A global three-dimensional seismic horizon tracking method and system under multiple seed point constraints

Abstract

The present invention provides a global three-dimensional seismic layer tracking method and system under multiple seed point constraints, the method comprising collecting post-stack seismic data; manually picking three-dimensional seismic layer seed points based on the post-stack seismic data, in combination with geophysical data and geological knowledge of the work area; thinning the seed points at equal intervals and scoring them based on a scoring criterion, and then sorting them from large to small according to the scoring results; selecting a number of top-ranked three-dimensional layer seed points to construct a global three-dimensional layer initial value; and automatically tracking the global three-dimensional layer based on the three-dimensional layer initial value and a number of top-ranked three-dimensional layer seed points. The present invention significantly improves the efficiency of global three-dimensional seismic layer automatic tracking; the present invention improves the accuracy of target seismic layer automatic tracking by reducing the impact of insufficient seismic phase consistency of artificial seed points on global three-dimensional seismic layer automatic tracking; the algorithm of the present invention is simple and easy to implement, has high operating efficiency, and is easy to form a software function module for promotion and application.

Description

Technical Field

[0001] The present invention belongs to the technical field of oil and gas field seismic exploration, and in particular relates to a global three-dimensional seismic layer tracking method and system under multiple seed point constraints. Background Art

[0002] Automatic 3D seismic horizon tracking is crucial for improving the efficiency and accuracy of seismic data interpretation. Compared to traditional algorithms that track horizons along specific paths, 3D seismic horizon tracking algorithms based on global optimization often achieve better accuracy and higher tracking efficiency in structurally complex areas. Therefore, global 3D seismic horizon tracking algorithms have become a key application technology in current seismic interpretation.

[0003] However, compared with traditional horizon tracking algorithms that follow specific paths, the global 3D seismic horizon automatic 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 time required to construct the initial 3D seismic horizon value increases significantly, significantly affecting the efficiency of global 3D seismic horizon automatic tracking and also severely challenging the memory requirements of the computing hardware. When the number of seed points is insufficient, the number of optimization iterations during global 3D seismic horizon automatic tracking increases significantly, reducing algorithm efficiency and resulting in reduced accuracy. Furthermore, practical application tests have shown that because manually selected seed points do not guarantee seismic phase consistency, the inclusion of some non-critical seed points not only reduces the efficiency of global 3D seismic horizon automatic tracking but also has a certain negative impact on the accuracy of the final results. Summary of the Invention

[0004] In response to the above problems, the present invention proposes a global three-dimensional seismic horizon tracking method and system under multiple seed point constraints.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A global three-dimensional seismic horizon tracking method under multiple seed point constraints includes the following steps:

[0007] Acquire post-stack seismic data;

[0008] Based on post-stack seismic data, combined with geophysical data and geological knowledge of the work area, 3D seismic horizon seed points are manually picked;

[0009] The seed points are thinned out at equal intervals and scored based on the scoring criteria, and then sorted from large to small according to the scoring results;

[0010] Select several top-ranked 3D layer seed points to construct the global 3D layer initial value;

[0011] Global 3D layer automatic tracking is carried out based on the initial 3D layer value and several top-ranked 3D layer seed points.

[0012] Preferably, the post-stack seismic data is recorded as s(t, x, y), wherein t represents the two-way travel time of the seismic wave, and [x, y] represents the plane coordinates of the corresponding seismic trace.

[0013] Preferably, picking up the three-dimensional seismic horizon seed point also includes the following steps:

[0014] Determine the target seismic horizon;

[0015] Pick the seed point of the target seismic horizon.

[0016] Preferably, scoring and sorting the seed points comprises the following steps:

[0017] Select a prime number greater than the maximum value of the three-dimensional layer seed point row and column to achieve equal spacing thinning of the seed points;

[0018] On the basis of equal-interval thinning, the vertical and horizontal distance ratios between each seed point and any other seed point are calculated and summed up and sorted.

[0019] Preferably, a spline interpolation algorithm is used to construct the three-dimensional layer initial value through the three-dimensional layer seed point.

