Mark point extraction and stratum classification method and system based on phase attribute driving

Abstract

The invention belongs to the field of geological analysis, particularly relates to a mark point extraction and stratum classification method and system based on phase attribute driving, and aims to solve the problems of weak interpretability and poor display effect of a stratum obtained through decomposition in the prior art. The method comprises the following steps: performing three-dimensional coordinate transformation on multiple pieces of stratum mark point data extracted according to phase attributes; clustering the data points after the three-dimensional coordinate transformation, and performing horizon division on the data points according to a clustering result; counting the number N of scatter points belonging to the same seismic trace in the horizon category vectors belonging to the same category in the horizon category vectors obtained after horizon division; and displaying the three-dimensional scatter point coordinates corresponding to the scatter points of which the N is less than or equal to a set value in a hierarchical bit category manner. According to the invention, layer division from a three-dimensional view angle is proposed for the first time; the accuracy of horizon division is improved through flexible parameter adjustment in the DBSCAN algorithm; and geological concepts are introduced from a three-dimensional perspective through a flexible frame selection layering method for layering.

Description

Background Art

[0002] Existing stratigraphic analysis methods can be roughly divided into three categories: (1) Image-based stratigraphic tracking methods. Seismic profiles are processed as a series of two-dimensional images. For example, the seismic profile image edge detection method is used to combine neural networks with image classification technology, and the CNN deep convolutional neural network is used to achieve synchronous automatic multi-layer tracking. The horizon is manually annotated on the image, and the neural network is trained to learn the characteristics of seismic data to predict the stratigraphic layers. (2) Stratum tracking methods based on seismic attributes. For example, the seismic phase attribute is used to track the stratigraphic layers. (3) Stratum tracing methods based on seismic coherence. This method can automatically track the phase axis based on the similar amplitudes of adjacent channels within the same horizon. In this method, some layer points are manually selected as constraint points, and then the complete layer is obtained by interpolation. The pinch-out phenomenon between stratigraphic layers in the related technology greatly interferes with the stratigraphic interpretation. Only the stratigraphic changes near the constraint points are considered, which leads to a problem of reduced stratigraphic division accuracy. Summary of the invention

[0003] In order to solve the above problems in the prior art, that is, the problem that the strata obtained by decomposition in the prior art are weak in interpretability and poor in display effect, the present invention provides a landmark point extraction and strata classification method based on phase attribute driving, the method comprising:

[0004] Performing three-dimensional coordinate transformation on a plurality of stratigraphic marker point data extracted according to phase attributes;

[0005] Cluster the data points after the three-dimensional coordinate transformation, and divide the data points into layers according to the clustering results;

[0006] Count the number of scattered points N belonging to the same seismic trace in the layer category vectors belonging to the same category in the layer category vectors obtained after the layer division;

[0007] The stratified bit category displays the three-dimensional scatter point coordinates corresponding to the scatter points whose number N of scatter points belonging to the same seismic trace is less than or equal to the set end value.

[0008] In some implementations, the coordinates of phase attribute jump points are found out by performing Hilbert transformation on the seismic waveform data, and used as formation marker point data.

[0009] In some embodiments, the formation marker point data is three-dimensionally transformed into a set feature space by using the PCA method.

[0010] In some embodiments, clustering is performed using the DBSCAN algorithm.

[0011] In some embodiments, after obtaining the number of scattered points belonging to the same seismic trace in the layer category vector belonging to the same category, it also includes determining the distance multiple of the largest nearest neighbor distance and the smallest nearest neighbor distance in the layer category vector belonging to the same category, and judging whether the clustering parameter is qualified if the distance multiple reaches a set threshold;

[0012] If the clustering parameters are not qualified, adjust the clustering parameters and perform clustering again.

[0013] In some embodiments, clustering the data points after the three-dimensional coordinate transformation includes:

[0014] The three-dimensionally transformed data points are subjected to perspective transformation so that the data point coverage is greater than a set threshold;

[0015] Map the data points after the perspective transformation to two-dimensional coordinates, and select the layer category vector for the data points in the two-dimensional coordinates.

