A speed spectrum label making method, device, equipment and medium

Abstract

This invention belongs to the field of seismic exploration technology, specifically disclosing a method for creating velocity spectrum tags, including the following steps: S1, acquiring the seismic stacked velocity spectrum; S2, picking velocity tag data; S3, performing velocity prediction through an intelligent velocity spectrum interpretation system, locating tag anomalies, and correcting them to obtain corrected velocity tag data; S4, calculating the energy extrema of the corrected velocity tag data, and optimizing the corrected velocity tag data based on the energy extrema curve and set velocity fine-tuning parameters; S5, further optimizing the optimized corrected velocity tag data based on the energy extrema curve and set minimum time parameters for adding points to obtain the final optimized corrected velocity tag data. This invention can improve the quantity and quality of velocity tag data, providing quality assurance for intelligent velocity spectrum picking. This invention is applicable to velocity spectrum interpretation.

Description

Technical Field

[0001] This invention belongs to the field of seismic exploration technology, specifically a method, apparatus, equipment, and medium for producing velocity spectrum tags. Background Technology

[0002] Seismic velocity spectrum picking is a crucial step in the seismic data processing workflow, and the results directly impact the quality of seismic imaging. In typical velocity spectrum interpretation, technicians need to repeatedly compare and verify multiple reference data points, completing numerous human-computer interactions. This process is not only time-consuming and labor-intensive but also requires extensive regional processing experience, making it one of the most time-consuming manual steps in the seismic data processing workflow. With the surge in acquired data volume and the increasing demands for velocity density from clients, the cost of manual high-density interpretation is prohibitively high. Therefore, there is an urgent need to automate the velocity picking process to ensure picking quality while reducing time and labor costs, shortening the seismic data processing cycle, and ultimately improving productivity.

[0003] In recent years, with the advancement of artificial intelligence technology, it has also attracted widespread attention from seismic exploration researchers and fierce competition from oil and gas service companies. The quality of intelligent velocity spectrum acquisition is influenced by both the subjective factors of the "intelligent velocity spectrum interpretation system" and the objective factors such as the quantity and quality of velocity tags. In particular, the quality of velocity tags significantly affects the accuracy of the "intelligent velocity spectrum interpretation system's" judgment of the velocity spectrum of the target layer. A certain number of velocity tags is the foundation for model training of the "intelligent velocity spectrum interpretation system," so rapidly and accurately increasing the number of tags is imperative.

[0004] In actual production, due to the influence of velocity spectrum data and limitations in display accuracy, some tags may be missing vertically or have picking deviations, leading to velocity spectrum picking errors. Using incorrect velocities prevents accurate imaging of the reflected waves from the target layer, causing geological artifacts and ultimately resulting in exploration failure. Typically, severe shallow interference and weak energy in deep seismic reflection signals, along with a low signal-to-noise ratio, lead to missing velocity spectrum energy in both shallow and deep layers. If velocity spectrum energy is missing in the area, the velocity tag data picked up by processing personnel will be incomplete. Therefore, a velocity tag production technology that can correctly guide the "intelligent velocity spectrum interpretation system" has attracted attention. This technology can improve the quality of velocity tags while also enhancing the quality of intelligent velocity spectrum picking. Summary of the Invention

[0005] The purpose of this invention is to provide a method, apparatus, device, and medium for manufacturing velocity spectrum tags, so as to improve the quantity and quality of velocity tags.

[0006] To achieve the above objectives, the present invention employs the following technical methods:

[0007] A method for creating velocity spectrum tags includes the following steps:

[0008] S1. Obtain the earthquake stacking velocity spectrum;

[0009] S2. Based on the distribution of energy clusters, the morphology of large sets, and the characteristics of small stacking segments of the seismic stacking velocity spectrum, velocity label data is extracted.

[0010] S3. Predict velocity using an intelligent velocity spectrum interpretation system, locate abnormal points in the labels, and correct them to obtain corrected velocity label data;

[0011] S4. Calculate the energy extreme value of the corrected speed tag data, and optimize the corrected speed tag data based on the energy extreme value curve and the set speed fine-tuning parameters;

[0012] S5. Calculate the energy extreme value of the corrected velocity label data. Based on the energy extreme value curve and the set minimum time parameter for the increase point, further optimize the optimized corrected velocity label data to obtain the final optimized corrected velocity label data.

