Chromatographic inversion iterative computation quality control method, device and equipment, storage medium and product

Abstract

The invention provides a tomographic inversion iterative computation quality control method, device and equipment, a storage medium and a product, relates to the technical field of seismic exploration, and aims to solve the problem that a specific reason causing an error result is difficult to intuitively analyze by a related tomographic inversion technology. The method comprises the following steps: carrying out forward modeling on an initial velocity model, carrying out difference calculation on second first arrival time of each receiving channel obtained by calculation and corresponding picked first first arrival time, and obtaining an error corresponding to each receiving channel; performing root-mean-square error statistics according to the errors corresponding to all the receiving channels of each shot point to obtain a root-mean-square error corresponding to each shot point; performing multi-round iterative updating on the initial velocity model based on a tomographic inversion algorithm, and performing visual display on seismic wave inversion errors of all shot points after each round of iterative calculation to obtain a multi-round error distribution diagram; and calculating the seismic wave inversion error reduction percentage of each shot point after multiple rounds of iteration, and analyzing the tomographic inversion effect.

Description

Technical Field

[0001] This application relates to the field of seismic exploration technology, and more specifically, to a tomographic inversion iterative calculation quality control method, apparatus, equipment, storage medium, and product. Background Technology

[0002] As exploration efforts intensify and targets penetrate deeper, the surface of the work area becomes more complex, with significant lateral variations in shallow velocities. Traditional near-surface modeling and static correction techniques are no longer fully adequate for handling complex work areas and cannot provide a detailed characterization of near-surface velocities. First-arrival tomography inversion technology has become the mainstream modeling technique due to its strong adaptability and more advanced principles. However, in practical applications, this technology has limitations in quality control methods. For example, the root mean square error between the fitted travel time and the picked travel time, and the fact that each iteration yields a single value, result in a high average effect and make it difficult to fully reflect the details of the calculation results. Summary of the Invention

[0003] This application provides a method, apparatus, equipment, storage medium, and product for quality control of tomographic inversion iterative calculations, aiming to solve the problem that the calculation process of related first-arrival tomographic inversion techniques is not intuitive and it is difficult to analyze the specific reasons for the magnitude of the error results.

[0004] A first aspect of this application provides a quality control method for torsion inversion iterative calculation, the method comprising: Acquire shot gather records for the target area, the shot gather records including seismic wave information from multiple receiver channels corresponding to each shot point, and extract the first arrival time of the seismic waves from each receiver channel from the shot gather records. Based on prior information, an initial velocity model is established, and forward modeling is performed on the initial velocity model using the ray tracing method to obtain the second first arrival time of seismic waves for each receiver channel. The difference between the second first arrival time of the seismic waves of each receiving channel and the corresponding first first arrival time is calculated to obtain the error corresponding to each receiving channel. The root mean square error of each shot point is obtained by statistically analyzing the errors of all the receiving channels at each shot point. Based on the root mean square error corresponding to each shot point, the initial velocity model is iteratively updated multiple times using the tomographic inversion algorithm, and the seismic wave inversion error of all shot points after each iteration is visualized to obtain a multi-round error distribution map. Based on the multi-round error distribution map, the reduction ratio of the seismic wave inversion error of each shot point in different iteration rounds is calculated to obtain the percentage reduction of the seismic wave inversion error of each shot point after multi-round iteration calculation. The tomographic inversion effect is analyzed based on the percentage reduction of the seismic wave inversion error.

[0005] In one optional implementation, the step of performing root mean square error statistics based on the errors corresponding to all the receiving channels at each shot point to obtain the root mean square error for each shot point includes: The root mean square error corresponding to each shot point is calculated according to the following formula: , Among them, X rms x represents the root mean square error corresponding to each shot point; i represents the error corresponding to the i-th receiving channel; n represents the number of receiving channels corresponding to this shot point.

[0006] In one optional implementation, the step of iteratively updating the initial velocity model based on the root mean square error corresponding to each shot point using a tomographic inversion algorithm includes: Based on the seismic wave inversion error, the velocity values ​​in the initial velocity model are adjusted to obtain the initial velocity model after multiple iterations.

[0007] In one optional implementation, the analysis of the tomographic inversion effect based on the percentage reduction in seismic wave inversion error includes: If the percentage reduction in seismic wave inversion error at all shot points is less than the preset value, the initial velocity model updated in this iteration is used as the target velocity model.