[0016] In some implementations, if the number of scattered points belonging to the same seismic trace is greater than the set value, the corresponding scattered points are marked as substandard scattered points and the three-dimensional coordinate transformation is performed again.

[0017] Another aspect of the present invention provides a landmark point extraction and stratum classification system based on phase attribute driving, comprising:

[0018] A marker point transformation module is configured to perform three-dimensional coordinate transformation on a plurality of stratigraphic marker point data extracted according to phase attributes;

[0019] A layer division module is configured to cluster the data points after the three-dimensional coordinate transformation, and divide the data points into layers according to the clustering results;

[0020] The scattered point number statistics module of the same seismic trace is configured to count the scattered point number N belonging to the same seismic trace in the layer category vectors belonging to the same category in the layer category vectors obtained after the layer division;

[0021] The hierarchical display module is configured to display the three-dimensional scattered point coordinates corresponding to the scattered points whose N is less than or equal to the set value in a hierarchical manner.

[0022] According to a third aspect of the present invention, an electronic device is provided, comprising:

[0023] at least one processor; and

[0024] a memory communicatively connected to at least one of the processors; wherein,

[0025] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the above-mentioned landmark point extraction and formation classification method driven by phase attributes.

[0026] In a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to be executed by the computer to implement the above-mentioned phase attribute-driven landmark point extraction and formation classification method.

[0027] Beneficial effects of the present invention:

[0028] (1) The present invention proposes for the first time to perform stratum division from a three-dimensional perspective, directly performing simultaneous calculations of all scattered points in the three-dimensional space based on actual data, quantifying the problem of stratum pinch-out interpretation, and improving the accuracy of stratum division;

[0029] (2) The present invention improves the accuracy of horizon division by flexible parameter adjustment in the DBSCAN algorithm;

[0030] (3) The present invention introduces geological concepts from a three-dimensional perspective for stratification through a flexible frame selection stratification method. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0032] Figure 1 It is a flow chart of a method for extracting landmark points and classifying strata based on phase attribute driving in an embodiment of the present invention;

[0033] Figure 2 is an original seismic profile in an embodiment of the present invention;

[0034] Figure 3 is a schematic diagram of the phase attribute of a seismic profile in an embodiment of the present invention;

[0035] Figure 4 is a schematic diagram of obtaining the three-dimensional coordinates of a jump point in an embodiment of the present invention;

[0036] Figure 5 is a schematic diagram of data points undergoing three-dimensional coordinate transformation in an embodiment of the present invention;

[0037] Figure 6 is a schematic diagram of clustering results of clustering completed for the first time in an embodiment of the present invention;

[0038] Figure 7 is a schematic diagram of clustering results when clustering parameters are unqualified in an embodiment of the present invention;

[0039] Figure 8 Schematic diagram of clustering results when the final clustering parameters are qualified in an embodiment of the present invention;

[0040] Fig. 9Schematic diagram of the principle of evaluating whether classification parameters are qualified in an embodiment of the present invention;

[0041] Fig.10 is the original image that undergoes perspective transformation in the embodiment of the present invention;

[0042] Fig.11 is a process image of perspective transformation in an embodiment of the present invention;

[0043] Fig.12 is an image after the perspective transformation in the embodiment of the present invention;

[0044] Fig.13 Schematic diagram of the principle of box-selecting layer category vectors in an embodiment of the present invention;

[0045] Fig.14 is a schematic diagram of the principle of marking scattered points as non-standard scattered points in an embodiment of the present invention;

[0046] Fig.15 is a schematic diagram of scattered points of each seismic trace in an embodiment of the present invention;

[0047] Fig.16 is a schematic diagram of the effect after multiple types of strata in the strata pinch-out relationship are effectively distinguished in an embodiment of the present invention;

[0048] Fig.17 It is a schematic diagram of the effect of displaying three-dimensional scattered point coordinates according to hierarchical categories in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the relevant invention are shown in the accompanying drawings.

