Imaging domain reflected wave waveform inversion method, device, equipment, medium and product

Abstract

This disclosure relates to the field of oil and gas exploration technology, and in particular to a method, apparatus, equipment, medium, and product for imaging domain reflection wave waveform inversion. The method includes: calculating the gradient corresponding to each offset and a pre-constructed velocity model; defining an objective function for imaging domain reflection wave waveform inversion based on the gradients corresponding to all offsets; calculating the update gradient of the objective function corresponding to the velocity model; updating the velocity model based on the update gradient; and performing imaging domain reflection wave waveform inversion based on the converged velocity model. By defining the objective function for imaging domain reflection wave waveform inversion and calculating the update gradient of the objective function corresponding to the velocity model, and updating the velocity model, imaging domain reflection wave waveform inversion is achieved. This directly improves the imaging quality in the depth domain and compensates for the shortcomings of reflection wave waveform inversion applications with depth domain imaging as the ultimate goal.

Description

Technical Field

[0001] This disclosure relates to the field of oil and gas exploration technology, and in particular to an imaging domain reflected wave waveform inversion method, apparatus, equipment, medium and product. Background Technology

[0002] Common methods for reflecting wave waveform inversion are based on the data domain and involve two alternating iterative steps: 1) reflector construction; 2) velocity model update. Reflector construction can be achieved using different methods, such as ray migration imaging, reverse time migration, least squares reverse time migration, and wave impedance inversion. Velocity model update is based on the reflector construction results. Predicted reflection data corresponding to the reflector is generated through inverse migration or forward modeling of the wave equation, and the velocity model is updated by optimizing the inversion to reduce the mismatch between the predicted reflection data and the actual observed reflection data until the mismatch meets a preset threshold, at which point the iteration stops.

[0003] However, this method of reflecting wave waveform inversion requires a reflector model based on the imaging domain and the construction of a velocity model driven by an objective function built in the data domain, which cannot directly improve the imaging focus or imaging quality in the depth domain. Summary of the Invention

[0004] In view of the above problems, this disclosure is made to provide an imaging domain reflected wave waveform inversion method, apparatus, device, medium and product.

[0005] According to one aspect of this disclosure, an imaging domain reflected wave waveform inversion method is provided, comprising:

[0006] Based on each offset and the pre-built velocity model, calculate the gradient corresponding to each offset;

[0007] Based on the gradients corresponding to all the aforementioned offsets, the objective function for inverting the reflected wave waveform in the imaging domain is defined.

[0008] Calculate the updated gradient of the objective function corresponding to the velocity model;

[0009] The velocity model is updated based on the updated gradient;

[0010] Based on the converged velocity model, the waveform of the reflected wave in the imaging domain is inverted.

[0011] Furthermore, according to one aspect of the imaging domain reflected wave waveform inversion method of this disclosure, it further includes: defining an objective function for imaging domain reflected wave waveform inversion based on the gradients corresponding to all the offsets, including:

[0012] The gradients corresponding to each offset are normalized.

[0013] Based on the normalized gradients, the objective function for inverting the reflected wave waveform in the imaging domain is defined.

[0014] Furthermore, according to one aspect of the imaging domain reflected wave waveform inversion method of this disclosure, the method further includes: calculating the updated gradient of the objective function corresponding to the velocity model, including:

[0015] Based on the objective function and the gradients corresponding to each offset, the update gradient of the objective function corresponding to the velocity model is calculated.

[0016] Furthermore, according to one aspect of the imaging domain reflected wave waveform inversion method of this disclosure, it further includes: defining an objective function for imaging domain reflected wave waveform inversion based on each of the normalized gradients, including:

[0017] Calculate the product of each of the normalized gradients, and based on the negative of the square of the product, calculate the objective function for inverting the reflected wave waveform in the imaging domain.

[0018] Furthermore, according to one aspect of the imaging domain reflected wave waveform inversion method of this disclosure, the method further includes: calculating the gradient corresponding to each offset based on each offset and a pre-constructed velocity model, including:

[0019] If a target offset exists among the various offsets, the target offset is divided into multiple segment offsets. The gradient corresponding to each segment offset is calculated by taking the derivative of the pre-built velocity model based on each segment offset. The target offset is the offset whose distance coverage exceeds a preset threshold.