[0020] Preferably, carrying out the global three-dimensional layer automatic tracking further includes the following steps:

[0021] Select several seed points with the highest scores to participate in the global 3D seismic layer automatic tracking;

[0022] Eliminate several seed points with low ranking.

[0023] A global three-dimensional seismic layer tracking system under multiple seed point constraints includes an acquisition unit, a picking unit, a sorting unit, a calculation unit and a tracking unit; wherein,

[0024] An acquisition unit for acquiring post-stack seismic data;

[0025] Picking unit: manually picking 3D seismic layer seed points based on post-stack seismic data, combined with geophysical data and geological knowledge of the work area;

[0026] The sorting unit is used to thin out the seed points at equal intervals and score them based on the scoring criteria, and then sort them from large to small according to the scoring results;

[0027] A calculation unit is used to select a number of top-ranked 3D layer seed points to construct a global 3D layer initial value;

[0028] The tracking unit performs global 3D layer automatic tracking based on the initial value of the 3D layer and several top-ranked 3D layer seed points.

[0029] Preferably, the post-stack seismic data of the acquisition unit is recorded as s(t, x, y), wherein t represents the two-way travel time of the seismic wave, and [x, y] represents the plane coordinates of the corresponding seismic trace.

[0030] Preferably, the picking unit picks up the three-dimensional seismic horizon seed point, further comprising the following steps:

[0031] Determine the target seismic horizon;

[0032] Pick the seed point of the target seismic horizon.

[0033] Preferably, the ranking unit scores and ranks the seed points, comprising the following steps:

[0034] Select a prime number greater than the maximum value of the three-dimensional layer seed point row and column to achieve equal spacing thinning of the seed points;

[0035] On the basis of equal-interval thinning, the vertical and horizontal distance ratios between each seed point and any other seed point are calculated and summed up and sorted.

[0036] Preferably, the calculation unit constructs the initial value of the three-dimensional layer through the three-dimensional layer seed point using a spline interpolation algorithm.

[0037] Preferably, the tracking unit performs global three-dimensional layer automatic tracking, further comprising the following steps:

[0038] Select several seed points with the highest scores to participate in the global 3D seismic layer automatic tracking;

[0039] Eliminate several seed points with low ranking.

[0040] Beneficial effects of the present invention:

[0041] 1. The present invention quantitatively evaluates multiple seed points for global 3D seismic horizon automatic tracking by using equal-interval thinning and specific scoring criteria, selects some seed points with the highest ranking to construct the initial value of the 3D horizon, and significantly improves the efficiency of global 3D seismic horizon automatic tracking;

[0042] 2. The present invention takes into account the problem of seismic phase deviation between some manually picked seed points and the target horizon. By performing equal-interval thinning and scoring sorting, some seed points with lower rankings are eliminated, thereby reducing the impact of insufficient seismic phase consistency of artificial seed points on the automatic tracking of the global 3D seismic horizon, and improving the automatic tracking accuracy of the target seismic horizon.

[0043] 3. The algorithm of the present invention is simple and easy to implement, has high operating efficiency, and is easy to form a software function module for promotion and application.

[0044] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 A flow chart of a global three-dimensional seismic horizon tracking method under multiple seed point constraints of the present invention is shown;

[0047] Figure 2 shows a planar distribution view of seed points;

[0048] Figure 3 shows the three-dimensional spatial distribution view of seed points;

[0049] Figure 4 The planar distribution view of the sparse seed points is shown;

[0050] Figure 5 The three-dimensional spatial distribution view of the sparse seed points is shown;

[0051] Figure 6 A schematic diagram of the scoring graph of all seed points after thinning is shown;

[0052] Figure 7 A schematic diagram of the top 200 scoring results selected by scoring sorting is shown;

[0053] Figure 8 The spatial distribution of some seed points with high scores is shown;

[0054] Figure 9 The figure shows the 3D seismic horizon map obtained by automatically tracking the global 3D seismic horizon using the seed point;

[0055] Figure 10 The inline direction comparison diagram of the layer tracking calculation results of the selected seed points (solid line) and all the seed points after thinning (dashed line) is shown;

