Method and device for overcoming cycle skipping in full-wave inversion, electronic equipment and storage medium

Abstract

The application provides a method and device for overcoming period jump in full-wave inversion, electronic equipment and storage medium. The method comprises the following steps: obtaining observation data and synthetic data; constructing a distance matrix between the observation data and the synthetic data based on the observation data and the synthetic data; determining a permutation matrix based on the distance matrix; determining the total deviation of the observation data and the synthetic data based on the distance matrix and the permutation matrix; updating the model by taking the total deviation as a target function; repeating the above steps until the model converges; and enabling the target function corresponding to the model to be in a convex domain where the global minimum value is located, thereby overcoming the problem of period jump.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of full waveform inversion, in particular to a method and device for overcoming cycle skipping in full waveform inversion, electronic equipment and storage medium. BACKGROUND

[0002] Full waveform inversion is the most powerful imaging technique so far. Full waveform inversion relies on solving wave equation to simulate wave field propagation in the ground, then comparing the difference between synthetic seismogram and observed data, transmitting data error to form gradient, updating model to reduce data error, new model as initial model and repeating iteration until the algorithm converges to the optimal value. Generally, full waveform inversion uses least square waveform error, which not only fits the amplitude of seismic signal, but also fits the phase of seismic signal; therefore, theoretically, the size that full waveform inversion can distinguish can reach half a wavelength.

[0003] Full waveform inversion restores the model of the earth's interior, including P-wave velocity, anisotropic parameters and density, etc., by optimizing the objective function constrained by wave equation. Now full waveform inversion has been widely used in oil and gas exploration field, however, the nonlinearity of objective function is an important factor restricting the development of this technology. In addition, due to the very large dimension of model and data, the existing computer processing capacity can only use local minimum inversion algorithm, so if the objective function value corresponding to the initial model is in the convex domain of local minimum value instead of global minimum value, then full waveform inversion will converge to local minimum value, making the finally restored model deviate from the true model greatly, even worse than the initial model. A significant manifestation of the nonlinearity of full waveform inversion is cycle skipping, that is, when the predicted data deviates from the corresponding observed data more than half a cycle, cycle skipping will occur. Therefore, cycle skipping will lead full waveform inversion to converge to a local minimum value and give an incorrect inversion model. SUMMARY

[0004] In view of the above problems, the present application provides a method and device for overcoming cycle skipping in full waveform inversion, electronic equipment and storage medium.

[0005] The present application provides a method for overcoming cycle skipping in full waveform inversion, the method comprising:

[0006] Obtaining observed data and synthetic data;

[0007] Based on the observed data and the synthetic data, constructing a distance matrix between the observed data and the synthetic data;

[0008] Determining a permutation matrix based on the distance matrix;

[0009] determine a total deviation of the observation data and the synthetic data based on the distance matrix and the permutation matrix;

[0010] update a model as a target function based on the total deviation;

[0011] repeat the above steps until the model converges.

[0012] In some embodiments, the distance matrix is:

[0013] ;

[0014] wherein, is the distance matrix, is a time corresponding to an i-th record point of the synthetic data, is a time corresponding to a j-th record point of the observation data, is a total observation time, is a user input parameter, is the synthetic data at the time, is the observation data at the time, is a maximum value of the observation data, , each is a norm.

[0015] In some embodiments, the total deviation is:

[0016] ;

[0017] wherein, is the total deviation, is the distance matrix, is the permutation matrix.

[0018] In some embodiments, the determining the permutation matrix based on the distance matrix comprises:

[0019] solving the distance matrix based on a bid ranking algorithm to obtain an optimal solution;

[0020] determining the permutation matrix based on the optimal solution.

[0021] Embodiments of the present application provide a device for overcoming cycle skipping in full-wave inversion, the device comprising:

[0022] an acquisition module configured to acquire observation data and synthetic data;

[0023] a construction module configured to construct a distance matrix between the observation data and the synthetic data based on the observation data and the synthetic data;

[0024] a first determining module configured to determine a permutation matrix based on the distance matrix;

[0025] a second determining module configured to determine a total deviation between the observation data and the synthetic data based on the distance matrix and the permutation matrix;

[0026] an updating module configured to update a model by taking the total deviation as a target function;

[0027] a repeating module configured to repeat the above steps until the model converges.

