CN117368988A - Method, device, equipment and medium for analyzing aliasing data separation effect - Google Patents
Method, device, equipment and medium for analyzing aliasing data separation effect Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
- G01V1/30—Analysis
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V13/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices covered by groups G01V1/00 – G01V11/00
Abstract
The application discloses an aliasing data separation effect analysis method, device, equipment and medium, and belongs to the field of seismic exploration. The method comprises the following steps: acquiring first original seismic data and second original seismic data; simulating and aliasing the first original seismic data and the second original seismic data to obtain aliased seismic data after aliasing; performing aliasing data separation on the aliasing seismic data by adopting an ith separation method to obtain first separated seismic data and second separated seismic data; the first separated seismic data corresponds to the first original seismic data, i being sequentially assigned an integer from 1 to n; calculating an ith PSNR from the first raw seismic data and the first split seismic data; repeating the steps until i is equal to n, and obtaining n PSNR; the separation method corresponding to the maximum value of the n PSNR is determined as the target separation method. The scheme can objectively and quantitatively evaluate the aliasing data separation effect.
Description
Technical Field
The embodiment of the application relates to the field of seismic exploration, in particular to an aliasing data separation effect analysis method, device, equipment and medium.
Background
The aliasing acquisition technology greatly improves the seismic data acquisition efficiency, shortens the exploration period and reduces the acquisition cost. The aliased acquisition technique detects the seismic intensities from individual sources by setting a set of detection points at the surface and then firing a plurality of firing points (sources) in the subsurface at intervals.
Because of the short firing time interval of the seismic sources, the aliasing of the seismic waves generated by different seismic sources is caused, and the signal-to-noise ratio and imaging quality of the seismic data are seriously affected. Thus, seismic data acquired using an aliased acquisition technique requires an aliased separation process. Meanwhile, an aliasing separation method needs to be optimized to further improve the aliasing separation effect.
In the multi-source aliasing acquisition construction design process, the seismic data of at least two seismic sources which are obtained through independent detection are subjected to simulated aliasing to obtain simulated aliasing data, then the simulated aliasing data are separated by adopting an aliasing separation method, the separated seismic data are compared with original seismic data to verify the aliasing separation effect, and the aliasing separation method is optimized.
The traditional aliasing separation effect analysis adopts qualitative analysis, and subjective analysis is carried out by manually observing whether the separated seismic data are mixed with the seismic data of other seismic sources, so as to evaluate the aliasing separation effect.
Disclosure of Invention
The application provides a method, a device, equipment and a medium for analyzing an aliasing data separation effect, which can objectively and quantitatively evaluate the aliasing data separation effect. The technical scheme is as follows:
according to an aspect of the present application, there is provided an aliasing data separation effect analysis method, the method including:
collecting first original seismic data A and second original seismic data B;
simulating and aliasing the first original seismic data A and the second original seismic data B to obtain aliased seismic data C after aliasing;
performing aliasing data separation on the aliasing seismic data C by adopting an ith separation method to obtain first separated seismic data A 'and second separated seismic data B'; the first separated seismic data A 'corresponds to the first original seismic data A, the second separated seismic data B' corresponds to the second original seismic data B, and i is sequentially assigned as an integer from 1 to n;
calculating an ith peak signal-to-noise ratio (PSNR) according to the first original seismic data A and the first separated seismic data A';
repeating the steps until i is equal to n, and obtaining n PSNR;
and determining a separation method corresponding to the maximum value in the n PSNR as a target separation method.
According to another aspect of the present application, there is provided an aliasing data separation effect analysis apparatus, the apparatus comprising:
the acquisition module is used for acquiring the first original seismic data A and the second original seismic data B;
the aliasing module is used for carrying out simulated aliasing on the first original seismic data A and the second original seismic data B to obtain aliasing seismic data C after aliasing;
the separation module is used for carrying out aliasing data separation on the aliasing seismic data C by adopting an ith separation device to obtain first separated seismic data A 'and second separated seismic data B'; the first separated seismic data A 'corresponds to the first original seismic data A, the second separated seismic data B' corresponds to the second original seismic data B, and i is sequentially assigned as an integer from 1 to n;
the calculation module is used for calculating an ith peak signal-to-noise ratio PSNR according to the first original seismic data A and the first separated seismic data A';
the calculation module is used for enabling i to be equal to i+1, repeating the steps until i is equal to n, and obtaining n PSNR;
and the determining module is used for determining the separation device corresponding to the maximum value in the n PSNR as a target separation device.