[0013] As a limitation, step S1 specifically refers to:

[0014] Using regional reference velocities and pre-stack CMP seismic data gathers acquired in the field, the seismic stacking velocity spectrum P(T) was obtained by scanning according to the pre-set minimum and maximum percentage values ​​of the regional reference velocity. i V j ), i=1,2,…,N,j=1,2,…,M, where P represents the superposition velocity spectrum, T represents the two-way travel time of the reflected wave at zero shot-receiver distance, V represents the superposition velocity of the reflected wave at zero shot-receiver distance, i is the index of the sampling point along the time direction, j is the index of the sampling point along the velocity direction, N is the total number of sampling points along the time direction, and M is the total number of sampling points along the velocity direction.

[0015] As a further clarification: the velocity label data in step S2 is P. i (T) i V i ), i=1,2,…,N.

[0016] As a further clarification: Step S3 specifically includes:

[0017] S31. Input the velocity label data into the intelligent velocity spectrum interpretation system to start velocity prediction and obtain the predicted velocity data;

[0018] S32. Locate abnormal points on the tag using the maximum speed error map; the formula for calculating the maximum speed error is... ,in, It is the predicted velocity data in step S31. It is the speed at the point in the speed label data that corresponds to the predicted speed data;

[0019] S33. Manually correct label anomalies to obtain corrected speed label data.

[0020] To further specify: Step S4 is as follows:

[0021] S41. Apply a sliding time window to each time sample point of the corrected velocity label data, calculate the energy value within each window, and select the maximum energy value as the energy extreme value. The energy extreme value point is (T). k V k );

[0022] S42. Set multiple sets of speed fine-tuning parameters, each set including a time parameter T. k Minimum value limit parameter L k and maximum value limit parameter U k That is (T) k ,L k U k Multiple sets of speed fine-tuning parameters are based on the time parameter T. k The numerical values ​​are linearly interpolated into the corrected velocity label data in ascending order of light to dark;

[0023] S43. Calculate the energy extreme point (T) at the same time position as the pick point P(T,V) in the predicted velocity data. k V k Error σ; the formula for calculating error σ is: ;

[0024] S44. Determine whether the error σ satisfies the condition. If the condition is met, then the pick point P(T,V) will be moved to the energy extremum point (T). k V k The position of the pickup point P(T,V) is determined to obtain the optimized corrected velocity label data. If the condition is not met, the pickup point P(T,V) will not be moved.

[0025] As a final limitation, step S5 specifically refers to:

[0026] S51. Apply a sliding time window to each time sample point of the corrected velocity label data, calculate the energy value within each window, and select the maximum energy value as the energy extreme value. The energy extreme value point is (T). k V k );

[0027] S52. Set multiple sets of minimum time parameters for adding points, each set of minimum time parameters for adding points including the time parameter T. k and minimum time increment Inc k That is (T)k ,Inc k Multiple sets of additional points with minimum time parameters according to time parameter T k The numerical values ​​are linearly interpolated from smallest to largest, with intervals from lightest to darkest, into the optimized corrected velocity label data;

[0028] S53. Calculate the time difference T2-T1 between the pick points P1(T1,V1) and P2(T2,V2) in the predicted velocity data;

[0029] S54. Determine whether the time difference T2-T1 satisfies the condition. If the condition is met, then one point far from the energy extreme point is discarded from P1(T1,V1) and P2(T2,V2); if not met, both P1(T1,V1) and P2(T2,V2) are retained. Furthermore, if P2 is the first picking point, then based on retaining the original predicted velocity data curve trend, according to Inc... k Add shallow points one by one; if point P1 is the last picking point, then while preserving the original predicted velocity data curve trend, according to Inc k By adding deeper points one by one, the final optimized corrected speed label data is obtained.

[0030] This invention also discloses a velocity spectrum tag manufacturing device, comprising:

[0031] Seismic stacking velocity spectrum acquisition module, used to acquire seismic stacking velocity spectrum;

[0032] The velocity label data picking module picks velocity label data based on the distribution of energy clusters, the morphology of large sets, and the characteristics of small stacking segments in the seismic stack velocity spectrum.

