Depth domain deconvolution method, device and equipment of seismic data and storage medium

Abstract

The application discloses a depth domain deconvolution method, device and equipment of seismic data and a storage medium, and belongs to the technical field of seismic data processing. The method comprises the following steps: acquiring seismic data and a point spread function corresponding to each data point of the seismic data; the point spread function is an operator generated in the depth domain of the seismic data; based on the point spread function corresponding to each data point of the seismic data, the seismic data is divided into blocks to obtain at least two seismic data blocks; the at least two seismic data blocks are respectively deconvoluted in the depth domain to obtain at least two deconvoluted seismic data blocks; and the at least two deconvoluted seismic data blocks are spliced to obtain processed seismic data. The above scheme can improve the deconvolution effect and significantly improve the resolution of the seismic data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of seismic data processing, and in particular to a depth domain deconvolution method and device for seismic data, equipment and a storage medium. BACKGROUND

[0002] Deconvolution, also known as inverse filter or deconvolution, is a processing method for eliminating the effect of previous filtering. It is a processing procedure for improving the vertical resolution of seismic data by compressing basic wavelets. Deconvolution of seismic data can improve the resolution of seismic data.

[0003] In related technologies, seismic data is divided into blocks, and a point spread function and seismic data at a corresponding position are deconvolved in the wave number domain. The deconvolution result is spliced to obtain the final seismic data.

[0004] However, the deconvolution of seismic data in the wave number domain in related technologies is not obvious enough in improving the resolution of seismic data. SUMMARY

[0005] The present application provides a depth domain deconvolution method and device for seismic data, equipment, a storage medium and a program product. The technical solution is as follows:

[0006] According to one aspect of the present application, a depth domain deconvolution method for seismic data is provided, the method comprising:

[0007] obtaining the seismic data and a point spread function corresponding to each data point of the seismic data; the point spread function is an operator generated in the depth domain of the seismic data;

[0008] based on the point spread function corresponding to each data point of the seismic data, dividing the seismic data into blocks to obtain at least two seismic data blocks;

[0009] deconvolving the at least two seismic data blocks in the depth domain to obtain at least two deconvolved seismic data blocks;

[0010] splicing the at least two deconvolved seismic data blocks to obtain processed seismic data.

[0011] According to another aspect of the present application, a depth domain deconvolution device for seismic data is provided, the device comprising:

[0012] an acquisition module for acquiring the seismic data and a point spread function corresponding to each data point of the seismic data; the point spread function is an operator generated in the depth domain of the seismic data;

[0013] a blocking module configured to block the seismic data based on the point spread function corresponding to each data point of the seismic data, to obtain at least two seismic data blocks;

[0014] a processing module configured to deconvolve the at least two seismic data blocks in the depth domain, respectively, to obtain at least two deconvolved seismic data blocks;

[0015] a splicing module configured to splice the at least two deconvolved seismic data blocks, to obtain processed seismic data.

[0016] According to another aspect of the present application, a computer device is provided, which comprises a processor and a memory, the memory storing a computer program, the computer program being loaded and executed by the processor to implement the depth domain deconvolution method of seismic data as described above.

[0017] According to another aspect of the present application, a computer readable storage medium is provided, which stores a computer program, the computer program being loaded and executed by a processor to implement the depth domain deconvolution method of seismic data as described above.

[0018] According to another aspect of the present application, a computer program product is provided, which comprises computer instructions stored in a computer readable storage medium, the computer instructions being acquired by a processor from the computer readable storage medium, so that the processor is loaded and executed to implement the depth domain deconvolution method of seismic data as described above.

[0019] The technical scheme provided by the embodiments of the present application has at least the following beneficial effects:

[0020] By acquiring seismic data and a point spread function corresponding to each data point of the seismic data, the point spread function being an operator generated in the depth domain of the seismic data, the seismic data is blocked based on the point spread function corresponding to each data point of the seismic data, to obtain at least two seismic data blocks, the at least two seismic data blocks are deconvolved in the depth domain, respectively, to obtain at least two deconvolved seismic data blocks, and the at least two deconvolved seismic data blocks are spliced, to obtain processed seismic data. The above scheme can improve the deconvolution effect by performing deconvolution processing on the seismic data in the depth domain, thereby significantly improving the resolution of the seismic data. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0022] Figure 1 A structural block diagram of a computer system provided by an example embodiment is shown;

[0023] Figure 2 A flow chart of a depth domain deconvolution method of seismic data provided by an example embodiment is shown;

[0024] Figure 3 A flow chart of a depth domain deconvolution method of seismic data provided by an example embodiment is shown;

[0025] Figure 4 A flow chart of a depth domain deconvolution method of seismic data provided by an example embodiment is shown;

[0026] Figure 5 A flow chart of a depth domain deconvolution method of seismic data provided by an example embodiment is shown;

[0027] Figure 6 A flow chart of a depth domain deconvolution method of seismic data provided by an example embodiment is shown;

[0028] Figure 7 A flow chart of a depth domain deconvolution method of seismic data provided by an example embodiment is shown;

[0029] Figure 8 A schematic diagram of a migration result profile provided by an example embodiment is shown;

[0030] Figure 9 A schematic diagram of a deconvolution result profile of a related art provided by an example embodiment is shown;

[0031] Figure 10 A schematic diagram of a deconvolution result profile of the present embodiment provided by an example embodiment is shown;

[0032] Figure 11 A schematic diagram of a seismic data spectrum comparison provided by an example embodiment is shown;

[0033] Figure 12 A structural block diagram of a depth domain deconvolution device of seismic data provided by an example embodiment is shown;

[0034] Figure 13A structural block diagram of a computer device provided by an example embodiment is shown. DETAILED DESCRIPTION

[0035] For the purposes of the present application, the term "about" means that the following recited numerical data should have a tolerance of ± 0.1% unless otherwise stated.