According to one aspect of the present application, there is provided a computer device comprising: a processor and a memory storing a computer program that is loaded and executed by the processor to implement the aliased data separation effect analysis method as described above.
According to another aspect of the present application, there is provided a computer-readable storage medium storing a computer program loaded and executed by a processor to implement the aliasing data separation effect analysis method as described above.
According to another aspect of the present application, a computer program product is provided, the computer program product comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the aliasing data separation effect analysis method provided in the above aspect.
The beneficial effects that technical scheme that this application embodiment provided include at least:
and simulating and aliasing the two groups of original seismic data by using the acquired two groups of original seismic data to obtain aliased seismic data, then sequentially separating the aliased seismic data by adopting a plurality of separation methods to obtain separated seismic data, and evaluating the advantages and disadvantages of the separation methods by calculating the peak signal-to-noise ratio of the original seismic data and the separated seismic data. The higher the peak signal-to-noise ratio, the higher the fidelity of the separation, and the better the corresponding separation method.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed 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 application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 illustrates a block diagram of a computer system provided by an exemplary embodiment;
FIG. 2 illustrates a flow chart of a method for aliased data separation effect analysis provided by an exemplary embodiment;
FIG. 3 illustrates a schematic diagram of aliased data separation as provided by one exemplary embodiment;
FIG. 4 is a block diagram showing the structure of an aliasing data separation effect analysis apparatus according to an exemplary embodiment;
fig. 5 shows a block diagram of a computer device provided by an exemplary embodiment.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.
It should be understood that references herein to "a number" means one or more, and "a plurality" means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.
FIG. 1 illustrates a block diagram of a computer system provided in an exemplary embodiment of the present application. The computer system includes: a computer device 120 and a detection point 140.
The computer device 120 includes: at least one of a terminal, a server, a smart phone, a smart watch, a vehicle-mounted computer device, a wearable device, a smart television, a tablet computer, an electronic book reader, an MP3 player, an MP4 player, a laptop portable computer, and a desktop computer.
The computer device 120 includes a processor and a memory, where the memory stores an aliasing data separation effect analysis program, and the processor is configured to execute the program to implement the aliasing data separation effect analysis method provided in the embodiment of the present application.
The computer device 120 is connected to the detection point 140. Alternatively, the computer device 120 obtains the seismic data detected at the detection point through a relay device (e.g., a mobile storage device).
Detection points 140 are provided on the ground for detecting seismic waves. The number of detection points may be one or more. Illustratively, the detection points are arranged linearly or in a matrix on the ground. For example, 60×100 detection points are provided on the ground.
The detection points are used for periodically detecting seismic waves and storing seismic data. The sampling period of the detection point can be arbitrarily adjusted. Illustratively, detection point 140 transmits the seismic data to computer device 120 after the seismic data is acquired.
Those skilled in the art will appreciate that the number of computer devices described above may be greater or lesser. For example, the number of the above-mentioned computer devices may be only one, or the number of the above-mentioned computer devices may be several tens or hundreds, or more. The number and type of computer devices are not limited in the embodiments of the present application.
Fig. 2 shows a flowchart of an aliasing data separation effect analysis method according to an exemplary embodiment of the present application, where the method is applied to the computer device shown in fig. 1 for illustration, and the method includes:
in step 201, first raw seismic data A and second raw seismic data B are acquired.
The embodiment of the application takes the seismic data of two aliased seismic sources as an example to verify the separation method. Of course, by adopting the method provided by the embodiment of the application, a person skilled in the art can also verify the separation effect of the separation method by aliasing more seismic data of the seismic sources.
And the computer equipment respectively acquires the seismic data of the seismic source a and the seismic source b through detection points.
First, a plurality of detection points are set on the ground, for example, x detection points are set, and the x detection points may be arranged in a line or in a matrix. For example, x is equal to 6000, then an array of 60 x 100 inspection points may be set on the ground.
