Noise Removal Methods and Devices

Abstract

This invention discloses a noise removal method and apparatus. The method includes: acquiring seismic data from a preset work area, the seismic data containing linear noise data; obtaining the time-distance curve of the linear noise based on the linear noise data; sliding a time window along the time direction based on the time-distance curve of the linear noise to obtain a median sequence within a preset apparent velocity range; using adaptive stacking technology to obtain the predicted linear noise from the median sequence; and using adaptive filtering technology to filter the predicted linear noise based on the seismic data to obtain data with linear noise removed. This invention does not utilize two-dimensional filtering technology designed for velocity differences, thus minimizing the risk of significant signal distortion. It eliminates interference without filtering out effective components, thereby improving the fidelity of seismic data.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration technology, and in particular to a noise removal method and apparatus. Background Technology

[0002] Improving the signal-to-noise ratio (SNR) of seismic data is a crucial and fundamental step in seismic data processing. Different denoising methods have varying degrees of specificity and adaptability; no single method is effective against all types of interference. In exploration areas with highly complex surface conditions, such as mountains, loess plateaus, and vast deserts, there are dramatic topographical changes and complex and varied surface geological structures. The acquired seismic data often contains multiple sets of strong linear interference waves, such as high-energy surface waves and shallow, multiple-refracted waves. The presence of these interference waves severely impacts the SNR of the seismic data, negatively affecting subsequent data processing. Therefore, to ensure the effectiveness of seismic data processing, effective methods must be employed to suppress and attenuate these interference waves during the data processing phase.

[0003] The causes and types of noise are complex and diverse. Seismic noise can be either coherent or incoherent. Coherent noise includes surface waves, reflected waves from near-surface structures, and refracted waves. Near-surface structures such as underground rivers and high-velocity thin layers produce refracted waves and multiples. Coherent noise is characterized by a certain dominant frequency and a certain apparent velocity. Its difference from effective waves lies in the difference between the maximum true velocity of regular interference waves and the apparent velocity range of effective waves. Two-dimensional velocity filters can be designed to eliminate this noise by utilizing the velocity difference between effective and interference waves. However, traditional two-dimensional filtering techniques based on velocity differences easily cause significant signal distortion, filtering out some effective components while eliminating interference, thus reducing data fidelity. Summary of the Invention

[0004] This invention provides a noise removal method that does not utilize two-dimensional filtering techniques designed for velocity differences, thus minimizing the risk of significant signal distortion. While eliminating interference, it does not filter out effective components, thereby improving the fidelity of seismic data. The method includes:

[0005] Collect seismic data from a pre-defined work area; the seismic data includes linear noise data.

[0006] Based on the linear noise data, obtain the time interval curve of the linear noise;

[0007] Based on the time-distance curve of the linear noise, slide the time window along the time direction to obtain the median sequence within a preset apparent velocity range;

[0008] The predicted linear noise is obtained from the median sequence using an adaptive superposition technique;

[0009] Based on the earthquake data, adaptive filtering technology is used to filter the predicted linear noise to obtain the data after removing the linear noise.

[0010] This invention also provides a noise removal device that does not utilize two-dimensional filtering technology designed for velocity differences, thus minimizing the risk of significant signal distortion. It eliminates interference without filtering out effective components, thereby improving the fidelity of seismic data. The device includes:

[0011] The data acquisition module is used to acquire seismic data of a preset work area, and the seismic data includes linear noise data.

[0012] The time-distance curve acquisition module is used to acquire the time-distance curve of the linear noise based on the linear noise data.

[0013] The median sequence acquisition module is used to slide a time window along the time direction according to the time interval curve of the linear noise to obtain the median sequence within a preset apparent velocity range;

[0014] A linear noise prediction module is used to obtain the predicted linear noise from the median sequence using an adaptive superposition technique.

[0015] The linear noise removal module is used to filter the predicted linear noise using adaptive filtering technology based on the seismic data, and obtain the data after linear noise removal.

[0016] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method.

[0017] This invention also provides a computer-readable storage medium storing a computer program for performing the above-described methods.

