An ultra-resolution imaging method and device based on focused image fusion

Abstract

The application relates to the technical field of seismic wave imaging, and discloses a super-resolution imaging method and device based on focused image fusion. The method comprises the following steps: performing regional block processing on reverse-time migration images to obtain a plurality of sub-modules, and respectively determining wave number domain reverse-time migration images of the sub-modules; respectively determining wave number domain observation illumination operators of the sub-modules according to a preset velocity model; respectively performing image fusion processing on the wave number domain observation illumination operators and the wave number domain reverse-time migration images of the sub-modules to construct imaging focusing functions of the sub-modules; respectively determining super-resolution imaging results of the sub-modules according to the wave number domain reverse-time migration images and the imaging focusing functions of the sub-modules; and performing splicing processing on the super-resolution imaging results of all the sub-modules to obtain a super-resolution imaging result of the reverse-time migration images. The method realizes super-resolution imaging in the field of imageology, improves the imaging resolution, and greatly improves the imaging detail description effect.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of seismic wave imaging, in particular to a super-resolution imaging method and device based on focused image fusion, a storage medium and an electronic device. BACKGROUND

[0002] The purpose of the background description provided herein is to generally present the context of the application. The statements described herein are not necessarily prior art.

[0003] With the continuous development and progress of seismic exploration technology, exploration targets are gradually refined, and oil and gas reservoir types are gradually changing from structural oil and gas reservoirs to subtle oil and gas reservoirs, which puts forward higher requirements for high-resolution processing technology of seismic exploration.

[0004] Under the current technology, conventional resolution enhancement processing techniques such as deconvolution and inverse Q filtering are usually based on one-dimensional assumption and have observable processing effect for horizontal structures or small dip structures, but the processing effect is poor for high-steep structures or complex structures such as faults.

[0005] Super-resolution processing is widely used in the field of imageology. Among them, the imageology deblurring algorithm based on high-dimensional space processing is computationally efficient and effective, and has shown very good application potential. However, due to the high complexity of seismic wave propagation theory, this technology has certain difficulties in popularization and application in the field of geophysics.

[0006] Therefore, there is an urgent need for a new imageology-based super-resolution seismic imaging method to solve the above-mentioned defects. SUMMARY

[0007] In view of the above problems, the present application provides a super-resolution imaging method and device based on focused image fusion, a storage medium and an electronic device. By the technical scheme disclosed in the present application, the imageology super-resolution processing and seismic imaging are combined, the imaging focusing function is constructed using the imaging wave number spectrum and the illumination information under the data driving, and the imageology high-dimensional deconvolution operator is used to realize the imageology super-resolution imaging, thereby improving the imaging resolution and greatly improving the imaging detail description effect.

[0008] The first aspect of the present application provides a super-resolution imaging method based on focused image fusion, which comprises:

[0009] The reverse-time migration image is regionally blocked to obtain a plurality of sub-modules, and the wave number domain reverse-time migration image of each sub-module is determined respectively;

[0010] The wave number domain observation illumination operator of each sub-module is determined according to the preset velocity model;

[0011] The wavenumber domain observation illumination operator and wavenumber domain reverse time migration image of each submodule are fused to construct the imaging focusing function of each submodule.

[0012] Determine the super-resolution imaging results of each submodule based on the wavenumber domain reverse time migration image and imaging focusing function of each submodule;

[0013] The super-resolution imaging results of all submodules are spliced ​​to obtain the super-resolution imaging result of the reverse time migration image.

[0014] Furthermore, determining the wavenumber domain reverse time migration image of each submodule includes:

[0015] The wavenumber domain reverse time migration image of each submodule is determined by fast Fourier transform.

[0016] Furthermore, determining the super-resolution imaging results of each submodule includes:

[0017] The super-resolution imaging results of each submodule are determined by inverse fast Fourier transform.

[0018] Furthermore, the wavenumber domain observation illumination operator includes:

[0019] K psf =[G(x0;x s )G(x0;x r )] T G(x n ;x s )G(x n ;x r )

[0020] Among them, K psf is the wavenumber domain observation illumination operator, x0 is the center point coordinate of the submodule, x n For all points in the submodule, x s and x r are the coordinates of the shot point and the check point respectively, and G() is the Green's function. The Green's function of any two points can be constructed by the following formula:

[0021] G(x0;x)=Aexp[2πf(t-τ)]

[0022] where τ and A are the travel time field and amplitude determined by the ray tracing method, respectively, and f is the frequency.

[0023] Furthermore, determining the wavenumber domain observation illumination operator of each submodule according to the preset velocity model includes:

[0024] The wave number domain observation illumination operator of each sub-module is determined according to a preset velocity model through a preset seismic data observation system.

[0025] Further, the imaging focusing function comprises:

[0026]

[0027] wherein, is a wave number domain reverse time migration image of the sub-module, Fsmth(.) is a smoothing function, and Filt(alpha,.) is an angle filtering function; wherein the angle filtering function comprises:

[0028] Filt(alpha,.) = Envlp(K psf (alpha i ))

[0029] wherein Filt(alpha,.) is an angle filtering function, K psf (alpha i ) is a focusing operator on an illumination angle, and Envlp() is an envelope operation.

[0030] Further, the super-resolution imaging result of each sub-module is determined by:

[0031]

[0032] wherein, is a reverse time migration image; is an imaging focusing function; gamma is a regularization factor, is a super-resolution imaging result of the sub-module.

[0033] In a second aspect of the present application, a super-resolution imaging device based on focused image fusion is provided, and the device comprises:

[0034] a reverse time migration image determination module, configured to perform regional block processing on a reverse time migration image to obtain a plurality of sub-modules, and determine a wave number domain reverse time migration image of each sub-module;

[0035] an observation illumination operator determination module, configured to determine a wave number domain observation illumination operator of each sub-module according to a preset velocity model;

[0036] an imaging focusing function construction module, configured to perform image fusion processing on the wave number domain observation illumination operator and the wave number domain reverse time migration image of each sub-module, respectively, to construct an imaging focusing function of each sub-module;

[0037] a sub-module imaging result determination module, configured to determine a super-resolution imaging result of each sub-module according to the wave number domain reverse time migration image and the imaging focusing function of each sub-module, respectively;

[0038] The splicing module is configured to splice all the super-resolution imaging results of the sub-modules to obtain the super-resolution imaging result of the reverse-time migration image.

