Method for reconstructing background velocity field based on reflection wave waveform inversion gradient preprocessing

Abstract

The application provides a background velocity field reconstruction method and device based on reflected wave waveform inversion gradient preprocessing, a computer readable storage medium and an electronic device. The method comprises the following steps: based on near-offset information, inverse time migration is performed by calculating a Poynting vector, and a reflected wave field is obtained by reverse migration; the gradient of the velocity is calculated by using a reflected wave full waveform inversion technology based on an adaptive error function, the gradient of the velocity is preprocessed by using illumination energy compensation, the gradient of the preprocessed velocity is used to update the background velocity field, and a more accurate background velocity model is obtained. When the method is applied to model inversion, a more accurate background velocity model can be obtained, and the quality of a migration profile is significantly improved.

Description

Technical Field

[0001] This invention relates to the field of oil and gas seismic exploration technology, and in particular to a method, apparatus, computer-readable storage medium, and electronic device for reconstructing a background velocity field based on reflection wave waveform inversion gradient preprocessing. Background Technology

[0002] As oil and gas exploration progresses, target strata gradually shift from shallow to deep layers, making high-precision deep velocity modeling a pressing issue. Full waveform inversion, based on the least squares criterion, reconstructs the subsurface medium by reducing the error between observational and simulated records; however, it is an ill-conditioned nonlinear problem. Since its inception in the 1980s, it has been a hot topic in geophysics research. Full waveform inversion technology, driven by data and based on optimization algorithms and wave equation theory, offers higher resolution and modeling accuracy compared to ray-based travel-time tomography, enabling a more accurate description of seismic wave propagation in the subsurface medium. Conventional full waveform inversion primarily utilizes groundwater wave information, but onshore seismic exploration is limited by the lack of large offset information in the observation system, affecting the modeling accuracy of deep layers. In such cases, it is necessary to fully utilize reflected wave information from the observation records.

[0003] To address this issue, Xu et al. (2012) proposed the concept of reflected wave waveform inversion. Their method utilizes true amplitude reverse time migration (Zhang et al., 2007) to provide the high-frequency velocity perturbation of the model. While this true amplitude migration theoretically provides an accurate relative AVO, it cannot guarantee that the amplitude of the simulated data in the reverse migration will accurately match the observed data. In practical applications, noise contamination and the lack of low-frequency information in the observed data further complicate the matching process. Therefore, effectively matching the observed records with the reverse-migrated simulated records is crucial for achieving reflected wave waveform inversion. Currently, the conventional least quadratic error function cannot effectively match the observed records with the reverse-migrated records. Furthermore, the weak energy of deep effective signals in the observed records and the slow iteration speed of deep signals during the full waveform inversion process are also urgent problems to be solved. Summary of the Invention

[0004] To address the aforementioned problems, embodiments of the present invention provide a method, apparatus, computer-readable storage medium, and electronic device for reconstructing a background velocity field based on gradient preprocessing of reflected wave waveform inversion.

[0005] In a first aspect, embodiments of the present invention provide a background velocity field reconstruction method based on gradient preprocessing of reflected wave waveform inversion, comprising:

[0006] S100 performs reverse time migration and denoising on the shot receiver field observation records with respect to the background velocity field to obtain the corresponding migration profile.

[0007] S200, perform reverse offset processing on the data in the offset profile to obtain the reflected wave field in the background velocity field, and determine the back propagation background wave field based on the reflected wave field.

[0008] S300, based on the forward propagation background wave field, the reflected wave field, and the reverse propagation background wave field, obtains the gradient of the background velocity in the background velocity field by performing full waveform inversion of the reflected wave based on an adaptive error function.

[0009] S400, through illumination compensation at the gun receiver, preprocesses the gradient of the background velocity;

[0010] S500 updates the background velocity field based on the gradient of the preprocessed background velocity, and obtains a new background velocity model based on the updated background velocity field.

[0011] S600: Determine whether the new background velocity model meets the convergence condition. If it does not meet the convergence condition, repeat steps S100 to S500 based on the updated background velocity field until the new background velocity model meets the convergence condition.

[0012] According to an embodiment of the present invention, in step S100, the denoising process includes eliminating inverse time offset noise by obtaining the Poynting vector of the shot-receiver wavefield propagation.

