A reflected wave waveform inversion method based on envelope error function and electronic equipment

Abstract

The present invention provides a reflection wave waveform inversion method based on an envelope error function, a computer-readable storage medium, and an electronic device. The method comprises: obtaining a background wave field through migration and demigration; updating the background velocity field using a reflection wave full waveform inversion technique based on the envelope error function; and using the inverted background velocity field as the initial velocity field and updating the initial velocity field using a reflection wave full waveform inversion technique based on the L2 error function to obtain a disturbance velocity model. When applied to model inversion, the present invention can produce a more accurate velocity model, significantly improving the quality of the migration profile.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas seismic exploration, and in particular to a reflection wave waveform inversion method based on an envelope error function, a computer-readable storage medium, and an electronic device. Background Art

[0002] The theoretical framework for seismic inversion has been established since the 1980s. Tarantola (1984) pioneered the theoretical framework for acoustic full-waveform inversion in the time and space domains. Full-waveform inversion, based on the perspective of prestack shot gathers, reconstructs subsurface parameters by solving constrained optimization problems. It is a high-precision, high-resolution velocity modeling method.

[0003] As oil and gas exploration continues to deepen, target horizons are gradually transitioning from shallow to deep, even ultra-deep, layers. Effectively modeling high-precision velocity in these deep regions has become a hot topic in geophysical research. Full waveform inversion (FWI) technology, driven by data, offers higher resolution and modeling accuracy than travel-time tomography based on ray theory. Conventional FWI primarily utilizes diving waves, but in actual seismic exploration, limited by field observation systems, FWI alone cannot effectively reconstruct deep layers. Therefore, it is necessary to fully utilize the reflected wave information in the observation records.

[0004] To address this issue, Xu et al. (2012) proposed the concept of reflection waveform inversion. Their method uses true amplitude migration to perturb the model's high-frequency parameters, but this method cannot guarantee that the amplitude of the simulated data during the de-migration accurately matches the observed data. Xu's method is based on a least-squares error function in the data domain. However, when the initial model is inaccurate or low-frequency information is missing, the inversion results are prone to local extrema and failure to converge. et al. (2011) proposed using the envelope and instantaneous phase to construct a target functional for inversion. The Hilbert transform used to obtain the envelope can separate amplitude and phase, reducing inversion nonlinearity. However, there is no literature examining how to use the envelope error function to invert reflection waveforms, reconstruct mid-depth velocity models, and improve migration imaging quality when the initial model accuracy is low. Summary of the Invention

[0005] In response to the above problems, embodiments of the present invention provide a reflected wave waveform inversion method based on an envelope error function, a computer-readable storage medium, and an electronic device.

[0006] In a first aspect, an embodiment of the present invention provides a method for inverting a reflected wave waveform based on an envelope error function, comprising:

[0007] S100, performing reverse time migration and denoising processing on the shot receiver field observation record with respect to the background velocity field to obtain a corresponding migration profile;

[0008] S200, performing de-migration processing on the data in the migration profile to obtain a reflection wave field in the background velocity field;

[0009] S300, based on the reflected wave field, obtaining a background velocity gradient in the background velocity field by performing full waveform inversion of the reflected wave based on an envelope error function;

[0010] S400, updating the background velocity field according to the gradient of the background velocity, and obtaining a new background velocity model according to the updated background velocity field;

[0011] S500: Determine whether the new background velocity model meets the convergence condition:

[0012] If the new background velocity model does not meet the convergence condition, then based on the updated background velocity field, steps S100 to S400 are re-executed until the new background velocity model meets the convergence condition;

[0013] If the new background velocity model meets the convergence condition, step S600 is executed;

[0014] S600, using the updated background velocity field as the initial velocity field, and performing reverse time migration and denoising on the initial velocity field to obtain a corresponding migration profile;

[0015] S700, obtaining a forward background wave field and a backward residual wave field, and performing a full waveform inversion of the reflected wave based on an L2 error function by cross-correlating the forward background wave field and the backward residual wave field to obtain a gradient of the disturbance velocity in the disturbance velocity field;

[0016] S800, updating the initial velocity field according to the gradient of the disturbance velocity, and obtaining a new disturbance velocity model according to the updated initial velocity field;

[0017] S900: Determine whether the new perturbation velocity model meets the convergence conditions:

[0018] If the new perturbation velocity model does not meet the convergence condition, steps S600 to S800 are re-executed based on the updated initial velocity field until the new perturbation velocity model meets the convergence condition.

