Super-resolution imaging method and device based on focusing image fusion

Abstract

The invention 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: carrying out regional blocking processing on a reverse time migration image to obtain a plurality of sub-modules, and respectively determining a wavenumber domain reverse time migration image of each sub-module; wave number domain observation illumination operators of all the sub-modules are determined according to a preset speed model; respectively carrying out image fusion processing on the wave number domain observation illumination operator and the wave number domain reverse time migration image of each sub-module so as to construct an imaging focusing function of each sub-module; determining 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; and splicing the super-resolution imaging results of all the sub-modules to obtain the super-resolution imaging result of the reverse time migration image. According to the invention, imaging super-resolution imaging is realized, the imaging resolution is improved, and the imaging detail description effect is greatly improved.

Description

Technical Field

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

[0002] The purpose of the background technology description provided here is to give an overall background of the present application. The statements in this section merely provide background related to the present application and do not necessarily constitute prior art.

[0003] With the continuous development and progress of seismic exploration technology, exploration targets have gradually become more refined, and the types of oil and gas reservoirs have gradually shifted from structural oil and gas reservoirs to concealed oil and gas reservoirs, which has put forward higher requirements on the high-resolution processing technology of seismic exploration.

[0004] Under current technology, conventional resolution improvement processing techniques, such as deconvolution and inverse Q filtering, are usually based on one-dimensional assumptions and have considerable processing effects on horizontal structures or structures with small dips, but have poor processing effects on complex structures such as high and steep structures or faults.

[0005] Super-resolution processing is widely used in the field of image science. Among them, the image deblurring algorithm based on high-dimensional space processing is computationally efficient and effective, showing great application potential. However, due to the high complexity of seismic wave propagation theory, this technology is difficult to promote and apply in the field of geophysics.

[0006] Therefore, a new super-resolution seismic imaging method based on imaging is urgently needed to address the defects mentioned above. Summary of the invention

[0007] In response to the above problems, this application proposes a super-resolution imaging method, device, storage medium and electronic device based on focused image fusion. Through the technical solution disclosed in this application, image super-resolution processing and seismic imaging are combined, and imaging focusing function is constructed using imaging wave number spectrum and illumination information under data drive, and image high-dimensional deconvolution operator is used to achieve image super-resolution imaging, thereby improving imaging resolution and greatly improving the effect of imaging detail depiction.

[0008] In a first aspect of the present application, a super-resolution imaging method based on focused image fusion is provided, the method comprising:

[0009] 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;

[0010] Determine the wavenumber domain observation illumination operator of each submodule according to the preset velocity model;

[0011] The wavenumber domain observation illumination operator and the wavenumber domain reverse time migration image of each submodule are subjected to image fusion processing respectively to construct the imaging focusing function of each submodule;

[0012] Determine the super-resolution imaging result of each submodule according to 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] Further, the wavenumber domain reverse time migration image of each submodule is determined, including:

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

[0016] Furthermore, determining the super-resolution imaging result 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 coordinate of the center point 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 is 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, the wavenumber domain observation illumination operator of each submodule is determined according to the preset velocity model, including:

[0024] According to the preset velocity model, the wavenumber domain observation illumination operator of each submodule is determined respectively through the preset seismic data observation system.

[0025] Furthermore, the imaging focusing function includes:

[0026]

[0027] in, is the wavenumber domain reverse time migration image of the submodule, Fsmth(.) is a smoothing function, and Filt(α,.) is an angle filtering function; wherein the angle filtering function includes:

[0028] Filt(α,.)=Envlp(K psf (α i ))

[0029] 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.

[0030] Furthermore, the super-resolution imaging results of each submodule are determined by the following formula:

[0031]

[0032] 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.

[0033] A second aspect of the present application provides a super-resolution imaging device based on focused image fusion, the device comprising:

[0034] A reverse time migration image determination module is used to perform regional block processing on the reverse time migration image to obtain multiple sub-modules, and respectively determine the wavenumber domain reverse time migration image of each sub-module;

[0035] 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;

[0036] 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 respectively, so as to construct the imaging focusing function of each submodule;

[0037] A submodule imaging result determination module is used to determine the super-resolution imaging result of each submodule according to the wavenumber domain reverse time migration image and imaging focusing function of each submodule;

[0038] 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.