[0056] Figure 11The crossline direction comparison diagram of the layer tracking calculation results of the selected seed points (solid line) and all the seed points after thinning (dashed line) is shown;

[0057] Figure 12 A bar chart comparing accuracy and efficiency is shown;

[0058] Figure 13 A sector chart comparing accuracy and efficiency is shown. DETAILED DESCRIPTION

[0059] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0060] A global 3D seismic horizon tracking method under multiple seed point constraints, such as Figure 1 As shown, the following steps are included:

[0061] Acquire post-stack seismic data;

[0062] Based on post-stack seismic data, combined with geophysical data and geological knowledge of the work area, 3D seismic horizon seed points are manually picked;

[0063] The seed points are thinned out at equal intervals and scored based on the scoring criteria, and then sorted from large to small according to the scoring results;

[0064] Select several top-ranked 3D layer seed points to construct the global 3D layer initial value;

[0065] Global 3D layer automatic tracking is carried out based on the initial 3D layer value and several top-ranked 3D layer seed points.

[0066] Further, if Figure 2 As shown, the post-stack seismic data is recorded as s(t, x, y), where t represents the two-way travel time of the seismic wave, and [x, y] represents the plane coordinates of the corresponding seismic trace.

[0067] It should be noted that the post-stack seismic data collected after a series of seismic data processing steps, including but not limited to: static correction, denoising, amplitude compensation, velocity analysis, dynamic correction, migration, etc., are equivalent to the data collected under self-excitation and self-collection conditions.

[0068] Further, picking up the three-dimensional seismic horizon seed point also includes the following steps:

[0069] Determine the target seismic horizon;

[0070] Pick the seed point of the target seismic horizon.

[0071] It should be noted that if Figure 3 As shown in the figure, for a 3D seismic data volume s(t,x,y), the target seismic horizon for automatic global 3D seismic horizon picking is determined by combining the geophysical data, core data, well logging and geological knowledge of the work area. Then, on this basis, the seed points of the target seismic horizon are picked manually, which are recorded as seed(t1,x1,y1), seed(t2,x2,y2),…,seed(t N ,x N ,y N ), where (t1,x1,y1),(t2,x2,y2),...,(t N ,x N ,y N ) represent the two-way travel time and plane coordinates corresponding to these N seed points in the 3D seismic data volume.

[0072] Further, if Figure 4 As shown, scoring and sorting the seed points include the following steps:

[0073] Select a prime number greater than the maximum value of the three-dimensional layer seed point row and column to achieve equal spacing thinning of the seed points;

[0074] On the basis of equal-interval thinning, the vertical and horizontal distance ratios between each seed point and any other seed point are calculated and summed up and sorted.

[0075] It should be noted that if Figure 5 As shown, for the above seed points, first select a prime number greater than the maximum number of seed point rows and columns to carry out equal-interval thinning, ensuring that the seed points of each row and column are thinned out at equal intervals, thereby avoiding excessive thinning in a certain row or column, and ensuring that the seed points after thinning are evenly distributed in the two-dimensional plane. Let the seed point after thinning be seed(t i ,x i ,y i )(i=1,2,...,N s ), where N s Much smaller than N.

[0076] like Figure 6 As shown, for any seed point seed(t i ,x i ,y i )(i=1,2,...,N s ), calculate its difference with any other seed point seed(t j,x j ,y j )(j≠i,j=1,2,...,N s ) of the longitudinal and transverse distances. The transverse distance D horizon It can be expressed as:

[0077]

[0078] Among them, X grid ,Y grid are the spacing of seismic traces in the line and trace direction respectively, and sqrt(.) means taking the square root.

[0079] Correspondingly, the vertical distance D between it and various sub-points vertical It can be expressed as:

[0080]

[0081] Where sp is the seismic sampling time interval; vp represents the P-wave velocity of the formation. From the above formula, we can see that the vertical distance between seed points is half the product of the two-way travel time difference between them and the P-wave velocity of the formation.

[0082] Based on the above, calculate the ratio of the vertical and horizontal distances between the seed points:

[0083]

[0084] 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, which is as follows:

[0085]

[0086] Among them, r (i,j) is the element of Ratio at position (i,j), and r (i,j) =r (j,i) .

