Anisotropy analysis method, device and equipment of geochemical data and storage medium

Abstract

The application provides a geochemical data anisotropy analysis method, device, equipment and storage medium, the method comprises the following steps: taking each sampling point in a research area as a center sampling point respectively, determining a plurality of statistical windows of each center sampling point; for each statistical window, determining a non-empty subset corresponding to a sampling point set composed of all sampling points in the statistical window; determining the non-empty subset with the largest difference between the element concentration and the average element concentration corresponding to the statistical window as the target non-empty subset corresponding to the statistical window; according to the window size of each statistical window and the average value of the element concentration corresponding to the target non-empty subset corresponding to each statistical window, a double logarithmic diagram for determining the anisotropy distribution information of the target element corresponding to the center sampling point is constructed. In this way, the application is helpful to reduce the influence of data anomalies at individual sampling points on the anisotropy distribution analysis result of the element concentration in the whole research area.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geochemical data analysis, in particular to a method and device for anisotropy analysis of geochemical data, an apparatus and a storage medium. BACKGROUND

[0002] Geochemical data is also known as geochemical data. When analyzing mineralization in a study area, multiple sampling points are often selected from the study area, geochemical data corresponding to each sampling point is collected, and the element concentration of a specific element required for mineralization at each sampling point is determined according to the collected geochemical data. The anisotropic distribution of the element concentration of the specific element in the study area (i.e., the element concentration of the specific element changes differently in different directions in the study area) is analyzed to determine the mineralization in the study area (e.g., the area where the specific element is enriched in the study area has a higher probability of mineralization).

[0003] At present, the existing technology usually selects a sampling point in the study area as a center sampling point, divides a plurality of regular shapes (such as rectangles, sectors, ellipses, etc.) of research windows from the center sampling point in the study area, calculates the element concentration average of each research window according to the element concentration of all sampling points contained in each research window, projects the window size of each research window and its corresponding element concentration average onto a double logarithmic graph, and through linear fitting, a fitting straight line corresponding to each coordinate point in the double logarithmic graph can be obtained. The slope of the fitting straight line is used to determine the anisotropic distribution of the element concentration at the center sampling point (where a positive slope indicates that the element concentration diffuses from the center sampling point to the surrounding area, and a negative slope indicates that the element concentration converges from the surrounding area to the center sampling point).

[0004] Here, when analyzing the anisotropic distribution of the element concentration of a certain specific element in the study area according to the above-mentioned existing technology, on the one hand, since the above-mentioned existing technology often uses a certain regular shape window for anisotropy analysis, and the anisotropy characteristics of geochemical data are often irregular and have no specific shape, the accuracy of the analysis results obtained by the above-mentioned existing technology based on a certain regular shape window for anisotropy analysis is low. On the other hand, since the sampling points in different research windows overlap, and the double logarithmic graph needs to use the element concentration average of different research windows, when the element concentration of an individual sampling point in a certain research window is abnormal (i.e., abnormally high or abnormally low), the numerical abnormality may cause the calculation results of the element concentration averages of multiple research windows to be inaccurate, thereby reducing the accuracy of the final anisotropic distribution analysis results. SUMMARY

[0005] Therefore, the application provides a geochemical data anisotropy analysis method, device, equipment and storage medium, which can reduce the influence of data anomaly at individual sampling points on the analysis result of the anisotropy distribution of element concentration in the whole study area, and effectively improve the accuracy of the analysis result of the anisotropy distribution of element concentration in the whole study area.

[0006] In order to make the above objectives, characteristics and advantages of the application more obvious and easy to understand, the following preferred embodiments are described in detail below, and the accompanying drawings are referred to.

[0007] In a first aspect, the embodiments of the application provide a geochemical data anisotropy analysis method, which comprises:

[0008] Collecting geochemical data of a plurality of sampling points in a study area, and performing element analysis on the collected geochemical data at each sampling point to determine the element concentration of a target element at each sampling point;

[0009] For each sampling point, taking the sampling point as a center sampling point of a statistical window, a plurality of statistical windows corresponding to the center sampling point are determined from the study area by changing the window size of the statistical window;

[0010] For each statistical window corresponding to the center sampling point, a sampling point set composed of all sampling points in the statistical window is determined, and all non-empty subsets corresponding to the sampling point set are determined from all sampling points in the statistical window;

[0011] According to the element concentration corresponding to the sampling points in each non-empty subset, the non-empty subset with the largest difference between the element concentration and the element concentration mean corresponding to the statistical window is determined from all non-empty subsets corresponding to the sampling point set as the target non-empty subset corresponding to the statistical window, wherein the element concentration mean is determined according to the element concentration corresponding to all sampling points in the statistical window;

[0012] Taking the window size of each statistical window as the abscissa and the average value of the element concentration corresponding to the target non-empty subset corresponding to each statistical window as the ordinate, a log-log graph corresponding to the abscissa and the ordinate is constructed, and the anisotropy distribution information corresponding to the target element at the center sampling point is determined according to the log-log graph.

[0013] In a second aspect, the embodiments of the application provide a geochemical data anisotropy analysis device, which comprises:

[0014] The sampling module is configured to collect geochemical data at a plurality of sampling points in a study area, and perform element analysis on the collected geochemical data at each sampling point to determine element concentrations of a target element at each sampling point.

[0015] The first statistical module is configured to, for each sampling point, take the sampling point as a center sampling point of a statistical window, and determine a plurality of statistical windows corresponding to the center sampling point by changing a window size of the statistical window in the study area.

[0016] The second statistical module is configured to, for each statistical window corresponding to the center sampling point, determine all non-empty subsets corresponding to a sampling point set composed of all sampling points in the statistical window from all sampling points in the statistical window.

[0017] The third statistical module is configured to determine, from all non-empty subsets corresponding to the sampling point set, a non-empty subset with the largest difference between element concentrations of sampling points in the non-empty subset and an average value of element concentrations corresponding to the statistical window as a target non-empty subset corresponding to the statistical window according to the element concentrations of the sampling points in each non-empty subset, wherein the average value of the element concentrations is determined according to the element concentrations of all sampling points in the statistical window.

