Method and device for extracting deep earth non-stationary diffraction fields

Abstract

The present invention provides a method and device for extracting deep-earth non-stationary diffraction fields, relating to the technical field of deep-earth resource exploration. The method comprises the following steps: obtaining seismic shot gather data of deep-earth viscoelastic media; performing a reflection wave continuation non-additive transformation on the seismic shot gather data to obtain shot gather reflection continuation gather data; constructing a sparse regularization-constrained least squares problem for solving the adaptive prediction filter coefficients based on the shot gather reflection continuation gather data, a plane wave destruction filter, and an adaptive prediction filter; solving the least squares problem to obtain the target adaptive prediction filter coefficients; and extracting non-stationary diffraction wave data of deep-earth viscoelastic media based on the target adaptive prediction filter coefficients, the shot gather reflection continuation gather data, and the plane wave destruction filter. This method can effectively preserve the spectral and amplitude characteristics of the diffraction waves of deep-earth viscoelastic media, providing technical support for high-precision imaging of deep oil and gas, hot dry rocks, and other deep-layer structures.

Description

Technical Field

[0001] The present invention relates to the technical field of deep earth resource exploration, and in particular to a method and device for extracting a deep earth non-stationary diffraction field. Background Art

[0002] Elastic diffraction seismic imaging, a cutting-edge technology, has been widely reported in leading exploration journals, and the domestic petroleum industry and research institutions have also invested heavily in research. Literature reviews and technical research indicate that while commercial software exists, none specifically targets deep-earth viscoelastic diffraction extraction, making it difficult to precisely locate small-scale geological anomalies in reservoirs.

[0003] Hot dry rock is a viscoelastic medium. When processing diffraction wave signals from hot dry rock reservoirs, the traditional three-dimensional pre-stack diffraction wave separation method can remove the shielding effect of strong wave impedance interfaces on weak seismic wave signals. However, it is difficult to effectively maintain the spectrum and amplitude characteristics of the diffraction waves, which is not conducive to subsequent parameter modeling and imaging research.

[0004] In summary, there is an urgent need for a method that can effectively extract the non-stationary diffraction field in deep viscoelastic media. Summary of the Invention

[0005] The object of the present invention is to provide a method and device for extracting a deep-earth non-stationary diffraction field, so as to alleviate the technical problem of low precision in the existing deep-earth non-stationary diffraction field extraction method.

[0006] In a first aspect, the present invention provides a method for extracting a deep-earth non-stationary diffraction field, comprising: acquiring seismic shot gather data of a deep-earth viscoelastic medium; performing a reflection wave continuation non-additive transformation on the seismic shot gather data to obtain shot gather reflection continuation track gather data; constructing a sparse regularization constrained least squares problem for solving an adaptive prediction filter coefficient based on the shot gather reflection continuation track gather data, a plane wave destruction filter, and an adaptive prediction filter; wherein the plane wave destruction filter is used to extract reflection wave data from the shot gather reflection continuation track gather data; the adaptive prediction filter is a mode operator of a non-stationary diffraction wave; solving the least squares problem to obtain a target adaptive prediction filter coefficient; and extracting the non-stationary diffraction wave data of the deep-earth viscoelastic medium based on the target adaptive prediction filter coefficient, the shot gather reflection continuation track gather data, and the plane wave destruction filter.

[0007] In an optional embodiment, based on the shot gather reflection continuation gather data, the plane wave destruction filter and the adaptive prediction filter, a sparse regularization constrained least squares problem for solving the adaptive prediction filter coefficients is constructed, including: using the plane wave destruction filter to filter the shot gather reflection continuation gather data to obtain reflected wave data; based on the shot gather reflection continuation gather data, the reflected wave data and the adaptive prediction filter, a non-stationary diffraction wave adaptive extraction model is constructed; based on the non-stationary diffraction wave adaptive extraction model and the adaptive prediction filter, the least squares problem is constructed.

