Method, device and medium for lunar surface dating of impact craters by cluster analysis

Abstract

The present disclosure provides a method for selecting impact craters for lunar surface dating by cluster analysis, comprising: obtaining image data of a lunar surface study area, identifying impact craters in the study area, and forming an impact crater vector data set; extracting various characteristic data of the impact craters based on the impact crater vector data, the characteristic data including grayscale image data, roughness data, TiO2 abundance data, mineral characteristic data, and spectral absorption peak characteristic data; constructing a clustering model based on the various characteristic data and a K-means ++ algorithm, classifying the impact craters of the lunar surface according to the clustering results of the various characteristic data; selecting impact craters with characteristic types meeting expectations, calculating the cumulative frequency of the impact craters, and substituting the cumulative frequency into an empirical formula for lunar impact crater dating to calculate the absolute mode age of the impact craters in the study area. This method can efficiently and intelligently classify impact craters based on a clustering model, and can effectively improve the precision of dating the geological unit in the region by selecting impact craters for statistical dating from the classified impact craters.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of lunar and planetary remote sensing and geology, and particularly relates to a method and device for lunar surface dating of impact craters by cluster analysis, an electronic device, and a medium. BACKGROUND

[0002] Impact craters are the most widespread and distinctive landform on the lunar surface, and have always been a research hotspot in the field of lunar and planetary science. Studies have shown that the greater the density of original impact craters on the lunar surface, the older the age, and the frequency distribution of impact craters is an effective method for measuring the relative age of the lunar surface, which is of great significance for studying the impact evolution history of the Moon.

[0003] Currently, the main methods for obtaining the age of celestial bodies are stratigraphic superposition, impact crater degradation, isotope dating, and the crater size-frequency distribution (CSFD) method. The CSFD method was created by Neukum, and the method first constructs an impact crater production function (NPF) of the relationship between the diameter (D) of the impact crater and the cumulative impact crater frequency (Ncum) by statistically analyzing the size-diameter relationship of impact craters in 18 regions on the Moon; then, Ncum(D>1km) of each region is calculated based on the NPF, and is correlated with the radioisotope age of the corresponding sample (Apollo sample or Lunar sample), and a function relationship between the cumulative frequency Ncum(D>1km) and the lunar surface (exposed) age (t) is established by least squares fitting, that is, the lunar impact dating formula. By using the impact dating formula, the absolute geological age of any region on the lunar surface can be estimated by statistically analyzing the frequency distribution of impact craters. Due to the limited conditions of lunar field investigation and lunar sample acquisition, the CSFD method for estimating the geological age of the lunar surface has become an effective method widely used and recognized in the field of lunar and planetary science.

[0004] Although the CSFD method has certain universality, in the specific application process, different researchers (or the same researcher) will obtain different statistical results for the geological dating of the same region. For example, in the CE-5 landing area, multiple scholars obtained ages in the range of 1.21-3.46 Ga using the CSFD method, and the extreme value of the estimated age is 22.5 billion years apart. One of the main reasons for the difference in age estimation results is the frequency statistics of impact craters. Excluding the interference of secondary craters, the surface is remodeled by geological events (such as impact lava flow covering process, etc.) in a research area, and new impact craters cover old impact craters, or the mineral composition of the local area is changed due to the impact, resulting in impact craters of different evolution stages existing in the region. Due to the objective conditions such as full moon gray scale image and complex terrain, it is very difficult to distinguish and identify impact craters with different evolution stages, resulting in deviation in the frequency distribution statistics of impact craters, and finally leading to large differences in the estimated age of the geological unit model. SUMMARY

[0005] In view of the above problems, the present application provides a lunar surface dating method for selecting impact craters by cluster analysis to solve the above technical problems.

[0006] The first aspect of the present disclosure provides a lunar surface dating method for selecting impact craters by cluster analysis, comprising: obtaining image data of a lunar surface research area, identifying impact craters in the research area, and forming an impact crater vector data set; extracting a plurality of feature data of the impact craters based on the impact crater vector data, the feature data including gray scale image data, roughness data, TiO2 abundance data, mineral feature data, and spectral absorption peak feature data; constructing a clustering model based on the plurality of feature data and the K-means++ algorithm, and classifying the impact craters of the lunar surface according to the clustering results of the plurality of feature data; selecting impact craters with feature types meeting the expectations, calculating the cumulative frequency of the impact craters, and substituting into the empirical formula for lunar impact crater dating to calculate the absolute model age of the impact craters in the research area.

[0007] Optionally, the obtained image data of the lunar surface research area, the identification of the impact craters in the research area, and the formation of the impact crater vector data set comprise: excluding secondary craters from the impact craters, and the impact crater vector data set does not include the vector data of the secondary craters.