[0036] The example embodiments will be described in detail with reference to the drawings, wherein like reference numerals refer to like parts throughout the several views. The following detailed description of the example embodiments is not representative of all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the appended claims.

[0037] The terminology used in the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. As used in the present application and the appended claims, the singular forms "a," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0038] It should be noted that, before collecting the relevant data of the earthquake (such as: seismic data, point spread data) and in the process of collecting the relevant data of the earthquake, the application can display a prompt interface, a pop-up window or output voice prompt information, which is used to prompt that the relevant data of the earthquake is currently being collected, so that the application only starts to perform the relevant steps of acquiring the relevant data of the earthquake after obtaining the confirmation operation issued by the prompt interface or the pop-up window, otherwise (i.e. without obtaining the confirmation operation issued by the prompt interface or the pop-up window), ending the relevant steps of acquiring the relevant data of the earthquake, i.e. not acquiring the relevant data of the earthquake. In other words, all the relevant data of the earthquake collected by the application is collected with the consent and authorization, and the collection, use and processing of the relevant data need to comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0039] It should be understood that, although the terms first, second, etc. can be employed in this application to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one piece of information from another piece of information. For example, a first parameter can also be referred to as a second parameter, and similarly, a second parameter can also be referred to as a first parameter, without departing from the scope of the present application. Depending on the context, the word "if" as used herein can be interpreted as "when" or "upon" or "in response to determining".

[0040] Figure 1A structural block diagram of a computer system provided by an example embodiment of the present application is shown. The computer system can implement a system architecture of a depth domain deconvolution method for seismic data. The computer system 100 includes a terminal 120 and a server 140.

[0041] The terminal 120 can be an electronic device such as a mobile phone, a tablet computer, a vehicle terminal (car machine), a wearable device, a PC (Personal Computer), an unmanned terminal, etc. A client running a target application program can be installed in the terminal 120, which can be a depth domain deconvolution application program for seismic data, including but not limited to a three-dimensional visualization modeling software Petrel, a seismic prediction software VVA, and a seismic interpretation software Landmark. The three-dimensional visualization modeling software Petrel is mainly used for seismic interpretation, structure modeling, facies modeling, reservoir attribute modeling, and reservoir numerical simulation display and virtual reality, etc. The seismic prediction software VVA is mainly used for reservoir spatial distribution prediction. The seismic interpretation software Landmark is mainly used for fine structure interpretation work. It can also be other application programs that provide depth domain deconvolution functions for seismic data, which are not limited in the present application. In addition, the form of the target application program is not limited in the present application, including but not limited to an App (Application) installed in the terminal 120, a mini program, etc., and can also be in the form of a web page.

[0042] The server 140 can be a standalone physical server, a server cluster or a distributed system composed of multiple physical servers, a cloud server providing cloud computing services, a cloud database, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content distribution networks (CDN), and basic cloud computing services such as big data and artificial intelligence platforms, etc. The server 140 can be a background server of the above-mentioned target application program, used to provide background services for the client of the target application program.

[0043] The cloud technology refers to a hosting technology of unifying a series of resources such as hardware, software, network, and the like in a wide area network or a local area network to realize data calculation, storage, processing and sharing. The cloud technology is a general term of network technology, information technology, integration technology, management platform technology, application technology and the like applied based on a cloud computing business model, can form a resource pool, and is used on demand, flexibly and conveniently. The cloud computing technology will become an important support. The background service of a technical network system needs a large amount of calculation and storage resources, such as a video website, a picture website and more portal websites. With the high development and application of the Internet industry, in the future, each item is likely to have its own identification mark and needs to be transmitted to the background system for logical processing. Different levels of data will be processed separately, and the data of various industries needs strong system support, which can only be realized through cloud computing.

[0044] In some embodiments, the server 140 described above can also be implemented as a node in a blockchain system. The blockchain is a new application mode of distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm and other computer technologies. The blockchain is essentially a decentralized database, which is a series of data blocks associated using cryptographic methods, each data block contains information of a batch of network transactions, and is used to verify the validity (anti-fake) of the information and generate the next block. The blockchain can include a blockchain underlying platform, a platform product service layer and an application service layer.

[0045] The terminal 120 and the server 140 can communicate through a network, such as a wired or wireless network.

