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

Abstract

The invention discloses a depth domain deconvolution method and device for seismic data, equipment and a storage medium, and belongs to the technical field of seismic data processing. The method comprises the following steps: acquiring seismic data and point spread functions respectively corresponding to data points of the seismic data; the point spread function is an operator generated in a depth domain of the seismic data; based on the point spread function corresponding to each data point of the seismic data, partitioning the seismic data to obtain at least two seismic data blocks; performing deconvolution on the at least two seismic data blocks in a depth domain to obtain at least two deconvolution seismic data blocks; and splicing the at least two deconvolution seismic data blocks to obtain processed seismic data. According to the scheme, the deconvolution effect can be improved, and the resolution of the seismic data is remarkably improved.

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, device, equipment and storage medium for seismic data. Background Art

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

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

[0004] However, the method of performing deconvolution on seismic data in the wavenumber domain in the related art does not significantly improve the resolution of seismic data. Summary of the invention

[0005] The present application provides a method, device, equipment, storage medium and program product for depth domain deconvolution of seismic data. 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] 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;

[0008] Based on the point spread functions corresponding to the data points of the seismic data, the seismic data is divided into blocks to obtain at least two seismic data blocks;

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

[0010] The at least two deconvolution seismic data blocks are spliced ​​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, used for acquiring the seismic data and point spread functions 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 block division module, configured to divide the seismic data into blocks based on the point spread functions corresponding to the data points of the seismic data, to obtain at least two seismic data blocks;

[0014] A processing module, configured to perform deconvolution on the at least two seismic data blocks in the depth domain respectively to obtain at least two deconvolution seismic data blocks;

[0015] The splicing module is used to splice the at least two deconvolution seismic data blocks to obtain processed seismic data.

[0016] According to another aspect of the present application, a computer device is provided, comprising: a processor and a memory, wherein the memory stores a computer program, and the computer program is 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, wherein the computer-readable storage medium 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 as described above.

[0018] According to another aspect of the present application, a computer program product is provided, which includes computer instructions stored in a computer-readable storage medium, and a processor obtains the computer instructions from the computer-readable storage medium, so that the processor loads and executes to implement the depth domain deconvolution method of seismic data as described above.

[0019] The beneficial effects brought by the technical solution provided by the embodiment of the present application include at least:

[0020] By obtaining seismic data and point spread functions 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 functions 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 deconvolved in the depth domain to obtain at least two deconvolved seismic data blocks; 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 THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

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

[0023] Figure 2 A flow chart showing a method for depth-domain deconvolution of seismic data provided by an exemplary embodiment;

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

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

[0026] Figure 5 A flow chart showing a method for depth-domain deconvolution of seismic data provided by an exemplary embodiment;

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

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

[0029] Figure 8 A schematic diagram showing a cross section of a migration result provided by an exemplary embodiment is shown;

[0030] Fig. 9 A schematic diagram showing a cross section of a deconvolution result of a related art provided by an exemplary embodiment;

[0031] Fig.10 A schematic diagram of a cross section of a deconvolution result of this embodiment provided by an exemplary embodiment is shown;

[0032] Fig.11 A schematic diagram of comparing seismic data spectra provided by an exemplary embodiment is shown;

[0033] Fig.12 A structural block diagram of a depth domain deconvolution device for seismic data provided by an exemplary embodiment is shown;

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

[0035] 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.