[0008] In an optional embodiment, the plane wave destruction operator of the plane wave destruction filter is: Where I represents the identity matrix, C k,k+1 (p k ) represents the plane wave destruction filter that predicts the k+1-th shot gather reflection continuation gather data from the k-th shot gather reflection continuation gather data, ,p=[p1,p2,...,p N-1 ], N represents the number of shot gather reflection continuation gather data, p k represents the event slope of the k-th shot gather reflection continuation gather data, Z x It represents the Z transformation of the shot gather reflection continuation gather data in the spatial direction, Z t Represents the Z transform of the shot reflection continuation gather data in the time direction.

[0009] In an optional embodiment, the non-stationary diffraction wave adaptive extraction model is expressed as: Among them, A i,j (t,x) represents the initial adaptive prediction filter coefficients, represents the target adaptive prediction filter coefficient, i represents the displacement variable in the time direction, j represents the displacement variable in the space direction, t represents the time direction, x represents the space direction, S(t,x) represents the shot gather reflection continuation gather data, I represents the unit matrix, C(p) represents the plane wave destruction operator of the plane wave destruction filter, M represents the maximum displacement variable in the time direction, W represents the maximum displacement variable in the space direction, E i,j (t,x)=S(t,x)(IC(p)) represents the time and space transformation data after the reflection wave data is removed from the shot gather reflection continuation gather data.

[0010] In an optional embodiment, the least squares problem is expressed as: Here, ∈ represents the scalar regularization parameter and D represents the regularization operator.

[0011] In an optional implementation, solving the least squares problem to obtain target adaptive prediction filter coefficients includes: solving the least squares problem using a quasi-Newton method to obtain target adaptive prediction filter coefficients.

[0012] In a second aspect, the present invention provides a deep-earth non-stationary diffraction field extraction device, comprising: an acquisition module for acquiring seismic shot gather data of a deep-earth viscoelastic medium; a transformation module for performing a reflection wave extension non-superposition transformation on the seismic shot gather data to obtain shot gather reflection extension track data; a construction module for constructing a sparse regularization constrained least squares problem for solving an adaptive prediction filter coefficient based on the shot gather reflection extension track data, a plane wave destruction filter and an adaptive prediction filter; wherein the plane wave destruction filter is used to extract reflection wave data from the shot gather reflection extension track data; the adaptive prediction filter is a mode operator of non-stationary diffraction waves; a solution module for solving the least squares problem to obtain target adaptive prediction filter coefficients; and an extraction module for extracting the non-stationary diffraction wave data of the deep-earth viscoelastic medium based on the target adaptive prediction filter coefficients, the shot gather reflection extension track data and the plane wave destruction filter.

[0013] In an optional embodiment, the construction module is specifically used to: use the plane wave destruction filter to filter the shot gather reflection continuation track data to obtain reflection wave data; construct a non-stationary diffraction wave adaptive extraction model based on the shot gather reflection continuation track data, the reflection wave data and the adaptive prediction filter; construct the least squares problem based on the non-stationary diffraction wave adaptive extraction model and the adaptive prediction filter.

[0014] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the steps of the deep earth non-stationary diffraction field extraction method described in any one of the aforementioned embodiments are implemented.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, implement the deep earth non-stationary diffraction field extraction method described in any one of the aforementioned embodiments.

[0016] The deep non-stationary diffraction field extraction method provided by the present invention, after obtaining the seismic shot gather data of the deep viscoelastic medium, first performs a reflection wave extension non-additive transformation on it to obtain the shot gather reflection extension track data, thereby eliminating the reflection wave propagation characteristics caused by the offset distance, so that the reflection wave and the diffraction wave in the shot gather reflection extension track data have a strong kinematic difference, so as to facilitate the separation of the diffraction wave. Next, it is combined with a plane wave destruction filter that can extract the reflection wave data and an adaptive prediction filter as a mode operator of the non-stationary diffraction wave to construct a least squares problem for solving the sparse regularization constraint of the adaptive prediction filter coefficient. By solving the least squares problem, the target adaptive prediction filter coefficient is obtained, and then the non-stationary diffraction wave data of the deep viscoelastic medium is extracted based on the coefficient. This method can effectively maintain the spectrum and amplitude characteristics of the diffraction wave of the deep viscoelastic medium, providing technical support for high-precision imaging of deep oil and gas, hot dry rocks, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are 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.