Firing the seismic source a to enable the x detection points to detect the seismic data according to a preset sampling period. For example, the sampling period is set to 2 milliseconds, and detection starts from the time source a is fired to the end of detection 5 seconds after source a is fired. Then within 5 seconds, each detection point detects 5 seconds x (1000 ms/2 ms) =2500 seismic data, and x detection points detect 2500 x total seismic data.
Wherein each seismic data includes at least: the distance from the detection point corresponding to the seismic data to the seismic source a, the sampling time of the seismic data and the amplitude of the seismic data. Taking the distance in the seismic data as the x-axis and the sampling time as the y-axis, a two-dimensional point diagram can be drawn, each data point on the point diagram represents the seismic data, the position of the data point on the two-dimensional point diagram represents the distance and the sampling time in the seismic data, and the numerical value of the data point on the two-dimensional point diagram represents the amplitude in the seismic data.
Taking the example that the distance from each detection point in the x detection points to the seismic source a is different, a two-dimensional point diagram drawn by 2500 x seismic data includes: a rectangular lattice of horizontal x data points, vertical 2500 data points, which contains 2500 x data points in total.
Based on the above description, assuming that there are x detection points, each of which is sampled y times, that is, y sampling times, x×y seismic data corresponding to the source a, that is, the first raw seismic data a, will be detected. The first original seismic data A is the seismic data corresponding to the seismic source a.
Assuming x detection points, each of which samples z times, i.e., has z sampling times, x×z seismic data corresponding to the source B, i.e., the second raw seismic data B, are detected. The second original seismic data B is the seismic data corresponding to the seismic source B. Wherein z and y may be the same or different.
Source a and source b are located differently with respect to the detection point or the explosion intensity of source a and source b is different.
The first original seismic data A and the second original seismic data B are seismic data obtained by respectively and independently detecting the seismic source a and the seismic source B, and the two seismic data do not affect each other and have no aliasing.
That is, the computer device receives the amplitudes from the source a detected by the x detection points at y sampling times, resulting in first raw seismic data a comprising x y data points, each data point comprising three data: distance of detection point from source a, sampling time and amplitude.
The computer equipment receives the amplitudes from the seismic source B detected by the x detection points at z sampling times to obtain second original seismic data B, wherein the second original seismic data B comprises x z data points, and each data point comprises three data: distance between detection point and seismic source b, sampling time and amplitude, x, y and z are integers greater than 1.
The first raw seismic data a comprises a three-dimensional data map of m raw data points, the raw data points comprising coordinates determined by distance, sampling time, and a value determined by amplitude, m being a positive integer.
The first split seismic data a' includes a three-dimensional data map made up of m split data points including coordinates determined by distance, sampling time, and a value determined by amplitude, the coordinates of the split data points being in one-to-one correspondence with the original data points.
Step 202, performing simulated aliasing on the first original seismic data A and the second original seismic data B to obtain aliased seismic data C after aliasing.
Illustratively, the first raw seismic data A and the second raw seismic data B are added. Respectively generating a dot diagram of the first original seismic data A and the second original seismic data B, wherein the x-axis represents sampling time, the y-axis represents distance between a detection point and a seismic source, and adding the two dot diagrams in the same coordinate system: and adding two amplitudes of points with the same coordinates to finally obtain aliasing seismic data: and a dot pattern obtained by adding the two dot patterns.
By way of example, the aliased seismic data obtained in different aliased scenes may be simulated by adjusting the second raw seismic data B. For example, according to the adjustment parameters: and adjusting the second original seismic data B, and aliasing the adjusted second original seismic data B with the first original seismic data A to obtain different aliasing seismic data, wherein the detonation delay time of the seismic source a and the seismic source B, the distance between the seismic source a and the seismic source B and the like.
For example, if the firing delay time of the source a and the source B is increased, the dot pattern of the second original seismic data B is shifted rightward in the coordinate system (the horizontal axis represents the sampling time, and the rightward shift represents the sampling time delay). For another example, if the distance between the source a and the source B is increased, and if the distance between the source B and the detection point is further away after the increase, the dot pattern of the second original seismic data B is moved upward in the coordinate system (the vertical axis represents the distance between the detection point and the source, and the upward movement represents the distance between the detection point and the source B). And (3) aliasing the dot diagram of the adjusted second original seismic data B with the dot diagram of the first original seismic data A to obtain aliasing seismic data C.