[0020] Based on the gradients corresponding to all the segment offsets, the gradient corresponding to the target offset is obtained.

[0021] Furthermore, according to one aspect of the imaging domain reflected wave waveform inversion method of this disclosure, after updating the velocity model based on the updated gradient, the method further includes:

[0022] When the velocity model converges, a waveform inversion model for the reflected wave in the imaging domain is obtained;

[0023] If the velocity model fails to converge, the steps of taking the derivative of the pre-built velocity model with respect to each offset and calculating the gradient corresponding to each offset are repeated until the velocity model converges.

[0024] According to another aspect of this disclosure, an imaging domain reflected wave waveform inversion apparatus is provided, comprising:

[0025] The first calculation module is used to calculate the gradient corresponding to each offset based on each offset and the pre-built velocity model.

[0026] A definition module is used to define the objective function for inverting the reflected wave waveform in the imaging domain based on the gradients corresponding to all the offsets.

[0027] The second calculation module is used to calculate the updated gradient of the objective function corresponding to the velocity model;

[0028] The update module is used to update the velocity model based on the update gradient;

[0029] The waveform inversion module is used to perform waveform inversion of the reflected wave in the imaging domain based on the converged velocity model.

[0030] According to another aspect of this disclosure, a computer device is provided, including a memory, a processor, and a computer program stored in the memory, the processor executing the computer program to implement the method of one aspect above.

[0031] According to another aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the method of one aspect above.

[0032] According to another aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the method of the above-described aspect.

[0033] As will be described in detail below, an imaging domain reflected wave waveform inversion method, apparatus, device, medium, and product according to embodiments of the present disclosure, by defining an objective function for imaging domain reflected wave waveform inversion and calculating the update gradient of the velocity model corresponding to the objective function, updates the velocity model, thereby realizing imaging domain reflected wave waveform inversion, which can directly improve the imaging quality in the depth domain and make up for the deficiencies in the reflected wave waveform inversion application technology with depth domain imaging as the ultimate goal.

[0034] It should be understood that both the foregoing general description and the following detailed description are exemplary and intended to provide further illustration of the claimed technology. Attached Figure Description

[0035] The above and other objects, features, and advantages of this disclosure will become more apparent from the more detailed description of the embodiments thereof in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the embodiments of this disclosure to explain the disclosure and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0036] Figure 1This is a flowchart illustrating an imaging domain reflected wave waveform inversion method according to an embodiment of the present disclosure.

[0037] Figure 2 This is a flowchart illustrating another method for inverting the waveform of an imaging domain reflected wave according to an embodiment of the present disclosure.

[0038] Figure 3 The illustration shows a test image of an initial velocity model applied according to an embodiment of this disclosure.

[0039] Figure 4 The image is an illustration of a real reference velocity model applied according to an embodiment of this disclosure.

[0040] Figure 5 The diagram shows the waveform inversion result of the imaging domain reflected wave according to an embodiment of this disclosure.

[0041] Figure 6 The illustration shows the near-offset imaging results of an initial velocity model according to an embodiment of this disclosure.

[0042] Figure 7 The illustration shows the long-offset imaging results of an initial velocity model according to an embodiment of this disclosure.

[0043] Figure 8 The illustration shows the far-offset imaging result obtained by inverting the reflected wave waveform in the imaging domain according to an embodiment of this disclosure.

[0044] Figure 9 This is a schematic diagram of the structure of an imaging domain reflected wave waveform inversion device according to an embodiment of the present disclosure.

[0045] Figure 10 This is a schematic diagram illustrating the structure of a computer device according to an embodiment of the present disclosure.

[0046] Figure 11 This is a schematic diagram illustrating a computer program product according to an embodiment of the present disclosure. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this disclosure more apparent, exemplary embodiments according to this disclosure will now be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this disclosure, and not all embodiments of this disclosure. It should be understood that this disclosure is not limited to the exemplary embodiments described herein.

[0048] Common methods for reflecting wave waveform inversion are based on the data domain and involve two alternating iterative steps: 1) reflector construction; 2) velocity model update. Reflector construction can be achieved using different methods, such as ray migration imaging, reverse time migration, least squares reverse time migration, and wave impedance inversion. Velocity model update is based on the reflector construction results. Predicted reflection data corresponding to the reflector is generated through inverse migration or forward modeling of the wave equation, and the velocity model is updated by optimizing the inversion to reduce the mismatch between the predicted reflection data and the actual observed reflection data until the mismatch meets a preset threshold, at which point the iteration stops.