[0013] According to an embodiment of the present invention, eliminating reverse-time migration noise by obtaining the Poynting vector of the shot-receiver field includes:

[0014] The Poynting vector for the propagation of the shot-receiver wavefield is obtained according to the following formula, and the propagation angle is calculated based on the vector.

[0015] ;

[0016] According to the following formula, the counter-time offset is filtered by setting the included angle constraint to eliminate counter-time offset noise.

[0017]

[0018]

[0019] In the formula, For imaging values, The background wave field representing the forward propagation of the source signal at the epicenter at time t. Let t be the background wave field propagated back from the receiver point. The weighting function is related to the propagation angle. As an angle constraint, Let P be the propagation angle, x and z be the wave field coordinate axes, and P be the propagation angle.sx It is the pressure field of the shot point in the x-direction, P sz It is the pressure field of the shot point in the z-direction, P rx It is the pressure field of the detector point in the x-direction, P rz It is the pressure field of the detector point in the z-direction.

[0020] According to an embodiment of the present invention, in step S200, the data in the offset profile is subjected to inverse offset processing to obtain the reflected wave field in the background velocity field, including:

[0021] Obtain the propagating background wave field obtained by forward continuation through shot point excitation, and calculate the time derivative of the propagating wave field.

[0022] The zero-delay cross-correlation of the time derivatives of the offset profile and the propagating wave field is used as the conjugate source for forward extension to obtain the reflected wave field.

[0023] According to an embodiment of the present invention, based on the forward propagation background wave field, the reflected wave field, and the backward propagation background wave field, the gradient of the background velocity in the background velocity field is obtained by performing full waveform inversion of the reflected wave based on an adaptive error function, including:

[0024] Find the derivative of the adaptive error function with respect to the background velocity:

[0025]

[0026] In the formula, This refers to the forward propagation background wave field after correction by the Wiener filter. This refers to the simulated reflected wave field after correction by the Wiener filter. Refers to the backpropagating background wave field after correction by the Wiener filter. This refers to the simulated backpropagation background wave field after correction by the Wiener filter; m is the velocity.

[0027] Adaptive error function:

[0028]

[0029] In the formula, It is the Toeplitz matrix related to the simulation record u. Here, h is a one-dimensional Wiener filter, which makes the simulated record approximate the observed record as a whole. h is the observed record, t is time, s is the shot point, and r is the receiver point.

[0030] According to an embodiment of the present invention, in step S400, the gradient of the background velocity is preprocessed by illumination compensation at the shot receiver according to the following formula:

[0031] .

[0032] According to an embodiment of the present invention, in step S500, updating the background velocity field based on the gradient of the preprocessed background velocity includes:

[0033] Based on the gradient of the preprocessed background velocity, the update step size is obtained according to the strong Wolf criterion, and the background velocity field is updated according to the update step size.

[0034] Secondly, the present invention also provides a background velocity field reconstruction device based on gradient preprocessing of reflected wave waveform inversion, characterized in that it comprises:

[0035] The reverse time migration processing module is used to acquire the shot and receiver wavefield observation records. Based on the near offset information in the shot and receiver wavefield observation records, it performs reverse time migration and noise reduction processing on the background velocity field to obtain the corresponding migration profile.

[0036] The reverse offset processing module is used to perform reverse offset processing on the data in the offset profile to obtain the reflected wave field in the background velocity field, and determine the back propagation background wave field based on the reflected wave field.

[0037] The full waveform inversion module for reflected waves is used to obtain the velocity gradient in the background velocity field by performing full waveform inversion of reflected waves based on an adaptive error function, based on the forward propagation background wave field, the reflected wave field, and the reverse propagation background wave field.

[0038] The gradient preprocessing module is used to preprocess the velocity gradient through illumination compensation at the gun receiver.

[0039] The background velocity update module is used to update the background velocity field based on the gradient of the preprocessed background velocity, and to obtain a new background velocity model based on the updated background velocity field.