[0046] The depth domain deconvolution method for seismic data provided by the embodiments of the present application can be executed by a computer device, which is an electronic device with data calculation, processing and storage capabilities. Figure 1 For example, the depth domain deconvolution method for seismic data can be executed by the terminal 120 (for example, a client of a target application program installed and running in the terminal 120 executes the depth domain deconvolution method for seismic data), or can be executed by the server 140, or can be executed by the terminal 120 and the server 140 in cooperation, which is not limited in the present application.

[0047] Those skilled in the art can know that the number of the terminal 120 described above can be more or less. For example, the terminal 120 described above can be only one, or the terminal 120 described above can be dozens or hundreds, or more. The number and type of the terminal 120 are not limited in the embodiments of the present application.

[0048] In seismic data processing and imaging, imaging domain least square is widely used as a key technology. The traditional least square migration needs to perform migration and de-migration once per iteration, which is costly. Least square migration can be regarded as a process of de-blurring using Hessian matrix. Therefore, point spread function can be used to reduce the cost by combining migration and deconvolution, providing a compromise for least square migration.

[0049] In related technologies, there are two ways for depth domain migration de-blurring. The first way is to take the entire data volume as an iteration space, establish a least square objective function, and iteratively process the deconvolution result as a whole. This method has a problem that it cannot obtain an effective iteration step, and thus cannot effectively distinguish resolution. The second way is to divide the data into blocks, deconvolve the point spread function and the corresponding data in the wave number domain, and then splice the deconvolution result to obtain the final result. This method has a problem that the wave number domain division is not obvious enough in widening the frequency band, and the resolution is not obvious enough in improving the resolution.

[0050] Figure 2 An example embodiment of the present application provides a flowchart of a depth domain deconvolution method of seismic data. The method is applied to Figure 1 The server 140 is used as an example to illustrate the method, which includes the following steps.

[0051] In step 220, seismic data and point spread functions corresponding to each data point of the seismic data are obtained. The point spread function is an operator generated in the depth domain of the seismic data.

[0052] The seismic data can be two-dimensional or three-dimensional seismic data, which is not limited in the present embodiment.

[0053] The physical meaning of the point scatter function (PSF) is the seismic imaging response of a single diffraction point underground, which is the basis of seismic imaging data.

[0054] The point scatter function can be obtained by direct imaging of the underground model, or can be obtained by interpolation of the Hessian matrix.

[0055] In the present embodiment, the point scatter function is an operator generated in the depth domain of the seismic data. Each data point of the seismic data corresponds to a point scatter function. The size of the point scatter function can be represented as N.

[0056] The point scatter function and the seismic data can be generated by various imaging methods, including but not limited to at least one of the following: integral method, Gaussian beam, one-way wave, reverse time migration (RTM) imaging method, etc.

[0057] Optionally, seismic data is acquired, and each data point of the seismic data corresponds to a point spread function.

[0058] At step 240, the seismic data is divided into blocks based on the point spread functions corresponding to each data point of the seismic data, to obtain at least two seismic data blocks.

[0059] A seismic data block is a part of seismic data obtained after the seismic data is divided into blocks. The seismic data block is a subset of the seismic data.

[0060] Optionally, the at least two seismic data blocks obtained by dividing the seismic data can have the same size or different sizes. The seismic data contained in the different at least two seismic data blocks can have a part of data overlap.

[0061] Optionally, the seismic data is divided into blocks based on the point spread functions corresponding to each data point of the seismic data, to obtain at least two seismic data blocks.

[0062] At step 260, the at least two seismic data blocks are respectively deconvolved in the depth domain, to obtain at least two deconvolved seismic data blocks.

[0063] Deconvolution, also known as inverse filter or deconvolution, is a processing method for eliminating the effect of a previous filter. It is a processing process for improving the vertical resolution of seismic data by compressing the basic wavelet. Deconvolution of seismic data can improve the resolution of seismic data.

[0064] A deconvolved seismic data block is a seismic data block obtained after a seismic data block is deconvolved.

[0065] Optionally, the same processing is performed on each seismic data block. The at least two seismic data blocks are respectively deconvolved in the depth domain, to obtain at least two deconvolved seismic data blocks.

[0066] At step 280, the at least two deconvolved seismic data blocks are spliced, to obtain processed seismic data.

[0067] Splicing refers to splicing the at least two deconvolved seismic data blocks into the entire seismic data.

[0068] Optionally, the at least two deconvolved seismic data blocks are spliced, to obtain processed seismic data.

[0069] In summary, the method provided by the embodiment of the application obtains seismic data and a point spread function corresponding to each data point of the seismic data; the point spread function is an operator generated in the depth domain of the seismic data; the seismic data is divided into at least two seismic data blocks based on the point spread function corresponding to each data point of the seismic data; the at least two seismic data blocks are deconvoluted in the depth domain respectively to obtain at least two deconvoluted seismic data blocks; and the at least two deconvoluted seismic data blocks are spliced to obtain processed seismic data. The above scheme can improve the deconvolution effect by deconvoluting the seismic data in the depth domain, thereby significantly improving the resolution of the seismic data.

[0070] Figure 3 A flowchart of a method for deconvoluting seismic data in a depth domain according to an example embodiment of the application is shown. The step 260 can optionally include the following steps 320 and 340.

