A method and system for statistically analyzing geological data

Abstract

The present invention relates to the technical field of geological data storage, and specifically discloses a method and system for statistically analyzing geological data acquisition, wherein the method comprises performing autocorrelation identification on a geological data matrix to obtain an autocorrelation sequence; performing extreme value analysis on the autocorrelation sequence, comparing the extreme value analysis results of the autocorrelation sequences corresponding to different types of geological data, and calculating the correlation between different types; when the correlation between two types is greater than a preset correlation threshold, marking the two types as a feature type group; storing each type of geological data matrix based on time sequence, and constructing hyperlinks of different storage results according to the feature type group. The present invention compares different types of geological data, calculates their correlation, and establishes hyperlinks on the names of geological data with a sufficiently high correlation. When a staff member reads a certain type of geological data, the geological data that is sufficiently related to it can be read very simply and quickly, and the convenience is extremely high.

Description

Technical Field

[0001] The present invention relates to the technical field of geological data storage, and in particular to a method and system for statistically analyzing geological acquisition data. Background Art

[0002] Geological data statistics is an important geological work task, which mainly involves the organization, analysis and summary of the collected geological data. There are many types of geological data, and each type of geological data has an independent collection method. After the geological data is collected, it needs to be stored.

[0003] The existing storage methods for geological collection data are mostly classified storage methods. The storage processes of different types of geological collection data are independent. When faced with geological analysis tasks, staff need to combine multiple types of data for analysis. When the number of types is large, staff need to compare them type by type. At this time, multiple data reading processes are required, which is very inconvenient. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for statistically analyzing geological data collected to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for statistically analyzing geological data collection, comprising:

[0007] Count different types of geological data containing sampling points and sampling times, and create a geological data matrix labeled by type and sampling time;

[0008] Counting geological data matrices of the same type at different sampling times, performing autocorrelation identification on the geological data matrices, and obtaining an autocorrelation sequence;

[0009] Performing extreme value analysis on the autocorrelation sequence, comparing the extreme value analysis results of the autocorrelation sequences corresponding to different types of geological data, and calculating the correlation between different types according to the extreme value analysis results;

[0010] When the correlation between two types is greater than a preset correlation threshold, the two types are marked as a feature type group;

[0011] The geological data matrix of each type is stored in chronological order, and hyperlinks of different stored results are constructed according to the feature type group.

[0012] As a further solution of the present invention: the step of counting different types of geological data containing sampling points and sampling times and creating a geological data matrix with types and sampling times as labels includes:

[0013] Query the boundary information of the geological sampling area and obtain the bounding rectangle of the boundary information;

[0014] Statistics of different types of geological data containing sampling points and sampling times;

[0015] For the same type of geological data containing sampling points and sampling time, the distances between sampling points are compared two by two, and the minimum distance is recorded;

[0016] Create a fundamental matrix based on the minimum distance and bounding rectangle;

[0017] Clustering the geological data according to the sampling time, inserting the geological data into the basic matrix based on the sampling points, and obtaining the geological data matrix; in the same type of geological data, the difference in the sampling time of any two geological data is less than the preset difference threshold;

[0018] Calculate the average time corresponding to the same type of geological data as the time tag, and read the type of geological data as the type tag.

[0019] As a further solution of the present invention: the step of creating a basic matrix based on the minimum distance and the circumscribed rectangle includes:

[0020] Create a grid using the minimum distance as the side length of the grid cell;

[0021] Align the upper left endpoint of the grid with the upper left endpoint of the circumscribed rectangle;

[0022] The nodes of the grid cells are used as row and column positions to create a basic matrix, wherein the row and column positions in the basic matrix correspond one-to-one to the nodes in the circumscribed rectangle;

[0023] The step of clustering the geological data according to the sampling time and inserting the geological data into the basic matrix based on the sampling points to obtain the geological data matrix includes:

[0024] Calculate the time difference between the sampling times of any two geological data, and when the time difference is less than a preset time difference threshold, classify the geological data into one category;

[0025] Query the sampling location of geological data and locate the node closest to the sampling location;

[0026] The row and column positions corresponding to the query nodes are used as the insertion positions of the geological data.

