Method and device for overcoming cycle jump, electronic equipment and storage medium

Abstract

The invention provides a method and a device for overcoming cycle jump, electronic equipment and a storage medium. The method comprises the following steps: acquiring 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; based on the distance matrix and the permutation matrix, determining the total deviation of the observation data and the synthetic data; updating the model by taking the total deviation as a target function; repeating the steps until the model converges; the objective function corresponding to the model can be located in the convex domain where the global minimum value is located, so that the problem of cycle jump is solved.

Description

Technical Field

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

[0002] Full waveform inversion is the most powerful imaging technology to date. Full waveform inversion relies on solving the wave equation to simulate the propagation of the wave field underground, and then compares the difference between the synthetic seismogram and the observed data. The error of the back-transmitted data forms a gradient, and the model is updated to reduce the data error. The new model is used as the initial model and it is iterated repeatedly until the algorithm converges to the optimal value. Usually, full waveform inversion uses the least squares waveform error, which not only fits the amplitude of the seismic signal, but also fits the phase of the seismic signal; therefore, theoretically, the size that can be resolved by full waveform inversion can reach half a wavelength.

[0003] Full waveform inversion restores the model of the earth's interior by optimizing the objective function constrained by the wave equation, including P-wave velocity, anisotropy parameters, and density. Full waveform inversion has now been widely used in the field of oil and gas exploration. However, the nonlinearity of the objective function is an important factor restricting the development of this technology. In addition, due to the large dimensions of the model and data, the existing computer processing power is limited to the use of local minimization inversion algorithms. Therefore, if the objective function value corresponding to the initial model is in the convex domain where the local minimum of the objective function is located rather than the global minimum, then the full waveform inversion will converge to the local minimum, making the final restored model deviate greatly from the true model, or even worse than the initial model. A significant manifestation of the nonlinearity of full waveform inversion is cycle jump, that is, when the predicted data deviates from the corresponding observed data by more than half a cycle, a cycle jump will occur. Therefore, cycle jump will cause the full waveform inversion to converge to a local minimum and give an erroneous inversion model. Summary of the invention

[0004] In response to the above problems, the present application provides a method, device, electronic device and storage medium for overcoming cycle jumping.

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

[0006] Obtain observational and synthetic data;

[0007] Based on the observed data and the synthesized data, construct a distance matrix between the observed data and the synthesized data;

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

[0009] determining a total deviation between the observed data and the synthetic data based on the distance matrix and the permutation matrix;

[0010] Using the total deviation as an objective function to update the model;

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

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

[0013]

[0014] In the formula, c ij is the distance matrix, t i is the time corresponding to the i-th recording point of the synthetic data, t j is the time corresponding to the jth record point of the observed data, T is the total observation time, η is the user input parameter, and u i Synthetic Data i The data corresponding to the moment, d j is the observed data t j The data corresponding to the moment, A is the maximum value of the observed data, and p and q are both norms.

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

[0016] h=∑ ij c ij m ij ;

[0017] Where h is the total deviation, c ij is the distance matrix, m ij is the permutation matrix.

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

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

[0020] The permutation matrix is ​​determined based on the optimal solution.

[0021] An embodiment of the present application provides a device for overcoming cycle skipping, the device comprising:

[0022] Acquisition module, used 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 determination module, configured to determine a permutation matrix based on the distance matrix;

[0025] A second determination module, configured to determine a total deviation between the observed data and the synthetic data based on the distance matrix and the permutation matrix;

[0026] An updating module, used for updating the model by taking the total deviation as an objective function;

[0027] The repeat module is used to repeat the above steps until the model converges.

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

[0029]

[0030] In the formula, c ij is the distance matrix, t i is the time corresponding to the i-th recording point of the synthetic data, t j is the time corresponding to the jth record point of the observed data, T is the total observation time, η is the user input parameter, and u i Synthetic Data i The data corresponding to the moment, d j is the observed data t j The data corresponding to the moment, A is the maximum value of the observed data, and p and q are both norms.