[0018] Figure 1 A flowchart of a method for extracting a deep-earth non-stationary diffraction field provided by an embodiment of the present invention;

[0019] Figure 2 A flowchart of constructing a least squares problem for solving a sparse regularization constraint of an adaptive prediction filter coefficient is provided in an embodiment of the present invention;

[0020] Figure 3 A functional module diagram of a deep-earth non-stationary diffraction field extraction device provided by an embodiment of the present invention;

[0021] Figure 4 A schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0023] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort shall fall within the scope of protection of the present invention.

[0024] The following embodiments of the present invention are described in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other.

[0025] Example 1

[0026] Figure 1 A flowchart of a method for extracting a deep-earth non-stationary diffraction field provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, the method specifically includes the following steps:

[0027] Step S102: Acquire seismic shot gather data of deep-earth viscoelastic media.

[0028] Step S104: performing reflection wave continuation non-additive transformation on the seismic shot gather data to obtain shot gather reflection continuation gather data.

[0029] In seismic shot gather data, both reflected and diffracted waves exhibit hyperbolic morphology. Furthermore, diffracted waves are weaker than reflected waves and are often submerged in the background reflected energy from strong impedance interfaces. Therefore, extracting diffracted waves directly from seismic shot gather data is undesirable. The propagation characteristics of reflected waves in seismic shot gather data (hyperbolic morphology) are caused by offset. In view of this, embodiments of the present invention propose studying the time-distance relationship for non-zero offsets based on the local slope of reflected waves and constructing a velocity-independent extended non-additive transformation of reflected waves to eliminate the reflected wave propagation characteristics caused by offset, resulting in linearized reflected waves and hyperbolic diffracted waves.

[0030] Specifically, in the exploration of hot dry rock reservoirs, the strong wave impedance interface of the overlying strata has a strong shielding effect on seismic waves. For this type of curved reflection interface, the geometric relationship between the dip angle α, reflection angle θ, seismic wave propagation velocity v in viscoelastic media, half-offset h, common center point position y, and reflection wave travel time t is defined as follows: Among them, the reflection angle in the dip domain and the travel time derivative related: The travel time derivative is the local slope of the reflected wave, also known as the event slope of the seismic shot gather data, p h represents the slope of the event in the offset direction, p y Indicates the slope of the event in the direction of the common center point.

[0031] Based on the above equations (1.1) and (1.2), the reflection wave parameters can be solved: the dip angle α, the reflection angle θ, the propagation velocity v of the seismic wave in the viscoelastic medium, and the coordinates t and h of the shot gather data (the position of a sampling point in the shot gather can be expressed by the travel time t and the half-offset h) and the travel time derivative p h and p y The solution of the reflected wave parameters is as follows:

[0032] The relationship between the vertical round-trip travel time τ, the reflected wave travel time t, and the travel time t0 at the zero offset position (i.e., the non-zero offset time-distance relationship) is: By substituting the inclination angle α and reflection angle θ solved in the above equation (1.3), we can construct a reflection wave extension non-additive transformation that is independent of velocity, thereby eliminating the reflection wave propagation characteristics caused by the offset distance h, and the reflection can be mapped to the non-additive zero offset position. The reflection wave extension non-additive transformation is expressed as: Among them, the event slope p h and p y The numerical value can be obtained by processing the seismic shot gather data through a plane wave destruction filter.

[0033] Seismic shot gather data can be transformed into a new gather using the reflection continuation non-additive transform. In this new gather, the reflection waves exhibit linear characteristics, while the diffraction waves are hyperbolic. This means that there is a strong kinematic difference between the reflection and diffraction waves, providing a foundation for diffraction wave extraction in subsequent steps.

[0034] Step S106 : constructing a sparse regularized least squares problem for solving the coefficients of the adaptive prediction filter based on the shot gather reflection continuation gather data, the plane wave destruction filter and the adaptive prediction filter.