[0027] As a further solution of the present invention, the step of counting geological data matrices of the same type with different sampling times, performing autocorrelation identification on the geological data matrices, and obtaining an autocorrelation sequence comprises:

[0028] Statistics of geological data matrix of the same type with different sampling times;

[0029] Extract the eigenvalues ​​of the geological data matrix based on a preset feature recognition model, arrange the eigenvalues ​​according to the sampling time, and obtain the eigenvalue sequence;

[0030] The autocorrelation coefficients of the eigenvalue sequence at different lag steps are calculated as the autocorrelation sequence.

[0031] As a further solution of the present invention, the step of performing extreme value analysis on the autocorrelation sequence, comparing the extreme value analysis results of the autocorrelation sequences corresponding to different types of geological data, and calculating the correlation between different types according to the extreme value analysis results comprises:

[0032] Traverse each value of the autocorrelation sequence and calculate the difference of each value; the difference is the subtraction of the previous data from the next data; the tail element is not calculated;

[0033] Locate the extreme value number and its extreme value according to the sign of the difference;

[0034] Compare the extreme value numbers and extreme values ​​corresponding to different types of geological data and calculate the correlation.

[0035] As a further solution of the present invention: the step of storing each type of geological data matrix based on time sequence and constructing hyperlinks of different storage results according to feature type groups includes:

[0036] Obtain each type of geological data matrix, use the time dimension as the third dimension to count the geological data matrix, and obtain a three-dimensional geological data matrix;

[0037] Types are used as labels for the three-dimensional geological data matrix;

[0038] Create hyperlinks between labels based on the determined feature type groups.

[0039] The technical solution of the present invention also provides a geological acquisition data statistics system, the system comprising:

[0040] The data matrix creation module is used to count different types of geological data containing sampling points and sampling times, and create a geological data matrix with type and sampling time as labels;

[0041] An autocorrelation identification module is used to count geological data matrices of the same type with different sampling times, perform autocorrelation identification on the geological data matrices, and obtain an autocorrelation sequence;

[0042] An extreme value analysis identification module is used to perform extreme value analysis on the autocorrelation sequence, compare the extreme value analysis results of the autocorrelation sequences corresponding to different types of geological data, and calculate the correlation between different types according to the extreme value analysis results;

[0043] A type marking module, used for marking two types as a feature type group when the correlation between two types is greater than a preset correlation threshold;

[0044] The data storage module is used to store each type of geological data matrix based on time sequence and to construct hyperlinks of different stored results according to feature type groups.

[0045] As a further solution of the present invention: the data matrix creation module includes:

[0046] A bounding rectangle creation unit is used to query the boundary information of the geological sampling area and obtain the bounding rectangle of the boundary information;

[0047] The geological data statistics unit is used to count different types of geological data containing sampling points and sampling times;

[0048] The minimum distance recording unit is used to compare the distances between sampling points for the same type of geological data containing sampling points and sampling time, and record the minimum distance;

[0049] A basic matrix creation unit, used for creating a basic matrix based on a minimum distance and a bounding rectangle;

[0050] A data insertion unit is used to cluster the geological data according to the sampling time, insert the geological data into the basic matrix based on the sampling points, and obtain a geological data matrix; in the same type of geological data, the difference in sampling time between any two geological data is less than a preset difference threshold;

[0051] The label generation unit is used to calculate the average time corresponding to the same type of geological data as the time label, and read the type of geological data as the type label.

[0052] As a further solution of the present invention: the autocorrelation identification module includes:

[0053] Matrix statistics unit, used to count the geological data matrices of the same type with different sampling times;

[0054] A sequence generation unit is used to extract eigenvalues ​​of the geological data matrix based on a preset feature recognition model, arrange the eigenvalues ​​according to sampling time, and obtain an eigenvalue sequence;

[0055] The calculation execution unit is used to calculate the autocorrelation coefficient of the eigenvalue sequence at different lag step sizes as the autocorrelation sequence.

