A two-dimensional seismic horizon automatic tracking method and device

Abstract

A method and device for automatic tracking of two-dimensional seismic horizons, the method comprising: obtaining N seed points of a two-dimensional seismic horizon of a study area; calculating a score for each seed point based on a preset scoring criterion; selecting B seed points from the N seed points to construct a global two-dimensional horizon initial value based on the score and the positional relationship of the seed points; and automatically tracking the two-dimensional horizon based on the two-dimensional horizon initial value and A seed points.

Description

Technical Field

[0001] This article relates to the field of seismic exploration technology, and in particular to a two-dimensional seismic layer automatic tracking method and device. Background Art

[0002] Automatic seismic horizon tracking is crucial for improving the efficiency of seismic data interpretation. Compared to algorithms that track horizons along specific paths, those based on global optimization often offer greater stability and efficiency in structurally complex areas. Consequently, global seismic horizon tracking has become a key application technology in seismic interpretation.

[0003] However, compared with traditional algorithms that track horizons along specific paths, the global seismic horizon tracking algorithm is more sensitive to the number of manually selected seismic horizon seed points. When the number of seed points is too large, the computational time required to construct the initial horizon value increases significantly, significantly affecting the efficiency of global seismic horizon tracking. However, when the number of seed points is insufficient, the increase in the number of optimization iterations during global seismic horizon tracking reduces the algorithm's efficiency, and the accuracy of the results is also reduced due to the insufficient number of seed points. In addition, according to actual application tests, because the seismic phase consistency corresponding to the manually selected seed points is not strictly uniform, the participation of some seed points not only reduces the efficiency of global seismic horizon tracking but also has a certain negative impact on the accuracy of the final results.

[0004] Therefore, realizing a new two-dimensional seismic layer automatic tracking method is an urgent problem to be solved. Summary of the Invention

[0005] The inventors discovered that:

[0006] (1) The global 2D seismic horizon tracking method requires the use of manually picked seed points to construct the 2D horizon initial value. If the number of seed points in this step is too small, the accuracy of the calculated horizon initial value will be low, affecting the convergence speed and convergence accuracy of the subsequent 2D horizon tracking algorithm. However, if the number of seed points is too large, the calculation time of the horizon initial value will be greatly increased, reducing the overall efficiency of the global 2D seismic horizon automatic tracking.

[0007] (2) Among the manually picked seed points, some are critical to the construction of the initial values ​​of the 2D horizon, but the contribution of a small number of seed points can be basically ignored. Therefore, quantitative evaluation of seed points can be carried out to construct the initial values ​​of the horizon for some key seed points, thereby improving the efficiency of global 2D seismic horizon tracking.

[0008] (3) Some of the manually picked seed points may have a certain phase deviation from the target layer. Such seed points will affect the accuracy of the overall results in the global two-dimensional seismic layer automatic tracking. Therefore, by performing seed point scoring, some seed points with high comprehensive rankings are selected for layer tracking and some seed points with low rankings are discarded, thereby improving the accuracy of global two-dimensional seismic layer automatic tracking and seismic interpretation.

[0009] Based on the above findings, the present application provides a two-dimensional seismic layer automatic tracking method and device. This method can improve the efficiency of global two-dimensional layer tracking by selecting seed points with higher comprehensive rankings to construct initial values ​​of two-dimensional seismic layers; and eliminate some seed points with lower rankings through comprehensive ranking results, thereby improving the accuracy of global two-dimensional layer automatic tracking results.

[0010] In a first aspect, the present application provides a two-dimensional seismic horizon automatic tracking method, characterized in that the method comprises:

[0011] Obtain N seed points of the 2D seismic horizon in the study area;

[0012] Calculate the score of each seed point based on the preset scoring criteria;

[0013] Selecting B seed points from the N seed points according to the positional relationship between the scores and the seed points to construct a global two-dimensional layer initial value;

[0014] Automatic tracking of the two-dimensional layer is performed based on the two-dimensional layer initial value and A seed points.