[0008] Optionally, the extracting the plurality of characteristic data of the impact craters based on the impact crater vector data comprises: spatially superimposing the impact vector data and digital orthographic image data of the lunar surface study area to obtain the gray scale image data; spatially superimposing the impact vector data and topographic data of the lunar surface study area to obtain roughness data; spatially superimposing the impact vector data and TiO2 component image inversion data of the lunar surface study area to obtain the TiO2 abundance data; spatially superimposing the impact vector data and inversion data of multi-band imager data of the lunar surface study area to obtain mineral characteristic data; and spatially superimposing the impact vector data and inversion data of a lunar mineral mapper of the lunar surface study area to obtain spectral absorption peak characteristic data.

[0009] Optionally, after the plurality of characteristic data of the impact craters is extracted based on the impact crater vector data, the method further comprises: performing correlation analysis on the plurality of characteristic data to screen out characteristic data with a correlation coefficient higher than a preset threshold.

[0010] Optionally, the constructing a clustering model based on the plurality of characteristic data and K-means ++ algorithm comprises: selecting K cluster centers from the plurality of characteristic data based on a K-means ++ algorithm; calculating distances from each group of data in the plurality of characteristic data to the K cluster centers, and dividing data sets of the plurality of characteristic data into K data sets based on the distances; calculating data mean values of each data set, and taking the data mean values as new cluster centers; repeating the above steps until the cluster centers and the data sets satisfy a preset condition, to obtain the clustering model; and classifying the impact craters corresponding to the plurality of characteristic data into a plurality of categories based on a clustering result of the clustering model.

[0011] Optionally, the method further comprises: performing clustering experiments by setting different numbers of clusters, evaluating experimental results by applying an elbow method and a silhouette coefficient method, and determining the number K of cluster centers.

[0012] Optionally, the selecting the impact craters of the expected feature type calculates the impact crater cumulative frequency of the impact craters, and substitutes the impact crater cumulative frequency into a lunar impact crater dating empirical formula to calculate the absolute mode age of the impact craters in the study area, including: selecting the impact craters of the expected feature type; segmenting the selected impact craters according to a diameter range for a first time, judging the saturation of the impact craters in each diameter range, and selecting the impact craters in the diameter range under the saturation line; segmenting the diameter range of the selected impact craters according to a preset interval for a second time, counting the number of impact craters in each segment, and calculating the segment impact crater cumulative frequency corresponding to each segment; calculating the impact crater cumulative frequency of the impact craters of the expected feature type based on the segment impact crater cumulative frequency and an impact crater production function formula; and substituting the impact crater cumulative frequency into the lunar impact crater dating empirical formula to calculate the absolute mode age of the region.

[0013] A second aspect of the present disclosure provides a lunar surface dating device for cluster analysis and selection of impact craters, including: an impact crater data acquisition module configured to acquire image data of a lunar surface study area, identify impact craters in the study area, and form an impact crater vector data set; an impact crater feature extraction module configured to extract a plurality of feature data of the impact craters based on the impact crater vector data, the feature data including grayscale image data, roughness data, TiO2 abundance data, mineral feature data, and spectral absorption peak feature data; an impact crater feature classification module configured to construct a clustering model based on the plurality of feature data and a K-means++ algorithm, and classify the impact craters of the lunar surface according to the clustering results of the plurality of feature data; and an impact crater dating module configured to select impact craters of an expected feature type, calculate the impact crater cumulative frequency of the impact craters, and substitute the impact crater cumulative frequency into a lunar impact crater dating empirical formula to calculate the absolute mode age of the impact craters in the study area.

[0014] A third aspect of the present disclosure provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements each step of the lunar surface dating method for cluster analysis and selection of impact craters according to any one of the first aspect when executing the computer program.

[0015] A fourth aspect of the present disclosure provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executable by a processor to implement each step of the lunar surface dating method for cluster analysis and selection of impact craters according to any one of the first aspect.

