A method and apparatus for seismic data noise attenuation

Abstract

The application provides a seismic data noise attenuation method and device, the method comprising: obtaining seismic data to be denoised; inputting the seismic data into a pre-constructed artificial neural network to output noise intensity data; the artificial neural network is a seismic data noise intensity prediction model obtained by training seismic data samples based on a machine learning algorithm; limiting noisy seismic data based on Casado filtering setting space parameters, determining the size of the Hankel matrix in Casado filtering; obtaining noise intensity data limited by Casado filtering setting space parameters; performing Casado filtering on the noisy seismic data limited by the setting space parameters with the number of ranks to complete random noise attenuation of the seismic data; and performing iterative cycles with the setting space parameters to complete random noise attenuation of all seismic data. The application converts the noise intensity of the seismic data into the number of ranks, so that the rank of the Casado filtering changes with the signal-to-noise ratio of the seismic data, and the random noise attenuation effect is good.

Description

Technical Field

[0001] This application relates to the field of petroleum geophysical exploration technology, and in particular to a method and apparatus for attenuating seismic data noise. Background Technology

[0002] Seismic exploration refers to the geophysical exploration method of artificially generating and receiving seismic waves, and inferring the properties and morphology of underground rock strata by observing and analyzing the propagation patterns of seismic waves underground. It plays a key role in oil and gas, coalfield, and engineering geological exploration, as well as in the detection of the deep structure of the Earth's crust and upper mantle.

[0003] During seismic data acquisition, a large amount of interference noise is often recorded simultaneously. Based on its characteristics, noise can be mainly divided into random noise and coherent noise. However, regardless of the type of noise, it will interfere with or even obscure the effective signal, leading to a decrease in the signal-to-noise ratio of the seismic data. One of the key steps in seismic data processing is to attenuate noise and highlight the effective signal, thereby providing reliable data for seismic interpretation and inversion.

[0004] In existing technologies, there are denoising methods designed based on the differences between signals and noise in order to improve the signal-to-noise ratio of seismic data. Random noise attenuation methods are mainly divided into two categories: one is conventional methods based on physical principles, and the other is artificial intelligence methods.

[0005] However, conventional methods for attenuating random noise require manual parameter tuning, which is time-consuming and labor-intensive. Due to limitations in human experience and time costs, optimal parameters are often not found during the processing, resulting in suboptimal processing effects. Furthermore, even if optimal parameters are obtained from experimental data, the signal-to-noise ratio (SNR) varies within a dataset; not only do different profiles have different SNRs, but the SNR also varies within the same profile. Therefore, when using optimal parameters obtained from experimental data to attenuate noise across the entire dataset, the processing parameters are often inappropriate, leading to unsatisfactory noise attenuation results.

[0006] Understandably, when using a trained model for random noise attenuation, AI methods do not require manual parameter tuning and generally achieve good noise attenuation results with fast computation speed. However, in real-world data applications, AI methods are not suitable for data with uneven energy distribution or for random noise attenuation of seismic data containing other types of noise, resulting in poor performance in practical applications. Summary of the Invention

[0007] This application discloses a method and apparatus for attenuating noise in seismic data.

[0008] In a first aspect, this application discloses a method for attenuating noise in seismic data, the method comprising:

[0009] Obtain the seismic data to be denoised;

[0010] The earthquake data is input into a pre-constructed artificial neural network, which outputs noise intensity data; wherein, the artificial neural network is an earthquake data noise intensity prediction model trained on earthquake data samples based on machine learning algorithms;

[0011] Spatial parameters were set based on Cadzow filtering to limit noisy seismic data, and the size of the Hankel matrix in Cadzow filtering was determined.

[0012] Obtain noise intensity data within the spatial parameters of the Cadzow filter setting;

[0013] Using the number of rank values ​​of the Hankel matrix, Cadzow filtering is applied to noisy seismic data with defined spatial parameters to attenuate random noise in the seismic data.

[0014] The random noise attenuation of all seismic data is completed by iteratively looping through set spatial parameters.