[0015] In the second aspect, an embodiment of the present invention also provides a two-dimensional seismic layer automatic tracking device, which includes: a memory and a processor; the memory is used to store a program for automatic tracking of two-dimensional seismic layers, and the processor is used to read and execute the program for automatic tracking of two-dimensional seismic layers, and execute any one of the methods described in the above embodiments.

[0016] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a data processing program is stored, and the data processing program is executed by a processor to perform the two-dimensional seismic layer automatic tracking method described in any one of the above embodiments.

[0017] Compared with related technologies, the present application provides a method and device for automatic tracking of two-dimensional seismic horizons, the method comprising: obtaining N seed points of the two-dimensional seismic horizon of the study area; calculating the score of each seed point based on a preset scoring criterion; selecting B seed points from the N seed points to construct a global two-dimensional horizon initial value based on the positional relationship between the score and the seed point; and performing automatic two-dimensional horizon tracking based on the two-dimensional horizon initial value and A seed points. The present application improves the efficiency of global two-dimensional horizon tracking by selecting seed points with a high comprehensive ranking to construct the two-dimensional seismic horizon initial value; and improves the accuracy of the global two-dimensional horizon automatic tracking result by eliminating some seed points with a low ranking based on the comprehensive ranking result, which has important guiding significance for high-precision horizon automatic tracking and seismic interpretation.

[0018] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present application. Other advantages of the present application can be realized and obtained by the solutions described in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are used to provide an understanding of the technical solution of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.

[0020] Figure 1 This is a flow chart of the automatic tracking method of two-dimensional seismic horizons according to an embodiment of the present application;

[0021] Figure 2 A schematic diagram of a two-dimensional seismic layer automatic tracking device according to an embodiment of the present application;

[0022] Figure 3 Schematic cross-sectional diagram of the results of global two-dimensional seismic layer automatic tracking interpretation based on different seed points in some exemplary embodiments;

[0023] Figure 4 Schematic diagram of amplitude variance comparison of automatic tracing horizon results in some exemplary embodiments;

[0024] Figure 5 Schematic diagram of 10 seed points of a two-dimensional seismic horizon based on manual picking in some exemplary embodiments;

[0025] Figure 6 A schematic diagram of the ratio of the vertical and horizontal distances between each seed point and other seed points in some exemplary embodiments;

[0026] Figure 7 is a schematic diagram of a first sequence of seed points in some exemplary embodiments;

[0027] Figure 8 is a schematic diagram of a second sequence of seed points in some exemplary embodiments;

[0028] Figure 9 A cross-sectional diagram of global two-dimensional seismic horizon automatic tracking interpretation results constrained by selecting first three, first four, and first five seed points according to the second sequence in some exemplary embodiments;

[0029] Figure 10 Graphs comparing the amplitude variance results corresponding to the automatic tracking interpretation results using the first three, first four, and first five seed points in some exemplary embodiments. DETAILED DESCRIPTION

[0030] This application describes multiple embodiments, but this description is exemplary rather than restrictive, and it will be apparent to those skilled in the art that there may be more embodiments and implementations within the scope of the embodiments described herein. Although many possible feature combinations are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are also possible. Unless specifically limited, any feature or element of any embodiment may be used in combination with any other feature or element in any other embodiment, or may replace any other feature or element in any other embodiment.

[0031] This application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features, and elements disclosed in this application may also be combined with any conventional features or elements to form a unique inventive solution defined by the claims. Any features or elements of any embodiment may also be combined with features or elements from other inventive solutions to form another unique inventive solution defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in this application may be implemented individually or in any appropriate combination. Therefore, except for the limitations made according to the appended claims and their equivalents, the embodiments are not subject to other limitations. In addition, various modifications and changes may be made within the scope of protection of the appended claims.