[0016] The above at least one technical solution adopted in the embodiments of the present disclosure can achieve the following beneficial effects:

[0017] (1) Based on the crater data in the identified lunar surface area, high-resolution image data, mineral content and multispectral inversion data are fused, and K-means clustering algorithm is used for unsupervised classification of craters in the area, so as to realize the differentiation of craters with different evolution stages in the study area;

[0018] (2) The crater clustering model based on K-means algorithm can realize efficient and intelligent classification and identification of craters, which not only overcomes the difficulty of distinguishing craters with different evolution stages by using gray image alone, but also overcomes the difficulty of distinguishing craters with different evolution stages in different regions by using only one feature data;

[0019] (3) The selection of craters from the classified craters for statistical dating can effectively improve the accuracy of dating of the geological unit in the region, and the classification of craters can determine the evolution stage of the region, which provides effective information for subsequent fine study of the geological impact events and formation and evolution process of the region. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more completely understand the present disclosure and its advantages, reference will now be made to the following description taken together with the accompanying drawings, in which:

[0021] Figure 1 A schematic diagram of a lunar surface dating method for cluster analysis and selection of craters provided by an embodiment of the present disclosure is schematically shown;

[0022] Figure 2 A structural block diagram of a lunar surface dating device for cluster analysis and selection of craters provided by an embodiment of the present disclosure is schematically shown;

[0023] Figure 3 A structural block diagram of an electronic device provided by an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0024] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary, and are not intended to limit the scope of the present disclosure. In the following detailed description, many specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments can be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted to avoid unnecessarily obscuring the concept of the present disclosure.

[0025] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the terms "comprises", "comprising", "includes", "including" and the like are specifically intended to be open-ended and to mean that other features, steps, operations, and / or components can be added.

[0026] All terms used herein, including technical and scientific terms, have the meanings commonly understood by one of ordinary skill in the art unless otherwise defined herein. It should be noted that the terms used herein are defined as having meanings that are consistent with the context of the specification in which the terms are used.

[0027] Some of the blocks of the diagrams and / or flowcharts, and combination thereof, can be implemented by computer program instructions. The computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the block diagrams and / or flowchart block or blocks.

[0028] Accordingly, the techniques of this disclosure can be realized in hardware and / or in software (including firmware, microcode, etc.). Furthermore, the techniques of this disclosure can be realized as a computer program product, which can be stored on a computer-readable medium that can be accessed by a computing-based device. In the context of this disclosure, a computer-readable medium can be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be, by way of example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. Specific examples of a computer-readable medium include magnetic tapes, hard disk drives (HDD), a magnetic disk, a CD-ROM, a random access memory (RAM), a flash memory, and / or a wired / wireless communication links.

[0029] Figure 1 A schematic diagram of a method for lunar surface dating of selected impact craters by cluster analysis is shown.

[0030] As Figure 1 The method for lunar surface dating of selected impact craters by cluster analysis provided by the embodiments of the present disclosure includes S110-S140.

[0031] S110, image data of a lunar surface study area is acquired, impact craters in the study area are identified, and impact crater vector data sets are formed.

[0032] In this embodiment, after selecting the lunar surface study area, impact craters were identified and labeled within the study area using ArcMap software based on image data with a spatial resolution of 7m acquired by Chang'e-2. Simultaneously, secondary craters exhibiting obvious chain or cluster patterns were removed based on morphological characteristics, and secondary craters within the initial impact crater were also eliminated. Finally, the labeled and confirmed impact craters were compiled into an impact crater vector dataset, which does not include vector data for secondary craters.

[0033] S120 extracts various feature data of impact craters based on impact crater vector data. The feature data includes grayscale image data, roughness data, TiO2 abundance data, mineral feature data, and spectral absorption peak feature data.

[0034] In this embodiment, impact vector data can be spatially overlaid with digital orthophoto data of the lunar surface study area to obtain grayscale image data. The digital orthophoto data can be the Chang'e-2 20mDOM image data. Specifically, the grayscale image data can include features such as impact crater longitude, center latitude, diameter, image grayscale, average grayscale, grayscale co-occurrence matrix contrast, grayscale co-occurrence matrix correlation, grayscale co-occurrence matrix energy, grayscale co-occurrence matrix entropy, and grayscale co-occurrence matrix homogeneity.

[0035] Among them, longitude features are the longitude coordinates of the impact crater center, ranging from -180 to 180 degrees; latitude features are the latitude coordinates of the impact crater center, ranging from -90 to 90 degrees; image grayscale is the grayscale value of the pixel at the center of the impact crater; average grayscale is the average grayscale value of all pixels within the impact crater; coarsness is the interquartile range of the curvature of all pixels within the impact crater; grayscale co-occurrence matrix contrast is the contrast value of the depth of texture grooves between pixels in the impact crater image region; grayscale co-occurrence matrix correlation is the linear correlation of grayscale between adjacent pixels in the impact crater image region; grayscale co-occurrence matrix energy is a measure of the uniformity of pixel grayscale distribution in the impact crater image region; grayscale co-occurrence matrix entropy is a measure of the information of texture image in the impact crater image region; and grayscale co-occurrence matrix homogeneity is a measure of the local smoothness of the image in the impact crater image region.