[0018] The analysis module is configured to construct a double logarithmic graph corresponding to a horizontal coordinate and a vertical coordinate, wherein the horizontal coordinate is a window size of each statistical window, and the vertical coordinate is an average value of the element concentrations corresponding to the target non-empty subset corresponding to each statistical window, and determine anisotropy distribution information of the target element corresponding to the center sampling point according to the double logarithmic graph.

[0019] In a third aspect, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements steps of the anisotropy analysis method when executing the computer program.

[0020] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, and the computer readable storage medium stores a computer program, and the computer program implements steps of the anisotropy analysis method when executed by a processor.

[0021] The technical solution provided by the embodiments of the present application can have the following beneficial effects:

[0022] The method, device, equipment and storage medium for anisotropy analysis of geochemical data provided by the embodiments of the present application collect geochemical data of a plurality of sampling points in a research area, perform element analysis on the collected geochemical data of each sampling point, and determine the element concentration of a target element at each sampling point. For each sampling point, the sampling point is taken as a center sampling point of a statistical window, a plurality of statistical windows corresponding to the center sampling point are determined from the research area by changing the window size of the statistical window. For each statistical window corresponding to the center sampling point, a sampling point set composed of all sampling points in the statistical window is determined, and all non-empty subsets corresponding to the sampling point set are determined from all sampling points in the statistical window. The non-empty subset with the largest difference between the element concentration of each non-empty subset and the average element concentration of the statistical window is determined as the target non-empty subset of the statistical window according to the element concentration of each non-empty subset. The window size of each statistical window is taken as the abscissa, and the average of the element concentration corresponding to the target non-empty subset of each statistical window is taken as the ordinate, a double logarithmic graph corresponding to the abscissa and the ordinate is constructed, and the anisotropy distribution information of the target element at the center sampling point is determined according to the double logarithmic graph. In this way, the present application is helpful to reduce the influence of data anomalies at individual sampling points on the anisotropy distribution analysis result of the element concentration in the whole research area, and effectively improves the accuracy of the anisotropy distribution analysis result of the element concentration in the whole research area. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be considered as limiting the scope. For those skilled in the art, other related drawings can also be obtained without creative labor.

[0024] Figure 1 A research window division schematic diagram when the anisotropy distribution of the element concentration in the research area is analyzed by the prior art is shown;

[0025] Figure 2 A flowchart of the anisotropy analysis method of geochemical data provided by the embodiments of the present application is shown;

[0026] Figure 3 A division schematic diagram of a circular statistical window provided by the embodiments of the present application is shown;

[0027] Figure 4aA display schematic diagram of a first target non-empty subset corresponding to the center sampling point in different scales is shown.

[0028] Figure 4b A display schematic diagram of a second target non-empty subset corresponding to the center sampling point in different scales is shown.

[0029] Figure 5 A structure schematic diagram of an anisotropy analysis device for geochemical data is shown.

[0030] Figure 6 A structure schematic diagram of an electronic device 600 is shown. DETAILED DESCRIPTION

[0031] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the following will be combined with the accompanying drawings for the embodiments of the present application to make a clear and complete description of the technical solutions in the embodiments of the present application. It should be understood that the accompanying drawings in the present application are only for the purpose of illustration and description, and are not used to limit the protection scope of the present application. In addition, it should be understood that the schematic drawings are not drawn according to the actual proportions. The flowchart shows the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowchart can not be implemented in sequence, and the steps without logical context relationship can be reversed in sequence or implemented simultaneously. In addition, one or more other operations can be added to the flowchart or removed from the flowchart under the guidance of the content of the present application by those skilled in the art.

[0032] In addition, the described embodiments are only some of the embodiments of the present application, not all the embodiments. The components of the embodiments of the present application described and shown in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0033] It should be noted that the term "comprising" will be used in the embodiments of the present application to indicate the presence of the features declared thereafter, but does not exclude the addition of other features.

[0034] Here, Figure 1 A research window division schematic diagram when analyzing the anisotropy distribution of element concentration in the research area by the prior art solution is shown. Figure 1As shown, the prior art generally selects a sampling point in the study area as a central sampling point (such as the sampling point represented by the star symbol in Figure 1 , and a plurality of square study windows with different side lengths can be divided in the study area by changing the side length of the square.

[0035] Specifically, after obtaining the element concentration of the chemical element to be analyzed at each sampling point in the study area through the collection and analysis of geochemical data, taking sampling point a as the central sampling point, the element concentration of the chemical element to be analyzed at each sampling point in the study area is analyzed according to the square side length - Taking the n square study windows centered on the above-mentioned central sampling point as an example, the anisotropic distribution of the element concentration of the above-mentioned chemical element to be analyzed in the study area can be analyzed by the following steps 1-5:

[0036] Step 1, for each square study window, the average element concentration C of all sampling points in the square study window can be calculated according to the number of all sampling points contained in the square study window and the element concentration corresponding to each sampling point.

[0037] Step 2, the window size of each square study window (for example, the window size of the nth square study window is × ) and the average element concentration corresponding to each square study window (for example, the average element concentration of the nth square study window is ) are used to construct a double logarithmic graph, wherein each scatter point in the double logarithmic graph can correspond to a square study window, the horizontal coordinate of the scatter point can be the window size of the square study window, and the vertical coordinate of the scatter point can be the average element concentration of the square study window.

[0038] Step 3, the window size of each square study window and the above-mentioned average element concentration corresponding thereto have a logarithmic linear relationship, so that the logarithmic linear relationship between the window size of each square study window and the above-mentioned average element concentration corresponding thereto can be determined by linear fitting, and the slope k (k is the slope of the fitted double logarithmic curve) fitted by linear fitting is used to represent the above-mentioned logarithmic linear relationship; wherein k>0 represents that the element concentration in the study area is attenuated (equivalent to indicating that the element concentration spreads from the sampling point a to the surrounding) with the sampling point a (i.e. the above-mentioned central sampling point) as the center, and k<0 represents that the element concentration in the study area is increased (equivalent to indicating that the element concentration from the surrounding of the sampling point a towards the sampling point a) with the sampling point a as the center.