[0036] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0037] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0038] It should be noted that before collecting earthquake-related data (such as earthquake data, point spread data) and during the process of collecting earthquake-related data, the present application can display a prompt interface, pop-up window or output voice prompt information, and the prompt interface, pop-up window or voice prompt information is used to prompt that the relevant data is currently being collected, so that the present application only starts to execute the relevant steps of obtaining earthquake-related data after obtaining the confirmation operation issued to the prompt interface or pop-up window, otherwise (that is, when the confirmation operation issued to the prompt interface or pop-up window is not obtained), the relevant steps of obtaining earthquake-related data are terminated, that is, the earthquake-related data are not obtained. In other words, all earthquake-related data collected by this application are collected with consent and authorization, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0039] It should be understood that, although the terms first, second, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first parameter may also be referred to as the second parameter, and similarly, the second parameter may also be referred to as the first parameter. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0040] Figure 1The block diagram of the computer system provided by an exemplary embodiment of the present application is shown. The computer system can be implemented as 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-mounted terminal (vehicle computer), a wearable device, a PC (Personal Computer), an unmanned reservation terminal, etc. A client that runs a target application can be installed in the terminal 120, and the target application can be a deep domain deconvolution application for seismic data, including but not limited to the three-dimensional visualization modeling software Petrel, the seismic prediction software VVA, and the seismic interpretation software Landmark. The three-dimensional visualization modeling software Petrel is mainly used for seismic interpretation, structural modeling, lithofacies modeling, reservoir attribute modeling, reservoir numerical simulation display and virtual reality, etc. The seismic prediction software VVA is mainly used for reservoir spatial distribution prediction, and the seismic interpretation software Landmark is mainly used for fine structural interpretation work. It can also be other applications that provide the deep domain deconvolution function of seismic data, which is not limited in this application. In addition, this application does not limit the form of the target application, including but not limited to App (Application, application) installed in the terminal 120, applets, etc., and can also be in the form of a web page.

[0042] Server 140 may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services, cloud database, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDN), and cloud servers for basic cloud computing services such as big data and artificial intelligence platforms. Server 140 may be a backend server of the above-mentioned target application, used to provide backend services for the client of the target application.

[0043] Among them, cloud technology refers to a hosting technology that unifies hardware, software, network and other resources in a wide area network or local area network to realize data computing, storage, processing and sharing. Cloud technology is a general term for network technology, information technology, integration technology, management platform technology, application technology, etc. based on the cloud computing business model. It can form a resource pool, which is used on demand and is flexible and convenient. Cloud computing technology will become an important support. The background services of technical network systems require a large amount of computing and storage resources, such as video websites, picture websites and more portal websites. With the high development and application of the Internet industry, in the future, each item may have its own identification mark, and all need to be transmitted to the background system for logical processing. Data of different levels will be processed separately. All kinds of industry data require strong system backing support, which can only be achieved through cloud computing.

[0044] In some embodiments, the server 140 can also be implemented as a node in a blockchain system. Blockchain is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithm. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the blockchain underlying platform, platform product service layer, and application service layer.

[0045] The terminal 120 and the server 140 may communicate with each other via a network, such as a wired or wireless network.

[0046] In the depth domain deconvolution method for seismic data provided in the embodiment of the present application, the execution subject of each step may be a computer device, which refers to an electronic device with data calculation, processing and storage capabilities. Figure 1 Taking the implementation environment of the scheme shown as an example, the depth domain deconvolution method of seismic data can be executed by the terminal 120 (such as the client of the target application installed and running in the terminal 120 executes the depth domain deconvolution method of seismic data), or the server 140 can execute the depth domain deconvolution method of seismic data, or the terminal 120 and the server 140 can cooperate with each other to execute it, and this application does not limit this.

[0047] Those skilled in the art will appreciate that the number of the terminals 120 may be more or less. For example, there may be only one terminal 120, or there may be dozens or hundreds of terminals 120, or a greater number. The embodiment of the present application does not limit the number and device type of the terminals 120.

[0048] In seismic data processing and imaging, imaging domain least squares is widely used as a key technology. Traditional least squares migration requires one migration and one de-migration in each iteration, which has high computational cost. Least squares migration can be regarded as a defuzzification process using the Hessian matrix. Therefore, the point spread function can be used to reduce the computational cost through the combination of migration and deconvolution, providing a compromise solution for least squares migration.

[0049] In the related art, there are two main methods for depth domain offset deblurring. The first method is to use the entire data volume as the iteration space, establish the least squares objective functional, and iterate the deconvolution result as a whole. The problem with this method is that it is impossible to obtain an effective iteration step, and thus it is impossible to obtain effective resolution. The second method is to divide the data into blocks, deconvolve the point spread function and the data at the corresponding position in the wavenumber domain, and then splice the deconvolution results to obtain the final result. The problem with this method is that the wavenumber domain division does not significantly broaden the frequency band, and does not significantly improve the resolution.

[0050] Figure 2 A flowchart of a depth domain deconvolution method for seismic data provided by an exemplary embodiment of the present application is shown, in which the method is applied to Figure 1 The server 140 is illustrated as an example, and the method includes:

[0051] Step 220, obtaining seismic data and point spread functions 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.