[0039] Further, the reverse-time migration image determination module comprises a splitting unit and a reverse-time migration image determination unit.

[0040] The splitting unit is configured to split the reverse-time migration image into sub-modules.

[0041] The reverse-time migration image determination unit is configured to determine the wave-number domain reverse-time migration image of each sub-module through Fourier transform.

[0042] Further, the sub-module imaging result determination module is configured to determine the super-resolution imaging result of each sub-module through inverse fast Fourier transform.

[0043] Further, the wave-number domain observation illumination operator comprises:

[0044] K psf = [G(x0; x s ) G(x0; x r )] T G(x n ; x s ) G(x n ; x r )

[0045] wherein K psf is the wave-number domain observation illumination operator, x0 is the center point coordinate of the sub-module, x n is all points in the sub-module, x s and x r are shot point coordinates and receiver point coordinates respectively, and G() is the Green function; wherein the Green function of any two points is constructed by the following formula:

[0046] G(x0; x) = Aexp[2πf(t-τ)]

[0047] wherein τ and A are the travel time field and amplitude respectively determined according to the ray tracing method, and f is the frequency.

[0048] Further, the observation illumination operator determination module is configured to determine the wave-number domain observation illumination operator of each sub-module through a preset seismic data observation system according to a preset velocity model.

[0049] Further, the imaging focusing function comprises:

[0050]

[0051] wherein, is the wavenumber domain reverse time migration image of the submodule, Fsmth(.) is the smoothing function, and Filt(α,.) is the angle filtering function; wherein the angle filtering function includes:

[0052] Filt(α,.)=Envlp(K psf (α i ))

[0053] Among them, Filt(α,.) is the angle filter function, K psf (α i ) is the focusing operator on the lighting angle, and Envlp() is the envelope operation.

[0054] Furthermore, the super-resolution imaging results of each submodule are determined by the following formula:

[0055]

[0056] in, It is the reverse time migration image; is the imaging focusing function; γ is the regularization factor, This is the super-resolution imaging result of the submodule.

[0057] According to a third aspect of the present application, a computer-readable storage medium is provided. The computer program stored in the computer-readable storage medium can be executed by one or more processors to implement the steps of the method described above.

[0058] The fourth aspect of the present application provides an electronic device comprising a memory and one or more processors, wherein the memory stores a computer program, the memory and the one or more processors are communicatively connected to each other, and when the computer program is executed by the one or more processors, the steps of the method described above are implemented.

[0059] Compared with the prior art, the advantages or beneficial effects of the technical solution of this application include:

[0060] By combining image super-resolution processing with seismic imaging, the imaging wavenumber spectrum and illumination information are used to construct an imaging focusing function under data-driven conditions, and an image high-dimensional deconvolution operator is used to achieve image super-resolution imaging, thereby improving the imaging resolution and significantly improving the imaging detail depiction effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced. Obviously, the accompanying drawings in the following description only relate to the embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of the provided drawings.

[0062] In addition, it should be further pointed out that, for the convenience of description, only the parts related to the present disclosure are shown in the drawings. The drawings accompanying the specification form part of the present application, which are used to provide further understanding of the present application. The schematic embodiments and their descriptions in the present application are used to explain the present application and do not constitute improper limitation on the present application. In the drawings:

[0063] Figure 1 A flowchart of a super-resolution imaging method based on focused image fusion provided by the embodiments of the present application;

[0064] Figure 2 A reverse-time migration result schematic diagram provided by the embodiments of the present application;

[0065] Figure 3 A wave number spectrum schematic diagram obtained according to the reverse-time migration result provided by the embodiments of the present application;

[0066] Figure 4 An illumination observation operator schematic diagram obtained by using a velocity model and an observation system provided by the embodiments of the present application;

[0067] Figure 5 A focused function wave number spectrum schematic diagram provided by the embodiments of the present application;

[0068] Figure 6 A final super-resolution imaging result schematic diagram provided by the embodiments of the present application;

[0069] Figure 7 A vertical resolution spectrum schematic diagram calculated according to Figure 1 and Figure 5 provided by the embodiments of the present application. DETAILED DESCRIPTION

[0070] The embodiments of the present application will be described in detail below in combination with the drawings and the embodiments, by which the process of how to apply technical means to solve technical problems and achieve corresponding technical effects of the present application can be fully understood and implemented. The embodiments of the present application and each feature in the embodiments can be combined with each other without conflict, and the technical solutions formed by the combination are all within the protection scope of the present application.

[0071] It should be noted that the embodiments described below are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0072] As can be known from the background, with the continuous development and progress of seismic exploration technology means, the exploration target is gradually refined, and the oil and gas reservoir type is gradually changed from structural oil and gas reservoir to subtle oil and gas reservoir, which puts forward higher requirements for high-resolution processing technology of seismic exploration.

[0073] Under the current technology, conventional resolution enhancement processing technologies such as deconvolution and inverse Q filtering are usually based on one-dimensional assumption, and have observable processing effect for horizontal structure or small dip structure, but the processing effect is poor when facing high-steep structure or complex structure such as fault.

[0074] Super-resolution processing is widely used in the field of imageology. Among them, the imageology deblurring algorithm based on high-dimensional space processing is efficient in calculation, remarkable in effect, and has shown very good application potential. However, due to the high complexity of seismic wave propagation theory, this technology has certain difficulty in popularization and application in the field of geophysics.