[0019] According to an embodiment of the present invention, the above step S100 includes the following steps:

[0020] Extend the shot point excitation forward to obtain the source wave field and calculate the time derivative of the source wave field;

[0021] Extend the observation record backward from the maximum time point to obtain the wave field of the detection point;

[0022] Based on the time derivative of the source wavefield and the receiver wavefield, imaging is performed using a cross-correlation imaging condition;

[0023] The imaging images are smoothed and denoised to obtain the migration profile.

[0024] According to an embodiment of the present invention, the above-mentioned smoothing and denoising process of the imaging image to obtain the migration profile includes:

[0025] The imaging image is smoothed using Gaussian filtering, and then the migration profile is obtained using Laplacae filtering.

[0026] According to an embodiment of the present invention, the above step 200 includes the following steps:

[0027] The zero-delay cross-correlation of the migration profile and the time derivative of the source wavefield is used as the conjugate source forward continuation to obtain the reflected wavefield.

[0028] According to an embodiment of the present invention, the above-mentioned time derivative of the source wavefield is a first-order time derivative of the source wavefield.

[0029] According to an embodiment of the present invention, step 300 includes the following steps:

[0030] Find the derivative of the envelope error function with respect to velocity:

[0031]

[0032] Where H is the Hilbert transform;

[0033] Wherein, the envelope error function is:

[0034]

[0035]

[0036]

[0037] Where, e mod is the envelope of the analog recording, e obs is the envelope of the observation record, m is the velocity of the velocity field to be updated, d is the reflected wave field in the observation record, u ref (z, x, t) is the reflected wave field obtained by demigration.

[0038] According to an embodiment of the present invention, in step S400, updating the background velocity field according to the gradient of the background velocity includes:

[0039] Based on the gradient of the velocity, an update step size is obtained according to the strong Wolf criterion to update the background velocity field.

[0040] According to an embodiment of the present invention, step S700 includes the following steps:

[0041] Find the derivative of the L2 norm error function with respect to velocity:

[0042]

[0043] Among them, the L2 norm error function is:

[0044]

[0045] Where, L * is the reverse extension process, R is the residual between the observation record and the simulation record at the detection point, d ref (z,x,t) is the reflected wave field recorded in the field observation, u ref (z, x, t) is the reflected wave field obtained by demigration.

[0046] In a second aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method for inverting a reflected wave waveform based on an envelope error function as described in the first aspect is implemented.

[0047] In a third aspect, an embodiment of the present invention provides an electronic device, comprising:

[0048] processor;

[0049] a memory for storing instructions executable by the processor;

[0050] Wherein, the processor is configured to execute the instructions to implement a reflected wave waveform inversion method based on an envelope error function as described in the first aspect.

[0051] Compared with the prior art, the above technical solution of the present invention has the following beneficial effects:

[0052] The present invention extracts reflection wave information from a smooth background velocity field through migration and demigration operators. When low-frequency information is missing from field seismic exploration data, the reflection wave path is constructed based on an envelope function. The reflection wave travel time error between the observed and simulated records is estimated, and the gradient is obtained to update the low-wavenumber background velocity field, providing a more accurate initial model for subsequent inversion. The deep-layer velocity information is then updated through full waveform inversion of the reflection wave based on the L2 error function. The final high-precision velocity model is obtained through multiple iterative updates, thereby forming a robust, adaptable, and highly accurate velocity modeling scheme, providing a high-precision velocity model for migration imaging and seismic data interpretation. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 A flowchart showing the steps of a reflected wave waveform inversion method based on an envelope error function according to an embodiment of the present invention is shown;