[0049] However, this method of reflecting wave waveform inversion requires a reflector model based on the imaging domain and the construction of a velocity model driven by an objective function built in the data domain, which cannot directly improve the imaging focus or imaging quality in the depth domain.

[0050] The above description, with reference to the accompanying drawings, describes an imaging domain reflected wave waveform inversion method, apparatus, device, medium, and product according to embodiments of the present disclosure. By defining the objective function for imaging domain reflected wave waveform inversion and calculating the update gradient of the velocity model corresponding to the objective function, the velocity model is updated, thereby realizing the imaging domain reflected wave waveform inversion. This can directly improve the imaging quality in the depth domain and make up for the shortcomings in the application technology of reflected wave waveform inversion with depth domain imaging as the ultimate goal.

[0051] To facilitate understanding of this embodiment, a detailed description of the imaging domain reflected wave waveform inversion method disclosed in this disclosure is provided first. The execution entity of the imaging domain reflected wave waveform inversion method provided in this disclosure is generally a computer device with certain computing capabilities. This computer device may include, for example, a terminal device, a server, or other processing devices. The terminal device may be a user equipment (UE), mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, vehicle-mounted device, wearable device, etc. In some possible implementations, the imaging domain reflected wave waveform inversion method can be implemented by a processor calling computer-readable instructions stored in memory.

[0052] Example 1

[0053] like Figure 1 The diagram shows a flowchart of an imaging domain reflected wave waveform inversion method provided in an embodiment of this disclosure. The method includes steps S101-S105:

[0054] S101: Calculate the gradient corresponding to each offset based on each offset and the pre-built velocity model.

[0055] The offset distance can be either a near offset distance or a far offset distance. For example, 0-2km can be classified as a near offset distance, and 2-10km as a far offset distance. The specific number and distance of offset distance divisions can be selected according to actual needs. This solution only provides one exemplary solution. The velocity model is based on commonly used construction methods in this field, and will not be described in detail in this embodiment.

[0056] The gradient corresponding to each offset is calculated by taking the derivative of the pre-built velocity model with respect to each offset.

[0057] Optionally, S101 further includes the following steps:

[0058] 1) If a target offset exists in each offset, the target offset is divided into multiple offset segments. The gradient corresponding to each offset segment is calculated by taking the derivative of the pre-built velocity model based on each offset segment.

[0059] The target offset is the offset distance that exceeds the preset threshold. The preset threshold can be set by the user. Taking the far offset distance as an example, if the distance coverage of the far offset distance is 2-10km, which exceeds the preset threshold, the far offset distance can be divided into multiple offset segments, such as 2-4km, 4-6km, 6-8km, and 8-10km. The gradient corresponding to each offset segment is calculated by taking the derivative of the velocity model based on each offset segment.

[0060] 2) Based on the gradients corresponding to the offsets of all segments, obtain the gradient corresponding to the target offset.

[0061] Specifically, the average of the gradients corresponding to all segment offsets is used as the gradient corresponding to the far offset. Alternatively, the median of the gradients corresponding to all segment offsets is obtained and used as the gradient corresponding to the far offset.

[0062] S102: Define the objective function for inverting the reflected wave waveform in the imaging domain based on the gradients corresponding to all offsets.

[0063] The objective function, also known as cross-correlation, is the structural similarity of images at each offset, such as the similarity between near-offset and far-offset images. The closer the near-offset and far-offset images match, the higher the structural similarity. To improve structural similarity, the objective function needs to be minimized.

[0064] S102 specifically includes the following steps:

[0065] 1) Normalize the gradients corresponding to each offset.

[0066] For example, the gradient corresponding to the near offset is... The gradient corresponding to the far offset is The normalized gradients are as follows:

[0067]

[0068] in, This represents the gradient corresponding to the near offset after normalization. Φ represents the gradient corresponding to the normalized far offset, and Φ represents the least-squares objective function for fitting the data domain reflection energy amplitude at the corresponding offset.

[0069] 2) Based on the normalized gradients, define the objective function for inverting the reflected wave waveform in the imaging domain.