[0056] As a further solution of the present invention: the extreme value analysis and identification module includes:

[0057] An element traversal unit is used to traverse each value of the autocorrelation sequence and calculate the difference of each value; the difference is the latter data minus the previous data; the tail element is not calculated;

[0058] A positioning unit, used for positioning the extreme value sequence number and its extreme value according to the sign of the difference;

[0059] The comparison unit is used to compare the extreme value serial numbers and their extreme values ​​corresponding to different types of geological data and calculate the correlation.

[0060] Compared with the prior art, the beneficial effects of the present invention are: the present invention stores various types of geological data at various times in the form of a three-dimensional matrix, compares different types of geological data, calculates their correlation, and establishes hyperlinks on the names of geological data with a sufficiently high correlation. When the staff reads a certain type of geological data, they can quickly and easily read the geological data that is sufficiently relevant to it, which is extremely convenient. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0062] Figure 1 This is a flowchart of the statistical method for geological data acquisition.

[0063] Figure 2 This is the first sub-flow chart of the statistical method for geological acquisition data.

[0064] Figure 3 This is the second sub-process flowchart of the statistical method for geological acquisition data.

[0065] Figure 4 This is the third sub-process flowchart of the statistical method for geological acquisition data.

[0066] Figure 5 This is the fourth sub-process flowchart of the statistical method for geological acquisition data.

[0067] Figure 6 This is a structural block diagram of the geological data collection statistical system. DETAILED DESCRIPTION

[0068] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0069] Figure 1The flowchart of the method for statistically analyzing geological data acquisition is shown in the following figure. In an embodiment of the present invention, a method for statistically analyzing geological data acquisition is provided. The method comprises:

[0070] Step S100: Count different types of geological data containing sampling points and sampling times, and create a geological data matrix with type and sampling time as labels;

[0071] There are many types of geological data, including geophysical data, geochemical data and remote sensing data. Most of these data are point-by-point measurements, that is, in a geological sampling area, multiple sampling points are set up to sample data. After the data sampling is completed, it is quantified and the sampling time is recorded synchronously to obtain geological data containing the sampling points and sampling time. After obtaining the geological data of the sampling points and sampling time, the type corresponding to the geological data is queried, and the type and sampling time are used as labels to store the geological data of different sampling points in a matrix form to obtain a geological data matrix. The row and column positions of the geological data matrix represent the sampling points.

[0072] Step S200: Counting geological data matrices of the same type at different sampling times, and performing autocorrelation identification on the geological data matrix to obtain an autocorrelation sequence;

[0073] For each type of geological data, each sampling time corresponds to a geological data matrix. These geological data matrices are arranged in chronological order, and then autocorrelation identification is performed on them. The autocorrelation identification process provided in this application is a method in statistical analysis and signal processing, which is used to measure the correlation of geological data matrices of various sampling times at different times. The autocorrelation analysis process needs to determine a lag parameter, which indicates the time span over which the autocorrelation analysis is performed. The lag parameter is used as an independent variable, and all calculated autocorrelation results are counted to obtain an autocorrelation sequence; the autocorrelation sequence represents the change of a type of data.

[0074] Step S300: performing extreme value analysis on the autocorrelation sequence, comparing the extreme value analysis results of the autocorrelation sequences corresponding to different types of geological data, and calculating the correlation between different types according to the extreme value analysis results;

[0075] Perform extreme value analysis on the autocorrelation sequence, which is to locate the maximum and minimum values. Since the autocorrelation sequence itself is a sequence, the extreme value analysis result is also a sequence, called the extreme value sequence, which is a subset of the autocorrelation sequence. By comparing the extreme value analysis results of the autocorrelation sequence corresponding to different types of geological data, that is, the extreme value sequence, the degree of correlation between different types of geological data can be determined. Two types with high correlation indicate that the corresponding two types of geological data in the geological sampling area have highly similar changes, which actually contains very important information and can reflect the characteristics of the geological sampling area. For example, when the two types of geological data, gravity and magnetism, have extremely high similarity, it indicates the existence of an ore body. For another example, when the two types of data, sedimentary phase type and paleoclimate characteristics, have extremely high similarity, the paleoenvironment can be reconstructed based on them to predict the distribution of sedimentary reservoirs.