[0055] Figure 2 The model established by the method of the first embodiment 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);

[0056] Figure 3 The figure shows the migration profile obtained by the method of the first embodiment of the present invention (where a, b, and c are the reverse time migration results based on the true velocity field, the initial velocity field, and the inversion result, respectively);

[0057] Figure 4 A schematic diagram showing the structure of an electronic device according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0058] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0059] Example 1

[0060] In real-world seismic exploration, the quality of velocity modeling in deep layers is often affected by offset, missing low frequencies, and the accuracy of the initial model. Conventional deep-layer FWI models based on subsurface waves are limited in accuracy. To provide a more accurate velocity model for migration and seismic data interpretation, this paper utilizes reflected waves and employs full waveform inversion technology based on an envelope error function to obtain a high-precision velocity model.

[0061] The working principle and implementation process of this method are described in detail below.

[0062] like Figure 1As shown, the reflected wave waveform inversion method based on the envelope error function proposed in this embodiment can be divided into two major steps:

[0063] The first step is to extract the reflected wave information based on the demigration operator;

[0064] The second step is to perform full waveform inversion of the reflected wave based on the envelope error function;

[0065] The third step is to perform full waveform inversion of the reflected wave based on the L2 error function.

[0066] The first step is to extract the reflected wave information based on the de-migration operator. The specific implementation process is as follows:

[0067] First, reverse time migration is performed based on the initial velocity field, and the migration profile is obtained after local Laplace filtering.

[0068] In this embodiment, reverse time migration is the zero-delay cross-correlation of the first-order time derivative of the forward source wavefield with the reverse wavefield at the receiver. For a point underground, if the cross-correlation value is large, it indicates that the point is a reflection point of the underground medium; if the value is small, it is not a reflection point. In-phase stacking suppresses noise while highlighting significant layers. Its expression is as follows:

[0069]

[0070] Among them, I(z,x) is the imaging value, u(t,z,x) is the forward wavefield of the source, and r(t,z,x) is the reverse wavefield of the receiver.

[0071] In this embodiment, the specific implementation process is as follows:

[0072] 1) Extend the shot point excitation forward to obtain the source wave field and calculate the first-order time derivative of the source wave field;

[0073] 2) Extend the observation record backward from the maximum time point to obtain the wave field of the detection point;

[0074] 3) Based on the first-order time derivative of the source wavefield and the receiver wavefield, imaging is performed using the cross-correlation imaging condition.

[0075] 4) Use Gaussian filtering to smooth the image, and then use Laplacae filtering to obtain the final migration profile.

[0076] Then, the obtained migration profile is used as data input, and the reflection wave information in the background velocity field is obtained after de-migration.

[0077] In the processing of actual seismic data, the initial model is usually obtained by ray travel time tomography, which contains relatively rich low-frequency information, but less layer information that can generate effective reflection waves. Using the wave field equation forward simulation based on this type of model cannot obtain effective reflection wave information, and it is also impossible to perform full waveform inversion of the reflection wave to reconstruct the medium parameters of the deep layer. Therefore, this embodiment adopts the wave equation type of anti-migration method, and uses the first-order time derivative of the incident wave field and the zero-delay cross-correlation of the imaging value as a conjugate source to generate the required reflection wave, thereby not requiring high wave number information like general forward simulation. Based on the acoustic wave equation under the first-order Born approximation, the anti-migration expression is as follows:

[0078]

[0079]

[0080] u=u0+u ref

[0081] Where u0(z,x,t) is the background wave field, u ref (z,x,t) is the reflected wave field, and s is the smoothed slowness field.