[0036] In this embodiment, the calculation formulas for gray-level co-occurrence matrix contrast, gray-level co-occurrence matrix correlation, gray-level co-occurrence matrix energy, gray-level co-occurrence matrix entropy, and gray-level co-occurrence matrix homogeneity are as shown in (1) to (5):

[0037]

[0038]

[0039]

[0040]

[0041]

[0042] Wherein, the gray level co-occurrence matrix p(i, j) represents the number of times or frequency of occurrence of two pixels with gray levels i and j having a certain spatial position relationship, the commonly used position relationships are 0°, 45°, 90° and 135°; the gray level co-occurrence matrix is calculated by using a pixel spacing of 1, a direction of 0 degrees and a gray level classification n = 256.

[0043] In the embodiment, the impact vector data is spatially superimposed with the terrain data of the lunar surface research area to obtain roughness data. Specifically, the Chang'e-2 20-meter resolution DEM data and the impact crater vector data are used to extract the terrain data of each impact crater, and the roughness of each impact crater image is counted.

[0044] First, the curvature of all pixel points in the impact crater is calculated, and the interquartile range (IQR) calculated by the quartile method is the roughness of the impact crater image. The curvature calculation formula of a single pixel point is as follows:

[0045] C = (h + +h - -2h) / l 2 (6)

[0046] Wherein, l is the baseline length, l takes the resolution of 20 meters of the Chang'e-2 DEM data, h is the elevation of the current point h + is the elevation of the horizontal positive direction with a baseline length of 1, and h - is the elevation of the horizontal negative direction with a baseline length of 1.

[0047] In the embodiment, the impact vector data is spatially superimposed with the TiO2 component image inversion data of the lunar surface research area to obtain TiO2 abundance data. The TiO2 abundance feature can be the TiO2 abundance data obtained by inversion of the original data of the wide-angle camera (WAC) of the American lunar reconnaissance orbiter (LROC) system, and the value is the average value of all pixel TiO2 abundance values in the impact crater image area.

[0048] In the embodiment, the impact vector data is spatially superimposed with the inversion data of the multi-band imager data of the lunar surface research area to obtain mineral feature data.

[0049] The mineral characteristic data includes data of FeO, diopside, olivine, plagioclase, pyroxene, submicroscopic metallic iron and optical maturity, and can be specifically data of multispectral image data of the lunar surface acquired by the Moon Mineralogy Mapper (M3) of the Indian lunar probe-1 lunar probe at 5 wavelength positions (UVVIS; 415 nm, 750 nm, 900 nm, 950 nm, 1001 nm) in the ultraviolet-visible light band and 4 wavelength positions (NIR; 1000 nm, 1050 nm, 1100 nm, 1250 nm) in the near-infrared band. The FeO content, the contents of four common minerals (diopside, olivine, plagioclase, pyroxene), the abundance of submicroscopic metallic iron (SMFe) and the optical maturity (OMAT) data covering nearly the entire moon are calculated by inversion, and the values are the average values of the characteristic values of all pixels in the impact crater image area.

[0050] In the embodiment, the impact vector data is spatially superimposed with the inversion data of the lunar mineral mapper of the lunar surface research area to obtain 2976 spectral absorption peak characteristic data. The 2976 spectral absorption peak characteristic is the average value of the 2976 spectral absorption peak values of all pixels in the impact crater image area by using the inversion data of the Moon Mineralogy Mapper (M3) of the Indian lunar probe-1 lunar probe.

[0051] After extracting various characteristic data of the impact crater, in order to improve the accuracy of the data, correlation analysis can be performed on the data characteristics, and the characteristics with strong correlation are screened out, the characteristic data with a correlation coefficient higher than a preset threshold is screened out, and the relatively independent data characteristics are retained. Two methods of Pearson correlation coefficient and Spearman correlation coefficient are mainly used.

[0052] The Pearson correlation coefficient is a statistical index reflecting the close degree of the correlation between characteristic variables, and the formula is as follows:

[0053]

[0054] Wherein, x and y are characteristic variables, and r is a correlation coefficient, -1≤r≤+1.

[0055] When r>0, the two variables are positively correlated, and when r<0, the two variables are negatively correlated; when |r|=1, the two variables are completely correlated, and when r=0, the two variables are not correlated; when 0<|r|<1, the two variables exist to a certain extent. The closer |r| is to 1, the closer the linear relationship between the two variables is; the closer |r| is to 0, the weaker the linear correlation between the two variables is; generally, it can be divided into three levels: |r|<0.4 is low correlation; 0.4≤|r|<0.7 is significant correlation; 0.7≤|r|<1 is high linear correlation.