[0018] In this embodiment of the invention, seismic data from a preset work area is collected, and the time-distance curve of the linear noise is obtained based on the linear noise data. Based on the time-distance curve of the linear noise, a time window is slid along the time direction to obtain the median sequence within a preset apparent velocity range. The predicted linear noise is obtained from the median sequence using adaptive superposition technology. Then, based on the seismic data, the predicted linear noise is filtered using adaptive filtering technology to obtain the data after linear noise removal. This completes the noise removal process. The entire process does not utilize two-dimensional filtering technology designed for velocity differences, which is less likely to cause significant signal distortion. While eliminating interference, it does not filter out effective components, thus improving the fidelity of seismic data. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0020] Figure 1 This is a flowchart of the noise removal method in an embodiment of the present invention;

[0021] Figure 2 This is a schematic diagram of the noise removal device in an embodiment of the present invention;

[0022] Figure 3 This is a single-shot data profile of linear noise predicted before linear noise removal in a complex mountainous area in western China, as shown in an embodiment of the present invention.

[0023] Figure 4 This is a single-shot data profile of linear noise predicted after linear noise removal in a complex mountainous area in western China, as shown in an embodiment of the present invention.

[0024] Figure 5 This is a profile of single-shot data of predicted linear noise in a complex mountainous area in western China, as described in an embodiment of the present invention.

[0025] Figure 6 This is a cross-sectional view of a complex mountainous area in western China before linear noise removal, as described in this embodiment of the invention.

[0026] Figure 7 This is a cross-sectional view of a complex mountainous area in western China after linear noise removal, as described in an embodiment of the present invention.

[0027] Figure 8 This is a cross-sectional view of a complex mountainous area in western China before noise removal in the common detector point domain, as described in this embodiment of the invention.

[0028] Figure 9 This is a cross-sectional view of a complex mountainous area in western China after noise removal in the common detector point domain, as described in an embodiment of the present invention.

[0029] Figure 10 This is a seismic overlay profile of a complex mountainous area in western China before linear noise removal, as described in this embodiment of the invention.

[0030] Figure 11 This is a seismic overlay profile after linear noise removal in a complex mountainous area in western China, as shown in an embodiment of the present invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0032] Figure 1 A flowchart of a noise removal method provided in an embodiment of the present invention is shown below. Figure 1 As shown, the method includes:

[0033] Step 101: Collect seismic data for the preset work area. The seismic data includes linear noise data.

[0034] Step 102: Obtain the time-distance curve of the linear noise based on the linear noise data.

[0035] In this embodiment, the time-distance curve of the linear noise is as follows:

[0036]

[0037] Among them, t ln For the travel time of linear noise, v ln denoted as linear noise velocity, x as offset distance, and T0 as a constant term.

[0038] The time-distance curve is a straight line, and its slope is the reciprocal of the linear disturbance speed.

[0039] Step 103: Based on the time-distance curve of the linear noise, slide the time window along the time direction to obtain the median sequence within the preset apparent velocity range.

[0040] Step 104: Use adaptive superposition technology to obtain the predicted linear noise in the median sequence.

[0041] In this embodiment, the adaptive overlay technique can be multiple correlation, and the median algorithm for this multiple correlation is a hybrid algorithm. Specifically, the median method is first used to extract seismic data, and then the multiple correlation method is applied to the seismic data extracted by the median algorithm. The median algorithm is a method used in statistics; it combines the median with weights to give small weights to amplitude outliers that do not match the median seismic amplitude, thus reducing the impact of these outliers on the seismic record and resulting in a result that closely matches the center of the seismic record source dataset.

[0042] Specifically, a certain set of input seismic record points is set. Given a point x in the corresponding space, move this point to the location that is closest to a point in the point set Q. Let this point be x. * Then we have the equation:

[0043]

[0044] Equation (4) above shows that the median earthquake amplitude has a relatively small weight on outliers, which can prevent the negative impact of amplitude outliers on them and make the results closer to the main body of the earthquake data.

[0045] Multiple correlation is an adaptive weighted stacking technique. Its algorithm is based on the estimation and application of two weighting functions: the similarity between seismic traces and the energy of the stacked traces. The weighting function is calculated by sliding a time window along the time direction. A median sequence is obtained within a certain apparent velocity range. Then, multiple correlation is used to find the optimal median in the median sequence as the predicted coherent signal. Through weighted stacking, all dissimilar seismic traces and traces with energy anomalies are weakened. Adaptive stacking automatically assigns different weights to traces based on their similarity; traces with better similarity have higher weights, and traces with poorer similarity have lower weights.