[0040] The convergence judgment module is used to determine whether the new background velocity model meets the convergence condition. If the convergence condition is not met, the inverse time migration processing module, the inverse migration processing module, the reflected wave full waveform inversion module, the gradient preprocessing module and the background velocity update module are called again based on the updated background velocity field to perform the functions of each module until the new background velocity model meets the convergence condition.

[0041] Thirdly, embodiments of the present invention provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a background velocity field reconstruction method based on reflected wave waveform inversion gradient preprocessing as described in the first aspect.

[0042] Fourthly, embodiments of the present invention provide an electronic device comprising:

[0043] processor;

[0044] Memory used to store the processor's executable instructions;

[0045] The processor is configured to execute the instructions to implement a background velocity field reconstruction method based on gradient preprocessing of reflected wave waveform inversion as described in the first aspect above.

[0046] Compared with the prior art, the above-mentioned technical solution of the present invention has the following beneficial effects:

[0047] The embodiments of this invention are based on near-offset information. By obtaining the Poynting vector, inverse time migration noise is eliminated. The reflected wave information is extracted from the smooth background velocity field using the inverse migration operator. Based on the adaptive error function, the gradient is obtained and preprocessed. This accelerates the iteration convergence speed while improving the accuracy of deep inversion. After multiple iterations to update and reconstruct the background velocity field, a robust, adaptable, and high-precision velocity modeling scheme is finally formed, providing a high-precision velocity model for the interpretation of migration imaging and seismic data. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 The flowchart of the background velocity field reconstruction method based on gradient preprocessing of reflected wave waveform inversion according to an embodiment of the present invention is shown.

[0050] Figure 2 The model established using the method of Embodiment 1 of the present invention and its inversion results are shown (where a, b, and c are the true velocity field, the initial velocity field, and the inversion result, respectively).

[0051] Figure 3 The diagram shows the migration profile obtained using the method of Embodiment 1 of the present invention (where a and b are the initial velocity field and the reverse time migration result of the inversion result, respectively).

[0052] Figure 4 A schematic diagram of the composition structure of an electronic device according to an embodiment of the present invention is shown. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] Example 1

[0055] In actual seismic exploration, especially onshore seismic exploration, the quality of velocity modeling in the middle and deep layers is often affected by small migration distances, missing low-frequency data, and the accuracy of the initial model. To provide a more accurate velocity model for migration and seismic data interpretation, unlike conventional waveform inversion using latent waves, reflected wave waveform inversion fully utilizes reflected wave information from observation records, possessing the ability to recover deep medium parameters. However, matching observation records with back-migration records and the iterative convergence speed in the deep layers are problems that urgently need to be solved. To address these two issues, this invention proposes a novel gradient preprocessing method for reflected wave waveform inversion, and uses this method to establish a more accurate velocity model.

[0056] The working principle and implementation process of this method are explained in detail below.

[0057] like Figure 1 As shown, the background velocity field reconstruction method based on gradient preprocessing of reflected wave waveform inversion proposed in this embodiment can be divided into two main steps:

[0058] The first step is to reconstruct the reflected wave field;

[0059] The second step is to reconstruct the velocity model based on the gradient preprocessing of the full waveform inversion of the reflected wave.

[0060] Specifically, the first step is to reconstruct the reflected wave field, and the specific implementation process is as follows:

[0061] First, the shot receiver wavefield observation records were acquired, and the data were sorted according to the offset size in the shot receiver wavefield observation records. The data with the closest offset was selected as the main analysis object. Based on the data with the closest offset, the reverse time offset with respect to the background velocity field was performed, and the Poynting vector was obtained for noise reduction to obtain the corresponding offset profile.

[0062] In this embodiment, the reverse-time migration is the zero-delay cross-correlation between the forward propagation wavefield of the seismic source and the backward propagation wavefield of the receiver. Its expression is as follows:

[0063]

[0064] In the formula, For imaging values, The positive propagation wave field of the source signal at time t (also known as the positive propagation background wave field) is the wave field at the source of the earthquake. The back propagation wave field at the receiver point at time t (also known as the back propagation background wave field).

[0065] The specific implementation process is as follows:

[0066] 1) The source wave field is obtained by positive extension of the shot point.

[0067] 2) The observation record is extended backward from the maximum time point to obtain the wavefield at the receiver point;

[0068] 3) Imaging using cross-correlation imaging conditions;

[0069] 4) Eliminate imaging noise by obtaining the Poynting vector of the shot-receiver wavefield propagation.