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

[0032] h=∑ ij c ij m ij ;

[0033] Where h is the total deviation, c ij is the distance matrix, m ij is the permutation matrix.

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

[0035] A solving unit, used to solve the distance matrix based on a paid ranking algorithm to obtain an optimal solution;

[0036] A determination unit is used to determine the permutation matrix based on the optimal solution.

[0037] An embodiment of the present application provides an electronic device, including: a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, any one of the above-mentioned methods is executed.

[0038] An embodiment of the present application provides a storage medium, which stores a computer program that can be executed by one or more processors and can be used to implement any of the methods described above.

[0039] The present application provides a method, device, electronic device and storage medium for overcoming cycle jumps, which obtain observation data and synthetic data, construct a distance matrix between the observation data and the synthetic data, and determine a permutation matrix based on the distance matrix; based on the distance matrix and the permutation matrix, determine the total deviation between the observation data and the synthetic data, use the total deviation as the objective function to update the model, and repeat the above steps until the model converges, so that the objective function corresponding to the model is in the convex domain where the global minimum is located, thereby overcoming the problem of cycle jumps. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0041] Figure 1 A schematic diagram of a process for a method provided in an embodiment of the present application;

[0042] Figure 2 It is a schematic diagram of a real model provided in the embodiment of the present application;

[0043] Figure 3 It is a schematic diagram of the initial model provided in the embodiment of the present application;

[0044] Figure 4 is a schematic diagram of observation data provided in an embodiment of the present application;

[0045] Figure 5 is a schematic diagram of initial synthetic data provided in an embodiment of the present application;

[0046] Figure 6 is a schematic diagram of the difference between the observed data and the synthetic data provided in the embodiment of the present application;

[0047] Figure 7 is a schematic diagram of the inversion result obtained by the objective function provided in the embodiment of the present application;

[0048] Figure 8 It is a schematic diagram of the final synthetic data provided in the embodiment of the present application;

[0049] Fig. 9 It is the final data comparison schematic diagram provided in the embodiment of the present application;

[0050] Fig.10 A schematic diagram of the structure of the device provided in the embodiment of the present application;

[0051] Fig.11 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

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

[0054] If similar descriptions of "first\second\third" appear in the application documents, the following instructions will be added. In the following description, the terms "first\second\third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged in a specific order or sequence where permitted, 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 those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0056] Embodiment 1

[0057] Based on the problems existing in the related art, the embodiment of the present application provides a method for overcoming cycle jumps, and the execution subject of the method for overcoming cycle jumps can be an electronic device. The electronic device can be various types of terminals such as a laptop, 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 messaging device, a portable gaming device), and can also be implemented as a server. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms.

[0058] The functions implemented by the method for overcoming cycle jumping provided in the embodiment of the present application can be implemented by calling program codes by a processor of an electronic device, wherein the program codes can be stored in a computer storage medium.

[0059] The present application embodiment provides a method for overcoming cycle jumps. Figure 1 A flow chart of the method provided in the embodiment of the present application is as follows: Figure 1 As shown, the method includes:

[0060] Step S1: Obtain observation data and synthetic data;

[0061] In the embodiments of the present application, observation data refers to seismic data recorded in the field by an instrument, which corresponds to simulated data generated by a real earth model. Synthetic data refers to seismic data generated by computer simulation, which corresponds to simulated data generated by a computational model in a computer (e.g., an initial velocity model at the beginning of inversion).

[0062] In the embodiment of the present application, the observed data may be displacement, velocity, or acceleration. Similarly, the synthesized data may be displacement, velocity, or acceleration.

[0063] Step S2: constructing a distance matrix between the observed data and the synthesized data based on the observed data and the synthesized data;

[0064] In the embodiment of the present application, full waveform inversion compares synthetic data with observation data. In the observation record, the observation data are all recorded in discrete form; meanwhile, in the digital synthetic record, the synthetic data are also in discrete form.