[0056] Spearman correlation coefficient reflects the statistical index of the degree of correlation between the characteristic variables, and the formula is as follows:

[0057]

[0058] Wherein, n is the number of grades, d is the grade difference of two columns of paired variables. The Spearman correlation coefficient indicates the correlation direction of the characteristic variable X (independent variable) and the characteristic variable Y (dependent variable). If X increases, Y tends to increase, and the Spearman correlation coefficient is positive. Like the size property of Pearson correlation coefficient, the value is between [-1, 1].

[0059] S130, based on a plurality of characteristic data and K-means++ algorithm, a clustering model is constructed, and according to the clustering results of the plurality of characteristic data, the impact craters on the lunar surface are classified.

[0060] S130 includes S131, according to the feature selection result, the corresponding feature is selected to form several data sets, and the KMeans algorithm is applied to combine the data set to construct a clustering model for clustering analysis of the data; S130 further includes S132, the KMeans++ centroid initialization method is used instead of the method of randomly selecting the initial centroid, so as to improve the clustering efficiency and the effect of clustering.

[0061] In S131, by setting different clustering numbers, the KMeans algorithm is used for clustering experiment, and the elbow method and the contour coefficient method are used to evaluate the experimental results to determine the number K of clustering centers.

[0062] The elbow method includes:

[0063] (1) For a data set of n points, iteratively calculate k from 1 to n, and calculate the sum of squares of distances from each point to the cluster center to which it belongs after each clustering is completed;

[0064] (2) The sum of squares will gradually decrease until k == n, because each point is the cluster center itself.

[0065] (3) In the process of changing the sum of squares, there will be an inflection point, that is, the elbow point, and when the decrease rate suddenly slows down, it is considered as the best k value.

[0066] The contour coefficient is an evaluation index of the density and dispersion of the class, and the formula is expressed as follows:

[0067]

[0068] Wherein, a is the average value of the sample i to the other points in the same cluster, b is the minimum value of the average dissimilarity of the sample i to other clusters, and s is in the range of [-1, 1]. If s is close to 1, it represents that the sample is in the reasonable cluster, if s is close to -1, it represents that s should be divided into other clusters, and if s is close to 0, it represents that the sample i is on the boundary of two clusters. The average value of s of all samples is called the silhouette coefficient of the clustering result, which is a measure of whether the clustering is reasonable and effective. We can use the silhouette coefficient to determine the number of clusters, and select the k value corresponding to the larger coefficient.

[0069] After determining the initial number of cluster centers K according to S131, the embodiment uses the KMeans++ algorithm to construct a clustering model to classify the impact craters on the lunar surface, including the following steps S132-S136.

[0070] S132, selecting K cluster centers based on the K-means ++ algorithm from a plurality of feature data.

[0071] In the embodiment, selecting K cluster centers based on the K-means ++ algorithm includes:

[0072] (1) randomly selecting a point from the input data point set X as the first cluster center c1;

[0073] (2) for each point xi in the data set, calculating the distance D(xi) of the point to the nearest cluster center selected from the selected cluster centers, r = 1, 2,... k;

[0074] (3) calculating the probability P(x) of each sample point being selected as the next cluster center, and finally selecting the sample point corresponding to the maximum probability value as the next cluster center. Wherein, the point with larger D(x) has a larger probability of being selected as the cluster center, and the calculation formula of the probability P(x) is:

[0075]

[0076] (4) repeating 2 and 3 until k cluster centers are selected, and using the k cluster centers as the initial cluster centers.

[0077] S133, calculating the distance of each group of data in the plurality of feature data to the K cluster centers, and dividing the data set of the plurality of feature data into K data sets based on the distance.

[0078] S134, calculating the data mean of each data set, and taking the data mean as a new cluster center.

[0079] S135, repeating the above steps until the cluster centers and data sets satisfy the preset condition, and obtaining a clustering model.

[0080] S136, based on the clustering result of the clustering model, the impact craters corresponding to the plurality of feature data are classified into a plurality of categories.

[0081] S140, the cumulative frequency of the impact craters of the feature type meeting the expectation is calculated, and the cumulative frequency of the impact craters in the study area is calculated by substituting the lunar impact crater formation dating empirical formula.

[0082] In this embodiment, S140 can include S141-S144.

[0083] S141, selecting appropriate impact craters, segmenting the selected impact craters according to the diameter range, and selecting impact craters in the diameter range below the saturation line.