[0039] Furthermore, the reverse time migration image determination module comprises: a splitting unit and a reverse time migration image determination unit; wherein,

[0040] A splitting unit is used for performing regional block processing on the reverse time migration image to obtain multiple sub-modules;

[0041] The reverse time migration image determination unit is used to determine the wave number domain reverse time migration image of each submodule through Fourier transform.

[0042] Furthermore, the submodule imaging result determination module is used to determine the super-resolution imaging result of each submodule by inverse fast Fourier transform.

[0043] Furthermore, the wavenumber domain observation illumination operator includes:

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

[0045] Among them, K psf is the wavenumber domain observation illumination operator, x0 is the coordinate of the center point 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 is constructed by the following formula:

[0046] G(x0;x)=Aexp[2πf(t-τ)]

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

[0048] Furthermore, the observation illumination operator determination module is used to preset the velocity model and respectively determine the wavenumber domain observation illumination operator of each submodule through a preset seismic data observation system.

[0049] Furthermore, the imaging focusing function includes:

[0050]

[0051] in, is the wavenumber domain reverse time migration image of the submodule, Fsmth(.) is a smoothing function, and Filt(α,.) is an 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, wherein a 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, including 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 the present 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 embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are merely embodiments of the present application. For ordinary technicians in the relevant field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0062] It should also be noted that, for the convenience of description, only the parts related to the present disclosure are shown in the drawings. The drawings constituting a part of the specification of this application are used to provide a further understanding of the present application. The illustrative embodiments and their descriptions in this application are used to explain the present application and do not constitute an 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 in an embodiment of the present application;

[0064] Figure 2 A schematic diagram of a reverse time migration result provided in an embodiment of the present application;

[0065] Figure 3 A schematic diagram of a wave number spectrum obtained according to a reverse time migration result provided in an embodiment of the present application;

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

[0067] Figure 5 A schematic diagram of a focusing function wavenumber spectrum provided in an embodiment of the present application;

[0068] Figure 6 A schematic diagram of a final super-resolution imaging result provided in an embodiment of the present application;

[0069] Figure 7 A method according to the embodiment of the present application Figure 1 and Figure 5 Schematic diagram of the calculated vertical resolution spectrum. DETAILED DESCRIPTION

[0070] The following will describe the implementation methods of the present application in detail with reference to the accompanying drawings and embodiments, so that the implementation process of how the present application applies technical means to solve technical problems and achieve corresponding technical effects can be fully understood and implemented accordingly. The embodiments of the present application and the various features in the embodiments can be combined with each other without conflict, and the technical solutions formed are all within the protection scope of the present application.

[0071] It should be clear that the embodiments described below are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by technicians in the relevant field without making creative work are within the scope of protection of the present application.

[0072] As can be seen from the background technology, with the continuous development and progress of seismic exploration technology, exploration targets have gradually become more refined, and the types of oil and gas reservoirs have gradually shifted from structural oil and gas reservoirs to concealed oil and gas reservoirs, which has put forward higher requirements on the high-resolution processing technology of seismic exploration.

[0073] Under current technology, conventional resolution improvement processing techniques, such as deconvolution and inverse Q filtering, are usually based on one-dimensional assumptions and have considerable processing effects on horizontal structures or structures with small dips, but have poor processing effects on complex structures such as high and steep structures or faults.

[0074] Super-resolution processing is widely used in the field of image science. Among them, the image deblurring algorithm based on high-dimensional space processing is computationally efficient and effective, showing great application potential. However, due to the high complexity of seismic wave propagation theory, this technology is difficult to promote and apply 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 image super-resolution processing with seismic imaging, constructs an imaging focusing function using imaging wavenumber spectrum and illumination information under data drive, and adopts image high-dimensional deconvolution operator to realize image super-resolution imaging, thereby improving the imaging resolution and greatly improving the imaging detail portrayal effect.

[0076] Embodiment 1

[0077] This 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 in an embodiment of the present application is shown in FIG. Figure 1 As shown, the method disclosed in this embodiment includes the following steps:

[0078] Step 110: performing regional block processing on the reverse time migration image to obtain multiple sub-modules, and determining the wavenumber domain reverse time migration image of each sub-module respectively.

[0079] Optionally, the input RTM image is processed by regional blocks, and the RTM image in the wavenumber domain is calculated by using FFT (fast Fourier transform, FFT for short) in each submodule.