[0076] Step S400: When the correlation between two types is greater than a preset correlation threshold, the two types are marked as a feature type group;

[0077] When the correlation between two types is high enough, the two types can be used as a group of types and then as the geological characteristics of the geological sampling area. It should be noted that in the technical solution of the present invention, the correlation of any two types of geological data will be calculated once, and a judgment will be made. The obtained feature type group includes two types, and the total number of feature type groups is not limited.

[0078] Step S500: storing each type of geological data matrix based on time sequence, and constructing hyperlinks of different stored results according to feature type groups;

[0079] Each type of geological data is the geological data matrix of each time. The geological data matrix of each time is counted in chronological order to obtain a three-dimensional geological data matrix. The data structure is extremely clear and can be stored in a three-dimensional array. The label of the three-dimensional array (also called the name) is the type of geological data. On this basis, the feature type group indicates that two types of data have highly similar changes. The three-dimensional arrays corresponding to the two types in the feature type group are located, and hyperlinks are established between the types. When a certain type of geological data needs to be read, the hyperlink can be used to quickly read the geological data of the type with a high degree of correlation.

[0080] Figure 2 This is a first sub-flow chart of the method for counting geological data. The steps of counting different types of geological data containing sampling points and sampling times and creating a geological data matrix with types and sampling times as labels include:

[0081] Step S101: querying the boundary information of the geological sampling area and obtaining the circumscribed rectangle of the boundary information;

[0082] Step S102: Counting different types of geological data containing sampling points and sampling times;

[0083] Step S103: for geological data of the same type containing sampling points and sampling time, the distances between the sampling points are compared two by two, and the minimum distance is recorded;

[0084] Step S104: creating a basic matrix based on the minimum distance and the bounding rectangle;

[0085] Step S105: clustering the geological data according to the sampling time, inserting the geological data into the basic matrix based on the sampling points, and obtaining a geological data matrix; in the same type of geological data, the difference in sampling time between any two geological data is less than a preset difference threshold;

[0086] Step S106: Calculate the average time corresponding to the same type of geological data as the time tag, and read the type of geological data as the type tag.

[0087] In an example of the technical solution of the present invention, a specific description is given of the generation process of a geological data matrix. First, the boundary information of the geological sampling area is queried. This information has been determined before sampling and can be directly read. The boundary information is an irregular shape and needs to be regularized. The regularization method is to obtain a circumscribed rectangle. Then, different types of geological data containing sampling points and sampling times are counted, and each type is analyzed separately. For geological data of the same type containing sampling points and sampling times, the distances between the sampling points are compared two by two, and the minimum distance is recorded. The minimum distance is used as the side length, and the length and width of the circumscribed rectangle are divided to obtain multiple points on the length and width. A grid can be created based on the obtained points, and the nodes of the grid are used as row and column positions to obtain a matrix, which is called a basic matrix. Finally, the geological data of the same type and within the same time range are classified into one category according to the sampling time, and the insertion position is determined according to the nodes corresponding to the actual sampling points, and the geological data is inserted into the basic matrix to obtain a geological data matrix. The geological data matrix contains two labels, one is the type label corresponding to the geological data, and the other is the time label. The time label of the geological data matrix adopts the average value of the sampling time corresponding to the same type of geological data.

[0088] Furthermore, the step of creating a basic matrix based on the minimum distance and the bounding rectangle includes:

[0089] Create a grid using the minimum distance as the side length of the grid cell;

[0090] Align the upper left endpoint of the grid with the upper left endpoint of the circumscribed rectangle;

[0091] The nodes of the grid cells are used as row and column positions to create a basic matrix, wherein the row and column positions in the basic matrix correspond one-to-one to the nodes in the circumscribed rectangle.