[0087] For any manually picked seed point seed(t i ,x i ,y i )(i=1,2,...,N s ), find all the aspect ratios r related to it (i,j) The sum sum(t i ,x i ,y i )(i=1,2,...,N s ):

[0088]

[0089] From the above formula, we can see that sum(t i ,x i ,y i ) is the sum of the elements in the i-th row or i-th column of the symmetric matrix Ratio.

[0090] When a seed point is located in a structurally complex location, such as a fault, it typically has a larger aspect ratio than other seed points. Conversely, in structurally simple areas, seed points are often located on continuously horizontal seismic axes, and the corresponding aspect ratio approaches zero. Therefore, manually picked seed points can be quantitatively ranked based on their aspect ratios: a larger sum(t,x,y) indicates a higher score.

[0091] It should be noted that if Figure 7 As shown, Figure 7 Yes Figure 6 After thinning, the 3D seismic horizon seed points are scored, using the sum of the vertical and horizontal distance ratios of each seed point to all other seed points as the criterion. The higher the comprehensive score, the higher the ranking. This score is then normalized, with the score range set to [0, 100].

[0092] like Figure 8 As shown, Figure 8 corresponds to Figure 7 The spatial distribution of some seed points with high scores.

[0093] like Figure 9 As shown, it is based on Figure 8 The 3D seismic horizon is obtained by automatically tracking the global 3D seismic horizon based on the seed points in .

[0094] Further, if Figure 10 As shown, the spline interpolation algorithm is used to construct the initial value of the three-dimensional layer through the three-dimensional layer seed points.

[0095] It should be noted that if Figure 11 As shown in the figure, interpolation is performed on some of the top seed points identified above to obtain initial values ​​for global 3D seismic horizon tracking. The interpolation algorithm used is spline interpolation. When sufficient seed points are available, this type of algorithm can achieve an interpolation effect that is relatively close to the actual horizon, thereby reducing the number of subsequent horizon optimization iterations to improve the accuracy and efficiency of global 3D seismic horizon 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, quantitative evaluation and ranking of seed points are carried out, and on this basis, initial values ​​are constructed. This allows global 3D seismic horizon tracking to significantly improve computational efficiency while ensuring accuracy.

[0096] Furthermore, carrying out the global three-dimensional layer automatic tracking also includes the following steps:

[0097] Select several seed points with the highest scores to participate in the global 3D seismic layer automatic tracking;

[0098] Eliminate several seed points with low ranking.

[0099] It should be noted that after the initial 3D horizon values ​​are constructed, most of the seed points with the highest scores are selected for global 3D seismic horizon automatic tracking. During this process, some seed points with lower scores are eliminated. First, the vast majority of seed points with lower scores are located in relatively simple structural locations. Eliminating some seed points in these locations has little impact on the results of global 3D seismic horizon automatic tracking and can improve horizon tracking efficiency. Second, the seismic phases of manually picked seed points are not strictly guaranteed to be consistent, but the tracking results of the horizon automatic tracking algorithm in structurally simple areas generally have good seismic phase consistency. Therefore, eliminating some seed points with lower scores can, in most cases, reduce the negative impact of seed point seismic phase inconsistency, thereby comprehensively improving the accuracy and efficiency of global 3D seismic horizon automatic tracking.

[0100] like Figure 12 As shown, (a, c) are the normalized results of the amplitude variance values ​​of the layers (dashed lines) of all seed points tracked after normalization in the Inlien and Crossline directions, and (b, d) are the corresponding Figure 6 The amplitude variance value corresponding to the middle layer (solid line). It can be seen from this that: in areas with relatively simple structures, the global automatic tracking algorithm can obtain layer tracking results that are basically consistent with those in the case of abundant seed points even when there are fewer seed points; in addition, due to the existence of phase errors, too many manually picked seed points will affect the accuracy of the final results. In areas with simple structures, the global 3D seismic layer automatic tracking algorithm can obtain better phase consistency results by optimizing some seed points. In terms of computational efficiency, such as Figure 13 As shown in the figure, after the seed point optimization, the overall efficiency of layer tracking calculation is improved by about 35%.

