Three-dimensional seismic data processing device and method

Abstract

The invention provides a three-dimensional seismic data processing device and method. The method comprises the following steps that a sensor detector arranged on the earth surface or in a well records a wave field signal; the vibrator vehicle moves and excites according to a preset grid, and the recording system synchronously stores seismic trace data; the original three-dimensional seismic data blocks are input into the generator, and the processed three-dimensional seismic data blocks are output; inputting a data block output by the generator or a real high-quality data block through a discriminator, and distinguishing the local statistical characteristics of the generated data and the real data; preprocessing the data; according to the method, noise in the seismic data can be effectively reduced, a data missing region can be repaired, and detail features of the data can be enhanced, so that the reliability of subsequent interpretation and imaging can be improved, the problem of overlarge original data deviation caused by self errors of a detector can be avoided, and the accuracy of data processing can be improved. And the protection effect on the detector is improved.

Description

Technical Field

[0001] The present invention relates to the field of geophysical exploration and computer artificial intelligence technology, and in particular to a device and method for processing three-dimensional seismic data. Background Art

[0002] In geophysical exploration, seismic data acquisition involves placing seismic sources on the surface or underwater to generate artificial seismic waves and record the signals reflected by these seismic waves at different stratum interfaces underground. In this way, important data about stratum interfaces, rock and soil properties, and geological structures can be obtained.

[0003] In geophysical exploration, the acquisition and processing of seismic data is an important means to obtain underground geological structure information. In recent years, Generative Adversarial Network (GAN) has achieved remarkable results in image processing, speech signal enhancement and other fields. GAN can effectively learn the distribution characteristics of data through adversarial training of generators and discriminators. However, the existing three-dimensional seismic data processing solutions face many challenges, such as noise interference, data loss, and resolution degradation during data acquisition. Traditional processing methods have certain limitations, which makes it difficult for the processing results to achieve the expected accuracy. Existing processing methods (such as traditional filtering and interpolation algorithms) show limitations when processing complex underground structures, and it is difficult to balance data quality and computational efficiency. In addition, in the process of collecting three-dimensional seismic data, there are also problems such as insufficient accuracy, difficulty in collection, and easy damage. Summary of the invention

[0004] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a three-dimensional seismic data processing device and method to solve the problems raised in the above-mentioned background technology. The present invention can effectively reduce the noise in the seismic data, repair the data missing areas, and enhance the detailed features of the data to improve the reliability of subsequent interpretation and imaging, avoid the problem of excessive deviation of the original data due to the detector's own errors, and improve the protection effect of the detector.

[0005] In order to achieve the above-mentioned purpose, the present invention is implemented through the following technical scheme: a three-dimensional seismic data processing device, including a processing device body, the processing device body including a seismic data recorder, a verification module, a signal line and a detector, the verification module is installed on the side of the seismic data recorder, and the seismic data recorder is connected to the signal line, the signal line passes through the interior of the verification module, the signal line is connected to multiple detectors, a shell is installed on the top of the detector, and a plug-in rod is inserted at the bottom of the detector, a reel is arranged on the top of the shell, the bottom end of the shell is used to press on the ground, the plug-in rod is used to be embedded under the ground, a verification compartment is opened inside the verification module, and each of the detectors is pushed vertically into the interior from the opening end of the verification compartment.

[0006] Furthermore, the detector includes an outer shell, a receiving shell, an insert rod and a fixing rod, the bottom of the outer shell is integrally formed with a receiving shell, both sides of the outer shell are provided with lifting grooves, the top side of the outer shell is integrally formed with a fixing plate, and the side of the receiving shell is mounted with an impact plate.

[0007] Furthermore, a transmission sleeve is integrally formed on the top of the plug-in rod, a movable plate is installed on the side of the transmission sleeve, a fixed rod is inserted at the end of the movable plate, a spring is sleeved on the top of the fixed rod, and the movable plate extends outward from the inside of the lifting slot.

[0008] Furthermore, the bottom of the receiving sleeve is in an open state, the bottom end of the plug rod passes downward from the bottom of the receiving sleeve, the two ends of the spring are respectively connected to the surfaces of the movable plate and the fixed plate, and the transmission sleeve is embedded in the interior of the outer shell.

[0009] Furthermore, the verification module includes a motor and a vibration simulation chamber, a driving shaft is inserted into the interior of the vibration simulation chamber, a motor is installed at one end of the driving shaft, a cam is integrally formed on the surface of the driving shaft, and a pressure rod is also inserted into the interior of the vibration simulation chamber, and end plates are integrally formed at both ends of the pressure rod.