[0033] The velocity label data correction module uses an intelligent velocity spectrum interpretation system to predict velocity, locate abnormal points in the labels, and correct them to obtain corrected velocity label data.

[0034] The speed tag data optimization module calculates the energy extreme value of the corrected speed tag data and optimizes the corrected speed tag data based on the energy extreme value curve and the set speed fine-tuning parameters.

[0035] The final optimization module for corrected velocity label data calculates the energy extreme value of the corrected velocity label data. Based on the energy extreme value curve and the set minimum time parameter for the increase point, it further optimizes the corrected velocity label data to obtain the final optimized corrected velocity label data.

[0036] The present invention also discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to implement the above-described method.

[0037] The present invention also discloses a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0038] The beneficial effects achieved by this invention, due to the adoption of the above-described solution, compared with the prior art, are as follows:

[0039] (1) The present invention provides a method for making velocity spectrum labels, which performs maximum velocity error checks on velocity label data, quickly locates data points with large errors, corrects velocity label data, and optimizes it. If there are data points that deviate from the energy extreme point, the velocity level is finely adjusted. For shallow and deep layers with missing energy, the vertical adjustment of velocity labels is performed based on the velocity trend and the inference of effective energy clusters. This method quickly improves the quantity and quality of velocity label data, provides quality assurance for intelligent velocity spectrum picking, and thus improves imaging quality.

[0040] (2) The present invention also provides corresponding implementation devices, electronic devices and readable storage media, which further make the method more practical, and the devices, electronic devices and readable storage media have corresponding advantages.

[0041] This invention is applicable to velocity spectrum interpretation. Attached Figure Description

[0042] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0043] Figure 1 The seismic stacking velocity spectrum P(T) of Embodiment 1 of the present invention i V j ) Schematic diagram;

[0044] Figure 2 The speed tag data P in Embodiment 1 of the present invention i (T) i V i ) Schematic diagram;

[0045] Figure 3 This is a schematic diagram of the location of abnormal points on the tag in Embodiment 1 of the present invention;

[0046] Figure 4 This is a schematic diagram of the corrected speed tag data in Embodiment 1 of the present invention;

[0047] Figure 5 This is a schematic diagram showing the position of the pick point P(T,V) in the velocity spectrum in the predicted velocity data of Embodiment 1 of the present invention;

[0048] Figure 6 This is a schematic diagram of the optimized corrected speed label data according to Embodiment 1 of the present invention;

[0049] Figure 7 This is a schematic diagram of the final optimized corrected speed label data in Embodiment 1 of the present invention;

[0050] Figure 8 This is a schematic diagram of a velocity spectrum tag manufacturing device according to Embodiment 2 of the present invention;

[0051] Figure 9 This is a schematic diagram of the structure of an electronic device according to Embodiment 2 of the present invention. Detailed Implementation

[0052] The present invention will be further described below with reference to the embodiments. However, those skilled in the art should understand that the present invention is not limited to the following embodiments. Any improvements and equivalent changes made based on the specific embodiments of the present invention are within the scope of protection of the claims of the present invention.

[0053] Example 1: A method for manufacturing velocity spectrum tags

[0054] A method for creating velocity spectrum tags includes the following steps:

[0055] S1. Using the regional reference velocity and pre-stack CMP seismic data gathers acquired in the field, the seismic stacking velocity spectrum P(T) is obtained by scanning according to the pre-set minimum and maximum percentage values ​​of the regional reference velocity. i V j ), i=1, 2, ..., N, j=1, 2, ..., M, where P represents the stacking velocity spectrum, T represents the two-way travel time of the reflected wave at zero shot-receiver distance, V represents the stacking velocity of the reflected wave at zero shot-receiver distance, i is the index of the sampling point along the time direction, j is the index of the sampling point along the velocity direction, N is the total number of sampling points along the time direction, and M is the total number of sampling points along the velocity direction. Seismic stacking velocity spectrum P(T i V j (See diagram) Figure 1 ;

[0056] S2. Based on the distribution of energy clusters in the seismic stacked velocity spectrum, the morphology of large aggregations, and the characteristics of small stacking segments, velocity label data P is extracted. i (T) i V i ), i=1, 2,…,N, see Figure 2 ;

