Thin-layer structure high-resolution seismic inversion method and system, and readable storage medium

Abstract

The invention provides a thin-layer structure high-resolution seismic inversion method and system, and a readable storage medium. The thin-layer structure high-resolution seismic inversion method comprises the steps of obtaining seismic data; extracting the seismic data to obtain seismic wavelets; acquiring an inclination angle of the seismic data; constructing a spatial prediction error filter according to the inclination angle; establishing a target functional of wave impedance seismic inversion according to the seismic data, the seismic wavelets and a spatial prediction error filter; and solving the target functional through an odd-even decomposition basis tracking algorithm to obtain high-resolution wave impedance data. According to the technical scheme of the invention, wave impedance inversion can be simultaneously carried out on multiple channels of seismic data under the driving of a space structure, and the capacity of an inversion result for revealing a thin-layer structure is effectively enhanced.

Description

Technical Field

[0001] The present application relates to the field of oil and gas exploitation, and more particularly, to a high-resolution seismic inversion method and system for thin-layer structures and a readable storage medium. Background Art

[0002] Seismic records are the seismic responses of underground media, containing information on the formation structure and its physical property changes. However, due to the frequency band limitation of seismic records and the interference effect between different reflections, although we can obtain the macroscopic trend of the underground structure from seismic records, it is difficult to predict and analyze thin-layer structures and their internal features.

[0003] Wave impedance inversion is the most commonly used method to improve the resolution of seismic records. Based on the seismic record convolution model, this method weakens the interference effect of seismic wavelets while converting seismic reflections into formation wave impedance, improving the ability of seismic data to reflect thin-layer structures. Like other inversion problems, wave impedance inversion also has strong non-uniqueness and uncertainty. Applying certain regularization conditions to the inversion problem is an effective way to reduce non-uniqueness. Among them, assuming that the reflection coefficient sequence satisfies a certain form of sparse distribution is a currently commonly used regularization condition. This wave impedance inversion method based on sparse structure regularization constraints is called sparse pulse wave impedance inversion.

[0004] There are different mathematical functions to describe the sparse characteristics, resulting in many different forms of sparse pulse inversion methods. Different from conventional seismic inversion, sparse pulse inversion belongs to a typical non-linear optimization problem. This type of inversion method has the theoretical advantage of breaking through the effective frequency band of seismic records and greatly improving the resolution of inversion results, but the implementation process is relatively complex, and the high-frequency components of inversion results have strong non-uniqueness. Spatial structure and its continuity are important indicators for judging the reliability of inversion results. However, the existing sparse pulse inversion mostly adopts a running mode of inverting each trace one by one. For a certain seismic trace, it cannot reflect the spatial structure and its continuity characteristics. Therefore, this sparse pulse inversion method that runs trace by trace cannot distinguish and judge the reliability of inversion results. Summary of the Invention

[0005] The present application aims to solve or improve the above technical problems.

[0006] To this end, the first object of the present application is to provide a high-resolution seismic inversion method for thin-layer structures.

[0007] The second object of the present application is to provide a high-resolution seismic inversion system for thin-layer structures.

[0008] The third object of the present application is to provide another high-resolution seismic inversion system for thin-layer structures.

[0009] The fourth object of the present application is to provide a readable storage medium.

[0010] To achieve the first object of the present application, the technical solution of the first aspect of the present application provides a thin-layer structure high-resolution seismic inversion method, including: acquiring seismic data; extracting the seismic data to obtain a seismic wavelet; acquiring the dip angle of the seismic data; constructing a spatial prediction error filter according to the dip angle; establishing an objective functional for wave impedance seismic inversion based on the seismic data, the seismic wavelet, and the spatial prediction error filter; and solving the objective functional by the even-odd decomposition basis pursuit algorithm to obtain high-resolution wave impedance data.