And processing the second original seismic data B according to the adjustment parameters to obtain processed second original seismic data B, wherein the adjustment parameters comprise at least one of firing delay time of the seismic source a and the seismic source B and distance between the seismic source a and the seismic source B. And aliasing the first original seismic data A and the processed second original seismic data B to obtain aliasing seismic data C.
Of course, the computer device may also adjust the first original seismic data a according to the adjustment parameter, and alias the adjusted first original seismic data a with the adjusted second original seismic data B to obtain the aliased seismic data C, or alias the adjusted first original seismic data a with the adjusted second original seismic data B to obtain the aliased seismic data C.
By means of the adjustment parameters, aliasing data possibly generated in various aliasing scenes can be obtained, for example, the detonation time interval of two seismic sources is adjusted, the distance between the two seismic sources and the detection point is adjusted, and therefore a more suitable separation method can be selected for different aliasing scenes.
Step 203, performing aliasing data separation on the aliasing seismic data C by adopting an ith separation method to obtain first separated seismic data A 'and second separated seismic data B'; the first split seismic data a 'corresponds to the first original seismic data a, the second split seismic data B' corresponds to the second original seismic data B, and i is sequentially assigned an integer of 1 to n.
By way of example, there may be a number of different separation methods. Alternatively, the same separation method may be used with different separation parameters, or a plurality of separation methods described in this embodiment may be used.
And adopting a plurality of separation methods to separate the aliased seismic data C respectively to obtain first separated seismic data A 'and first separated seismic data B'.
The first split seismic data a' is seismic data of source a split from aliased seismic data. That is, the first split seismic data a 'corresponds to the first original seismic data a, and the closer the first split seismic data a' is to the first original seismic data a, the better the split effect is.
The second split seismic data B' is the seismic data of the source B split from the aliased seismic data. That is, the second separated seismic data B' corresponds to the second original seismic data B, and the closer the second separated seismic data B is to the second original seismic data B, the better the separation effect is.
Illustratively, the first split seismic data a 'is the same dimension as the first original seismic data a, i.e., the first split seismic data a' is also x y data points. The second separated seismic data B' is the same dimension as the second original seismic data B, i.e., the second separated seismic data B is also x×z data points.
For example, as shown in fig. 3 (1), the aliased seismic data C is separated into the first separated seismic data a ' and the second separated seismic data B ' by a separation method, and as shown in fig. 3 (3), the aliased seismic data C is a dot map of the first separated seismic data a '. As shown in (2) of fig. 3, in the plot of the first original seismic data a, PSNR is calculated using the first original seismic data a and the first split seismic data a ', and a larger PSNR indicates a higher fidelity of the first split seismic data a', that is, a better split method.
In this embodiment, an example of separating the aliased seismic data C into two separated seismic data by using a separation method is described, and the separation effect of the separation method is quantitatively analyzed to obtain an evaluation value (PSNR), and other separation methods can also calculate the corresponding PSNR by this method, and then compare the PSNR of each separation method to select the separation method that is ultimately used.
In step 204, an i-th PSNR (Peak Signal to Noise Ratio, peak signal-to-noise ratio) is calculated from the first raw seismic data A and the first split seismic data A'.
The formula for calculating PSNR is:
wherein MAX is the maximum amplitude value in the first original seismic data A; the MSE is the mean square error of the first original seismic data A and the first separated seismic data A'.
The formula for calculating the MSE of the first original seismic data A and the first separated seismic data A' is as follows:
where x is the longitudinal total number of data points in the seismic data (which can be understood as the surveyTotal number of points), y is the lateral total number of data points in the seismic data (which can be understood as the total number of sampling times), i is a non-negative number less than x, j is a non-negative number less than y, (i, j) characterizes the coordinates of one data point in the seismic data. data 1,x,y Data is the amplitude of the (x, y) point in the first original seismic data A 2,x,y Is the amplitude of the (x, y) point in the first split seismic data a'.