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

[0053] The physical meaning of the point spread function (PSF) is the seismic imaging response of a single diffraction point in the ground, and it is the basis of seismic imaging data.

[0054] The point spread function can be obtained by directly obtaining the imaging results through the underground model for seismic forward modeling, or by interpolating the Hessian matrix for inversion.

[0055] In this embodiment, the point spread function is an operator generated in the depth domain of the seismic data. There is a point spread function corresponding to each data point of the seismic data. The size of the point spread function can be expressed as N.

[0056] The point spread function and 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, wave equation reverse time migration imaging method (Reverse Time Migration, RTM), etc.

[0057] Optionally, seismic data and point spread functions corresponding to respective data points of the seismic data are obtained.

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

[0059] A seismic data block is a portion of seismic data obtained by dividing the entire seismic data into blocks. A seismic data block is a subset of seismic data.

[0060] Optionally, the sizes of the at least two seismic data blocks into which the seismic data is divided may be the same or different. The seismic data respectively contained in the at least two different seismic data blocks may have a portion of data overlapped.

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

[0062] Step 260: Deconvolute the at least two seismic data blocks in the depth domain to obtain at least two deconvoluted seismic data blocks.

[0063] Deconvolution, also known as inverse filter or deconvolution, is a processing method that eliminates the previous filtering effect. It is a processing process that improves the vertical resolution of seismic data by compressing basic wavelets. Deconvolution of seismic data can improve the resolution of seismic data.

[0064] The deconvolution seismic data block is a seismic data block obtained after deconvolution of the seismic data block.

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

[0066] Step 280, concatenate at least two deconvolved seismic data blocks to obtain processed seismic data.

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

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

[0069] In summary, the method provided by the embodiment of the present application obtains seismic data and point spread functions 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 functions 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 deconvolved in the depth domain to obtain at least two deconvolved seismic data blocks; 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.

[0070] Figure 3 The flowchart of the depth domain deconvolution method of seismic data provided by an exemplary embodiment of the present application is shown. The above step 260 may optionally include the following steps 320 and 340:

[0071] Step 320: Based on the point spread functions corresponding to the data points of the sub-seismic data contained in the at least two seismic data blocks, respectively, obtain point spread function matrices corresponding to the at least two seismic data blocks.

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

[0073] Optionally, based on the point spread functions corresponding to the respective data points of the sub-seismic data contained in the at least two seismic data blocks, point spread function matrices corresponding to the at least two seismic data blocks are obtained.

[0074] Step 340, based on the point spread function matrices and seismic profiles corresponding to the at least two seismic data blocks, respectively, deconvolute the at least two seismic data blocks in the depth domain to obtain at least two deconvoluted seismic data blocks.

[0075] A seismic profile is also called a seismic record profile. It is a seismic data map that marks a certain survey line. According to the different physical dimensions used in the vertical axis, seismic record profiles can be divided into time profiles and depth profiles.

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

[0077] Optionally, the same processing is performed on each seismic data block. Based on the point spread function matrices and seismic profiles corresponding to the at least two seismic data blocks, the at least two seismic data blocks are deconvolved in the depth domain to obtain at least two deconvolved seismic data blocks.

[0078] Figure 4A flowchart of a depth domain deconvolution method for seismic data provided by an exemplary embodiment of the present application is shown. In some embodiments, deconvolution is performed on each seismic data block in an iterative manner. Specifically, the above step 340 includes the following steps 400, 420, 440, 460, and 480:

[0079] Step 400, based on the point spread function matrix and the seismic profile corresponding to the at least two seismic data blocks, determine the first objective function corresponding to the at least two seismic data blocks respectively.

[0080] The first objective function is an objective function for a seismic data block determined when performing the first deconvolution.

[0081] Optionally, based on the point spread function matrix and the seismic profile corresponding to the at least two seismic data blocks, the first objective functions corresponding to the at least two seismic data blocks are determined, wherein the first objective functions corresponding to different seismic data blocks are different.

[0082] Optionally, taking a seismic data block as an example, the first objective function is represented as J(m), the point spread function matrix is ​​represented as H, and the seismic profile of the seismic data block is represented as m0. Then the first objective function of the seismic data block is represented as follows:

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

[0084] Here, ||·|| indicates the calculation of the L2 norm.