[0084] First, the selected impact crater data is saturated, and the impact craters in the diameter range below the saturation line are selected for calculation. For example, it is detected that the impact craters less than 200m in the study area are saturated (above the saturation line), so impact craters greater than 200m need to be selected for calculation in this area. Assuming that it is set to 300m, the impact craters in the range of 300m to the maximum diameter of the impact craters are counted.

[0085] S142, the diameter range of the selected impact craters is segmented twice according to the preset interval, the number of impact craters in each segment is counted, and the cumulative frequency of the impact craters corresponding to each segment is calculated.

[0086] For example, the diameter range [300m, Max] is segmented according to certain rules (usually segmented by a factor of 2, such as 300m, 600m, 1200m,..., max), the number of impact craters in each segment is counted, and the cumulative frequency of the impact craters corresponding to each segment Ncum value is calculated by combining the area value of the study area. ...., max), the number of impact craters in each segment is counted, and the cumulative frequency of the impact craters corresponding to each segment Ncum value is calculated by combining the area value of the study area.

[0087] S143, based on the segmented impact crater cumulative frequency and the impact crater production function formula, the cumulative frequency of the impact craters of the feature type meeting the expectation is calculated.

[0088] In this embodiment, according to the impact crater production function formula (as follows), a0 is fitted by the least square method, and finally N1 value is obtained according to log 10 (N1) = a0.

[0089]

[0090] ​In the formula, D is the diameter of the impact crater (unit: km); N is the number of impact craters with a diameter of D km per unit area accumulated in the region (unit: number / km2); the coefficient ai is a fitting parameter, which is valid for 10 m≤D≤300 km, wherein a0 represents the length of time during which the geological unit is exposed to meteorite bombardment. The most commonly used production functions are NPF1983 and NPF2001, which have the same form, and only differ in the specific values of the coefficients a1-a11, as shown in the following table.

[0091] Table Coefficients of impact crater production function

[0092]

[0093]

[0094] S144, the impact crater cumulative frequency is substituted into the empirical formula for lunar impact crater dating, and the absolute mode age of the region is calculated.

[0095] The impact crater cumulative frequency N1 value is substituted into the empirical formula for lunar impact crater dating (as follows), and the absolute mode age of the region is calculated.

[0096]

[0097] In the formula, t is the age (unit: 10 9 years), k1=5.44×10 -14 ; k2=6.93; k3=8.38×10 -4 .

[0098] The lunar surface dating method for selecting impact craters provided by the embodiments of the present disclosure selects impact craters based on the impact crater data in the identified lunar surface region, fuses high-resolution image data, mineral content, and multispectral inversion data, and performs non-supervised classification on the impact craters in the region through a K-means clustering algorithm, thereby achieving the differentiation of impact craters with different evolution stages in the study region. The impact crater clustering model based on the K-means algorithm established by the method can achieve efficient and intelligent classification and identification of impact craters, not only overcoming the difficulty of distinguishing impact craters with different evolution stages through gray image data alone, but also overcoming the difficulty of distinguishing impact craters with different evolution stages in different regions using only one feature data due to different geological events experienced by different regions. The method selects impact craters for statistical dating from the classified impact craters, which can effectively improve the accuracy of dating the geological unit in the region, and the classification of impact craters can determine the evolution stage experienced by the region, thereby providing effective information for subsequent fine research on the geological impact events and formation and evolution process of the region.

[0099] Figure 2A structural block diagram of a lunar surface dating device for selecting impact craters by cluster analysis is shown.

[0100] As shown in Figure 2 The present embodiment provides a lunar surface dating device for selecting impact craters by cluster analysis, which comprises an impact crater data acquisition module 210, an impact crater feature extraction module 220, an impact crater feature classification module 230 and an impact crater dating module 240.

[0101] The impact crater data acquisition module 210 is configured to acquire image data of a lunar surface study area, identify impact craters in the study area, and form an impact crater vector data set.

[0102] The impact crater feature extraction module 220 is configured to extract various feature data of the impact craters based on the impact crater vector data, including grayscale image data, roughness data, TiO2 abundance data, mineral feature data and spectral absorption peak feature data.

[0103] The impact crater feature classification module 230 is configured to construct a clustering model based on the various feature data and the K-means ++ algorithm, and classify the impact craters on the lunar surface according to the clustering results of the various feature data.

[0104] The impact crater dating module 240 is configured to calculate the cumulative frequency of impact craters that meet the expected feature type, and substitute it into the empirical formula for dating impact crater formation on the moon to calculate the absolute mode age of the impact craters in the study area.