[0080] Step 120: Determine the wavenumber domain observation illumination operator of each submodule according to the preset velocity model.

[0081] Optionally, an observation illumination operator in the wavenumber domain is calculated using a velocity model and a seismic data observation system.

[0082] Step 130: perform image fusion processing on the wavenumber domain observation illumination operator and the wavenumber domain reverse time migration image of each submodule respectively to construct the imaging focusing function of each submodule.

[0083] Optionally, an imaging focusing function is constructed by performing image fusion based on the observed illumination operator in the wavenumber domain and the reverse time migration image.

[0084] Step 140: Determine the super-resolution imaging result of each sub-module according to the wavenumber domain reverse time migration image and the imaging focus function of each sub-module.

[0085] 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.

[0086] Step 150: stitching the super-resolution imaging results of all submodules 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 stitched together to finally obtain the super-resolution imaging result of the entire module.

[0088] The super-resolution imaging method based on focused image fusion provided in this embodiment specifically includes the following steps: step 110, performing regional block processing on the reverse time migration image to obtain multiple sub-modules, and determining the wavenumber domain reverse time migration image of each sub-module respectively; step 120, determining the wavenumber domain observation illumination operator of each sub-module respectively according to a preset velocity model; step 130, performing image fusion processing on the wavenumber domain observation illumination operator and the wavenumber domain reverse time migration image of each sub-module respectively to construct the imaging focusing function of each sub-module; step 140, determining the super-resolution imaging result of each sub-module respectively according to the wavenumber domain reverse time migration image and the imaging focusing function of each sub-module; step 150, performing 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 this embodiment combines image super-resolution processing with seismic imaging, constructs an imaging focusing function using imaging wavenumber spectrum and illumination information under data drive, and adopts an image high-dimensional deconvolution operator to achieve image super-resolution imaging, thereby improving the imaging resolution and greatly improving the imaging detail portrayal effect.

[0089] Embodiment 2

[0090] Based on the first embodiment, this embodiment further explains and illustrates the super-resolution imaging method based on focused image fusion disclosed in the first embodiment.

[0091] The first step is to perform regional block processing on the reverse time migration image to obtain multiple sub-modules, and determine the wavenumber domain reverse time migration image of each sub-module respectively.

[0092] In some embodiments, determining the wavenumber domain reverse time migration image of each submodule includes:

[0093] The wavenumber domain reverse time migration image of each submodule is determined by fast Fourier transform.

[0094] Optionally, the input RTM image is processed by regional blocks, and the RTM image in the wavenumber domain is calculated by using FFT (fast Fourier transform, FFT for short) in each submodule.

[0095] The second step is to determine the wavenumber domain observation illumination operator of each submodule according to the preset velocity model.

[0096] Optionally, an observation illumination operator in the wavenumber domain is calculated using a velocity model and a seismic data observation system.

[0097] In some embodiments, determining the wavenumber domain observation illumination operator of each submodule according to the preset velocity model includes:

[0098] According to the preset velocity model, the wavenumber domain observation illumination operator of each submodule is determined respectively through the preset seismic data observation system.

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

[0100] In some embodiments, the wavenumber domain observation illumination operator comprises:

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

[0102] Among them, K psfis the wavenumber domain observation illumination operator, x0 is the coordinate of the center point 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 is constructed by the following formula:

[0103] G(x0;x)=Aexp[2πf(t-τ)]

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

[0105] As an example, based on the velocity model and observation system, the travel time field τ and amplitude A are obtained using the ray tracing method. Given the calculated frequency f, the Green's function of any two points can be constructed in the following form:

[0106] G(x0;x)=Aexp[2πf(t-τ)]

[0107] The observed illumination operator has the following form:

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

[0109] Among them, x0 represents the center point coordinate of the submodule, x n Represents all points in the submodule, x s and x r Represent the shot point coordinates and check point coordinates respectively.

[0110] The third step is to perform image fusion processing on the wavenumber domain observation illumination operator and the wavenumber domain reverse time migration image of each submodule respectively to construct the imaging focusing function of each submodule.

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

[0112]

[0113] in, is the wavenumber domain reverse time migration image of the submodule, Fsmth(.) is a smoothing function, and Filt(α,.) is an angle filtering function; wherein the angle filtering function includes:

[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, image fusion can be performed 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, which is 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 result of each submodule according to 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 the regularization factor, This is the super-resolution imaging result of the submodule.