Namely, calculating the amplitude difference between the original data point in the first original seismic data A and the corresponding separated data point in the first separated seismic data A', and obtaining m amplitude differences; squaring and adding the m amplitude differences to obtain a first sum; dividing the first sum by m to obtain MSE; reading the maximum value of the amplitude in the original data point to obtain MAX; substituting MSE and MAX into a PSNR formula to calculate the ith PSNR.
Step 205, let i equal i+1 and repeat the above steps until i equals n, resulting in n PSNR.
And (3) repeating the methods from step 203 to step 204, and calculating PSNR of the separated seismic data obtained by separating the aliased seismic data by using different separation methods and the original seismic data to obtain PSNR corresponding to each separation method.
In step 206, the separation method corresponding to the maximum value of the n PSNRs is determined as the target separation method.
For example, if the PSNR of the separation method 1 is 20 and the PSNR of the separation method 2 is 50, the separation method 2 is preferably used for the aliasing data separation in this construction.
In summary, according to the method provided by the embodiment of the application, the acquired two groups of original seismic data are utilized to perform simulated aliasing on the two groups of original seismic data to obtain aliased seismic data, then a plurality of separation methods are sequentially adopted to separate the aliased seismic data to obtain separated seismic data, and the advantages and disadvantages of the separation methods are evaluated by calculating the peak signal-to-noise ratio of the original seismic data and the separated seismic data. The higher the peak signal-to-noise ratio, the higher the fidelity of the separation, and the better the corresponding separation method.
Fig. 4 is a block diagram of an apparatus for analyzing an aliasing data separation effect according to an exemplary embodiment of the present application, the apparatus including:
an acquisition module 401, configured to acquire first original seismic data a and second original seismic data B;
an aliasing module 402, configured to perform simulated aliasing on the first original seismic data a and the second original seismic data B to obtain aliased seismic data C after aliasing;
the separation module 403 is configured to perform aliasing data separation on the aliased seismic data C by using an ith separation device, to obtain first separated seismic data a 'and second separated seismic data B'; the first separated seismic data A 'corresponds to the first original seismic data A, the second separated seismic data B' corresponds to the second original seismic data B, and i is sequentially assigned as an integer from 1 to n;
a calculation module 404, configured to calculate an ith peak signal-to-noise ratio PSNR according to the first original seismic data a and the first separated seismic data a';
the calculation module 404 is configured to repeat the above steps until i is equal to n, thereby obtaining n PSNRs;
a determining module 405, configured to determine a separation device corresponding to a maximum value of the n PSNRs as a target separation device.
In an alternative embodiment, the formula for calculating the i-th PSNR is:
wherein MAX is the maximum amplitude value in the first original seismic data A; MSE is the mean square error of the first raw seismic data A and the first split seismic data A'.
In an alternative embodiment, the first raw seismic data a includes a three-dimensional data plot made up of m raw data points, the raw data points including coordinates determined by distance, sampling time, and a value determined by amplitude, m being a positive integer;
the first separated seismic data A' comprises a three-dimensional data point diagram formed by m separated data points, wherein the separated data points comprise coordinates determined by distance and sampling time and numerical values determined by amplitude, and the coordinates of the separated data points are in one-to-one correspondence with the original data points;
the calculating module 404 is configured to calculate amplitude differences between the original data point and the corresponding separated data point, so as to obtain m amplitude differences;
the calculating module 404 is configured to square and add the m amplitude differences to obtain a first sum;
the calculating module 404 is configured to divide the first sum by m to obtain the MSE;
the calculation module 404 is configured to read a maximum value of the amplitudes in the raw data points to obtain the MAX;
the calculating module 404 is configured to calculate the ith PSNR by substituting the MSE and the MAX into the formula of the PSNR.
In an alternative embodiment, the acquisition module 401 is configured to receive amplitudes from the source a detected by x detection points at y sampling times, and obtain the first raw seismic data a, where the first raw seismic data a includes x times y data points, and each of the x times y data points includes three data: distance between the detection point and the seismic source a, sampling time and amplitude;
the acquisition module 401 is configured to receive amplitudes from the seismic source B detected by the x detection points at z sampling times, to obtain the second raw seismic data B, where the second raw seismic data B includes x times z data points, and each of the x times z data points includes three data: distance between detection point and seismic source b, sampling time and amplitude, x, y and z are integers greater than 1.