[0057] S3. Velocity prediction is performed using an intelligent velocity spectrum interpretation system to locate and correct anomalies in the labels, resulting in corrected velocity label data; specifically:

[0058] S31. Input the velocity label data into the intelligent velocity spectrum interpretation system to start velocity prediction and obtain the predicted velocity data;

[0059] S32. Using the maximum speed error map, locate the tag anomaly points. Tag anomaly points are data points with large maximum speed error values ​​and a small number of data points. See Figure 3 In this embodiment, the minimum maximum speed error is 32, and the maximum maximum speed error is 420; maximum speed error The calculation formula is ,in, It is the predicted velocity data in step S31. It is the speed at the point in the speed label data that corresponds to the predicted speed data;

[0060] S33. Manually correct label anomalies to obtain corrected speed label data. See Figure 4 .

[0061] S4. Calculate the energy extreme value of the corrected speed tag data, and optimize the corrected speed tag data based on the energy extreme value curve and the set speed fine-tuning parameters; specifically:

[0062] S41. Apply a sliding time window to each time sample point of the corrected velocity label data, calculate the energy value within each window, and select the maximum energy value as the energy extreme value. The energy extreme value point is (T). k V k ), k=1, 2, ..., N;

[0063] S42. Set four sets of speed fine-tuning parameters, each set including a time parameter T. k Minimum value limit parameter L k and maximum value limit parameter U k That is (T) k ,L k U k The four sets of speed fine-tuning parameters are based on the time parameter T. k The numerical values ​​are linearly interpolated into the corrected velocity label data in ascending order from light to dark; in this embodiment, the four sets of velocity fine-tuning parameters are (200, -2, 2), (800, -2, 2), (2000, -1.5, 1.5) and (5000, -1, 1).

[0064] S43. Calculate the energy extreme point (T) at the same time position as the pick point P(T,V) in the predicted velocity data. k V k Error σ; the position of the pick point P(T,V) in the velocity spectrum in the predicted velocity data, as shown in the figure. Figure 5 As shown, the formula for calculating the error σ is: ;

[0065] S44. Determine whether the error σ satisfies the condition. If the condition is met, then the pick point P(T,V) will be moved to the energy extremum point (T). k V k The optimized corrected velocity label data is obtained by taking the position of ( ), see Figure 6 If the conditions are not met, the pick point P(T,V) will not be moved.

[0066] S5. Calculate the energy extreme value of the corrected velocity label data. Based on the energy extreme value curve and the set minimum time parameter for the increment point, further optimize the optimized corrected velocity label data to obtain the final optimized corrected velocity label data; specifically:

[0067] S51. Apply a sliding time window to each time sample point of the corrected velocity label data, calculate the energy value within each window, and select the maximum energy value as the energy extreme value. The energy extreme value point is (T). k V k );

[0068] S52. Set four sets of minimum time parameters for adding points, each set of minimum time parameters for adding points includes a time parameter T. k and minimum time increment Inc k That is (T) k ,Inc k The minimum time parameter for the four groups of added points is based on the time parameter T. k The values ​​are linearly interpolated from smallest to largest, from shallowest to deepest, into the optimized corrected velocity label data; in this embodiment, the minimum time parameters for the four sets of added points are (200, 70), (800, 100), (2000, 200), and (5000, 500).

[0069] S53. Calculate the time difference T2-T1 between the pick points P1(T1,V1) and P2(T2,V2) in the predicted velocity data;

[0070] S54. Determine whether the time difference T2-T1 satisfies the condition. If the condition is met, then one point far from the energy extreme point is discarded from P1(T1,V1) and P2(T2,V2); if not met, both P1(T1,V1) and P2(T2,V2) are retained. Furthermore, if P2 is the first picking point, then based on retaining the original predicted velocity data curve trend, according to Inc... k Add shallow points one by one, Inc k Decided to increase the density of data points, Inc k The smaller the value, the more data points are added. kThe larger the value, the fewer data points are added; if point P1 is the last picking point, then while preserving the original trend of the predicted velocity data curve, according to Inc... k By adding points to deeper layers one by one, the final optimized corrected velocity label data is obtained. In this embodiment, there is energy deficiency in the shallow layers but not in the deeper layers. The final optimized corrected velocity label data can be found in [link to documentation]. Figure 7 .