[0032] In addition, when describing representative embodiments, the specification may have presented the method and / or process as a specific sequence of steps. However, to the extent that the method or process does not rely on the specific order of the steps described herein, the method or process should not be limited to the steps in the specific order described. As will be understood by those skilled in the art, other orders of steps are also possible. Therefore, the specific order of the steps set forth in the specification should not be interpreted as a limitation to the claims. In addition, the claims for the method and / or process should not be limited to performing their steps in the order written, and those skilled in the art can readily understand that these orders can be changed and still remain within the spirit and scope of the embodiments of the present application.

[0033] The embodiment of the present invention provides a two-dimensional seismic layer automatic tracking method, such as Figure 1 As shown, the method includes steps S100-S130:

[0034] S100: Obtain N seed points of the 2D seismic horizon of the study area;

[0035] S110: Calculate the score of each seed point based on a preset scoring criterion;

[0036] S120: Selecting B seed points from the N seed points according to the positional relationship between the scores and the seed points to construct a global two-dimensional layer initial value;

[0037] S130: Automatically track the two-dimensional layer based on the two-dimensional layer initial value and A seed points.

[0038] In an exemplary embodiment, obtaining N seed points of a two-dimensional seismic horizon in a study area includes:

[0039] Step 1: Obtain a 2D seismic profile of the study area;

[0040] In this step, the two-dimensional seismic profile of the study area is post-stack seismic data, which is the data obtained by a series of seismic data processing operations on the original seismic data collected; the seismic data processing operations include but are not limited to: static correction, denoising, amplitude compensation, velocity analysis, dynamic correction, offset, etc. The final processed post-stack seismic data is equivalent to the data collected under self-excitation and self-collection conditions, which can be recorded as s(t,x), where t represents the two-way travel time of the seismic wave and x is the seismic channel number.

[0041] Step 2: Determine the 2D seismic horizon for automatic horizon tracking by combining geophysical data, core data, well logging, and geological information of the work area;

[0042] Step 3: Pick N seed points of the 2D seismic horizon. Figure 5The diagram shows N=10 seed points of a manually picked 2D seismic horizon, numbered 1-10 from left to right.

[0043] In this step, the seed points of the 2D seismic horizon are manually picked and recorded as seed(t1,x1), seed(t2,x2), ..., seed(t N ,x N ), where (t1,x1), (t2,x2), ..., (t N ,x N ) represent the two-way travel time and lateral coordinate position of these N seed points on the 2D seismic profile.

[0044] In an exemplary embodiment, the score of each seed point is calculated based on a preset scoring criterion, including:

[0045] Step 1, corresponding to any seed point seed(t i ,x i )(i=1,2,...,N), first calculate its relationship with any other seed point seed(t j ,x j )(j≠i, j=1,2,...,N) longitudinal and transverse distances;

[0046] The horizontal distance is:

[0047]

[0048] in, is the horizontal distance between the i-th seed point and the j-th seed point, L grid is the distance between seismic traces, abs(.) means to find the absolute value, x i Indicates the trace number corresponding to the i-th seed point on the 2D seismic profile, x j Indicates the trace number corresponding to the j-th seed point on the 2D seismic profile.

[0049] The vertical distance is:

[0050]

[0051] in, is the longitudinal distance between the i-th seed point and the j-th seed point, sp is the seismic sampling time interval, vp represents the longitudinal wave velocity of the formation, t i represents the two-way travel time corresponding to the i-th seed point on the two-dimensional seismic profile, t j It represents the two-way travel time corresponding to the j-th seed point on the 2D seismic profile. From the above formula, we can see that the vertical distance between seed points is half of the product of the two-way travel time difference and the formation compressional wave velocity.

[0052] Step 2: Establish a seed point symmetric matrix based on the aspect ratio of each seed point;

[0053] The aspect ratio is:

[0054]

[0055] Among them, Ratio (i,j) is the ratio of the vertical and horizontal distances between the i-th seed point and the j-th seed point. Figure 6 As shown in the figure, it is a schematic diagram of the ratio of the vertical and horizontal distances between each seed point and other seed points.