[0070] The process involves obtaining the Poynting vector for the propagation of the shot-receiver wavefield and calculating the propagation angle based on this vector. The expression for this is:

[0071]

[0072] Based on the propagation angle, the cross-correlation imaging conditions are modified as follows:

[0073]

[0074]

[0075] In the formula, The weighting function is related to the propagation angle. To limit the included angle, Let P be the propagation angle, x and z be the wave field coordinate axes, and P be the propagation angle. sx It is the pressure field of the shot point in the x-direction, P sz It is the pressure field of the shot point in the z-direction, P rx It is the pressure field of the detector point in the x-direction, P rz It is the pressure field of the detector point in the z-direction.

[0076] By selecting different limiting angles, the reverse-time offset can be filtered to eliminate low-frequency offset noise, thereby obtaining the offset profile.

[0077] Then, the data in the offset profile is subjected to inverse offset processing to obtain the reflected wave field in the background velocity field.

[0078] According to existing technologies, when the initial velocity model is relatively smooth, forward modeling of the wave equation cannot simulate effective reflected wave information, thus making it impossible to reconstruct deep medium parameters through full waveform inversion of the reflected wave. Therefore, this invention proposes to use an inverse migration method similar to the wave equation to obtain the reflected wave field, the expression of which is shown below:

[0079]

[0080] In the formula, It is the background wave field. It is a reflected wave field. It is a smooth velocity field.

[0081] The specific implementation process is as follows:

[0082] 1) The background wave field under the smooth parametric model is obtained by shooting point excitation and forward continuation.

[0083] 2) The zero-delay cross-correlation of the time derivative of the filtered offset profile and the background wave field is used as the positive extension of the conjugate source to obtain the perturbation wave field under the smooth parameter model, that is, the reflection wave field.

[0084] Specifically, the second step, reconstructing the velocity model based on gradient preprocessing of the full waveform inversion of the reflected wave, is as follows:

[0085] First, the gradient is obtained based on the adaptive error function.

[0086] According to existing technology, full waveform inversion of reflected waves requires approximating the observation record with the inverse migration record to update the subsurface parameter model. However, due to the low accuracy of the initial model and the inability of the migration profile to accurately represent the reflection coefficient, the kinematic and dynamic information of the inverse migration record differs significantly from that of the observation record, making the conventional L2 norm error function unusable. To enable the observation record to more reasonably and effectively approximate the observation record, this invention employs the following adaptive error function (Hansen, 2002) to obtain the velocity gradient distribution:

[0087]

[0088] In the formula, It is the Toeplitz matrix related to the simulation record u. For a one-dimensional Wiener filter, the simulated record is made to approximate the observed record as a whole. h is the observed record, t is time, s is the shot point, and r is the receiver point.

[0089] The derivative of the adaptive error function with respect to velocity is obtained from the following formula:

[0090]

[0091] In the formula, This refers to the forward propagation background wave field after correction by the Wiener filter. This refers to the simulated reflected wave field after correction by the Wiener filter. Refers to the backpropagating background wave field after correction by the Wiener filter. This refers to the simulated backpropagation background wave field after correction by the Wiener filter.

[0092] The reverse propagating wavefield can be obtained from the following formula:

[0093]

[0094] Then, the velocity gradient is preprocessed.

[0095] In this embodiment, the gradient is preprocessed using illumination compensation at the shot receiver, and the specific expression is as follows:

[0096]

[0097] This gradient preprocessing method does not require additional computation or storage space, and can effectively improve depth lighting and increase the speed of iterative convergence.

[0098] Finally, based on the velocity gradient, the update step size is obtained through the strong Wolf criterion. The background velocity field is updated according to the update step size, and a new background velocity model is obtained based on the updated background velocity field. Then, it is determined whether the new background velocity model meets the convergence condition. If it does not meet the convergence condition, the above steps (first step and second step) are repeated based on the updated background velocity field until the new background velocity model meets the convergence condition.