[0008] In one optional implementation, the analysis of the tomographic inversion effect based on the percentage reduction in seismic wave inversion error includes: If the percentage reduction in the seismic wave inversion error at a certain shot point is not less than a preset value, the quality control adjustment of the shot point is carried out based on the multi-round error distribution map.

[0009] In one optional implementation, the quality control adjustment of a certain shot point based on the multi-round error distribution map includes: Analyze the waveform characteristics of a certain shot point, the waveform characteristics including: amplitude, frequency, and phase; If abnormal amplitude, missing frequency, or phase discontinuity exists in the waveform characteristics, corresponding adjustments are made, including: adjusting source parameters, receiving device settings, sampling rate, filter settings, and data alignment.

[0010] A second aspect of this application provides a tomographic inversion iterative calculation quality control device, the device comprising: The acquisition module is used to acquire shot gather records of the target area. The shot gather records include seismic wave information from multiple receiver channels corresponding to each shot point. The first arrival time of the seismic waves from each receiver channel is extracted from the shot gather records. The simulation module is used to establish an initial velocity model based on prior information, and to perform forward modeling of the initial velocity model based on the ray tracing method to obtain the second first arrival time of seismic waves for each receiver channel. The first calculation module is used to calculate the difference between the second first arrival time of the seismic waves of each receiving channel and the corresponding first first arrival time, so as to obtain the error corresponding to each receiving channel. The second calculation module is used to perform root mean square error statistics based on the errors corresponding to all the receiving channels of each shot point, and obtain the root mean square error corresponding to each shot point. The inversion display module is used to perform multiple iterations of updating the initial velocity model based on the root mean square error corresponding to each shot point and the tomographic inversion algorithm, and to visualize the seismic wave inversion error of all shot points after each iteration, thus obtaining a multi-round error distribution map. The analysis module is used to calculate the reduction ratio of the seismic wave inversion error of each shot point in different iterations based on the multi-round error distribution map, to obtain the percentage reduction of the seismic wave inversion error of each shot point after multi-round iteration calculation, and to analyze the tomographic inversion effect based on the percentage reduction of the seismic wave inversion error.

[0011] According to a third aspect of this application, an electronic device is provided, comprising: a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the tomographic inversion iterative calculation quality control method described in the first aspect.

[0012] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the tomographic inversion iterative calculation quality control method described in the first aspect.

[0013] According to a fifth aspect of this application, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the tomographic inversion iterative calculation quality control method described in the first aspect.

[0014] In this embodiment, the initial velocity model is forward-modeled using ray tracing to obtain the second arrival time. The difference between the second arrival time of each receiver channel and the first arrival time of the same channel is calculated to obtain the error corresponding to each receiver channel. The root mean square error (RMSE) of each shot point is obtained by statistically analyzing the errors of all receiver channels at each shot point. The initial velocity model is then iteratively updated multiple times using a tomographic inversion algorithm. The inversion errors of all shot points after each iteration are visualized to obtain a multi-round error distribution map. The percentage reduction in inversion error for each shot point is calculated to analyze the tomographic inversion effect. This allows for a direct analysis of the error convergence degree of each shot point region during the calculation process, which helps to further analyze the causes of error magnitude in different regions and provides a strong basis for the accuracy evaluation and analysis of the finally established velocity model. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of the steps of the tomographic inversion iterative calculation quality control method proposed in an embodiment of this application; Figure 2 This is a multi-round error distribution diagram of the tomographic inversion iterative calculation quality control method proposed in an embodiment of this application; Figure 3 This is a conventional tomographic inversion error convergence diagram of the tomographic inversion iterative calculation quality control method proposed in an embodiment of this application; Figure 4 This is a schematic diagram of an electronic device according to this application. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] As exploration efforts intensify and targets are explored more deeply, the surface of the work area becomes increasingly complex, with dramatic lateral variations in shallow velocities. The requirements for near-surface modeling and static correction techniques are becoming increasingly demanding. In terms of static correction, existing methods such as field static correction, elevation, and refraction static correction are no longer fully adequate for handling the complex conditions of current work areas. Furthermore, in terms of near-surface velocity modeling, methods such as surface result interpolation and refraction static correction modeling, due to the limitations of their respective velocity models, cannot provide a precise depiction of near-surface velocities.