[0082] The specific implementation process is as follows:

[0083] 1) Extend the shot point excitation forward to obtain the background wave field under the smooth parameter model;

[0084] 2) The zero-delay cross-correlation of the time derivative of the migration profile and the forward background wave field is used as the conjugate source forward extension to obtain the disturbance wave field under the smoothing parameter model, that is, the reflection wave field.

[0085] The second step is to perform full waveform inversion of the reflected wave based on the envelope error function. The specific process is as follows:

[0086] First, the gradient is obtained based on the envelope error function.

[0087] Reflection wave full waveform inversion is data-driven and has a high degree of nonlinearity. It is prone to falling into local extremes and failing to converge when low-frequency information and initial model accuracy are low. Field observation seismic data often suffer from the problem of missing low-frequency information due to the observation instrument itself. In order to improve the accuracy of velocity modeling in the absence of low-frequency information, the present invention uses an envelope error function to reconstruct the background velocity field using reflection wave information. The envelope error function is as follows:

[0088]

[0089]

[0090]

[0091] Among them, e mod is the envelope of the analog recording, e obs is the envelope of the field observation record, m is the velocity field to be updated, d is the reflection wave field in the field observation record, u ref (z, x, t) is the reflected wave field obtained by demigration.

[0092] The gradient of the envelope error function with respect to velocity m is obtained according to the following formula:

[0093]

[0094] Where H is the Hilbert transform;

[0095] Finally, based on the velocity gradient, the strong Wolf criterion is used to determine the update step size. 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. It is then determined whether the new background velocity model meets the convergence criteria. If it does not, the above steps (the first and second steps) are repeated based on the updated background velocity field until the new background velocity model meets the convergence criteria.

[0096] The third step is to perform full waveform inversion of the reflected wave based on the L2 norm error function. The specific process is as follows:

[0097] The L2 norm error functional of the reflected wave full waveform inversion is as follows:

[0098]

[0099] The corresponding gradient is:

[0100]

[0101] Where, L * is the reverse extension process, R is the residual between the observation record and the simulation record at the detection point; d ref (z,x,t) is the reflected wave field recorded in the field observation. ref (z,x,t) is the reflected wave field obtained by demigration.

[0102] u0(z,x,t) is given by The realization process is to load the earthquake source and then perform forward simulation of the wave field equation.

[0103] Reflected wave field u ref (z,x,t) is given by The process of obtaining the background wave field is similar to that of obtaining the background wave field. It is only necessary to replace the source with

[0104] The residual back propagation process is Replace f(t) with u ref (t,z,x)-d ref (t,z,x), and its realization process is to simulate the reverse time propagation of the wave field by forward modeling after loading the residual source.

[0105] The implementation process and technical effects of the technical solution of the present invention are explained below with reference to an example.

[0106] like Figure 2 and Figure 3 As shown, the Sigsbee2A model was intercepted to test the full waveform inversion technique of reflected waves based on the envelope error function of the present invention. The initial parameter model was a linear model with low accuracy. The number of longitudinal sampling points was 140, the number of transverse sampling points was 460, and the longitudinal and transverse sampling intervals were both 10m. Finite difference forward modeling was performed using a Ricker wavelet with a main frequency of 15Hz, a time sampling interval of 1.0ms, and a sampling time of 2.5s. A total of 150 shots were fired in the forward modeling, with a shot spacing of 30m and 460 channels received per shot. Figure 2 ac are the true velocity field, initial velocity field, and inversion result, respectively. Figure 3 ac are the reverse time migration results based on the true velocity field, initial velocity field, and inversion results, respectively. It can be seen that the accuracy of the migration profile is improved, which proves the accuracy of the method.

[0107] When the initial velocity model accuracy is low, the present invention uses a full-waveform inversion technique based on an envelope error function to determine the function gradient, iteratively update the low-wavenumber background medium parameters, and simultaneously obtain the migration profile based on the new velocity. After the velocity model accuracy meets the iterative convergence conditions, the full-waveform inversion technique based on the L2 error function is then used to iteratively update the high-wavenumber perturbed medium parameters. This ultimately effectively improves the modeling accuracy of the full-waveform inversion and provides a high-precision velocity field for subsequent migration imaging and seismic data interpretation. The above examples demonstrate that this method can achieve excellent results when applied to model inversion, significantly improving the quality of the velocity model.