[0071] In step 320, a point spread function matrix corresponding to each of the at least two seismic data blocks is obtained based on the point spread function corresponding to each data point of the sub-seismic data contained in each of the at least two seismic data blocks.

[0072] The sub-seismic data refers to the seismic data contained in the seismic data block.

[0073] Optionally, a point spread function matrix corresponding to each of the at least two seismic data blocks is obtained based on the point spread function corresponding to each data point of the sub-seismic data contained in each of the at least two seismic data blocks.

[0074] In step 340, the at least two seismic data blocks are deconvoluted in the depth domain based on the point spread function matrix corresponding to each of the at least two seismic data blocks and a seismic profile, to obtain at least two deconvoluted seismic data blocks.

[0075] The seismic profile, also called a seismic record profile, is a seismic data graph indicating a certain survey line. According to the different physical quantities used in the longitudinal coordinates, the seismic record profile can be divided into a time profile and a depth profile.

[0076] In some embodiments, the seismic profile selected by the embodiment is a depth profile in the depth domain.

[0077] Optionally, the same processing is performed on each seismic data block. The at least two seismic data blocks are deconvoluted in the depth domain based on the point spread function matrix corresponding to each of the at least two seismic data blocks and a seismic profile, to obtain at least two deconvoluted seismic data blocks.

[0078] Figure 4A flow chart of a method of depth domain deconvolution of seismic data according to an example embodiment of the present application is shown. In some embodiments, deconvolution is performed on each seismic data chunk in an iterative manner. Specifically, step 340 described above comprises the following steps 400, 420, 440, 460 and 480:

[0079] Step 400, based on the point spread function matrix corresponding to each of the at least two seismic data chunks and the seismic profile, determining a first objective function corresponding to each of the at least two seismic data chunks.

[0080] The first objective function is an objective function of one of the seismic data chunks determined when performing the first deconvolution.

[0081] Optionally, based on the point spread function matrix corresponding to each of the at least two seismic data chunks and the seismic profile, determining a first objective function corresponding to each of the at least two seismic data chunks. Wherein the first objective function corresponding to different seismic data chunks is different.

[0082] Optionally, taking one of the seismic data chunks as an example, the first objective function is denoted as J(m), the point spread function matrix is denoted as H, and the seismic profile of the seismic data chunk is denoted as m0. The first objective function of the seismic data chunk has the following expression:

[0083] J(m) = ||Hm - m0||

[0084] Wherein, ||·|| represents the calculation of L2 norm.

[0085] Step 420, solving the first objective function to obtain at least two first deconvolution seismic data chunks, with the objective of minimizing the first objective function.

[0086] The first deconvolution seismic data chunk is a seismic data chunk obtained after performing the first deconvolution on the seismic data chunk.

[0087] Optionally, taking one of the seismic data chunks as an example, the first objective function is denoted as J(m), the point spread function matrix is denoted as H, and the seismic profile of the seismic data chunk is denoted as m0. The first objective function of the seismic data chunk has the following expression:

[0088] Optionally, taking one of the seismic data chunks as an example, after step 400, the first objective function of the seismic data chunk is solved using the conjugate direction method, and m in the first objective function is obtained, i.e. the first deconvolution seismic data chunk is obtained.

[0089] Step 440, based on the at least two first deconvolution seismic data chunks, determining a second objective function corresponding to each of the at least two seismic data chunks.

[0090] The second objective function is an objective function of one of the seismic data chunks determined when performing the second deconvolution.

[0091] Optionally, based on the at least two first deconvolution seismic data blocks, a second objective function corresponding to each of the at least two seismic data blocks is determined. The second objective function corresponding to different seismic data blocks is different.

[0092] Optionally, taking one seismic data block as an example, the second objective function is denoted as F(m). After obtaining the first deconvolution seismic data block in step 420, the seismic profile of the first deconvolution seismic data block is taken as the initial value m0 of the seismic profile on the right side of the equation in the second objective function, and the second objective function is expressed as follows:

[0093]

[0094] Wherein, ||·||1 represents the calculation of L1 norm.

[0095] In step 460, the second objective function is solved by taking minimizing the second objective function as the goal, and at least two second deconvolution seismic data blocks are obtained.

[0096] The second deconvolution seismic data block is a seismic data block obtained by performing second deconvolution on the first deconvolution seismic data block.

[0097] Optionally, the Split Bregman Method is used to solve the second objective function by taking minimizing the second objective function as the goal, and at least two second deconvolution seismic data blocks are obtained.

[0098] Optionally, taking one seismic data block as an example, after step 440, the Split Bregman Method is used to solve the second objective function of the seismic data block, and m in the second objective function is solved, that is, the second deconvolution seismic data block is obtained.

[0099] In step 480, the at least two second deconvolution seismic data blocks are taken as the at least two seismic data blocks, and the step of determining the first objective function corresponding to each of the at least two seismic data blocks based on the point spread function matrix and the seismic profile corresponding to each of the at least two seismic data blocks is returned until a set number of iterations is reached, and at least two deconvolution seismic data blocks are obtained.

[0100] Optionally, the at least two second deconvolution seismic data blocks are taken as the at least two seismic data blocks in step 400, and step 400 is returned to repeat the above steps 400-460 until a set number of iterations is reached, and at least two deconvolution seismic data blocks are obtained.

