CN113900146A - Surface wave pressing method and system - Google Patents
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- 238000003825 pressing Methods 0.000 title claims abstract description 27
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- 238000004590 computer program Methods 0.000 claims description 15
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Abstract
The invention provides a surface wave pressing method and a surface wave pressing system. The surface wave pressing method comprises the following steps: acquiring current pre-stack seismic data; inputting the current pre-stack seismic data into a pre-established surface wave optimal identification network to obtain current surface wave data; and determining a surface wave suppression result according to the current pre-stack seismic data and the current surface wave data. The invention can effectively press the interference of the surface wave, improve the pressing efficiency and precision and reduce the pressing cost.
Description
Technical Field
The invention relates to the technical field of seismic data denoising, in particular to a surface wave suppression method and a surface wave suppression system.
Background
In the desert area, due to the fact that the surface sand dunes are large in fluctuation and loose in sand layers, the surface wave characteristics are extremely complex due to the fact that seismic waves are absorbed and attenuated seriously, conventional surface waves and scattering surface waves exist, and the difficulty of surface wave suppression is remarkably increased. At present, a great amount of denoising technologies aiming at three-dimensional surface waves are applied to desert areas, such as a frequency division surface wave suppression method, an abnormal amplitude suppression method, a three-dimensional cone filtering method and the like, although the technologies achieve a certain application effect, the traditional single method is limited by a certain hypothesis or condition, the conventional surface waves and scattering surface waves are difficult to be effectively suppressed at the same time, and the adaptability and the fidelity capability need to be further improved.
In order to solve the problem of intelligent and efficient denoising of massive pre-stack seismic data, an intelligent denoising technology based on deep learning is rapidly developed in recent years. Deep learning is a latest artificial intelligence algorithm, and can automatically learn target features by utilizing big data advantages so as to intelligently identify targets. The intelligent denoising network has huge advantages and good development prospect, and nevertheless, a three-dimensional intelligent denoising network aiming at complex surface waves in desert regions does not appear.
Disclosure of Invention
The embodiment of the invention mainly aims to provide a surface wave pressing method and system, so as to effectively press the interference of the surface wave, improve the pressing efficiency and precision and reduce the pressing cost.
In order to achieve the above object, an embodiment of the present invention provides a surface wave pressing method, including:
acquiring current pre-stack seismic data;
inputting the current pre-stack seismic data into a pre-established surface wave optimal identification network to obtain current surface wave data;
and determining a surface wave suppression result according to the current pre-stack seismic data and the current surface wave data.
An embodiment of the present invention further provides a surface wave suppression system, including:
the acquisition unit is used for acquiring current pre-stack seismic data;
the input unit is used for inputting the current pre-stack seismic data into a pre-established surface wave optimal identification network to obtain current surface wave data;
and the surface wave suppression result unit is used for determining a surface wave suppression result according to the current pre-stack seismic data and the current surface wave data.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program stored on the memory and running on the processor, wherein the processor realizes the steps of the surface wave pressing method when executing the computer program.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the steps of the surface wave pressing method.
According to the surface wave suppression method and system provided by the embodiment of the invention, the current pre-stack seismic data are input into the pre-established surface wave optimal identification network to obtain the current surface wave data, and the surface wave suppression result is determined according to the current pre-stack seismic data and the current surface wave data, so that the interference of the surface wave can be effectively suppressed, the suppression efficiency and precision are improved, and the suppression cost is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a flow chart of a method of surface wave suppression in an embodiment of the present invention;
FIG. 2 is a flow chart of creating a surface wave optimal recognition network in an embodiment of the present invention;
FIG. 3 is a schematic representation of current pre-stack seismic data after preprocessing in an embodiment of the invention;
FIG. 4 is a schematic diagram of the current surface wave data after denormalization according to an embodiment of the invention;
FIG. 5 is a graph illustrating the results of surface wave suppression in an embodiment of the present invention;
FIG. 6 is a schematic illustration of pre-processed historical pre-stack seismic data in an embodiment of the invention;
FIG. 7 is a schematic illustration of actual surface wave data in an embodiment of the invention;
FIG. 8 is a graphical illustration of surface wave training data in an embodiment of the present invention;
FIG. 9 is a schematic illustration of surface wave tag data in an embodiment of the invention;
FIG. 10 is a block diagram of a surface wave suppression system in an embodiment of the present invention;
fig. 11 is a block diagram of a computer device in the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As will be appreciated by one skilled in the art, embodiments of the present invention may be embodied as a system, apparatus, device, method, or computer program product. Accordingly, the present disclosure may be embodied in the form of: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), or a combination of hardware and software.