[0099] The specific implementation process is as follows:

[0100] 1) The source wave field is obtained by positive continuation of the shot point excitation, and the simulated record is made to approximate the observation record by Wiener filtering;

[0101] 2) The residuals of the observation record and the simulation record are extended backward from the point of maximum time to obtain the backpropagating wave field;

[0102] 3) Cross-correlate the two to obtain the velocity gradient;

[0103] 4) Preprocess the velocity gradient and use the preprocessed velocity gradient to update the background velocity information.

[0104] The following example illustrates the implementation process and technical effects of the technical solution of this invention.

[0105] like Figure 2 and Figure 3As shown, the Marmousi model was used to test the technology of this invention. The initial parameter model was a linear model. The number of longitudinal sampling points was 155, the number of transverse sampling points was 300, and the longitudinal and transverse sampling intervals were both 15m. Finite difference forward modeling was performed using a Ricker wavelet with a main frequency of 12Hz. The time sampling interval was 1.0ms, the sampling time was 2s, and a total of 150 shots were fired in the forward modeling, with a shot interval of 20m and 300 receivers per shot. Figure 2 ac represent the true velocity field, the initial velocity field, and the inversion result, respectively. Figure 3 ab are the reverse-time migration results based on the initial velocity field and the inversion results, respectively. It can be seen that the accuracy of the migration profile at the box is improved and the phase axis is more continuous, which proves the accuracy of the method.

[0106] This invention addresses the issue of low initial velocity model accuracy by acquiring reflected wave information through wave equation migration and inverse migration, and then achieving high-precision velocity modeling based on this reflected wave information. This is primarily achieved through the following technical steps: First, based on near-offset information, inverse time migration is performed by calculating the Poynting vector, and the reflected wave field is obtained through inverse migration. Second, the velocity gradient is calculated using a full-waveform inversion technique based on an adaptive error function, and the gradient is preprocessed using illumination compensation. Finally, the background velocity field is updated using this preprocessed gradient. The above examples demonstrate that applying this method to model inversion yields good results, significantly improving the quality of the velocity model.

[0107] Example 2

[0108] The following are embodiments of the apparatus of the present invention, which can be used to execute embodiments of the method of the present invention. For details not disclosed in the embodiments of the apparatus of the present invention, please refer to the embodiments of the method of the present invention.

[0109] This embodiment provides a background velocity field reconstruction device based on gradient preprocessing for reflected wave waveform inversion, characterized in that it includes:

[0110] The reverse time migration processing module is used to acquire the shot and receiver wavefield observation records. Based on the near offset information in the shot and receiver wavefield observation records, it performs reverse time migration and noise reduction processing on the background velocity field to obtain the corresponding migration profile.

[0111] The reverse offset processing module is used to perform reverse offset processing on the data in the offset profile to obtain the reflected wave field in the background velocity field, and determine the back propagation background wave field based on the reflected wave field.

[0112] The full waveform inversion module for reflected waves is used to obtain the velocity gradient in the background velocity field by performing full waveform inversion of reflected waves based on an adaptive error function, based on the forward propagation background wave field, the reflected wave field, and the reverse propagation background wave field.

[0113] The gradient preprocessing module is used to preprocess the velocity gradient through illumination compensation at the gun receiver.

[0114] The background velocity update module is used to update the background velocity field based on the gradient of the preprocessed background velocity, and to obtain a new background velocity model based on the updated background velocity field.

[0115] The convergence judgment module is used to determine whether the new background velocity model meets the convergence condition. If the convergence condition is not met, the inverse time migration processing module, the inverse migration processing module, the reflected wave full waveform inversion module, the gradient preprocessing module and the background velocity update module are called again based on the updated background velocity field to perform the functions of each module until the new background velocity model meets the convergence condition.

[0116] Example 3

[0117] This embodiment provides a computer-readable medium storing a computer program that, when executed by a processor, implements the various steps of a background velocity field reconstruction method based on reflected wave waveform inversion gradient preprocessing as described in the above embodiment.

[0118] It should be noted that all or part of the processes in the methods of the above embodiments of the present invention can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. Of course, there are other readable storage media, such as quantum memories, graphene memories, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0119] Example 4

[0120] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Figure 4As shown, at the hardware level, this electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or it may include non-volatile memory, such as at least one disk drive. Of course, this electronic device may also include other hardware required for other business operations.