[0039] Step 4, after obtaining the slope k, the singularity index corresponding to the sampling point a (i.e. the above-mentioned center sampling point) can be calculated E+k; wherein E is the Euclidean dimension, which can generally be taken as two for the sampling points in the study area to collect geochemical data, and E can be taken as 2 by default; when the singularity index is less than E, it indicates that the element concentration is gathered, indicating favorable mineralization; when the singularity index is greater than E, it indicates that the element concentration is attenuated, indicating unfavorable mineralization; and when the singularity index is equal to E, it indicates that the element concentration is uniformly distributed and is not affected by geological activities (equivalent to the singularity index can be used to quantitatively represent the anisotropic distribution of the element concentration of the chemical element to be analyzed in the study area).

[0040] Step 5, for each sampling point in the study area, the above steps 1-4 are repeatedly executed with the sampling point as the center sampling point, so as to obtain the singularity index corresponding to each sampling point in the study area.

[0041] Here, according to the prior art scheme shown in steps 1-5, on the one hand, the prior art scheme generally considers the anisotropic characteristics in a certain regular form of window (such as the square research window shown in Figure 1 , which assumes that the element changes in each direction are uniform), and does not truly show the anisotropy of the geochemical element distribution (i.e. the irregularity of the element distribution); on the other hand, when the prior art scheme analyzes the anisotropy of the geochemical data, the high anomaly or loss of the selected window in a certain scale (i.e. different window sizes correspond to different scales) can cause the anomaly or loss of other scales in that direction to be suppressed, which cannot accurately express the true singularity index of the region and the spatial distribution characteristics of the elements (i.e. due to the overlapping of the sampling points in different research windows, and the need to use the element concentration mean values corresponding to different research windows for the double logarithmic graph, when the element concentration of an individual sampling point in a certain research window is abnormal, the abnormal value can cause the calculation results of the element concentration mean values corresponding to multiple research windows to be inaccurate, thereby reducing the accuracy of the analysis results of the anisotropic distribution of the element concentration in the whole study area).

[0042] Therefore, the embodiments of the present application provide a geochemical data anisotropy analysis method, device, equipment and storage medium, which can reduce the influence of the data abnormality of an individual sampling point on the analysis results of the anisotropic distribution of the element concentration in the whole study area, and effectively improve the accuracy of the analysis results of the anisotropic distribution of the element concentration in the whole study area.

[0043] In one embodiment of this application, a method for anisotropic analysis of localized data can run on a terminal device or a server. The terminal device can be a local terminal device. When the anisotropic analysis method for localized data runs on a server, it can be implemented and executed based on a cloud interaction system, which includes a server and client devices (i.e., terminal devices).

[0044] To facilitate understanding of the embodiments of this application, the following provides a detailed description of a method, apparatus, device, and storage medium for anisotropy analysis of geochemical data provided by the embodiments of this application.

[0045] Reference Figure 2 As shown, Figure 2 The diagram illustrates a flowchart of an anisotropy analysis method for geochemical data provided in an embodiment of this application. The anisotropy analysis method for geochemical data includes steps S201-S205; specifically:

[0046] S201 involves collecting geochemical data from multiple sampling points within the study area, and performing elemental analysis on the geochemical data collected at each sampling point to determine the elemental concentration of the target element at each sampling point.

[0047] Here, when collecting geochemical data at sampling points within the study area, different sampling media can be collected at the sampling points as specific geochemical data for subsequent elemental analysis, depending on different research needs. For example, stream sediments can be collected at the sampling points as the aforementioned geochemical data, or soil, rocks, etc. can be collected at the sampling points as the aforementioned geochemical data. This application embodiment does not limit the data type of the specific geochemical data collected.

[0048] It should be noted that the specific element type of the target element can also be determined according to the actual research needs. For example, the target element can be copper or iron. This application embodiment does not limit the specific element type of the target element.

[0049] S202, for each sampling point, using that sampling point as the center sampling point of the statistical window, multiple statistical windows corresponding to the center sampling point are determined from the study area by changing the window size of the statistical window.

[0050] In this embodiment of the application, the shape of the statistical window does not affect the final anisotropy analysis results (i.e., the anisotropic distribution of the element concentration of the target element in the study area). Therefore, this embodiment of the application does not limit the specific shape of the statistical window.

[0051] Specifically, as an optional embodiment, in the execution of the above step S202, a circular statistical window can be selected, at this time, for each sampling point, the terminal device can take the sampling point as a center sampling point of the circular statistical window, and determine a plurality of circular statistical windows corresponding to the center sampling point from the study area by changing the window radius of the circular statistical window.

[0052] Exemplary description, Figure 3 A division schematic diagram of a circular statistical window provided by an embodiment of the present application is shown, as Figure 3 As shown, taking a sampling point represented by a star-shaped mark in the study area as an example, taking the sampling point as a center sampling point (i.e. a center) of a circular statistical window, n circular statistical windows with the sampling point as the center can be determined from the study area according to radii r1-rn, respectively.

[0053] Specifically, as another optional embodiment, in the execution of the above step S202, a square statistical window can also be selected, as in the prior art solution shown in Figure 1 At this time, for each sampling point, the terminal device can take the sampling point as a center sampling point of the square statistical window, and determine a plurality of square statistical windows corresponding to the center sampling point from the study area by changing the window length of the square statistical window.

[0054] Exemplary description, as Figure 1 As shown, taking a sampling point represented by a star-shaped mark in the study area as an example, taking the sampling point as a center sampling point of a square statistical window, n square statistical windows with the sampling point as the center can be divided from the study area according to square side lengths - , respectively.