[0028] In some embodiments, the distance matrix is:

[0029] ;

[0030] wherein, is the distance matrix, is a time corresponding to an i-th record point of the synthetic data, is a time corresponding to a j-th record point of the observation data, is a total observation time, is a user input parameter, is the synthetic data at the time, is the observation data at the time, is a maximum value of the observation data, , all are norms.

[0031] In some embodiments, the total deviation is:

[0032] ;

[0033] wherein, is the total deviation, is the distance matrix, is the permutation matrix.

[0034] In some embodiments, the first determining module comprises:

[0035] a solving unit configured to solve the distance matrix based on a bidding ranking algorithm to obtain an optimal solution;

[0036] a determining unit configured to determine the permutation matrix based on the optimal solution.

[0037] Embodiments of the present application provide an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to perform any of the above methods.

[0038] The embodiment of the present application provides a storage medium, which stores a computer program capable of being executed by one or more processors and capable of being used to implement the method.

[0039] The present application provides a method and device for overcoming cycle jump in full-wave inversion, an electronic device and a storage medium. The method comprises the following steps: obtaining observation data and synthetic data; constructing a distance matrix between the observation data and the synthetic data; determining a permutation matrix based on the distance matrix; determining a total deviation of the observation data and the synthetic data based on the distance matrix and the permutation matrix; and updating a model by taking the total deviation as a target function, and repeating the above steps until the model converges, so that the target function corresponding to the model is in a convex domain where a global minimum value is located, thereby overcoming the cycle jump problem. BRIEF DESCRIPTION OF DRAWINGS

[0040] The present application will be described in more detail below based on the embodiments and with reference to the drawings.

[0041] Figure 1 A flowchart of the method provided by the embodiment of the present application is shown in the figure;

[0042] Figure 2 A real model diagram provided by the embodiment of the present application is shown in the figure;

[0043] Figure 3 An initial model diagram provided by the embodiment of the present application is shown in the figure;

[0044] Figure 4 An observation data diagram provided by the embodiment of the present application is shown in the figure;

[0045] Figure 5 An initial synthetic data diagram provided by the embodiment of the present application is shown in the figure;

[0046] Figure 6 A difference between the observation data and the synthetic data provided by the embodiment of the present application is shown in the figure;

[0047] Figure 7 An inversion result made by a target function provided by the embodiment of the present application is shown in the figure;

[0048] Figure 8 A final synthetic data diagram provided by the embodiment of the present application is shown in the figure;

[0049] Figure 9 A final data comparison diagram provided by the embodiment of the present application is shown in the figure;

[0050] Figure 10 A structure diagram of the device provided by the embodiment of the present application is shown in the figure;

[0051] Figure 11A schematic diagram of a constituent structure of an electronic device provided by an embodiment of the present application is shown. DETAILED DESCRIPTION

[0052] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings, and the described embodiments should not be regarded as limiting the present application. All other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0053] In the following description, “some embodiments” are described, which describe a subset of all possible embodiments, but it can be understood that “some embodiments” can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0054] If similar descriptions of “first\second\third” appear in the application file, the following description is added. In the following description, the terms “first\second\third” referred to are only to distinguish similar objects, and do not represent a specific order of the objects. It can be understood that “first\second\third” can be interchanged in a specific order or sequence as allowed, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0056] Embodiment One

[0057] Based on the problems in the related art, an embodiment of the present application provides a method for overcoming cycle jumps in full-wave inversion. The execution subject of the method for overcoming cycle jumps in full-wave inversion can be an electronic device. The electronic device can be a notebook computer, a tablet computer, a desktop computer, a set-top box, a mobile device (for example, a mobile phone, a portable music player, a personal digital assistant, a dedicated message device, a portable game device), and various types of terminals, and can also be implemented as a server. The server can be a standalone physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud services, cloud databases, 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.

[0058] The function of the method for overcoming period jumps in full-wave inversion provided in this application embodiment can be implemented by the processor of an electronic device calling program code, wherein the program code can be stored in a computer storage medium.

[0059] This application provides a method for overcoming period jumps in full-wave inversion. Figure 1 A flowchart illustrating the method provided in the embodiments of this application, as shown below. Figure 1 As shown, the method includes:

[0060] Step S1: Acquire observational data and synthetic data;

[0061] In the embodiments of this application, observational data refers to seismic data recorded in the field using instruments, which corresponds to simulation data generated by a real Earth model. Synthetic data refers to seismic data generated by computer simulation, which corresponds to simulation data generated by a computational model in the computer (e.g., the initial velocity model at the start of the inversion).