[0015] Preferably, the step of setting spatial parameters to limit noisy seismic data based on Cadzow filtering and determining the size of the Hankel matrix in Cadzow filtering includes:

[0016] The noisy seismic data is constrained based on the spatial parameters [2Lx+1, 2Ly+1, Lt] of the Cadzow filter, and the size of the Hankel matrix in the Cadzow filter is determined to be [Lh, Lh]. .

[0017] Preferably, the step of performing Cadzow filtering on the noisy seismic data with predetermined spatial parameters, based on the number of rank values ​​of the Hankel matrix, to attenuate the random noise in the seismic data, includes:

[0018] The noise intensity is converted into the number of ranks K according to the following expression, and Cadzow filtering is performed on the noisy seismic data bounded by the spatial parameters [2Lx+1, 2Ly+1, Lt] using the number of ranks of the Hankel matrix to complete the random noise attenuation of the seismic data:

[0019]

[0020] Where Sigma represents the random noise intensity of the seismic data, sigmaHigh represents the highest random noise intensity of the seismic data, and sigmaLow represents the lowest random noise intensity of the seismic data.

[0021] Preferably, the artificial neural network is constructed in the following manner:

[0022] S1. Using the label data A as input data for the artificial neural network, the direct output of the neural network... and The predicted noise intensity EstiSigma of the indirect output of the neural network is calculated using the following expression:

[0023] ;

[0024] S2. Apply Gaussian filtering to the label data B, with a filter window size of Lg; determine the Gaussian-filtered label data B as the actual noise intensity and use it as the output target of the artificial neural network.

[0025] S3. Calculate the error between the predicted noise intensity EstiSigma indirectly output by the artificial neural network in S1 and the actual noise intensity in S2. Use the error as feedback to train the artificial neural network until the residual is less than a set threshold. Save the various parameters of the artificial neural network to the parameter configuration file to complete the construction of the artificial neural network.

[0026] Preferably, the tag pairs are formed in the following manner:

[0027] Generate corresponding noisy data and random noise data based on forward modeling methods;

[0028] The first label data corresponding to the noisy seismic data and the second label data corresponding to the random noise data are used to form a label pair.

[0029] Preferably, the step of generating the corresponding noisy data and random noise data based on the forward modeling method includes:

[0030] Simulate noiseless seismic data and random noise of different intensities, respectively;

[0031] Random noise of varying intensities is added to noise-free seismic data to obtain noisy seismic data with different signal-to-noise ratios. The noise intensity range is [SigmaLow, SigmaHigh], which covers the noise intensity range of actual seismic data.

[0032] Secondly, this application discloses a seismic data noise attenuation device, the device comprising:

[0033] The earthquake data acquisition module is used to acquire earthquake data to be denoised.

[0034] The noise intensity prediction module is used to input the seismic data into a pre-constructed artificial neural network and output noise intensity data; wherein, the artificial neural network is a model for predicting the noise intensity of seismic data, which is trained on seismic data samples based on machine learning algorithms.

[0035] The filtering module is used to limit noisy seismic data by setting spatial parameters based on Cadzow filtering and to determine the size of the Hankel matrix in Cadzow filtering.

[0036] The noise intensity limiting module is used to obtain noise intensity data limited by the spatial parameters of the Cadzow filter setting.

[0037] The noise attenuation module is used to perform Cadzow filtering on noisy seismic data limited by a set spatial parameter, based on the number of rank values ​​of the Hankel matrix, to complete the random noise attenuation of the seismic data; and to perform iterative loops based on the set spatial parameter to complete the random noise attenuation of all seismic data.

[0038] Thirdly, this application discloses an electronic device comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to perform the method as described in any of the preceding aspects.

[0039] Fourthly, this application discloses a non-transitory computer-readable storage medium in which, when the instructions in the storage medium are executed by a processor of an electronic device, enable the electronic device to perform the methods described in any of the preceding aspects.

[0040] Fifthly, this application discloses a computer program product in which, when the instructions in the computer program product are executed by a processor of an electronic device, the electronic device is enabled to perform the method described in any of the preceding aspects.

