Stratum over-peeling point identification method based on breakpoint rejection

Abstract

This invention provides a method for identifying stratigraphic over-stripping points based on discontinuity removal. The method includes: Step 1, inputting seismic data and unconformity interpretation results, and segmenting the seismic data; Step 2, calculating the inter-channel correlation coefficient for the segmented seismic data volume and assigning it as the correlation coefficient value for the current channel; Step 3, using the isolated forest algorithm to find abrupt changes for each channel's correlation coefficient value; Step 4, removing discontinuities and retaining over-stripping points based on the density of abrupt changes. This method for identifying stratigraphic over-stripping points more accurately overcomes the difficulty of relying heavily on stratigraphic interpretation results when identifying abrupt changes using phase attributes. Compared with traditional methods, this method is more applicable when identifying stratigraphic over-stripping points in fault-developed areas.

Description

Technical Field

[0001] This invention relates to the field of exploration geophysics, and in particular to a method for identifying stratigraphic over-stripping points based on breakpoint removal. Background Technology

[0002] Formation oil reservoirs are characterized by low oil-bearing height and strip-like distribution in plan. The over-stripping line boundary directly controls the size of the oil-bearing area, so the identification of over-stripping points is crucial for formation oil reservoirs.

[0003] Phase attribute and waveform classification techniques are commonly used methods for identifying stratigraphic over-stripping points. These techniques rely on changes in seismic waveform morphology to determine the location of over-stripping points. By opening time windows upwards and downwards along the unconformity surface and extracting phase attributes or performing waveform classification, they can effectively reveal the planar distribution pattern of stratigraphic over-stripping lines, making them an effective method for identification. However, in fault-developed areas, the over-stripping points identified by phase attribute and waveform classification techniques are often mixed with many faults, posing significant challenges to the identification of stratigraphic over-stripping points.

[0004] The patented technology, "A Method for Identifying Stratigraphic Over-stripping Lines Based on Iterative Seismic DNA Detection," introduces the seismic DNA algorithm for the first time into the identification of stratigraphic over-stripping points. Differences in seismic reflection structures exist on either side of the over-stripping point, corresponding to mutations in different strings (seismic DNA). Over-stripping points are identified by searching for these mutation points. However, mutations in seismic reflection structures are not only caused by over-stripping points; faults and lithological pinch-out points can also cause mutations. Therefore, the stratigraphic over-stripping points identified by this method still contain many faults or lithological pinch-out points.

[0005] How to accurately identify stratigraphic over-stripping points by eliminating the influence of faults from seismic data is an urgent problem to be solved.

[0006] Chinese patent application CN201410638655.1 discloses a method for identifying stratigraphic overstripping lines based on iterative seismic DNA detection. This method includes: Step 1, inputting a 3D seismic data volume and unconformity stratigraphic data; Step 2, acquiring target seismic data; Step 3, converting the target seismic data into character data; Step 4, setting search parameters; Step 5, identifying and recording wavegroup characteristic changes that satisfy the search parameters as overstripping points; and Step 6, outputting overstripping lines to obtain the distribution of stratigraphic overstripping lines on a plane. This method, based on iterative seismic DNA detection, can achieve accurate and efficient detection results, reducing the workload of geological researchers and improving the quality and efficiency of exploration deployment.

[0007] Chinese patent application CN201610727136.1 discloses a bio-inspired computational method for identifying stripping lines in stratigraphic traps and oil and gas reservoirs. This method includes: Step 1, inputting a 3D seismic data volume; Step 2, employing an edge detection technique to enhance the spatial continuity of the seismic data, targeting the approximate range of the stripping lines to be identified; Step 3, setting search parameters; Step 4, calculating parameters that strongly satisfy stratigraphic continuity sensitivity to form a bio-inspired computational continuity attribute data volume; and Step 5, tracing and interpreting the bio-inspired computational continuity attribute data volume with stripping characteristics to derive the distribution of the stripping lines on a plane. This bio-inspired computational method for identifying stripping lines in stratigraphic traps and oil and gas reservoirs can achieve efficient detection results and accurately determine the location of stripping lines, reducing the ambiguity of stripping line identification and improving the quality and efficiency of exploration deployment.