[0070] Specifically, the product of each of the normalized gradients is calculated, and the objective function for inverting the reflected wave waveform in the imaging domain is calculated based on the negative of the square of the product.

[0071] The objective function is expressed as follows:

[0072]

[0073] Where Ψ(m) represents the structural similarity (i.e., the objective function), This represents the gradient corresponding to the near offset after normalization. This represents the gradient corresponding to the far offset after normalization.

[0074] S103: Calculate the update gradient of the objective function corresponding to the velocity model.

[0075] Specifically, based on the objective function and the gradients corresponding to each offset, the update gradient of the velocity model corresponding to the objective function is calculated. To improve structural similarity, the update gradient of the velocity model corresponding to the structural similarity needs to be calculated, and the model is updated along the negative direction of the update gradient to improve structural similarity.

[0076] The expression for updating the gradient is as follows:

[0077]

[0078] in, This indicates that the objective function corresponds to the update gradient of the velocity model. Expressing partial differentials and finding partial derivatives This represents the gradient corresponding to the near offset after normalization. This represents the gradient corresponding to the far offset after normalization.

[0079] S104: Update the velocity model based on the updated gradient.

[0080] Optionally, after updating the velocity model, the following may also be included:

[0081] When the velocity model converges, a waveform inversion model for the reflected wave in the imaging domain is obtained;

[0082] If the velocity model does not converge, repeat S101-104 until the velocity model converges.

[0083] S105: Based on the converged velocity model, perform waveform inversion of the reflected wave in the imaging domain.

[0084] Example 2

[0085] like Figure 2 The diagram shown is another flowchart of the imaging domain reflected wave waveform inversion method provided in this disclosure, the method including S201-S205:

[0086] S201: Select a reasonable imaging domain energy focusing metric and derive the gradient of this metric with respect to the inversion velocity model.

[0087] Taking the normalized cross-correlation of near-offset and far-offset FWI gradients (imaging) as an example, we obtain near-offset conventional LS-FWI imaging. Compared with long-offset conventional LS-FWI imaging The normalized gradients are as follows:

[0088]

[0089] in, This represents the gradient corresponding to the near offset after normalization. Φ represents the gradient corresponding to the normalized far offset, and Φ represents the least-squares objective function for fitting the data domain reflection energy amplitude at the corresponding offset.

[0090] S202: Define the energy focusing metric as the cross-correlation between near offset and far offset (objective function), and minimize the objective function.

[0091] Among them, the energy focusing metric is the structural similarity of the imaging at near offset and far offset. The larger the energy focusing metric value, the higher the focusing degree and the higher the structural similarity.

[0092] Therefore, the objective function is minimized, and its expression is as follows:

[0093]

[0094] Where Ψ(m) represents the structural similarity (i.e., the objective function), This represents the gradient corresponding to the near offset after normalization. This represents the gradient corresponding to the far offset after normalization.

[0095] S203: Derive the updated gradient of the objective function with respect to the velocity model.

[0096] To improve structural similarity, the velocity model needs to be updated during the inversion process to increase cross-correlation. This requires first deriving the gradient of the objective function with respect to the velocity model. The update gradient represents the direction of velocity model updates; updating along the negative direction of the update gradient will improve structural similarity.

[0097] The expression for updating the gradient is as follows:

[0098]

[0099] in, This indicates that the objective function corresponds to the update gradient of the velocity model. Expressing partial differentials and finding partial derivatives This represents the gradient corresponding to the near offset after normalization. This represents the gradient corresponding to the far offset after normalization.

[0100] S204: Using the near offset image as a reference image, the far offset is divided into multiple offset segments, and the gradients of all offset segments are superimposed as the gradient of the far offset.

[0101] Specifically, the average gradient of all offset segments can be calculated, and this average can be used as the gradient of the far offset. By dividing the far offset into multiple segments and calculating the gradient for each segment, the accuracy of gradient calculation can be improved, thus enhancing imaging quality.

[0102] S205: Repeat S201-204 until the velocity model converges, and use the converged velocity model to perform waveform inversion of the reflected wave in the imaging domain.

[0103] This embodiment provides the following test data obtained based on this solution: the work area is 7.68km wide horizontally and 2km deep, and the test results are as follows. Figures 3-8 As shown in the test results above, a velocity model suitable for depth domain imaging was constructed by improving the structural similarity between long-offset imaging and short-offset imaging.