[0055] It should be noted that when defining the minimum window size of the above statistical window (such as the minimum window radius of the above circular statistical window, and the minimum square side length of the above square statistical window) and the window spacing of two adjacent statistical windows, the point distance between a plurality of sampling points in the study area can be flexibly adjusted, so that each statistical window can contain a sufficient number of sampling points, and the proportion of overlapping sampling points between two adjacent statistical windows can be less than or equal to a certain preset threshold, thereby facilitating the improvement of the accuracy and reliability of the final anisotropy analysis result.

[0056] S203, for each statistical window corresponding to the center sampling point, according to a sampling point set composed of all sampling points in the statistical window, determining all non-empty subsets corresponding to the sampling point set from all sampling points in the statistical window.

[0057] Specifically, taking an example of that a statistical window contains 5 sampling points in total, by performing maximum non-empty combination on the 5 sampling points (i.e. all the sampling points) contained in the statistical window, it can be determined that the sampling point set composed of the above 5 sampling points corresponds to 31 different non-empty subsets in total, that is, a combination manner of a non-empty subset.

[0058] S204, determining, from all the non-empty subsets corresponding to the sampling point set, a non-empty subset with the largest difference between the element concentration and the element concentration average corresponding to the statistical window as the target non-empty subset corresponding to the statistical window according to the element concentration corresponding to the sampling points in each non-empty subset.

[0059] Here, the element concentration average can be determined according to the element concentration corresponding to all the sampling points in the statistical window; for example, if the statistical window contains 5 sampling points, the sum of the element concentrations corresponding to the 5 sampling points (i.e. the element concentrations of the target element corresponding to the 5 sampling points) can be calculated, and then the sum value obtained is divided by 5 (i.e. the number of sampling points contained in the statistical window) to obtain the element concentration average corresponding to the statistical window.

[0060] In the embodiments of the present application, when performing the above step S204, for each non-empty subset, a U-statistic (i.e. target statistic) suitable for spatial statistics can be constructed according to the element concentration corresponding to the sampling points in the non-empty subset and the element concentration average corresponding to the statistical window, so as to determine the target non-empty subset according to the value of the U-statistic corresponding to each non-empty subset.

[0061] Specifically, as an optional embodiment, for each non-empty subset, the target statistic (i.e. the U-statistic) corresponding to the non-empty subset can be calculated according to the element concentration corresponding to each sampling point in the non-empty subset and the element concentration average corresponding to the statistical window according to the following formula:

[0062]

[0063] Wherein, U is the target statistic (i.e. the U-statistic) corresponding to the non-empty subset;

[0064] n is the number of sampling points contained in the non-empty subset;

[0065] is the element concentration corresponding to the i-th sampling point in the non-empty subset;

[0066] z is the element concentration average corresponding to the statistical window.

[0067] Here, after the U value (i.e. the above target statistic) corresponding to each non-empty subset is calculated according to the above formula, the non-empty subset with the largest target statistic (i.e. the non-empty subset with the largest U value calculated above) and the non-empty subset with the smallest target statistic (i.e. the non-empty subset with the smallest U value calculated above) can be determined from all non-empty subsets corresponding to the sample point set according to the target statistic corresponding to each non-empty subset, as the target non-empty subset corresponding to the statistical window.

[0068] It should be noted that in another optional embodiment, when performing the above step S204, for each non-empty subset, the element concentration corresponding to the non-empty subset can of course be represented by calculating the element concentration mean according to the element concentration corresponding to each sample point in the non-empty subset and the number of sample points contained in the non-empty subset, so as to determine the non-empty subset with the largest difference between the element concentration and the element concentration mean corresponding to the statistical window as the target non-empty subset corresponding to the statistical window; however, compared with the above embodiment introducing the U statistic, since there are a large number of overlapping sample points between different statistical windows, the way of representing the element concentration corresponding to a non-empty subset by calculating the element concentration mean may still have the risk of affecting the analysis result of the anisotropic distribution of the element concentration in the whole research area due to data anomalies at individual sample points, based on which, in the embodiment of the present application, the above step S204 can be preferably performed by using the above embodiment introducing the U statistic.

[0069] For example, the non-empty subset with the largest U value is denoted as a first target non-empty subset, Figure 4a A display diagram provided by the embodiment of the present application for displaying the first target non-empty subset corresponding to the central sample point under different scales is shown in FIG. 2, which shows a display diagram for displaying the first target non-empty subset corresponding to the central sample point under different scales. Figure 4a As shown in FIG. 2, taking three statistical windows with the central sample point as the center and the radius of 500 meters, 1500 meters and 2500 meters respectively as an example, after all sample points in the research area are marked by using a graphic identifier (such as a triangular identifier) as shown in FIG. 2, the central sample point and the sample points belonging to the first target non-empty subset in each statistical window can be marked by using different graphic identifiers, so that the researchers can more intuitively determine the first target non-empty subset corresponding to the central sample point under different scales (i.e. in different statistical windows) by drawing the display diagram. Figure 4a As shown in FIG. 2, taking three statistical windows with the central sample point as the center and the radius of 500 meters, 1500 meters and 2500 meters respectively as an example, after all sample points in the research area are marked by using a graphic identifier (such as a triangular identifier) as shown in FIG. 2, the central sample point and the sample points belonging to the first target non-empty subset in each statistical window can be marked by using different graphic identifiers, so that the researchers can more intuitively determine the first target non-empty subset corresponding to the central sample point under different scales (i.e. in different statistical windows) by drawing the display diagram.