[0019] First-arrival tomography inversion is a technique that uses the first-arrival information of seismic waves to invert underground velocity structures. Due to its strong adaptability and more advanced principles, this method has significant advantages in near-surface modeling and static correction, and has become a mainstream modeling technique in the industry. However, the quality control methods used have two main limitations: firstly, the fitted first-arrival travel times after forward modeling are characterized by large data volumes and unintuitive display; secondly, the root mean square error between the fitted and picked travel times is calculated as a single value in each iteration, resulting in a high average effect. Therefore, new quality control methods are needed to provide more specific quality control and display of the calculation results, ensuring their rationality and accuracy.

[0020] Reference Figure 1 , Figure 1 This is a flowchart of the steps of a tomographic inversion iterative calculation quality control method proposed in an embodiment of this application, which includes the following steps S11 to S16: Step S11: Obtain the shot gather record of the target area. The shot gather record includes seismic wave information from multiple receiver channels corresponding to each shot point. Extract the first arrival time of the seismic waves from each receiver channel from the shot gather record.

[0021] Shot gather records are obtained by setting up multiple excitation points (shot points) and receiver points (geophones) on the ground or in wells within the target area. After seismic waves are generated at each shot point, they propagate underground and are reflected at different geological interfaces. These reflected waves are then received and recorded by the geophones. The seismic wave information received by all the corresponding geophones after each shot point is generated constitutes the shot gather record for that shot point. The receiver channel is a data channel composed of the seismic wave information received and recorded by the geophones.

[0022] First arrival time (FAT) refers to the time when a seismic wave, after traveling through the subsurface medium from the shot point, first arrives at the surface receiver and is recorded. It is the earliest arriving waveform in the seismic record and is crucial for determining velocity changes in subsurface structures. Extracting the FAT from each receiver channel in the shot gather record can be done manually or using automated extraction software.

[0023] Step S12: Based on prior information, establish an initial velocity model, and perform forward modeling on the initial velocity model using the ray tracing method to obtain the second first arrival time of seismic waves for each receiver channel.

[0024] Prior information about the target area is collected, including at least geological structure, lithological distribution, drilling data, well logging data, and previous seismic exploration results. Based on the collected prior information, an initial velocity model of the subsurface medium is constructed using geological modeling software or manually. This model is typically a three-dimensional grid, where each grid point or block is assigned a velocity value representing the wave velocity of the subsurface medium at that location.

[0025] Ray tracing is a method for calculating the propagation path and travel time of seismic waves in subsurface media. This study uses ray tracing algorithms to perform forward modeling on an initial velocity model. During the simulation, seismic waves are emitted from the shot point, and ray tracing is used to calculate their propagation path and travel time in the subsurface media. Through forward modeling, the theoretical second arrival time of the seismic waves at each receiver point under the initial velocity model can be obtained.

[0026] Step S13: Calculate the difference between the second first arrival time of the seismic waves of each receiving channel and the corresponding first first arrival time to obtain the error corresponding to each receiving channel.

[0027] The difference between the second first arrival time of each receiver channel obtained from the simulation calculation and the first first arrival time picked up by the same receiver channel is calculated to obtain the error corresponding to each receiver channel. Calculating the error magnitude of each channel is an important basis for evaluating the accuracy of the current initial velocity model, which is used for subsequent analysis and processing.

[0028] Step S14: Perform root mean square error statistics based on the errors corresponding to all the receiving channels at each shot point to obtain the root mean square error corresponding to each shot point.

[0029] Root mean square error (RMS) is a quantitative metric that measures the difference between model predictions and actual measurements. After each shot point is fired, multiple receivers collect seismic wave information. When calculating the RMS, the errors of all receiver channels corresponding to each shot point are considered to obtain the RMS for each shot point. This helps identify which shot points (or areas) have larger errors, allowing for more targeted corrections to the velocity model.

[0030] Further, the step of performing root mean square error statistics based on the errors corresponding to all the receiving channels at each shot point to obtain the root mean square error corresponding to each shot point includes: The root mean square error corresponding to each shot point is calculated according to the following formula: , Among them, X rms x represents the root mean square error corresponding to each shot point; i represents the error corresponding to the i-th receiving channel; n represents the number of receiving channels corresponding to this shot point.