[0008] Chinese patent application CN201710704161.2 discloses a method for identifying and extracting stratigraphic overstripping lines using interlayer integration. This method includes: Step 1, performing Booleanization of seismic data based on instantaneous phase; Step 2, identifying overstripping lines using interlayer integration after Booleanization; and Step 3, extracting overstripping lines using a finite difference method after identification. This method utilizes seismic data, performs global normalization of instantaneous phase to achieve Booleanization, identifies overstripping lines through interlayer integration based on the Booleanized seismic data, and extracts stratigraphic overstripping lines using a finite difference method after identification.

[0009] The existing technologies described above are significantly different from the present invention and have failed to solve the technical problem we want to address. Therefore, we have invented a new method for identifying stratigraphic over-stripping points based on breakpoint removal. Summary of the Invention

[0010] The purpose of this invention is to provide a more accurate method for identifying stratigraphic over-stripping points based on breakpoint removal.

[0011] The objective of this invention can be achieved through the following technical measures: a method for identifying stratigraphic over-stripping points based on breakpoint removal, which includes:

[0012] Step 1: Input seismic data and unconformity interpretation results, and perform segmentation processing on the seismic data;

[0013] Step 2: Calculate the inter-channel correlation coefficient for the cut seismic data volume and assign it as the correlation coefficient value of the current channel;

[0014] Step 3: Use the Isolation Forest algorithm to find abrupt change points for the correlation coefficient value of each path;

[0015] Step 4: Based on the density of mutation points, remove breakpoints and retain over-peeling points.

[0016] The objective of this invention can also be achieved through the following technical measures:

[0017] In step 1, depending on the type of unconformity, whether it is an overlying stratum or an eroded stratum, a time window is opened upwards or downwards along the unconformity surface. Within this time window, the 3D seismic data is segmented.

[0018] In step 2, for the cut 3D seismic data, along the main survey line direction, the correlation coefficients of the left and right adjacent traces of each trace are calculated and assigned as the correlation coefficient value of the current trace. At the same time, the location information of the trace, including the horizontal coordinate, vertical coordinate and time, is recorded.

[0019] In step 2, where there are abrupt change points such as overstripping points and breakpoints, the seismic reflection structures on both sides of the abrupt change point are quite different, and the correlation between the two seismic data on both sides of the abrupt change point is low; where there are no abrupt change points, the correlation between the two seismic data is good; therefore, the location of the abrupt change point can be determined by the magnitude of the correlation, with a good correlation indicating a normal point and a poor correlation indicating an abrupt change point.

[0020] In step 2, assuming the seismic data on both sides of the current trace are denoted as x and y respectively, and the number of elements is n, the formula for calculating the correlation r is as follows:

[0021]

[0022] In step 3, a small portion of the main survey lines are selected, and the correlation coefficient value is used as an attribute. A value is randomly selected between 0 and 1. This value can distinguish between abrupt changes and continuous points in the earthquake reflection structure, thus completing the training process of the isolated forest algorithm.

[0023] In step 3, within the three-dimensional survey area, for each main survey line, the trained isolated forest model is used to predict the mutation points.

[0024] In step 4, based on the density of mutation points, breakpoints with higher density are removed, and other points are retained as over-peeling points.

[0025] In step 4, using the dataset of mutation points found as input data, along the main survey line, the density of these mutation points is determined based on coordinate and time information. More densely packed points are considered breakpoints, while more isolated points are considered over-peeling points.

[0026] In step 4, assume Q is the set of points where the main survey line direction changes abruptly, and X(x1,x2,t) xY(y1, y2, t) is a mutation point in the point set Q. y Let ) be any other abrupt change point in the point set Q, and let a be the interval distance between tracks along the main survey line; determine the abrupt change point X(x1,x2,t). x The formulas for dense points (breakpoints) and isolated points (overstripping points) are as follows:

[0027]

[0028] The mutation point Y(y1,y2,t) in the above formula y It is necessary to traverse the point set Q, excluding X(x1, x2, t). x All points other than ), where Class=1 indicates that the point is a dense point (i.e., a breakpoint), and Class=0 indicates that the point is an isolated point (i.e., a superstripped point).