[0011] According to the thin-layer structure high-resolution seismic inversion method provided by the present application, first, seismic data is acquired. Then the seismic data is extracted to obtain a seismic wavelet. Based on the continuity and coherence of seismic signals, the dip angle of seismic reflection is estimated from the seismic record, and a spatial prediction error filter is designed along the dip angle direction. The spatial prediction error filter is introduced into the regularization condition of multi-channel wave impedance inversion to construct a multi-channel simultaneous inversion system with spatial structure constraints. The even-odd decomposition basis pursuit technique is used to numerically solve the multi-channel inversion system to obtain a high-resolution wave impedance inversion result. By introducing the spatial structure into the regularization condition of seismic inversion and simultaneously performing wave impedance inversion on multi-channel seismic data driven by the spatial structure, the revealing ability of the inversion result for the thin-layer structure is effectively enhanced. The operation is simple, the operation is stable, and the effect is obvious, providing technical support for high-resolution thin-layer structure seismic characterization.

[0012] In addition, the technical solution provided by the present application may further have the following additional technical features:

[0013] In some technical solutions, optionally, establishing an objective functional for wave impedance seismic inversion based on the seismic data, the seismic wavelet, and the spatial prediction error filter includes: connecting the seismic data end to end to form an input seismic record vector; constructing a large sparse block matrix according to the seismic wavelet; adjusting the spatial prediction error filter to the direction of the dip angle of each sample point, and constructing a spatial prediction error filter matrix through a Helix transform; and establishing an objective functional for wave impedance seismic inversion based on the input seismic record vector, the large sparse block matrix, and the spatial prediction error filter matrix.

[0014] In this technical solution, based on seismic data, seismic wavelets, and a spatial prediction error filter, an objective functional for wave impedance seismic inversion is established. Specifically, first, all seismic data are joined end to end to form an input seismic record vector. Then, a large sparse block matrix composed of seismic wavelets is constructed. Then, the spatial prediction error filter is adjusted to the dip direction of each sample point, and the Helix transform is used to construct a spatial prediction error filter matrix composed of the spatial prediction error filters. Finally, based on the input seismic record vector, the large sparse block matrix, and the spatial prediction error filter matrix, the objective functional for wave impedance seismic inversion is established.

[0015] In some technical solutions, optionally, the seismic data includes reflection time and surface location.

[0016] In this technical solution, the seismic data includes reflection time and surface location.

[0017] In some technical solutions, optionally, obtaining the dip angle of seismic data includes: determining the dip angle on the seismic data through a dip angle scanning method.

[0018] In this technical solution, obtaining the dip angle of seismic data specifically means determining the dip angle on the seismic data through a dip angle scanning method.

[0019] In some technical solutions, optionally, the formula for the spatial prediction error filter is:

[0020]

[0021] where x is the surface location, L is the length, and q(x) is the spatial prediction error filter.

[0022] In this technical solution, designing a spatial prediction error filter with a length of 3L along the dip direction can introduce the spatial prediction error filter into the regularization condition of multi-channel wave impedance inversion.

[0023] In some technical solutions, optionally, the large sparse block matrix is:

[0024]

[0025] where is the large sparse block matrix and w is the submatrix.

[0026] In this technical solution, a large sparse block matrix composed of seismic wavelets is constructed. Each column in the submatrix w is the seismic wavelet after delay.

[0027] In some technical solutions, optionally, the formula for the objective functional is:

[0028]

[0029] Among them, represents the L2 norm, ||||1 represents the L1 norm, v is a vector formed by connecting the head and tail of the wave impedance data v(x,t) after inversion, F is a first-order difference matrix, λ1 and λ2 are two regularization parameters, a is a spatial prediction error filter matrix, u is an input seismic record vector, is a large sparse block matrix.

[0030] In this technical solution, the objective functional of wave impedance seismic inversion is established, and the objective functional is solved by the odd-even decomposition basis pursuit algorithm to obtain high-resolution wave impedance data. The basic idea of the odd-even decomposition basis pursuit algorithm is to first construct a seismic reflection dictionary, and each element in the dictionary represents the seismic reflection of different thickness formations. Then, calculate the projection of the actual seismic record on the dictionary elements, and thus invert and estimate the underground wave impedance data.