[0041] The technical solution provided in this application may include the following beneficial effects:

[0042] This application provides a Cadzow filtering method based on intelligent noise intensity prediction. The automated rank selection Cadzow filtering strategy based on intelligent noise intensity prediction is as follows: artificial intelligence is used to predict the noise intensity of seismic data, and the noise intensity is converted into the number of ranks. This allows the rank of the Cadzow filtering to change with the signal-to-noise ratio of the seismic data, ultimately achieving the best random noise attenuation effect. This method has good application prospects in seismic data processing. Attached Figure Description

[0043] Figure 1 A flowchart of a seismic data noise attenuation method provided in this application;

[0044] Figure 2 A schematic diagram of the noiseless seismic data provided in this application;

[0045] Figure 3 A schematic diagram of random noise provided in this application;

[0046] Figure 4 A schematic diagram of noisy seismic data provided for this application;

[0047] Figure 5 A schematic diagram of the artificial neural network provided in this application;

[0048] Figure 6 A schematic diagram of the actual data to be denoised provided in this application;

[0049] Figure 7 A schematic diagram illustrating the noise intensity predicted by the artificial neural network provided in this application;

[0050] Figure 8 A schematic diagram of seismic data after Cadzow filtering provided in this application;

[0051] Figure 9 A structural diagram of the seismic data noise attenuation device provided in this application;

[0052] Figure 10 A block diagram of an electronic device provided in this application;

[0053] Figure 11 A block diagram of another electronic device provided in this application. Detailed Implementation

[0054] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0055] To address the shortcomings of existing technologies, this application provides a method and apparatus for attenuating seismic data noise. The seismic data noise attenuation method provided in this application is described below.

[0056] Example 1

[0057] Reference Figure 1 This is a flowchart of a seismic data noise attenuation method provided in this application. This method can be applied to electronic devices, and specifically includes the following steps:

[0058] Step S101: Obtain the seismic data to be denoised.

[0059] Step S102: Input the earthquake data into a pre-constructed artificial neural network and output noise intensity data; wherein, the artificial neural network is an earthquake data noise intensity prediction model obtained by training earthquake data samples based on machine learning algorithms.

[0060] Step S103: Based on the Cadzow filter, set spatial parameters to limit the noisy seismic data and determine the size of the Hankel matrix in the Cadzow filter.

[0061] Step S104: Obtain noise intensity data limited by the spatial parameters of the Cadzow filter settings.

[0062] Step S105: Using the number of ranks of the Hankel matrix, perform Cadzow filtering on the noisy seismic data with set spatial parameters to attenuate the random noise of the seismic data.

[0063] Step S106: Iterate through the set spatial parameters to complete the random noise attenuation of all seismic data.

[0064] In one implementation, the noisy seismic data can be constrained and the size of the Hankel matrix in the Cadzow filter can be determined as follows: the noisy seismic data is constrained based on the spatial parameters of the Cadzow filter [2Lx+1, 2Ly+1, Lt], and the size of the Hankel matrix in the Cadzow filter is determined to be [Lh, Lh], where... .

[0065] In one implementation, random noise attenuation of seismic data can be achieved as follows: the noise intensity is converted into the number of ranks K according to the following expression, and Cadzow filtering is performed on the noisy seismic data bounded by the spatial parameters [2Lx+1, 2Ly+1, Lt] using the number of ranks of the Hankel matrix, thus completing the random noise attenuation of the seismic data.

[0066]

[0067] Where Sigma represents the random noise intensity of the seismic data, sigmaHigh represents the highest random noise intensity of the seismic data, and sigmaLow represents the lowest random noise intensity of the seismic data.

[0068] In one implementation, the artificial neural network can be constructed in the following manner:

[0069] S1. Using the label data A as input data for the artificial neural network, the direct output of the neural network... and The predicted noise intensity EstiSigma of the indirect output of the neural network is calculated using the following expression:

[0070] ;

[0071] S2. Apply Gaussian filtering to the label data B, with a filter window size of Lg; determine the Gaussian-filtered label data B as the actual noise intensity and use it as the output target of the artificial neural network.

[0072] S3. Calculate the error between the predicted noise intensity EstiSigma indirectly output by the artificial neural network in S1 and the actual noise intensity in S2. Use the error as feedback to train the artificial neural network until the residual is less than a set threshold. Save the various parameters of the artificial neural network to the parameter configuration file to complete the construction of the artificial neural network.