[0108] Example 2

[0109] This embodiment provides a computer-readable medium having a computer program stored thereon. When the program is executed by a processor, the steps of the background velocity field reconstruction method based on reflected wave waveform inversion gradient preprocessing as described in the above embodiment are implemented.

[0110] It should be noted that the present invention can implement all or part of the processes in the above-mentioned embodiment method by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above-mentioned various method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. Of course, there are other types of readable storage media, such as quantum memory, graphene memory, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased 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 electric carrier signals and telecommunication signals.

[0111] Example 3

[0112] Figure 4 FIG. 1 is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Figure 4 As shown, at the hardware level, the electronic device includes a processor and, optionally, an internal bus, a network interface, and memory. The memory may include internal memory, such as high-speed random-access memory (RAM), and may also include non-volatile memory, such as at least one disk storage device. Of course, the electronic device may also include other hardware required for its services.

[0113] The processor, network interface, and memory can be interconnected via an internal bus, such as an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus. These buses can be categorized as address buses, data buses, and control buses. For ease of illustration, the diagram uses only line segments, but this does not imply that there is only one bus or only one type of bus.

[0114] The memory is configured to store a program. Specifically, the program may include program code, which includes computer operating instructions. The memory may include internal 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 the internal memory and then runs it. The processor executes the program stored in the memory to perform all steps of the aforementioned method for reconstructing the background velocity field based on gradient preprocessing of reflected wave waveform inversion.

[0115] The communication bus mentioned in the above devices may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. This communication bus can be divided into address buses, data buses, control buses, etc. For ease of illustration, only one thick line is used in the figure, but this does not mean that there is only one bus or only one type of bus. The communication interface is used for communication between the above electronic devices and other devices.

[0116] Bus comprises hardware, software or both, for above-mentioned parts are coupled together.For example, bus can comprise accelerated graphics port (AGP) or other graphics bus, enhanced industry standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industry standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more above these combinations.In suitable case, bus can comprise one or more buses.Although the embodiment of the present invention describes and shows specific bus, the present invention considers any suitable bus or interconnection.

[0117] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage. Alternatively, the memory may be at least one storage device located away from the processor.

[0118] The memory may include a large capacity memory for data or instructions. By way of example and not limitation, the memory may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory may include a removable or non-removable (or fixed) medium. In a specific embodiment, the memory is a non-volatile solid-state memory. In a specific embodiment, the memory includes a read-only memory (ROM). Where appropriate, 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 a flash memory, or a combination of two or more of these.

[0119] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components.

[0120] It should be noted that those skilled in the art can clearly understand that for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by 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 embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0121] The devices, apparatuses, systems, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, an in-vehicle human-computer 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 a combination of any of these devices.

[0122] Although the present invention provides 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 only one way of executing the order of many steps and does not represent the only execution order. When an actual device or terminal product is executed, it can be executed in sequence or in parallel according to the method shown in the embodiments or the drawings (for example, in a parallel processor or multi-threaded processing environment, or even a distributed data processing environment).

[0123] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0124] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0125] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1A step that specifies a function in one or more boxes.

[0126] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0127] Each embodiment in this specification is described in a related manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from other embodiments. In particular, the device, electronic device, and readable storage medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simple. For related portions, reference can be made to the descriptions of the method embodiments.

[0128] The above description is only a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included in the scope of protection of the present invention.