[0031] To achieve the second object of the present application, the technical solution of the second aspect of the present application provides a high-resolution seismic inversion system for thin-layer structures, including: a first acquisition module for acquiring seismic data; an extraction module for extracting the seismic data to obtain a seismic wavelet; a second acquisition module for acquiring the dip angle of the seismic data; a filter module for constructing a spatial prediction error filter according to the dip angle; an objective functional construction module for establishing the objective functional of wave impedance seismic inversion according to the seismic data, the seismic wavelet, and the spatial prediction error filter; a calculation module for solving the objective functional by the odd-even decomposition basis pursuit algorithm to obtain high-resolution wave impedance data.

[0032] According to the high-resolution seismic inversion system for thin-layer structures provided by the present application, it includes a first acquisition module, an extraction module, a second acquisition module, a filter module, an objective functional construction module, and a calculation module. Among them, the first acquisition module is used to acquire seismic data. The extraction module is used to extract the seismic data to obtain a seismic wavelet. The second acquisition module is used to acquire the dip angle of the seismic data. The filter module is used to construct a spatial prediction error filter according to the dip angle. The objective functional construction module is used to establish the objective functional of wave impedance seismic inversion according to the seismic data, the seismic wavelet, and the spatial prediction error filter. The calculation module is used to solve the objective functional by the odd-even decomposition basis pursuit algorithm to obtain high-resolution wave impedance data. By introducing the spatial structure into the regularization condition of seismic inversion and performing wave impedance inversion on multi-channel seismic data simultaneously under the drive of the spatial structure, the revealing ability of the inversion result for thin-layer structures is effectively enhanced, the operation is simple, the operation is stable, the effect is obvious, and it provides technical support for high-resolution thin-layer structure seismic characterization.

[0033] To achieve the third objective of the present application, the technical solution of the third aspect of the present application provides another thin-layer structure high-resolution seismic inversion system, including: a memory and a processor. Among them, a program or instruction that can run on the processor is stored on the memory. When the processor executes the program or instruction, it implements the thin-layer structure high-resolution seismic inversion method of any one of the technical solutions in the first aspect. Therefore, it has the technical effects of any one of the technical solutions in the above-mentioned first aspect, which will not be elaborated here.

[0034] To achieve the fourth objective of the present application, the technical solution of the fourth aspect of the present application provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, it implements the steps of the thin-layer structure high-resolution seismic inversion method of any one of the technical solutions in the first aspect. Therefore, it has the technical effects of any one of the technical solutions in the above-mentioned first aspect, which will not be elaborated here.

[0035] The additional aspects and advantages of the present application will become obvious in the following description section, or be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The above and / or additional aspects and advantages of the present application will become obvious and easy to understand from the description of the embodiments in conjunction with the following drawings, where:

[0037] Figure 1 is a schematic flowchart of the steps of the thin-layer structure high-resolution seismic inversion method according to an embodiment of the present application;

[0038] Figure 2 is a schematic flowchart of the steps of the thin-layer structure high-resolution seismic inversion method according to an embodiment of the present application;

[0039] Figure 3 is a schematic flowchart of the steps of the thin-layer structure high-resolution seismic inversion method according to an embodiment of the present application;

[0040] Figure 4 is a schematic block diagram of the structure of the thin-layer structure high-resolution seismic inversion system according to an embodiment of the present application;

[0041] Figure 5 is a schematic block diagram of the structure of the thin-layer structure high-resolution seismic inversion system according to another embodiment of the present application;

[0042] Figure 6 is a schematic diagram of the continental sedimentary thin-layer geological model of the thin-layer structure high-resolution seismic inversion method according to an embodiment of the present application;

[0043] Figure 7 is a schematic diagram of the synthetic seismic record of the geological model of the thin-layer structure high-resolution seismic inversion method according to an embodiment of the present application;

[0044] Figure 8 Schematic diagram of the synthetic seismic record inversion result of the thin-layer structure high-resolution seismic inversion method according to an embodiment of the present application;

[0045] Figure 9 Schematic diagram of the actual seismic record of the thin-layer structure high-resolution seismic inversion method according to an embodiment of the present application; and

[0046] Figure 10 Schematic diagram of the actual seismic record inversion result of the thin-layer structure high-resolution seismic inversion method according to an embodiment of the present application.