[0092] The above content explains the generation process of the basic matrix. The minimum distance is used as the side length of the grid unit to create a grid. Then, the upper left endpoint of the grid is aligned with the upper left endpoint of the circumscribed rectangle. At this time, a grid is obtained inside the rectangle. The nodes of the grid unit are used as row and column positions to create the basic matrix. It can be seen from the above content that the row and column positions in the basic matrix correspond one-to-one to the nodes in the circumscribed rectangle.

[0093] The step of clustering the geological data according to the sampling time and inserting the geological data into the basic matrix based on the sampling points to obtain the geological data matrix includes:

[0094] Calculate the time difference between the sampling times of any two geological data, and when the time difference is less than a preset time difference threshold, classify the geological data into one category;

[0095] Query the sampling location of geological data and locate the node closest to the sampling location;

[0096] The row and column positions corresponding to the query nodes are used as the insertion positions of the geological data.

[0097] The above content explains the data filling process. First, the time of each geological data in the same geological data matrix needs to be approximate. The time difference between the sampling times of any two geological data of the same type is calculated. When the time difference is small enough, the geological data are classified into one category. The same type of geological data is inserted into the same geological data matrix. Specifically, since the row and column positions correspond one-to-one to the nodes of the circumscribed rectangle, for any geological data, its sampling position is queried, and the node closest to the sampling position is queried in the circumscribed rectangle. The row and column position corresponding to the node is the insertion position of the geological data. In layman's terms, the circumscribed rectangle represents the actual map information, which is used to compare with each sampling position. The basic matrix is ​​a data structure. The row and column positions correspond one-to-one to the nodes (actual coordinates) in the circumscribed matrix, which is used to fill the actual data into the basic matrix.

[0098] Figure 3 This is a second sub-flow diagram of the method for statistically analyzing geological data collected. The steps of statistically analyzing geological data matrices of the same type with different sampling times, performing autocorrelation identification on the geological data matrices, and obtaining autocorrelation sequences include:

[0099] Step S201: Counting geological data matrices of the same type at different sampling times;

[0100] Step S202: extracting eigenvalues ​​of the geological data matrix based on a preset feature recognition model, arranging the eigenvalues ​​according to sampling time, and obtaining an eigenvalue sequence;

[0101] Step S203: Calculate the autocorrelation coefficients of the eigenvalue sequence at different lag steps as the autocorrelation sequence.

[0102] In an example of the technical solution of the present invention, a specific description is given of the autocorrelation identification process. For each type of geological data, multiple geological data matrices can be obtained at different times. The geological data matrices of the same type at different sampling times are statistically analyzed, and then each geological data matrix is ​​simplified to obtain eigenvalues. The eigenvalues ​​are arranged according to the sampling time to obtain an eigenvalue sequence. The autocorrelation coefficient of the eigenvalue sequence at different lag steps is calculated as an autocorrelation sequence. The element number of each element in the autocorrelation sequence corresponds to the lag step, and the element value of each element corresponds to the autocorrelation coefficient. It should be noted that the eigenvalue extraction method is to calculate the mean of all elements in the geological data matrix and directly use it as the eigenvalue. In addition, the eigenvalue concept of the matrix itself can also be used. The eigenvector and eigenvalue are themselves a parameter of the matrix. After obtaining multiple eigenvalues, the mean can be calculated or the mode value can be extracted as the final eigenvalue.

[0103] Specifically, the process of determining the autocorrelation coefficient is:

[0104] ; In the formula, The lag step length is The autocorrelation coefficient of is the first eigenvalues, is the mean of the eigenvalue sequence, is the total number of data in the eigenvalue sequence.

[0105] Figure 4 The third sub-flow chart of the statistical method for geological acquisition data is as follows: the steps of performing extreme value analysis on the autocorrelation sequence, comparing the extreme value analysis results of the autocorrelation sequences corresponding to different types of geological data, and calculating the correlation between different types according to the extreme value analysis results include:

[0106] Step S301: traverse each value of the autocorrelation sequence and calculate the difference of each value; the difference is the subtraction of the previous data from the next data; the tail element is not calculated;

[0107] Step S302: Locate the extreme value sequence number and its extreme value according to the sign of the difference;

[0108] Step S303: Compare the extreme value numbers and extreme values ​​corresponding to different types of geological data, and calculate the correlation.