[0010] Furthermore, the end plate is embedded in the inner wall of the vibration simulation chamber, an oscillation plate is installed on the side of the pressure-bearing rod, an elastic sheet is attached to the side of the end plate, the elastic sheet has an overall arc-shaped structure, and the other end of the elastic sheet rests on the inner wall of the vibration simulation chamber, and the end of the oscillation plate is used to rest on the impact plate of each detector.

[0011] A method for processing three-dimensional seismic data, comprising the following steps:

[0012] Step 1: The sensor detectors arranged on the surface or in the well record the wave field signal, and multiple detectors are combined according to rules to suppress ground interference;

[0013] Step 2: The source vehicle moves and excites according to the preset grid, and the recording system stores the seismic trace data synchronously;

[0014] Step 3: inputting the original 3D seismic data block into the generator, and outputting the processed 3D seismic data block;

[0015] Step 4: Input the data block output by the generator or the real high-quality data block through the discriminator to distinguish the local statistical characteristics of the generated data and the real data;

[0016] Step 5: Preprocess the data, use the training set to perform adversarial training on GAN, and optimize the generator and discriminator alternately;

[0017] Step 6: Use quantitative indicators and qualitative comparisons to evaluate the processing results and perform visualization.

[0018] Furthermore, the core architecture of the discriminator includes an input layer, a dimensional convolution layer, a batch normalization layer and a fully connected layer, wherein the number of convolution kernels is set to 16 or 32 for extracting different types of features; the step size is set to (1,1,1) or (2,2,2) for downsampling the data while extracting features to reduce the spatial dimension of the data.

[0019] Furthermore, it also includes the training process of the adversarial network, which combines the adversarial loss with the seismic data characteristic constraints, including the adversarial loss, the reconstruction loss and the total loss of the generator. The gradient penalty adopts Wasserstein GAN, and the gradient penalty term needs to be added to stabilize the training. The total loss of the generator L G It is the weighted sum of adversarial loss and reconstruction loss, and the formula is:

[0020] LG=L Gadv +γL Grec

[0021] Among them, γ is a hyperparameter used to control the weight of reconstruction loss in the total loss. By adjusting the value of γ, it is used to balance the emphasis of the generator between deceiving the discriminator and generating content similar to real data.

[0022] Furthermore, the training process of the data is also included, including: pre-training the generator using only content loss: β = 1, α = 0, quickly converging to a rough solution; jointly training the generator and the discriminator, gradually increasing the adversarial loss weight: α = 0.1, β = 0.9; introducing a multi-scale discriminator to improve the ability to distinguish details; using the Adam optimizer: β 1 =0.5,β 2 =0.999, the initial learning rate is adjusted dynamically.

[0023] Beneficial effects of the present invention:

[0024] 1. The three-dimensional seismic data processing device can provide protection for each detector during the acquisition process, reducing the difficulty of three-dimensional seismic data acquisition operations, and can quickly calibrate each detector through a calibration module to ensure the effectiveness of the final three-dimensional seismic data processing and avoid the problem of excessive deviation of the original data due to the detector's own errors.

[0025] 2. This method for 3D seismic data processing has stronger noise reduction capabilities and can effectively suppress random noise and system noise in seismic data. GAN can accurately complete missing data caused by equipment or environmental problems during the acquisition process.

[0026] 3. This method for processing three-dimensional seismic data can effectively enhance data features and strengthen the detailed structure of seismic data, which is helpful for subsequent geological interpretation and modeling. At the same time, compared with traditional methods, GAN shows higher computational efficiency when processing complex three-dimensional data. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A flowchart of a method for processing three-dimensional seismic data according to the present invention;

[0028] Figure 2 It is a structural schematic diagram of a three-dimensional seismic data processing device of the present invention;

[0029] Figure 3 It is a schematic diagram of the structure of the detector part of the present invention;

[0030] Figure 4 This is a disassembled diagram of the detector of the present invention;

[0031] Figure 5 It is a structural schematic diagram of the verification module part of the present invention;

[0032] Figure 6 This is an internal split diagram of the verification module of the present invention;

[0033] In the figure: 1. seismic data recorder; 2. calibration module; 3. signal line; 4. detector; 5. housing; 6. reeling column; 7. receiving casing; 8. impact plate; 9. plug-in rod; 10. transmission sleeve; 11. lifting slot; 12. fixed plate; 13. threaded sleeve; 14. threaded rod; 15. movable plate; 16. fixed rod; 17. spring; 18. storage bin; 19. calibration bin; 20. motor; 21. vibration simulation chamber; 22. drive shaft; 23. cam; 24. pressure rod; 25. end plate; 26. elastic sheet; 27. oscillation plate. DETAILED DESCRIPTION