In view of the problems of poor adaptability, high complexity, low denoising efficiency, incapability of meeting the efficient processing of pre-stack data of massive desert regions in the broadband wide-azimuth era and the like in the prior art, the embodiment of the invention provides a surface wave pressing method, which can effectively press the interference of surface waves, improve the pressing efficiency and precision and reduce the pressing cost. The present invention will be described in detail below with reference to the accompanying drawings.
FIG. 1 is a flow chart of a method of surface wave suppression in an embodiment of the present invention. As shown in fig. 1, the surface wave pressing method includes:
s101: and acquiring current pre-stack seismic data.
The data in the invention are three-dimensional data, and the identification network is also a three-dimensional network.
S102: and inputting the current pre-stack seismic data into a pre-established surface wave optimal identification network to obtain the current surface wave data.
In an embodiment, before executing S102, the method further includes: and preprocessing and maximum value normalization are carried out on the current pre-stack seismic data. S102 includes: and inputting the preprocessed and maximum normalized current pre-stack seismic data into a pre-established surface wave optimal identification network to obtain current surface wave data.
The current pre-stack seismic data after the maximum value normalization can be obtained by dividing the current pre-stack seismic data by the maximum value in the current pre-stack seismic data. The data range of the current pre-stack seismic data after maximum normalization is between-1.0 and 1.0. The preprocessing includes geometric diffusion compensation and static correction.
S103: and determining a surface wave suppression result according to the current pre-stack seismic data and the current surface wave data.
In specific implementation, S103 includes: and performing inverse normalization on the current surface wave data, and subtracting the preprocessed current pre-stack seismic data (including the surface waves) from the current surface wave data after the inverse normalization to obtain a surface wave suppression result.
Where the inverse normalization is to multiply the current front wave data by the maximum value.
FIG. 3 is a schematic representation of current pre-stack seismic data after preprocessing in an embodiment of the invention. FIG. 4 is a schematic diagram of the current surface wave data after denormalization according to an embodiment of the invention. FIG. 5 is a graph showing the result of surface wave suppression in the example of the present invention. The vertical coordinate in fig. 3-5 is the vertical offset in kilometers (km), the horizontal coordinate is the horizontal offset in kilometers (km), and the vertical coordinate is time in seconds(s). As shown in fig. 5, the surface wave in fig. 3 is identified without effective information loss, manual participation and complex parameter testing in the prior art are eliminated, and the denoising efficiency is improved by more than 40 times.
The main body of execution of the surface wave pressing method shown in fig. 1 is a computer. As can be seen from the flow shown in fig. 1, the surface wave suppression method according to the embodiment of the present invention inputs the current prestack seismic data into the pre-created surface wave optimal identification network to obtain the current surface wave data, and determines the surface wave suppression result according to the current prestack seismic data and the current surface wave data, so that the interference of the surface wave can be effectively suppressed, the suppression efficiency and accuracy are improved, and the suppression cost is reduced.
Fig. 2 is a flowchart of creating a surface wave optimal recognition network in the embodiment of the present invention. As shown in fig. 2, the step of creating the optimal surface wave identification network in advance includes:
the following iterative process is performed:
s201: and inputting the pre-stack seismic training data into a surface wave recognition network to obtain surface wave training data.
In an embodiment, before performing S201, the method further includes: preprocessing historical pre-stack seismic data, and intercepting the historical pre-stack seismic data according to a certain sampling point step length (such as 2 sampling point step lengths, 4 sampling point step lengths or 8 sampling point step lengths) along the time direction and the space direction to obtain pre-stack seismic training data. The size of the pre-stack seismic training data is 40 × 40 × 40.
S201 comprises: and randomly ordering and maximum value normalization are carried out on the pre-stack seismic training data, and a plurality of groups of pre-stack seismic training data subjected to random ordering and maximum value normalization are input into a surface wave identification network to obtain surface wave training data. In specific implementation, 16 groups of pre-stack seismic training data subjected to random sorting and maximum value normalization can be input into a surface wave recognition network. The data range of the pre-stack seismic training data after maximum normalization is between-1.0 and 1.0.
S202: and determining an objective function according to the surface wave label data and the surface wave training data corresponding to the pre-stack seismic training data.
In an embodiment, before performing S202, the method further includes:
and sequentially carrying out frequency division surface wave suppression, abnormal amplitude suppression and three-dimensional cone filtering on the preprocessed historical pre-stack seismic data to obtain actual surface wave data.