[0047] Among them, Figure 4 and Figure 5 The corresponding relationship between the reference numerals and the component names in is:

[0048] 10: Thin-layer structure high-resolution seismic inversion system; 110: First acquisition module; 120: Extraction module; 130: Second acquisition module; 140: Filter module; 150: Target functional construction module; 160: Calculation module; 20: Thin-layer structure high-resolution seismic inversion system; 300: Memory; 400: Processor. Detailed implementation manners

[0049] In order to be able to more clearly understand the above objects, features, and advantages of the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific implementation manners. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.

[0050] Many specific details are set forth in the following description in order to fully understand the present application. However, the present application may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present application is not limited by the specific embodiments disclosed below.

[0051] Next, refer to Figures 1 to 10 Describe the thin-layer structure high-resolution seismic inversion method, system, and readable storage medium according to some embodiments of the present application.

[0052] As Figure 1 shown, an embodiment of the first aspect of the present application provides a thin-layer structure high-resolution seismic inversion method, including the following steps:

[0053] Step S102: Acquire seismic data;

[0054] Step S104: Extract the seismic data to obtain a seismic wavelet;

[0055] Step S106: Acquire the dip angle of the seismic data;

[0056] Step S108: Construct a spatial prediction error filter according to the dip angle;

[0057] Step S110: Establish an objective functional for wave impedance seismic inversion based on seismic data, seismic wavelets, and the spatial prediction error filter;

[0058] Step S112: Solve the objective functional through the even-odd decomposition basis pursuit algorithm to obtain high-resolution wave impedance data.

[0059] According to the high-resolution seismic inversion method for thin-layer structures provided in this embodiment, seismic data is first acquired. Then, the seismic data is extracted to obtain seismic wavelets. Based on the continuity and coherence of seismic signals, the dip angle of seismic reflections is estimated from seismic records, and a spatial prediction error filter is designed along the dip angle direction. The spatial prediction error filter is introduced into the regularization condition of multi-channel wave impedance inversion to construct a multi-channel simultaneous inversion system with spatial structure constraints. The even-odd decomposition basis pursuit technique is used to numerically solve the multi-channel inversion system to obtain the high-resolution wave impedance inversion result. By introducing the spatial structure into the regularization condition of seismic inversion and performing wave impedance inversion on multi-channel seismic data simultaneously under the drive of the spatial structure, the ability of the inversion result to reveal thin-layer structures is effectively enhanced. The operation is simple, the operation is stable, and the effect is obvious, providing technical support for high-resolution thin-layer structure seismic characterization.

[0060] As Figure 2 shown, according to the high-resolution seismic inversion method for thin-layer structures of an embodiment proposed in this application, an objective functional for wave impedance seismic inversion is established based on seismic data, seismic wavelets, and the spatial prediction error filter, including the following steps:

[0061] Step S202: Connect the head and tail of the seismic data to form an input seismic record vector;

[0062] Step S204: Construct a large sparse block matrix according to the seismic wavelets;

[0063] Step S206: Adjust the spatial prediction error filter to the direction of the dip angle of each sample point, and construct a spatial prediction error filter matrix through the Helix transform;

[0064] Step S208: Establish an objective functional for wave impedance seismic inversion based on the input seismic record vector, the large sparse block matrix, and the spatial prediction error filter matrix.