[0031] The root mean square error (RMSE) is calculated based on the errors of all receiver channels corresponding to each shot point. This is achieved by squaring the errors of each receiver channel, averaging the results, and then taking the square root of the average. This yields a single numerical value that reflects the overall error level of all receiver channels at that shot point. The larger this value, the greater the error at that shot point, and the less accurate the velocity model may be in that region. Specifically, the RMS error for each shot point is calculated using the following formula (1): (1) Among them, X rms x represents the root mean square error corresponding to each shot point; i represents the error corresponding to the i-th receiving channel; n represents the number of receiving channels corresponding to this shot point.

[0032] Step S15: Based on the root mean square error corresponding to each shot point, the initial velocity model is iterated and updated multiple times using the tomographic inversion algorithm, and the seismic wave inversion error of all shot points calculated in each iteration is visualized to obtain a multi-round error distribution map.

[0033] The layered inversion algorithm is an iterative algorithm used for seismic velocity model inversion. It is typically based on optimization methods such as gradient descent or Newton's method, adjusting the velocity model parameters by calculating the gradient or Hessian matrix of the velocity parameters to optimize the velocity model.

[0034] Based on the root mean square error (RMSE) corresponding to each shot point, the initial velocity model is iteratively updated multiple times using a tomographic inversion algorithm. Specifically, the forward modeling and error calculation process is repeated using the velocity model updated in the previous iteration. Based on the newly calculated seismic wave inversion error information, the velocity model is updated again using the tomographic inversion algorithm. After each iteration, the calculated seismic wave inversion errors for all shot points are used to generate an error distribution map using visualization tools (such as plotting software, programming libraries, etc.). After multiple iterations (e.g., 7 iterations), a multi-round error distribution map is obtained, as shown below. Figure 2 As shown in the diagram, by observing the error distribution diagrams from multiple rounds, the velocity calculation results of the tomographic inversion can be intuitively analyzed, allowing for further analysis and optimization of the velocity model.

[0035] Furthermore, the step of iteratively updating the initial velocity model based on the root mean square error corresponding to each shot point using a tomographic inversion algorithm includes: Based on the seismic wave inversion error, the velocity values ​​in the initial velocity model are adjusted to obtain the initial velocity model after multiple iterations.

[0036] The root mean square error (RMSE) is calculated as a gradient (i.e., sensitivity or partial derivative) with respect to each parameter in the initial velocity model. Based on this gradient information, optimization algorithms (such as the conjugate gradient method, L-BFGS, etc.) are used to update the parameters in the velocity model, typically involving increasing or decreasing velocity values ​​in certain regions of the model. This process is repeated multiple times based on the newly calculated seismic wave inversion error information from the previous round, adjusting the velocity values ​​in the initial velocity model to ultimately obtain a multi-round iteratively updated initial velocity model.

[0037] Step S16: Based on the multi-round error distribution map, calculate the reduction ratio of the seismic wave inversion error of each shot point in different iteration rounds, and obtain the percentage reduction of the seismic wave inversion error of each shot point after multi-round iteration calculation. Analyze the tomographic inversion effect based on the percentage reduction of the seismic wave inversion error.

[0038] Multi-round error distribution plots illustrate the error changes for each shot point across different iterations. For each shot point, the percentage reduction in seismic wave inversion error across different iterations is calculated. By combining the multi-round error distribution plots with the further quantified percentage reduction, the overall trend of the percentage reduction in error for all shot points is observed, determining whether the inversion process is stable and effective. This allows for further analysis of the quality of picked first arrivals and individual seismic shots in areas with significant errors, providing key insights and directions for adjusting the accuracy of picked first arrivals, improving the accuracy and reliability of the inversion results, and providing a strong basis for the final velocity model accuracy evaluation and analysis.

[0039] Furthermore, the analysis of the tomographic inversion effect based on the percentage reduction in seismic wave inversion error includes: If the percentage reduction in seismic wave inversion error at all shot points is less than the preset value, the initial velocity model updated in this iteration is used as the target velocity model.

[0040] Observe whether the percentage reduction in error at all shot points shows a downward trend and whether it has reached a stable state. Set a preset value (e.g., 5%) to judge whether the error reduction is significant. When the percentage reduction in seismic wave inversion error at all shot points is less than the preset value, the inversion process is close to convergence or has reached the preset accuracy requirement, and no further iterations are needed. Use the initial velocity model updated in this iteration as the target velocity model to better reflect the underground velocity structure.