[0062] In the embodiments of this application, the observed data can be displacement, velocity, or acceleration. Similarly, the synthesized data can be displacement, velocity, or acceleration.

[0063] Step S2: Based on the observed data and the synthesized data, construct the distance matrix between the observed data and the synthesized data;

[0064] In the embodiments of this application, the full waveform inversion compares the synthetic data with the observation data. In the observation record, the observation data are all discrete records; at the same time, in the digital synthetic record, the synthetic data are also discrete.

[0065] In the embodiments of this application, it is assumed that single-channel seismic observation data is in yes Synthesized single-channel data in yes Then a distance matrix can be constructed. .

[0066] In the embodiments of this application, the distance matrix is:

[0067] ;

[0068] In the formula, It is a distance matrix. Let i be the time corresponding to the i-th record point in the synthesized data. Let j be the time corresponding to the j-th record point in the observation data. For the total observation time, Input parameters for the user, Synthetic data Data corresponding to the time point, is observation data Data corresponding to the time point, is the maximum value of the observation data, , All are norms.

[0069] In the embodiment of the application, the total deviation is:

[0070] ;

[0071] In the formula, is the total deviation, is the distance matrix, is the permutation matrix.

[0072] In the embodiment of the application, is the distance matrix, is the permutation matrix, and the product corresponding to each pair of seismic traces is the deviation, and then the sum of the deviations of all seismic traces can obtain the total deviation.

[0073] Step S3: determining a permutation matrix based on the distance matrix;

[0074] In the embodiment of the application, the permutation matrix can be composed of 0 and 1, wherein each row and each column in the permutation matrix has only one 1. According to the distance matrix and the permutation matrix, the deviation value of each pair of seismic traces can be obtained, and then the sum of the deviation values of all seismic traces can obtain a total deviation, and the permutation matrix with the minimum total deviation can be taken as the target matrix.

[0075] In the embodiment of the application, the permutation matrix can be equivalent to a permutation vector, or the permutation matrix can be represented by a vector. For example, , indicates that the i-th position is replaced to the j-th position; wherein, is a permutation vector equivalent to the permutation matrix.

[0076] Step S4: determining the total deviation of the observation data and the synthetic data based on the distance matrix and the permutation matrix;

[0077] In the embodiment of the application, in the case of determining the permutation matrix, the difference value corresponding to each pair of seismic traces can be obtained through the distance matrix and the permutation matrix, and the sum of the difference values corresponding to all seismic traces can obtain the total deviation.

[0078] Step S5: updating the model by taking the total deviation as the objective function;

[0079] In the embodiment of the application, the total deviation minimized is taken as the objective function to realize the updating of the model.

[0080] Step S6: repeat the above steps until the model converges.

[0081] In the embodiments of the present application, repeating the above steps means obtaining new synthetic data, constructing a distance matrix between the new synthetic data and the observation data based on the new synthetic data and the observation data, determining a permutation matrix based on the distance matrix, determining a total deviation of the observation data and the synthetic data based on the distance matrix and the permutation matrix, updating the model with the total deviation as a target function, and repeating the above steps until the model converges, so that the target function is in a convex domain where the global minimum value is located.

[0082] In the embodiments of the present application, the number of model updates can also be set, and the model can be stopped updating when the number of model updates reaches the set number of updates, at which time it is assumed that the model converges.

[0083] In summary, the observation data and the synthetic data are obtained, a distance matrix between the observation data and the synthetic data is constructed, a permutation matrix is determined based on the distance matrix, a total deviation of the observation data and the synthetic data is determined based on the distance matrix and the permutation matrix, the model is updated with the total deviation as a target function, and the above steps are repeated until the model converges, so that the model corresponding to the target function is in a convex domain where the global minimum value is located, thereby overcoming the problem of period skipping.

[0084] Embodiment Two

[0085] Based on the problems in the related art, the embodiments of the present application provide a method for overcoming period skipping in full-wave inversion. The execution subject of the method for overcoming period skipping in full-wave inversion can be an electronic device. The electronic device can be a notebook computer, a tablet computer, a desktop computer, a set-top box, a mobile device (such as a mobile phone, a portable music player, a personal digital assistant, a dedicated messaging device, a portable game device), and various types of terminals, and can also be implemented as a server. The server can be a standalone physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content distribution networks (CDN), and big data and artificial intelligence platforms, etc. basic cloud computing services.