[0034] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0035] See also Figures 1 to 6 The present invention provides the following technical solutions: a three-dimensional seismic data processing device, comprising a processing device body, the processing device body comprising a seismic data recorder 1, a verification module 2, a signal line 3 and a detector 4, the verification module 2 is installed on the side of the seismic data recorder 1, and the seismic data recorder 1 is connected with a signal line 3, the signal line 3 passes through the inside of the verification module 2, the signal line 3 is connected with a plurality of detectors 4, a housing 5 is installed on the top of the detector 4, and a plug-in rod 9 is inserted at the bottom of the detector 4, a reeling column 6 is arranged on the top of the housing 5, the bottom end of the housing 5 is used to press on the ground, the plug-in rod 9 is used to be embedded under the ground, a verification chamber 19 is opened inside the verification module 2, and each of the detectors 4 is vertically pushed into the inside from the opening end of the verification chamber 19. The three-dimensional seismic data processing device can be used to collect seismic data in the early stage, and the modules collected in the collection process are calibrated to improve the accuracy of the data processing process.

[0036] When the invention is used, multiple detectors 4 are directly embedded into the ground, and the buffer structure on the outer shell 5 is used to provide protection for each detector 4 during the acquisition process, thereby reducing the difficulty of three-dimensional seismic data acquisition operations, and each detector 4 can be quickly verified through the verification module 2 to ensure the effectiveness of the final three-dimensional seismic data processing and avoid the problem of excessive deviation of the original data due to the errors of the detector 4 itself.

[0037] In this embodiment, the detector 4 includes a housing 5, a receiving sleeve 7, a plug rod 9 and a fixed rod 16. The bottom of the housing 5 is integrally formed with the receiving sleeve 7, and lifting grooves 11 are provided on both sides of the housing 5. The top side of the housing 5 is integrally formed with a fixed plate 12, and the side of the receiving sleeve 7 is attached with an impact plate 8. The top of the plug rod 9 is integrally formed with a transmission sleeve 10, and the side of the transmission sleeve 10 is installed with a movable plate 15. The end of the movable plate 15 is inserted with a fixed rod 16, and the top of the fixed rod 16 is sleeved with a spring 17. The movable plate 15 passes out from the inside of the lifting groove 11. The bottom of the receiving sleeve 7 is in an open state, and the bottom end of the plug rod 9 passes downward from the bottom of the receiving sleeve 7. The two ends of the spring 17 are respectively connected to the surface of the movable plate 15 and the fixed plate 12, and the transmission sleeve 10 is embedded in the inside of the housing 5.

[0038] Specifically, the detector 4 itself has a double-layer structure, and the bottom layer of the plug-in rod 9 is partially in a separated state through the top movable plate 15 and the fixed plate 12. The spring 17 applies downward pressure to push the movable plate 15 downward. This pushing effect causes the tip part of the bottom of the plug-in rod 9 to pass downward from the bottom of the receiving sleeve 7, and maintain this state to install the entire detector 4. During installation, the bottom plug-in rod 9 will be embedded in the ground. When there are hard obstacles such as stones under the ground, the plug-in rod 9 is blocked by the bottom and cannot continue to move downward. At this time, the movable plate 15 will move up relative to the fixed plate 12, and the spring 17 will be compressed, thereby avoiding excessive impact force from the top during installation, which will cause damage to the tip part of the plug-in rod 9, so as to protect the end of the plug-in rod 9.

[0039] In this embodiment, the verification module 2 includes a motor 20 and a vibration simulation chamber 21, a driving shaft 22 is inserted into the interior of the vibration simulation chamber 21, a motor 20 is installed at one end of the driving shaft 22, a cam 23 is integrally formed on the surface of the driving shaft 22, and a pressure rod 24 is also inserted into the interior of the vibration simulation chamber 21, and end plates 25 are integrally formed at both ends of the pressure rod 24. The end plate 25 is embedded in the inner wall of the vibration simulation chamber 21, an oscillation plate 27 is installed on the side of the pressure rod 24, and an elastic sheet 26 is attached to the side of the end plate 25. The elastic sheet 26 is an arc-shaped structure as a whole, and the other end of the elastic sheet 26 is against the inner wall of the vibration simulation chamber 21, and the end of the oscillation plate 27 is used to abut against the impact plate 8 of each detector 4.