[0129] As an example, based on the wavenumber domain reverse time migration results of the submodule The imaging focusing function of the submodule Computational super-resolution imaging results Its form is as follows:

[0130]

[0131] Among them, γ is the regularization factor, and its value can be 5% of maximum value.

[0132] Step 5: stitching the super-resolution imaging results of all sub-modules to obtain the super-resolution imaging result of the reverse time migration image.

[0133] Optionally, the super-resolution imaging results of all sub-modules are obtained and stitched together 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 this embodiment specifically includes the following steps: a first step, performing regional block processing on the reverse time migration image to obtain multiple sub-modules, and determining the wavenumber domain reverse time migration image of each sub-module respectively; a second step, determining the wavenumber domain observation illumination operator of each sub-module according to a preset velocity model; a third step, performing image fusion processing on the wavenumber domain observation illumination operator and the wavenumber domain reverse time migration image of each sub-module respectively to construct an imaging focusing function of each sub-module; a fourth step, determining the super-resolution imaging result of each sub-module according to the wavenumber domain reverse time migration image and the imaging focusing function of each sub-module respectively; and a fifth step, performing 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 this embodiment combines image super-resolution processing with seismic imaging, constructs an imaging focusing function using imaging wavenumber spectrum and illumination information under data drive, and adopts an image high-dimensional deconvolution operator to achieve image super-resolution imaging, thereby improving the imaging resolution and greatly improving the imaging detail portrayal effect.

[0135] Embodiment 3

[0136] Based on the first embodiment, this embodiment further explains and illustrates the super-resolution imaging method based on focused image fusion disclosed in the first embodiment in an illustrative manner. Specifically:

[0137] The first step is to perform regional block processing on the reverse time migration image to obtain multiple sub-modules, and determine the wavenumber domain reverse time migration image of each sub-module respectively.

[0138] The input reverse time migration image can be referenced Figure 2 The reverse time migration image in the wavenumber domain can be found in Figure 3 .

[0139] The second step is to determine the wavenumber domain observation illumination operator of each submodule according to the preset velocity model.

[0140] The observation illumination operator in the wavenumber domain can be found in Figure 4 .

[0141] The third step is to perform image fusion processing on the wavenumber domain observation illumination operator and the wavenumber domain reverse time migration image of each submodule respectively to construct the imaging focusing function of each submodule.

[0142] Among them, the imaging focusing function can be referred to Figure 5 .

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

[0144] Step 5: stitching the super-resolution imaging results of all sub-modules to obtain the super-resolution imaging result of the reverse time migration image.

[0145] Among them, after the stitching operation, the final super-resolution imaging result can be referred to Figure 6 .

[0146] Depend on Figure 1 and Figure 6 As shown, the super-resolution imaging method based on focused image fusion disclosed in the present application can significantly improve the image resolution, especially for shallow and middle 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 4

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

[0149] A reverse time migration image determination module is used to perform regional block processing on the reverse time migration image to obtain multiple sub-modules, and respectively determine the wavenumber domain reverse time migration image of each sub-module;

[0150] 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;

[0151] 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 respectively, so as to construct the imaging focusing function of each submodule;

[0152] A submodule imaging result determination module is used to determine the super-resolution imaging result of each submodule according to the wavenumber domain reverse time migration image and imaging focusing function of each submodule;

[0153] 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.

[0154] In some embodiments, the reverse time migration image determination module includes: a splitting unit, a reverse time migration image determination unit; wherein,

[0155] A splitting unit is used for performing regional block processing on the reverse time migration image to obtain multiple sub-modules;

[0156] The reverse time migration image determination unit is used to determine the wave number domain reverse time migration image of each submodule through Fourier transform.

[0157] In some embodiments, the submodule imaging result determination module is used to determine the super-resolution imaging result of each submodule by inverse fast Fourier transform.

[0158] In some embodiments, the wavenumber 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] Among them, K psf is the wavenumber domain observation illumination operator, x0 is the coordinate of the center point 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 is constructed by the following formula:

[0161] G(x0;x)=Aexp[2πf(t-τ)]

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

[0163] In some embodiments, the observation illumination operator determination module is used to preset a velocity model and determine the wavenumber domain observation illumination operator of each submodule respectively through a preset seismic data observation system.