[0029] The stratigraphic over-stripping point identification method based on breakpoint removal in this invention starts from seismic data, opens a time window along the unconformity surface, and uses the isolated forest algorithm to identify abrupt changes in seismic reflection structures. This overcomes the difficulty of phase attribute identification of abrupt changes being highly dependent on stratigraphic interpretation results. This method identifies stratigraphic abrupt changes more accurately and in line with the surface. Furthermore, by using abrupt change density analysis technology to remove abrupt changes caused by breakpoints, it identifies stratigraphic over-stripping points more accurately. Compared with traditional methods, this method has greater applicability when identifying stratigraphic over-stripping points in fault-developed areas. Attached Figure Description

[0030] Figure 1 This is a flowchart of a specific embodiment of the formation over-stripping point identification method based on breakpoint removal of the present invention;

[0031] Figure 2 This is a comparison image before and after earthquake cutting in a specific embodiment of the present invention;

[0032] Figure 3 This is a schematic diagram of the isolated forest principle in a specific embodiment of the present invention;

[0033] Figure 4 This is a schematic diagram illustrating the differences in seismic reflection structures at the over-stripping point and the breakpoint in a specific embodiment of the present invention;

[0034] Figure 5 This is a comparative analysis diagram of the results of identifying over-peeling points before and after removing breakpoints in a specific embodiment of the present invention. Detailed Implementation

[0035] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0036] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the present invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, and / or combinations thereof.

[0037] The method for identifying stratigraphic over-stripping points based on breakpoint removal of the present invention includes the following steps:

[0038] Step 1: Input seismic data and unconformity interpretation results, and perform segmentation processing on the seismic data;

[0039] Step 2: Calculate the inter-channel correlation coefficient for the cut seismic data volume and assign it as the correlation coefficient value of the current channel;

[0040] Step 3: Use the Isolation Forest algorithm to find abrupt change points for the correlation coefficient value of each path;

[0041] Step 4: Based on the density of mutation points, remove breakpoints and retain over-peeling points.

[0042] The following are several specific embodiments of the application of the present invention.

[0043] Example 1

[0044] In a specific embodiment 1 of the present invention, the method for identifying stratigraphic over-stripping points based on breakpoint removal includes the following steps:

[0045] In step 1, input the three-dimensional seismic data of the work area, as well as the interpretation results of the unconformity surface (layer data).

[0046] Depending on the type of unconformity—whether it is an overlying stratum or an eroded stratum—a time window is opened upwards or downwards along the unconformity surface. Within this time window, the 3D seismic data is segmented.

[0047] In step 2, for the cut 3D seismic data, along the main survey line, the correlation coefficients of the left and right adjacent traces of each trace are calculated and assigned as the correlation coefficient value of the current trace. At the same time, the location information of the trace (including the horizontal coordinate, vertical coordinate, and time) is recorded.

[0048] In step 3, a small portion of the main survey lines are selected, and the correlation coefficient value is used as an attribute. A value is randomly selected between 0 and 1 (this value can distinguish between abrupt changes and continuous points in the earthquake reflection structure) to complete the training process of the isolated forest algorithm.

[0049] Within the three-dimensional work area, for each main survey line, a trained isolated forest model is used to predict mutation points.

[0050] In step 4, based on the density of mutation points, breakpoints with higher density are removed, while other points are retained as over-peeling points.

[0051] Example 2

[0052] In a specific embodiment 2 of the present invention, such as Figure 1 As shown, Figure 1 This is a flowchart of the formation over-stripping point identification method based on breakpoint removal according to the present invention. The formation over-stripping point identification method based on breakpoint removal includes the following steps:

[0053] Step 101: Input the 3D seismic data of the work area and the interpretation results of the unconformity surface (layer data).

[0054] Step 102: Based on the type of unconformity, whether it is an overlying stratum or an eroded stratum, open a time window upwards or downwards along the unconformity surface. Within this time window, perform segmentation processing on the 3D seismic data.

[0055] Step 2: Calculate the correlation between adjacent traces. For the cut 3D seismic data, along the main survey line, calculate the correlation coefficient between the left and right adjacent traces of each trace and assign it as the correlation coefficient value of the current trace. At the same time, record the location information of the trace (including x-coordinate, y-coordinate, and time).

[0056] In areas with abrupt changes such as overstripping points and faults, the seismic reflection structures on both sides of the abrupt change point differ significantly, and the correlation between the two seismic data streams on either side of the abrupt change point is low. In areas without abrupt changes, the correlation between the two seismic data streams is good. Therefore, the location of abrupt changes can be determined by the magnitude of the correlation; a good correlation indicates a normal point, while a poor correlation indicates an abrupt change point.