[0109] In an example of the technical solution of the present invention, the extreme value analysis process and the application process of the extreme value analysis results are described in detail. For each type of autocorrelation sequence of geological data, each value of the autocorrelation sequence is traversed, and the difference of each value is calculated. The calculation method is to subtract the previous data from the latter data (the tail element is not calculated because there is no latter element). The extreme value number can be located according to the sign of the difference, and then the extreme value at the extreme value number can be read. This process is actually a discretized application of the extreme value calculation process of the function (see the derivative sign), which is not complicated; after locating the extreme value number and its extreme value, the extreme value number and its extreme value corresponding to different types of geological data are compared, and the correlation is calculated.

[0110] Among them, a feasible way to calculate the correlation is to first normalize the extreme values ​​so that they become data between 0 and 1. The normalization method is to select the maximum and minimum values ​​among the extreme values, calculate the maximum difference, and then for each extreme value, calculate the difference between it and the minimum value, calculate the ratio of the difference to the maximum difference, and obtain data between 0 and 1; finally, construct a histogram based on all the normalized extreme values, calculate the area of ​​the histogram, use the area of ​​the histogram as the feature of the geological data, and determine the correlation based on the inverse proportion of the area difference; that is, the closer the areas of the histograms are, the more similar the two types of geological data are.

[0111] Figure 5 The fourth sub-flow chart of the geological data collection statistical method is as follows: the step of storing each type of geological data matrix based on time sequence and constructing hyperlinks of different storage results according to feature type groups includes:

[0112] Step S401: Obtain each type of geological data matrix, and use the time dimension as the third dimension to count the geological data matrix to obtain a three-dimensional geological data matrix;

[0113] Step S402: using the type as a label of the three-dimensional geological data matrix;

[0114] Step S403: creating hyperlinks between tags based on the determined feature type group.

[0115] In an example of the technical solution of the present invention, the final data storage process is specifically limited. For each type of geological data matrix, the time dimension is used as the third dimension to statistically analyze the geological data matrix to obtain a three-dimensional geological data matrix. The type of geological data is used as a label of the three-dimensional geological data matrix. When the labels of two three-dimensional geological data matrices belong to the same feature type group, a hyperlink is established between the two labels, so that the management party can read other more relevant three-dimensional geological data matrices when reading a certain three-dimensional geological data matrix.

[0116] Figure 6 1 is a structural block diagram of a geological data collection statistics system. In an embodiment of the present invention, a geological data collection statistics system, the system 10 includes:

[0117] A data matrix creation module 11 is used to count different types of geological data containing sampling points and sampling times, and create a geological data matrix with types and sampling times as labels;

[0118] An autocorrelation identification module 12 is used to count geological data matrices of the same type with different sampling times, perform autocorrelation identification on the geological data matrices, and obtain an autocorrelation sequence;

[0119] The extreme value analysis identification module 13 is used to perform extreme value analysis on the autocorrelation sequence, compare the extreme value analysis results of the autocorrelation sequences corresponding to different types of geological data, and calculate the correlation between different types according to the extreme value analysis results;

[0120] A type marking module 14 is used to mark two types as a feature type group when the correlation between two types is greater than a preset correlation threshold;

[0121] The data storage module 15 is used to store each type of geological data matrix based on time sequence and to construct hyperlinks of different storage results according to feature type groups.

[0122] Furthermore, the data matrix creation module 11 includes:

[0123] A bounding rectangle creation unit is used to query the boundary information of the geological sampling area and obtain the bounding rectangle of the boundary information;

[0124] The geological data statistics unit is used to count different types of geological data containing sampling points and sampling times;

[0125] The minimum distance recording unit is used to compare the distances between sampling points for the same type of geological data containing sampling points and sampling time, and record the minimum distance;

[0126] A basic matrix creation unit, used for creating a basic matrix based on a minimum distance and a bounding rectangle;

[0127] A data insertion unit is used to cluster the geological data according to the sampling time, insert the geological data into the basic matrix based on the sampling points, and obtain a geological data matrix; in the same type of geological data, the difference in sampling time between any two geological data is less than a preset difference threshold;

[0128] The label generation unit is used to calculate the average time corresponding to the same type of geological data as the time label, and read the type of geological data as the type label.