[0070] For example, the non-empty subset with the smallest U value is denoted as a second target non-empty subset, Figure 4b A display diagram provided by the embodiment of the present application for displaying the first target non-empty subset corresponding to the central sample point under different scales is shown in FIG. 2, which shows a display diagram for displaying the first target non-empty subset corresponding to the central sample point under different scales.Figure 4b As shown, the statistical windows include three circles with center sampling points as the center and radius of 500 meters, 1500 meters and 2500 meters respectively, which represent statistical windows of different scales. After marking all the sampling points in the research area with graphical marks (such as triangles as shown), the center sampling points and the sampling points belonging to the second target non-empty subset in each statistical window can be marked with different graphical marks, so that the researchers can more intuitively determine the corresponding second target non-empty subset of the center sampling points at different scales (i.e. in different statistical windows) through the display diagram. Figure 4b

[0071] It should be noted that, Figure 4a Figure 4b Only for example, the first target non-empty subset and the second target non-empty subset corresponding to the center sampling points at different scales, for actual drawing, the specific scale represented by each statistical window (i.e. the window size of each statistical window, such as the radius range corresponding to the circular statistical window), the number of selected statistical windows, the shape of the statistical window and other specific drawing parameter information are not limited by the embodiments of the present application.

[0072] S205, taking the window size of each statistical window as the abscissa and the average value of the element concentration corresponding to the target non-empty subset corresponding to each statistical window as the ordinate, constructing a double logarithmic graph corresponding to the abscissa and the ordinate, and determining the anisotropy distribution information of the target element corresponding to the center sampling point according to the double logarithmic graph.

[0073] Here, according to the specific implementation of step S204 described above, when the implementation of introducing U-statistics described above is used to execute step S204, each statistical window will correspond to a target non-empty subset with the maximum U value (i.e. target statistics) (which can be denoted as the first target non-empty subset) and a target non-empty subset with the minimum U value (which can be denoted as the second target non-empty subset). At this time, when executing step S205, the average value of the element concentration of the first target non-empty subset and the second target non-empty subset corresponding to each statistical window can be taken as the ordinate while the abscissa remains unchanged (i.e. taking the window size of each statistical window as the abscissa) to construct two double logarithmic graphs corresponding to the same center sampling point, as shown in steps a1-a2 below.

[0074] Step a1, for the scatter points corresponding to each statistical window, taking the window size of the statistical window as the first abscissa corresponding to the scatter point and the average value of the element concentration corresponding to the first target non-empty subset corresponding to the statistical window as the first ordinate corresponding to the scatter point, constructing a first double logarithmic graph corresponding to the first abscissa and the first ordinate.​​

[0075] Here, the first target non-empty subset is a non-empty subset corresponding to the maximum target statistic (i.e., the U statistic) of the statistical window; the specific calculation method of the target statistic can refer to the specific implementation of the preceding step S204, and repeated parts will not be described here.

[0076] Specifically, for the first target non-empty subset, the average element concentration can be calculated according to the element concentration corresponding to each sampling point in the first target non-empty subset and the number of sampling points contained in the first target non-empty subset, to represent the average value of the element concentration corresponding to the first target non-empty subset (i.e., the first ordinate).

[0077] Step a2, for the scatter point corresponding to each statistical window, the window size of the statistical window is taken as the second abscissa corresponding to the scatter point, and the average value of the element concentration corresponding to the second target non-empty subset corresponding to the statistical window is taken as the second ordinate corresponding to the scatter point. A second double logarithmic graph corresponding to the second abscissa and the second ordinate is constructed.

[0078] Here, the second target non-empty subset is a non-empty subset corresponding to the minimum target statistic (i.e., the U statistic) of the statistical window; the specific calculation method of the target statistic can refer to the specific implementation of the preceding step S204, and repeated parts will not be described here.

[0079] Specifically, for the second target non-empty subset, the average element concentration can be calculated according to the element concentration corresponding to each sampling point in the second target non-empty subset and the number of sampling points contained in the second target non-empty subset, to represent the average value of the element concentration corresponding to the second target non-empty subset (i.e., the second ordinate).

[0080] On the basis of the steps a1-a2, after obtaining the first double logarithmic graph and the second double logarithmic graph, the anisotropy distribution information of the target element corresponding to the central sampling point can be determined by the following steps b1-b4, specifically:

[0081] Step b1, linear fitting is performed on all scatter points in the first double logarithmic graph by the least square method, and a first slope corresponding to the linear fitting result is determined.

[0082] Here, by the least square method, linear fitting can be performed on all scatter points in the first double logarithmic graph, the linear relationship between the window size (i.e., the first abscissa) of each statistical window and the average value of the element concentration (i.e., the first ordinate) corresponding to the first target non-empty subset corresponding to the statistical window is determined, and the first slope k1 obtained by linear fitting is used to represent the linear relationship.

[0083] Step b2, linearly fitting all the scattered points in the second double logarithmic graph by least square method, and determining the second slope corresponding to the linear fitting result.

[0084] Here, by least square method, the linear relationship between the window size (i.e. the second abscissa) of each statistical window and the average value of the element concentration (i.e. the second ordinate) corresponding to the above-mentioned second target non-empty subset corresponding to the linear relationship can also be determined by linearly fitting the second slope k2.

[0085] Step b3, determining the slope with the largest absolute value from the first slope and the second slope as the target slope.

[0086] Here, after obtaining the first slope k1 and the second slope k2, the absolute value of the larger one can be determined as the target slope K by comparing the absolute values of the first slope k1 and the second slope k2.

[0087] Step b4, determining the singularity index corresponding to the center sampling point according to the target slope.

[0088] Here, after obtaining the target slope K, the singularity index corresponding to the center sampling point can be calculated E+k (different from the formula for calculating the singularity index in the prior art).

[0089] It should be noted that E still represents the Euclidean dimension, which can generally be taken as two-dimensional plane to collect geochemical data of the sampling points in the study area, at which time E can be taken as 2 by default.

[0090] Here, as described above in the prior art, the singularity index can be used to represent the anisotropic distribution information of the target element at the center sampling point, wherein the singularity index reflects the anisotropy of the spatial distribution of the element concentration affected by multiple geological activities (the singularity index).