[0071] Depend on Figure 3 and Figure 4 As can be seen, this embodiment can quickly and with high quality correct label data, by Figures 5-7 As can be seen, the velocity label data generated in this embodiment can not only fine-tune unreasonable velocity spectra, but also thin out overly dense areas and encrypt missing areas, effectively improving the quantity and quality of velocity label data and providing a guarantee for subsequent intelligent prediction.

[0072] Example 2: A velocity spectrum tag manufacturing device, equipment, and medium

[0073] A velocity spectrum tag manufacturing device, the structural schematic diagram of which is shown below. Figure 8 As shown, it includes:

[0074] Seismic stacking velocity spectrum acquisition module, used to acquire seismic stacking velocity spectrum;

[0075] The velocity label data picking module picks velocity label data based on the distribution of energy clusters, the morphology of large sets, and the characteristics of small stacking segments in the seismic stack velocity spectrum.

[0076] The velocity label data correction module uses an intelligent velocity spectrum interpretation system to predict velocity, locate abnormal points in the labels, and correct them to obtain corrected velocity label data.

[0077] The speed tag data optimization module calculates the energy extreme value of the corrected speed tag data and optimizes the corrected speed tag data based on the energy extreme value curve and the set speed fine-tuning parameters.

[0078] The final optimization module for corrected velocity label data calculates the energy extreme value of the corrected velocity label data. Based on the energy extreme value curve and the set minimum time parameter for the increase point, it further optimizes the corrected velocity label data to obtain the final optimized corrected velocity label data.

[0079] This embodiment also provides an electronic device, the structure of which is as follows: Figure 9 As shown, it includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, it implements the method described in Embodiment 1.

[0080] This embodiment also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method described in Embodiment 1.

Claims

1. A method of making a velocity spectrum tag, the method comprising: The method comprises the following steps: S1, acquiring a seismic stack velocity spectrum; S2, picking up velocity label data based on the distribution of energy groups of the seismic stack velocity spectrum, the shape of a major event set, and the characteristics of a small stack section; S3, performing velocity prediction through an intelligent velocity spectrum interpretation system, locating label abnormal points, and performing correction to obtain corrected velocity label data; S4, calculating the energy extreme value of the corrected velocity label data, and optimizing the corrected velocity label data according to an energy extreme value curve and a set velocity fine-tuning parameter; Step S4 is specifically: S41, apply a sliding time window at each time point of the corrected speed label data, calculate an energy value in each window, select a maximum energy value as an energy extreme value, and the energy extreme point is ; S42, set multiple groups of speed fine tuning parameters, each group of speed fine tuning parameters includes time parameter , minimum value limit parameter , and maximum value limit parameter , that is , multiple groups of speed fine tuning parameters are linearly interpolated from shallow to deep interval line according to time parameter value from small to large order S43, calculating pickup points in the predicted speed data extremum point of energy at the same time as it error error The calculation formula is: ; S44, Judgment Error Does it meet the requirements? If the condition is met, the pick point will be selected. Move to the energy extreme point The location is used to obtain optimized corrected velocity label data; if the conditions are not met, the pickup point is not moved. ; S5, calculating the energy extreme value of the corrected velocity label data, and further optimizing the optimized corrected velocity label data according to an energy extreme value curve and a set increase point minimum time parameter to obtain finally optimized corrected velocity label data; Step S5 is specifically: S51, apply a sliding time window at each time point of the corrected speed label data, calculate an energy value in each window, select a maximum energy value as an energy extreme value, and the energy extreme point is (x, y) ); S52, set multiple groups of increase point minimum time parameters, each group of increase point minimum time parameters includes a time parameter and a minimum time increment that is , the multiple groups of increase point minimum time parameters are linearly interpolated from shallow to deep in the order of time parameter values from small to large into the optimized corrected speed tag data; S53, calculating pick-up points in the predicted speed data time difference ; S54. Determining the time difference Does it meet the requirements? If the condition is met, then from , Discard a point that is far from the energy extremum; if this condition is not met, then... All are retained, and if If the point is the first picking point, then while preserving the original trend of the predicted speed data curve, based on... Add shallow points one by one; if If the point is the last picking point, then while preserving the original trend of the predicted speed data curve, based on... By adding deeper points one by one, the final optimized corrected speed label data is obtained.