[0057] Assuming the seismic data on both sides of the current trace are denoted as X and Y respectively, and both have n elements, the correlation calculation formula is as follows:

[0058]

[0059] Step 301: Select a small portion of the main survey lines, use the correlation coefficient value as the attribute value, and randomly select a value between 0 and 1 (this value can distinguish between abrupt changes and continuous points in the earthquake reflection structure) to complete the training process of the isolated forest algorithm.

[0060] An isolation forest is a random binary tree where each node either has two children or none (a leaf node). For finding mutations based on correlation, each node in an isolation forest either has two children (a normal node and an anomalous node) or is a leaf node (an anomalous node). The random binary tree structure of an isolation forest makes it easy to categorize anomalous and normal nodes into two classes.

[0061] Step 302: Within the three-dimensional work area, for each main survey line, use the trained isolated forest model to predict mutation points.

[0062] Once iTree is built, it can be used to predict real-world data. The prediction process involves traversing the test records through iTree and observing which leaf node each record lands on. iTree can effectively detect outliers (mutations) because outliers are generally very rare and are quickly assigned to leaf nodes in iTree. Therefore, the path length from a leaf node to the root node can be used to determine whether a point is an outlier.

[0063] From steps 301 and 302, the distribution location of the mutation points can be obtained. The mutation points are recorded using three columns of data: (horizontal axis, vertical axis, time).

[0064] Step 4: Based on the density of mutation points, remove the breakpoints with higher density and retain the other points as overpeeling points.

[0065] Using the identified mutation point dataset as input, along the main survey line, the density of these mutation points is determined based on coordinate and time information. More densely packed points are identified as breakpoints, while more isolated points are identified as over-peeling points. A sliding time window is designed; sliding left or right reveals that mutation points consistently exist around breakpoints, indicating a denser distribution of breakpoints. Over-peeling points, on the other hand, do not exhibit a second mutation point around them, indicating a more isolated distribution of over-peeling points.

[0066] Suppose Q is the set of points where the main survey line direction changes abruptly, and X(x1,x2,t) x Y(y1, y2, t) is a mutation point in the point set Q. y Let ) be any other abrupt change point in point set Q, and let a be the distance between tracks along the main survey line. Determine the abrupt change point X(x1,x2,t). x The formulas for dense points (breakpoints) and isolated points (overstripping points) are as follows:

[0067]

[0068] The mutation point Y(y1,y2,t) in the above formula y It is necessary to traverse the point set Q, excluding X(x1, x2, t). x All points other than ), where Class=1 indicates that the point is a dense point (breakpoint), and Class=0 indicates that the point is an isolated point (overstripping point).

[0069] Based on actual application in the work area, this method significantly reduces interference from discontinuities when locating stratigraphic over-exfoliation points. The patented technology effectively eliminates the influence of discontinuities, thus making the identification results of the erosion points under the fourth sand layer in this work area clearer and more definite.

[0070] Example 3

[0071] In a specific embodiment 3 of the present invention, the method for identifying over-stripping points in formation based on breakpoint removal includes the following steps:

[0072] Step 101: Input the 3D seismic data of the work area and the interpretation results of the unconformity surface (layer data).

[0073] Step 102: Based on the unconformity type (whether it's an overburden stratum or an erosional stratum), open a time window upwards or downwards along the unconformity surface. Within this time window, segment the 3D seismic data, such as... Figure 2 As shown. Figure 2 These are before-and-after comparison images of the earthquake cutting process; Figure 2 (a) is a cross-sectional view before the earthquake cutting; Figure 2 (b) is a cross-sectional view after earthquake cutting. The stratum is an eroded stratum, so a certain time window is opened down along the unconformity surface, and the earthquake data is cut within the time window.

[0074] Step 2: Calculate the correlation between adjacent traces. For the cut 3D seismic data, along the main survey line, calculate the correlation coefficient between the left and right adjacent traces of each trace and assign it as the correlation coefficient value of the current trace. At the same time, record the location information of the trace (including x-coordinate, y-coordinate, and time).

