Ultra-deep imaging method and device, equipment and storage medium

Abstract

The invention provides an ultra-deep imaging method, device and equipment and a storage medium, and belongs to the technical field of seismic wave imaging. The method comprises the following steps: acquiring a target seismic data volume; performing phase inversion calculation on the target seismic data volume in the spatial wave number domain, and generating a least square image based on the phase information of the target seismic data volume; and generating a phase inversion imaging data volume of the target seismic data volume based on the least square image. The phase error caused by observation missing is corrected by using the phase information of the seismic data, so that the stability of a high-dimensional space deconvolution algorithm can be improved, the problems of low signal-to-noise ratio and poor continuity of ultra-deep imaging are solved, and the quality of ultra-deep imaging is improved.

Description

Technical Field

[0001] The present application relates to the field of seismic wave imaging technology, and in particular to an ultra-deep imaging method, device, equipment and storage medium. Background Art

[0002] Least squares migration imaging is currently the most theoretically accurate seismic imaging technology. It achieves quantitative estimation of underground reflectivity models by matching seismic observation data. Imaging domain least squares imaging obtains the point spread function and uses high-dimensional spatial deconvolution to replace the iterative data matching process of de-migration / migration in the traditional data domain least squares imaging. It has the advantages of high computational efficiency, low dependence on model accuracy, and significant effects when processing actual data. However, the high-dimensional spatial deconvolution algorithm in the frequency wavenumber domain requires high accuracy of the complex field of the point spread function, so usually only the energy information of the point spread function is used. However, when facing the problem of ultra-deep imaging, the deep illumination conditions are poor and the data signal-to-noise ratio is significantly reduced. The use of energy inversion algorithms will amplify the noise signal while broadening the frequency, resulting in instability in the imaging results. Summary of the invention

[0003] The present application provides an ultra-deep imaging method, device, equipment and storage medium, which can improve the stability of the high-dimensional space deconvolution algorithm and solve the problems of low signal-to-noise ratio and poor continuity of ultra-deep imaging. The technical solution is as follows:

[0004] On the one hand, an embodiment of the present application provides an ultra-deep imaging method, comprising:

[0005] Acquire target seismic data volume;

[0006] Performing phase inversion calculation on the target seismic data volume in the spatial wave number domain, and generating a least squares image based on the phase information of the target seismic data volume;

[0007] A phase inversion imaging data volume of the target seismic data volume is generated based on the least squares image.

[0008] Optionally, before performing phase inversion calculation on the target seismic data volume in the spatial wavenumber domain and generating a least squares image based on phase information of the target seismic data volume, the method includes:

[0009] Performing a three-dimensional spatial Fourier transform on the target seismic data volume to obtain the target seismic data volume in the spatial wavenumber domain;

[0010] The step of generating a phase inversion imaging data volume of the target seismic data volume based on the least squares image comprises:

[0011] Performing a three-dimensional spatial inverse Fourier transform on the least squares image in the spatial wavenumber domain to obtain the phase inversion imaging data volume.

[0012] Optionally, acquiring the target seismic data volume includes:

[0013] Obtain three-dimensional reverse time migration image and point spread function;

[0014] The performing three-dimensional spatial Fourier transform on the target seismic data volume to obtain the target seismic data volume in the spatial wavenumber domain includes:

[0015] Performing a three-dimensional Fourier transform on the reverse time migration image and the point spread function to obtain the reverse time migration image and the point spread function in the spatial wave number domain.

[0016] Optionally, performing phase inversion calculation on the target seismic data volume in the spatial wavenumber domain and generating a least squares image based on phase information of the target seismic data volume includes:

[0017] A phase inversion calculation is performed on the reverse time migration image and the point spread function in the spatial wave number domain, and the least squares image is generated based on the phase information.

[0018] Optionally, the expression for the phase inversion calculation includes:

[0019]

[0020] Among them, m lsm is the least squares imaging, m rtm is the reverse time migration image, K psf is the point spread function and * is the conjugate symbol.

[0021] Optionally, the expression of the three-dimensional space Fourier transform includes:

[0022] F(u,v,w)=∫∫∫f(x,y,z)e^(-i2π(ux+vy+wz))dxdydz

[0023] Among them, f(x, y, z) is the target seismic data volume in the three-dimensional space, and F(u, v, w) is the target seismic data volume in the spatial wave number domain.