[0035] Among them, the plane wave destruction filter is used to extract the reflection wave data from the shot gather reflection continuation gather data; the adaptive prediction filter is the mode operator of the non-stationary diffraction wave.

[0036] After obtaining the shot gather reflection continuation gather data, the reflected wave signal in the shot gather reflection continuation gather data can be described by a plane wave destruction filter, and the time-frequency variation characteristics of the diffraction wave signal can be characterized by an adaptive prediction filter. Then, an equation for solving the coefficients of the adaptive prediction filter can be constructed. In an embodiment of the present invention, the solution equation for the filter coefficients is a least squares problem with sparse regularization constraints.

[0037] Step S108: Solve the least squares problem to obtain target adaptive prediction filter coefficients.

[0038] Before solving the equation, the filter coefficients in the equation are only initial values. Only after solving the equation can the target filter coefficients (i.e., target adaptive prediction filter coefficients) that can accurately characterize the time-frequency variation characteristics of the diffraction wave signal be obtained.

[0039] Step S110 , extracting non-stationary diffraction wave data of deep-earth viscoelastic medium based on target adaptive prediction filter coefficients, shot gather reflection continuation gather data, and plane wave destruction filter.

[0040] First, the shot gather reflection continuation gather data are filtered using a plane wave destruction filter to obtain the reflection wave data. Then, the reflection wave data are removed from the shot gather reflection continuation gather data to obtain the remaining data. Finally, the remaining data are filtered based on the target adaptive prediction filter coefficient to extract the non-stationary diffraction wave data of the deep viscoelastic medium.

[0041] The deep-earth non-stationary diffraction field extraction method provided by an embodiment of the present invention, after obtaining seismic shot gather data of a deep viscoelastic medium, first performs a reflection wave extension non-additive transformation on it to obtain shot gather reflection extension trace data, thereby eliminating the reflection wave propagation characteristics caused by the offset distance, so that the reflection wave and the diffraction wave in the shot gather reflection extension trace data have a strong kinematic difference, which facilitates the separation of the diffraction wave. Next, it is combined with a plane wave destruction filter that can extract reflection wave data and an adaptive prediction filter as a mode operator for non-stationary diffraction waves to construct a least squares problem for solving the sparse regularization constraint of the adaptive prediction filter coefficient. By solving the least squares problem, the target adaptive prediction filter coefficient is obtained, and then the non-stationary diffraction wave data of the deep viscoelastic medium is extracted based on the coefficient. This method can effectively maintain the spectrum and amplitude characteristics of the diffraction wave of the deep viscoelastic medium, providing technical support for high-precision imaging of deep oil and gas, hot dry rocks, etc.

[0042] In an optional embodiment, if Figure 2 As shown, the above step S106 constructs a sparse regularization constrained least squares problem for solving the coefficients of the adaptive prediction filter based on the shot gather reflection continuation gather data, the plane wave destruction filter and the adaptive prediction filter, which specifically includes the following steps:

[0043] Step S1061: Filter the shot gather reflection continuation gather data using a plane wave destruction filter to obtain reflection wave data.

[0044] Specifically, after the seismic shot gather data are subjected to reflection wave extension non-additive transformation, the linearized reflection waves in the shot gather reflection extension gather data can be described using a plane wave destruction filter. Specifically, the shot gather reflection extension gather data are filtered by a plane wave destruction filter to obtain reflection wave data. The above filtering process can be expressed as: R = C(p)S(t,x); wherein R represents the reflection wave data, C(p) represents the plane wave destruction operator of the plane wave destruction filter, S(t,x) represents the shot gather reflection extension gather data, and S(t,x) = [S1,S2,...S N ] T , N represents the number of shot gather reflection continuation gather data.