And intercepting actual surface wave data corresponding to the historical pre-stack seismic data according to a certain sampling point step length (such as 2 sampling point step lengths, 4 sampling point step lengths or 8 sampling point step lengths) along the time direction and the space direction to obtain surface wave label data. The size of the surface wave tag data is 40 × 40 × 40.
S202 comprises the following steps: and carrying out maximum value normalization on the surface wave label data, and determining the surface wave label data and the surface wave training data which are corresponding to the pre-stack seismic training data and subjected to maximum value normalization to obtain a target function.
FIG. 6 is a schematic illustration of pre-processed historical pre-stack seismic data in an embodiment of the invention. FIG. 7 is a diagram of actual surface wave data in an embodiment of the invention. The vertical coordinate in fig. 6-7 is the vertical offset in kilometers (km), the horizontal coordinate is the horizontal offset in kilometers (km), and the vertical coordinate is time in seconds(s).
FIG. 8 is a graphical representation of surface wave training data in an embodiment of the present invention. Fig. 9 is a schematic diagram of surface wave tag data in an embodiment of the present invention. As shown in fig. 8 to 9, fig. 8 has 8 sets of surface wave training data in total, and fig. 9 shows 8 sets of surface wave label data corresponding to the 8 sets of surface wave training data one by one.
S203: and judging whether the current iteration times reach the preset iteration times or not.
Wherein, the preset iteration number can be 20.
S204: and when the current iteration times reach the preset iteration times, taking the surface wave identification network in the current iteration as the optimal surface wave identification network.
S205: and when the current iteration times do not reach the preset iteration times, updating the surface wave identification network according to the target function and the preset learning rate, and continuously executing the iteration processing.
The surface wave identification network can be updated through a small batch random gradient descent method according to the target function and the learning rate. The objective function may be a mean square error objective function with a learning rate of 0.001.
In one embodiment, before executing S201, the method further includes building a surface wave identification network, including:
1. and constructing an input layer, wherein the input layer is a convolutional layer, and the size of the convolutional layer is 3 multiplied by 1 multiplied by nf (nf convolutional filters with convolution kernels of 3 multiplied by 3 and the number of channels is 1). Wherein nf may be 100.
2. And constructing an intermediate layer, wherein the intermediate layer is composed of a plurality of construction blocks, each construction block is composed of a convolution layer, a batch standardization layer and a correction linear unit layer which are sequentially connected, and the sizes of the convolution layers are all 3 multiplied by nc multiplied by nf (nf convolution kernels are convolution filters with 3 multiplied by 3, and the number of channels is nc). Where nc may be 100.
3. And (3) constructing an output layer, wherein the output layer is a convolutional layer, and the size of the convolutional layer is 3 multiplied by nc multiplied by 1(1 convolutional core is a convolution filter with 3 multiplied by 3, and the number of channels is nc).
The specific process of the embodiment of the invention is as follows:
1. preprocessing historical pre-stack seismic data, and intercepting the historical pre-stack seismic data along a time direction and a space direction according to a certain sampling point step length to obtain pre-stack seismic training data.
2. And randomly ordering and carrying out maximum value normalization on the seismic training data before stacking.
3. And inputting a plurality of groups of pre-stack seismic training data subjected to random sequencing and maximum value normalization into a surface wave identification network to obtain surface wave training data.
4. And sequentially carrying out frequency division surface wave suppression, abnormal amplitude suppression and three-dimensional cone filtering on the preprocessed historical pre-stack seismic data to obtain actual surface wave data.
5. And intercepting actual surface wave data corresponding to the historical pre-stack seismic data according to a certain sampling point step length along the time direction and the space direction to obtain surface wave label data.
6. And carrying out maximum value normalization on the surface wave label data, and determining the surface wave label data and the surface wave training data which are corresponding to the pre-stack seismic training data and subjected to maximum value normalization to obtain a target function.
7. And judging whether the current iteration times reach the preset iteration times or not. And when the current iteration times reach the preset iteration times, taking the surface wave identification network in the current iteration as the optimal surface wave identification network, otherwise, updating the surface wave identification network according to the target function and the preset learning rate, and returning to the step 3.
8. And preprocessing and maximum value normalization are carried out on the current pre-stack seismic data. S102 includes: and inputting the preprocessed and maximum normalized current pre-stack seismic data into a pre-established surface wave optimal identification network to obtain current surface wave data.