[0086] The method for overcoming period skipping in full-wave inversion provided in the embodiments of the present application can realize the functions by calling program codes by the processor of the electronic device, wherein the program codes can be saved in a computer storage medium.

[0087] The embodiments of the present application provide a method for overcoming period skipping in full-wave inversion, Figure 1 The flowchart of the method provided in the embodiments of the present application is shown in FIG. 1.Figure 1 The method comprises:

[0088] Step S1: obtaining observation data and synthetic data;

[0089] In an embodiment of the present application, the observation data refers to seismic data recorded in the field by an instrument, which corresponds to simulated data generated by a real earth model. The synthetic data refers to seismic data simulated by a computer, which corresponds to simulated data generated by a calculation model (for example, an initial velocity model at the start of inversion) in the computer.

[0090] In an embodiment of the present application, the observation data can be displacement, velocity, or acceleration. Similarly, the synthetic data can be displacement, velocity, or acceleration.

[0091] Step S2: constructing a distance matrix between the observation data and the synthetic data based on the observation data and the synthetic data;

[0092] In an embodiment of the present application, full waveform inversion is a comparison between synthetic data and observation data. In the observation record, the observation data is all discrete records. At the same time, in the digital synthetic record, the synthetic data is also discrete.

[0093] In an embodiment of the present application, it is assumed that single-channel seismic observation data is in is , and the synthetic single-channel data is in is , then a distance matrix can be constructed.

[0094] In an embodiment of the present application, the distance matrix is:

[0095] ;

[0096] In the formula, d is the distance matrix, ti is the time corresponding to the i th record point of the synthetic data, tj is the time corresponding to the j th record point of the observation data, T is the total observation time, a is a user input parameter, is the data at the time ti of the synthetic data, is the data at the time tj of the observation data, is the maximum value of the observation data, and ‖·‖ is a norm.

[0097] In an embodiment of the present application, the total deviation is: ​​​​​​​​​​​​

[0098] ;

[0099] wherein, is the total deviation, is the distance matrix, is the permutation matrix.

[0100] In the embodiments of the present application, is the distance matrix, is the permutation matrix, the product corresponding to each pair of seismic traces is the deviation, and the sum of the deviations of all seismic traces can obtain the total deviation.

[0101] Step S3: determining a permutation matrix based on the distance matrix;

[0102] In the embodiments of the present application, the permutation matrix can be composed of 0 and 1, wherein each row and each column in the permutation matrix has only one 1. According to the distance matrix and the permutation matrix, the deviation value of each pair of seismic traces can be obtained, and the sum of the deviation values of all seismic traces can obtain a total deviation. The permutation matrix with the minimum total deviation can be taken as the target matrix.

[0103] In the embodiments of the present application, the permutation matrix can be equivalent to a permutation vector, or the permutation matrix can be represented by a vector. Exemplarily, , indicates that the i-th position is permuted to the j-th position; wherein, is the permutation vector equivalent to the permutation matrix.

[0104] In the embodiments of the present application, the permutation matrix is determined based on the distance matrix, comprising:

[0105] Step 31: solving the distance matrix based on a bidding ranking algorithm to obtain an optimal solution;

[0106] Step 32: determining the permutation matrix based on the optimal solution.

[0107] In the embodiments of the present application, the value of the distance matrix solution is not unique, so the distance matrix can be solved by the bidding ranking algorithm, and the optimal solution corresponding to the distance matrix can be obtained. The optimal solution is the minimum value of the total deviation of the distance matrix. The permutation matrix is determined based on the minimum value of the total deviation, so that the total deviation of the observed data and the synthetic data determined by the distance matrix and the permutation matrix can be guaranteed to be the minimum value.

[0108] Step S4: determining the total deviation of the observed data and the synthetic data based on the distance matrix and the permutation matrix;

[0109] In the embodiments of the present application, after the permutation matrix is determined, the difference corresponding to each pair of seismic traces can be obtained through the distance matrix and the permutation matrix, and the total deviation can be obtained by summing the differences corresponding to all seismic traces.

[0110] Step S5: updating the model as a target function with the total deviation;

[0111] In the embodiments of the present application, the model is updated by taking the minimized total deviation as the target function.

[0112] Step S6: repeating the above steps until the model converges.