[0065] In this embodiment, according to seismic data, seismic wavelets, and a spatial prediction error filter, an objective functional for wave impedance seismic inversion is established. Specifically, first, all seismic data are concatenated end to end to form an input seismic record vector. Then, a large sparse block matrix composed of seismic wavelets is constructed. Then, the spatial prediction error filter is adjusted to the dip direction of each sample point, and the Helix transform is used to construct a spatial prediction error filter matrix composed of the spatial prediction error filters. Finally, based on the input seismic record vector, the large sparse block matrix, and the spatial prediction error filter matrix, the objective functional for wave impedance seismic inversion is established.

[0066] In some embodiments, optionally, the seismic data includes reflection time and surface location.

[0067] As Figure 3 shown, for the thin-layer structure high-resolution seismic inversion method according to an embodiment proposed in the present application, obtaining the dip angle of seismic data specifically includes the following steps:

[0068] Step S302: Determine the dip angle on the seismic data through the dip angle scanning method.

[0069] In this embodiment, obtaining the dip angle of seismic data specifically means determining the dip angle on the seismic data through the dip angle scanning method.

[0070] In some embodiments, optionally, the formula for the spatial prediction error filter is:

[0071]

[0072] where x is the surface location, L is the length, and q(x) is the spatial prediction error filter.

[0073] In this technical solution, designing a spatial prediction error filter with a length of 3L along the dip direction can introduce the spatial prediction error filter into the regularization condition of multi-channel wave impedance inversion.

[0074] In some embodiments, optionally, the large sparse block matrix is:

[0075]

[0076] where is the large sparse block matrix and w is the sub-matrix. A large sparse block matrix composed of seismic wavelets is constructed. Each column in the sub-matrix w is the seismic wavelet after delay.

[0077] In some embodiments, optionally, the formula for the objective functional is:

[0078]

[0079] where denotes the L2 norm, ||||1 denotes the L1 norm, v is a vector formed by connecting the head and tail of the inverted wave impedance data v(x,t), F is the first-order difference matrix, λ1 and λ2 are two regularization parameters, a is the spatial prediction error filter matrix, and u is the input seismic record vector. is a large sparse block matrix. The objective functional for wave impedance seismic inversion is established, and the objective functional is solved by the even-odd decomposition basis pursuit algorithm to obtain high-resolution wave impedance data. The basic idea of the even-odd decomposition basis pursuit algorithm is to first construct a seismic reflection dictionary, where each element in the dictionary represents the seismic reflection of a formation with different thicknesses. Then, the projection of the actual seismic record on the dictionary elements is calculated, and the underground wave impedance data is inverted and estimated accordingly.

[0080] As Figure 4 shown, an embodiment of the second aspect of the present application provides a high-resolution seismic inversion system 10 for thin-layer structures, including: a first acquisition module 110 for acquiring seismic data; an extraction module 120 for extracting the seismic data to obtain a seismic wavelet; a second acquisition module 130 for acquiring the dip angle of the seismic data; a filter module 140 for constructing a spatial prediction error filter according to the dip angle; an objective functional construction module 150 for establishing an objective functional for wave impedance seismic inversion based on the seismic data, the seismic wavelet, and the spatial prediction error filter; and a calculation module 160 for solving the objective functional by the even-odd decomposition basis pursuit algorithm to obtain high-resolution wave impedance data.

[0081] According to the high-resolution seismic inversion system 10 for thin-layer structures provided by this embodiment, it includes a first acquisition module 110, an extraction module 120, a second acquisition module 130, a filter module 140, an objective functional construction module 150, and a calculation module 160. Among them, the first acquisition module 110 is used to acquire seismic data. The extraction module 120 is used to extract the seismic data to obtain a seismic wavelet. The second acquisition module 130 is used to acquire the dip angle of the seismic data. The filter module 140 is used to construct a spatial prediction error filter according to the dip angle. The objective functional construction module 150 is used to establish an objective functional for wave impedance seismic inversion based on the seismic data, the seismic wavelet, and the spatial prediction error filter. The calculation module 160 is used to solve the objective functional by the even-odd decomposition basis pursuit algorithm to obtain high-resolution wave impedance data. By introducing the spatial structure into the regularization condition of seismic inversion and performing wave impedance inversion on multi-channel seismic data simultaneously under the drive of the spatial structure, the ability of the inversion result to reveal thin-layer structures is effectively enhanced. The operation is simple, the operation is stable, and the effect is obvious, providing technical support for high-resolution thin-layer structure seismic characterization.