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

[0165]

[0166] in, is the wavenumber domain reverse time migration image of the submodule, Fsmth(.) is a smoothing function, and Filt(α,.) is an angle filtering function; wherein the angle filtering function includes:

[0167] Filt(α,.)=Envlp(K psf (α i ))

[0168] 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.

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

[0170]

[0171] 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.

[0172] Those skilled in the art will appreciate that the structure described in this embodiment does not constitute a limitation on the device of the embodiment of the present application, and may include more or fewer modules / units than shown in the figure, or a combination of certain modules / units, or different module / unit arrangements.

[0173] It should be understood by those skilled in the art that the above modules or steps of the present application can be implemented by a general-purpose computing device, and they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices. Optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation.

[0174] Technicians in the relevant field can clearly understand that, for the convenience and conciseness of description, the specific working process of each module in the super-resolution imaging device based on focused image fusion can refer to the corresponding process in the aforementioned method embodiment, and this embodiment will not be repeated here.

[0175] The device provided in this embodiment includes: a reverse time migration image determination module, which is used to perform regional block processing on the reverse time migration image to obtain multiple sub-modules, and respectively determine the wavenumber domain reverse time migration image of each sub-module; an observation illumination operator determination module, which is used to determine the wavenumber domain observation illumination operator of each sub-module according to a preset velocity model; an imaging focusing function construction module, which is used to perform image fusion processing on the wavenumber domain observation illumination operator and the wavenumber domain reverse time migration image of each sub-module respectively to construct the imaging focusing function of each sub-module; a sub-module imaging result determination module, which is used to determine the super-resolution imaging result of each sub-module according to the wavenumber domain reverse time migration image and the imaging focusing function of each sub-module respectively; and a splicing module, which 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. According to the device disclosed in this embodiment, image super-resolution processing and seismic imaging are combined, and an imaging focusing function is constructed using imaging wavenumber spectrum and illumination information under data drive, and an image high-dimensional deconvolution operator is used to achieve image super-resolution imaging, thereby improving the imaging resolution and greatly improving the imaging detail portrayal effect.

[0176] Embodiment 5

[0177] This embodiment provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it can implement some or all steps of the method in the above method embodiment:

[0178] 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;

[0179] Determine the wavenumber domain observation illumination operator of each submodule according to the preset velocity model;

[0180] The wavenumber domain observation illumination operator and the wavenumber domain reverse time migration image of each submodule are subjected to image fusion processing respectively to construct the imaging focusing function of each submodule;

[0181] Determine the super-resolution imaging result of each submodule according to 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] Further, the wavenumber domain reverse time migration image of each submodule is determined, including:

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

[0185] Furthermore, determining the super-resolution imaging result 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 coordinate of the center point 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 is 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, the wavenumber domain observation illumination operator of each submodule is determined according to the preset velocity model, including:

[0193] According to the preset velocity model, the wavenumber domain observation illumination operator of each submodule is determined respectively through the preset seismic data observation system.

[0194] Furthermore, the imaging focusing function includes:

[0195]

[0196] in, is the wavenumber domain reverse time migration image of the submodule, Fsmth(.) is a smoothing function, and Filt(α,.) is an angle filtering function; wherein the angle filtering function includes:

[0197] Filt(α,.)=Envlp(K psf (α i ))

[0198] 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.

[0199] Furthermore, the super-resolution imaging results of each submodule are determined by the following formula:

[0200]

[0201] 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.

[0202] Among them, the computer-readable storage medium may also include computer programs, data files, data structures, etc. alone, or include a combination thereof. The computer-readable storage medium or computer program may be specifically designed and understood by a technician in the field of computer software, or the computer-readable storage medium may be known and available to a technician in the field of computer software. Examples of computer-readable storage media include: magnetic media, such as hard disks, floppy disks, and tapes; optical media, such as CDROM 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 computer programs include machine code (e.g., code generated by a compiler) and files containing high-level code, which can be executed by a computer using an interpreter. The described hardware device may be configured to be used as one or more software modules to perform the operations and methods described above, and vice versa. In addition, the computer-readable storage medium may be distributed in a networked computer system, and program code or computer programs may be stored and executed in a decentralized manner.

