should not be understood as a limitation of the present application.

Claims

1. A method for quickly separating regional fields from local fields, characterized in that: The method for quickly separating the regional field and the local field includes: Acquiring geophysical measurement data within the area to be measured; the geophysical measurement data includes regional field data and local field data; Gridding the geophysical measurement data to obtain geophysical gridded data; An improved interpolation cutting method is used to perform multiple iterative cutting of geophysical gridded data until the difference between the regional field data of the current cutting and the regional field data of the previous cutting is less than a preset cutting difference, thereby obtaining the regional field data within the area to be measured. The improved interpolation cutting method is obtained by replacing the 4-point circle average of the interpolation cutting method with an 8-point window average and introducing a dynamic search threshold. The local field data in the area to be measured are obtained by subtracting the regional field data from the geophysical measurement data.

2. The method for rapidly separating a regional field and a local field according to claim 1, characterized in that: The improved interpolation cutting method is used to perform multiple iterative cutting of the geophysical grid data until the difference between the regional field data of the current cutting and the regional field data of the previous cutting is less than the preset cutting difference, and the regional field data in the area to be measured is obtained, which specifically includes: Use geophysical gridded data as initial grid data; Calculate the dynamic search threshold corresponding to each grid point data in the initial grid data respectively; Traverse each grid point data. If the average value of the difference between the current grid data and a preset number of adjacent grid data is greater than the current dynamic search threshold, the current grid data is used as the local abnormal area data until all grid points are traversed and multiple local abnormal area data are obtained. Traverse each local abnormal area data, calculate the 8-point interpolation cutting operator of the current local abnormal area data, and replace the current local abnormal area data with the corresponding 8-point interpolation cutting operator until all local abnormal area data are replaced, and obtain the replaced grid point data; The initial grid data and the replaced grid point data are added together by a weighted method to obtain the regional field data of the current cut; Use the currently cut regional field data as the initial grid data and return to the step of "calculating the dynamic search threshold corresponding to each grid point data in the initial grid data" until the difference between the currently cut regional field data and the previously cut regional field data is less than the preset cutting difference, ending the iteration and obtaining the regional field data.

3. The method for rapidly separating a regional field and a local field according to claim 2, characterized in that: The calculation formula for the dynamic search threshold is: Where E represents the dynamic search threshold; k represents the search threshold coefficient; (i, j) represents the grid point in the i-th row and j-th column; G represents the grid point set; represents the remaining maximum value among all grid points obtained by the first 8-point interpolation cut; σ represents the mean square error of all grid points.

4. The method for rapidly separating a regional field and a local field according to claim 2, characterized in that: The grid point data after replacement is: Among them, A(x,y) represents the replaced grid point data corresponding to the coordinate (x, y); G(·,·) represents the initial grid data at the coordinate corresponding to the grid point data at the cutting radius distance of the current local abnormal area data; r represents the cutting radius.

5. The method for rapidly separating a regional field and a local field according to claim 2, characterized in that: The initial grid data and the replaced grid point data are added together by a weighted method to obtain the regional field data of the current cut, including: R(x,y)=aA(x,y)+bG(x,y); Among them, R(x,y) represents the regional field data of the current cut corresponding to the coordinate (x,y); A(x,y) represents the replaced grid point data corresponding to the coordinate (x,y); G(x,y) represents the initial grid data corresponding to the coordinate (x,y); a and b represent the first weighting coefficient and the second weighting coefficient respectively; a≥0; b≥0; and a+b=1.

6. The method for rapidly separating a regional field and a local field according to claim 5, characterized in that: The calculation formulas for the first weighting coefficient and the second weighting coefficient are: g=c+d+e+f(0≤g≤4); Among them, g represents the comprehensive weight coefficient; c represents the weight coefficient of the impact of data changes in the x direction; d represents the weight coefficient of the impact of data changes in the y direction; e represents the weight coefficient of the impact of data changes in the diagonal direction that increases along the x direction and increases in the y direction; f represents the weight coefficient of the impact of data changes in the diagonal direction that decreases along the x direction and increases in the y direction.

7. A device for rapidly separating a regional field from a local field, characterized in that: The device for quickly separating the regional field and the local field comprises: A data acquisition module is used to acquire geophysical measurement data within the area to be measured; the geophysical measurement data includes regional field data and local field data; A grid processing module is used to perform grid processing on geophysical measurement data to obtain geophysical grid data; A regional field data acquisition module is used to perform multiple iterative cuts on the geophysical gridded data using an improved interpolation cutting method, ending the iteration when the difference between the regional field data of the current cut and the regional field data of the previous cut is less than a preset cutting difference, thereby obtaining the regional field data within the area to be measured; the improved interpolation cutting method is obtained by replacing the 4-point circular average of the interpolation cutting method with an 8-point window average and introducing a dynamic search threshold; The local field data acquisition module is used to subtract the regional field data from the geophysical measurement data to obtain the local field data in the area to be measured.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for rapidly separating a regional field from a local field according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for quickly separating a regional field from a local field according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for quickly separating a regional field from a local field according to any one of claims 1 to 6 is implemented.

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