[0075] In view of this, the present application proposes a super-resolution imaging method based on focused image fusion, which combines imageology super-resolution processing and seismic imaging, constructs an imaging focusing function using imaging wave number spectrum and illumination information under data driving, and uses an imageology high-dimensional deconvolution operator to realize imageology super-resolution imaging, thereby improving imaging resolution and greatly improving imaging detail description effect.

[0076] Embodiment one

[0077] The embodiment provides a super-resolution imaging method based on focused image fusion, Figure 1 A flowchart of a super-resolution imaging method based on focused image fusion provided by the embodiment of the present application is shown in Figure 1 The method disclosed by the embodiment includes the following steps:

[0078] Step 110, performing regional block processing on the reverse-time migration image to obtain a plurality of sub-modules, and respectively determining wave number domain reverse-time migration images of the sub-modules.

[0079] Optionally, the input reverse-time migration image is subjected to regional block processing, and the wave number domain reverse-time migration image is calculated by using FFT (fast Fourier transform, FFT) in each sub-module.

[0080] Step 120, respectively determine the wave number domain observation illumination operator of each sub-module according to the preset velocity model.

[0081] Optionally, the wave number domain observation illumination operator is calculated by using the velocity model and the seismic data observation system.

[0082] Step 130, respectively perform image fusion processing on the wave number domain observation illumination operator and the wave number domain reverse time migration image of each sub-module to construct the imaging focusing function of each sub-module.

[0083] Optionally, the imaging focusing function is constructed by performing image fusion on the wave number domain observation illumination operator and the reverse time migration image.

[0084] Step 140, respectively determine the super-resolution imaging result of each sub-module according to the wave number domain reverse time migration image and the imaging focusing function of each sub-module.

[0085] Optionally, the super-resolution imaging result of the sub-module is obtained by using the wave number domain reverse time migration image and the imaging focusing function and by using the inverse FFT.

[0086] Step 150, perform splicing processing on the super-resolution imaging results of all sub-modules to obtain the super-resolution imaging result of the reverse time migration image.

[0087] Optionally, the super-resolution imaging results of all sub-modules are obtained and spliced to finally obtain the super-resolution imaging result of the whole module.

[0088] The super-resolution imaging method based on focused image fusion provided in the embodiment specifically includes the following steps: Step 110, perform regional block processing on a reverse time migration image to obtain a plurality of sub-modules, and respectively determine the wave number domain reverse time migration image of each sub-module; Step 120, respectively determine the wave number domain observation illumination operator of each sub-module according to a preset velocity model; Step 130, respectively perform image fusion processing on the wave number domain observation illumination operator and the wave number domain reverse time migration image of each sub-module to construct the imaging focusing function of each sub-module; Step 140, respectively determine the super-resolution imaging result of each sub-module according to the wave number domain reverse time migration image and the imaging focusing function of each sub-module; and Step 150, perform splicing processing on the super-resolution imaging results of all sub-modules to obtain the super-resolution imaging result of the reverse time migration image. The super-resolution imaging method based on focused image fusion disclosed in the embodiment combines image super-resolution processing and seismic imaging, constructs the imaging focusing function by using the imaging wave number spectrum and the illumination information under the data driving, and adopts the image high-dimensional deconvolution operator to realize the image super-resolution imaging, thereby improving the imaging resolution and greatly improving the imaging detail description effect.

[0089] Embodiment two

[0090] The embodiment is based on the embodiment one, and further explains and illustrates the super-resolution imaging method based on focused image fusion.

[0091] The first step is to perform regional block processing on the reverse-time migration image to obtain a plurality of sub-modules, and wave number domain reverse-time migration images of the sub-modules are determined respectively.

[0092] In some embodiments, the wave number domain reverse-time migration image of each sub-module is determined by:

[0093] The wave number domain reverse-time migration image of each sub-module is determined by fast Fourier transform.

[0094] Optionally, the input reverse-time migration image is subjected to regional block processing, and the wave number domain reverse-time migration image is calculated by using FFT (fast Fourier transform) in each sub-module.

[0095] The second step is to determine the wave number domain observation illumination operator of each sub-module according to a preset velocity model.

[0096] Optionally, the wave number domain observation illumination operator is calculated by using the velocity model and a seismic data observation system.

[0097] In some embodiments, the wave number domain observation illumination operator of each sub-module is determined according to the preset velocity model by:

[0098] The wave number domain observation illumination operator of each sub-module is determined according to the preset velocity model by a preset seismic data observation system.

[0099] It should be noted that the preset observation system includes an observation system set during seismic data acquisition, which belongs to the basic standard in the industry; the reverse-time migration image includes seismic imaging data obtained by using the seismic reverse-time migration technology, which also belongs to the conventional technology in the industry; the preset velocity model includes a migration velocity model that needs to be input during migration, which is also a basic condition for seismic imaging and belongs to the industry specification.

[0100] In some embodiments, the wave number domain observation illumination operator includes:

[0101] K psf =[G(x0;x s )G(x0;x r )] T G(x n ;x s )G(x n ;x r )

[0102] wherein, K psfis the wave number domain observed illumination operator, x0is the center point coordinate of the sub-module, x n is all points in the sub-module, x s and x r are shot point coordinates and receiver point coordinates respectively, G() is the Green's function; wherein, the Green's function of any two points is constructed by the following formula:

[0103] G(x0; x) = Aexp[2πf(t-τ)]

[0104] Wherein, τ and A are the travel time field and amplitude determined according to the ray tracing method respectively, and f is the frequency.

[0105] As an example, according to the velocity model and the observation system, the travel time field τ and the amplitude A are obtained by using the ray tracing method, and the frequency f of the given calculation can be constructed to construct the Green's function of any two points, which is as follows:

[0106] G(x0; x) = Aexp[2πf(t-τ)]

[0107] The form of the observed illumination operator is as follows:

[0108] K psf = [G(x0; x s )G(x0; x r )] T G(x n ; x s )G(x n ; x r )

[0109] Wherein, x0represents the center point coordinate of the sub-module, x n represents all points in the sub-module, x s and x r represent shot point coordinates and receiver point coordinates respectively.