[0046] In practice, the similarity coefficient between two adjacent records is calculated using the following formula (5):

[0047]

[0048] Calculate the average of the similarity coefficients of all pairwise adjacent channels within the range of channel L using the following formula (6):

[0049]

[0050] Where L is the given number of spatial channels, and L is an odd number; the average similarity coefficient R k As the weight of the central channel within the range of channel L. When R k When ≤0, R k =r m Among them, r m Defined as the minimum weight; when R0 = MAX{R k}<r n At that time, R k =1, r n Defined as the threshold value for the weighting coefficients. The superposition at this point is equivalent to equal-weight superposition.

[0051] To make the tracks with good similarity stand out more during the superposition process, R... k Perform index weighting.

[0052] Based on the above analysis, the superimposed seismic traces will reflect noise characteristics, which can be used to extract linear noise. Specifically, multiple seismic traces x(i) are horizontally superimposed, and the predicted linear noise is then obtained.

[0053]

[0054] Step 105: Based on the earthquake data, use adaptive filtering technology to filter the predicted linear noise and obtain the data after removing the linear noise.

[0055] In this embodiment, if the noise prediction is fairly accurate, simple subtraction can eliminate the noise. However, since the mismatch between the noise prediction and the actual noise may change with time and location, simple subtraction is usually not effective in eliminating noise, so a nonlinear adaptive subtraction method is preferable.

[0056] The noise removal method provided in this invention collects seismic data from a preset work area, obtains the time-distance curve of the linear noise based on the linear noise data, slides a time window along the time direction based on the time-distance curve of the linear noise to obtain the median sequence within a preset apparent velocity range, uses adaptive superposition technology to obtain the predicted linear noise in the median sequence, and then uses adaptive filtering technology to filter the predicted linear noise based on the seismic data to obtain the data after linear noise removal. The entire process does not utilize two-dimensional filtering technology designed for velocity differences, which is less likely to cause significant signal distortion. While eliminating interference, it does not filter out effective components, thus improving the fidelity of seismic data.

[0057] To improve the accuracy of the seismic data, after obtaining the time-distance curve of the linear noise based on the linear noise data, the method further includes:

[0058] The seismic data is linearly time-difference corrected based on the time-distance curve of the linear noise to obtain the travel time of the corrected linear noise.

[0059] Specifically, the travel time of the corrected linear noise is:

[0060] t ln_new =t ln -Δt=T0 (2)

[0061] Where, Δt=x / v ln , t ln For the travel time of linear noise, v ln denoted as linear noise velocity, x as offset distance, and T0 as a constant term.

[0062] According to the above formula, the travel time of the corrected linear noise is a constant, meaning the linear noise is corrected to be horizontally in phase and has no time or phase difference. Horizontal superposition of the corrected seismic traces will amplify the amplitude of the linear noise.

[0063] When time difference correction is applied to seismic records based on the velocity of linear interference, the travel time of the effective signal is:

[0064]

[0065] Among them, v ln Let x be the linear interference velocity, h be the offset distance, and h be the depth of the reflection layer. Clearly, equation (3) is not a constant; therefore, the effective hyperbolic signal cannot be corrected to a horizontally in-phase axis, and time and phase differences exist. In this case, horizontal stacking of seismic records will not only fail to strengthen the effective signal but will also weaken it.

[0066] The principle of adaptive weighted superposition based on the sliding window along the time direction of the time-distance curve of linear interference is similar to the horizontal superposition of linear time difference correction. The effective signal is in a random state, that is, the effective signal will not be affected and will be filtered out.

[0067] Specifically, the data after linear noise removal is as follows:

[0068]

[0069] Where k is time, j is the filter coefficient, w is the matched filter coefficient, and L is the filter length.

[0070] At each time sample K, the current filter coefficients are adjusted using the learned signal δ:

[0071] w(k+1,j)=w(k,j)+δ(k,j)

[0072] If the matched filter coefficients are correct, then the noise estimated after filtering will match the actual noise, and thus it can be eliminated.

[0073] The invention will now be illustrated with test results of actual seismic data from a complex mountainous region in western China:

[0074] To test the accuracy and stability of the invention, and also to test its universality, particularly its effectiveness in using actual field data, actual seismic data from a complex mountainous region in western China—a region considered the most typical and complex in the geophysical community—were selected for testing.