[0045] In the embodiment of the present invention, the plane wave destruction operator C(p) of the plane wave destruction filter is:

[0046] Where I represents the identity matrix, C k,k+1 (p k ) represents the plane wave destruction filter that predicts the k+1-th shot gather reflection continuation gather data from the k-th shot gather reflection continuation gather data, ,p=[p1,p2,...,p N-1 ], N represents the number of shot gather reflection continuation gather data, p k represents the event slope of the k-th shot gather reflection continuation gather data, Z x It represents the Z transformation of the shot gather reflection continuation gather data in the spatial direction, Z t Represents the Z transform of the shot reflection continuation gather data in the time direction.

[0047] Step S1062: constructing a non-stationary diffraction wave adaptive extraction model based on the shot gather reflection continuation gather data, the reflection wave data and the adaptive prediction filter.

[0048] In order to characterize the time-frequency variation characteristics of the diffraction wave signal in the shot gather reflection continuation gather, the embodiment of the present invention adopts an adaptive prediction filter as the mode operator of the non-stationary diffraction wave. In order to estimate the adaptive prediction filter coefficients of the non-stationary diffraction signal, the coefficient matrix A is set. i,j It changes with the two dimensions of time t and space x, where i represents the displacement variable in the time direction and j represents the displacement variable in the space direction. That is, the filter coefficient is expressed as: A i,j (t, x). The embodiment of the present invention does not impose any specific restrictions on the number of filter coefficients (i.e., filter length) in the adaptive prediction filter. Users can select the filter length based on actual needs. Generally, the filter length selection depends on the maximum dip angle of the seismic response in the data. The larger the maximum dip angle of the seismic response, the longer the filter length.

[0049] Taking setting ten filter coefficients as an example, the format is as follows:

[0050] In an embodiment of the present invention, based on the plane wave decomposition of the reflected wave and the non-stationary mode characterization of the diffraction response, the non-stationary diffraction wave adaptive extraction model is expressed as:

[0051] Among them, A i,j (t,x) represents the initial adaptive prediction filter coefficients, represents the coefficient of the target adaptive prediction filter, i represents the displacement variable in the time direction, j represents the displacement variable in the spatial direction, t represents the time direction, x represents the spatial direction, S(t,x) represents the shot reflection continuation gather data, I represents the unit matrix, C(p) represents the plane wave destruction operator of the plane wave destruction filter, M represents the maximum displacement variable in the time direction, W represents the maximum displacement variable in the spatial direction, E i,j (t,x)=S(t,x)(IC(p)) represents the time and space transformation data after the reflection wave data is removed from the shot gather reflection continuation gather data.

[0052] Step S1063 : constructing a least squares problem based on the non-stationary diffraction wave adaptive extraction model and the adaptive prediction filter.

[0053] Alternatively, the least squares problem is expressed as:

[0054] ; where ∈ represents the scalar regularization parameter and D represents the regularization operator.

[0055] From the above expression, we can see that in the least squares problem, the coefficient matrix A i,j (t,x) is subject to regularization constraints. By solving the least squares problem with sparse regularization constraints, the non-stationary phase mode characteristics of the diffraction response can be captured, and the deep non-stationary diffraction wave signal can be reconstructed.

[0056] Since the scale of three-dimensional pre-stack data is large, in an embodiment of the present invention, the above-mentioned step S108 solves the least squares problem to obtain the target adaptive prediction filter coefficients, including: using the quasi-Newton method to solve the least squares problem to obtain the target adaptive prediction filter coefficients.

[0057] In summary, the deep-earth non-stationary diffraction field extraction method provided by the embodiment of the present invention can eliminate the shielding effect of strong wave impedance interfaces on seismic waves, capture the non-stationary diffraction response of hot dry rock reservoirs, extract the time-frequency variation characteristics of the diffraction signal through plane wave destruction filters and pattern recognition, reconstruct the non-stationary diffraction wave signal of the discontinuous geological body of the hot dry rock reservoir, obtain three-dimensional shot gather non-stationary diffraction data, and provide a data basis for diffraction imaging of thermal storage media.

[0058] Example 2

[0059] An embodiment of the present invention further provides a deep-earth non-stationary diffraction field extraction device, which is mainly used to execute the deep-earth non-stationary diffraction field extraction method provided in the above-mentioned embodiment 1. The deep-earth non-stationary diffraction field extraction device provided in the embodiment of the present invention is specifically introduced below.