[0101] In some embodiments, the number of iterations is determined according to a resolution required by deconvolution of the seismic data block, a data processing complexity corresponding to the resolution, an occupied space, an artificial experience, and the like.

[0102] In the above embodiments, the deconvolution processing is performed on each seismic data block by the iterative processing manner, so that the deconvolution effect of each seismic data block is ensured, and the depth domain deconvolution accuracy and resolution of the seismic data are improved.

[0103] Figure 5 A flowchart of a depth domain deconvolution method of seismic data provided by an example embodiment of the present application is shown. In some embodiments, before the seismic data is processed, a point spread function of a data point of the seismic data is also windowed. The step 320 includes the following steps 520, 540 and 560:

[0104] In step 520, a window function corresponding to each data point is determined based on the point spread function corresponding to each data point of the sub-seismic data contained in the at least two seismic data blocks and the coordinate data of each data point.

[0105] The window function (Window Function) is also called a weight function. A part of the spectrum value of the function is selected as the spectrum of the whole function. This is equivalent to opening a "window" in the whole function, and studying the values in the window, which can represent the overall properties of the function.

[0106] Optionally, a window function corresponding to each data point is determined based on the point spread function corresponding to each data point of the sub-seismic data contained in the at least two seismic data blocks and the coordinate data of each data point. One data point corresponds to one window function. The coordinate data of the data point has different values, and the corresponding window function is different.

[0107] Optionally, taking one data point of one seismic data block as an example, the coordinate data of the data point are x and y, the size of the point spread function is represented as N, and the window function is represented as w(x, y). The window function of the data point of the seismic data block has the following representation:

[0108]

[0109] In step 540, the point spread function corresponding to each data point is windowed according to the window function corresponding to each data point, to obtain a windowed point spread function corresponding to each data point.

[0110] The windowed point spread function refers to the point spread function multiplied by the window function.

[0111] Optionally, the point spread function corresponding to each data point is multiplied by a window function corresponding to the data point to window the point spread function corresponding to the data point, to obtain a windowed point spread function corresponding to each data point.

[0112] At step 560, a point spread function matrix corresponding to each of the at least two seismic data blocks is obtained based on the windowed point spread function corresponding to each data point.

[0113] The point spread function matrix of each seismic data block is a matrix composed of the windowed point spread functions of all data points of the seismic data block.

[0114] Optionally, a point spread function matrix corresponding to each of the at least two seismic data blocks is obtained based on the windowed point spread function corresponding to each data point.

[0115] In the embodiment, the data denoising can be achieved by windowing the point spread function, so that the noise of the seismic data is smaller, and the resolution and processing accuracy of the seismic data are improved.

[0116] Figure 6 A flowchart of a method for depth domain deconvolution of seismic data according to an example embodiment of the present application is shown. The step 240 includes the following steps 620 and 640:

[0117] At step 620, the size of each seismic data block of the seismic data is determined based on the point spread function corresponding to each data point of the seismic data.

[0118] The entire seismic data block is divided into blocks according to the size of the point spread function of a single data point.

[0119] Optionally, the size of each seismic data block of the seismic data is determined by multiplying the point spread function by a preset multiple based on the point spread function corresponding to each data point of the seismic data.

[0120] In some embodiments, the preset multiple is set to 3. That is, the size of each seismic data block of the seismic data is 3 times the size of a single point spread function.

[0121] At step 640, the seismic data is divided into blocks according to the size of each seismic data block of the seismic data, to obtain at least two seismic data blocks.

[0122] Optionally, the seismic data is divided into blocks according to the size of each seismic data block of the seismic data, to obtain at least two seismic data blocks. There can be data overlap between different seismic data blocks.

[0123] In the embodiment, the seismic data is divided into blocks, and the plurality of seismic data blocks are obtained, so that the processing accuracy and efficiency of the seismic data are ensured.

[0124] Figure 7 A flowchart of a method for deconvolution of seismic data in depth domain according to an example embodiment of the present application is shown. In some embodiments, after deconvolution of each seismic data block, the deconvoluted seismic data blocks need to be spliced into the whole seismic data. The step 280 includes the following steps 720 and 740.

[0125] In step 720, the splicing functions corresponding to the at least two deconvoluted seismic data blocks are obtained.

[0126] The splicing function is a function for splicing the deconvoluted seismic data blocks.

[0127] Optionally, the splicing functions corresponding to the at least two deconvoluted seismic data blocks are obtained.

[0128] In some embodiments, each data point of each deconvoluted seismic data block corresponds to a splicing function, and the splicing function can be consistent with the solving formula of the window function. That is, based on the point spread functions corresponding to each data point of the sub-seismic data included in the at least two seismic data blocks and the coordinate data of each data point, the splicing function corresponding to each data point is determined.

[0129] In step 740, the at least two deconvoluted seismic data blocks are spliced based on the splicing functions to obtain processed seismic data.

[0130] Optionally, each data point of the at least two deconvoluted seismic data blocks is multiplied by the corresponding splicing function, so that the at least two deconvoluted seismic data blocks are spliced based on the splicing functions to obtain the processed seismic data.