[0121] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This bus can be categorized as an address bus, data bus, control bus, etc. For ease of illustration, only line segments are used in the diagram, but this does not imply that there is only one bus or one type of bus.

[0122] A memory is used to store programs. Specifically, the program may include program code, which includes computer operation instructions. The memory may include main memory and non-volatile memory, and provides instructions and data to the processor. The processor reads the corresponding computer program from the non-volatile memory into main memory and then runs it. The processor executes the program stored in the memory to perform all the steps in the aforementioned background velocity field reconstruction method based on reflected wave waveform inversion gradient preprocessing.

[0123] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus. The communication interface is used for communication between the above electronic devices and other devices.

[0124] A bus, including hardware, software, or both, is used to couple the aforementioned components together. For example, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, a bus may include one or more buses. Although specific buses are described and illustrated in embodiments of the invention, the invention contemplates any suitable bus or interconnect.

[0125] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0126] The memory may include a large-capacity storage device for data or instructions. For example, and not limitingly, the memory may include a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disk drive, a magneto-optical disk drive, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where suitable, the memory may include removable or non-removable (or fixed) media. In a particular embodiment, the memory is a non-volatile solid-state memory. In a particular embodiment, the memory includes a read-only memory (ROM). Where suitable, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0127] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0128] It should be noted that those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments 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. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0129] The apparatus, device, system, module, or unit described in the above embodiments can be implemented by a computer chip or entity, or by a product with a certain function. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, an in-vehicle human-machine interaction device, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0130] While this invention provides the method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive means. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only execution order. In actual devices or terminal products, the methods shown in the embodiments or drawings can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even a distributed data processing environment).

[0131] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0132] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0133] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0134] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, 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. Without further limitation, 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 said element.

[0135] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, electronic devices, and readable storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0136] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A method for reconstructing the background velocity field based on gradient preprocessing of reflected wave waveform inversion, characterized in that, include: S100 performs reverse time migration and denoising on the shot receiver field observation records with respect to the background velocity field to obtain the corresponding migration profile. S200, perform reverse offset processing on the data in the offset profile to obtain the reflected wave field in the background velocity field, and determine the back propagation background wave field based on the reflected wave field. S300, based on the forward propagation background wave field, the reflected wave field, and the reverse propagation background wave field, obtains the gradient of the background velocity in the background velocity field by performing full waveform inversion of the reflected wave based on an adaptive error function. S400, through illumination compensation at the gun receiver, preprocesses the gradient of the background velocity; S500 updates the background velocity field based on the gradient of the preprocessed background velocity, and obtains a new background velocity model based on the updated background velocity field. S600, determine whether the new background velocity model meets the convergence condition. If it does not meet the convergence condition, then based on the updated background velocity field, repeat steps S100 to S500 until the new background velocity model meets the convergence condition. The step of obtaining the gradient of the background velocity in the background velocity field by performing full waveform inversion of the reflected wave based on an adaptive error function, based on the forward propagation background wave field, the reflected wave field, and the reverse propagation background wave field, includes: Find the derivative of the adaptive error function with respect to the background velocity: In the formula, This refers to the forward propagation background wave field after correction by the Wiener filter. This refers to the simulated reflected wave field after correction by the Wiener filter. Refers to the backpropagating background wave field after correction by the Wiener filter. This refers to the simulated backpropagation background wave field after correction by the Wiener filter; m is the velocity. Adaptive error function: In the formula, It is the Toeplitz matrix related to the simulation record u. Here, h is a one-dimensional Wiener filter, which makes the simulated record approximate the observed record as a whole. h is the observed record, t is time, s is the shot point, and r is the receiver point.

2. The background velocity field reconstruction method based on reflected wave waveform inversion gradient preprocessing as described in claim 1, characterized in that, In step S100, the denoising process includes eliminating inverse time offset noise by obtaining the Poynting vector of the shot-receiver wavefield propagation.