[0105] It can be understood that the impact crater data acquisition module 210, the impact crater feature extraction module 220, the impact crater feature classification module 230 and the impact crater dating module 240 can be combined in one module, or any one of them can be split into multiple modules. Alternatively, at least part of the function of one or more of these modules can be combined with at least part of the function of other modules and implemented in one module. According to an embodiment of the present application, at least one of the impact crater data acquisition module 210, the impact crater feature extraction module 220, the impact crater feature classification module 230 and the impact crater dating module 240 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on chip, a system on board, a system on package, an application specific integrated circuit (ASIC), or any other reasonable way to integrate or package circuits, etc. Hardware or firmware, or a suitable combination of software, hardware and firmware. Alternatively, at least one of the impact crater data acquisition module 210, the impact crater feature extraction module 220, the impact crater feature classification module 230 and the impact crater dating module 240 can be at least partially implemented as a computer program module that can perform the functions of the corresponding module when the program is run by a computer.

[0106] Figure 3 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown schematically.

[0107] As shown in Figure 3 , the electronic device described in the present embodiment includes an electronic device 300 including a processor 310, a computer readable storage medium 320. The electronic device 300 can perform the method described above with reference to Figure 1 to achieve detection of specific operations.

[0108] Specifically, the processor 310 may, for example, include a general purpose microprocessor, an instruction set processor, and / or a related chipset and / or a special purpose microprocessor (e.g., an application specific integrated circuit (ASIC)), etc. The processor 310 can also include on-board memory for cache purposes. The processor 310 can be a single processing unit or a plurality of processing units for executing the different actions of the method flow according to the embodiments of the present disclosure described with reference to Figure 1 .

[0109] The computer readable storage medium 320, for example, can be any medium that can contain, store, communicate, propagate or transport instructions. For example, the readable storage medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared or semiconductor system, apparatus, device or propagation medium. Specific examples of the readable storage medium include a magnetic storage device such as a hard disk or hard drive (HDD), an optical storage device such as a compact disc (CD-ROM), a memory such as a random access memory (RAM) or flash memory, and / or a wired / wireless communication link.

[0110] The computer readable storage medium 320 can include a computer program 321, which can include code / computer executable instructions that, when executed by the processor 310, cause the processor 310 to perform, for example, the method flow described above in conjunction with Figure 1 the method flow described above in conjunction with

[0111] The computer program 321 can be configured to have, for example, computer program code including computer program modules. For example, in an example embodiment, the code in the computer program 321 can include one or more program modules, for example, including 321A, module 321B, …. It should be noted that the division and number of modules are not fixed, and a person skilled in the art can use appropriate program modules or combinations of program modules according to actual conditions, when these program modules or combinations of program modules are executed by the processor 310, the processor 310 can perform, for example, the method flow described above in conjunction with Figure 1 to Figure 1 the method flow described above in conjunction with

[0112] According to an embodiment of the present application, at least one of the impact crater data acquisition module 210, the impact crater feature extraction module 220, the impact crater feature classification module 230 and the impact crater dating module 240 can be implemented as a computer program module described above, which, when executed by the processor 310, can implement the corresponding operation described above. Figure 3 The computer program module described above, when executed by the processor 310, can implement the corresponding operation described above.

[0113] The present disclosure also provides a computer readable medium, which can be contained in the device / apparatus / system described in the above embodiments; or can exist independently without being assembled into the device / apparatus / system. The above computer readable medium carries one or more programs, when the one or more programs are executed, the method according to the embodiments of the present disclosure is implemented.

[0114] Those skilled in the art can understand that the features recited in various embodiments of the present disclosure and / or claims can be combined or / and integrated, even if such combination or integration is not explicitly recited in the present disclosure. In particular, the features recited in various embodiments of the present disclosure and / or claims can be combined and / or integrated in various combinations, without departing from the spirit and teachings of the present disclosure. All such combinations and / or integrations fall within the scope of the present disclosure.

[0115] While the present disclosure has been shown and described with reference to certain exemplary embodiments thereof, it will be understood by those skilled in the art that various changes in form and details can be made therein without departing from the spirit and scope of the disclosure as defined by the appended claims and their equivalents. Therefore, the scope of the present disclosure should not be limited to the above-described embodiments, but should be defined by the appended claims and equivalents thereof only.

Claims

1. A method of lunar surface dating of impact craters by cluster analysis selection, characterized in that, The method comprises the following steps: acquiring image data of a lunar surface study area, identifying impact craters in the study area, and forming an impact crater vector data set; extracting multiple characteristic data of the impact craters based on the impact crater vector data, wherein the characteristic data comprises grayscale image data, roughness data, TiO2 abundance data, mineral characteristic data, and spectral absorption peak characteristic data; based on the plurality of feature data and K-means ++ algorithm constructs a clustering model, and classifies the impact craters on the moon surface according to a clustering result of the plurality of feature data. selecting impact craters with diameters in a non-saturated range from impact crater categories divided by classification, calculating the cumulative frequency of the impact craters, and substituting the cumulative frequency into an empirical formula for lunar impact crater dating to calculate the absolute mode age of the impact craters in the study area.