[0024] On the other hand, an embodiment of the present application provides an ultra-deep imaging device, comprising:

[0025] An acquisition module, used for acquiring a target seismic data volume;

[0026] A calculation module, used for performing phase inversion calculation on the target seismic data volume in a spatial wave number domain, and generating a least squares image based on the phase information of the target seismic data volume;

[0027] An imaging module is used to generate a phase inversion imaging data volume of the target seismic data volume based on the least squares image.

[0028] Optionally, the device further comprises:

[0029] A transformation module, used for performing a three-dimensional spatial Fourier transform on the target seismic data volume to obtain the target seismic data volume in the spatial wave number domain;

[0030] The imaging module is further used for:

[0031] Performing a three-dimensional spatial inverse Fourier transform on the least squares image in the spatial wavenumber domain to obtain the phase inversion imaging data volume.

[0032] On the other hand, an embodiment of the present application provides an electronic device, which includes a memory and a processor; a computer program is stored in the memory, and when the computer program is executed by the processor, the method described in the above aspects is implemented.

[0033] On the other hand, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is loaded and executed by a processor to implement the method as described in the above aspects.

[0034] The technical solution provided by this application includes at least the following beneficial effects:

[0035] The ultra-deep imaging method, apparatus, device and storage medium provided in the present application perform phase inversion calculation on a target seismic data volume in the spatial wavenumber domain, generate a least squares image based on the phase information of the target seismic data volume, and then generate a least squares phase inversion imaging data volume of the target seismic data volume based on the least squares image. The phase information of the seismic data is used to correct the phase error caused by the missing observation, thereby improving the stability of the high-dimensional space deconvolution algorithm, solving the problems of low signal-to-noise ratio and poor continuity of ultra-deep imaging, and improving the quality of ultra-deep imaging. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for describing the embodiments.

[0037] Figure 1 is a flow chart of an ultra-deep imaging method provided by an exemplary embodiment of the present application;

[0038] Figure 2is a flow chart of an ultra-deep imaging method provided by another exemplary embodiment of the present application;

[0039] Figure 3 It is a schematic diagram of a two-dimensional slice of a three-dimensional actual data point spread function provided by an exemplary embodiment of the present application;

[0040] Figure 4 It is a schematic diagram of a two-dimensional slice of a reverse time migration image of three-dimensional actual data provided by an exemplary embodiment of the present application;

[0041] Figure 5 It is a schematic diagram of the least square image result obtained by using the correlation technology;

[0042] Figure 6 is a schematic diagram of least squares phase inversion imaging provided by an exemplary embodiment of the present application;

[0043] Figure 7 is a structural block diagram of an ultra-deep imaging device provided by an exemplary embodiment of the present application;

[0044] Figure 8 It is a structural block diagram of an electronic device provided by an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0045] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0046] Embodiment 1

[0047] Please refer to Figure 1 , which shows a flow chart of an ultra-deep imaging method provided by an exemplary embodiment of the present application. The method comprises the following steps:

[0048] Step 101: Acquire a target seismic data volume.

[0049] Seismic tomography is a technology that uses seismic data to invert the material properties of underground structures and draw their images layer by layer. Tomography technology can intuitively and clearly display the properties of underground material structures in the form of images.

[0050] When executing the ultra-deep imaging instruction, the electronic device acquires the target seismic data volume to analyze and process the target seismic data volume to obtain a corresponding image.

[0051] Step 102: Perform phase inversion calculation on the target seismic data volume in the spatial wave number domain, and generate a least square image based on the phase information of the target seismic data volume.

[0052] The spatial domain is also called image space. It is a space composed of image pixels. Directly processing pixel values ​​with length (distance) as the independent variable in image space is called spatial domain processing. Research using wave number as a variable is called wave number domain.

[0053] Related technologies usually use least squares migration imaging, and high-dimensional spatial deconvolution algorithms in the frequency wavenumber domain have high requirements for the accuracy of the point spread function complex field, so usually only the energy information of the point spread function is used. However, when facing ultra-deep imaging problems, the deep lighting conditions are poor and the data signal-to-noise ratio drops significantly. The use of energy inversion algorithms will amplify the noise signal while broadening the frequency, resulting in instability in the imaging results.