[0056] Let Ratio (i,j) = 0 when i = j; It can be seen that for all manually picked seed points, Ratio is a symmetric matrix with zero diagonal elements, as follows:

[0057]

[0058] Among them, r (i,j) is the element corresponding to Ratio at position (i, j), r (i,j) =r (j,i) .

[0059] Step 3: Calculate the sum of the i-th row or i-th column of the symmetric matrix of each seed point, and use the sum as the score of each seed point.

[0060] For any manually picked seed point seed(t i ,x i )(i=1,2,...,N), find all the aspect ratios r related to it (i,j) The sum sum(t i ,x i )(i=1,2,...,N):

[0061]

[0062] From the above formula, we can see that sum(t i ,x i ) is the sum of the i-th row or i-th column of the symmetric matrix Ratio. When a seed point is located in a structurally complex location, such as a fault, it typically has a large aspect ratio relative to other seed points. Conversely, in structurally simple areas, seed points are typically located on continuously horizontal seismic axes, and their corresponding aspect ratios are very close to zero. Therefore, manually selected seed points can be quantitatively ranked based on their aspect ratios: a larger sum(t,x) indicates a higher score.

[0063] In an exemplary embodiment, selecting B seed points from the N seed points based on the positional relationship between the scores and the seed points to construct a global two-dimensional layer initial value includes:

[0064] Step 1: construct a first sequence of seed points by sorting them according to the score of each seed point, and determine M key seed points from the first sequence;

[0065] like Figure 7 As shown, based on Figure 6 The sum of the vertical and horizontal distance ratios of each seed point is the score diagram of each seed point. The first sequence of seed points is constructed according to the score, and M = 2 key seed points are selected from the first sequence, such as Figure 7 As shown, the key seed points are 4 and 5.

[0066] Step 2, constructing a second sequence of seed points by sorting the NM seed points according to their positional relationship with the M key seed points, and selecting B seed points from the second sequence, specifically includes:

[0067] Step 21, classify the NM seed points into categories of key seed points that are consistent with their positional relationships;

[0068] Step 22, sorting the plurality of seed points in each class according to the scores;

[0069] Step 23, based on the score ranking results of each category, a comprehensive ranking is performed according to the category to construct a second sequence of seed points; Figure 8 As shown, Figure 8 is based on Figure 6 The results and the comprehensive ranking results obtained by taking the 4th and 5th seed points with the highest scores as key seed points are taken into account. Figure 6 The scoring results in the MATLAB software and the relative position relationship between various sub-points and key seed points avoid the problem that the uneven distribution of seed points on both sides of the key seed point affects the accuracy and stability of the global two-dimensional seismic layer automatic tracking results.

[0070] Step 24: Select B seed points from the second sequence.

[0071] like Figure 8 As shown, according to Figure 8 For the second sequence in , B = 4 seed points (4, 5, 1, 6) or B = 5 seed points (4, 5, 1, 6, 2) can be selected according to the specific work area conditions.

[0072] Step 3: construct a global two-dimensional layer initial value based on the B seed points.

[0073] This step can use an interpolation algorithm to construct the initial value of the global 2D horizon. The interpolation algorithm can use spline interpolation. When there are sufficient seed points, this type of algorithm can obtain an interpolation effect that is relatively close to the actual horizon, thereby reducing the number of subsequent iterations and improving the accuracy and efficiency of global 2D seismic horizon automatic tracking. However, when there are many seed points, the efficiency of spline interpolation is greatly reduced, which significantly affects the efficiency of horizon tracking. Therefore, we conduct seed point sorting and optimization and implement initial horizon value construction based on this. This allows global 2D seismic horizon automatic tracking to maximize efficiency while ensuring accuracy.