[0060] Figure 3 is a functional module diagram of a deep earth non-stationary diffraction field extraction device provided by an embodiment of the present invention, such as Figure 3 As shown, the device mainly includes: an acquisition module 10, a transformation module 20, a construction module 30, a solution module 40, and an extraction module 50, wherein:

[0061] The acquisition module 10 is used to acquire seismic shot gather data of deep-earth viscoelastic media.

[0062] The transformation module 20 is used to perform reflection wave extension non-addition transformation on the seismic shot gather data to obtain shot gather reflection extension gather data.

[0063] Construction module 30 is used to construct a sparse regularization constrained least squares problem for solving the coefficients of the adaptive prediction filter based on the shot gather reflection continuation gather data, the plane wave destruction filter and the adaptive prediction filter; wherein the plane wave destruction filter is used to extract the reflection wave data from the shot gather reflection continuation gather data; the adaptive prediction filter is a mode operator of non-stationary diffraction waves.

[0064] The solving module 40 is used to solve the least squares problem to obtain target adaptive prediction filter coefficients.

[0065] The extraction module 50 is used to extract the non-stationary diffraction wave data of the deep viscoelastic medium based on the target adaptive prediction filter coefficient, the shot gather reflection continuation gather data and the plane wave destruction filter.

[0066] The deep non-stationary diffraction field extraction device provided by an embodiment of the present invention, after obtaining seismic shot gather data of a deep viscoelastic medium, first performs a reflection wave extension non-additive transformation on it to obtain shot gather reflection extension trace data, thereby eliminating the reflection wave propagation characteristics caused by the offset distance, so that the reflection wave and the diffraction wave in the shot gather reflection extension trace data have a strong kinematic difference, which facilitates the separation of the diffraction wave. Next, it is combined with a plane wave destruction filter that can extract reflection wave data and an adaptive prediction filter as a mode operator for non-stationary diffraction waves to construct a least squares problem for solving the sparse regularization constraint of the adaptive prediction filter coefficient. By solving the least squares problem, the target adaptive prediction filter coefficient is obtained, and then the non-stationary diffraction wave data of the deep viscoelastic medium is extracted based on the coefficient. This method can effectively maintain the spectrum and amplitude characteristics of the diffraction wave of the deep viscoelastic medium, providing technical support for high-precision imaging of deep oil and gas, hot dry rocks, etc.

[0067] Optionally, the building module 30 is specifically configured to:

[0068] The plane wave destruction filter is used to filter the shot gather reflection continuation gather data to obtain the reflection wave data.

[0069] Based on the shot reflection continuation gather data, reflection wave data and adaptive prediction filter, an adaptive extraction model for non-stationary diffraction waves is constructed.

[0070] Based on the non-stationary diffraction wave adaptive extraction model and adaptive prediction filter, a least squares problem is constructed.

[0071] Optionally, the plane wave destruction operator of the plane wave destruction filter is:

[0072] Where I represents the identity matrix, C k,k+1 (p k ) represents the plane wave destruction filter that predicts the k+1-th shot gather reflection continuation gather data from the k-th shot gather reflection continuation gather data, ,p=[p1,p2,...,p N-1 ], N represents the number of shot gather reflection continuation gather data, p k represents the event slope of the k-th shot gather reflection continuation gather data, Z x It represents the Z transformation of the shot gather reflection continuation gather data in the spatial direction, Z t Represents the Z transform of the shot reflection continuation gather data in the time direction.

[0073] Optionally, the non-stationary diffraction wave adaptive extraction model is expressed as:

[0074] Among them, A i,j(t,x) represents the initial adaptive prediction filter coefficients, represents the coefficient of the target adaptive prediction filter, i represents the displacement variable in the time direction, j represents the displacement variable in the spatial direction, t represents the time direction, x represents the spatial direction, S(t,x) represents the shot reflection continuation gather data, I represents the unit matrix, C(p) represents the plane wave destruction operator of the plane wave destruction filter, M represents the maximum displacement variable in the time direction, W represents the maximum displacement variable in the spatial direction, E i,j (t,x)=S(t,x)(IC(p)) represents the time and space transformation data after the reflection wave data is removed from the shot gather reflection continuation gather data.