2. The method of claim 1, wherein, The step of acquiring image data of a lunar surface study area, identifying impact craters in the study area, and forming an impact crater vector data set comprises the following steps: removing secondary craters from the impact craters, and excluding vector data of the secondary craters from the impact crater vector data set.

3. The method of claim 1, wherein, The step of extracting multiple characteristic data of the impact craters based on the impact crater vector data comprises the following steps: spatially superimposing the impact crater vector data on digital orthographic image data of the lunar surface study area to obtain the grayscale image data; spatially superimposing the impact crater vector data on topographic data of the lunar surface study area to obtain the roughness data; spatially superimposing the impact crater vector data on TiO2 component image inversion data of the lunar surface study area to obtain the TiO2 abundance data; spatially superimposing the impact crater vector data on inversion data of multi-band imager data of the lunar surface study area to obtain the mineral characteristic data; spatially superimposing the impact crater vector data on inversion data of a lunar mineral mapper of the lunar surface study area to obtain the spectral absorption peak characteristic data.

4. The method of claim 1, wherein, After the step of extracting multiple characteristic data of the impact craters based on the impact crater vector data, the method further comprises the following steps: performing correlation analysis on the multiple characteristic data to filter out characteristic data with a correlation coefficient higher than a preset threshold.

5. The method of claim 1, wherein, The K-means algorithm is based on the plurality of feature data and the plurality of feature data ++ The algorithm constructs a clustering model, and classifies the impact craters on the lunar surface according to the clustering results of the plurality of feature data. Based on K-means ++ algorithm selects K cluster centers from the plurality of feature data; calculating the distance of each group of data in the multiple characteristic data to the K clustering centers, and dividing the data set of the multiple characteristic data into K data sets based on the distance; calculating the data mean of each data set, and taking the data mean as a new clustering center; repeating the above steps until the clustering centers and the data sets meet a preset condition, to obtain the clustering model; based on the clustering result of the clustering model, dividing the impact craters corresponding to the multiple characteristic data into multiple categories.

6. The method of claim 5, wherein, The method further comprises the following steps: performing clustering experiments by setting different numbers of clusters, and evaluating the experimental results to determine the number K of clustering centers.

7. The method of claim 1, wherein, The step of selecting impact craters with diameters in a non-saturated range from impact crater categories divided by classification, calculating the cumulative frequency of the impact craters, and substituting the cumulative frequency into an empirical formula for lunar impact crater dating to calculate the absolute mode age of the impact craters in the study area comprises the following steps: selecting impact craters of a target category from the impact crater categories divided by classification; segmenting the selected impact craters according to diameter ranges, performing saturation judgment on the impact craters in each diameter range, and selecting impact craters in a diameter range below a saturation line. The selected impact crater diameter range is twice segmented at preset intervals, the number of impact craters in each segment is counted, and the corresponding segment impact crater cumulative frequency is calculated; The impact crater cumulative frequency is calculated based on the segment impact crater cumulative frequency and the impact crater production function formula; The impact crater cumulative frequency is substituted into the lunar impact crater dating empirical formula to calculate the absolute mode age of the region.

8. A device for dating the lunar surface by cluster analysis selection of impact craters, characterized in that, The method comprises the following steps: An impact crater data acquisition module is used to acquire image data of a lunar surface study area, identify impact craters in the study area, and form an impact crater vector data set; An impact crater feature extraction module is used to extract various feature data of the impact craters based on the impact crater vector data, wherein the feature data includes grayscale image data, roughness data, TiO2 abundance data, mineral feature data, and spectral absorption peak feature data; a crater feature classification module configured to classify craters of the lunar surface based on the plurality of feature data and a K-means ++ algorithm to build a clustering model, and classify the craters of the lunar surface according to a clustering result of the plurality of feature data; An impact crater dating module is used to select impact craters with diameters in the non-saturated range from the impact crater categories classified by the impact crater classification, calculate the impact crater cumulative frequency, and substitute it into the lunar impact crater dating empirical formula to calculate the absolute mode age of the impact craters in the study area.

9. An electronic device comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the computer program, each step of the lunar surface dating method for selecting impact craters by cluster analysis according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by the processor, each step of the lunar surface dating method for selecting impact craters by cluster analysis according to any one of claims 1 to 7 is implemented.

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