[0091] Here, after obtaining the singularity index corresponding to each sampling point in the study area in the manner shown in steps b1-b4, in order to comprehensively and multi-scalely reflect the anisotropic distribution of the element concentration of the target element in the study area, the singularity index corresponding to other location points in the study area other than the sampling points can also be determined in the manner shown in steps c1-c3, specifically:

[0092] Step c1, obtaining the singularity index corresponding to each sampling point in the research area.

[0093] Here, the specific implementation of step c1 can refer to the method of determining the singularity index corresponding to a sampling point in the foregoing steps b1-b4, and the repeated parts will not be described here.

[0094] Step c2, performing interpolation operation between the singularity indexes corresponding to adjacent sampling points to obtain the singularity indexes corresponding to a plurality of interpolation points.

[0095] Specifically, in the embodiments of the present application, the interpolation operation method adopted can be an IDW (Inverse Distance Weighted, inverse distance weighted) spatial interpolation method, wherein the IDW spatial interpolation method is to take the distance between the interpolation point and the sampling point as the weight for weighted average, that is, the closer to the interpolation point, the greater the weight corresponding to the sampling point, and for each interpolation point, the singularity index corresponding to the interpolation point can be calculated by weighted average according to the weights and singularity indexes corresponding to the adjacent sampling points of the interpolation point.

[0096] It should be noted that the specific number and specific interpolation position of the interpolation points can be flexibly adjusted according to the actual anisotropy analysis requirement, and the embodiments of the present application do not make any limitation thereto.

[0097] Step c3, determining the singularity index distribution map corresponding to the research area according to the singularity indexes corresponding to each interpolation point respectively.

[0098] Here, after calculating the singularity index corresponding to each interpolation point by the IDW spatial interpolation method, taking the singularity index corresponding to each interpolation point as the pixel value of the pixel point corresponding to each interpolation point, the singularity index distribution map corresponding to the research area can be obtained by color rendering.

[0099] It should be noted that, compared with the prior art, the embodiment of the present application shown by the above steps S201-S205, by screening the optimal sampling point set (i.e. the above target non-empty subset) from each statistical window and making a double logarithmic graph (i.e. the above first double logarithmic graph and the above second double logarithmic graph) of the average element concentration of the sampling points in the optimal sampling point set corresponding to each scale (i.e. the window size of each statistical window), it is beneficial to reduce the influence of data anomalies at individual sampling points on the analysis result of the anisotropy distribution of element concentration in the whole study area, effectively improving the accuracy of the analysis result of the anisotropy distribution of element concentration in the whole study area, so that the anisotropy analysis method provided by the embodiment of the present application is not limited by the window of the statistical window, and can more truly reflect the influence of geological activities on the spatial distribution of element concentration, fully considering the heterogeneity of the geological body and the anisotropy characteristics of geochemical elements in different spatial scales, and can maximize the reflection of the anisotropy characteristics of geochemical elements.

[0100] Based on the same inventive concept, the present application also provides a geochemical data anisotropy analysis device corresponding to the above geochemical data anisotropy analysis method. Since the principle of solving problems of the geochemical data anisotropy analysis device in the embodiment of the present application is similar to that of the above geochemical data anisotropy analysis method in the embodiment of the present application, the implementation of the geochemical data anisotropy analysis device can be referred to the implementation of the above geochemical data anisotropy analysis method, and the repeated parts will not be described again.

[0101] Referring to Figure 5 , it is shown that Figure 5 a structure diagram of a geochemical data anisotropy analysis device provided by the embodiment of the present application is shown, wherein the anisotropy analysis device comprises:

[0102] The sampling module 501 is configured to collect geochemical data of a plurality of sampling points in a study area, and perform element analysis on the collected geochemical data at each sampling point to determine the element concentration of a target element at each sampling point.

[0103] The first statistical module 502 is configured to, for each sampling point, take the sampling point as a center sampling point of a statistical window, and determine a plurality of statistical windows corresponding to the center sampling point from the study area by changing the window size of the statistical window.

[0104] The second statistical module 503 is configured to, for each statistical window corresponding to the center sampling point, determine all non-empty subsets corresponding to the sampling point set formed by all sampling points in the statistical window from all sampling points in the statistical window according to the sampling point set.

[0105] The third statistical module 504 is configured to determine, from all non-empty subsets corresponding to the sampling point set, a non-empty subset with the largest difference between the element concentration and the element concentration mean corresponding to the statistical window as the target non-empty subset corresponding to the statistical window according to the element concentration corresponding to each sampling point in each non-empty subset.

[0106] The analysis module 505 is configured to construct a double logarithmic graph corresponding to the horizontal coordinate and the vertical coordinate, where the horizontal coordinate is the window size of each statistical window, and the vertical coordinate is the average of the element concentration corresponding to the target non-empty subset corresponding to each statistical window, and determine the anisotropy distribution information of the target element corresponding to the central sampling point according to the double logarithmic graph.

[0107] In an optional implementation, when the central sampling point is taken as the center of the statistical window, and a plurality of statistical windows corresponding to the central sampling point are determined from the research region by changing the window size of the statistical window, the first statistical module 502 is configured to:

[0108] In an optional implementation, when the central sampling point is taken as the center of the statistical window, and a plurality of circular statistical windows corresponding to the central sampling point are determined from the research region by changing the window radius of the circular statistical window, the first statistical module 502 is configured to:

[0109] In an optional implementation, when the central sampling point is taken as the center of the statistical window, and a plurality of statistical windows corresponding to the central sampling point are determined from the research region by changing the window size of the statistical window, the first statistical module 502 is further configured to:

[0110] In an optional implementation, when the central sampling point is taken as the center of the statistical window, and a plurality of square statistical windows corresponding to the central sampling point are determined from the research region by changing the window length of the square statistical window, the first statistical module 502 is configured to:

[0111] In an optional implementation, when the non-empty subset with the largest difference between the element concentration and the element concentration mean corresponding to the statistical window is determined from all non-empty subsets corresponding to the sampling point set as the target non-empty subset corresponding to the statistical window according to the element concentration corresponding to each sampling point in each non-empty subset, the third statistical module 504 is configured to:

[0112] For each non-empty subset, the target statistical quantity corresponding to the non-empty subset is calculated according to the element concentration corresponding to each sampling point in the non-empty subset and the element concentration mean corresponding to the statistical window according to the following formula:

[0113]

[0114] wherein, U is the target statistical quantity corresponding to the non-empty subset, n is the number of sampling points contained in the non-empty subset, is the element concentration corresponding to the i-th sampling point in the non-empty subset, and z is the mean value of the element concentration corresponding to the statistical window;

[0115] According to the target statistical quantity corresponding to each non-empty subset, from all non-empty subsets corresponding to the sampling point set, the non-empty subset with the largest target statistical quantity and the non-empty subset with the smallest target statistical quantity are determined as the target non-empty subset corresponding to the statistical window.