9. And performing inverse normalization on the current surface wave data, and subtracting the preprocessed current pre-stack seismic data from the current surface wave data subjected to inverse normalization to obtain a surface wave suppression result.
In summary, the surface wave suppression method according to the embodiment of the present invention inputs the current pre-stack seismic data into the pre-established surface wave optimal identification network to obtain the current surface wave data, and determines the surface wave suppression result according to the current pre-stack seismic data and the current surface wave data, so that the interference of the surface wave can be effectively suppressed, the suppression efficiency and precision are improved, and the suppression cost is reduced.
Based on the same inventive concept, the embodiment of the invention also provides a surface wave pressing system, and as the principle of solving the problems of the system is similar to that of a surface wave pressing method, the implementation of the system can refer to the implementation of the method, and repeated parts are not described again.
Fig. 10 is a block diagram showing the structure of a surface wave suppression system in the embodiment of the present invention. As shown in fig. 10, the surface wave suppression system includes:
the acquisition unit is used for acquiring current pre-stack seismic data;
the input unit is used for inputting the current pre-stack seismic data into a pre-established surface wave optimal identification network to obtain current surface wave data;
and the surface wave suppression result unit is used for determining a surface wave suppression result according to the current pre-stack seismic data and the current surface wave data.
In one embodiment, the method further comprises the following steps:
an iteration unit for performing an iterative process of:
inputting the pre-stack seismic training data into a surface wave recognition network to obtain surface wave training data;
determining a target function according to the surface wave label data and the surface wave training data corresponding to the pre-stack seismic training data;
judging whether the current iteration times reach preset iteration times or not;
and when the current iteration times reach the preset iteration times, taking the surface wave identification network in the current iteration as the optimal surface wave identification network, otherwise, updating the surface wave identification network according to the target function and the preset learning rate, and continuously executing the iteration processing.
In one embodiment, the method further comprises the following steps:
the first interception unit is used for intercepting historical pre-stack seismic data to obtain pre-stack seismic training data;
and the second interception unit is used for intercepting actual surface wave data corresponding to the historical pre-stack seismic data to obtain surface wave label data.
In one embodiment, the method further comprises the following steps:
and the denoising unit is used for sequentially carrying out frequency division surface wave suppression processing, abnormal amplitude suppression processing and centrum filtering processing on the historical pre-stack seismic data to obtain actual surface wave data.
In summary, the surface wave suppression system of the embodiment of the invention inputs the current pre-stack seismic data into the pre-established surface wave optimal identification network to obtain the current surface wave data, and determines the surface wave suppression result according to the current pre-stack seismic data and the current surface wave data, so that the interference of the surface wave can be effectively suppressed, the suppression efficiency and precision are improved, and the suppression cost is reduced.
The embodiment of the invention also provides a specific implementation mode of computer equipment capable of realizing all the steps in the surface wave pressing method in the embodiment. Fig. 11 is a block diagram of a computer device in an embodiment of the present invention, and referring to fig. 11, the computer device specifically includes the following contents:
a processor (processor)1101 and a memory (memory) 1102.
The processor 1101 is configured to call a computer program in the memory 1102, and the processor implements all the steps in the surface wave suppression method in the above embodiments when executing the computer program, for example, the processor implements the following steps when executing the computer program:
acquiring current pre-stack seismic data;
inputting the current pre-stack seismic data into a pre-established surface wave optimal identification network to obtain current surface wave data;
and determining a surface wave suppression result according to the current pre-stack seismic data and the current surface wave data.
To sum up, the computer device of the embodiment of the present invention inputs the current pre-stack seismic data into the pre-created optimal surface wave identification network to obtain the current surface wave data, and determines the surface wave suppression result according to the current pre-stack seismic data and the current surface wave data, so that the interference of the surface wave can be effectively suppressed, the suppression efficiency and precision are improved, and the suppression cost is reduced.
An embodiment of the present invention further provides a computer-readable storage medium capable of implementing all the steps in the surface wave suppression method in the foregoing embodiment, where the computer-readable storage medium stores a computer program, and the computer program, when executed by a processor, implements all the steps in the surface wave suppression method in the foregoing embodiment, for example, when the processor executes the computer program, implements the following steps:
acquiring current pre-stack seismic data;
inputting the current pre-stack seismic data into a pre-established surface wave optimal identification network to obtain current surface wave data;
and determining a surface wave suppression result according to the current pre-stack seismic data and the current surface wave data.