[0074] In an exemplary embodiment, two-dimensional layer automatic tracking is performed based on the two-dimensional layer initial value and A seed points; in this embodiment, on the basis of the completion of the construction of the two-dimensional layer initial value, most of the seed points with a high comprehensive ranking are selected to carry out global two-dimensional seismic layer automatic tracking. In this process, some seed points with a ranking near the end can be eliminated: first, most of the seed points with a low ranking are located in the work area with a relatively simple structure. Eliminating some seed points at this location has basically no effect on the final global two-dimensional seismic layer automatic tracking result, and can also improve the operating efficiency of the global two-dimensional seismic layer automatic tracking algorithm. Secondly, since the phase consistency of the manually picked seed points does not strictly meet the consistency requirements, the tracking results of the layer automatic tracking algorithm in the area with simple structure usually have good phase consistency; for this reason, eliminating some seed points with a low comprehensive ranking can, in most cases, reduce the adverse effects of the seed point phase inconsistency on the final result, thereby improving the accuracy of the global two-dimensional seismic layer automatic tracking result. Specific examples, such as Figure 9 As shown, Figure 9 Figure (a) is a cross-section of the global 2D seismic layer automatic tracking interpretation results constrained by the first three seed points selected in the second sequence. Figure 9 Figure (b) is a cross-sectional diagram of the global 2D seismic layer automatic tracking interpretation result constrained by the first four seed points selected in the second sequence. Figure 9 Figure (c) is a cross-sectional diagram of the global 2D seismic layer automatic tracking interpretation result constrained by the first 5 seed points selected in the second sequence. For the interpretation result diagram obtained by the above automatic tracking, the corresponding amplitude variance is calculated and compared, and we can get Figure 10 The comparison diagram shown is from Figure 10 The comparative analysis shows that the amplitude variance of the global 2D seismic layer automatic tracking results based on a small number of seed points with the highest comprehensive ranking is much smaller than Figure 3The corresponding amplitude variance; secondly, after the comprehensive sorting, as the number of high-scoring seed points increases, the amplitude variance of the layer tracking result gradually decreases, indicating that the accuracy of the result gradually improves. Therefore, selecting some seed points with high comprehensive sorting to carry out global two-dimensional seismic layer automatic tracking can meet the stability of layer tracking and reduce the influence of some seed points with poor phase consistency in the lower sorting on the accuracy of the final result. At the same time, it also greatly improves the computational efficiency of global two-dimensional seismic layer automatic tracking, which has important guiding significance for high-precision seismic interpretation.

[0075] The present invention has the following advantages due to the adoption of the above technical solution:

[0076] 1. This technical solution uses specific scoring criteria to quantitatively evaluate multiple seed points for global 2D seismic horizon automatic tracking, selects multiple key seed points with the highest ranking to construct the initial value of the 2D horizon, and significantly improves the efficiency of global 2D seismic horizon automatic tracking;

[0077] 2. This technical solution takes into account the mutual positional relationship of each seed point and conducts a comprehensive ranking based on the scoring results and the above positional relationship, further improving the accuracy and efficiency of global 2D seismic horizon automatic tracking;

[0078] 3. The algorithm of this technical solution is simple and easy to implement, with high operating efficiency, and it is easy to form software functional modules for promotion and application.

[0079] An embodiment of the present invention also provides a two-dimensional seismic layer automatic tracking device, which includes: a memory 210 and a processor 220; the memory is used to store a program for automatic tracking of two-dimensional seismic layers, and the processor is used to read and execute the program for automatic tracking of two-dimensional seismic layers, and execute any one of the methods described in the above embodiments.

[0080] An embodiment of the present invention further provides a computer-readable storage medium having a data processing program stored thereon. The data processing program is used by a processor to execute the two-dimensional seismic layer automatic tracking method described in any one of the above embodiments.