[0041] Furthermore, the analysis of the tomographic inversion effect based on the percentage reduction in seismic wave inversion error includes: If the percentage reduction in the seismic wave inversion error at a certain shot point is not less than a preset value, the quality control adjustment of the shot point is carried out based on the multi-round error distribution map.

[0042] If the percentage reduction in seismic wave inversion error at a particular shot point is not less than a preset value (i.e., the error reduction is insignificant or even increases), quality control adjustments are needed for that shot point. A comprehensive analysis combining topography, surface conditions, and single-shot quality should be conducted to analyze the quality of the picked first arrivals and individual seismic shots. The single-shot seismic records for that shot point should be re-examined to confirm any data quality issues, and re-sampling or reprocessing should be performed if necessary. If the shot point is located in a special geological area (such as a fault zone or lithological change zone), where seismic wave propagation paths are complex, relevant parameters in the initial velocity model can be adjusted or constraints can be added. The accuracy of the first arrival time picking directly affects the accuracy of the inversion results; the first arrival times for areas with large errors can be re-picked to ensure accuracy.

[0043] Furthermore, the quality control adjustment of a certain shot point based on the multi-round error distribution map includes: Analyze the waveform characteristics of a certain shot point, the waveform characteristics including: amplitude, frequency, and phase; If abnormal amplitude, missing frequency, or phase discontinuity exists in the waveform characteristics, corresponding adjustments are made, including: adjusting source parameters, receiving device settings, sampling rate, filter settings, and data alignment.

[0044] To analyze the single-shot quality at a specific shot point, a detailed analysis of its waveform characteristics is necessary. These characteristics include amplitude, frequency, and phase. Check for abnormal waveform amplitudes, such as excessively high, low, or unstable amplitudes. If abnormal amplitudes are found, adjustments can be made by modifying source parameters and receiver settings, such as adjusting the source's energy output, excitation method, or excitation time. Analyze the waveform for missing or abnormal frequency components. If missing frequencies are present, adjustments can be made by modifying the sampling rate and filter settings, such as increasing the sampling rate to capture a wider frequency range. Check for continuous waveform phase, including any phase jumps or inconsistencies. If phase discontinuities are found, adjustments can be made through data alignment, such as using cross-correlation or phase matching methods to realign the data.

[0045] Compared to conventional methods for calculating convergence error in tomographic inversion, this method uses root mean square error (RMSE) statistics for all channels, resulting in a single value, such as... Figure 3As shown. This method provides a global error estimate, but it cannot reflect the error characteristics of different shots. The tomographic inversion iterative calculation quality control method of this application performs first arrival error statistics according to the receiver channel of each shot, and uses the statistical results as the current root mean square error of that shot. Each shot has a root mean square error, and the information is stored. It can analyze the error situation of each shot in more detail, which helps to identify which shot points (or areas) have larger errors, so as to make more targeted corrections to the velocity model.

[0046] This application, by performing error statistics and analysis on a shot-by-shot basis, can further extend to different gathers in seismic data, including the error convergence of each trace in the shot gather, as well as the error convergence of receiver gathers, offset gathers, and common center point gathers. For example, for receiver gathers, receiver gather extraction is performed on all traces to obtain data information after excitation by different shot points at the same receiver. Then, root mean square error statistics are performed for each receiver to obtain the error statistics results for each receiver.

[0047] In this embodiment, the initial velocity model is forward-modeled using ray tracing to obtain the second arrival time. The difference between the second arrival time of each receiver channel and the first arrival time of the same channel is calculated to obtain the error corresponding to each receiver channel. The root mean square error (RMSE) of each shot point is obtained by statistically analyzing the errors of all receiver channels at each shot point. The initial velocity model is then iteratively updated multiple times using a tomographic inversion algorithm. The inversion errors of all shot points after each iteration are visualized to obtain a multi-round error distribution map. The percentage reduction in inversion error for each shot point is calculated to analyze the tomographic inversion effect. This allows for a direct analysis of the error convergence degree of each shot point region during the calculation process, which helps to further analyze the causes of error magnitude in different regions and provides a strong basis for the accuracy evaluation and analysis of the finally established velocity model.

[0048] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of this disclosure are not limited to the described order of actions, because according to the embodiments of this disclosure, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of this disclosure.