[0116] In an optional implementation, when the window size of each statistical window is taken as the abscissa, and the average value of the element concentration corresponding to the target non-empty subset corresponding to each statistical window is taken as the ordinate, a double logarithmic graph corresponding to the abscissa and the ordinate is constructed, the analysis module 505 is configured to:

[0117] For the scatter point corresponding to each statistical window, the window size of the statistical window is taken as the first abscissa corresponding to the scatter point, and the average value of the element concentration corresponding to the first target non-empty subset corresponding to the statistical window is taken as the first ordinate corresponding to the scatter point. A first double logarithmic graph corresponding to the first abscissa and the first ordinate is constructed; wherein the first target non-empty subset is the non-empty subset with the largest target statistical quantity corresponding to the statistical window;

[0118] For the scatter point corresponding to each statistical window, the window size of the statistical window is taken as the second abscissa corresponding to the scatter point, and the average value of the element concentration corresponding to the second target non-empty subset corresponding to the statistical window is taken as the second ordinate corresponding to the scatter point. A second double logarithmic graph corresponding to the second abscissa and the second ordinate is constructed; wherein the second target non-empty subset is the non-empty subset with the smallest target statistical quantity corresponding to the statistical window.

[0119] In an optional implementation, when the target element corresponding to the anisotropy distribution information at the central sampling point is determined according to the double logarithmic graph, the analysis module 505 is configured to:

[0120] The least square method is used to linearly fit all scatter points in the first double logarithmic graph, and a first slope corresponding to the linear fitting result is determined;

[0121] The least square method is used to linearly fit all scatter points in the second double logarithmic graph, and a second slope corresponding to the linear fitting result is determined;

[0122] From the first slope and the second slope, the slope with the largest absolute value is determined as the target slope;

[0123] According to the target slope, a singularity index corresponding to the center sampling point is determined, wherein the singularity index is used to represent an anisotropy distribution information of the target element at the center sampling point.

[0124] In an optional implementation, the anisotropy analysis apparatus further includes a display module, wherein the display module is configured to:

[0125] The singularity index corresponding to each sampling point in the study area is obtained.

[0126] An interpolation operation is performed between the singularity indexes corresponding to adjacent sampling points to obtain singularity indexes corresponding to a plurality of interpolation points.

[0127] According to the singularity index corresponding to each interpolation point, a singularity index distribution map corresponding to the study area is determined.

[0128] As shown in Figure 6 The embodiments of the present application provide an electronic device 600 for performing the anisotropy analysis method of geochemical data in the present application, the device includes a memory 601, a processor 602, and a computer program stored in the memory 601 and executable on the processor 602, wherein the memory 601 and the processor 602 are connected by a bus for communication, and the processor 602 executes the computer program to implement the steps of the anisotropy analysis method of geochemical data.

[0129] Specifically, the memory 601 and the processor 602 can be general memory and processor, which are not specifically limited here, and when the processor 602 runs the computer program stored in the memory 601, the anisotropy analysis method of geochemical data can be executed.

[0130] Corresponding to the anisotropy analysis method of geochemical data in the present application, the embodiments of the present application further provide a computer readable storage medium, and the computer readable storage medium stores a computer program, and the computer program is run by a processor to execute the steps of the anisotropy analysis method of geochemical data.

[0131] Specifically, the storage medium can be a general storage medium, such as a mobile disk, a hard disk, etc., and the computer program on the storage medium can be executed to execute the anisotropy analysis method of geochemical data.

[0132] In the embodiments of the present application, it should be understood that the disclosed system and method can be implemented in other manners. The embodiments described above are merely exemplary, for example, the division of the units is only a logical function division, and there can be another division manner in actual implementation; for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, and electrical, mechanical or other forms.

[0133] The units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments.

[0134] In addition, each functional unit in the embodiments of the present application can be integrated into one processing unit, or each unit can exist physically, or two or more units can be integrated into one unit.

[0135] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts that make contributions to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various other media that can store program codes.

[0136] It should be noted that: similar reference numerals and letters in the following drawings represent similar items, and therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. In addition, the terms "first", "second", "third" and the like are used only to distinguish descriptions, and cannot be understood as indicating or implying relative importance.