[0129] Specifically, the autocorrelation identification module 12 includes:

[0130] Matrix statistics unit, used to count the geological data matrices of the same type with different sampling times;

[0131] A sequence generation unit is used to extract eigenvalues ​​of the geological data matrix based on a preset feature recognition model, arrange the eigenvalues ​​according to sampling time, and obtain an eigenvalue sequence;

[0132] The calculation execution unit is used to calculate the autocorrelation coefficient of the eigenvalue sequence at different lag step sizes as the autocorrelation sequence.

[0133] In addition, the extreme value analysis and identification module 13 includes:

[0134] An element traversal unit is used to traverse each value of the autocorrelation sequence and calculate the difference of each value; the difference is the latter data minus the previous data; the tail element is not calculated;

[0135] A positioning unit, used for positioning the extreme value sequence number and its extreme value according to the sign of the difference;

[0136] The comparison unit is used to compare the extreme value serial numbers and their extreme values ​​corresponding to different types of geological data and calculate the correlation.

[0137] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for statistically analyzing geological data, characterized in that: The method comprises: Count different types of geological data containing sampling points and sampling times, and create a geological data matrix labeled by type and sampling time; Counting geological data matrices of the same type at different sampling times, performing autocorrelation identification on the geological data matrices, and obtaining an autocorrelation sequence; Performing extreme value analysis on the autocorrelation sequence, comparing the extreme value analysis results of the autocorrelation sequences corresponding to different types of geological data, and calculating the correlation between different types according to the extreme value analysis results; When the correlation between two types is greater than a preset correlation threshold, the two types are marked as a feature type group; The geological data matrix of each type is stored in chronological order, and hyperlinks of different stored results are constructed according to the feature type group.

2. The method for statistically analyzing geological data according to claim 1, characterized in that: The step of counting different types of geological data containing sampling points and sampling times to create a geological data matrix with types and sampling times as labels includes: Query the boundary information of the geological sampling area and obtain the bounding rectangle of the boundary information; Statistics of different types of geological data containing sampling points and sampling times; For the same type of geological data containing sampling points and sampling time, the distances between sampling points are compared two by two, and the minimum distance is recorded; Create a fundamental matrix based on the minimum distance and bounding rectangle; Clustering the geological data according to the sampling time, inserting the geological data into the basic matrix based on the sampling points, and obtaining the geological data matrix; in the same type of geological data, the difference in the sampling time of any two geological data is less than the preset difference threshold; Calculate the average time corresponding to the same type of geological data as the time tag, and read the type of geological data as the type tag.

3. The method for statistically analyzing geological data according to claim 2, characterized in that: The step of creating a basic matrix based on the minimum distance and the bounding rectangle comprises: Create a grid using the minimum distance as the side length of the grid cell; Align the upper left endpoint of the grid with the upper left endpoint of the circumscribed rectangle; The nodes of the grid cells are used as row and column positions to create a basic matrix, wherein the row and column positions in the basic matrix correspond one-to-one to the nodes in the circumscribed rectangle; The step of clustering the geological data according to the sampling time and inserting the geological data into the basic matrix based on the sampling points to obtain the geological data matrix includes: Calculate the time difference between the sampling times of any two geological data, and when the time difference is less than a preset time difference threshold, classify the geological data into one category; Query the sampling location of geological data and locate the node closest to the sampling location; The row and column positions corresponding to the query nodes are used as the insertion positions of the geological data.