2. The speed spectrum label making method according to claim 1, wherein, The step S1 is specifically: using the regional reference velocity and the prestack CMP seismic data gathers collected in the field, scanning to obtain the seismic stacking velocity spectrum according to the preset minimum value of the percentage of the regional reference velocity and the maximum value of the percentage of the regional reference velocity Wherein, P represents the stacking velocity spectrum, T represents the two-way travel time of the reflected wave at zero offset, V represents the stacking velocity of the reflected wave at zero offset, i is the serial number of the sampling point along the time direction, j is the serial number of the sampling point along the velocity direction, N is the total sampling point number along the time direction, and M is the total sampling point number along the velocity direction.

3. A speed spectrum label making method according to claim 2, wherein The speed tag data in step S2 is .

4. The method of claim 3, wherein Step S3 is specifically: S31, inputting the velocity label data into the intelligent velocity spectrum interpretation system to start velocity prediction and obtain predicted velocity data; S32, locating the label abnormal points through the maximum absolute speed error map; the maximum speed error calculation formula is wherein, is the predicted speed data in step S31, is the speed of the corresponding point of the predicted speed data in the speed label data; S33, manually correcting the label abnormal points to obtain corrected velocity label data.

5. A velocity spectrum label making apparatus characterized by comprising: The method comprises: a seismic stack velocity spectrum acquisition module that acquires a seismic stack velocity spectrum; a velocity label data picking module that picks up velocity label data based on the distribution of energy groups of the seismic stack velocity spectrum, the shape of a major event set, and the characteristics of a small stack section; a velocity label data correction module that performs velocity prediction through an intelligent velocity spectrum interpretation system, locates label abnormal points, and performs correction to obtain corrected velocity label data; A sliding time window is applied at each time sample of the modified speed label data, an energy value is calculated in each window, a maximum energy value is selected as an energy extreme value, and the energy extreme point is ; a corrected velocity label data optimization module that calculates the energy extreme value of the corrected velocity label data and optimizes the corrected velocity label data according to an energy extreme value curve and a set velocity fine-tuning parameter; A plurality of sets of speed fine-tuning parameters are set, each set of speed fine-tuning parameters including a time parameter , a minimum limit parameter , and a maximum limit parameter , that is , the plurality of sets of speed fine-tuning parameters are linearly interpolated from shallow to deep in the order of time parameter values from small to large into the corrected speed label data; Picking points in computed predicted velocity data Energy extrema at the same time as the time ) error ; error The formula for calculating the error is: ; Judgment error Whether the condition is met If the condition is met, the pickup point is moved to a position where the energy extreme point is ; if not, the pickup point is not moved ; and the optimized correction speed label data is obtained . a corrected velocity label data final optimization module that calculates the energy extreme value of the corrected velocity label data and further optimizes the optimized corrected velocity label data according to an energy extreme value curve and a set increase point minimum time parameter to obtain finally optimized corrected velocity label data; A sliding time window is applied at each time sample of the modified speed label data, an energy value is calculated in each window, a maximum energy value is selected as an energy extreme value, and an energy extreme point is (0, 0); ); A plurality of sets of increase point minimum time parameters are set, each set of increase point minimum time parameters including a time parameter and a minimum time increment i.e. The plurality of sets of increase point minimum time parameters are linearly interpolated from shallow to deep in the order of time parameter values from small to large into the optimized corrected speed tag data; Picking up points in computed predicted velocity data Time difference ; Judging the time difference Does it meet the requirements? If the condition is met, then from , Discard a point that is far from the energy extremum; if this condition is not met, then... All are retained, and if If the point is the first picking point, then while preserving the original trend of the predicted speed data curve, based on... Add shallow points one by one; if If the point is the last picking point, then while preserving the original trend of the predicted speed data curve, based on... By adding deeper points one by one, the final optimized corrected speed label data is obtained.

6. An electronic device, comprising: The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the method in any one of claims 1-4.

7. A computer readable storage medium characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the method in any one of claims 1-4.

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