[0073] It should be noted that in step S1, the label pairs can be formed in the following manner, including: generating corresponding noisy data and random noise data based on the forward modeling method; and forming a label pair by combining the first label data corresponding to the noisy seismic data and the second label data corresponding to the random noise data.

[0074] In one scenario, the step of generating corresponding noisy data and random noise data based on the forward modeling method includes: simulating noiseless seismic data and random noise of different intensities respectively; adding random noise of different intensities to the noiseless seismic data to obtain noisy seismic data with corresponding signal-to-noise ratios; wherein the noise intensity range is [SigmaLow, SigmaHigh], covering the noise intensity range of actual seismic data.

[0075] The following section provides a detailed explanation of the seismic data noise attenuation method provided in this application, using a specific example. Please refer to [link to relevant documentation]. Figures 2 to 8 .

[0076] (1) For the noise-free seismic data, random noise and noisy seismic data obtained by forward modeling, please refer to [link to relevant documentation]. Figure 2 , Figure 3 , Figure 4 ;

[0077] (2) For constructing artificial neural networks, please refer to [link / reference]. Figure 5 The artificial neural network was trained using forward-modeled noisy seismic data and random noise.

[0078] (3) Use the trained artificial neural network to predict the actual data to be denoised. Please refer to [link to documentation]. Figure 6 ;

[0079] (4) Obtain the noise intensity predicted by the artificial neural network, such as Figure 7 As shown;

[0080] (5) Obtain the corresponding noise intensity according to the spatial parameters set by Cadzow filtering, calculate the number of corresponding ranks, and perform Cadzow filtering on the seismic data volume limited by the set spatial parameters to obtain the random noise attenuation result of the seismic data volume limited by the set spatial parameters.

[0081] (6) Iterative processing yields the random noise attenuation result for the entire data volume, such as Figure 8 As shown.

[0082] It's important to note that while Cadzow filtering can be used to attenuate random noise in conventional denoising schemes, the most crucial parameter is the number of ranks, which plays a key role in random noise attenuation. The choice of the number of ranks is directly related to the signal-to-noise ratio (SNR) of the seismic data. In conventional denoising, the number of ranks needs to be manually selected. Generally, when using Cadzow filtering to denoise data, only one value can be chosen for the number of ranks; however, the SNR of seismic data varies, so when the number of ranks is fixed, the denoising effect of Cadzow filtering is not ideal.

[0083] This application provides a Cadzow filtering method based on intelligent noise intensity prediction, which, based on conventional denoising schemes, automatically selects the rank of the Cadzow filter by using artificial intelligence to predict the noise intensity of seismic data and converts the noise intensity into the number of ranks. This allows the rank of the Cadzow filter to change with the signal-to-noise ratio of the seismic data, ultimately achieving the best random noise attenuation effect. This method has good application prospects in seismic data processing.

[0084] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions involved are not necessarily required by this application.

[0085] Example 2

[0086] Reference Figure 9 This is a structural diagram of a seismic data noise attenuation device provided in this application. The device includes:

[0087] The seismic data acquisition module 210 is used to acquire seismic data to be denoised;

[0088] The noise intensity prediction module 220 is used to input the seismic data into a pre-constructed artificial neural network and output noise intensity data; wherein, the artificial neural network is a model for predicting the noise intensity of seismic data obtained by training seismic data samples based on machine learning algorithms;

[0089] The filtering module 230 is used to limit noisy seismic data by setting spatial parameters based on Cadzow filtering and to determine the size of the Hankel matrix in Cadzow filtering.

[0090] The noise intensity limiting module 240 is used to acquire noise intensity data limited by the spatial parameters of the Cadzow filter setting.

[0091] The noise attenuation module 250 is used to perform Cadzow filtering on noisy seismic data limited by a set spatial parameter, based on the number of rank values ​​of the Hankel matrix, to complete the random noise attenuation of the seismic data; and to perform iterative looping based on the set spatial parameter to complete the random noise attenuation of all seismic data.