[0075] In areas with abrupt changes such as overstripping points and faults, the seismic reflection structures on both sides of the abrupt change point differ significantly, and the correlation between the two seismic data streams on either side of the abrupt change point is low. In areas without abrupt changes, the correlation between the two seismic data streams is good. Therefore, the location of abrupt changes can be determined by the magnitude of the correlation; a good correlation indicates a normal point, while a poor correlation indicates an abrupt change point.

[0076] Assuming the seismic data on both sides of the current trace are denoted as X and Y respectively, and both have n elements, the correlation calculation formula is as follows:

[0077]

[0078] Step 301: Select a small portion of the main survey lines, use the correlation coefficient value as the attribute value, and randomly select a value between 0 and 1 (this value can distinguish between abrupt changes and continuous points in the earthquake reflection structure) to complete the training process of the isolated forest algorithm.

[0079] An isolation forest is a random binary tree where each node either has two children or none (a leaf node). For finding mutation points based on correlation, each node in an isolation forest either has two children (a normal node and an anomalous node) or is a leaf node (an anomalous node). The principle is as follows: Figure 3 As shown, Figure 3 This is a schematic diagram of the Isolation Forest principle. The attribute values ​​of gray outliers and black normal points differ significantly. Through the random binary tree of the Isolation Forest, outliers and normal points can be easily divided into two categories.

[0080] The training process for a single tree is as follows:

[0081] Input: The correlation coefficient value (vector X) of a certain main survey line, the current tree height (e), the upper limit of the tree height (l), and any selected value p∈(0,1):

[0082] Output: iTree

[0083] 1: Choose any one of the correlation coefficient values ​​for the main survey line, i.e., choose any q∈X;

[0084] 2. If q < p, then the point containing that number is an outlier;

[0085] 3: If q≥p, then the point containing that number is a normal point;

[0086] 4. If the current tree height exceeds the height limit, i.e., e≥l, then exit the training process;

[0087] 5: Traverse all values ​​in vector X and find all outliers.

[0088] Suppose a tree considers q to be an outlier. This result may not be accurate because p is any value between (0,1), and there are many random factors involved in the relationship between q and p. However, if 100 trees are randomly split in this way, and 80 of them consider q to be an outlier, then this result is more reliable.

[0089] Step 302: Within the three-dimensional work area, for each main survey line, use the trained isolated forest model to predict mutation points.

[0090] Once iTree is built, it can be used to predict real-world data. The prediction process involves traversing the test records through iTree and observing which leaf node each record lands on. iTree can effectively detect outliers (mutations) because outliers are generally very rare and are quickly assigned to leaf nodes in iTree. Therefore, the path length from a leaf node to the root node can be used to determine whether a point is an outlier.

[0091] From steps 301 and 302, the distribution location of the mutation points can be obtained. The mutation points are recorded using three columns of data: (horizontal axis, vertical axis, time).

[0092] Step 4: Based on the density of mutation points, remove the breakpoints with higher density and retain the other points as overpeeling points.

[0093] Using the identified mutation point dataset as input, along the main survey line, based on coordinate and time information, the density of these mutation points is determined. More densely packed points are identified as breakpoints, while more isolated points are identified as over-peeling points. For example... Figure 4 As shown, Figure 4 A schematic diagram illustrating the differences in seismic reflection structures at overstripping points and fault points; Figure 4 (a) is a schematic diagram of the seismic reflection structure at the overstripping point. Figure 4 (b) is a schematic diagram of the seismic reflection structure at the fault point. As can be seen from the diagram, different seismic reflection structures correspond to the two sides of the over-stripping point or fault point, and the over-stripping point or fault point is abruptly changed. When the sliding window is designed, abruptly changed points always exist around the fault point when sliding left or right, and the distribution of fault points is relatively dense. No second abruptly changed point appears around the over-stripping point, and the distribution of over-stripping points is relatively isolated.

[0094] Suppose Q is the set of points where the main survey line direction changes abruptly, and X(x1,x2,t) x Y(y1, y2, t) is a mutation point in the point set Q. y Let ) be any other abrupt change point in point set Q, and let a be the distance between tracks along the main survey line. Determine the abrupt change point X(x1,x2,t). x The formulas for dense points (breakpoints) and isolated points (overstripping points) are as follows:

[0095]

[0096] The mutation point Y(y1,y2,t) in the above formula y It is necessary to traverse the point set Q, excluding X(x1, x2, t). x All points other than ), where Class=1 indicates that the point is a dense point (breakpoint), and Class=0 indicates that the point is an isolated point (overstripping point).