[0081] Example 1

[0082] This example demonstrates a method for automatically tracking 2D seismic horizons based on optimized seed points. The specific process is as follows:

[0083] Step a. Acquire post-stack seismic data;

[0084] Step b. Manually picking 2D seismic horizon seed points based on the combined geophysical data and geological knowledge of the work area;

[0085] Step c. Calculating the score of each seed point based on a preset scoring criterion, and sorting the seed points from large to small according to the score results to obtain a first sequence of seed points;

[0086] Step d. Determine M key seed points from the first sequence, and sort the remaining seed points again according to their mutual positional relationships with the key seed points and the score results to construct a second sequence of seed points; in this step, after determining M key seed points based on the first sequence, it is necessary to further sort the remaining seed points again according to their mutual positional relationships with the key seed points and the score results to construct a second sequence of seed points. An important reason for carrying out this step is that the top-ranked seed points evaluated by the above-mentioned scoring results may be unevenly distributed on both sides of the key seed points, thereby affecting the accuracy and stability of the global two-dimensional seismic layer automatic tracking results. Figure 3 As shown, Figure 3 Figure (a) is the final interpretation result profile of the global 2D seismic layer automatic tracking based on four seed points. Figure 3 Figure (b) is the final interpretation result profile of the global 2D seismic layer automatic tracking based on the two middle seed points. Figure 3 Figure (c) is a cross-section of the final interpretation results of automatic tracking of global two-dimensional seismic layers based on two seed points on the edge. Figure 4 for Figure 3 A schematic diagram of the amplitude variance comparison of the three automatic tracking layer results. Figure 4 The comparative analysis shows that: 1) The smaller the amplitude variance, the better the seismic phase consistency of global 2D seismic horizon automatic tracking, and the higher the 2D seismic horizon tracking accuracy. 2) For global 2D seismic horizon automatic tracking, the more manually picked seed points, the better. Some key seed points have a much greater guiding role in horizon automatic tracking than most other seed points. Figure 3 (a) Figure 4 The amplitude variance corresponding to the global 2D seismic layer automatic tracking result constrained by seed points is greater than Figure 3 The amplitude variance of the results constrained by the two seed points in the middle of Figure (b) is the error caused by the phase inconsistency problem of manually picking seed points. Figure 3 The amplitude variance of the results constrained by the two seed points in the middle of Figure (b) is Figure 3 A comparison of the amplitude variances of the results constrained by the two middle seed points in Figure (c) shows that manually selected seed points at different locations have varying impacts on the global 2D seismic horizon automatic tracking results. The contribution and impact of the two middle seed points on the accuracy and stability of the 2D seismic horizons picked by the global 2D seismic horizon automatic tracking are far greater than those of the two edge seed points. This comparative analysis demonstrates that the positional relationship of seed points influences the accuracy and stability of the global 2D seismic horizon automatic tracking results.

[0087] Step e. Selecting B seed points from the second sequence to construct a global two-dimensional layer initial value;

[0088] Step f. Perform global 2D layer automatic tracking based on the constructed 2D layer initial value and the majority of seed points ranked at the top. In this step, the majority of seed points ranked at the top can be selected based on the specific situation, for example, selecting the top 70%-80% of seed points, that is, excluding the bottom 20-30% of seed points for global 2D layer automatic tracking.

[0089] The method implemented in this example has the following technical effects:

[0090] By selecting seed points with high comprehensive ranking to construct the initial value of 2D seismic horizon, the efficiency of global 2D horizon tracking can be improved; and by eliminating some seed points with low ranking through comprehensive ranking results, the accuracy of global 2D horizon automatic tracking results can be improved, which has important guiding significance for high-precision horizon automatic tracking and seismic interpretation.