[0085] Step 420, solving the first objective function with minimizing the first objective function as the goal, and obtaining at least two first deconvolution seismic data blocks.

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

[0087] Optionally, the first objective function is solved by using a conjugate direction method with the minimization of the first objective function as the goal, so as to obtain at least two first deconvolution seismic data blocks.

[0088] Optionally, taking a seismic data block as an example, after step 400, the conjugate direction method is used to solve the first objective function of the seismic data block to obtain m in the first objective function, that is, to obtain the first deconvolution seismic data block.

[0089] Step 440: Determine second objective functions corresponding to the at least two seismic data blocks respectively based on the at least two first deconvolved seismic data blocks.

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

[0091] Optionally, based on the at least two first deconvolution seismic data blocks, second objective functions corresponding to the at least two seismic data blocks are determined, wherein the second objective functions corresponding to different seismic data blocks are different.

[0092] Optionally, taking a seismic data block as an example, the second objective function is represented as F(m). After the first deconvolution seismic data block is obtained in step 420, the seismic profile of the first deconvolution seismic data block is used 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 represented as follows:

[0093]

[0094] Among them, ||·||1 indicates the calculation of the L1 norm.

[0095] Step 460 , solving the second objective function with minimizing the second objective function as the goal, and obtaining at least two second deconvolution seismic data blocks.

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

[0097] Optionally, the second objective function is solved by using a Split Bregman Method with the goal of minimizing the second objective function to obtain at least two second deconvolution seismic data blocks.

[0098] Optionally, taking a 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 to obtain the second deconvolution seismic data block.

[0099] Step 480, taking at least two second deconvolution seismic data blocks as at least two seismic data blocks, returning to the step of determining the first objective function corresponding to at least two seismic data blocks based on the point spread function matrix and the seismic profile corresponding to the at least two seismic data blocks, until the set number of iterations is reached to obtain at least two deconvolution seismic data blocks.

[0100] Optionally, at least two second deconvolution seismic data blocks are used as at least two seismic data blocks in step 400, and the process returns to step 400 to repeatedly execute steps 400 to 460 until a set number of iterations is reached to obtain at least two deconvolution seismic data blocks.

[0101] In some embodiments, the number of iterations is set based on factors such as the resolution required for deconvolution of the seismic data block, the data processing complexity corresponding to the resolution, the occupied space, and manual experience.

[0102] In the above embodiment, deconvolution processing is performed on each seismic data block of the seismic data through an iterative processing method, which can ensure the deconvolution effect of each seismic data block and improve the depth domain deconvolution accuracy and resolution of the seismic data.

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

[0104] Step 520, based on the point spread functions corresponding to the data points of the sub-seismic data contained in at least two seismic data blocks and the coordinate data of the data points, determine the window functions corresponding to the data points.

[0105] Window function is also called "weight function". It selects a part of the spectrum value of the function as the spectrum of the whole function. This is equivalent to opening a "window" from the whole function. Studying the value in the window can represent the overall properties of the function.

[0106] Optionally, based on the point spread functions corresponding to the data points of the sub-seismic data contained in at least two seismic data blocks, and the coordinate data of each data point, the window functions corresponding to each data point are determined. One data point corresponds to one window function. Different values ​​of the coordinate data of the data point correspond to different window functions.

[0107] Optionally, taking a data point of a seismic data block as an example, the coordinate data of the data point are x and y respectively, the size of the point spread function is represented by N, and the window function is represented by w(x, y). Then the window function of the data point of the seismic data block is represented as follows:

[0108]

[0109] Step 540 , windowing the point spread function corresponding to each data point according to the window function corresponding to each data point, to obtain the 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, according to the window function corresponding to each data point, the point spread function corresponding to each data point is multiplied by the window function to window the point spread function corresponding to each data point, so as to obtain the windowed point spread function corresponding to each data point.

[0112] Step 560: Based on the windowed point spread functions corresponding to each data point, a point spread function matrix corresponding to at least two seismic data blocks is obtained.

[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 in the seismic data block.

[0114] Optionally, based on the windowed point spread functions corresponding to each data point, point spread function matrices corresponding to at least two seismic data blocks are obtained.

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

[0116] Figure 6 The flowchart of the depth domain deconvolution method of seismic data provided by an exemplary embodiment of the present application is shown. The above step 240 includes the following steps 620 and 640:

[0117] Step 620, 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.