[0104] Example 3

[0105] According to another aspect of the embodiments of this disclosure, an imaging domain reflected wave waveform inversion apparatus is provided, such as... Figure 9 As shown, the device includes:

[0106] The first calculation module 101 is used to calculate the gradient corresponding to each offset based on each offset and the pre-built velocity model.

[0107] Definition module 102 is used to define the objective function for inverting the reflected wave waveform in the imaging domain based on the gradients corresponding to all the offsets.

[0108] The second calculation module 103 is used to calculate the updated gradient of the objective function corresponding to the velocity model;

[0109] Update module 104 is used to update the velocity model based on the update gradient;

[0110] The waveform inversion module 105 is used to perform waveform inversion of the reflected wave in the imaging domain based on the converged velocity model.

[0111] In one or more embodiments, the definition module 102 is used to: normalize the gradient corresponding to each of the offsets;

[0112] Based on the normalized gradients, the objective function for inverting the reflected wave waveform in the imaging domain is defined.

[0113] In one or more embodiments, the second calculation module 103 is used to: calculate the updated gradient of the objective function corresponding to the velocity model based on the objective function and the gradients corresponding to each of the offsets.

[0114] In one or more embodiments, the definition module 102 is further configured to: calculate the product of each of the normalized gradients, and calculate the objective function for inverting the reflected wave waveform in the imaging domain based on the negative of the square of the product.

[0115] In one or more embodiments, the first calculation module 101 is further configured to: when there is a target offset among the offsets, divide the target offset into multiple segment offsets, differentiate the pre-built velocity model based on each segment offset, calculate the gradient corresponding to each segment offset, wherein the target offset is an offset whose distance coverage exceeds a preset threshold.

[0116] Based on the gradients corresponding to all the segment offsets, the gradient corresponding to the target offset is obtained.

[0117] The imaging domain reflected wave waveform inversion device is further configured to: after updating the velocity model based on the update gradient, obtain an imaging domain reflected wave waveform inversion model if the velocity model converges.

[0118] If the velocity model fails to converge, the steps of taking the derivative of the pre-built velocity model with respect to each offset and calculating the gradient corresponding to each offset are repeated until the velocity model converges.

[0119] The imaging domain reflected wave waveform inversion device and the imaging domain reflected wave waveform inversion method provided in this disclosure are based on the same inventive concept and have the same beneficial effects as the methods they employ, operate, or implement.

[0120] Example 4

[0121] This disclosure also provides a computer device for performing the above-described imaging domain reflected wave waveform inversion method. Please refer to... Figure 10 It illustrates a schematic diagram of a computer device provided by some embodiments of this disclosure. For example... Figure 10 As shown, the computer device 8 includes: a processor 800, a memory 801, a bus 802, and a communication interface 803. The processor 800, the communication interface 803, and the memory 801 are connected via the bus 802. The memory 801 stores a computer program that can run on the processor 800. When the processor 800 runs the computer program, it executes the imaging domain reflection wave waveform inversion method provided in any of the foregoing embodiments of this disclosure.

[0122] The memory 801 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this device network element and at least one other network element is achieved through at least one communication interface 803 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0123] Bus 802 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory 801 is used to store programs. After receiving an execution instruction, the processor 800 executes the program. The imaging domain reflected wave waveform inversion method disclosed in any of the foregoing embodiments of this disclosure can be applied to the processor 800, or implemented by the processor 800.

[0124] The processor 800 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 800 or by instructions in software form. The processor 800 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPTA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this disclosure. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this disclosure can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 801. Processor 800 reads the information in memory 801 and, in conjunction with its hardware, completes the steps of the above method.

[0125] The computer device provided in this disclosure and the imaging domain reflected wave waveform inversion method provided in this disclosure are based on the same inventive concept and have the same beneficial effects as the methods they employ, operate, or implement.

[0126] Example 5

[0127] This disclosure also provides a computer-readable storage medium corresponding to the imaging domain reflection waveform inversion method provided in the foregoing embodiments. The computer-readable storage medium is an optical disc, on which a computer program (i.e., a computer program product) is stored. When the computer program is run by a processor, it executes the imaging domain reflection waveform inversion method provided in any of the foregoing embodiments.