4. The method for statistically analyzing geological data according to claim 1, characterized in that: The step of counting geological data matrices of the same type at different sampling times, performing autocorrelation identification on the geological data matrices, and obtaining an autocorrelation sequence comprises: Statistics of geological data matrix of the same type with different sampling times; Extract the eigenvalues ​​of the geological data matrix based on a preset feature recognition model, arrange the eigenvalues ​​according to the sampling time, and obtain the eigenvalue sequence; The autocorrelation coefficients of the eigenvalue sequence at different lag steps are calculated as the autocorrelation sequence.

5. The method for statistically analyzing geological data according to claim 1, characterized in that: The step of performing extreme value analysis on the autocorrelation sequence, comparing the extreme value analysis results of the autocorrelation sequences corresponding to different types of geological data, and calculating the correlation between different types according to the extreme value analysis results comprises: Traverse each value of the autocorrelation sequence and calculate the difference of each value; the difference is the subtraction of the previous data from the next data; the tail element is not calculated; Locate the extreme value number and its extreme value according to the sign of the difference; Compare the extreme value numbers and extreme values ​​corresponding to different types of geological data and calculate the correlation.

6. The method for statistically analyzing geological data according to claim 1, characterized in that: The step of storing each type of geological data matrix based on time sequence and constructing hyperlinks of different stored results according to feature type groups comprises: Obtain each type of geological data matrix, use the time dimension as the third dimension to count the geological data matrix, and obtain a three-dimensional geological data matrix; Types are used as labels for the three-dimensional geological data matrix; Create hyperlinks between labels based on the determined feature type groups.

7. A geological data collection statistical system, characterized in that: The system comprises: The data matrix creation module is used to count different types of geological data containing sampling points and sampling times, and create a geological data matrix with type and sampling time as labels; An autocorrelation identification module is used to count geological data matrices of the same type with different sampling times, perform autocorrelation identification on the geological data matrices, and obtain an autocorrelation sequence; An extreme value analysis identification module is used to perform extreme value analysis on the autocorrelation sequence, compare the extreme value analysis results of the autocorrelation sequences corresponding to different types of geological data, and calculate the correlation between different types according to the extreme value analysis results; A type marking module, used for marking two types as a feature type group when the correlation between two types is greater than a preset correlation threshold; The data storage module is used to store each type of geological data matrix based on time sequence and to construct hyperlinks of different stored results according to feature type groups.

8. The geological data collection statistical system according to claim 7, characterized in that: The data matrix creation module includes: A bounding rectangle creation unit is used to query the boundary information of the geological sampling area and obtain the bounding rectangle of the boundary information; The geological data statistics unit is used to count different types of geological data containing sampling points and sampling times; The minimum distance recording unit is used to compare the distances between sampling points for the same type of geological data containing sampling points and sampling time, and record the minimum distance; A basic matrix creation unit, used for creating a basic matrix based on a minimum distance and a bounding rectangle; A data insertion unit is used to cluster the geological data according to the sampling time, insert the geological data into the basic matrix based on the sampling points, and obtain a geological data matrix; in the same type of geological data, the difference in sampling time between any two geological data is less than a preset difference threshold; The label generation unit is used to calculate the average time corresponding to the same type of geological data as the time label, and read the type of geological data as the type label.

9. The geological data collection statistical system according to claim 7, characterized in that: The autocorrelation identification module comprises: Matrix statistics unit, used to count the geological data matrices of the same type with different sampling times; A sequence generation unit is used to extract eigenvalues ​​of the geological data matrix based on a preset feature recognition model, arrange the eigenvalues ​​according to sampling time, and obtain an eigenvalue sequence; The calculation execution unit is used to calculate the autocorrelation coefficient of the eigenvalue sequence at different lag step sizes as the autocorrelation sequence.

10. The geological data collection statistical system according to claim 7, characterized in that: The extreme value analysis and identification module comprises: An element traversal unit is used to traverse each value of the autocorrelation sequence and calculate the difference of each value; the difference is the subtraction of the previous data from the next data; the tail element is not calculated; A positioning unit, used for positioning the extreme value sequence number and its extreme value according to the sign of the difference; The comparison unit is used to compare the extreme value serial numbers and their extreme values ​​corresponding to different types of geological data and calculate the correlation.

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