[0091] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As is well known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable, and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those skilled in the art that communication media generally embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

Claims

1. A two-dimensional seismic horizon automatic tracking method, characterized in that: The method comprises: Obtain N seed points manually picked from the 2D seismic horizon of the study area; Calculate the score of each seed point based on the preset scoring criteria; Selecting B seed points from the N seed points according to the positional relationship between the scores and the seed points to construct a global two-dimensional layer initial value; Automatically tracking the two-dimensional layer based on the two-dimensional layer initial value and A seed points; Where N is greater than A, A is greater than B, N, A, and B are all positive integers; A is to select the top A seed points in the order of scores from N seed points; The step of selecting B seed points from the N seed points to construct a global two-dimensional layer initial value based on the positional relationship between the score and the seed point includes: Sorting the scores of each seed point to construct a first sequence of seed points, and determining M key seed points from the first sequence; Sorting the NM seed points according to their positional relationship with the M key seed points to construct a second sequence of seed points, and selecting B seed points from the second sequence; Constructing a global two-dimensional layer initial value according to the B seed points; Sorting the NM seed points according to the positional relationship between the NM seed points and the M key seed points to construct a second sequence of seed points, and selecting B seed points from the second sequence, comprises: Divide the NM seed points into categories of key seed points that are consistent with their positional relationships; sorting the plurality of seed points in each class according to the scores; According to the score ranking results of each category, a comprehensive ranking is performed according to the category to construct the second sequence of seed points; B seed points are selected from the second sequence.

2. The two-dimensional seismic horizon automatic tracking method according to claim 1, characterized in that: The step of obtaining N seed points of the two-dimensional seismic horizon of the study area includes: Obtain a 2D seismic profile of the study area; Combining the geophysical data, core information, logging information and geological information of the work area, determine the 2D seismic horizon to be automatically tracked; Pick N seed points of the 2D seismic horizon.

3. The two-dimensional seismic horizon automatic tracking method according to claim 1, characterized in that: The score of each seed point is calculated based on the preset scoring criteria, including: Calculate the horizontal and vertical distances between each seed point and other seed points; Establish a seed point symmetric matrix based on the aspect ratio of each seed point; The sum of the i-th row or i-th column of the symmetric matrix of each seed point is calculated, and the sum is used as the score of each seed point.

4. The two-dimensional seismic horizon automatic tracking method according to claim 3, characterized in that: The lateral distance is: in, is the horizontal distance between the i-th seed point and the j-th seed point, x i Indicates the trace number corresponding to the i-th seed point on the 2D seismic profile, x j Indicates the trace number corresponding to the j-th seed point on the 2D seismic profile, L grid is the distance between seismic traces, and abs(.) means finding the absolute value.

5. The two-dimensional seismic horizon automatic tracking method according to claim 4, characterized in that: The longitudinal distance is: in, is the longitudinal distance between the i-th seed point and the j-th seed point, sp is the seismic sampling time interval, vp represents the longitudinal wave velocity of the formation, t i represents the two-way travel time corresponding to the i-th seed point on the two-dimensional seismic profile, t j It represents the two-way travel time corresponding to the j-th seed point on the two-dimensional seismic profile.

6. The two-dimensional seismic horizon automatic tracking method according to claim 5, characterized in that: The aspect ratio is: Among them, Ratio (i,j) is the ratio of the vertical and horizontal distances between the i-th seed point and the j-th seed point.

7. The two-dimensional seismic horizon automatic tracking method according to claim 6, characterized in that: The seed point symmetric matrix is: Among them, r (i,j) is the element corresponding to Ratio at position (i, j), r (i,j) =r (j,i) .

8. The two-dimensional seismic horizon automatic tracking method according to claim 7, characterized in that: The score of the seed point is: Among them, sum(t i ,x i ) is the sum of the ith row or ith column of the symmetric matrix, r (i,j) is the element corresponding to Ratio at position (i, j), i = 1, 2, ..., N.

9. A two-dimensional seismic layer automatic tracking device, characterized in that: The device includes: a memory and a processor; the memory is used to store a program for automatically tracking two-dimensional seismic layers, and the processor is used to read and execute the program for automatically tracking two-dimensional seismic layers, and execute the method described in any one of claims 1-8.

10. A computer-readable storage medium having a data processing program stored thereon, wherein the data processing program is executed by a processor to implement the two-dimensional seismic layer automatic tracking method according to any one of claims 1 to 8.

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