[0131] In the embodiment, the deconvoluted seismic data blocks are spliced to obtain the complete processed seismic data, so that the size of the processed seismic data is consistent with that of the original seismic data.

[0132] The overall steps of the method for deconvolution of seismic data in depth domain according to the embodiments of the present application are described below in combination with a specific embodiment. The steps of the embodiment mainly include:

[0133] 1. Obtain seismic data and a point spread function profile of the seismic data. The point spread function profile includes the point spread functions corresponding to each data point of the seismic data.

[0134] 2. Divide the seismic data according to the size of a single point spread function, and each seismic data block has a size of 3 times that of the single point spread function, to obtain a plurality of seismic data blocks.

[0135] 3. Multiply the window function of formula 1 by the point spread function of each data point in each seismic data block. Wherein, x, y are the coordinate data of each data point, and N is the size of the point spread function of the data point.

[0136]

[0137] 4. For each sub-seismic data contained in each seismic data block, solve the first objective function of formula 2 by using the conjugate direction method to obtain the first deconvolution seismic data block m. Wherein, H is the point spread function matrix composed of the single point spread function of each data point of the seismic data block obtained by step 3, and m0 is the seismic profile of the seismic data block.

[0138] J(m) = ||Hm-m0||

[0139] 5. Take the first deconvolution seismic data block m obtained by step 4 as the initial value m0 of the seismic profile of this step, and solve the second objective function of formula 3 by using the Split-Brgeman method to obtain the second deconvolution seismic data block m.

[0140]

[0141] 6. Repeat the above steps 4 and 5 until the set number of iterations is reached, and the deconvolution seismic data block of the seismic data block is obtained.

[0142] 7. Perform the above steps 4, 5 and 6 for each seismic data block.

[0143] 8. Multiply each data point of each deconvolution seismic data block by the corresponding formula 1 of the data point to splice the deconvolution seismic data block.

[0144] 9. Obtain the processed seismic data of the seismic data.

[0145] In summary, the depth domain deconvolution method of the seismic data provided by the embodiment optimizes the implementation process of deconvolution, blocks the point spread function, adds a window, and appropriately introduces a regularization method, iterates by using a least square framework, can improve the deconvolution effect, and improve the resolution of the seismic data after deconvolution.

[0146] The embodiment also verifies the deconvolution effect by using an integral method imaging to obtain a seismic data and a point spread function profile. Figure 8 Fig. 1 shows a schematic diagram of the migration result profile provided by an example embodiment of the present application. Figure 9 Fig. 2 shows a schematic diagram of the deconvolution result profile of the related art provided by an example embodiment of the present application. Figure 10A schematic diagram of a deconvolution result profile of the embodiment provided by one example embodiment of the present application is shown. Figure 11 A schematic diagram of a seismic data spectrum contrast provided by one example embodiment of the present application is shown. As shown by the above results, compared with the method of the related art, the method provided by the embodiment can have higher resolution, and in terms of the result of the spectrum contrast, the method provided by the embodiment has wider frequency width, i.e., the deconvolution effect is better.

[0147] Figure 12 A structural block diagram of a depth domain deconvolution device of seismic data provided by one example embodiment of the present application is shown. The depth domain deconvolution device 800 of seismic data includes:

[0148] An acquisition module 810 is configured to acquire the seismic data and a point spread function corresponding to each data point of the seismic data, wherein the point spread function is an operator generated in the depth domain of the seismic data;

[0149] A block division module 820 is configured to divide the seismic data based on the point spread function corresponding to each data point of the seismic data to obtain at least two seismic data blocks;

[0150] A processing module 830 is configured to deconvolute the at least two seismic data blocks in the depth domain respectively to obtain at least two deconvoluted seismic data blocks;

[0151] A splicing module 840 is configured to splice the at least two deconvoluted seismic data blocks to obtain processed seismic data.

[0152] In some embodiments, the processing module 830 is configured to:

[0153] acquire a point spread function matrix corresponding to each of the at least two seismic data blocks based on the point spread function corresponding to each data point of the sub-seismic data included in each of the at least two seismic data blocks;

[0154] deconvolute each of the at least two seismic data blocks in the depth domain based on the point spread function matrix corresponding to each of the at least two seismic data blocks and a seismic profile to obtain the at least two deconvoluted seismic data blocks.

[0155] In some embodiments, the processing module 830 is configured to:

[0156] determine a first objective function corresponding to each of the at least two seismic data blocks based on the point spread function matrix corresponding to each of the at least two seismic data blocks and the seismic profile;

[0157] solving the first objective function with the objective of minimizing the first objective function, to obtain at least two first deconvolution seismic data blocks;

[0158] determining a second objective function corresponding to each of the at least two seismic data blocks based on the at least two first deconvolution seismic data blocks;

[0159] solving the second objective function with the objective of minimizing the second objective function, to obtain at least two second deconvolution seismic data blocks;

[0160] returning the step of determining the first objective function corresponding to each of the at least two seismic data blocks based on the point spread function matrix and the seismic profile corresponding to each of the at least two seismic data blocks, until a set number of iterations is reached, to obtain the at least two deconvolution seismic data blocks, by taking the at least two second deconvolution seismic data blocks as the at least two seismic data blocks.