[0040] Specifically, after each detector 4 is placed back into the calibration chamber 19 with the aid of the signal line 3, two adjacent detectors 4 are placed side by side, and the motor 20 is started at this time. The drive shaft 22 and the cam 23 on the surface of the drive shaft 22 are driven to rotate by the motor 20. The side of the cam 23 will periodically collide with the side pressure rod 24 during the rotation. The pressure rod 24 is pushed by the elastic sheet 26 in the initial state, causing the side oscillation plate 27 to partially move to an area away from each detector 4. After being collided with the cam 23, the entire pressure rod 24 is pushed continuously. At this time, the end plate 25 translates toward the position of the detector 4, and the elastic sheet 26 is compressed, and finally the oscillation plate 27 collides with the impact plate 8 on the side of each detector 4. The collision effect acts on the sensor inside the detector 4, and the data collected by each detector 4 is compared to determine whether the collection result of each detector 4 is accurate and consistent, so as to achieve the purpose of synchronous calibration and detection of multiple detectors 4.

[0041] This embodiment also provides a method for processing three-dimensional seismic data, comprising the following steps:

[0042] Step 1: The sensor detectors 4 arranged on the surface or in the well record the wave field signal, and multiple detectors 4 are combined according to a rule to suppress ground interference;

[0043] Step 2: The source vehicle moves and excites according to the preset grid, and the recording system stores the seismic trace data synchronously;

[0044] Step 3: inputting the original 3D seismic data block into the generator, and outputting the processed 3D seismic data block;

[0045] Step 4: Input the data block output by the generator or the real high-quality data block through the discriminator to distinguish the local statistical characteristics of the generated data and the real data;

[0046] Step 5: Preprocess the data, use the training set to perform adversarial training on GAN, and optimize the generator and discriminator alternately;

[0047] Step 6: Use quantitative indicators and qualitative comparisons to evaluate the processing results and perform visualization.

[0048] In this embodiment, the core architecture of the discriminator includes an input layer, a dimensional convolution layer, a batch normalization layer and a fully connected layer, wherein the number of convolution kernels is set to 16 or 32 for extracting different types of features; the step size is set to (1,1,1) or (2,2,2) for downsampling the data while extracting features to reduce the spatial dimension of the data.

[0049] This embodiment also includes a training process of an adversarial network, which combines adversarial loss with seismic data characteristic constraints, including adversarial loss, reconstruction loss and total loss of the generator. The gradient penalty adopts Wasserstein GAN, and a gradient penalty term needs to be added to stabilize the training;

[0050] The reconstruction loss is used to measure the difference between the denoised data generated by the generator and the real clean data, ensuring that the generated data is consistent with the real data in content. The commonly used reconstruction loss is the mean squared error loss function (MSE). Assume that the real clean data is y, and the denoised data generated by the generator is The reconstruction loss The formula is:

[0051]

[0052] in:

[0053] n is the total number of elements in the data.

[0054] y i is the i-th element of the real clean data.

[0055] is the i-th element of the denoised data generated by the generator.

[0056] The total loss of the generator is L G It is the weighted sum of adversarial loss and reconstruction loss, and the formula is:

[0057] L G =L Gadv +γL Grec

[0058] Among them, γ is a hyperparameter used to control the weight of reconstruction loss in the total loss. By adjusting the value of γ, it is used to balance the emphasis of the generator between deceiving the discriminator and generating content similar to real data.

[0059] This embodiment also includes a data training process, including: pre-training the generator using only content loss: β = 1, α = 0, quickly converging to a rough solution; jointly training the generator and the discriminator, gradually increasing the adversarial loss weight: α = 0.1, β = 0.9; introducing a multi-scale discriminator to improve the ability to distinguish details; using the Adam optimizer: β 1 =0.5,β 2 =0.999, the initial learning rate is adjusted dynamically.

[0060] The basic principles and main features of the present invention and the advantages of the present invention are shown and described above. For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention.

[0061] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A three-dimensional seismic data processing device, comprising a processing device body, characterized in that: The processing device body comprises a seismic data recorder (1), a verification module (2), a signal line (3) and a detector (4); the verification module (2) is installed on the side of the seismic data recorder (1), and the seismic data recorder (1) is connected to the signal line (3); the signal line (3) passes through the interior of the verification module (2); the signal line (3) is connected to a plurality of detectors (4); a housing (5) is installed on the top of the detector (4); a plug-in rod (9) is inserted at the bottom of the detector (4); a reeling column (6) is arranged on the top of the housing (5); the bottom end of the housing (5) is used to be pressed on the ground; the plug-in rod (9) is used to be embedded under the ground; a verification chamber (19) is opened inside the verification module (2); each detector (4) is vertically pushed into the interior from the opening end of the verification chamber (19).