[0203] Embodiment 6

[0204] This embodiment provides a computer program product. The computer program product includes a computer program or instructions, and when the computer program or instructions are executed by a processor, all or part of the steps of the method in the above method embodiment are implemented:

[0205] 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;

[0206] Determine the wavenumber domain observation illumination operator of each submodule according to the preset velocity model;

[0207] The wavenumber domain observation illumination operator and the wavenumber domain reverse time migration image of each submodule are subjected to image fusion processing respectively to construct the imaging focusing function of each submodule;

[0208] Determine the super-resolution imaging result of each submodule according to the wavenumber domain reverse time migration image and imaging focusing function of each submodule;

[0209] The super-resolution imaging results of all submodules are spliced ​​to obtain the super-resolution imaging result of the reverse time migration image.

[0210] Further, the wavenumber domain reverse time migration image of each submodule is determined, including:

[0211] The wavenumber domain reverse time migration image of each submodule is determined by fast Fourier transform.

[0212] Furthermore, determining the super-resolution imaging result of each submodule includes:

[0213] The super-resolution imaging results of each submodule are determined by inverse fast Fourier transform.

[0214] Furthermore, the wavenumber domain observation illumination operator includes:

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

[0216] Among them, K psf is the wavenumber domain observation illumination operator, x0 is the coordinate of the center point 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 is constructed by the following formula:

[0217] G(x0;x)=Aexp[2πf(t-τ)]

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

[0219] Furthermore, the wavenumber domain observation illumination operator of each submodule is determined according to the preset velocity model, including:

[0220] According to the preset velocity model, the wavenumber domain observation illumination operator of each submodule is determined respectively through the preset seismic data observation system.

[0221] Furthermore, the imaging focusing function includes:

[0222]

[0223] in, is the wavenumber domain reverse time migration image of the submodule, Fsmth(.) is a smoothing function, and Filt(α,.) is an angle filtering function; wherein the angle filtering function includes:

[0224] Filt(α,.)=Envlp(K psf (α i ))

[0225] Among them, Filt(α,.) is the angle filter function, K psf (αi ) is the focusing operator on the lighting angle, and Envlp() is the envelope operation.

[0226] Furthermore, the super-resolution imaging results of each submodule are determined by the following formula:

[0227]

[0228] 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.

[0229] Further, the computer program product may include one or more computer executable components configured to perform the embodiments when the program is run; the computer program product may also include a computer program tangibly contained on a readable medium, the computer program including program code for performing any method in the embodiments of the present disclosure. In such an embodiment, the computer program may be downloaded and installed from a network through a communication portion, and / or installed from a removable medium.

[0230] Embodiment 7

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

[0232] One or more processors are used to execute all or part of the steps in the above method embodiments. The memory is used to store various types of data, which may include instructions of any application or method in the electronic device, and data related to the application.

[0233] The memory can be implemented by any type of volatile or non-volatile storage device or a combination of them, 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] One or more processors may 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 components, and are used to perform all or part of the steps in the aforementioned method embodiment:

[0235] 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;

[0236] Determine the wavenumber domain observation illumination operator of each submodule according to the preset velocity model;

[0237] The wavenumber domain observation illumination operator and the wavenumber domain reverse time migration image of each submodule are subjected to image fusion processing respectively to construct the imaging focusing function of each submodule;

[0238] Determine the super-resolution imaging result of each submodule according to the wavenumber domain reverse time migration image and imaging focusing function of each submodule;

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

[0240] Further, the wavenumber domain reverse time migration image of each submodule is determined, including:

[0241] The wavenumber domain reverse time migration image of each submodule is determined by fast Fourier transform.

[0242] Furthermore, determining the super-resolution imaging result of each submodule includes:

[0243] The super-resolution imaging results of each submodule are determined by inverse fast Fourier transform.

[0244] Furthermore, the wavenumber 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] Among them, K psf is the wavenumber domain observation illumination operator, x0 is the coordinate of the center point 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 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 by the ray tracing method, respectively, and f is the frequency.

[0249] Furthermore, the wavenumber domain observation illumination operator of each submodule is determined according to the preset velocity model, including:

[0250] According to the preset velocity model, the wavenumber domain observation illumination operator of each submodule is determined respectively through the preset seismic data observation system.