[0082] As Figure 5As shown in the figure, an embodiment of the third aspect of the present application provides another thin-layer structure high-resolution seismic inversion system 20, including: a memory 300 and a processor 400. Among them, a program or instruction that can run on the processor 400 is stored on the memory 300. When the processor 400 executes the program or instruction, the steps of the thin-layer structure high-resolution seismic inversion method in any one of the embodiments of the first aspect are implemented. Therefore, it has the technical effects of any one of the above-mentioned first aspects and will not be elaborated here.

[0083] An embodiment of the fourth aspect of the present application provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the thin-layer structure high-resolution seismic inversion method in any one of the embodiments of the first aspect are implemented. Therefore, it has the technical effects of any one of the above-mentioned first aspects and will not be elaborated here.

[0084] As Figures 6 to 10 shown, according to the thin-layer structure high-resolution seismic inversion method of a specific embodiment provided by the present application, a spatial structure is introduced into the regularization condition of seismic inversion, and wave impedance inversion is simultaneously performed on multi-channel seismic data under the drive of the spatial structure, effectively enhancing the revealing ability of the inversion result for the thin-layer structure.

[0085] The core content of this embodiment is: based on the continuity and coherence of seismic signals, estimate the dip angle of seismic reflections from seismic records, and design a spatial prediction error filter along the dip angle direction. Introduce the spatial prediction error filter into the regularization condition of multi-channel wave impedance inversion to construct a multi-channel simultaneous inversion system with spatial structure constraints. Use the odd-even decomposition basis pursuit technique to numerically solve the multi-channel inversion system to obtain a high-resolution wave impedance inversion result.

[0086] The specific steps are as follows:

[0087] Step 1: Input seismic data u(x, t), where t is the reflection time and x is the surface position.

[0088] Step 2: Extract the seismic wavelet w(t) from the seismic records using a conventional method.

[0089] Step 3: Determine the dip angle p(x, t) on the seismic record u(x, t) using a conventional method.

[0090] Step 4: Design a spatial prediction error filter q(x) with a length of 3L, and its specific form is:

[0091]

[0092] Step 5: Connect the heads and tails of all input seismic records u(x, t) to form an input seismic record vector u.

[0093] Step 6: Construct a large sparse block matrix composed of seismic wavelets w(t). Its specific form is as follows:

[0094]

[0095] Among them, each column in the sub-matrix w is the seismic wavelet after delay.

[0096] Step 7: Adjust the spatial prediction error filter q(x) to the dip direction p(x,t) of each sample point, and then use the Helix transform to construct a matrix a composed of the spatial prediction error filter q(x).

[0097] Step 8: Establish the objective functional ε(v) for wave impedance seismic inversion, and its specific form is as follows:

[0098]

[0099] Among them, represents the L2 norm, ||||1 represents the L1 norm, v is a vector formed by concatenating the wave impedance data v(x,t) after inversion from head to tail, F is the first-order difference matrix, and λ1 and λ2 are two regularization parameters.

[0100] Step 9: Use the odd-even decomposition basis pursuit algorithm to solve the objective functional to obtain the high-resolution wave impedance data v(x,t). The basic idea of the odd-even decomposition basis pursuit algorithm is as follows: First, construct a seismic reflection dictionary, and each element in the dictionary represents the seismic reflection of different thickness strata. Then, calculate the projection of the actual seismic record on the dictionary elements, and thus invert and estimate the underground wave impedance data.