[0065] In the embodiment of the present application, it is assumed that the single-channel seismic observation data is j Yes j , the synthesized single channel data is at t i is u i , then we can construct the distance matrix ci j .

[0066] In an embodiment of the present application, the distance matrix is:

[0067]

[0068] In the formula, c ij is the distance matrix, t i is the time corresponding to the i-th recording point of the synthetic data, t j is the time corresponding to the jth record point of the observed data, T is the total observation time, η is the user input parameter, and ui is the synthetic data t i The data corresponding to the moment, d j is the observed data t j The data corresponding to the moment, A is the maximum value of the observed data, and p and q are both norms.

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

[0070] h=∑ ij c ij m ij ;

[0071] Where h is the total deviation, c ij is the distance matrix, m ij is the permutation matrix.

[0072] In the embodiments of the present application, c ij is the distance matrix, m ij is a permutation matrix, the product of each pair of seismic traces is the deviation, and then the total deviation can be obtained by summing up the deviations of all seismic traces.

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

[0074] In an embodiment of the present application, the permutation matrix can be composed of 0s and 1s, wherein there is only one 1 in each row and each column of the permutation matrix. Based on the distance matrix and the permutation matrix, the deviation value for each pair of seismic traces can be calculated, and then the deviation values ​​of all seismic traces can be summed to obtain a total deviation. The permutation matrix with the smallest total deviation can be taken as the target matrix.

[0075] In the embodiments of the present application, the permutation matrix may be equivalent to the permutation vector, or the permutation matrix may be represented by a vector. For example, j=σ i , indicating that the i-th position is replaced to the j-th position; where σ i is a permutation vector equivalent to the permutation matrix.

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

[0077] In the embodiment of the present application, when 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.

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

[0079] In the embodiment of the present application, the model is updated by minimizing the total deviation as the objective function.

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

[0081] In an embodiment of the present application, repeating the above steps refers to obtaining new synthetic data, thereby constructing a 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, and using the total deviation as the objective function to update the model until the model converges and the objective function is in the convex domain where the global minimum is located.

[0082] In the embodiment of the present application, the number of model updates can also be set, and the model update can be stopped until the number of model updates reaches the set number of updates. At this time, the default model converges.

[0083] To summarize, the observed data and the synthetic data are obtained, a distance matrix between the observed data and the synthetic data is constructed, and a permutation matrix is ​​determined 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, and the total deviation is used as the objective function to update the model, and the above steps are repeated until the model converges, so that the objective function corresponding to the model is in the convex domain where the global minimum is located, thereby overcoming the problem of periodic jumping.

[0084] Embodiment 2

[0085] Based on the problems existing in the related art, the embodiment of the present application provides a method for overcoming cycle jumps, and the execution subject of the method for overcoming cycle jumps can be an electronic device. The electronic device can be various types of terminals such as a laptop, 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 messaging device, a portable gaming device), and can also be implemented as a server. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms.

[0086] The functions implemented by the method for overcoming cycle jumping provided in the embodiment of the present application can be implemented by calling program codes by a processor of an electronic device, wherein the program codes can be stored in a computer storage medium.

[0087] The present application embodiment provides a method for overcoming cycle jumps. Figure 1 A flow chart of the method provided in the embodiment of the present application is as follows: Figure 1 As shown, the method includes:

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

[0089] In the embodiments of the present application, observation data refers to seismic data recorded in the field by an instrument, which corresponds to simulated data generated by a real earth model. Synthetic data refers to seismic data generated by computer simulation, which corresponds to simulated data generated by a computational model in a computer (e.g., an initial velocity model at the beginning of inversion).

[0090] In the embodiment of the present application, the observed data may be displacement, velocity, or acceleration. Similarly, the synthesized data may be displacement, velocity, or acceleration.

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

[0092] In the embodiment of the present application, full waveform inversion compares synthetic data with observation data. In the observation record, the observation data are all recorded in discrete form; meanwhile, in the digital synthetic record, the synthetic data are also in discrete form.