[0113] In the embodiments of the present application, repeating the above steps means obtaining new synthetic data, thereby constructing a new distance matrix between the new synthetic data and the observed data based on the new synthetic data and the observed data, determining a permutation matrix based on the distance matrix, determining a total deviation based on the distance matrix and the permutation matrix, updating the model as a target function with the total deviation, and stopping updating the model until the model converges, which can make the target function in a convex domain where the global minimum value is located.

[0114] In the embodiments of the present application, the number of model updates can also be set, and the model can be stopped updating until the number of model updates reaches the set number of updates, at which time it is assumed that the model converges.

[0115] Embodiment Three

[0116] The method for overcoming the period jump in the full-wave inversion of the present application will be described in detail through specific embodiments.

[0117] A transmission model test is adopted to demonstrate the present application.

[0118] Figure 2 A real model is demonstrated, which has a uniform background and a circular abnormal velocity body in the middle. The background velocity is 3000 m / s, and the abnormal body velocity is 3600 m / s.

[0119] Figure 3 A uniform initial model is demonstrated, which has a velocity of 3000 m / s. There are 48 uniformly spaced explosive sources above the upper interface at a distance of 100 m, and 300 pressure receivers below the lower interface at a distance of 100 m. The source wavelet is a 10 Hz Ricker wavelet.

[0120] Figure 4 The observed data is demonstrated.

[0121] Figure 5 The initial synthetic data is demonstrated.

[0122] Figure 6The difference between the observation data and the synthetic data is shown, and it can be seen that the period jump phenomenon is serious, and the conventional method cannot correctly invert it, and always falls into local extremum. In the present inversion, no filtering or data truncation and other multi-stage inversion strategies are adopted, and all data are input at one time.

[0123] Figure 7 The inversion result is made by using the objective function of the present application, and p=1 and q=2 are selected when the distance matrix is constructed. Because the objective function is relatively convex, the velocity structure can be well inverted.

[0124] Figure 8 The final synthetic data is shown.

[0125] Figure 9 The final data comparison is shown, and the observation data has been fitted, and all seismic information has been completely extracted.

[0126] Embodiment Four

[0127] Based on the foregoing embodiments, the present application provides a device for overcoming period jump in full-wave inversion, each module included in the device and each unit included in each module can be realized by a processor in a computer device; of course, it can also be realized by a specific logic circuit; in the implementation process, the processor can be a central processing unit (CPU), a microprocessor unit (MPU), a digital signal processor (DSP) or a field programmable gate array (FPGA) and the like.

[0128] The present application provides a device for overcoming period jump in full-wave inversion, Figure 10 The structural diagram of the device provided by the present application is shown in Figure 10 The device includes:

[0129] The acquisition module is configured to acquire observation data and synthetic data.

[0130] In the present application, the observation data refers to the seismic data recorded in the field by the instrument, which corresponds to the simulation data generated by the real earth model. The synthetic data refers to the seismic data simulated by the computer, which corresponds to the simulation data generated by the calculation model (for example, the initial velocity model at the beginning of inversion) in the computer.

[0131] In the present application, the observation data can be displacement, velocity or acceleration. Similarly, the synthetic data can be displacement, velocity or acceleration.

[0132] The construction module is used to construct a distance matrix between the observed data and the synthesized data based on the observed data and the synthesized data;

[0133] In the embodiments of this application, the full waveform inversion compares the synthetic data with the observation data. In the observation record, the observation data are all discrete records; at the same time, in the digital synthetic record, the synthetic data are also discrete.

[0134] In the embodiments of this application, it is assumed that single-channel seismic observation data is in yes Synthesized single-channel data in yes Then a distance matrix can be constructed. .

[0135] In the embodiments of this application, the distance matrix is:

[0136] ;

[0137] In the formula, The distance matrix is... Let i be the time corresponding to the i-th record point in the synthesized data. Let j be the time corresponding to the j-th record point in the observation data. For the total observation time, Input parameters for the user, Synthetic data The data corresponding to the time. For observation data The data corresponding to the time. The maximum value of the observed data. , All are norms.

[0138] In this embodiment of the application, the total deviation is:

[0139] ;

[0140] In the formula, For the total deviation, It is a distance matrix. Let be the permutation matrix.

[0141] In the embodiments of this application, The distance matrix is... Given a permutation matrix, the product of each pair of seismic traces is the deviation. The total deviation is obtained by summing the deviations of all seismic traces.