Claims

1. A reflected wave waveform inversion method based on envelope error function, characterized in that: The following steps are involved: S100, performing reverse time migration and denoising processing on the shot receiver field observation record with respect to the background velocity field to obtain a corresponding migration profile; S200, performing de-migration processing on the data in the migration profile to obtain a reflection wave field in the background velocity field; S300, based on the reflected wave field, obtaining a background velocity gradient in the background velocity field by performing full waveform inversion of the reflected wave based on an envelope error function; S400, updating the background velocity field according to the gradient of the background velocity, and obtaining a new background velocity model according to the updated background velocity field; S500, determining whether the new background velocity model meets the convergence condition: if the new background velocity model does not meet the convergence condition, re-execute steps S100 to S400 based on the updated background velocity field until the new background velocity model meets the convergence condition; If the new background velocity model meets the convergence condition, step S600 is executed; S600, using the updated background velocity field as the initial velocity field, and performing reverse time migration and denoising on the initial velocity field to obtain a corresponding migration profile; S700, obtaining a forward background wave field and a backward residual wave field, and performing a full waveform inversion of the reflected wave based on an L2 error function by cross-correlating the forward background wave field and the backward residual wave field to obtain a gradient of the disturbance velocity in the disturbance velocity field; S800, updating the initial velocity field according to the gradient of the disturbance velocity, and obtaining a new disturbance velocity model according to the updated initial velocity field; S900: Determine whether the new perturbation velocity model meets the convergence conditions: If the new perturbation velocity model does not meet the convergence condition, steps S600 to S800 are re-executed based on the updated initial velocity field until the new perturbation velocity model meets the convergence condition.

2. The reflected wave waveform inversion method based on the envelope error function according to claim 1, characterized in that: The step S100 includes the following steps: Extend the shot point excitation forward to obtain the source wave field and calculate the time derivative of the source wave field; Extend the observation record backward from the maximum time point to obtain the wave field of the detection point; Based on the time derivative of the source wavefield and the receiver wavefield, imaging is performed using a cross-correlation imaging condition; The imaging images are smoothed and denoised to obtain the migration profile.

3. The reflected wave waveform inversion method based on envelope error function according to claim 2, characterized in that: The imaging image is smoothed and denoised to obtain the migration profile, including: The imaging image is smoothed using Gaussian filtering, and then the migration profile is obtained using Laplacae filtering.

4. The reflected wave waveform inversion method based on envelope error function according to claim 1, wherein: The step 200 includes the following steps: The zero-delay cross-correlation of the migration profile and the time derivative of the source wavefield is used as the conjugate source forward continuation to obtain the reflected wavefield.

5. The reflected wave waveform inversion method based on the envelope error function according to any one of claims 2 to 4, characterized in that: The time derivative of the source wavefield is the first-order time derivative of the source wavefield.

6. The reflected wave waveform inversion method based on envelope error function according to claim 1, characterized in that: The step 300 includes the following steps: Find the derivative of the envelope error function with respect to velocity: Where H is the Hilbert transform; Wherein, the envelope error function is: Where, is the envelope of the analog recording, is the envelope of the observation record, is the velocity of the velocity field to be updated, is the reflected wave field in the observation record, is the reflected wave field obtained through demigration processing.

7. The reflected wave waveform inversion method based on envelope error function according to claim 1, characterized in that: In step S400, updating the background velocity field according to the gradient of the background velocity includes: Based on the gradient of the velocity, an update step size is obtained according to the strong Wolf criterion to update the background velocity field.

8. The reflected wave waveform inversion method based on envelope error function according to claim 6, characterized in that: The step S700 includes the following steps: Find the derivative of the L2 norm error function with respect to velocity: Among them, the L2 norm error function is: Where, For the reverse extension process, is the residual between the observation record and the simulation record at the detection point, is the reflected wave field recorded in the field observation, is the reflected wave field obtained through demigration processing.

9. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the program is executed by a processor, the reflected wave waveform inversion method based on the envelope error function as described in any one of claims 1 to 8 is implemented.

10. An electronic device comprising: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the reflected wave waveform inversion method based on the envelope error function as described in any one of claims 1 to 8.

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