[0049] Based on the same inventive concept, embodiments of this application disclose a tomographic inversion iterative calculation quality control device, the device comprising: The acquisition module is used to acquire shot gather records of the target area. The shot gather records include seismic wave information from multiple receiver channels corresponding to each shot point. The first arrival time of the seismic waves from each receiver channel is extracted from the shot gather records. The simulation module is used to establish an initial velocity model based on prior information, and to perform forward modeling of the initial velocity model based on the ray tracing method to obtain the second first arrival time of seismic waves for each receiver channel. The first calculation module is used to calculate the difference between the second first arrival time of the seismic waves of each receiving channel and the corresponding first first arrival time, so as to obtain the error corresponding to each receiving channel. The second calculation module is used to perform root mean square error statistics based on the errors corresponding to all the receiving channels of each shot point, and obtain the root mean square error corresponding to each shot point. The inversion display module is used to perform multiple iterations of updating the initial velocity model based on the root mean square error corresponding to each shot point and the tomographic inversion algorithm, and to visualize the seismic wave inversion error of all shot points after each iteration, thus obtaining a multi-round error distribution map. The analysis module is used to calculate the reduction ratio of the seismic wave inversion error of each shot point in different iterations based on the multi-round error distribution map, to obtain the percentage reduction of the seismic wave inversion error of each shot point after multi-round iteration calculation, and to analyze the tomographic inversion effect based on the percentage reduction of the seismic wave inversion error.

[0050] In one optional implementation, the second computing module is specifically used for: The root mean square error corresponding to each shot point is calculated according to the following formula: , Among them, X rms x represents the root mean square error corresponding to each shot point; i represents the error corresponding to the i-th receiving channel; n represents the number of receiving channels corresponding to this shot point.

[0051] In one optional implementation, the inversion display module is specifically used for: Based on the seismic wave inversion error, the velocity values ​​in the initial velocity model are adjusted to obtain the initial velocity model after multiple iterations.

[0052] In one optional implementation, the analysis module is specifically used for: If the percentage reduction in seismic wave inversion error at all shot points is less than the preset value, the initial velocity model updated in this iteration is used as the target velocity model.

[0053] In one optional implementation, the analysis module is specifically used for: If the percentage reduction in the seismic wave inversion error at a certain shot point is not less than a preset value, the quality control adjustment of the shot point is carried out based on the multi-round error distribution map.

[0054] In one optional implementation, the analysis module is specifically used for: Analyze the waveform characteristics of a certain shot point, the waveform characteristics including: amplitude, frequency, and phase; If abnormal amplitude, missing frequency, or phase discontinuity exists in the waveform characteristics, corresponding adjustments are made, including: adjusting source parameters, receiving device settings, sampling rate, filter settings, and data alignment.

[0055] This disclosure also provides an electronic device, with reference to... Figure 4 , Figure 4 This is a schematic diagram of an electronic device illustrated in an embodiment of this disclosure. For example... Figure 4 As shown, the electronic device 100 includes a memory 110 and a processor 120. The memory 110 and the processor 120 are connected via a bus for communication. The memory 110 stores a computer program that can run on the processor 120 to implement the steps in the tomographic inversion iterative calculation quality control method disclosed in this embodiment.

[0056] This disclosure also provides a computer-readable storage medium that, when the instructions in the computer-readable storage medium are executed by a processor of a computer device, enables the computer device to perform the steps in the tomographic inversion iterative calculation quality control method disclosed in this disclosure.

[0057] This disclosure also provides a computer program product, including a computer program that, when executed by a processor of a computer device, is capable of performing the steps in the tomographic inversion iterative calculation quality control method disclosed in this disclosure.

[0058] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0059] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, apparatuses, electronic devices, and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0060] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0061] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0062] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.