[0050] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0051] In order to more clearly explain the landmark point extraction and stratum classification method based on phase attribute driving of the present invention, the following is combined with Figure 1 Each step in the embodiment of the present invention is described in detail.

[0052] First embodiment

[0053] The landmark point extraction and stratum classification method based on phase attribute driving includes steps S1 to S4, and each step is described in detail as follows:

[0054] Step S1, performing three-dimensional coordinate transformation on a plurality of stratigraphic marker point data extracted according to phase attributes.

[0055] The original seismic data obtained initially are as follows Figure 2 As shown, the three-dimensional original seismic data includes n×n seismic waveform data, each of which is an m×1 vector. The schematic diagram of the corresponding seismic profile phase attribute is as follows Figure 3 shown.

[0056] In this embodiment, the coordinates of the phase attribute jump point are found out by performing Hilbert transformation on the seismic waveform data, and are used as the formation marker point data.

[0057] Extract one of the seismic waveform data and obtain the seismic waveform signal through the Hilbert transform method:

[0058]

[0059] in, represents the complex number that changes with time after Hilbert transform, seis(t) represents the seismic waveform data, seis(τ) represents the seismic waveform amplitude value corresponding to the τ time displacement, τ represents the time displacement, * represents convolution, h(t) represents the Hilbert transform operator, The integral used is the Cauchy principal value integral;

[0060] The analytical signal JX(t) of the seismic waveform data seis(t) is constructed as:

[0061]

[0062] Where j represents the unit of the imaginary number and JX(t) represents The imaginary part of the method of expressing the conversion of a complex number that changes with time into a real part and an imaginary part after the Hilbert transform, J represents the imaginary unit, and X(t) represents the value of the imaginary part;

[0063] AMP(t) represents the instantaneous amplitude:

[0064]

[0065] θ(t) represents the instantaneous phase:

[0066]

[0067] The jump point time TB(t) of the instantaneous phase attribute data θ(t) is calculated by the differential algorithm:

[0068] CF(t) = θ(t) - θ(t-1) CF(t) is used to represent the sudden change of the instantaneous phase attribute data θ(t)

[0069]

[0070] According to the plane coordinates (X, Y) of the selected earthquake, the three-dimensional coordinates (X, Y, B(t)) of each jump point are obtained, and the jump point is recorded as the stratigraphic marker point data. The stratigraphic marker point data obtained is as follows: Figure 4 shown.

[0071] In this embodiment, the formation marker point data is transformed three-dimensionally into a set feature space by the PCA method. The feature space into which the formation marker point data is transformed by the PCA method can maximize the variance on the new coordinate axis, so that only valid information can be processed in subsequent analysis.

[0072] The stratigraphic marker data is recorded as z ij , where i represents the scatter point number and j represents the dimension number;

[0073] Standardize the stratigraphic marker data:

[0074]

[0075] BZ j represents the value of the jth dimension of all the scattered points after standardization, and shu represents the number of all the scattered points;

[0076] The matrix composed of all landmark point data is Z, calculate ZZ T The eigenvalue λ and eigenvector ξ of

[0077] ZZ T ξ=λξ;

[0078] Where Z is a 3*3 matrix, λ represents the eigenvalue matrix composed of eigenvalues ​​along the diagonal, and ξ represents the eigenvector matrix composed of eigenvector arrangements;

[0079] λ includes λ1, λ2 and λ3, λ1≥λ2≥λ3; ξ1 is the eigenvector corresponding to λ1, ξ2 is the eigenvector corresponding to λ2, and ξ3 is the eigenvector corresponding to λ3;

[0080] For each scattered point coordinate z in the center of the landmark point i Project according to the eigenvectors:

[0081] B ik =z i ·ξ k ;

[0082] Among them, B ik represents the coordinate of the kth dimension after the ith scattered point is transformed into a three-dimensional shape, z i represents the coordinates of the i-th scattered point in the original coordinate system, ξ krepresents the kth eigenvector.