[0101] A global three-dimensional seismic layer tracking system under multiple seed point constraints includes an acquisition unit, a picking unit, a sorting unit, a calculation unit and a tracking unit;

[0102] An acquisition unit for acquiring post-stack seismic data;

[0103] Picking unit: manually picking 3D seismic layer seed points based on post-stack seismic data, combined with geophysical data and geological knowledge of the work area;

[0104] The sorting unit is used to thin out the seed points at equal intervals and score them based on the scoring criteria, and then sort them from large to small according to the scoring results;

[0105] A calculation unit is used to select a number of top-ranked 3D layer seed points to construct a global 3D layer initial value;

[0106] The tracking unit performs global 3D layer automatic tracking based on the initial value of the 3D layer and several top-ranked 3D layer seed points.

[0107] It should be noted that, in response to the problems existing in the traditional global three-dimensional seismic layer automatic tracking method, the purpose of the present invention is to provide a new global three-dimensional seismic layer tracking method and device under the constraints of multiple seed points. The present invention is proposed on the basis of studying the following problems: (1) The global three-dimensional seismic layer tracking method needs to use manually picked seed points to construct the three-dimensional layer initial value; in this step, if the number of seed points is too small, the accuracy of the calculated layer initial value is low, affecting the convergence speed and convergence accuracy of the subsequent global three-dimensional layer automatic tracking algorithm; but when the number of seed points is too large, it not only seriously challenges the computing hardware device memory, but also greatly increases the layer initial value calculation time, greatly reducing the overall efficiency of the global three-dimensional seismic layer automatic tracking; (2) Among the manually picked seed points, some seed points have no effect on the three-dimensional layer initial value. The construction of initial values ​​is very critical, but the contribution of a small number of seed points can be basically ignored; therefore, some key seed points can be selected through quantitative evaluation of seed points to construct initial values ​​of layers, so as to improve the efficiency of global 3D seismic layer automatic tracking; (3) Some seed points picked manually may have a certain seismic phase deviation from the target layer. Such seed points will affect the accuracy of the overall results in global 3D seismic layer automatic tracking. Therefore, by scoring seed points, some seed points with high ranking are selected to carry out layer tracking and some seed points with low ranking are discarded, thereby greatly improving the accuracy of seismic interpretation.

[0108] The present invention starts from the global three-dimensional seismic layer automatic tracking and proposes a new global three-dimensional seismic layer tracking method and device under the constraints of multiple seed points: when the number of seed points is sufficient, seed points are thinned out and each seed point is scored and sorted to improve the accuracy and efficiency of the global three-dimensional seismic layer automatic tracking result. The present invention first selects a prime number greater than the maximum value of the three-dimensional layer seed point row and column to ensure that the seed points are thinned out at equal intervals, avoid excessive thinning for a certain row or column, and ensure that the thinned seed points are evenly distributed in the two-dimensional plane; then, on this basis, the vertical and horizontal distance ratios between each seed point and any other seed point are calculated and summed and sorted, and finally the seed points involved in constructing the initial value of the three-dimensional seismic layer are determined by the sorting results and some seed points with lower rankings are eliminated in the process of global three-dimensional seismic layer automatic tracking to improve the efficiency and accuracy of global layer automatic tracking. Actual test results show that the newly introduced seed point optimization method constructs the initial value of 3D seismic horizons by thinning and scoring at equal intervals and selecting seed points with higher rankings, which greatly improves the efficiency of global 3D seismic horizon automatic tracking. In addition, by eliminating some seed points with lower rankings through sorting results, the accuracy of global 3D horizon automatic tracking results is also improved while improving the computational efficiency, which has important guiding significance for high-precision seismic interpretation.