[0251] Furthermore, the imaging focusing function includes:

[0252]

[0253] in, is the wavenumber domain reverse time migration image of the submodule, Fsmth(.) is a smoothing function, and Filt(α,.) is an angle filtering function; wherein the angle filtering function includes:

[0254] Filt(α,.)=Envlp(K psf (α i ))

[0255] 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.

[0256] Furthermore, the super-resolution imaging results of each submodule are determined by the following formula:

[0257]

[0258] 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.

[0259] The multimedia component may include a screen and an audio component, the screen may be a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone, and the microphone is used to receive external audio signals. The received audio signal may be further stored in a memory or sent via a communication component. The audio component also includes at least one speaker for outputting audio signals.

[0260] The I / O interface provides an interface between one or more processors and other interface modules, which may be a keyboard, a mouse, buttons, etc. These buttons may 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 network ports, serial ports, etc.; wireless communication includes: Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, 5G, or one or a combination of them.

[0262] In summary, the present application provides a super-resolution imaging method, device, computer-readable storage medium and electronic device based on focused image fusion. The super-resolution imaging method based on focused image fusion disclosed in the present application specifically includes the following steps: the first step is to perform regional block processing on the reverse time migration image to obtain multiple sub-modules, and determine the wavenumber domain reverse time migration image of each sub-module respectively; the second step is to determine the wavenumber domain observation illumination operator of each sub-module according to the preset velocity model; the third step is to perform image fusion processing on the wavenumber domain observation illumination operator and the wavenumber domain reverse time migration image of each sub-module respectively to construct the imaging focusing function of each sub-module; the fourth step is to determine the super-resolution imaging result of each sub-module according to the wavenumber domain reverse time migration image and the imaging focusing function of each sub-module respectively; the fifth step is to splice 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 this embodiment combines image super-resolution processing with seismic imaging, constructs an imaging focusing function using imaging wavenumber spectrum and illumination information under data drive, and adopts an image high-dimensional deconvolution operator to achieve image super-resolution imaging, thereby improving the imaging resolution and greatly improving the imaging detail portrayal effect.

[0263] In addition, it should be understood that the method or device disclosed in the embodiments provided in the present application can also be implemented in other ways. The method or device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the methods and devices according to the multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a computer program segment or a part of a computer program, and the module, computer program segment or a part of a computer program contains one or more computer programs for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings, and can actually be executed in parallel. They can also be executed in the opposite order sometimes, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of dedicated hardware and computer programs.

[0264] In this application, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements that are inherent to such process, method, article, or apparatus. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, device or equipment including the elements; if there is a description of "first", "second", etc., it is only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features; in the description of this application, unless otherwise specified, the terms "multiple" and "multiple" mean at least two; if there is a description of a server, it should be noted that the server can be an independent physical server or terminal, or a server cluster composed of multiple physical servers, or a cloud server that can provide basic cloud computing services such as cloud servers, cloud databases, cloud storage and CDN; if there is a description of a smart terminal or mobile device in this 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 TV, a smart speaker, a personal computer (PC), a tablet computer, a smart phone, a smart TV, a smart speaker, a smart phone ... Computer, referred to as PC), etc., but not limited to this, the present application does not specifically limit the specific form of the smart terminal or mobile device.

[0265] Finally, it should be noted that, in the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "an example" or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may 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 the above embodiments are exemplary, and the contents described are only embodiments adopted for the convenience of understanding the present application, and are not intended to limit the present application. Any technician in the technical field to which the present application belongs can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present application, but the scope of protection of the present application shall still be based on the scope defined in the attached 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 the wavenumber domain reverse time migration image of each submodule are subjected to image fusion processing respectively to construct the imaging focusing function of each submodule; Determine the super-resolution imaging result of each submodule according to 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: The step of 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 comprises: 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 coordinate of the center point 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 is 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: According to the preset velocity model, the wavenumber domain observation illumination operator of each submodule is determined respectively through the preset seismic data observation system.

6. The super-resolution imaging method based on focused image fusion according to claim 1, characterized in that: The imaging focusing function comprises: in, is the wavenumber domain reverse time migration image of the submodule, Fsmth(.) is a smoothing function, and Filt(α,.) is an 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 sub-modules, and respectively determine the wavenumber domain reverse time migration image of each sub-module; 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 respectively, so as 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 according to the wavenumber domain reverse time migration image and imaging focusing 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 focused image fusion as described in any one of claims 1 to 7.

10. An electronic device, characterized in that: The invention 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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