[0083] The effect after completing the three-dimensional coordinate transformation is shown as follows Figure 5 shown.

[0084] Step S2, clustering the data points after the three-dimensional coordinate transformation, and dividing the data points into layers according to the clustering results.

[0085] In this embodiment, clustering is performed using the DBSCAN algorithm.

[0086] The DBSCAN algorithm first initializes the neighborhood parameters ∈=0.1, MinPts=10;

[0087] The coordinate B of the data point after the three-dimensional coordinate transformation is recorded as B={B k＝1 ,B k＝2 ,…,B k＝shu};

[0088] Initialize the core object collection at the same time Initialize the number of clusters o = 0, initialize the unvisited sample set Γ = B, and divide the clusters

[0089] For k = 1, 2, ..., shu, find sample B by distance metric k The ε neighborhood subsample set N ε (B k ):

[0090] N ε (B k )={B k ∈D|distance(B k , B l )≤ε};

[0091] Among them, B l In addition to B k Any other sample in foreign B;

[0092] If the number of samples in the neighborhood subsample set satisfies |N ε (B k )|≥MinPts, MinPts represents the minimum spacing, which is a constant parameter that is initialized at the beginning and can be adjusted in the process of adjusting the clustering parameters and re-clustering. k Add core object sample set: Ω=Ω∪{B k}; At this time, Ω is a collection of many core objects. The clustering result of the first clustering is as follows Figure 6 As shown,

[0093] If the core object sample collection The algorithm ends, otherwise continue to select core objects from the core object sample set to classify new sample clusters.

[0094] Based on the core object sample set, randomly select any core object B B , initialize the current cluster core object queue Ω cur = {B B}, initialize the category number p = p + 1, initialize the current cluster sample set C p = {B B}, update the unvisited sample set to Γ=Γ-{B B};

[0095] If the current cluster core object queue The current cluster C p After generation, update the cluster partition C = {C1, C2, ..., C p}, update the core object set Ω = Ω-C p , and then select another core object and update the core object set;

[0096] If the current cluster core object queue Update the core object sample set Ω = Ω - C p ;

[0097] In the current cluster core object queue Ω cur Take out a core object B k ′, find all ∈ neighborhood subsample sets N through the neighborhood distance threshold ∈ ∈ (B k ′), let Δ=N ∈ (B k ′)∩Γ, update the current cluster sample set C p =C p ∪Δ, update the unvisited sample set Γ=Γ-Δ, update Ω cur =Ω cur ∪(Δ∩Ω)-B k ′, transfer to the above judgment core object sample set

[0098] Until the core object sample set Then the algorithm ends;

[0099] Finally, we get the cluster partition C = {C1, C2, ..., C p}, get the current scatter point B according to C k Category L k , L k =C H , where B k ∈C H, the output layer category vector L corresponding to the sequence number k of the sample point.

[0100] Step S3, counting the number of scattered points N belonging to the same seismic trace in the layer category vectors belonging to the same category in the layer category vectors obtained after the layer division. The schematic diagram of the scattered points of each seismic trace is as follows Fig.15 shown.

[0101] Step S4, the three-dimensional scatter point coordinates corresponding to the scatter points whose N is less than or equal to the set value are displayed by stratum category. The effect after the multiple types of strata with stratum pinch-out relationship are effectively distinguished is as follows Fig.16 As shown, the final three-dimensional coordinates of the three points are as follows Fig.17 shown.

[0102] Although the various steps in the above embodiment are described in the above-mentioned order, those skilled in the art can understand that in order to achieve the effect of this embodiment, different steps do not have to be executed in such an order. They can be executed simultaneously (in parallel) or in a reverse order. These simple changes are within the scope of protection of the present invention.