[0110] The third step, the wave number domain observed illumination operator and the wave number domain reverse time migration image of each sub-module are respectively processed by image fusion to construct the imaging focusing function of each sub-module.

[0111] In some embodiments, the imaging focusing function comprises:

[0112]

[0113] Wherein, is the wave number domain reverse time migration image of the sub-module, Fsmth(.) is a smoothing function, and Filt(α,.) is an angle filtering function; wherein, the angle filtering function comprises:

[0114] Filt(α,.) = Envlp(K psf (αi ))

[0115] Among them, Filt(α,.) is the angle filter function, K psf (α i ) is the focusing operator on the lighting angle, and Envlp() is the envelope operation.

[0116] As an example, we can perform image fusion based on the observed illumination operator in the wavenumber domain and the reverse time migration image to extract the accurate imaging focus function. The fusion operator can be expressed as:

[0117]

[0118] in, is the reverse time migration image of the submodule in the wavenumber domain, Fsmth(.) is the smoothing function, Filt(α,.) is the angle filter function, i.e., the fusion operator, and the value range of α is K psf lighting angle range.

[0119] The calculation formula of the fusion operator includes:

[0120] Filt(α,.)=Envlp(K psf (α i ))

[0121] Among them, K psf (α i ) represents the focusing operator at each angle, and Envlp is the envelope operation. The fusion operator is obtained and applied to the reverse time migration image of the submodule to finally obtain the optimized imaging focusing function.

[0122] The fourth step is to determine the super-resolution imaging results of each submodule based on the wavenumber domain reverse time migration image and imaging focusing function of each submodule.

[0123] Optionally, a reverse time migration image and an imaging focus function in the wavenumber domain are used, and an inverse FFT is used to obtain a super-resolution imaging result of the submodule.

[0124] In some embodiments, determining the super-resolution imaging results of each submodule includes:

[0125] The super-resolution imaging results of each submodule are determined by inverse fast Fourier transform.

[0126] In some embodiments, the super-resolution imaging result of each submodule is determined by the following formula:

[0127]

[0128] in, It is the reverse time migration image; is the imaging focusing function; γ is a regularization factor, is the super-resolution imaging result of the sub-module.

[0129] As an example, the wave number domain reverse time migration result of the sub-module is and the imaging focusing function of the sub-module is the super-resolution imaging result is calculated in the form of

[0130]

[0131] wherein γ is a regularization factor, and its value can be taken as 5% of the maximum value.

[0132] Fifthly, the super-resolution imaging results of all the sub-modules are spliced to obtain the super-resolution imaging result of the reverse time migration image.

[0133] Optionally, the super-resolution imaging results of all the sub-modules are obtained and spliced to finally obtain the super-resolution imaging result of the entire module.

[0134] The super-resolution imaging method based on focused image fusion disclosed in the embodiment specifically comprises the following steps: first, the reverse time migration image is regionally blocked to obtain a plurality of sub-modules, and the wave number domain reverse time migration image of each sub-module is determined; second, the wave number domain observation illumination operator of each sub-module is determined according to a preset velocity model; third, the wave number domain observation illumination operator and the wave number domain reverse time migration image of each sub-module are respectively subjected to image fusion processing to construct the imaging focusing function of each sub-module; fourth, the super-resolution imaging result of each sub-module is determined according to the wave number domain reverse time migration image and the imaging focusing function of each sub-module; and fifth, the super-resolution imaging results of all the sub-modules are spliced to obtain the super-resolution imaging result of the reverse time migration image. The super-resolution imaging method based on focused image fusion disclosed in the embodiment combines imageology super-resolution processing and seismic imaging, constructs the imaging focusing function by using the imaging wave number spectrum and illumination information under data driving, and adopts the imageology high-dimensional deconvolution operator to realize imageology super-resolution imaging, thereby improving the imaging resolution and greatly improving the imaging detail description effect.

[0135] Embodiment Three

[0136] The embodiment further explains and describes the super-resolution imaging method based on focused image fusion disclosed in the embodiment one in an exemplary manner on the basis of the embodiment one. Specifically,

[0137] The first step is to divide the reverse-time migration image into sub-modules by region, and determine the wave number domain reverse-time migration image of each sub-module.

[0138] The input reverse-time migration image can refer to Figure 2 The wave number domain reverse-time migration image can refer to Figure 3 .

[0139] The second step is to determine the wave number domain observation illumination operator of each sub-module according to a preset velocity model.

[0140] The wave number domain observation illumination operator can refer to Figure 4 .

[0141] The third step is to perform image fusion processing on the wave number domain observation illumination operator and the wave number domain reverse-time migration image of each sub-module, to construct an imaging focusing function of each sub-module.

[0142] The imaging focusing function can refer to Figure 5 .

[0143] The fourth step is to determine the super-resolution imaging result of each sub-module according to the wave number domain reverse-time migration image and the imaging focusing function of each sub-module.

[0144] The fifth step is to perform splicing processing on the super-resolution imaging results of all sub-modules, to obtain a super-resolution imaging result of the reverse-time migration image.

[0145] After the splicing operation, the final super-resolution imaging result can refer to Figure 6 .

[0146] As shown in Figure 1 and Figure 6 , the super-resolution imaging method based on focus image fusion disclosed in the present application can significantly improve the image resolution, especially for shallow and medium layer models, the imaging result obtained by the method disclosed in the present application is very close to the reflection coefficient, which is very effective for widening the frequency band.

[0147] Embodiment Four

[0148] The embodiment provides a super-resolution imaging device based on focus image fusion. The device embodiment can be used to execute the method embodiments of the present application. For details not disclosed in the device embodiment, please refer to the method embodiments of the present application. The device disclosed in the embodiment comprises:

[0149] A reverse-time migration image determination module is configured to divide a reverse-time migration image into sub-modules by region, and determine the wave number domain reverse-time migration image of each sub-module.