[0101] Step 10: Output the high-resolution wave impedance data v(x,t).

[0102] As Figure 6 , Figure 7 and Figure 8 shown, specifically, taking the continental sedimentary thin-layer geological model shown in Figure 6 as an example, the specific implementation method and application effect of this embodiment are described in detail. Figure 7 is the synthetic seismic record corresponding to this geological model. The seismic wavelet used in the synthetic seismic record is the Ricker wavelet with a dominant frequency of 20 Hz. Due to the interference effect of seismic wavelets between different strata, it is difficult to distinguish and analyze the spatial structure of the geological model on the synthetic seismic record. Figure 8 is the wave impedance profile after inverting the seismic record shown in Figure 7 using this embodiment. The inversion result better reveals the spatial distribution and mutual relationship of each thin layer. The following details the specific implementation process of this embodiment:

[0103] Step 1: Input seismic data u(x,t), where t is the reflection time and x is the surface location. The input seismic data in this example is Figure 1 the seismic record shown.

[0104] Step 2: Extract the seismic wavelet w(t) from the seismic record using a conventional method. The seismic wavelet in this example is a Ricker wavelet with a dominant frequency of 20 Hz.

[0105] Step 3: Determine the dip p(x,t) on the seismic record u(x,t) using a conventional method. In this example, the dip scanning method is used to determine the dip on the seismic record.

[0106] Step 4: Design a spatial prediction error filter q(x) with a length of 3L, and its specific form is:

[0107]

[0108] L = 3 in this embodiment.

[0109] Step 5: Connect the head and tail of all input seismic records u(x,t) to form an input seismic record vector u.

[0110] Step 6: Construct a large sparse block matrix composed of the seismic wavelet w(t) Its specific form is:

[0111]

[0112] where each column in the sub-matrix w is the seismic wavelet after delay.

[0113] Step 7: Adjust the spatial prediction error filter a(x) to the dip direction p(x,t) of each sample point, and then use the Helix transform to construct a matrix a composed of the spatial prediction error filter a(x).

[0114] Step 8: Establish the objective functional ε(v) for wave impedance seismic inversion, and its specific form is:

[0115]

[0116] where represents the L2 norm, ||||1 represents the L1 norm, v is the vector formed by connecting the head and tail of the inverted wave impedance data v(x,t), F is the first-order difference matrix, and λ1 and λ2 are two regularization parameters. λ1 = 0.01 and λ1 = 0.1 in this example.

[0117] Step 9: Solve the objective functional using the odd-even decomposition basis pursuit algorithm to obtain the high-resolution wave impedance data v(x,t).

[0118] Step 10: Output the high-resolution wave impedance data v(x,t). The high-resolution wave impedance data in this example is Figure 3 the wave impedance profile shown.

[0119] As Figure 9 and Figure 10 shown, Figure 9 is the input seismic data of this embodiment. This area is a continental thin interbed sedimentary formation with a small sand body thickness. It is difficult to analyze and predict the spatial distribution and contact relationship of sand bodies on the seismic record shown in Figure 9 . Figure 10 is the wave impedance data after inversion using this embodiment. Compared with the seismic record shown in Figure 9 , the wave impedance data after inversion can more clearly reveal the spatial distribution and thickness variation of different sand bodies.

[0120] In summary, the beneficial effects of the embodiments of this application are as follows:

[0121] Adopting the technical solution of the present invention enhances the resolution and reliability of the inversion result, more clearly and accurately reveals the structure and form of the underground strata, is simple to operate, stable in operation, and has obvious effects, providing technical support for high-resolution thin layer structure seismic characterization.

[0122] In this application, the terms "first", "second", and "third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance; the term "multiple" means two or more, unless otherwise clearly defined. Terms such as "installation", "connection", "connection", and "fixation" should all be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; "connection" can be a direct connection or an indirect connection through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0123] In the description of this application, it should be understood that the orientation or positional relationship indicated by terms such as "upper", "lower", "front", and "rear" is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing this application and simplifying the description, rather than indicating or implying that the device or module referred to must have a specific direction, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to this application.