[0103] Second embodiment

[0104] After obtaining the number of scattered points belonging to the same seismic trace in the layer category vector belonging to the same category, it also includes judging whether the classification parameters are qualified according to the size relationship between the largest nearest neighbor distance and the smallest nearest neighbor distance in the layer category vector belonging to the same category; evaluating whether the classification parameters are qualified is as follows: Fig. 9 shown.

[0105] In this embodiment, the method for determining the number of scattered points belonging to the same seismic trace in the layer category vector belonging to the same category is:

[0106] Label the three points as z k (z k1 =X,z k2 =Y,z k3 =TB(t)), after three-dimensional coordinate transformation, it becomes B k (B k1 ,B k2 ,B k3 ), the corresponding category is L k =C H , where B k ∈C H ;

[0107]

[0108]

[0109] Q HIndicates the number of scattered points with repeated points on the same seismic trace for the H-th layer category. Refers to C H Any scattered point z of the category g

[0110] If the clustering parameters are not qualified, adjust the clustering parameters and re-cluster. The clustering results obtained when the clustering parameters are not qualified are as follows Figure 7 As shown in the figure, after repeatedly adjusting the clustering parameters, the qualified clustering results finally obtained are as follows Figure 8 shown.

[0111] Third embodiment

[0112] A method for obtaining clearer data points by perspective transformation is also disclosed, specifically:

[0113] Obtaining data points after three-dimensional coordinate transformation by the method in the first embodiment;

[0114] Before clustering, the three-dimensionally transformed data points are also subjected to perspective transformation so that the coverage is greater than a set threshold;

[0115] Map the data points after the perspective transformation to two-dimensional coordinates, and select the layer category vector for the data points in the two-dimensional coordinates. The effects before and after the perspective transformation are as follows: Fig.10 , Fig.11 and Fig.12 As shown in the figure, the scattered points after the perspective transformation can be obtained by Fig.13 The layer category vector is selected by using the method.

[0116] Fourth embodiment

[0117] The invention provides a method for judging whether the classification parameters are qualified and adjusting the clustering parameters to obtain the layer category vector again if they are not qualified.

[0118] In this embodiment, the method described in the first embodiment and / or the second embodiment is used to obtain the data points after the three-dimensional coordinate transformation. The difference is that if the number of scattered points belonging to the same seismic trace is greater than the set value, the corresponding scattered points are marked as non-standard scattered points and the three-dimensional coordinate transformation is performed again. Fig.14 shown.

[0119] Until all the scattered points obtained are qualified scattered points.

[0120] Fifth embodiment

[0121] Disclosed is a phase attribute driven landmark point extraction and stratum classification system, a landmark point transformation module configured to perform three-dimensional coordinate transformation on a plurality of stratum landmark point data extracted according to the phase attributes;

[0122] A layer division module is configured to cluster the data points after the three-dimensional coordinate transformation, and divide the data points into layers according to the clustering results;

[0123] The scattered point number statistics module of the same seismic trace is configured to count the scattered point number N belonging to the same seismic trace in the layer category vectors belonging to the same category in the layer category vectors obtained after the layer division;

[0124] The hierarchical display module is configured to display the three-dimensional scattered point coordinates corresponding to the scattered points whose N is less than or equal to the set value in a hierarchical manner.

[0125] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process and related instructions of the system described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0126] It should be noted that the above embodiment provides a phase attribute driven landmark point extraction and stratigraphic classification system, which is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be decomposed or combined. For example, the modules in the above embodiments can be combined into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the modules or steps, and are not regarded as improper limitations on the present invention.

[0127] Sixth embodiment

[0128] An electronic device, comprising:

[0129] at least one processor; and

[0130] a memory communicatively connected to at least one of the processors; wherein,

[0131] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the above-mentioned landmark point extraction and formation classification method driven by phase attributes.

[0132] Seventh embodiment

[0133] A computer-readable storage medium stores computer instructions, wherein the computer instructions are used to be executed by the computer to implement the above-mentioned phase attribute-driven landmark point extraction and formation classification method.