[0150] The observation illumination operator determination module is configured to determine wave number domain observation illumination operators of the sub-modules respectively according to a preset velocity model.

[0151] The imaging focusing function construction module is configured to perform image fusion processing on the wave number domain observation illumination operators and the wave number domain reverse time migration images of the sub-modules respectively to construct imaging focusing functions of the sub-modules.

[0152] The sub-module imaging result determination module is configured to determine super-resolution imaging results of the sub-modules respectively according to the wave number domain reverse time migration images and the imaging focusing functions of the sub-modules.

[0153] The splicing module is configured to perform splicing processing on the super-resolution imaging results of all the sub-modules to obtain the super-resolution imaging result of the reverse time migration image.

[0154] In some embodiments, the reverse time migration image determination module comprises a splitting unit and a reverse time migration image determination unit.

[0155] The splitting unit is configured to perform regional block processing on the reverse time migration image to obtain a plurality of sub-modules.

[0156] The reverse time migration image determination unit is configured to determine wave number domain reverse time migration images of the sub-modules through Fourier transform.

[0157] In some embodiments, the sub-module imaging result determination module is configured to determine the super-resolution imaging results of the sub-modules through inverse fast Fourier transform.

[0158] In some embodiments, the wave number domain observation illumination operator comprises:

[0159] K psf = [G(x0; x s ) G(x0; x r )] T G(x n ; x s ) G(x n ; x r )

[0160] wherein K psf is the wave number domain observation illumination operator, x0 is the center point coordinate of the sub-module, x n is all points in the sub-module, x s and x r are shot point coordinates and receiver point coordinates respectively, and G() is a Green function; wherein the Green function of any two points is constructed by the following formula:

[0161] G(x0; x) = Aexp[2πf(t-τ)]

[0162] Wherein, τ and A are respectively the travel time field and amplitude determined according to the ray tracing method, and f is the frequency.

[0163] In some embodiments, the observation illumination operator determination module is configured to determine the wave number domain observation illumination operator of each sub-module respectively by a preset velocity model and a preset seismic data observation system.

[0164] In some embodiments, the imaging focusing function comprises:

[0165]

[0166] Wherein, is the wave number domain reverse time migration image of the sub-module, Fsmth(.) is a smoothing function, and Filt(α,.) is an angle filtering function; wherein, the angle filtering function comprises:

[0167] Filt(α,.) = Envlp(K psf (α i ))

[0168] Wherein, Filt(α,.) is an angle filtering function, K psf (α i ) is a focusing operator on the illumination angle, and Envlp() is an envelope operation.

[0169] In some embodiments, the super-resolution imaging result of each sub-module is determined by the following formula:

[0170]

[0171] Wherein, is the reverse time migration image; is an imaging focusing function; γ is a regularization factor, is the super-resolution imaging result of the sub-module.

[0172] Those skilled in the art can understand that the structure described in the embodiments does not constitute a limitation on the device of the embodiments of the application, and can include more or less modules / cells than the illustration, or combine certain modules / cells, or different module / cell arrangement.

[0173] Those skilled in the art should understand that the modules or steps of the present application described above can be realized by general computing devices, which can be centralized on a single computing device or distributed on a network composed of multiple computing devices. Alternatively, they can be realized by program codes executable by computing devices, so that they can be stored in storage devices and executed by computing devices, and in some cases, the steps shown or described can be executed in different order, or they can be made into individual integrated circuit modules, or multiple modules or steps can be made into a single integrated circuit module.

[0174] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of each module in the imaging device based on the focused image fusion super-resolution can refer to the corresponding process in the foregoing method embodiment, and the present embodiment will not be repeated here.

[0175] The device provided by the embodiment includes: a reverse-time migration image determination module, configured to perform regional block processing on a reverse-time migration image to obtain a plurality of sub-modules, and determine wave number domain reverse-time migration images of the sub-modules respectively; an observed illumination operator determination module, configured to determine wave number domain observed illumination operators of the sub-modules respectively according to a preset velocity model; an imaging focus function construction module, configured to perform image fusion processing on the wave number domain observed illumination operators and the wave number domain reverse-time migration images of the sub-modules respectively, to construct imaging focus functions of the sub-modules; a sub-module imaging result determination module, configured to determine super-resolution imaging results of the sub-modules respectively according to the wave number domain reverse-time migration images and the imaging focus functions of the sub-modules; and a splicing module, configured to perform splicing processing on the super-resolution imaging results of all the sub-modules to obtain a super-resolution imaging result of the reverse-time migration image. According to the device disclosed by the embodiment, the imaging super-resolution processing and seismic imaging are combined, the imaging focus function is constructed by using the imaging wave number spectrum and the illumination information under the data driving, and the imaging super-resolution imaging is realized by using the imaging high-dimensional deconvolution operator, so that the imaging resolution is improved, and the imaging detail description effect is greatly improved.

[0176] Embodiment five

[0177] The embodiment provides a computer readable storage medium. The computer readable storage medium stores a computer program. The computer program is executed by a processor to implement some or all steps of the method in the foregoing method embodiments.

[0178] The reverse-time migration image is subjected to regional block processing to obtain a plurality of sub-modules, and wave number domain reverse-time migration images of the sub-modules are determined respectively;

[0179] The wave number domain observed illumination operators of the sub-modules are determined respectively according to a preset velocity model;

[0180] The wavenumber domain observation illumination operator and wavenumber domain reverse time migration image of each submodule are fused to construct the imaging focusing function of each submodule.

[0181] Determine the super-resolution imaging results of each submodule based on the wavenumber domain reverse time migration image and imaging focusing function of each submodule;

[0182] The super-resolution imaging results of all submodules are spliced ​​to obtain the super-resolution imaging result of the reverse time migration image.

[0183] Furthermore, determining the wavenumber domain reverse time migration image of each submodule includes:

[0184] The wavenumber domain reverse time migration image of each submodule is determined by fast Fourier transform.