[0128] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0129] The computer-readable storage medium provided in the above embodiments of this disclosure and the imaging domain reflected wave waveform inversion method provided in the embodiments of this disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0130] This disclosure also provides a computer program product; please refer to [reference needed]. Figure 11 The computer program product 600 carries program code, namely computer program 601. The instructions included in the computer program 601 can be used to execute the steps of the imaging domain reflected wave waveform inversion method described in the above method embodiments. For details, please refer to the above method embodiments, which will not be repeated here.

[0131] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium; in another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0132] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0133] The block diagrams of devices, apparatuses, devices, and systems disclosed herein are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0134] Additionally, as used herein, the “or” used in a list of items beginning with “at least one” indicates a separate list, such that a list of, for example, “at least one of A, B, or C” means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word “exemplary” does not imply that the described example is preferred or better than other examples.

[0135] It should also be noted that in the systems and methods of this disclosure, the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions to this disclosure.

[0136] Various changes, substitutions, and modifications can be made to the technology described herein without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of this disclosure is not limited to the specific aspects of the processes, machines, manufactures, events, means, methods, and actions described above. Currently existing or later-developed processes, machines, manufactures, events, means, methods, or actions that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein can be utilized. Therefore, the appended claims include such processes, machines, manufactures, events, means, methods, or actions within their scope.

[0137] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0138] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. An imaging domain reflected wave waveform inversion method characterized by, include: Based on each offset and the pre-built velocity model, calculate the gradient corresponding to each offset; Based on the gradients corresponding to all the aforementioned offsets, the objective function for inverting the reflected wave waveform in the imaging domain is defined. Calculate the updated gradient of the objective function corresponding to the velocity model; The velocity model is updated based on the updated gradient; Based on the converged velocity model, the waveform of the reflected wave in the imaging domain is inverted.

2. The imaging domain reflected wave waveform inversion method as described in claim 1, characterized in that, Based on the gradients corresponding to all the aforementioned offsets, an objective function for inverting the reflected wave waveform in the imaging domain is defined, including: The gradients corresponding to each offset are normalized. Based on the normalized gradients, the objective function for inverting the reflected wave waveform in the imaging domain is defined.

3. The imaging domain reflected wave waveform inversion method as described in claim 1, characterized in that, Calculating the update gradient of the objective function corresponding to the velocity model includes: Based on the objective function and the gradients corresponding to each offset, the update gradient of the objective function corresponding to the velocity model is calculated.

4. The imaging domain reflected wave waveform inversion method as described in claim 2, characterized in that, Based on the normalized gradients, the objective function for inverting the reflected wave waveform in the imaging domain is defined, including: Calculate the product of each of the normalized gradients, and based on the negative of the square of the product, calculate the objective function for inverting the reflected wave waveform in the imaging domain.

5. The imaging domain reflected wave waveform inversion method as described in claim 1, characterized in that, Based on each offset and the pre-built velocity model, the gradient corresponding to each offset is calculated, including: If a target offset exists among the various offsets, the target offset is divided into multiple segment offsets. The gradient corresponding to each segment offset is calculated by taking the derivative of the pre-built velocity model based on each segment offset. The target offset is the offset whose distance coverage exceeds a preset threshold. Based on the gradients corresponding to all the segment offsets, the gradient corresponding to the target offset is obtained.

6. The imaging domain reflected wave waveform inversion method as described in claim 1, characterized in that, After updating the velocity model based on the updated gradient, the method further includes: When the velocity model converges, a waveform inversion model for the reflected wave in the imaging domain is obtained; If the velocity model fails to converge, the steps of taking the derivative of the pre-built velocity model with respect to each offset and calculating the gradient corresponding to each offset are repeated until the velocity model converges.

7. An imaging domain reflected wave waveform inversion device, characterized in that, include: The first calculation module is used to calculate the gradient corresponding to each offset based on each offset and the pre-built velocity model. A definition module is used to define the objective function for inverting the reflected wave waveform in the imaging domain based on the gradients corresponding to all the offsets. The second calculation module is used to calculate the updated gradient of the objective function corresponding to the velocity model; The update module is used to update the velocity model based on the update gradient; The waveform inversion module is used to perform waveform inversion of the reflected wave in the imaging domain based on the converged velocity model.

8. A computer embedded device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1 to 6.

Images