[0075] First, the numerical simulation results of the method and apparatus described in this invention are provided. The predicted linear noise exhibits very obvious linear characteristics. After linear noise suppression using the method and apparatus described in this invention, the signal-to-noise ratio is significantly improved. See [link to relevant documentation]. Figure 3 , Figure 4 , Figure 5 Taking an earthquake record from a complex mountainous region in western China as an example, it can be seen that linear noise parallel to the first arrival refracted wave is very well-developed, severely affecting the signal-to-noise ratio. (See [link to relevant documentation]). Figure 6 After linear noise suppression using the method and apparatus described in this invention, it can be seen that the hyperbolic characteristics of the effective signal are revealed, and the signal-to-noise ratio is significantly improved. (See [link to related documentation]). Figure 7 The noise reduction effect in the detector domain is also quite ideal; the output noise gather does not damage the effective signal. (See [link]). Figure 8 and Figure 9 The application effect and advantages are also evident from the superimposed profile. Before linear noise suppression, the weak reflection underlying features are not obvious. (See [link to relevant documentation]). Figure 10 However, after linear noise suppression, the characteristics of weakly reflective strata become apparent, and the signal-to-noise ratio is significantly improved. (See [reference]). Figure 11 This demonstrates the effectiveness of the method and its adaptability to real-world data.

[0076] Based on the same inventive concept, this invention also provides a noise removal device, as described in the following embodiments. Since the principle by which the noise removal device solves the problem is similar to that of the noise removal method, the implementation of the noise removal device can refer to the implementation of the noise removal method, and repeated details will not be elaborated further. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0077] Figure 2 This is a schematic diagram of the structure of a noise removal device provided in an embodiment of the present invention, as shown below. Figure 2 As shown, the device includes:

[0078] Data acquisition module 201 is used to acquire seismic data of a preset work area, wherein the seismic data includes linear noise data;

[0079] The time interval curve acquisition module 202 is used to acquire the time interval curve of the linear noise based on the linear noise data;

[0080] The median sequence acquisition module 203 is used to slide a time window along the time direction according to the time interval curve of the linear noise to obtain the median sequence within a preset apparent velocity range;

[0081] The linear noise prediction module 204 is used to obtain the predicted linear noise from the median sequence using an adaptive superposition technique.

[0082] The linear noise removal module 205 is used to filter the predicted linear noise using adaptive filtering technology based on the seismic data to obtain the data after linear noise removal.

[0083] In this embodiment of the invention, the time-distance curve of the linear noise is as follows:

[0084]

[0085] Among them, t lnFor the travel time of linear noise, v ln denoted as linear noise velocity, x as offset distance, and T0 as a constant term.

[0086] In this embodiment of the invention, the device further includes:

[0087] The correction module is used to perform linear time difference correction on the seismic data based on the time distance curve of the linear noise, so as to obtain the travel time of the corrected linear noise.

[0088] In this embodiment of the invention, the travel time of the corrected linear noise is:

[0089] t ln_new =t ln -Δt=T0

[0090] Where, Δt=x / v ln , t ln For the travel time of linear noise, v ln denoted as linear noise velocity, x as offset distance, and T0 as a constant term.

[0091] In this embodiment of the invention, the data after linear noise removal is:

[0092]

[0093] Where k is time, j is the filter coefficient, w is the matched filter coefficient, and L is the filter length.

[0094] To achieve the above objectives, according to another aspect of this application, a computer device is also provided. This computer device includes a memory, a processor, a communication interface, and a communication bus. The memory stores a computer program executable on the processor, which, when executing the computer program, implements the steps of the methods described in the above embodiments.

[0095] The processor can be a central processing unit (CPU). The processor can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips.

[0096] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and units, such as the program units corresponding to the above-described method embodiments of the present invention. The processor executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions, and modules stored in the memory, thereby implementing the methods described in the above-described method embodiments.

[0097] The memory may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor, etc. Furthermore, the memory may include high-speed random access memory and non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, which can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0098] The one or more units are stored in the memory and, when executed by the processor, perform the methods described in the above embodiments.

[0099] This invention also provides a computer-readable storage medium storing a computer program for performing the above-described method.