[0093] In the embodiment of the present application, it is assumed that the single-channel seismic observation data is j Yes j , the synthesized single channel data is at t i is u i , then we can construct the distance matrix c ij .

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

[0095]

[0096] In the formula, c ij is the distance matrix, t i is the time corresponding to the i-th recording point of the synthetic data, t j is the time corresponding to the jth record point of the observed data, T is the total observation time, η is the user input parameter, and u i Synthetic Data i The data corresponding to the moment, d j is the observed data t j The data corresponding to the moment, A is the maximum value of the observed data, and p and q are both norms.

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

[0098] h=∑ ij c ij m ij ;

[0099] Where h is the total deviation, c ij is the distance matrix, mij is the permutation matrix.

[0100] In the embodiments of the present application, c ij is the distance matrix, m ij is a permutation matrix, the product of each pair of seismic traces is the deviation, and then the total deviation can be obtained by summing up the deviations of all seismic traces.

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

[0102] In an embodiment of the present application, the permutation matrix can be composed of 0s and 1s, wherein there is only one 1 in each row and each column of the permutation matrix. Based on the distance matrix and the permutation matrix, the deviation value for each pair of seismic traces can be calculated, and then the deviation values ​​of all seismic traces can be summed to obtain a total deviation. The permutation matrix with the smallest total deviation can be taken as the target matrix.

[0103] In the embodiments of the present application, the permutation matrix may be equivalent to the permutation vector, or the permutation matrix may be represented by a vector. For example, j=σ i , indicating that the i-th position is replaced to the j-th position; where σ i is a permutation vector equivalent to the permutation matrix.

[0104] In the embodiment of the present application, determining a permutation matrix based on the distance matrix includes:

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

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

[0107] In the embodiments of the present application, the value solved for the distance matrix is ​​not unique, so the distance matrix can be solved by a bidding ranking algorithm to obtain the optimal solution corresponding to the distance matrix. 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, thereby ensuring that the total deviation of the observed data and the synthetic data determined by the distance matrix and the permutation matrix is ​​the minimum value.

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

[0109] In the embodiment of the present application, when 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 by taking the total deviation as the objective function;

[0111] In the embodiment of the present application, the model is updated by minimizing the total deviation as the objective function.

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

[0113] In an embodiment of the present application, repeating the above steps refers to obtaining new synthetic data, thereby constructing a 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, and using the total deviation as the objective function to update the model until the model converges and the objective function is in the convex domain where the global minimum is located.

[0114] In the embodiment of the present application, the number of model updates can also be set, and the model update can be stopped until the number of model updates reaches the set number of updates. At this time, the default model converges.

[0115] Embodiment 3

[0116] The method for overcoming cycle skipping in the present application is described in detail below through specific embodiments.

[0117] This application uses a transmission model test for demonstration.

[0118] Figure 2 What is displayed is a real model, with a uniform background and a circular abnormal velocity body in the middle. The background velocity is 3000m / s and the abnormal velocity is 3600m / s.

[0119] Figure 3 The uniform initial model is shown with a velocity of 3000 m / s. There are 48 evenly spaced explosion sources above, 100 m from the upper interface, and 300 pressure receivers below, 100 m from the lower interface. The source wavelet is a 10 Hz Ricker wavelet.

[0120] Figure 4 Observational data are shown.

[0121] Figure 5 Shown are initial synthetic data.

[0122] Figure 6 The difference between the observed data and the synthetic data is shown. It can be seen that the periodic jump phenomenon is very serious. Conventional methods cannot be correctly inverted and always fall into local extreme values. In this inversion, no multi-level inversion strategy such as filtering or truncating data is adopted, and all data are input at one time.

[0123] Figure 7This is the inversion result obtained by using the objective function proposed by the present invention, and when constructing the distance matrix, p = 1 and q = 2 are selected. Since the objective function is relatively convex, the velocity structure can be well inverted.

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

[0125] Fig. 9 What is shown is the final data comparison, the observed data has been fitted and all the earthquake information has been fully extracted.