[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, based on the point spread functions corresponding to the data points of the seismic data, the point spread functions are multiplied by a preset multiple to determine the size of each seismic data block 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 that of a single point spread function.

[0121] Step 640, dividing the seismic data 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 to obtain at least two seismic data blocks. Seismic data between different seismic data blocks may have data overlap.

[0123] In this embodiment, the seismic data is divided into blocks to obtain a plurality of seismic data blocks, thereby ensuring the processing accuracy and efficiency of the seismic data.

[0124] Figure 7 A flowchart of a depth domain deconvolution method for seismic data provided by an exemplary embodiment of the present application is shown. In some embodiments, after deconvolution of each seismic data block, the deconvolution seismic data blocks need to be spliced ​​into the entire seismic data. The above step 280 includes the following steps 720 and 740:

[0125] Step 720: Obtain concatenation functions corresponding to at least two deconvolution seismic data blocks.

[0126] The splicing function is used to splice deconvoluted seismic data blocks.

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

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

[0129] Step 740: splice at least two deconvolution seismic data blocks based on the splicing function to obtain processed seismic data.

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

[0131] In this embodiment, by splicing the deconvolution seismic data blocks, complete processed seismic data can be obtained so as to keep the size consistent with the original seismic data.

[0132] The following is a description of the overall steps of the depth domain deconvolution method for seismic data provided by the embodiment of the present application in conjunction with a specific embodiment. The steps of this embodiment mainly include:

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

[0134] 2. Divide the seismic data into blocks according to the size of a single point spread function. The size of each seismic data block is three times that of a single point spread function, and multiple seismic data blocks are obtained.

[0135] 3. For each data point in each seismic data block, the point spread function is multiplied by the window function of the following formula 1. Wherein, x and 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 the sub-seismic data contained in each seismic data block, the conjugate direction method is used to solve the first objective function of the following formula 2 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 obtained in step 3 of the seismic data block, and m0 is the seismic profile of the seismic data block.

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

[0139] 5. Use the first deconvolution seismic data block m obtained in step 4 as the initial value m0 of the seismic profile in this step, and use the Split-Brgeman method to solve the second objective function of equation 3 below to obtain the second deconvolution seismic data block m.

[0140]

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

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

[0143] 8. Each data point of each deconvolution seismic data block is multiplied by the formula 1 corresponding to the data point to perform splicing of the deconvolution seismic data blocks.

[0144] 9. Obtain processed seismic data.

[0145] In summary, the depth domain deconvolution method for seismic data provided in this embodiment optimizes the deconvolution implementation process, divides the point spread function into blocks, adds windows, and appropriately introduces a regularization method, and uses a least squares framework for iteration, so as to improve the deconvolution effect and enhance the resolution of the seismic data after deconvolution.

[0146] This embodiment also verifies the deconvolution effect through seismic data and point spread function profiles obtained by imaging using the integral method. Figure 8 A schematic diagram of a cross section of a migration result provided by an exemplary embodiment of the present application is shown. Fig. 9 A schematic diagram of a cross section of a deconvolution result of a related technology provided by an exemplary embodiment of the present application is shown. Fig.10A schematic diagram of a cross section of a deconvolution result of this embodiment provided by an exemplary embodiment of the present application is shown. Fig.11 The schematic diagram of the seismic data spectrum comparison provided by an exemplary embodiment of the present application is shown. The above results show that compared with the method of the related art, the method provided by the present embodiment can have a higher resolution, and in terms of the result of the spectrum comparison, the method provided by the present embodiment has a wider bandwidth, that is, the deconvolution effect is better.

[0147] Fig.12 The structure block diagram of a depth domain deconvolution device for seismic data provided by an exemplary embodiment of the present application is shown. The depth domain deconvolution device 800 for seismic data includes:

[0148] An acquisition module 810 is used to acquire 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;

[0149] A block division module 820, configured to divide the seismic data into blocks based on the point spread functions corresponding to the data points of the seismic data, to obtain at least two seismic data blocks;

[0150] A processing module 830 is used to perform deconvolution on the at least two seismic data blocks in the depth domain to obtain at least two deconvolution seismic data blocks;

[0151] The splicing module 840 is used to splice the at least two deconvolution seismic data blocks to obtain processed seismic data.