[0075] Alternatively, the least squares problem is expressed as:

[0076] ; where ∈ represents the scalar regularization parameter and D represents the regularization operator.

[0077] Optionally, the solving module 40 is specifically configured to:

[0078] The quasi-Newton method is used to solve the least squares problem and obtain the target adaptive prediction filter coefficients.

[0079] Example 3

[0080] See also Figure 4 An embodiment of the present invention provides an electronic device, which includes: a processor 60, a memory 61, a bus 62 and a communication interface 63, wherein the processor 60, the communication interface 63 and the memory 61 are connected via the bus 62; the processor 60 is used to execute an executable module stored in the memory 61, such as a computer program.

[0081] The memory 61 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage. The system network element communicates with at least one other network element via at least one communication interface 63 (which may be wired or wireless), and may utilize the Internet, a wide area network, a local area network, a metropolitan area network, or the like.

[0082] The bus 62 may be an ISA bus, a PCI bus, or an EISA bus. The bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of representation, Figure 4 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0083] Among them, the memory 61 is used to store programs, and the processor 60 executes the program after receiving the execution instruction. The method executed by the device defined by the process disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 60 or implemented by the processor 60.

[0084] The processor 60 may be an integrated circuit chip with signal processing capabilities. During implementation, the steps of the above method may be performed by hardware integrated logic circuits or software instructions within the processor 60. The processor 60 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), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It may implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of the present invention may be directly executed by a hardware decoding processor or by a combination of hardware and software modules within the decoding processor. The software modules may be located in storage media well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or the like. The storage medium is located in the memory 61 , and the processor 60 reads the information in the memory 61 and completes the steps of the above method in combination with its hardware.

[0085] The computer program product of a method and apparatus for extracting a deep-Earth non-stationary diffraction field provided in an embodiment of the present invention includes a computer-readable storage medium storing non-volatile program code executable by a processor. The program code includes instructions that can be used to execute the methods described in the previous method embodiments. For specific implementation, please refer to the method embodiments and will not be described in detail here.

[0086] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0087] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0088] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0089] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer" and the like indicate positions or locations based on the positions shown in the accompanying drawings, or the positions or locations in which the inventive product is typically placed when in use. These terms are intended solely to facilitate the description of the present invention and to simplify the description, and are not intended to indicate or imply that the devices or components referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third," etc., are used solely to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0090] Furthermore, terms such as "horizontal," "vertical," and "overhanging" do not necessarily imply that a component must be absolutely horizontal or overhanging, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but rather that it can be slightly tilted.

[0091] In the description of the present invention, it should also be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections or electrical connections; they may refer to direct connections or indirect connections through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for extracting deep-earth non-stationary diffraction fields, characterized in that: include: Acquire seismic shot gather data of deep viscoelastic media; The seismic shot gather data are subjected to reflection wave extension non-additive transformation to obtain shot gather reflection extension gather data. The reflection wave extension non-additive transformation is expressed as: , Indicates the time at the zero offset position. represents the travel time of the reflected wave, Indicates the inclination angle, represents the reflection angle; Based on the shot gather reflection continuation gather data, the plane wave destruction filter, and the adaptive prediction filter, a sparse regularization constrained least squares problem for solving the coefficients of the adaptive prediction filter is constructed; wherein the plane wave destruction filter is used to extract reflection wave data from the shot gather reflection continuation gather data; and the adaptive prediction filter is a mode operator of non-stationary diffraction waves; Solving the least squares problem to obtain target adaptive prediction filter coefficients; Based on the target adaptive prediction filter coefficients, the shot gather reflection continuation gather data and the plane wave destruction filter, non-stationary diffraction wave data of the deep viscoelastic medium are extracted.