[0185] Furthermore, determining the super-resolution imaging results of each submodule includes:

[0186] The super-resolution imaging results of each submodule are determined by inverse fast Fourier transform.

[0187] Furthermore, the wavenumber domain observation illumination operator includes:

[0188] K psf =[G(x0;x s )G(x0;x r )] T G(x n ;x s )G(x n ;x r )

[0189] Among them, K psf is the wavenumber domain observation illumination operator, x0 is the center point coordinate of the submodule, x n For all points in the submodule, x s and x r are the coordinates of the shot point and the check point respectively, and G() is the Green's function. The Green's function of any two points can be constructed by the following formula:

[0190] G(x0;x)=Aexp[2πf(t-τ)]

[0191] where τ and A are the travel time field and amplitude determined by the ray tracing method, respectively, and f is the frequency.

[0192] Furthermore, determining the wavenumber domain observation illumination operator of each submodule according to the preset velocity model includes:

[0193] The wave number domain observation illumination operator of each sub-module is determined by a preset seismic data observation system according to a preset velocity model.

[0194] Further, the imaging focusing function comprises:

[0195]

[0196] wherein, is a wave number domain reverse time migration image of the sub-module, Fsmth(.) is a smoothing function, and Filt(alpha,.) is an angle filtering function; wherein the angle filtering function comprises:

[0197] Filt(alpha,.) = Envlp(K psf (alpha i ))

[0198] wherein Filt(alpha,.) is an angle filtering function, K psf (alpha i ) is a focusing operator on an illumination angle, and Envlp() is an envelope operation.

[0199] Further, the super-resolution imaging result of each sub-module is determined by the following formula:

[0200]

[0201] wherein, is a reverse time migration image; is an imaging focusing function; gamma is a regularization factor, is a super-resolution imaging result of the sub-module.

[0202] The computer readable storage medium can also include, or be separately comprised of, computer programs, data files, data structures, etc., or combinations thereof. The computer readable storage medium or computer program can be specifically designed and understood by those skilled in the computer software field, or can be known and available to those skilled in the computer software field. Examples of the computer readable storage medium include: magnetic media, such as hard disks, floppy disks, and magnetic tapes; optical media, such as CD ROM disks and DVDs; magneto-optical media, such as optical disks; and hardware devices specifically configured to store and execute computer programs, such as read-only memory (ROM), random access memory (RAM), flash memory; or servers, app application stores, etc. Examples of the computer program include machine code (e.g., code generated by a compiler) and files containing high-level code that can be executed by a computer by using an interpreter. The described hardware devices can be configured to function as one or more software modules to perform the above-described operations and methods, and vice versa. In addition, the computer readable storage medium can be distributed in a networked computer system, and the program code or computer program can be stored and executed in a decentralized manner.

[0203] Embodiment six

[0204] The embodiment provides a computer program product. The computer program product includes a computer program or instructions, which, when executed by a processor, implement all or part of the steps of the method in the foregoing method embodiments:

[0205] The reverse-time migration image is regionally blocked to obtain a plurality of sub-modules, and wave number domain reverse-time migration images of the sub-modules are respectively determined;

[0206] Wave number domain observation illumination operators of the sub-modules are respectively determined according to a preset velocity model;

[0207] Wave number domain observation illumination operators and wave number domain reverse-time migration images of the sub-modules are respectively subjected to image fusion processing to construct imaging focusing functions of the sub-modules;

[0208] Super-resolution imaging results of the sub-modules are respectively determined according to the wave number domain reverse-time migration images and the imaging focusing functions of the sub-modules;

[0209] Super-resolution imaging results of all the sub-modules are subjected to splicing processing to obtain a super-resolution imaging result of the reverse-time migration image.

[0210] Further, the wave number domain reverse-time migration images of the sub-modules are determined by:

[0211] The wave number domain reverse-time migration images of the sub-modules are determined by fast Fourier transform.

[0212] Further, the determining the super-resolution imaging result of each sub-module comprises:

[0213] The super-resolution imaging result of each sub-module is determined through inverse fast Fourier transform.

[0214] Further, the wave number domain observation illumination operator comprises:

[0215] K psf =[G(x0;x s )G(x0;x r )] T G(x n ;x s )G(x n ;x r )

[0216] Wherein, K psf is the wave number domain observation illumination operator, x0 is the center point coordinate of the sub-module, x n is all points in the sub-module, x s and x r are shot point coordinates and receiver point coordinates respectively, and G() is the Green function; wherein the Green function of any two points is constructed by the following formula:

[0217] G(x0;x)=Aexp[2πf(t-τ)]

[0218] Wherein, τ and A are respectively the travel time field and amplitude determined according to the ray tracing method, and f is the frequency.

[0219] Further, the wave number domain observation illumination operator of each sub-module is determined according to the preset velocity model, comprising:

[0220] The wave number domain observation illumination operator of each sub-module is determined according to the preset velocity model through the preset seismic data observation system.

[0221] Further, the imaging focusing function comprises:

[0222]

[0223] Wherein, is the wave number domain reverse time migration image of the sub-module, Fsmth(.) is a smoothing function, and Filt(α,.) is an angle filtering function; wherein the angle filtering function comprises:

[0224] Filt(α,.)=Envlp(K psf (α i ))

[0225] Wherein, Filt(α,.) is an angle filtering function, and K psf (αi ) is a focusing operator for the illumination angle, and Envlp() is an envelope operation.

[0226] Further, the super-resolution imaging result of each sub-module is determined by the following formula:

[0227]

[0228] wherein, is a reverse-time migration image; is an imaging focusing function; γ is a regularization factor, is the super-resolution imaging result of the sub-module.

[0229] Further, the computer program product can include one or more computer-executable components which, when executed by a computerized device, are configured to perform any of the methods of embodiments; the computer program product can also include a computer-readable medium having tangibly embodied thereon the computer program comprising instructions for performing any of the methods of embodiments. In such an implementation, the computer program can be downloaded and installed from a network, and / or installed from a removable media.