[0161] In some embodiments, the processing module 830 is configured to:

[0162] determining a window function corresponding to each data point in the sub-seismic data included in each of the at least two seismic data blocks based on a point spread function corresponding to each data point and coordinate data of each data point;

[0163] windowing the point spread function corresponding to each data point based on the window function corresponding to each data point, to obtain a windowed point spread function corresponding to each data point;

[0164] obtaining the point spread function matrix corresponding to each of the at least two seismic data blocks based on the windowed point spread function corresponding to each data point.

[0165] In some embodiments, the block dividing module 820 is configured to:

[0166] determining the size of each seismic data block of the seismic data based on the point spread function corresponding to each data point of the seismic data;

[0167] dividing the seismic data into the at least two seismic data blocks according to the size of each seismic data block of the seismic data.

[0168] In some embodiments, the splicing module 840 is configured to:

[0169] obtaining a splicing function corresponding to each of the at least two deconvolution seismic data blocks;

[0170] The at least two deconvolution seismic data blocks are spliced based on the splicing function to obtain the processed seismic data.

[0171] The embodiment of the application further provides a computer device, comprising a processor and a memory, the memory storing a computer program; and the processor is used for executing the computer program in the memory to implement the depth domain deconvolution method of seismic data provided by each method embodiment.

[0172] Optionally, the computer device is a server. For example, Figure 13 FIG. 1 is a structural block diagram of a server provided by an example embodiment of the application.

[0173] Generally, the server 1000 comprises a processor 1001 and a memory 1002.

[0174] The processor 1001 can comprise one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 1001 can be implemented in at least one of a hardware form of a digital signal processing (DSP), a field-programmable gate array (FPGA), a programmable logic array (PLA). The processor 1001 can also comprise a main processor and a coprocessor. The main processor is a processor for processing data in an awake state, also known as a central processing unit (CPU). The coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 1001 can be integrated with a graphics processor (GPU), which is used to render and draw the content required to be displayed on the display screen. In some embodiments, the processor 1001 can further comprise an artificial intelligence (AI) processor, which is used to process computing operations related to machine learning.

[0175] The memory 1002 can comprise one or more computer-readable storage media, which can be non-transitory. The memory 1002 can further comprise a high-speed random access memory, and a non-volatile memory such as one or more disk storage devices, flash storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 1002 is used to store at least one instruction for being executed by the processor 1001 to implement the depth domain deconvolution method of seismic data provided by the method embodiments.

[0176] In some embodiments, the server 1000 can further optionally include an input interface 1003 and an output interface 1004. The processor 1001, the memory 1002, and the input interface 1003 and the output interface 1004 can be connected through a bus or a signal line. Each peripheral device can be connected to the input interface 1003 and the output interface 1004 through a bus, a signal line, or a circuit board. The input interface 1003 and the output interface 1004 can be used to connect at least one peripheral device related to input / output (I / O) to the processor 1001 and the memory 1002. In some embodiments, the processor 1001, the memory 1002, and the input interface 1003 and the output interface 1004 are integrated on the same chip or circuit board; in some other embodiments, any one or both of the processor 1001, the memory 1002, and the input interface 1003 and the output interface 1004 can be implemented on a separate chip or circuit board, and the embodiments of the present application are not limited thereto.

[0177] Those skilled in the art can understand that the structure shown in the above is not a limitation on the computer device, and the computer device can include more or fewer components than those shown in the figure, or combine certain components, or use different component arrangements. Figure 13 The structure shown in the above does not constitute a limitation on the computer device, and the computer device can include more or fewer components than those shown in the figure, or combine certain components, or use different component arrangements.

[0178] In an example embodiment, a chip is also provided, which includes a programmable logic circuit and / or program instructions, and when the chip is running on a computer device, is used to implement the depth domain deconvolution method of seismic data provided by the above method embodiments.

[0179] The present application also provides a computer readable storage medium, which stores a computer program, and the computer program is loaded and executed by a processor to implement the depth domain deconvolution method of seismic data provided by the above method embodiments.

[0180] The present application provides a computer program product or computer program, which includes computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions, so that the computer device executes the depth domain deconvolution method of seismic data provided by the above method embodiments.

[0181] The above sequence numbers of the embodiments of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0182] Those skilled in the art can understand that all or part of the steps of the above-mentioned embodiments can be completed by hardware, or can be instructed by programs to complete the related hardware, and the programs can be stored in a computer readable storage medium, and the storage medium can be a read-only memory, a magnetic disk or an optical disk, etc.

[0183] Those skilled in the art should be aware that, in one or more examples described above, the functions described in the embodiments of the present application can be implemented in hardware, software, firmware or any combination thereof. When implemented in software, the functions can be stored in a computer readable medium or transmitted as one or more instructions or codes on a computer readable medium. The computer readable medium includes computer storage medium and communication medium, and the communication medium includes any medium that facilitates the transfer of computer programs from one place to another. The storage medium can be any available medium that can be accessed by a general or special purpose computer.