To sum up, the computer-readable storage medium according to the embodiment of the present invention inputs the current pre-stack seismic data into the pre-created optimal surface wave identification network to obtain the current surface wave data, and determines the surface wave suppression result according to the current pre-stack seismic data and the current surface wave data, so that the interference of the surface wave can be effectively suppressed, the suppression efficiency and precision can be improved, and the suppression cost can be reduced.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Those of skill in the art will further appreciate that the various illustrative logical blocks, units, and steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate the interchangeability of hardware and software, various illustrative components, elements, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design requirements of the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present embodiments.
The various illustrative logical blocks, or elements, or devices described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor, an Application Specific Integrated Circuit (ASIC), a field programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. For example, a storage medium may be coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC, which may be located in a user terminal. In the alternative, the processor and the storage medium may reside in different components in a user terminal.
In one or more exemplary designs, the functions described above in connection with the embodiments of the invention may be implemented in hardware, software, firmware, or any combination of the three. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media that facilitate transfer of a computer program from one place to another. Storage media may be any available media that can be accessed by a general purpose or special purpose computer. For example, such computer-readable media can include, but is not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store program code in the form of instructions or data structures and which can be read by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. Additionally, any connection is properly termed a computer-readable medium, and, thus, is included if the software is transmitted from a website, server, or other remote source via a coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), or wirelessly, e.g., infrared, radio, and microwave. Such discs (disk) and disks (disc) include compact disks, laser disks, optical disks, DVDs, floppy disks and blu-ray disks where disks usually reproduce data magnetically, while disks usually reproduce data optically with lasers. Combinations of the above may also be included in the computer-readable medium.
Claims (10)
1. A surface wave pressing method, comprising:
acquiring current pre-stack seismic data;
inputting the current pre-stack seismic data into a pre-established surface wave optimal identification network to obtain current surface wave data;
and determining a surface wave suppression result according to the current pre-stack seismic data and the current surface wave data.
2. The surface wave pressing method according to claim 1, wherein the step of creating the surface wave optimal recognition network in advance includes:
the following iterative process is performed:
inputting the pre-stack seismic training data into a surface wave recognition network to obtain surface wave training data;
determining a target function according to the surface wave label data corresponding to the pre-stack seismic training data and the surface wave training data;
judging whether the current iteration times reach preset iteration times or not;
and when the current iteration times reach preset iteration times, taking the surface wave identification network in the current iteration as the optimal surface wave identification network, otherwise, updating the surface wave identification network according to the target function and a preset learning rate, and continuously executing the iteration processing.
3. The surface wave pressing method according to claim 2, further comprising:
intercepting historical pre-stack seismic data to obtain the pre-stack seismic training data;
and intercepting actual surface wave data corresponding to the historical pre-stack seismic data to obtain the surface wave label data.
4. The surface wave pressing method according to claim 3, further comprising:
and sequentially carrying out frequency division surface wave suppression processing, abnormal amplitude suppression processing and cone filtering processing on the historical pre-stack seismic data to obtain the actual surface wave data.
5. A surface wave suppression system, comprising:
the acquisition unit is used for acquiring current pre-stack seismic data;
the input unit is used for inputting the current pre-stack seismic data into a pre-established surface wave optimal identification network to obtain current surface wave data;
and the surface wave suppression result unit is used for determining a surface wave suppression result according to the current pre-stack seismic data and the current surface wave data.
6. The surface wave suppression system according to claim 5, further comprising:
an iteration unit for performing an iterative process of:
inputting the pre-stack seismic training data into a surface wave recognition network to obtain surface wave training data;
determining a target function according to the surface wave label data corresponding to the pre-stack seismic training data and the surface wave training data;
judging whether the current iteration times reach preset iteration times or not;
and when the current iteration times reach preset iteration times, taking the surface wave identification network in the current iteration as the optimal surface wave identification network, otherwise, updating the surface wave identification network according to the target function and a preset learning rate, and continuously executing the iteration processing.
7. The surface wave suppression system according to claim 6, further comprising:
the first interception unit is used for intercepting historical pre-stack seismic data to obtain the pre-stack seismic training data;
and the second interception unit is used for intercepting actual surface wave data corresponding to the historical pre-stack seismic data to obtain the surface wave label data.
8. The surface wave suppression system according to claim 7, further comprising:
and the denoising unit is used for sequentially carrying out frequency division surface wave suppression processing, abnormal amplitude suppression processing and cone filtering processing on the historical pre-stack seismic data to obtain the actual surface wave data.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and running on the processor, characterized in that the processor implements the steps of the surface wave pressing method according to any one of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the surface wave pressing method according to any one of claims 1 to 4.
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