2. A three-dimensional seismic data processing device according to claim 1, characterized in that: The detector (4) comprises an outer shell (5), a receiving shell (7), a plug-in rod (9) and a fixing rod (16); the bottom of the outer shell (5) is integrally formed with the receiving shell (7); both sides of the outer shell (5) are provided with lifting grooves (11); the top side of the outer shell (5) is integrally formed with a fixing plate (12); and the side of the receiving shell (7) is mounted with an impact plate (8).

3. The three-dimensional seismic data processing device according to claim 2, characterized in that: A transmission sleeve (10) is integrally formed on the top of the plug-in rod (9), a movable plate (15) is installed on the side of the transmission sleeve (10), a fixed rod (16) is inserted at the end of the movable plate (15), a spring (17) is sleeved on the top of the fixed rod (16), and the movable plate (15) is extended outward from the inside of the lifting groove (11).

4. The three-dimensional seismic data processing device according to claim 3, characterized in that: The bottom of the receiving sleeve (7) is in an open state, the bottom end of the plug rod (9) extends downward from the bottom of the receiving sleeve (7), the two ends of the spring (17) are respectively connected to the surfaces of the movable plate (15) and the fixed plate (12), and the transmission sleeve (10) is embedded in the interior of the outer shell (5).

5. The three-dimensional seismic data processing device according to claim 2, characterized in that: The verification module (2) comprises a motor (20) and a vibration simulation chamber (21), a driving shaft (22) is inserted into the interior of the vibration simulation chamber (21), the motor (20) is mounted on one end of the driving shaft (22), a cam (23) is integrally formed on the surface of the driving shaft (22), a pressure rod (24) is also inserted into the interior of the vibration simulation chamber (21), and end plates (25) are integrally formed at both ends of the pressure rod (24).

6. A three-dimensional seismic data processing device according to claim 5, characterized in that: The end plate (25) is embedded in the inner wall of the vibration simulation chamber (21), an oscillation plate (27) is installed on the side of the pressure-bearing rod (24), and an elastic sheet (26) is attached to the side of the end plate (25). The elastic sheet (26) is an arc-shaped structure as a whole, and the other end of the elastic sheet (26) is against the inner wall of the vibration simulation chamber (21), and the end of the oscillation plate (27) is used to be against the impact plate (8) of each detector (4).

7. A method for processing three-dimensional seismic data, characterized in that: The following steps are involved: Step 1: The sensor detectors arranged on the surface or in the well record the wave field signal, and multiple detectors are combined according to rules to suppress ground interference; Step 2: The source vehicle moves and excites according to the preset grid, and the recording system stores the seismic trace data synchronously; Step 3: inputting the original 3D seismic data block into the generator, and outputting the processed 3D seismic data block; Step 4: Input the data block output by the generator or the real high-quality data block through the discriminator to distinguish the local statistical characteristics of the generated data and the real data; Step 5: Preprocess the data, use the training set to perform adversarial training on GAN, and optimize the generator and discriminator alternately; Step 6: Use quantitative indicators and qualitative comparisons to evaluate the processing results and perform visualization.

8. The three-dimensional seismic data processing method according to claim 7, characterized in that: The core architecture of the discriminator includes an input layer, a dimensional convolution layer, a batch normalization layer and a fully connected layer, where the number of convolution kernels is set to 16 or 32 to extract different types of features; the step size is set to (1,1,1) or (2,2,2) to downsample the data while extracting features and reduce the spatial dimension of the data.

9. The three-dimensional seismic data processing method according to claim 8, characterized in that: It also includes the training process of the adversarial network, which combines the adversarial loss with the seismic data characteristic constraints, including the adversarial loss, the reconstruction loss and the total loss of the generator. The gradient penalty adopts Wasserstein GAN, and the gradient penalty term needs to be added to stabilize the training. The total loss of the generator L G It is the weighted sum of adversarial loss and reconstruction loss, and the formula is: LG=L Gadv +γL Grec Among them, γ is a hyperparameter used to control the weight of reconstruction loss in the total loss. By adjusting the value of γ, it is used to balance the emphasis of the generator between deceiving the discriminator and generating content similar to real data.

10. The three-dimensional seismic data processing method according to claim 9, characterized in that: It also includes the training process of the data, including: the pre-trained generator only uses content loss: β=1, α=0, and converges quickly to a rough solution; jointly train the generator and discriminator, and gradually increase the adversarial loss weight: α=0.1, β=0.9; introduce a multi-scale discriminator to improve the ability to distinguish details; use the Adam optimizer: β1=0.5, β2=0.999, and dynamically adjust the initial learning rate.

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