[0063] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0064] The above provides a detailed description of the tomographic inversion iterative calculation quality control method, apparatus, equipment, storage medium, and product provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and its core ideas. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A quality control method for torsional inversion iterative calculation, characterized in that, The method includes: Acquire shot gather records for the target area, the shot gather records including seismic wave information from multiple receiver channels corresponding to each shot point, and extract the first arrival time of the seismic waves from each receiver channel from the shot gather records. Based on prior information, an initial velocity model is established, and forward modeling is performed on the initial velocity model using the ray tracing method to obtain the second first arrival time of seismic waves for each receiver channel. The difference between the second first arrival time of the seismic waves of each receiving channel and the corresponding first first arrival time is calculated to obtain the error corresponding to each receiving channel. The root mean square error of each shot point is obtained by statistically analyzing the errors of all the receiving channels at each shot point. Based on the root mean square error corresponding to each shot point, the initial velocity model is iteratively updated multiple times using the tomographic inversion algorithm, and the seismic wave inversion error of all shot points after each iteration is visualized to obtain a multi-round error distribution map. Based on the multi-round error distribution map, the reduction ratio of the seismic wave inversion error of each shot point in different iteration rounds is calculated to obtain the percentage reduction of the seismic wave inversion error of each shot point after multi-round iteration calculation. The tomographic inversion effect is analyzed based on the percentage reduction of the seismic wave inversion error.

2. The tomographic inversion iterative calculation quality control method according to claim 1, characterized in that, The step of performing root mean square error (RMSE) statistics on the errors corresponding to all receiver channels at each shot point to obtain the RMSE for each shot point includes: The root mean square error corresponding to each shot point is calculated according to the following formula: ， Among them, X rms x represents the root mean square error corresponding to each shot point; i The error corresponding to the i-th receiving channel is represented by ; n represents the number of receiving channels corresponding to this shot point.

3. The tomographic inversion iterative calculation quality control method according to claim 1, characterized in that, The step of updating the initial velocity model through multiple iterations based on the root mean square error corresponding to each shot point using a tomographic inversion algorithm includes: Based on the seismic wave inversion error, the velocity values ​​in the initial velocity model are adjusted to obtain the initial velocity model after multiple iterations.

4. The tomographic inversion iterative calculation quality control method according to claim 3, characterized in that, The analysis of the tomographic inversion effect based on the percentage reduction in seismic wave inversion error includes: If the percentage reduction in seismic wave inversion error at all shot points is less than the preset value, the initial velocity model updated in this iteration is used as the target velocity model.

5. The tomographic inversion iterative calculation quality control method according to claim 4, characterized in that, The analysis of the tomographic inversion effect based on the percentage reduction in seismic wave inversion error includes: If the percentage reduction in the seismic wave inversion error at a certain shot point is not less than a preset value, the quality control adjustment of the shot point is carried out based on the multi-round error distribution map.

6. The tomographic inversion iterative calculation quality control method according to claim 5, characterized in that, The quality control adjustment of a certain shot point based on the multi-round error distribution map includes: Analyze the waveform characteristics of a certain shot point, the waveform characteristics including: amplitude, frequency, and phase; If abnormal amplitude, missing frequency, or phase discontinuity exists in the waveform characteristics, corresponding adjustments are made, including: adjusting source parameters, receiving device settings, sampling rate, filter settings, and data alignment.

7. A quality control device for tomographic inversion iterative calculation, characterized in that, The device includes: The acquisition module is used to acquire shot gather records of the target area. The shot gather records include seismic wave information from multiple receiver channels corresponding to each shot point. The first arrival time of the seismic waves from each receiver channel is extracted from the shot gather records. The simulation module is used to establish an initial velocity model based on prior information, and to perform forward modeling of the initial velocity model based on the ray tracing method to obtain the second first arrival time of seismic waves for each receiver channel. The first calculation module is used to calculate the difference between the second first arrival time of the seismic waves of each receiving channel and the corresponding first first arrival time, so as to obtain the error corresponding to each receiving channel. The second calculation module is used to perform root mean square error statistics based on the errors corresponding to all the receiving channels of each shot point, and obtain the root mean square error corresponding to each shot point. The inversion display module is used to perform multiple iterations of updating the initial velocity model based on the root mean square error corresponding to each shot point and the tomographic inversion algorithm, and to visualize the seismic wave inversion error of all shot points after each iteration, thus obtaining a multi-round error distribution map. The analysis module is used to calculate the reduction ratio of the seismic wave inversion error of each shot point in different iterations based on the multi-round error distribution map, to obtain the percentage reduction of the seismic wave inversion error of each shot point after multi-round iteration calculation, and to analyze the tomographic inversion effect based on the percentage reduction of the seismic wave inversion error.

8. An electronic device, characterized in that, include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the tomographic inversion iterative computation quality control method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the electronic device, the electronic device is able to perform the tomographic inversion iterative calculation quality control method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the tomographic inversion iterative calculation quality control method as described in any one of claims 1 to 6.

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