[0230] Embodiment Seven

[0231] The embodiment provides an electronic device, which can include one or more processors, a memory, a multimedia component, an input / output (I / O) interface, and a communication component.

[0232] The one or more processors are configured to perform all or part of the steps in the foregoing method embodiments. The memory is configured to store various types of data, which can include, for example, instructions of any application program or method in the electronic device, and application-related data.

[0233] The memory can be implemented by any type of volatile or non-volatile storage devices or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0234] The one or more processors can be an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic device, which is configured to perform all or part of the steps in the foregoing method embodiments:

[0235] The reverse time migration image is regionally divided to obtain a plurality of sub-modules, and wave number domain reverse time migration images of the sub-modules are determined respectively;

[0236] Wave number domain observation illumination operators of the sub-modules are determined according to a preset velocity model respectively;

[0237] Image fusion processing is respectively performed on the wave number domain observation illumination operators and the wave number domain reverse time migration images of the sub-modules to construct imaging focusing functions of the sub-modules;

[0238] Super-resolution imaging results of the sub-modules are determined according to the wave number domain reverse time migration images and the imaging focusing functions of the sub-modules respectively;

[0239] The super-resolution imaging results of all the sub-modules are spliced to obtain a super-resolution imaging result of the reverse time migration image.

[0240] Further, the wave number domain reverse time migration images of the sub-modules are determined by:

[0241] The wave number domain reverse time migration images of the sub-modules are determined by fast Fourier transform.

[0242] Further, the super-resolution imaging results of the sub-modules are determined by:

[0243] The super-resolution imaging results of the sub-modules are determined by inverse fast Fourier transform.

[0244] Further, the wave number domain observation illumination operator includes:

[0245] K psf = [G(x0; x s ) G(x0; x r )] T G(x n ; xs )G(x n ; x r )

[0246] where K psf is the wave number domain observation illumination operator, x0 is the center point coordinate of the sub-module, x n is all points within the sub-module, x s and x r are the shot point coordinate and the receiver point coordinate respectively, and G() is the Green function; wherein the Green function of any two points is constructed by the following formula:

[0247] G(x0; x) = Aexp[2πf(t - τ)]

[0248] where τ and A are the travel time field and amplitude determined according to the ray tracing method respectively, and f is the frequency.

[0249] Further, the wave number domain observation illumination operator of each sub-module is determined according to the preset velocity model, comprising:

[0250] The wave number domain observation illumination operator of each sub-module is determined according to the preset velocity model through the preset seismic data observation system.

[0251] Further, the imaging focusing function comprises:

[0252]

[0253] wherein, is the wave number domain reverse time migration image of the sub-module, Fsmth(.) is a smoothing function, and Filt(α,.) is an angle filtering function; wherein the angle filtering function comprises:

[0254] Filt(α,.) = Envlp(K psf (α i ))

[0255] wherein Filt(α,.) is an angle filtering function, K psf (α i ) is a focusing operator on the illumination angle, and Envlp() is an envelope operation.

[0256] Further, the super-resolution imaging result of each sub-module is determined by the following formula:

[0257]

[0258] wherein, is the reverse time migration image; is the imaging focusing function; γ is a regularization factor, is the super-resolution imaging result of the sub-module.

[0259] The multimedia component can include a screen which can be a touch screen, and an audio component for outputting and / or inputting audio signals. For example, the audio component can include a microphone for receiving external audio signals. The received audio signals can be further stored in the memory or transmitted through the communication component. The audio component also includes at least one speaker for outputting audio signals.

[0260] The I / O interface provides an interface between the one or more processors and other interface modules, which can be a keyboard, a mouse, a button, etc. These buttons can be virtual buttons or physical buttons.

[0261] The communication component is used for wired or wireless communication between the electronic device and other devices. Wired communication includes communication through a network port, a serial port, etc.; wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, 5G, or a combination of one or more of them.

[0262] In summary, the present application provides an ultra-resolution imaging method and device based on focused image fusion, a computer readable storage medium and an electronic device. The ultra-resolution imaging method based on focused image fusion disclosed in the present application specifically includes the following steps: first, performing regional block processing on the reverse-time migration image to obtain a plurality of sub-modules, and determining the wave number domain reverse-time migration image of each sub-module; second, determining the wave number domain observation illumination operator of each sub-module according to a preset velocity model; third, performing image fusion processing on the wave number domain observation illumination operator and the wave number domain reverse-time migration image of each sub-module respectively to construct the imaging focusing function of each sub-module; fourth, determining the ultra-resolution imaging result of each sub-module according to the wave number domain reverse-time migration image and the imaging focusing function of each sub-module respectively; and fifth, performing splicing processing on the ultra-resolution imaging results of all sub-modules to obtain the ultra-resolution imaging result of the reverse-time migration image. The ultra-resolution imaging method based on focused image fusion disclosed in the present embodiment combines image-based ultra-resolution processing and seismic imaging, constructs an imaging focusing function using imaging wave number spectrum and illumination information under data driving, and adopts an image-based high-dimensional deconvolution operator to realize image-based ultra-resolution imaging, thereby improving imaging resolution and greatly improving imaging detail description effect.

[0263] It should also be understood that, wherever appropriate, the methods and apparatuses described herein can also be implemented in software and / or firmware. Furthermore, wherever appropriate, the methods and apparatuses described herein can also be implemented in one or more computer programs that are executable on one or more programmable computers or processors including, but not limited to, PCs, laptops, cell phones, and PDAs running software (e.g., web browsers) that can be used to operate a web server. In this regard, each step in the procedures can be implemented in software and / or firmware and / or hardware and / or other ways. Also, the software implemented instructions can be stored in one or more computer readable memory devices, such as a hard disk, floppy disk, CD ROM, RAM, EEPROM, and the like, and can be made available using one or more computer program products, one or more computer readable tangible storage devices, one or more computer program products, and / or one or more computer readable tangible storage devices.