[0184] The above description is only optional embodiments of the present application, and is not used to limit the present application, and any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A depth-domain deconvolution method for seismic data, characterized in that, The method includes: The seismic data and the point spread function corresponding to each data point of the seismic data are obtained; the point spread function is an operator generated in the depth domain of the seismic data. Based on the point spread function corresponding to each data point of the earthquake data, the size of each earthquake data block of the earthquake data is determined; The seismic data is divided into blocks according to the size of each seismic data block, resulting in at least two seismic data blocks; Based on the point spread function corresponding to each data point of the sub-seismic data contained in the at least two seismic data blocks, obtain the point spread function matrix corresponding to each of the at least two seismic data blocks; Based on the point spread function matrix and seismic profile corresponding to the at least two seismic data blocks respectively, determine the first objective function corresponding to the at least two seismic data blocks respectively; Minimizing the first objective function is the objective of solving the first objective function to obtain at least two first deconvolution seismic data blocks; Based on the at least two first deconvolution seismic data blocks, determine the second objective function corresponding to each of the at least two seismic data blocks; Minimizing the second objective function is the objective of solving the second objective function to obtain at least two second deconvolution seismic data blocks; The steps of taking the at least two second deconvolution seismic data blocks as the at least two seismic data blocks and returning the first objective function corresponding to the at least two seismic data blocks based on the point spread function matrix and seismic profile corresponding to the at least two seismic data blocks respectively, are repeated until a set number of iterations is reached to obtain at least two deconvolution seismic data blocks. The at least two deconvolution seismic data blocks are spliced ​​together to obtain the processed seismic data.

2. The method according to claim 1, characterized in that, The step of obtaining the point spread function matrix corresponding to each data point of the sub-seismic data contained in the at least two seismic data blocks includes: Based on the point spread function corresponding to each data point of the sub-seismic data contained in the at least two seismic data blocks, and the coordinate data of each data point, determine the window function corresponding to each data point. Based on the window function corresponding to each data point, the point spread function corresponding to each data point is windowed to obtain the windowed point spread function corresponding to each data point. Based on the windowed point spread function corresponding to each of the data points, the point spread function matrix corresponding to each of the at least two seismic data blocks is obtained.

3. The method according to claim 1 or 2, characterized in that, The step of stitching together the at least two deconvolution seismic data blocks to obtain processed seismic data includes: Obtain the splicing function corresponding to each of the at least two deconvolution seismic data blocks; The at least two deconvolution seismic data blocks are stitched together using the stitching function to obtain the processed seismic data.

4. A depth-domain deconvolution device for seismic data, characterized in that, The device includes: The acquisition module is used to acquire the seismic data and the point spread function corresponding to each data point of the seismic data; the point spread function is an operator generated in the depth domain of the seismic data. The block segmentation module is used to determine the size of each seismic data block based on the point spread function corresponding to each data point of the seismic data; and to segment the seismic data into blocks according to the size of each seismic data block to obtain at least two seismic data blocks. The processing module is configured to: obtain point spread function matrices corresponding to the at least two seismic data blocks based on the point spread function corresponding to each data point of the sub-seismic data contained in the at least two seismic data blocks; determine a first objective function corresponding to the at least two seismic data blocks based on the point spread function matrices and seismic profiles; solve the first objective function with the objective of minimizing the first objective function to obtain at least two first deconvolution seismic data blocks; determine a second objective function corresponding to the at least two seismic data blocks based on the at least two first deconvolution seismic data blocks; solve the second objective function with the objective of minimizing the second objective function to obtain at least two second deconvolution seismic data blocks; and return the step of determining the first objective function corresponding to the at least two seismic data blocks based on the point spread function matrices and seismic profiles, using the at least two second deconvolution seismic data blocks as the at least two seismic data blocks, until a set number of iterations is reached to obtain at least two deconvolution seismic data blocks. The stitching module is used to stitch together the at least two deconvolution seismic data blocks to obtain processed seismic data.

5. The apparatus according to claim 4, characterized in that, The processing module is configured to: determine a window function corresponding to each data point based on the point spread function corresponding to each data point in the sub-seismic data contained in the at least two seismic data blocks, and the coordinate data of each data point; window the point spread function corresponding to each data point according to the window function corresponding to each data point to obtain a windowed point spread function corresponding to each data point; and obtain the point spread function matrix corresponding to each of the at least two seismic data blocks based on the windowed point spread function corresponding to each data point.

6. The apparatus according to claim 4 or 5, characterized in that, The stitching module is used to obtain the stitching functions corresponding to the at least two deconvolution seismic data blocks respectively; and to stitch the at least two deconvolution seismic data blocks based on the stitching functions to obtain the processed seismic data.

7. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing a computer program that is loaded and executed by the processor to implement the depth domain deconvolution method for seismic data as described in any one of claims 1 to 3.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that is loaded and executed by a processor to implement the depth domain deconvolution method for seismic data as described in any one of claims 1 to 3.

9. A computer program product, characterized in that, The computer program product includes computer instructions stored in a computer-readable storage medium, from which a processor retrieves the computer instructions, causing the processor to load and execute them to implement the depth domain deconvolution method for seismic data as described in any one of claims 1 to 3.

Images