[0134] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process and related instructions of the storage device and processing device described above can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0135] Those skilled in the art should be able to appreciate that the modules and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software or a combination of the two, and the programs corresponding to the software modules and method steps can be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the technical field. In order to clearly illustrate the interchangeability of electronic hardware and software, the composition and steps of each example have been generally described in the above description according to the function. Whether these functions are performed in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0136] The terms "first", "second", etc. are used to distinguish similar objects rather than to describe or indicate a particular order or sequence.

[0137] The term "comprise" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that includes a list of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent to such process, method, article, or apparatus / device.

[0138] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A method for extracting landmark points and classifying strata based on phase attribute driving, characterized in that: include: Performing three-dimensional coordinate transformation on a plurality of stratigraphic marker point data extracted according to phase attributes; Cluster the data points after the three-dimensional coordinate transformation, and divide the data points into layers according to the clustering results; Count the number of scattered points N belonging to the same seismic trace in the layer category vectors belonging to the same category in the layer category vectors obtained after the layer division; The stratified bit category displays the three-dimensional scatter point coordinates corresponding to the scatter points whose number N of scatter points belonging to the same seismic trace is less than or equal to the set end value.

2. The method for extracting landmark points and classifying strata based on phase attribute driving according to claim 1 is characterized in that: The coordinates of the phase attribute jump points are found by performing Hilbert transform on the seismic waveform data and used as the stratigraphic marker point data.

3. The method for extracting landmark points and classifying strata based on phase attribute driving according to claim 1 is characterized in that: The PCA method is used to transform the stratigraphic landmark data into the set feature space in three dimensions.

4. The method for extracting landmark points and classifying strata based on phase attribute driving according to claim 1 is characterized in that: Clustering is performed using the DBSCAN algorithm.

5. The method for extracting landmark points and classifying strata based on phase attribute driving according to claim 1 is characterized in that: After obtaining the number of scattered points belonging to the same seismic trace in the layer category vector belonging to the same category, it also includes the distance multiple of the largest nearest neighbor distance and the smallest nearest neighbor distance in the layer category vector belonging to the same category, and judging whether the clustering parameter is qualified if the distance multiple reaches the set threshold; If the clustering parameters are not qualified, adjust the clustering parameters and perform clustering again.

6. The method for extracting landmark points and classifying strata based on phase attribute driving according to claim 1 is characterized in that: The clustering of the data points after the three-dimensional coordinate transformation includes: The three-dimensionally transformed data points are subjected to perspective transformation so that the data point coverage is greater than a set threshold; Map the data points after the perspective transformation to two-dimensional coordinates, and select the layer category vector for the data points in the two-dimensional coordinates.

7. The method for extracting landmark points and classifying strata based on phase attribute driving according to claim 6 is characterized in that: If the number of scattered points belonging to the same seismic trace is greater than the set value, the corresponding scattered points are marked as unqualified scattered points and the three-dimensional coordinate transformation is performed again.

8. A landmark point extraction and stratum classification system based on phase attribute drive, characterized in that: include: A marker point transformation module is configured to perform three-dimensional coordinate transformation on a plurality of stratigraphic marker point data extracted according to phase attributes; A layer division module is configured to cluster the data points after the three-dimensional coordinate transformation, and divide the data points into layers according to the clustering results; The scattered point number statistics module of the same seismic trace is configured to count the scattered point number N belonging to the same seismic trace in the layer category vectors belonging to the same category in the layer category vectors obtained after the layer division; The hierarchical display module is configured to display the three-dimensional scattered point coordinates corresponding to the scattered points whose number N of scattered points belonging to the same seismic channel is less than or equal to the set end value in a hierarchical manner.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to at least one of the processors; wherein, The memory stores instructions executable by the processor, and the instructions are used to be executed by the processor to implement the phase attribute driven landmark point extraction and formation classification method described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to be executed by the computer to implement the phase attribute-driven landmark point extraction and formation classification method described in any one of claims 1-7.

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