[0092] This application provides a Cadzow filtering scheme based on intelligent noise intensity prediction. The automated rank selection Cadzow filtering strategy based on intelligent noise intensity prediction is as follows: artificial intelligence is used to predict the noise intensity of seismic data, and the noise intensity is converted into the number of ranks. This allows the rank of the Cadzow filtering to change with the signal-to-noise ratio of the seismic data, ultimately achieving the best random noise attenuation effect. It has good application prospects in seismic data processing.

[0093] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0094] Example 3

[0095] Optionally, this application also provides an electronic device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the various processes of the above method embodiments and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0096] This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the various processes of the above-described method embodiments and achieves the same technical effects. To avoid repetition, it will not be described again here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0097] Figure 10 This application provides a block diagram of an electronic device 800. For example, the electronic device 800 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0098] Reference Figure 10 The electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.

[0099] Processing component 802 typically controls the overall operation of electronic device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0100] Memory 804 is configured to store various types of data to support the operation of device 800. Examples of this data include instructions for any application or method operating on electronic device 800, contact data, phonebook data, messages, images, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0101] Power supply component 806 provides power to various components of electronic device 800. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 800.

[0102] Multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0103] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when electronic device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0104] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0105] Sensor assembly 814 includes one or more sensors for providing state assessments of various aspects of electronic device 800. For example, sensor assembly 814 may detect the on / off state of device 800, the relative positioning of components such as the display and keypad of electronic device 800, changes in position of electronic device 800 or a component of electronic device 800, the presence or absence of user contact with electronic device 800, orientation or acceleration / deceleration of electronic device 800, and temperature changes of electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0106] Communication component 816 is configured to facilitate wired or wireless communication between electronic device 800 and other devices. Electronic device 800 can access wireless networks based on communication standards, such as WiFi, carrier networks (such as 2G, 3G, 4G, or 5G), or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast operation information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0107] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0108] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, which can be executed by a processor 820 of an electronic device 800 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0109] Example 4

[0110] Figure 11A block diagram of another electronic device 1900 provided for this application. For example, electronic device 1900 may be provided as a server.

[0111] Reference Figure 11 The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions, such as application programs, that can be executed by the processing component 1922. The application programs stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the methods described above.

[0112] Electronic device 1900 may also include a power supply component 1926 configured to perform power management of electronic device 1900, a wired or wireless network interface 1950 configured to connect electronic device 1900 to a network, and an input / output (I / O) interface 1958. Electronic device 1900 can operate on an operating system stored in memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or similar.

[0113] Example 5

[0114] Fifthly, this application discloses a computer program product in which, when the instructions in the computer program product are executed by a processor of an electronic device, the electronic device is enabled to perform the method described in any of the preceding aspects.

[0115] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0116] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0117] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

[0118] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0119] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0120] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0121] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0122] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0123] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0124] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for attenuating noise in seismic data, characterized in that, The method includes: Acquire the seismic data to be denoised; The earthquake data is input into a pre-constructed artificial neural network, which outputs noise intensity data; wherein, the artificial neural network is an earthquake data noise intensity prediction model trained on earthquake data samples based on machine learning algorithms; Spatial parameters were set based on Cadzow filtering to limit noisy seismic data, and the size of the Hankel matrix in Cadzow filtering was determined. Obtain noise intensity data within the spatial parameters of the Cadzow filter setting; Using the number of rank values ​​of the Hankel matrix, Cadzow filtering is applied to noisy seismic data with defined spatial parameters to attenuate random noise in the seismic data. The random noise attenuation of all seismic data is completed by iteratively looping with set spatial parameters. The steps of setting spatial parameters to limit noisy seismic data based on Cadzow filtering and determining the size of the Hankel matrix in Cadzow filtering include: The noisy seismic data is constrained based on the spatial parameters [2Lx+1, 2Ly+1, Lt] of the Cadzow filter, and the size of the Hankel matrix in the Cadzow filter is determined to be [Lh, Lh]. ; The step of performing Cadzow filtering on noisy seismic data with defined spatial parameters, based on the number of rank values ​​of the Hankel matrix, to attenuate random noise in the seismic data includes: The noise intensity is converted into the number of ranks K according to the following expression, and Cadzow filtering is performed on the noisy seismic data bounded by the spatial parameters [2Lx+1, 2Ly+1, Lt] using the number of ranks of the Hankel matrix to complete the random noise attenuation of the seismic data: Where Sigma represents the random noise intensity of the seismic data, sigmaHigh represents the highest random noise intensity of the seismic data, and sigmaLow represents the lowest random noise intensity of the seismic data.