In an alternative embodiment, the aliasing module 402 is configured to process the second raw seismic data B according to an adjustment parameter to obtain processed second raw seismic data B, where the adjustment parameter includes at least one of a firing delay time of the source a and the source B, and a distance between the source a and the source B;
the aliasing module 402 is configured to alias the first original seismic data a and the processed second original seismic data B to obtain the aliased seismic data C.
Fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present application. Specifically, the present invention relates to a method for manufacturing a semiconductor device. The computer device 500 includes a central processing unit (english: central Processing Unit, abbreviated as CPU) 501, a system Memory 504 including a random access Memory (english: random Access Memory, abbreviated as RAM) 502 and a Read-Only Memory (english: ROM) 503, and a system bus 505 connecting the system Memory 504 and the central processing unit 501. Computer device 500 also includes a basic Input/Output (I/O) system 506 that facilitates the transfer of information between various devices within the computer, and a mass storage device 507 for storage of an operating system 513, application programs 514, and other program modules 515.
The basic input/output system 506 includes a display 508 for displaying information and an input device 509, such as a mouse, keyboard, etc., for user input of information. Wherein both the display 508 and the input device 509 are coupled to the central processing unit 501 via an input/output controller 510 coupled to the system bus 505. The basic input/output system 506 may also include an input/output controller 510 for receiving and processing input from a number of other devices, such as a keyboard, mouse, or electronic stylus. Similarly, the input/output controller 510 also provides output to a display screen, a printer, or other type of output device.
The mass storage device 507 is connected to the central processing unit 501 through a mass storage controller (not shown) connected to the system bus 505. The mass storage device 507 and its associated computer-readable media provide non-volatile storage for the computer device 500. That is, the mass storage device 507 may include a computer readable medium (not shown) such as a hard disk or a compact disk-Only (CD-ROM) drive.
Computer readable media may include computer storage media and communication media without loss of generality. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes RAM, ROM, erasable programmable read-Only Memory (EPROM for short, english: erasable Programmable Read-Only Memory), electrically erasable programmable read-Only Memory (EEPROM for short, electrically Erasable Programmable Read-Only Memory), flash Memory or other solid state Memory technology, CD-ROM, digital versatile disks (DVD for short, digital Versatile Disc), or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage, or other magnetic storage devices. Of course, those skilled in the art will recognize that computer storage media are not limited to the ones described above. The system memory 504 and mass storage device 507 described above may be collectively referred to as memory.
According to various embodiments of the present application, the computer device 500 may also operate by being connected to a remote computer on a network, such as the Internet. I.e., the computer device 500 may be connected to the network 512 via a network interface unit 511 coupled to the system bus 505, or other types of networks or remote computer systems (not shown) may be coupled to the computer device using the network interface unit 511.
The application further provides a computer readable storage medium, in which at least one instruction, at least one section of program, a code set or an instruction set is stored, where the at least one instruction, the at least one section of program, the code set or the instruction set is loaded and executed by a processor to implement the aliasing data separation effect analysis method provided by the above method embodiment.
The present application provides a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the aliasing data separation effect analysis method provided by the above-described method embodiment.
The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The foregoing description of the preferred embodiments is merely exemplary in nature and is in no way intended to limit the invention, since it is intended that all modifications, equivalents, improvements, etc. that fall within the spirit and scope of the invention.
Claims (10)
1. A method for analyzing an aliasing data separation effect, the method comprising:
collecting first original seismic data A and second original seismic data B;
simulating and aliasing the first original seismic data A and the second original seismic data B to obtain aliased seismic data C after aliasing;
performing aliasing data separation on the aliasing seismic data C by adopting an ith separation method to obtain first separated seismic data A 'and second separated seismic data B'; the first separated seismic data A 'corresponds to the first original seismic data A, the second separated seismic data B' corresponds to the second original seismic data B, and i is sequentially assigned as an integer from 1 to n;
calculating an ith peak signal-to-noise ratio (PSNR) according to the first original seismic data A and the first separated seismic data A';
repeating the steps until i is equal to n, and obtaining n PSNR;
and determining a separation method corresponding to the maximum value in the n PSNR as a target separation method.