[0097] From practical application in work areas, this method significantly reduces interference from breakpoints when locating stratigraphic over-stripping points, such as... Figure 5 As shown. Figure 5 A comparative analysis chart showing the results of identifying over-peeling points before and after removing breakpoints; Figure 5 (a) shows the results of identifying the over-stripping point in a certain work area after 40ms of driving down from t7 (traditional method). Figure 5 (b) shows the identification results of over-peeling points in a certain work area, extending 40ms down from t7 (patented technology). The comparative analysis of the over-peeling point identification results in the two figures shows that the patented technology effectively eliminates the influence of faults in the fault development area indicated by the square, thus making the identification results of the erosion points under the fourth sandbar in this work area clearer and more explicit.

[0098] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0099] Except for the technical features described in the specification, all other technologies are known to those skilled in the art.

Claims

1. A method for identifying a super-unraveling point of a formation based on a breakpoint rejection, characterized in that, The method for identifying stratigraphic over-stripping points based on breakpoint removal includes: Step 1: Input seismic data and unconformity interpretation results, and perform segmentation processing on the seismic data; Step 2: Calculate the correlation coefficient between adjacent traces for the cut seismic data volume and assign it as the correlation coefficient value of the current trace. This includes: For the cut 3D seismic data, along the main survey line, calculate the correlation coefficient between the left and right adjacent traces of each trace and assign it as the correlation coefficient value of the current trace. At the same time, record the location information of the trace, including the x-coordinate, y-coordinate, and time. In places where there are abrupt change points such as overstripping points and breakpoints, the seismic reflection structures on both sides of the abrupt change point are significantly different, and the correlation between the two seismic data on both sides of the abrupt change point is low. In places where there are no abrupt change points, the correlation between the two seismic data is good. Therefore, the location of abrupt change points can be determined by the correlation magnitude. A good correlation indicates a normal point, and a poor correlation indicates an abrupt change point. Step 3: Use the Isolation Forest algorithm to find abrupt change points for the correlation coefficient value of each path; Step 4: Based on the density of mutation points, remove breakpoints and retain over-peeling points.

2. The breakpoint rejection based formation supertrip point identification method of claim 1, wherein, In step 1, depending on the type of unconformity, whether it is an overlying stratum or an eroded stratum, a time window is opened upwards or downwards along the unconformity surface. Within this time window, the 3D seismic data is segmented.

3. The breakpoint rejection based formation hyperbolic strip identification method of claim 1, wherein, In step 2, assuming the seismic data on both sides of the current trace are denoted as x and y respectively, and the number of elements is n, the formula for calculating the correlation r is as follows: 。 4. The breakpoint rejection based formation hyperbolic strip identification method of claim 1, wherein, In step 3, a small portion of the main survey lines are selected, and the correlation coefficient value is used as an attribute. A value is randomly selected between 0 and 1. This value can distinguish between abrupt changes and continuous points in the earthquake reflection structure, thus completing the training process of the isolated forest algorithm.

5. The breakpoint rejection based formation hyperbolic strip identification method of claim 4, wherein, In step 3, within the three-dimensional survey area, for each main survey line, the trained isolated forest model is used to predict the mutation points.

6. The breakpoint rejection based formation hyperbolic strip identification method of claim 1, wherein, In step 4, based on the density of mutation points, breakpoints with higher density are removed, and other points are retained as over-peeling points.

7. The breakpoint rejection based formation hyperbolic strip identification method of claim 6, wherein, In step 4, using the dataset of mutation points found as input data, along the main survey line, the density of these mutation points is determined based on coordinate and time information. More densely packed points are considered breakpoints, while more isolated points are considered over-peeling points.

8. The breakpoint rejection based formation supertrip point identification method of claim 7, wherein, In step 4, assume that Q is the set of points where the main survey line direction changes abruptly. It is a mutation point in the point set Q. It is any other abrupt change point in the point set Q, where 'a' is the distance between tracks along the main survey line; Determining abrupt change points The formulas for dense points (breakpoints) and isolated points (overstripping points) are as follows: ； The mutation point in the above formula It is necessary to traverse the point set Q, excluding All points other than, This indicates that the point belongs to a dense point, i.e., a breakpoint. This indicates that the point is an isolated point, or an overstripping point.

Images