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

[0153] Based on the point spread functions corresponding to the data points of the sub-seismic data contained in the at least two seismic data blocks, respectively, obtaining point spread function matrices corresponding to the at least two seismic data blocks respectively;

[0154] Based on the point spread function matrix and the seismic profile corresponding to the at least two seismic data blocks respectively, the at least two seismic data blocks are deconvolved in the depth domain respectively to obtain the at least two deconvolved seismic data blocks.

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

[0156] Determine first objective functions corresponding to the at least two seismic data blocks respectively based on the point spread function matrices and the seismic profiles corresponding to the at least two seismic data blocks respectively;

[0157] Taking minimizing the first objective function as a goal, solving the first objective function to obtain at least two first deconvolution seismic data blocks;

[0158] Based on the at least two first deconvolution seismic data blocks, determining second objective functions respectively corresponding to the at least two seismic data blocks;

[0159] Taking minimizing the second objective function as a goal, solving the second objective function to obtain at least two second deconvolution seismic data blocks;

[0160] The at least two second deconvolution seismic data blocks are used as the at least two seismic data blocks, and the step of determining the first objective function corresponding to the at least two seismic data blocks respectively based on the point spread function matrix and the seismic profile respectively corresponding to the at least two seismic data blocks is returned until the set number of iterations is reached to obtain the at least two deconvolution seismic data blocks.

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

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

[0163] According to the window functions corresponding to the data points, the point spread functions corresponding to the data points are windowed to obtain the windowed point spread functions corresponding to the data points;

[0164] Based on the windowed point spread functions corresponding to the data points, the point spread function matrices corresponding to the at least two seismic data blocks are obtained.

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

[0166] Determining the size of each seismic data block of the seismic data based on the point spread functions respectively corresponding to each of the data points of the seismic data;

[0167] The seismic data is divided into blocks according to the size of each of the seismic data blocks to obtain the at least two seismic data blocks.

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

[0169] Obtaining splicing functions corresponding to the at least two deconvolution seismic data blocks respectively;

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

[0171] An embodiment of the present application also provides a computer device, which includes: a processor and a memory, wherein a computer program is stored in the memory; the processor is used to execute the computer program in the memory to implement the depth domain deconvolution method of seismic data provided by the above-mentioned method embodiments.

[0172] Optionally, the computer device is a server. Fig.13 It is a structural block diagram of a server provided by an exemplary embodiment of the present application.

[0173] Typically, the server 1000 includes: a processor 1001 and a memory 1002 .

[0174] The processor 1001 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 1001 may be implemented in at least one hardware form of digital signal processing (DSP), field programmable gate array (FPGA), and programmable logic array (PLA). The processor 1001 may also include 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 may be integrated with a graphics processing unit (GPU), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 1001 may also include an artificial intelligence (AI) processor, which is used to process computing operations related to machine learning.

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

[0176] In some embodiments, the server 1000 may also optionally include: an input interface 1003 and an output interface 1004. The processor 1001, the memory 1002, the input interface 1003, and the output interface 1004 may be connected via a bus or a signal line. Each peripheral device may be connected to the input interface 1003 and the output interface 1004 via a bus, a signal line, or a circuit board. The input interface 1003 and the output interface 1004 may 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, 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 two of the processor 1001, the memory 1002, the input interface 1003, and the output interface 1004 may be implemented on a separate chip or circuit board, which is not limited in the embodiments of the present application.

[0177] Those skilled in the art will understand that Fig.13 The structure shown in the figure does not constitute a limitation on the computer device, and may include more or less components than shown in the figure, or combine some components, or adopt a different component arrangement.

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

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

[0180] The present application provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a 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 embodiment.

[0181] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0182] A person of ordinary skill in the art will appreciate that all or part of the steps for implementing the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which may be a read-only memory, a disk, or an optical disk, etc.

[0183] Those skilled in the art should be aware that in one or more of the above examples, the functions described in the embodiments of the present application can be implemented with hardware, software, firmware, or any combination thereof. When implemented using software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein the communication media include any media that facilitates the transmission of a computer program from one place to another. The storage medium can be any available medium that a general or special-purpose computer can access.