[0142] The first determining module is used to determine the permutation matrix based on the distance matrix;

[0143] In the embodiments of the present application, the permutation matrix can be composed of 0 and 1, wherein each row and each column of the permutation matrix has only one 1. According to the distance matrix and the permutation matrix, the deviation value of each pair of seismic traces can be obtained, and then the total deviation can be obtained by summing the deviation values of all seismic traces. The permutation matrix with the minimum total deviation can be taken as the target matrix.

[0144] In the embodiments of the present application, the permutation matrix can be equivalent to a permutation vector, or the permutation matrix can be represented by a vector. For example, , indicates that the i th position is permuted to the j th position; wherein, is a permutation vector equivalent to the permutation matrix.

[0145] The second determination module is configured to determine a total deviation between the observation data and the synthetic data based on the distance matrix and the permutation matrix.

[0146] In the embodiments of the present application, in the case where the permutation matrix is determined, the difference value corresponding to each pair of seismic traces can be obtained by the distance matrix and the permutation matrix, and the total deviation can be obtained by summing the difference values corresponding to all seismic traces.

[0147] The updating module is configured to update the model by taking the total deviation as a target function.

[0148] In the embodiments of the present application, the model is updated by taking the minimized total deviation as the target function.

[0149] The repeating module is configured to repeat the above steps until the model converges.

[0150] In the embodiments of the present application, repeating the above steps means obtaining new synthetic data, thereby constructing a new distance matrix between the new synthetic data and the observation data based on the new synthetic data and the observation data, determining a new permutation matrix based on the distance matrix, determining a new total deviation based on the distance matrix and the permutation matrix, updating the model by taking the new total deviation as a target function, and stopping updating the model until the model converges, so that the target function is in a convex domain where the global minimum value is located.

[0151] In the embodiments of the present application, the number of times of updating the model can be set, and the model can be stopped updating until the number of times of updating the model reaches the set number of times, at which time it is considered that the model converges.

[0152] In the embodiments of the present application, the first determination module comprises:

[0153] The solving unit is configured to solve the distance matrix based on the auction ranking algorithm to obtain an optimal solution.

[0154] The determination unit is configured to determine the permutation matrix based on the optimal solution.

[0155] In the embodiments of the present application, the value of the distance matrix solution is not unique, so the distance matrix can be solved by a bidding ranking algorithm, and the optimal solution corresponding to the distance matrix is obtained, the optimal solution is the minimum value of the total deviation of the distance matrix, and the permutation matrix is determined based on the minimum value of the total deviation, so that the total deviation of the observation data and the synthetic data determined by the distance matrix and the permutation matrix is the minimum value.

[0156] It should be noted that, in the embodiments of the present application, if the method for overcoming the period jump in full-wave inversion is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read Only Memory), a magnetic disk or an optical disk, and various storage medium that can store program codes. Thus, the embodiments of the present application are not limited to any specific hardware and software combination.

[0157] Embodiment five

[0158] Correspondingly, the embodiments of the present application provide a storage medium having a computer program stored thereon, characterized by the computer program being executed by a processor to implement the steps in the method for overcoming the period jump in full-wave inversion provided in the above embodiments.

[0159] Embodiment six

[0160] The embodiments of the present application provide an electronic device; Figure 11 The composition structure schematic diagram of the electronic device provided by the embodiments of the present application is shown in Figure 11 As shown in the figure, the electronic device 100 includes: a processor 101, at least one communication bus 102, a user interface 103, at least one external communication interface 104, and a memory 105. The communication bus 102 is configured to realize the connection and communication between the components. The user interface 103 can include a display screen, and the external communication interface 104 can include a standard wired interface and a wireless interface. The processor 101 is configured to execute the program of the method for overcoming the period jump in full-wave inversion stored in the memory to implement the steps in the method for overcoming the period jump in full-wave inversion provided in the above embodiments,

[0161] The embodiment of the present application provides a method for overcoming period jump in full-wave inversion, and the method comprises the following steps:

[0162] Step S1: acquiring observation data and synthetic data;

[0163] In the embodiment of the present application, the observation data refers to seismic data recorded in the field by an instrument, which corresponds to simulation data generated by a real earth model. The synthetic data refers to seismic data simulated by a computer, which corresponds to simulation data generated by a calculation model (for example, an initial velocity model at the beginning of inversion) in the computer.