[0126] Embodiment 4

[0127] Based on the foregoing embodiments, the embodiments of the present application provide a device for overcoming cycle jumping. The modules included in the device and the units included in the modules can be implemented by a processor in a computer device; of course, they can also be implemented by a specific logic circuit; in the implementation process, the processor can be a central processing unit (CPU, Central Processing Unit), a microprocessor (MPU, Microprocessor Unit), a digital signal processor (DSP, Digital Signal Processing) or a field programmable gate array (FPGA, Field Programmable Gate Array), etc.

[0128] The embodiment of the present application provides a device for overcoming cycle skipping. Fig.10 A schematic diagram of the structure of the device provided in the embodiment of the present application, such as Fig.10 As shown, the device comprises:

[0129] Acquisition module, used to acquire observation data and synthetic data;

[0130] In the embodiments of the present application, observation data refers to seismic data recorded in the field by an instrument, which corresponds to simulated data generated by a real earth model. Synthetic data refers to seismic data generated by computer simulation, which corresponds to simulated data generated by a computational model in a computer (e.g., an initial velocity model at the beginning of inversion).

[0131] In the embodiment of the present application, the observed data may be displacement, velocity, or acceleration. Similarly, the synthesized data may be displacement, velocity, or acceleration.

[0132] 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;

[0133] In the embodiment of the present application, full waveform inversion compares synthetic data with observation data. In the observation record, the observation data are all recorded in discrete form; meanwhile, in the digital synthetic record, the synthetic data are also in discrete form.

[0134] In the embodiment of the present application, it is assumed that the single-channel seismic observation data is j Yes j , the synthesized single channel data is at t i is u i , then we can construct the distance matrix c ij .

[0135] In an embodiment of the present application, the distance matrix is:

[0136]

[0137] In the formula, c ij is the distance matrix, t i is the time corresponding to the i-th recording point of the synthetic data, t j is the time corresponding to the jth record point of the observed data, T is the total observation time, η is the user input parameter, and u i Synthetic Data i The data corresponding to the moment, d j is the observed data t j The data corresponding to the moment, A is the maximum value of the observed data, and p and q are both norms.

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

[0139] h=∑ ij c ij m ij ;

[0140] Where h is the total deviation, c ij is the distance matrix, m ij is the permutation matrix.

[0141] In the embodiments of the present application, c ij is the distance matrix, m ij is a permutation matrix, the product of each pair of seismic traces is the deviation, and then the total deviation can be obtained by summing up the deviations of all seismic traces.

[0142] A first determination module, configured to determine a permutation matrix based on the distance matrix;

[0143] In an embodiment of the present application, the permutation matrix can be composed of 0s and 1s, wherein there is only one 1 in each row and each column of the permutation matrix. Based on the distance matrix and the permutation matrix, the deviation value for each pair of seismic traces can be calculated, and then the deviation values ​​of all seismic traces can be summed to obtain a total deviation. The permutation matrix with the smallest total deviation can be taken as the target matrix.

[0144] In the embodiments of the present application, the permutation matrix may be equivalent to the permutation vector, or the permutation matrix may be represented by a vector. For example, j=σ i , indicating that the i-th position is replaced to the j-th position; where σ i is a permutation vector equivalent to the permutation matrix.

[0145] A second determination module, configured to determine a total deviation between the observed data and the synthetic data based on the distance matrix and the permutation matrix;

[0146] In the embodiment of the present application, when 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.

[0147] An updating module, used for updating the model by taking the total deviation as an objective function;

[0148] In the embodiment of the present application, the model is updated by minimizing the total deviation as the objective function.

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

[0150] In an embodiment of the present application, repeating the above steps refers to obtaining new synthetic data, thereby constructing a 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, and using the total deviation as the objective function to update the model until the model converges and the objective function is in the convex domain where the global minimum is located.

[0151] In the embodiment of the present application, the number of model updates can also be set, and the model update can be stopped until the number of model updates reaches the set number of updates. At this time, the default model converges.