[0137] Finally, it should be noted that the above-described embodiments are merely specific embodiments of the present application, which are used to illustrate the technical solutions of the present application, but not to limit the same. The protection scope of the present application is not limited thereto. Although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that any skilled person in the art can still modify or easily think of changes to the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some of the technical features, within the technical range disclosed by the present application. The modifications, changes or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application. All should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for anisotropy analysis of geochemical data, characterized in that, The anisotropy analysis method includes: Geochemical data were collected from multiple sampling points within the study area, and elemental analysis was performed on the geochemical data collected at each sampling point to determine the elemental concentration of the target element at each sampling point. For each sampling point, the sampling point is used as the center sampling point of the statistical window. By changing the window size of the statistical window, multiple statistical windows corresponding to the center sampling point are determined from the study area. For each statistical window corresponding to the central sampling point, based on the sampling point set consisting of all sampling points within the statistical window, determine all non-empty subsets corresponding to the sampling point set from all sampling points within the statistical window; For each non-empty subset, the target statistic corresponding to the non-empty subset is calculated according to the element concentration corresponding to each sampling point in the non-empty subset and the mean element concentration corresponding to the statistical window, using the following formula: Where U is the target statistic corresponding to the non-empty subset, and n is the number of sampling points contained in the non-empty subset. is the element concentration corresponding to the i-th sampling point in the non-empty subset, and z is the average element concentration corresponding to all sampling points within the statistical window; Based on the target statistic corresponding to each non-empty subset, from all non-empty subsets corresponding to the sampling point set, determine the non-empty subset with the largest target statistic and the non-empty subset with the smallest target statistic as the target non-empty subsets corresponding to the statistical window; For each scatter point corresponding to a statistical window, the window size of the statistical window is used as the first horizontal axis of the scatter point, and the average value of the element concentrations corresponding to the first target non-empty subset of the statistical window is used as the first vertical axis of the scatter point. A first double logarithmic plot corresponding to the first horizontal axis and the first vertical axis is constructed. The first target non-empty subset is the non-empty subset with the largest target statistic corresponding to the statistical window. For each scatter point corresponding to a statistical window, the window size of the statistical window is used as the second horizontal axis of the scatter point, and the average value of the element concentration corresponding to the second target non-empty subset of the statistical window is used as the second vertical axis of the scatter point. A second double logarithmic plot corresponding to the second horizontal axis and the second vertical axis is constructed. The second target non-empty subset is the non-empty subset with the smallest target statistic corresponding to the statistical window. Based on the first double logarithmic plot and the second double logarithmic plot, the anisotropic distribution information of the target element at the central sampling point is determined.

2. The anisotropy analysis method according to claim 1, characterized in that, The method of using the sampling point as the center sampling point of the statistical window and determining multiple statistical windows corresponding to the center sampling point from the study area by changing the window size of the statistical window includes: Using this sampling point as the center sampling point of the circular statistical window, multiple circular statistical windows corresponding to the center sampling point are determined from the study area by changing the window radius of the circular statistical window.

3. The anisotropy analysis method according to claim 1, characterized in that, The method of using the sampling point as the center sampling point of the statistical window and determining multiple statistical windows corresponding to the center sampling point from the study area by changing the window size of the statistical window further includes: Using this sampling point as the center sampling point of the square statistical window, multiple square statistical windows corresponding to the center sampling point are determined from the study area by changing the window length of the square statistical window.

4. The anisotropy analysis method according to claim 1, characterized in that, The step of determining the anisotropic distribution information of the target element at the central sampling point based on the first logarithmic graph and the second logarithmic graph includes: By using the least squares method, a linear fit is performed on all the scattered points in the first double logarithmic plot, and the first slope corresponding to the linear fit result is determined. By using the least squares method, a linear fit is performed on all the scattered points in the second double logarithmic plot, and the second slope corresponding to the linear fit result is determined. From the first slope and the second slope, determine the slope with the largest absolute value as the target slope; Based on the target slope, the singularity index corresponding to the central sampling point is determined; wherein, the singularity index is used to represent the anisotropic distribution information of the target element at the central sampling point.

5. The anisotropy analysis method according to claim 1, characterized in that, The anisotropy analysis method further includes: Obtain the singularity index corresponding to each sampling point within the study area; Interpolation is performed between the singularity indices corresponding to adjacent sampling points to obtain singularity indices corresponding to multiple interpolation points; Based on the singularity index corresponding to each interpolation point, a singularity index distribution map corresponding to the study area is determined.

6. An anisotropy analysis device for geochemical data, characterized in that, The anisotropy analysis device includes: The sampling module is used to collect geochemical data from multiple sampling points within the study area, and to perform elemental analysis on the geochemical data collected at each sampling point to determine the elemental concentration of the target element at each sampling point. The first statistical module is used to determine multiple statistical windows corresponding to the center sampling point from the study area by changing the window size of the statistical window for each sampling point, using that sampling point as the center sampling point of the statistical window. The second statistical module is used to determine, for each statistical window corresponding to the central sampling point, all non-empty subsets corresponding to the sampling point set from all sampling points in the statistical window, based on the sampling point set composed of all sampling points in the statistical window. The third statistical module is used to calculate the target statistic for each non-empty subset according to the element concentration corresponding to each sampling point in the non-empty subset and the mean element concentration corresponding to the statistical window, using the following formula: Where U is the target statistic corresponding to the non-empty subset, and n is the number of sampling points contained in the non-empty subset. is the element concentration corresponding to the i-th sampling point in the non-empty subset, and z is the mean element concentration corresponding to the statistical window; Based on the target statistic corresponding to each non-empty subset, from all non-empty subsets corresponding to the sampling point set, determine the non-empty subset with the largest target statistic and the non-empty subset with the smallest target statistic as the target non-empty subsets corresponding to the statistical window; The analysis module is used to construct a first double logarithmic plot of the first horizontal axis and the first vertical axis for each scatter point corresponding to the statistical window, using the window size of the statistical window as the first horizontal axis and the average value of the element concentration corresponding to the first target non-empty subset of the statistical window as the first vertical axis; wherein, the first target non-empty subset is the non-empty subset with the largest target statistic corresponding to the statistical window. For each scatter point corresponding to a statistical window, the window size of the statistical window is used as the second horizontal axis of the scatter point, and the average value of the element concentration corresponding to the second target non-empty subset of the statistical window is used as the second vertical axis of the scatter point. A second double logarithmic plot corresponding to the second horizontal axis and the second vertical axis is constructed. The second target non-empty subset is the non-empty subset with the smallest target statistic corresponding to the statistical window. Based on the first double logarithmic plot and the second double logarithmic plot, the anisotropic distribution information of the target element at the central sampling point is determined.

7. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, they perform the steps of the anisotropy analysis method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the anisotropy analysis method as described in any one of claims 1 to 5.

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