[0164] In the embodiment of the present application, the observation data can be displacement, velocity or acceleration. Similarly, the synthetic data can be displacement, velocity or acceleration.

[0165] Step S2: constructing a distance matrix between the observation data and the synthetic data based on the observation data and the synthetic data;

[0166] In the embodiment of the present application, the full-wave inversion is to compare the synthetic data with the observation data. In the observation record, the observation data is discrete record. Meanwhile, in the digital synthetic record, the synthetic data is also discrete.

[0167] In the embodiment of the present application, it is assumed that single-channel seismic observation data is in is , the synthetic single-channel data is in is , and then the distance matrix .

[0168] In the embodiment of the present application, the distance matrix is as follows:

[0169]

[0170] In the formula, d is the distance matrix, ti is the time corresponding to the i th record point of the synthetic data, tj is the time corresponding to the j th record point of the observation data, T is the total observation time, is the data corresponding to the time of the synthetic data, is the data corresponding to the time of the observation data, is the maximum value of the observation data, and ‖·‖ is the norm.

[0171] In the embodiment of the present application, the total deviation is as follows:​​​​​​​​​​​​​

[0172] ;

[0173] wherein, is the total bias, is the distance matrix, is the permutation matrix.

[0174] In embodiments of the present application, is the distance matrix, is the permutation matrix, the product corresponding to each pair of seismic traces is the bias, and the sum of the biases of all seismic traces is the total bias.

[0175] Step S3: determining a permutation matrix based on the distance matrix;

[0176] In embodiments of the present application, the permutation matrix can be composed of 0 and 1, wherein each row and each column of the permutation matrix has only one 1. According to the distance matrix and the permutation matrix, the bias value of each pair of seismic traces can be calculated, and the sum of the bias values of all seismic traces is a total bias. The permutation matrix with the minimum total bias is taken as the target matrix.

[0177] In embodiments of the present application, the permutation matrix can be equivalent to a permutation vector, or the permutation matrix can be represented by a vector. For example, , indicates that the i-th position is permuted to the j-th position; wherein, is the permutation vector equivalent to the permutation matrix.

[0178] Step S4: determining the total bias of the observation data and the synthetic data based on the distance matrix and the permutation matrix;

[0179] In embodiments of the present application, after the permutation matrix is determined, the difference value corresponding to each pair of seismic traces can be calculated by the distance matrix and the permutation matrix, and the sum of the difference values corresponding to all seismic traces is the total bias.

[0180] Step S5: updating the model by taking the total bias as the objective function;

[0181] In embodiments of the present application, the total bias is minimized as the objective function to update the model.

[0182] Step S6: repeating the above steps until the model converges.

[0183] In the embodiments of the present application, repeating the above steps means obtaining new synthetic data, thereby constructing a distance matrix between the new synthetic data and the observation data based on the new synthetic data and the observation data, determining a permutation matrix based on the distance matrix, determining a total deviation of the observation data and the synthetic data based on the distance matrix and the permutation matrix, and updating the model with the total deviation as a target function until the model converges, which can enable the target function to be in a convex region where a global minimum value is located.

[0184] In the embodiments of the present application, the number of model updates can also be set, and the model can be stopped from being updated until the number of model updates reaches the set number of updates, at which time the model is considered to have converged.

[0185] In summary, the observation data and the synthetic data are obtained, a distance matrix between the observation data and the synthetic data is constructed, a permutation matrix is determined based on the distance matrix, a total deviation of the observation data and the synthetic data is determined based on the distance matrix and the permutation matrix, the model is updated with the total deviation as a target function, and the above steps are repeated until the model converges, which can enable the model corresponding to the target function to be in a convex region where a global minimum value is located, thereby overcoming the problem of periodic jumps.

[0186] In the embodiments of the present application, the determination of the permutation matrix based on the distance matrix comprises:

[0187] Step 31: solving the distance matrix based on a bidding ranking algorithm to obtain an optimal solution;

[0188] Step 32: determining the permutation matrix based on the optimal solution.

[0189] It should be noted that the descriptions of the storage medium and the electronic device embodiments above are similar to the descriptions of the method embodiments above, and have similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium and device embodiments of the present application, please refer to the description of the method embodiments of the present application.

[0190] It should be understood that the "one embodiment" or "an embodiment" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of each process does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The sequence number of the above embodiments of the present application is only for description, not representing the advantages and disadvantages of the embodiments.