[0152] In an embodiment of the present application, the first determining module includes:

[0153] A solving unit, used to solve the distance matrix based on a paid ranking algorithm to obtain an optimal solution;

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

[0155] In the embodiments of the present application, the value solved for the distance matrix is ​​not unique, so the distance matrix can be solved by a bidding ranking algorithm to obtain the optimal solution corresponding to the distance matrix. 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, thereby ensuring that the total deviation of the observed 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 embodiment of the present application, if the above-mentioned method for overcoming cycle jump 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 such an understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium, including several instructions to enable 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 each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM, Read Only Memory), a disk or an optical disk. In this way, the embodiment of the present application is not limited to any specific combination of hardware and software.

[0157] Embodiment 5

[0158] Accordingly, an embodiment of the present application provides a storage medium on which a computer program is stored, characterized in that when the computer program is executed by a processor, the steps in the method for overcoming cycle jumps provided in the above embodiment are implemented.

[0159] Embodiment 6

[0160] An embodiment of the present application provides an electronic device; Fig.11 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Fig.11 As shown, 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 connection and communication between these components. The user interface 103 may include a display screen, and the external communication interface 104 may include a standard wired interface and a wireless interface. The processor 101 is configured to execute a program of a method for overcoming cycle jumps stored in the memory to implement the steps in the method for overcoming cycle jumps provided in the above embodiment,

[0161] The present application embodiment provides a method for overcoming cycle jumping, the method comprising:

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

[0163] In the embodiments of the present application, observation data refers to seismic data recorded in the field by an instrument, which corresponds to simulated data generated by a real earth model. Synthetic data refers to seismic data generated by computer simulation, which corresponds to simulated data generated by a computational model in a computer (e.g., an initial velocity model at the beginning of inversion).

[0164] In the embodiment of the present application, the observed data may be displacement, velocity, or acceleration. Similarly, the synthesized data may be displacement, velocity, or acceleration.

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

[0166] In the embodiment of the present application, full waveform inversion compares synthetic data with observation data. In the observation record, the observation data are all recorded in discrete form; meanwhile, in the digital synthetic record, the synthetic data are also in discrete form.

[0167] In the embodiment of the present application, it is assumed that the single-channel seismic observation data is j Yes j , the synthesized single channel data is at t i is u i , then we can construct the distance matrix c ij .

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

[0169]

[0170] In the formula, c ij is the distance matrix, t i is the time corresponding to the i-th recording point of the synthetic data, t j is the time corresponding to the jth record point of the observed data, T is the total observation time, η is the user input parameter, and u i Synthetic Data i The data corresponding to the moment, d j is the observed data t j The data corresponding to the moment, A is the maximum value of the observed data, and p and q are both norms.

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

[0172] h=∑ ij c ij m ij ;

[0173] Where h is the total deviation, c ij is the distance matrix, mij is the permutation matrix.

[0174] In the embodiments of the present application, c ij is the distance matrix, m ij is a permutation matrix, the product of each pair of seismic traces is the deviation, and then the total deviation can be obtained by summing up the deviations of all seismic traces.

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

[0176] In an embodiment of the present application, the permutation matrix can be composed of 0s and 1s, wherein there is only one 1 in each row and each column of the permutation matrix. Based on the distance matrix and the permutation matrix, the deviation value for each pair of seismic traces can be calculated, and then the deviation values ​​of all seismic traces can be summed to obtain a total deviation. The permutation matrix with the smallest total deviation can be taken as the target matrix.

[0177] In the embodiments of the present application, the permutation matrix may be equivalent to the permutation vector, or the permutation matrix may be represented by a vector. For example, j=σ i , indicating that the i-th position is replaced to the j-th position; where σ i is a permutation vector equivalent to the permutation matrix.

[0178] Step S4: determining the total deviation between the observed data and the synthetic data based on the distance matrix and the permutation matrix;

[0179] In the embodiment of the present application, when 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.

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

[0181] In the embodiment of the present application, the model is updated by minimizing the total deviation as the objective function.