[0184] The above description is only an optional embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles 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 comprises: 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; Based on the point spread functions corresponding to the data points of the seismic data, the seismic data is divided into blocks to obtain at least two seismic data blocks; Deconvolving the at least two seismic data blocks in the depth domain respectively to obtain at least two deconvolved seismic data blocks; The at least two deconvolution seismic data blocks are spliced ​​to obtain processed seismic data.

2. The method according to claim 1, characterized in that The step of respectively performing deconvolution on the at least two seismic data blocks in the depth domain to obtain at least two deconvolution seismic data blocks comprises: Based on the point spread functions corresponding to the data points of the sub-seismic data contained in the at least two seismic data blocks, respectively, obtaining point spread function matrices corresponding to the at least two seismic data blocks respectively; Based on the point spread function matrix and the seismic profile corresponding to the at least two seismic data blocks respectively, the at least two seismic data blocks are deconvolved in the depth domain respectively to obtain the at least two deconvolved seismic data blocks.

3. The method according to claim 2, characterized in that The step of deconvolving the at least two seismic data blocks in the depth domain based on the point spread function matrix and the seismic profile respectively corresponding to the at least two seismic data blocks to obtain the at least two deconvolved seismic data blocks comprises: Determine first objective functions corresponding to the at least two seismic data blocks respectively based on the point spread function matrices and the seismic profiles corresponding to the at least two seismic data blocks respectively; Taking minimizing the first objective function as a goal, 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, determining second objective functions respectively corresponding to the at least two seismic data blocks; Taking minimizing the second objective function as a goal, solving the second objective function to obtain at least two second deconvolution seismic data blocks; The at least two second deconvolution seismic data blocks are used as the at least two seismic data blocks, and the step of determining the first objective function corresponding to the at least two seismic data blocks respectively based on the point spread function matrix and the seismic profile respectively corresponding to the at least two seismic data blocks is returned until the set number of iterations is reached to obtain the at least two deconvolution seismic data blocks.

4. The method according to claim 2, characterized in that: The step of obtaining point spread function matrices corresponding to the at least two seismic data blocks based on the point spread functions corresponding to the data points of the sub-seismic data contained in the at least two seismic data blocks respectively comprises: Determine the window function corresponding to each of the data points based on the point spread function corresponding to each of the data points of the sub-seismic data contained in the at least two seismic data blocks and the coordinate data of each of the data points; According to the window functions corresponding to the data points, the point spread functions corresponding to the data points are windowed to obtain the windowed point spread functions corresponding to the data points; Based on the windowed point spread functions corresponding to the data points, the point spread function matrices corresponding to the at least two seismic data blocks are obtained.

5. The method according to any one of claims 1 to 4, characterized in that: The step of dividing the seismic data into blocks based on the point spread functions corresponding to the data points of the seismic data to obtain at least two seismic data blocks includes: Determining the size of each seismic data block of the seismic data based on the point spread functions respectively corresponding to each of the data points of the seismic data; The seismic data is divided into blocks according to the size of each of the seismic data blocks to obtain the at least two seismic data blocks.

6. The method according to any one of claims 1 to 4, characterized in that: The step of splicing the at least two deconvolution seismic data blocks to obtain processed seismic data includes: Obtaining splicing functions corresponding to the at least two deconvolution seismic data blocks respectively; The at least two deconvolution seismic data blocks are spliced ​​based on the splicing function to obtain the processed seismic data.

7. A depth domain deconvolution device for seismic data, characterized in that: The device comprises: An acquisition module, used for acquiring the seismic data and point spread functions 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; A block division module, configured to divide the seismic data into blocks based on the point spread functions corresponding to the data points of the seismic data, to obtain at least two seismic data blocks; A processing module, configured to perform deconvolution on the at least two seismic data blocks in the depth domain respectively to obtain at least two deconvolution seismic data blocks; The splicing module is used to splice the at least two deconvolution seismic data blocks to obtain processed seismic data.

8. A computer device, characterized in that: The computer device comprises: a processor and a memory, wherein the memory stores a computer program, and the computer program is loaded and executed by the processor to implement the depth domain deconvolution method of seismic data as claimed in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which 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 6.

10. A computer program product, characterized in that The computer program product includes computer instructions, which are stored in a computer-readable storage medium. A processor obtains the computer instructions from the computer-readable storage medium so that the processor loads and executes them to implement the depth domain deconvolution method for seismic data as described in any one of claims 1 to 6.

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