[0191] It should be noted that, in the present document, the terms "comprising", "containing", or any other similar term are intended to encompass non-exclusive inclusion, such that processes, methods, articles, or apparatuses that comprise a list of elements are not limited to those elements, but can also include other elements not expressly listed, or also include elements inherent in such processes, methods, articles, or apparatuses. Without further limitation, an element defined by the phrase "comprising a" does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0192] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed components can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0193] The units described above as separate components can or can not be physically separated, and the components shown as units can or can not be physical units; they can be located in one place or distributed on multiple network units; and part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0194] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can be a unit alone, or two or more units can be integrated into one unit; the integrated unit can be realized in the form of hardware or in the form of hardware plus software functional unit.

[0195] Those of ordinary skill in the art can understand that all or part of the steps of the above method embodiments can be completed by program instruction related hardware, and the foregoing program can be stored in a computer readable storage medium, and the program executes the steps of the above method embodiments when executed; and the foregoing storage medium includes: mobile storage equipment, read only memory (ROM, Read Only Memory), magnetic disc or optical disc, and various storage program codes.

[0196] Alternatively, the above-mentioned integrated units of the present application, if realized in the form of software function modules and sold or used as independent products, can also be stored in a computer-readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing a controller to execute all or part of the methods described in the embodiments of the present application. The aforementioned storage medium includes mobile storage devices, ROM, magnetic disks or optical disks, and various media capable of storing program codes.

[0197] The above is only an embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for overcoming period jumps in full-wave inversion, characterized in that, The method includes: Acquire observational and synthetic data; Based on the observed data and the synthesized data, a distance matrix between the observed data and the synthesized data is constructed; Determine the permutation matrix based on the distance matrix; Based on the distance matrix and the permutation matrix, the total deviation between the observed data and the synthetic data is determined; Update the model using the total deviation as the objective function; Repeat the above steps until the model converges; The method based on the distance matrix and the permutation matrix includes: The distance matrix is ​​solved using a bidding ranking algorithm to obtain the optimal solution; The permutation matrix is ​​determined based on the optimal solution.

2. The method for overcoming period jumps in full-wave inversion according to claim 1, characterized in that, The distance matrix is: ； In the formula, It is a distance matrix. Let i be the time corresponding to the i-th record point in the synthesized data. Let j be the time corresponding to the j-th record point in the observation data. For the total observation time, Input parameters for the user. Synthetic data The data corresponding to the time. For observation data The data corresponding to the time. The maximum value of the observed data. , All are norms.

3. The method for overcoming period jumps in full-wave inversion according to claim 2, characterized in that, The total deviation is: h ； In the formula, h is the total deviation. It is a distance matrix. Let be the permutation matrix.

4. A device for overcoming period jumps in full-wave inversion, characterized in that, The device includes: The acquisition module is used to acquire observational data and synthetic data; The construction module is used to construct a distance matrix between the observed data and the synthesized data based on the observed data and the synthesized data; The first determining module is used to determine the permutation matrix based on the distance matrix; The second determining module is used to determine the total deviation between the observed data and the synthetic data based on the distance matrix and the permutation matrix; The update module is used to update the model using the total deviation as the objective function; The repeat module is used to repeat the above steps until the model converges. The first determining module includes: The solution unit is used to solve the distance matrix based on the bidding ranking algorithm to obtain the optimal solution; A determining unit is used to determine the permutation matrix based on the optimal solution.

5. The apparatus for overcoming period jumps in full-wave inversion according to claim 4, characterized in that, The distance matrix is: ； In the formula, It is a distance matrix. Let i be the time corresponding to the i-th record point in the synthesized data. Let j be the time corresponding to the j-th record point in the observation data. For the total observation time, Input parameters for the user. Synthetic data The data corresponding to the time. For observation data The data corresponding to the time. The maximum value of the observed data. , All are norms.

6. The apparatus for overcoming period jumps in full-wave inversion according to claim 5, characterized in that, The total deviation is: ； In the formula, For the total deviation, It is a distance matrix. Let be the permutation matrix.

7. An electronic device, characterized in that, include: A memory and a processor, wherein the memory stores a computer program that, when executed by the processor, performs the method as described in any one of claims 1 to 3.

8. A storage medium, characterized in that, The computer program stored in the storage medium can be executed by one or more processors and can be used to implement the method as described in any one of claims 1 to 3.

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