[0054] In a possible implementation, in order to make up for the above defects, the embodiment of the present application uses phase information to perform high-dimensional spatial deconvolution operations to increase the continuity and stability of the inversion imaging algorithm. The electronic device performs phase inversion calculations on the target seismic data volume in the spatial wavenumber domain and generates a least squares image based on the phase information of the target seismic data volume.

[0055] Step 103: Generate a phase inversion imaging data volume of the target seismic data volume based on the least squares image.

[0056] The electronic device processes the least square image obtained by phase inversion calculation in the wave number domain, obtains a three-dimensional least square phase inversion imaging data volume in the spatial domain and outputs it.

[0057] In summary, the ultra-deep imaging method provided in the embodiment of the present application performs phase inversion calculation on the target seismic data body in the spatial wavenumber domain, generates a least squares image based on the phase information of the target seismic data body, and then generates a least squares phase inversion imaging data body of the target seismic data body based on the least squares image. The phase information of the seismic data is used to correct the phase error caused by the missing observation, which can improve the stability of the high-dimensional space deconvolution algorithm, solve the problems of low signal-to-noise ratio and poor continuity of ultra-deep imaging, and improve the quality of ultra-deep imaging.

[0058] Embodiment 2

[0059] Please refer to Figure 2 , which shows a flow chart of an ultra-deep imaging method provided by another exemplary embodiment of the present application. The method comprises the following steps:

[0060] Step 201: Acquire target seismic data volume.

[0061] In a possible implementation, the target seismic data volume includes a three-dimensional reverse time migration image and a point spread function.

[0062] For an optical system, when the input object is a point light source, the light field distribution of the output image is called the point spread function, also known as the point expansion function. Mathematically, a point light source can be represented by a delta function (point pulse), and the light field distribution of the output image is called an impulse response, so the point spread function is also the impulse response function of the optical system.

[0063] Indicative, Figure 3 A two-dimensional slice of a three-dimensional real data point spread function is shown. Figure 4 A two-dimensional slice of a reverse time migration image of three-dimensional actual data is shown.

[0064] Step 202: Perform a three-dimensional spatial Fourier transform on the target seismic data volume to obtain the target seismic data volume in the spatial wave number domain.

[0065] The three-dimensional Fourier transform is a mathematical method that converts the time domain exponential representation of a three-dimensional signal into a frequency domain exponential representation. It can transform a three-dimensional signal into a three-dimensional spectrum, or map the spectrum back to a time domain signal. The basic principle of the three-dimensional Fourier transform is the spatial form of the two-dimensional Fourier transform. The three-dimensional signal can be described as a series of two-dimensional Fourier transforms, and each dimension can be described by an independent two-dimensional Fourier transform. This method can better reflect the spatial characteristics of the three-dimensional signal.

[0066] In a possible implementation, the target seismic data volume includes a 3D reverse time migration image and a point spread function, and step 202 specifically includes the following steps:

[0067] Step 202a, performing a three-dimensional spatial Fourier transform on the RTM image and the PSF to obtain the RTM image and the PSF in the spatial wave number domain.

[0068] Schematically, the expression of the three-dimensional Fourier transform is as follows:

[0069] F(u,v,w)=∫∫∫f(x,y,z)e^(-i2π(ux+vy+wz))dxdydz

[0070] Among them, f(x, y, z) is the target seismic data volume in three-dimensional space, and F(u, v, w) is the target seismic data volume in the spatial wavenumber domain.

[0071] Step 203: Perform phase inversion calculation on the target seismic data volume in the spatial wave number domain, and generate a least square image based on the phase information of the target seismic data volume.

[0072] In a possible implementation, the target seismic data volume includes a 3D reverse time migration image and a point spread function, and step 203 specifically includes the following steps:

[0073] Step 203a, performing phase inversion calculation on the RTM image and the PSF in the spatial wavenumber domain, and generating a least squares image based on the phase information.

[0074] Schematically, the expression for the phase inversion calculation includes:

[0075]

[0076] Among them, m lsm is the least squares imaging, m rtm is the reverse time migration image, K psf is the point spread function and * is the conjugate symbol.

[0077] Step 204: Perform a three-dimensional spatial inverse Fourier transform on the least squares image in the spatial wavenumber domain to obtain a phase inversion imaging data volume.