[0100] In summary, the method for noise suppression via adaptive wavefield separation along the time-distance curve of this invention effectively suppresses linear interference and improves the signal-to-noise ratio of the data. By using a sliding time window along the time direction based on the noise's time-distance curve and employing multiple correlated median adaptive superpositions to extract linear noise features, it avoids the problem of inaccurate coherence determination caused by large variations in regular interference waves within a fixed window. Furthermore, the adaptive filtering subtraction method effectively removes linear interference from the seismic record, avoiding spurious frequency interference and exhibiting good amplitude preservation. This method is an effective and amplitude-preserving linear noise suppression approach.

[0101] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0102] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0103] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0104] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0105] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A noise removal method, characterized in that, include: Collect seismic data from a pre-defined work area; the seismic data includes linear noise data. Based on the linear noise data, obtain the time interval curve of the linear noise; Based on the time-distance curve of the linear noise, slide the time window along the time direction to obtain the median sequence within a preset apparent velocity range; The predicted linear noise is obtained from the median sequence using an adaptive superposition technique; Based on the earthquake data, adaptive filtering technology is used to filter the predicted linear noise to obtain the data after removing the linear noise. The adaptive stacking method automatically assigns different weights to the tracks based on their similarity, stacking tracks with higher similarity and lower similarity. The predicted linear noise is obtained in the median sequence using an adaptive stacking technique, including: calculating the similarity coefficient between two adjacent trace records; calculating the average of the similarity coefficients of all pairs of adjacent traces within the range of L traces; applying an exponential weight to the average value; and horizontally stacking multiple seismic traces to obtain the predicted linear noise. After obtaining the time-distance curve of the linear noise based on the linear noise data, the method further includes: performing linear time difference correction on the seismic data based on the time-distance curve of the linear noise to obtain the travel time of the corrected linear noise. The travel time of the corrected linear noise is: t ln_new =t ln -Δt=T0 Where, Δt=x / v ln , t ln For the travel time of linear noise, v ln For linear noise velocity, x is the offset distance, and T0 is a constant term; The data after linear noise removal is as follows: Where k is time, j is the filter coefficient, w is the matched filter coefficient, and L is the filter length; At each time sample K, the current filter coefficients are adjusted by learning the signal δ: w(k+1,j)=w(k,j)+δ(k,j).

2. The method as described in claim 1, characterized in that, The time-distance curve of the linear noise is as follows: Among them, t ln For the travel time of linear noise, v ln denoted as linear noise velocity, x as offset distance, and T0 as a constant term.

3. A noise removal device, characterized in that, include: The data acquisition module is used to acquire seismic data of a preset work area, and the seismic data includes linear noise data. The time-distance curve acquisition module is used to acquire the time-distance curve of the linear noise based on the linear noise data. The median sequence acquisition module is used to slide a time window along the time direction according to the time interval curve of the linear noise to obtain the median sequence within a preset apparent velocity range; A linear noise prediction module is used to obtain the predicted linear noise from the median sequence using an adaptive superposition technique. The linear noise removal module is used to filter the predicted linear noise using adaptive filtering technology based on the seismic data, and obtain the data after linear noise removal. The adaptive stacking method automatically assigns different weights to the tracks based on their similarity, stacking tracks with higher similarity and lower similarity. The predicted linear noise is obtained in the median sequence using an adaptive stacking technique, including: calculating the similarity coefficient between two adjacent trace records; calculating the average of the similarity coefficients of all pairs of adjacent traces within the range of L traces; applying an exponential weight to the average value; and horizontally stacking multiple seismic traces to obtain the predicted linear noise. The device further includes: a correction module, used to perform linear time difference correction on the seismic data according to the time distance curve of the linear noise, so as to obtain the travel time of the corrected linear noise; The travel time of the corrected linear noise is: t ln_new =t ln -Δt=T0 Where, Δt=x / v ln , t ln For the travel time of linear noise, v ln For linear noise velocity, x is the offset distance, and T0 is a constant term; The data after linear noise removal is as follows: Where k is time, j is the filter coefficient, w is the matched filter coefficient, and L is the filter length; At each time sample K, the current filter coefficients are adjusted by learning the signal δ: w(k+1,j)=w(k,j)+δ(k,j).

4. The apparatus as described in claim 3, characterized in that, The time-distance curve of the linear noise is as follows: Among them, t ln For the travel time of linear noise, v ln denoted as linear noise velocity, x as offset distance, and T0 as a constant term.

5. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 2.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that performs the method of any one of claims 1 to 2.

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