[0109] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A global three-dimensional seismic horizon tracking method under multiple seed point constraints, characterized in that: The following steps are involved: Acquire post-stack seismic data; Based on post-stack seismic data, combined with geophysical data and geological knowledge of the work area, 3D seismic horizon seed points are manually picked; The seed points are thinned out at equal intervals and scored based on the scoring criteria, and then sorted from large to small according to the scoring results; Select several top-ranked 3D layer seed points to construct the global 3D layer initial value; Global 3D layer automatic tracking is carried out based on the initial 3D layer value and several top-ranked 3D layer seed points.

2. The global three-dimensional seismic horizon tracking method under multiple seed point constraints according to claim 1 is characterized in that: The post-stack seismic data is recorded as s(t, x, y), where t represents the two-way travel time of the seismic wave, and [x, y] represents the plane coordinates of the corresponding seismic trace.

3. The global three-dimensional seismic horizon tracking method under multiple seed point constraints according to claim 1 is characterized in that: Picking up the three-dimensional seismic horizon seed point also includes the following steps: Determine the target seismic horizon; Pick the seed point of the target seismic horizon.

4. The global three-dimensional seismic horizon tracking method under multiple seed point constraints according to claim 1, characterized in that: Scoring and sorting the seed points includes the following steps: Select a prime number greater than the maximum value of the three-dimensional layer seed point row and column to achieve equal spacing thinning of the seed points; On the basis of equal-interval thinning, the vertical and horizontal distance ratios between each seed point and any other seed point are calculated and summed up and sorted.

5. The global three-dimensional seismic horizon tracking method under multiple seed point constraints according to claim 1, characterized in that: The spline difference algorithm is used to construct the initial value of the three-dimensional layer through the three-dimensional layer seed points.

6. A global three-dimensional seismic horizon tracking method under multiple seed point constraints according to any one of claims 1 to 5, characterized in that: Carrying out the global three-dimensional layer automatic tracking also includes the following steps: Select several seed points with the highest scores to participate in the global 3D seismic layer automatic tracking; Eliminate several seed points with low ranking.

7. A global three-dimensional seismic horizon tracking system under multiple seed point constraints, characterized by: It includes a collection unit, a picking unit, a sorting unit, a calculation unit and a tracking unit; wherein, An acquisition unit for acquiring post-stack seismic data; Picking unit: manually picking 3D seismic layer seed points based on post-stack seismic data, combined with geophysical data and geological knowledge of the work area; The sorting unit is used to thin out the seed points at equal intervals and score them based on the scoring criteria, and then sort them from large to small according to the scoring results; A calculation unit is used to select a number of top-ranked 3D layer seed points to construct a global 3D layer initial value; The tracking unit performs global 3D layer automatic tracking based on the initial value of the 3D layer and several top-ranked 3D layer seed points.

8. The global three-dimensional seismic horizon tracking system under multiple seed point constraints according to claim 7, characterized in that: The post-stack seismic data of the acquisition unit is recorded as s(t, x, y), where t represents the two-way travel time of the seismic wave, and [x, y] represents the plane coordinates of the corresponding seismic trace.

9. The global three-dimensional seismic horizon tracking system under multiple seed point constraints according to claim 7, characterized in that: The picking unit picks up the three-dimensional seismic horizon seed point, further comprising the following steps: Determine the target seismic horizon; Pick the seed point of the target seismic horizon.

10. The global three-dimensional seismic horizon tracking system under multiple seed point constraints according to claim 7, characterized in that: The sorting unit scores and sorts the seed points, including the following steps: Select a prime number greater than the maximum value of the three-dimensional layer seed point row and column to achieve equal spacing thinning of the seed points; On the basis of equal-interval thinning, the vertical and horizontal distance ratios between each seed point and any other seed point are calculated and summed up and sorted.

11. The global three-dimensional seismic horizon tracking system under multiple seed point constraints according to claim 7, characterized in that: The calculation unit constructs the initial value of the three-dimensional layer through the three-dimensional layer seed point using the spline difference algorithm.

12. The global three-dimensional seismic horizon tracking system under multiple seed point constraints according to claim 7, characterized in that: The tracking unit performs global three-dimensional layer automatic tracking, further comprising the following steps: Select several seed points with the highest scores to participate in the global 3D seismic layer automatic tracking; Eliminate several seed points with low ranking.

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