[0078] Figure 5 The least squares image result obtained by using the correlation technique is shown. Figure 6 FIG. 4 shows a least squares phase inversion image generated by the method of an embodiment of the present application. It can be found that at the position indicated by the arrow, Figure 5 The reverse time migration image is not correctly imaged due to the limitation of the observation aperture. The least squares imaging in the related art cannot use the phase information to correct the observation missing problem. However, the least squares phase inversion imaging in the embodiment of the present application can correctly correct the phase deviation and improve the stability of the algorithm.

[0079] In the embodiment of the present application, the phase information of the point spread function is used to correct the phase error of the reverse time migration image, improve the stability of the high-dimensional space deconvolution algorithm, and solve the problems of low signal-to-noise ratio and poor continuity in ultra-deep imaging.

[0080] Embodiment 3

[0081] In combination with the above embodiments, the ultra-deep imaging method provided in the embodiments of the present application includes the following steps:

[0082] (1) Input 3D RTM image and point spread function data volume;

[0083] (2) performing a three-dimensional Fourier transform on the RTM image and the PSF to obtain the RTM image and the PSF in the spatial wavenumber domain;

[0084] (3) Applying phase inversion algorithm to the reverse time migration image and point spread function in the spatial wave number domain to obtain the least squares image in the wave number domain;

[0085] (4) performing a three-dimensional spatial inverse Fourier transform on the least squares image in the wavenumber domain to obtain a three-dimensional least squares phase inversion imaging data volume in the spatial domain;

[0086] (5) Output the three-dimensional least squares phase inversion imaging data volume in the spatial domain.

[0087] Embodiment 4

[0088] Please refer to Figure 7 , which shows a structural block diagram of an ultra-deep imaging device provided by an exemplary embodiment of the present application, the device comprising:

[0089] An acquisition module 701 is used to acquire a target seismic data volume;

[0090] A calculation module 702 is used to perform phase inversion calculation on the target seismic data volume in a spatial wave number domain, and generate a least square image based on the phase information of the target seismic data volume;

[0091] The imaging module 703 is used to generate a phase inversion imaging data volume of the target seismic data volume based on the least squares image.

[0092] Optionally, the device further comprises:

[0093] A transformation module, used for performing a three-dimensional spatial Fourier transform on the target seismic data volume to obtain the target seismic data volume in the spatial wave number domain;

[0094] The imaging module 703 is further used for:

[0095] Performing a three-dimensional spatial inverse Fourier transform on the least squares image in the spatial wavenumber domain to obtain the phase inversion imaging data volume.

[0096] Optionally, the acquisition module 701 is further used to:

[0097] Obtain three-dimensional reverse time migration image and point spread function;

[0098] The transformation module is further used for:

[0099] Performing a three-dimensional Fourier transform on the reverse time migration image and the point spread function to obtain the reverse time migration image and the point spread function in the spatial wave number domain.

[0100] Optionally, the calculation module 702 is further used for:

[0101] A phase inversion calculation is performed on the reverse time migration image and the point spread function in the spatial wave number domain, and the least squares image is generated based on the phase information.

[0102] Optionally, the expression for the phase inversion calculation includes:

[0103]

[0104] Among them, mlsm is the least squares imaging, m rtm is the reverse time migration image, K psf is the point spread function and * is the conjugate symbol.

[0105] Optionally, the expression of the three-dimensional space Fourier transform includes:

[0106] F(u,v,w)=∫∫∫f(x,y,z)e^(-i2π(ux+vy+wz))dxdydz

[0107] Among them, f(x, y, z) is the target seismic data volume in the three-dimensional space, and F(u, v, w) is the target seismic data volume in the spatial wave number domain.

[0108] In summary, the ultra-deep imaging device provided in the embodiment of the present application performs phase inversion calculation on the target seismic data body in the spatial wavenumber domain, generates a least squares image based on the phase information of the target seismic data body, and then generates a least squares phase inversion imaging data body of the target seismic data body based on the least squares image. The phase information of the seismic data is used to correct the phase error caused by the missing observation, which can improve the stability of the high-dimensional space deconvolution algorithm, solve the problems of low signal-to-noise ratio and poor continuity of ultra-deep imaging, and improve the quality of ultra-deep imaging.