2. The method of claim 1, wherein the formula for calculating the i-th PSNR is:
wherein MAX is the maximum amplitude value in the first original seismic data A; MSE is the mean square error of the first raw seismic data A and the first split seismic data A'.
3. The method of claim 2, wherein the first raw seismic data a comprises a three-dimensional data plot of m raw data points, the raw data points comprising coordinates determined by distance, sampling time, and a value determined by amplitude, m being a positive integer;
the first separated seismic data A' comprises a three-dimensional data point diagram formed by m separated data points, wherein the separated data points comprise coordinates determined by distance and sampling time and numerical values determined by amplitude, and the coordinates of the separated data points are in one-to-one correspondence with the original data points;
said calculating an i-th peak signal-to-noise ratio PSNR from said first raw seismic data a and said first separated seismic data a' comprising:
calculating the amplitude difference between the original data point and the corresponding separated data point to obtain m amplitude differences;
squaring and adding the m amplitude differences to obtain a first sum;
dividing the first sum by m to obtain the MSE;
reading the maximum value of the amplitude in the original data point to obtain the MAX;
and substituting the MSE and the MAX into a formula of the PSNR to calculate the ith PSNR.
4. A method according to any one of claims 1 to 3, wherein the acquiring of the first raw seismic data a and the second raw seismic data B comprises:
receiving amplitudes from a seismic source a detected by x detection points at y sampling times, and obtaining first original seismic data A, wherein the first original seismic data A comprises x times y data points, and each data point in the x times y data points comprises three data: distance between the detection point and the seismic source a, sampling time and amplitude;
receiving amplitudes from a seismic source B detected by the x detection points at z sampling times to obtain second original seismic data B, wherein the second original seismic data B comprises x times z data points, and each data point of the x times z data points comprises three data: distance between detection point and seismic source b, sampling time and amplitude, x, y and z are integers greater than 1.
5. The method of claim 4, wherein the performing simulated aliasing on the first original seismic data a and the second original seismic data B to obtain the aliased seismic data C comprises:
processing the second original seismic data B according to an adjustment parameter to obtain processed second original seismic data B, wherein the adjustment parameter comprises at least one of firing delay time of the seismic source a and the seismic source B and distance between the seismic source a and the seismic source B;
and aliasing the first original seismic data A and the processed second original seismic data B to obtain the aliasing seismic data C.
6. An aliased data separation effect analysis apparatus, the apparatus comprising:
the acquisition module is used for acquiring the first original seismic data A and the second original seismic data B;
the aliasing module is used for carrying out simulated aliasing on the first original seismic data A and the second original seismic data B to obtain aliasing seismic data C after aliasing;
the separation module is used for carrying out aliasing data separation on the aliasing seismic data C by adopting an ith separation device to obtain first separated seismic data A 'and second separated seismic data B'; the first separated seismic data A 'corresponds to the first original seismic data A, the second separated seismic data B' corresponds to the second original seismic data B, and i is sequentially assigned as an integer from 1 to n;
the calculation module is used for calculating an ith peak signal-to-noise ratio PSNR according to the first original seismic data A and the first separated seismic data A';
the calculation module is used for enabling i to be equal to i+1, repeating the steps until i is equal to n, and obtaining n PSNR;
and the determining module is used for determining the separation device corresponding to the maximum value in the n PSNR as a target separation device.
7. The apparatus of claim 6 wherein the formula for calculating PSNR is:
wherein MAX is the maximum amplitude value in the first original seismic data A; MSE is the mean square error of the first raw seismic data A and the first split seismic data A'.
8. A computer device, the computer device comprising: a processor and a memory storing a computer program that is loaded and executed by the processor to implement the aliasing data separation effect analysis method according to any one of claims 1 to 5.
9. A computer readable storage medium storing a computer program loaded and executed by a processor to implement the aliased data separation effect analysis method of any one of claims 1 to 5.
10. A computer program product, characterized in that it comprises computer instructions stored in a computer-readable storage medium, from which a processor obtains the computer instructions, such that the processor loads and executes to implement the aliased data separation effect analysis method of any one of claims 1 to 5.
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