[0264] In this application, the terms "comprise", "contain", or any other variant thereof are intended to cover a non-exclusive inclusion, so that processes, methods, articles, or devices that comprise a list of elements do not only include those elements, but also include other elements that are not expressly listed, or further include elements inherent in such processes, methods, articles, or devices. Without more limitations, the element defined by the statement "comprises a" does not exclude the presence of additional identical elements in the process, method, device or equipment comprising the element; if there is a description of "first", "second", etc., it is only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features or the sequence of indicated technical features; in the description of the present application, unless otherwise specified, the meaning of the term "a plurality of" or "a plurality" is at least two; if there is a description of a server, it should be noted that the server can be a stand-alone physical server or terminal, or a server cluster composed of multiple physical servers, or a cloud server capable of providing cloud server, cloud database, cloud storage and CDN and other basic cloud computing services; if there is a description of a smart terminal or a mobile device in the present application, it should be noted that the smart terminal or mobile device can be a mobile phone, a tablet computer, a smart watch, a netbook, a wearable electronic device, a personal digital assistant (PDA), an augmented reality technology device (AR), a virtual reality device (VR), a smart television, a smart sound, a personal computer (PC), etc., but is not limited thereto, and the specific form of the smart terminal or mobile device is not specially limited in the present application.

[0265] Finally, it should be noted that in the description of the present application, the description of the terms "one embodiment", "some embodiments", "example", "one example" or "some examples" means 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 the present application. In the present application, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0266] Although the embodiments of the present application have been shown and described above, it is understood that all the above-described embodiments are exemplary only, the contents described are merely adopted for the purpose of facilitating the understanding of the present application, and are not intended to limit the present application. Any person skilled in the art to which the present application belongs can make any modification and change in the form and details without departing from the spirit and scope of the present application, but the protection scope of the present application shall be subject to the scope defined by the appended claims.

Claims

1. A super-resolution imaging method based on focused image fusion, characterized in that: The method comprises: Performing regional block processing on the reverse time migration image to obtain multiple submodules, and determining the wavenumber domain reverse time migration image of each submodule respectively; Determine the wavenumber domain observation illumination operator of each submodule according to the preset velocity model; The wavenumber domain observation illumination operator and wavenumber domain reverse time migration image of each submodule are fused to construct the imaging focusing function of each submodule. Determine the super-resolution imaging results of each submodule based on the wavenumber domain reverse time migration image and imaging focusing function of each submodule; The super-resolution imaging results of all submodules are spliced ​​to obtain the super-resolution imaging result of the reverse time migration image.

2. The super-resolution imaging method based on focused image fusion according to claim 1, characterized in that: Determining the wavenumber domain reverse time migration image of each submodule includes: The wavenumber domain reverse time migration image of each submodule is determined by fast Fourier transform.

3. The super-resolution imaging method based on focused image fusion according to claim 1, characterized in that: Determining the super-resolution imaging results of each submodule includes: The super-resolution imaging results of each submodule are determined by inverse fast Fourier transform.

4. The super-resolution imaging method based on focused image fusion according to claim 1, characterized in that: The wavenumber domain observation illumination operator includes: K psf =[G(x0;x s )G(x0;x r )] T G(x n ;x s )G(x n ;x r ) Among them, K psf is the wavenumber domain observation illumination operator, x0 is the center point coordinate of the submodule, x n For all points in the submodule, x s and x r are the coordinates of the shot point and the check point respectively, and G() is the Green's function. The Green's function of any two points can be constructed by the following formula: G(x0;x)=Aexp[2πf(t-τ)] where τ and A are the travel time field and amplitude determined by the ray tracing method, respectively, and f is the frequency.

5. The super-resolution imaging method based on focused image fusion according to claim 1, characterized in that: The wavenumber domain observation illumination operator of each submodule is determined according to the preset velocity model, including: The wavenumber domain observation illumination operator of each submodule is determined respectively through a preset seismic data observation system according to a preset velocity model.

6. The super-resolution imaging method based on focused image fusion according to claim 1, characterized in that: The imaging focusing function includes: in, is the wavenumber domain reverse time migration image of the submodule, Fsmth(.) is the smoothing function, and Filt(α,.) is the angle filtering function; wherein the angle filtering function includes: Filt(α,.)=Envlp(K psf (α i )) Among them, Filt(α,.) is the angle filter function, K psf (α i ) is the focusing operator on the lighting angle, and Envlp() is the envelope operation.

7. The super-resolution imaging method based on focused image fusion according to claim 1, characterized in that: The super-resolution imaging results of each submodule are determined by the following formula: in, It is the reverse time migration image; is the imaging focusing function; γ is the regularization factor, This is the super-resolution imaging result of the submodule.

8. A super-resolution imaging device based on focused image fusion, characterized in that: include: A reverse time migration image determination module is used to perform regional block processing on the reverse time migration image to obtain multiple submodules, and to determine the wavenumber domain reverse time migration image of each submodule respectively; An observation illumination operator determination module is used to determine the wavenumber domain observation illumination operator of each submodule according to a preset velocity model; An imaging focusing function construction module is used to perform image fusion processing on the wavenumber domain observation illumination operator and the wavenumber domain reverse time migration image of each submodule to construct the imaging focusing function of each submodule; A submodule imaging result determination module is used to determine the super-resolution imaging result of each submodule based on the wavenumber domain reverse time migration image and imaging focus function of each submodule; The splicing module is used to splice the super-resolution imaging results of all sub-modules to obtain the super-resolution imaging result of the reverse time migration image.

9. A computer-readable storage medium, characterized in that The computer program stored in the computer-readable storage medium, when executed by one or more processors, implements the super-resolution imaging method based on focus image fusion according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The method comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the super-resolution imaging method based on focused image fusion as claimed in any one of claims 1 to 7 is implemented.

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