2. The deep earth non-stationary diffraction field extraction method according to claim 1, characterized in that: Based on the shot gather reflection continuation gather data, the plane wave destruction filter, and the adaptive prediction filter, a sparse regularization constrained least squares problem for solving the coefficients of the adaptive prediction filter is constructed, including: Using the plane wave destruction filter to filter the shot gather reflection continuation gather data to obtain reflection wave data; constructing a non-stationary diffraction wave adaptive extraction model based on the shot gather reflection continuation gather data, the reflection wave data and the adaptive prediction filter; The least squares problem is constructed based on the non-stationary diffraction wave adaptive extraction model and the adaptive prediction filter.

3. The deep earth non-stationary diffraction field extraction method according to claim 1, characterized in that: The plane wave destruction operator of the plane wave destruction filter for: ;in, represents the identity matrix, represents the plane wave destruction filter that predicts the k+1th shot gather reflection continuation gather data from the kth shot gather reflection continuation gather data, , , N represents the number of shot gather reflection continuation gather data, represents the event slope of the k-th shot gather reflection continuation gather data, It represents the Z transform of the shot gather reflection continuation gather data in the spatial direction. Represents the Z transform of the shot reflection continuation gather data in the time direction.

4. The method for extracting deep earth non-stationary diffraction fields according to claim 2, wherein: The non-stationary diffraction wave adaptive extraction model is expressed as: ;in, represents the initial adaptive prediction filter coefficients, represents the target adaptive prediction filter coefficient, i represents the displacement variable in the time direction, j represents the displacement variable in the spatial direction, Indicates the time direction, Indicates the spatial direction, represents the shot gather reflection continuation gather data, represents the identity matrix, represents the plane wave destruction operator of the plane wave destruction filter, M represents the maximum displacement variable in the time direction, W represents the maximum displacement variable in the space direction, , represents the time and space transformation data after removing the reflection wave data from the shot gather reflection continuation gather data.

5. The method for extracting deep earth non-stationary diffraction fields according to claim 4, wherein: The least squares problem is expressed as: ;in, represents the scalar regularization parameter, represents the regularization operator.

6. The method for extracting deep earth non-stationary diffraction fields according to claim 1, wherein: Solving the least squares problem to obtain target adaptive prediction filter coefficients includes: The least squares problem is solved using a quasi-Newton method to obtain target adaptive prediction filter coefficients.

7. A deep-earth non-stationary diffraction field extraction device, characterized in that: include: An acquisition module is used to obtain seismic shot gather data of deep viscoelastic media; The transformation module is used to perform reflection wave extension non-additive transformation on the seismic shot gather data to obtain shot gather reflection extension gather data; the reflection wave extension non-additive transformation is expressed as: , Indicates the time at the zero offset position. represents the travel time of the reflected wave, Indicates the inclination angle, represents the reflection angle; A construction module is used to construct a sparse regularization constrained least squares problem for solving the coefficients of the adaptive prediction filter based on the shot gather reflection continuation gather data, the plane wave destruction filter and the adaptive prediction filter; wherein the plane wave destruction filter is used to extract reflection wave data from the shot gather reflection continuation gather data; and the adaptive prediction filter is a mode operator of non-stationary diffraction waves; A solution module, configured to solve the least squares problem to obtain target adaptive prediction filter coefficients; An extraction module is used to extract the non-stationary diffraction wave data of the deep viscoelastic medium based on the target adaptive prediction filter coefficient, the shot gather reflection continuation gather data and the plane wave destruction filter.

8. The deep earth non-stationary diffraction field extraction device according to claim 7, characterized in that: The building blocks are specifically used for: Using the plane wave destruction filter to filter the shot gather reflection continuation gather data to obtain reflection wave data; constructing a non-stationary diffraction wave adaptive extraction model based on the shot gather reflection continuation gather data, the reflection wave data and the adaptive prediction filter; The least squares problem is constructed based on the non-stationary diffraction wave adaptive extraction model and the adaptive prediction filter.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, wherein: When the processor executes the computer program, the steps of the deep earth non-stationary diffraction field extraction method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method for extracting a deep-earth non-stationary diffraction field according to any one of claims 1 to 6 is implemented.

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