3. The background velocity field reconstruction method based on gradient preprocessing of reflected wave waveform inversion as described in claim 2, characterized in that, Counter-time migration noise is eliminated by calculating the Poynting vector propagating from the shot-receiver field, including: The Poynting vector for the propagation of the shot-receiver wavefield is obtained according to the following formula, and the propagation angle is calculated based on the vector. ； According to the following formula, the counter-time offset is filtered by setting the included angle constraint to eliminate counter-time offset noise. In the formula, For imaging values, The background wave field representing the forward propagation of the source signal at the epicenter at time t. Let t be the background wave field propagated back from the receiver point. The weighting function is related to the propagation angle. As an angle constraint, Let P be the propagation angle, x and z be the wave field coordinate axes, and P be the propagation angle. sx It is the pressure field of the shot point in the x-direction, P sz It is the pressure field of the shot point in the z-direction, P rx It is the pressure field of the detector point in the x-direction, P rz It is the pressure field of the detector point in the z-direction.

4. The background velocity field reconstruction method based on gradient preprocessing of reflected wave waveform inversion as described in claim 1, characterized in that, In step S200, the data in the offset profile is subjected to inverse offset processing to obtain the reflected wave field in the background velocity field, including: Obtain the propagating background wave field obtained by forward continuation through shot point excitation, and calculate the time derivative of the propagating wave field. The zero-delay cross-correlation of the time derivatives of the offset profile and the propagating wave field is used as the conjugate source for forward extension to obtain the reflected wave field.

5. The background velocity field reconstruction method based on gradient preprocessing of reflected wave waveform inversion as described in claim 1, characterized in that, In step S400, the gradient of the background velocity is preprocessed according to the following formula through illumination compensation at the shot detector: 。 6. The background velocity field reconstruction method based on gradient preprocessing of reflected wave waveform inversion as described in claim 1, characterized in that, In step S500, updating the background velocity field based on the gradient of the preprocessed background velocity includes: Based on the gradient of the preprocessed background velocity, the update step size is obtained according to the strong Wolf criterion, and the background velocity field is updated according to the update step size.

7. A background velocity field reconstruction device based on gradient preprocessing of reflected wave waveform inversion, characterized in that, include: The reverse time migration processing module is used to acquire the shot and receiver wavefield observation records. Based on the near offset information in the shot and receiver wavefield observation records, it performs reverse time migration and noise reduction processing on the background velocity field to obtain the corresponding migration profile. The reverse offset processing module is used to perform reverse offset processing on the data in the offset profile to obtain the reflected wave field in the background velocity field, and determine the back propagation background wave field based on the reflected wave field. The full waveform inversion module for reflected waves is used to obtain the velocity gradient in the background velocity field by performing full waveform inversion of reflected waves based on an adaptive error function, based on the forward propagation background wave field, the reflected wave field, and the reverse propagation background wave field. The gradient preprocessing module is used to preprocess the velocity gradient through illumination compensation at the gun receiver. The background velocity update module is used to update the background velocity field based on the gradient of the preprocessed background velocity, and to obtain a new background velocity model based on the updated background velocity field. The convergence judgment module is used to determine whether the new background velocity model meets the convergence condition. If the convergence condition is not met, the inverse time migration processing module, the inverse migration processing module, the full waveform inversion module of the reflected wave, the gradient preprocessing module and the background velocity update module are called again based on the updated background velocity field to perform the functions of each module until the new background velocity model meets the convergence condition. The full waveform inversion module for reflected waves is used to calculate the derivative of the adaptive error function with respect to the background velocity according to the following formula: In the formula, This refers to the forward propagation background wave field after correction by the Wiener filter. This refers to the simulated reflected wave field after correction by the Wiener filter. Refers to the backpropagating background wave field after correction by the Wiener filter. This refers to the simulated backpropagation background wave field after correction by the Wiener filter; m is the velocity. Adaptive error function: In the formula, It is the Toeplitz matrix related to the simulation record u. Here, h is a one-dimensional Wiener filter, which makes the simulated record approximate the observed record as a whole. h is the observed record, t is time, s is the shot point, and r is the receiver point.

8. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the background velocity field reconstruction method based on gradient preprocessing of reflected wave waveform inversion as described in any one of claims 1 to 6.

9. An electronic device comprising: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the background velocity field reconstruction method based on the gradient preprocessing of reflected wave waveform inversion as described in any one of claims 1 to 6.

Images