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

[0183] In an embodiment of the present application, repeating the above steps refers to obtaining new synthetic data, thereby constructing a 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, and using the total deviation as the objective function to update the model until the model converges and the objective function is in the convex domain where the global minimum is located.

[0184] In the embodiment of the present application, the number of model updates can also be set, and the model update can be stopped until the number of model updates reaches the set number of updates. At this time, the default model converges.

[0185] To summarize, the observed data and the synthetic data are obtained, a distance matrix between the observed data and the synthetic data is constructed, and a permutation matrix is ​​determined 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, and the total deviation is used as the objective function to update the model, and the above steps are repeated until the model converges, so that the objective function corresponding to the model is in the convex domain where the global minimum is located, thereby overcoming the problem of periodic jumping.

[0186] In the embodiment of the present application, determining a permutation matrix based on the distance matrix includes:

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

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

[0189] It should be noted here that the description of the above storage medium and electronic device embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

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

[0191] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

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

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

[0194] In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be a separate unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0195] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read Only Memory), disks or optical disks, etc. Various media that can store program codes.

[0196] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can essentially or in other words, the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a controller to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0197] The above is only an implementation method of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for overcoming cycle skipping, characterized in that: The method comprises: Obtain observational and synthetic data; Based on the observed data and the synthesized data, construct a distance matrix between the observed data and the synthesized data; determining a permutation matrix based on the distance matrix; determining a total deviation between the observed data and the synthetic data based on the distance matrix and the permutation matrix; Using the total deviation as an objective function to update the model; Repeat the above steps until the model converges.

2. The method for overcoming cycle skipping according to claim 1, characterized in that: The distance matrix is: In the formula, c ij is the distance matrix, t i is the time corresponding to the i-th recording point of the synthetic data, t j is the time corresponding to the jth record point of the observed data, T is the total observation time, η is the user input parameter, and u i Synthetic Data i The data corresponding to the moment, d j is the observed data t j The data corresponding to the moment, A is the maximum value of the observed data, and p and q are both norms.

3. The method for overcoming cycle skipping according to claim 2, characterized in that: The total deviation is: h=∑ ij c ij m ij ; Where h is the total deviation, c ij is the distance matrix, m ij is the permutation matrix.

4. The method for overcoming cycle skipping according to claim 1, characterized in that: The determining of a permutation matrix based on the distance matrix comprises: Solving the distance matrix based on a paid ranking algorithm to obtain an optimal solution; The permutation matrix is ​​determined based on the optimal solution.

5. A device for overcoming cycle skipping, characterized in that: The device comprises: Acquisition module, used to acquire observation data and synthetic data; 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; A first determination module, configured to determine a permutation matrix based on the distance matrix; A second determination module, configured to determine a total deviation between the observed data and the synthetic data based on the distance matrix and the permutation matrix; An updating module, used for updating the model by taking the total deviation as an objective function; The repeat module is used to repeat the above steps until the model converges.

6. The device for overcoming cycle skipping according to claim 5, characterized in that: The distance matrix is: In the formula, c ij is the distance matrix, t i is the time corresponding to the i-th recording point of the synthetic data, t j is the time corresponding to the jth record point of the observed data, T is the total observation time, η is the user input parameter, and u i Synthetic Data i The data corresponding to the moment, d j is the observed data t j The data corresponding to the moment, A is the maximum value of the observed data, and p and q are both norms.

7. The device for overcoming cycle skipping according to claim 6, characterized in that: The total deviation is: h=∑ ij c ij m ij ; Where h is the total deviation, c ij is the distance matrix, m ij is the permutation matrix.

8. The device for overcoming cycle skipping according to claim 5, characterized in that: The first determining module comprises: A solving unit, used to solve the distance matrix based on a paid ranking algorithm to obtain an optimal solution; A determination unit is used to determine the permutation matrix based on the optimal solution.

9. An electronic device, characterized in that: include: A memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method according to any one of claims 1 to 4 is performed.

10. 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 claimed in any one of claims 1 to 4.

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