[0124] In the description of this specification, the descriptions of terms such as "one embodiment", "some embodiments", "specific embodiments", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or instance. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0125] The above are only the preferred embodiments of this application and are not used to limit this application. For those skilled in the art, this application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application shall be included within the protection scope of this application.

Claims

1. A high-resolution seismic inversion method for thin-layer structures, characterized in that, Including: Obtaining seismic data; Extracting the seismic data to obtain a seismic wavelet; Obtaining the dip angle of the seismic data; Constructing a spatial prediction error filter according to the dip angle; Establishing an objective functional for wave impedance seismic inversion according to the seismic data, the seismic wavelet, and the spatial prediction error filter; Solving the objective functional by the even-odd decomposition basis pursuit algorithm to obtain high-resolution wave impedance data.

2. The high-resolution seismic inversion method for thin-layer structures according to claim 1, characterized in that, The establishing the objective functional for wave impedance seismic inversion according to the seismic data, the seismic wavelet, and the spatial prediction error filter includes: Connecting the head and tail of the seismic data to form an input seismic record vector; Constructing a large sparse block matrix according to the seismic wavelet; Adjusting the spatial prediction error filter to the direction of the dip angle of each sample point, and constructing a spatial prediction error filter matrix through a Helix transform; Establishing an objective functional for wave impedance seismic inversion according to the input seismic record vector, the large sparse block matrix, and the spatial prediction error filter matrix.

3. The high-resolution seismic inversion method for thin-layer structures according to claim 1, characterized in that, The seismic data includes reflection time and surface location.

4. The high-resolution seismic inversion method for thin-layer structures according to claim 1, characterized in that, The obtaining the dip angle of the seismic data includes: Determining the dip angle on the seismic data by a dip angle scanning method.

5. The high-resolution seismic inversion method for thin-layer structures according to any one of claims 1 to 4, characterized in that, The formula of the spatial prediction error filter is: where x is the surface location, L is the length, and q(x) is the spatial prediction error filter.

6. The high-resolution seismic inversion method for thin-layer structures according to claim 5, characterized in that, The large sparse block matrix is: Among them, is a large sparse block matrix, and w is a sub-matrix.

7. The high-resolution seismic inversion method for thin-layer structures according to claim 6, characterized in that, The formula of the objective functional is: Among them, represents the L2 norm, || ||1 represents the L1 norm, v is a vector formed by connecting the head and tail of the wave impedance data v(x,t) after inversion, F is the first-order difference matrix, λ1 and λ2 are two regularization parameters, a is the spatial prediction error filter matrix, and u is the input seismic record vector. is a large sparse block matrix.

8. A high-resolution seismic inversion system for thin-layer structures, characterized in that, Including: A first obtaining module for obtaining seismic data; An extraction module for extracting the seismic data to obtain a seismic wavelet; A second obtaining module for obtaining the dip angle of the seismic data; A filter module for constructing a spatial prediction error filter according to the dip angle; An objective functional construction module for establishing an objective functional for wave impedance seismic inversion according to the seismic data, the seismic wavelet, and the spatial prediction error filter; A calculation module for solving the objective functional by the even-odd decomposition basis pursuit algorithm to obtain high-resolution wave impedance data.

9. The thin-layer structure high-resolution seismic inversion system according to claim 8, wherein, Including: A memory and a processor, wherein a program or instruction that can run on the processor is stored on the memory, and when the processor executes the program or the instruction, the steps of the high-resolution seismic inversion method for thin layer structures according to any one of claims 1 to 7 are implemented.

10. A readable storage medium, on which a program or instruction is stored, wherein, When the program or the instruction is executed by the processor, the steps of the high-resolution seismic inversion method for thin layer structures according to any one of claims 1 to 7 are implemented.

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