2. The seismic data noise attenuation method according to claim 1, characterized in that, The artificial neural network is constructed in the following manner, including: S1. Using the label data A as input data for the artificial neural network, the direct output of the neural network... and The predicted noise intensity EstiSigma of the indirect output of the neural network is calculated using the following expression: ； S2. Apply Gaussian filtering to the label data B, with a filter window size of Lg; determine the Gaussian-filtered label data B as the actual noise intensity and use it as the output target of the artificial neural network. S3. Calculate the error between the predicted noise intensity EstiSigma indirectly output by the artificial neural network in S1 and the actual noise intensity in S2. Use the error as feedback to train the artificial neural network until the residual is less than a set threshold. Save the various parameters of the artificial neural network to the parameter configuration file to complete the construction of the artificial neural network.

3. The seismic data noise attenuation method according to claim 2, characterized in that, Tag pairs are formed in the following ways: Generate corresponding noisy data and random noise data based on forward modeling methods; The first label data corresponding to the noisy seismic data and the second label data corresponding to the random noise data are used to form a label pair.

4. The seismic data noise attenuation method according to claim 3, characterized in that, The steps for generating the corresponding noisy data and random noise data based on the forward modeling method include: Simulate noiseless seismic data and random noise of different intensities, respectively; Random noise of varying intensities is added to noise-free seismic data to obtain noisy seismic data with different signal-to-noise ratios. The noise intensity range is [SigmaLow, SigmaHigh], which covers the noise intensity range of actual seismic data.

5. A seismic data noise attenuation device, characterized in that, The device includes: The earthquake data acquisition module is used to acquire earthquake data to be denoised. The noise intensity prediction module is used to input the seismic data into a pre-constructed artificial neural network and output noise intensity data; wherein, the artificial neural network is a model for predicting the noise intensity of seismic data, which is trained on seismic data samples based on machine learning algorithms. The filtering module is used to limit noisy seismic data by setting spatial parameters based on Cadzow filtering and to determine the size of the Hankel matrix in Cadzow filtering. The noise intensity limiting module is used to obtain noise intensity data limited by the spatial parameters of the Cadzow filter setting. The noise attenuation module is used to perform Cadzow filtering on noisy seismic data limited by a set spatial parameter, based on the number of rank values ​​of the Hankel matrix, to complete the random noise attenuation of the seismic data; and to perform iterative looping based on the set spatial parameter to complete the random noise attenuation of all seismic data. The steps of setting spatial parameters to limit noisy seismic data based on Cadzow filtering and determining the size of the Hankel matrix in Cadzow filtering include: The noisy seismic data is constrained based on the spatial parameters [2Lx+1, 2Ly+1, Lt] of the Cadzow filter, and the size of the Hankel matrix in the Cadzow filter is determined to be [Lh, Lh]. ; The step of performing Cadzow filtering on noisy seismic data with defined spatial parameters, based on the number of rank values ​​of the Hankel matrix, to attenuate random noise in the seismic data includes: The noise intensity is converted into the number of ranks K according to the following expression, and Cadzow filtering is performed on the noisy seismic data bounded by the spatial parameters [2Lx+1, 2Ly+1, Lt] using the number of ranks of the Hankel matrix to complete the random noise attenuation of the seismic data: Where Sigma represents the random noise intensity of the seismic data, sigmaHigh represents the highest random noise intensity of the seismic data, and sigmaLow represents the lowest random noise intensity of the seismic data.

6. An electronic device, characterized in that, include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the method as described in any one of claims 1 to 4.

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

8. A computer program product, characterized in that, When the instructions in the computer program product are executed by the processor of the electronic device, the electronic device implements the method as described in any one of claims 1 to 4.

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