[0109] Embodiment 5

[0110] An embodiment of the present application provides an electronic device; Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Figure 8 As shown, the electronic device 800 includes: a processor 801, at least one communication bus 802, a user interface 803, at least one external communication interface 804, and a memory 805. The communication bus 802 is configured to realize the connection and communication between these components. The user interface 803 may include a display screen, and the external communication interface 804 may include a standard wired interface and a wireless interface. The processor 801 is configured to execute the program of the ultra-deep imaging method stored in the memory to implement the steps in the method provided in the above embodiment.

[0111] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored. The computer program is loaded and executed by a processor to implement the method described in the above embodiment.

[0112] The embodiment of the present application further provides a computer program product, which runs on a processor of a computer device, so that the computer device executes the method described in the above embodiment.

[0113] It should be noted here that the description of the above storage medium, electronic device, and remote control embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0114] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned sequence numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments.

[0115] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, object or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, object or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, object or device including the element.

[0116] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0117] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0118] In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be a separate unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0119] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read Only Memory), disks or optical disks, etc. Various media that can store program codes.

[0120] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can essentially or in other words, the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a controller to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0121] The above is only an implementation method of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An ultra-deep imaging method, characterized in that: include: Acquire target seismic data volume; Performing phase inversion calculation on the target seismic data volume in the spatial wave number domain, and generating a least squares image based on the phase information of the target seismic data volume; A phase inversion imaging data volume of the target seismic data volume is generated based on the least squares image.

2. The method according to claim 1, characterized in that Before performing phase inversion calculation on the target seismic data volume in the spatial wavenumber domain and generating a least squares image based on the phase information of the target seismic data volume, the method comprises: Performing a three-dimensional spatial Fourier transform on the target seismic data volume to obtain the target seismic data volume in the spatial wavenumber domain; The step of generating a phase inversion imaging data volume of the target seismic data volume based on the least squares image comprises: Performing a three-dimensional spatial inverse Fourier transform on the least squares image in the spatial wavenumber domain to obtain the phase inversion imaging data volume.

3. The method according to claim 2, characterized in that The step of acquiring the target seismic data volume comprises: Obtain three-dimensional reverse time migration image and point spread function; The performing three-dimensional spatial Fourier transform on the target seismic data volume to obtain the target seismic data volume in the spatial wavenumber domain includes: Performing a three-dimensional Fourier transform on the reverse time migration image and the point spread function to obtain the reverse time migration image and the point spread function in the spatial wave number domain.

4. The method according to claim 3, characterized in that The performing phase inversion calculation on the target seismic data volume in the spatial wave number domain and generating a least squares image based on the phase information of the target seismic data volume comprises: A phase inversion calculation is performed on the reverse time migration image and the point spread function in the spatial wave number domain, and the least squares image is generated based on the phase information.

5. The method according to claim 4, characterized in that The expression for the phase inversion calculation includes: Among them, m lsm is the least squares imaging, m rtm is the reverse time migration image, K psf is the point spread function and * is the conjugate symbol.

6. The method according to any one of claims 2 to 5, characterized in that: The expression of the three-dimensional space Fourier transform includes: F(u,v,w)=∫∫∫f(x,y,z)e^(-i2π(ux+vy+wz))dxdydz Among them, f(x, y, z) is the target seismic data volume in the three-dimensional space, and F(u, v, w) is the target seismic data volume in the spatial wave number domain.

7. An ultra-deep imaging device, characterized in that: include: An acquisition module, used for acquiring a target seismic data volume; A calculation module, used for performing phase inversion calculation on the target seismic data volume in a spatial wave number domain, and generating a least squares image based on the phase information of the target seismic data volume; An imaging module is used to generate a phase inversion imaging data volume of the target seismic data volume based on the least squares image.

8. The device according to claim 7, characterized in that The device also includes: A transformation module, used for performing a three-dimensional spatial Fourier transform on the target seismic data volume to obtain the target seismic data volume in the spatial wave number domain; The imaging module is further used for: Performing a three-dimensional spatial inverse Fourier transform on the least squares image in the spatial wavenumber domain to obtain the phase inversion imaging data volume.

9. An electronic device, characterized in that: The method comprises a memory and a processor; a computer program is stored in the memory, and when the computer program is executed by the processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium, characterized in that: A computer program is stored, and the computer program is loaded and executed by a processor to implement the method according to any one of claims 1 to 6.

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