Method and apparatus for multi-sampling rate seismic data reconstruction

Abstract

The application discloses a multi-sampling rate seismic data reconstruction method and device, and belongs to the technical field of petroleum geophysical exploration processing. The multi-sampling rate seismic data reconstruction method comprises the following steps: establishing a regular observation system based on an irregular observation system; establishing a three-dimensional array based on the regular observation system; obtaining the numerical value of an element in the three-dimensional array; obtaining a to-be-reconstructed receiving point set in the regular observation system based on the numerical value of the element in the three-dimensional array; performing two-dimensional curved wave transformation on each receiving line in the regular observation system to obtain a three-dimensional array after first reconstruction; and performing three-dimensional curved wave transformation on the numerical value of an element in the three-dimensional array after first reconstruction to obtain a three-dimensional array after second reconstruction. The application adopts two-dimensional curved wave transformation to reconstruct three-dimensional data, reduces the generation of random noise in the reconstruction process, reduces the iteration number of three-dimensional curved wave transformation, improves the calculation efficiency, and has good reliability and practicability.

Description

Technical Field

[0001] This application belongs to the field of petroleum geophysical exploration and processing technology, specifically relating to a method and apparatus for reconstructing seismic data with multiple sampling rates. Background Technology

[0002] Among the seismic data reconstruction methods in related technologies, the three-dimensional curvelet transform-based reconstruction algorithm has a large number of iterations and low computational efficiency. Furthermore, noise interference is generated during the data reconstruction process based on the three-dimensional curvelet transform. Summary of the Invention

[0003] The purpose of this application is to provide a multi-sampling-rate seismic data reconstruction method, apparatus, electronic device, and readable storage medium, which can solve the problems of high iteration count, low computational efficiency, and noise interference generated during the data reconstruction process based on three-dimensional curve transform in seismic data reconstruction.

[0004] In a first aspect, embodiments of this application provide a method for reconstructing seismic data at multiple sampling rates, comprising: establishing a regular observation system based on an irregular observation system; establishing a three-dimensional array based on the regular observation system; obtaining a second receiving point in the irregular observation system corresponding to a first receiving point in the regular observation system; obtaining the values ​​of elements in the three-dimensional array based on the first receiving point, the second receiving point, and seismic data; obtaining a set of receiving points to be reconstructed in the regular observation system based on the values ​​of elements in the three-dimensional array; performing a two-dimensional curvelet transform on each receiving line in the regular observation system to obtain the first value of the reconstructed receiving point; updating the set of receiving points to be reconstructed using the first value to obtain the three-dimensional array after the first reconstruction; performing a three-dimensional curvelet transform on the values ​​of elements in the three-dimensional array after the first reconstruction to obtain the three-dimensional array after the second reconstruction; the values ​​of elements in the three-dimensional array after the second reconstruction are the reconstructed seismic data.

[0005] This application uses two-dimensional curvelet transform to reconstruct three-dimensional data, providing basic data for three-dimensional curvelet transform, reducing the generation of random noise during the reconstruction process. At the same time, three-dimensional reconstruction is performed on the data reconstructed by two-dimensional curvelet transform, reducing the number of iterations of three-dimensional curvelet transform, improving computational efficiency, and having good reliability and practicality, meeting the needs of exploration and production.

[0006] In addition, the technical solution provided by this invention may also have the following additional technical features:

[0007] In the above technical solution, a regular observation system is established based on an irregular observation system. Specifically, this includes: acquiring the first receiver line spacing, the first number of receiver lines, the first number of sampling points at each receiver point, and the collected seismic data of the irregular observation system; and determining the second receiver line spacing, the second number of receiver lines, the distance between two receiver points, and the second number of sampling points at each receiver point of the regular observation system. The second receiver line spacing is the same as the first receiver line spacing, the second number of receiver lines is the same as the first number of receiver lines, and the second number of sampling points is the same as the first number of sampling points.

[0008] In this technical solution, an irregular observation system is designed based on the geological task, and a regular observation system is obtained through the irregular observation system, which provides the possibility for subsequent seismic data reconstruction through the regular observation system.

[0009] In the above technical solution, a three-dimensional array is established based on the rule-based observation system, specifically including: establishing a three-dimensional array based on the second receiving line spacing, the second number of receiving lines, and the second number of sampling points of the rule-based observation system.

[0010] In this technical solution, a three-dimensional array is established for each data point. By updating the values ​​in the three-dimensional array, the reconstructed seismic data is finally obtained, which can effectively improve the computational efficiency of the method.

[0011] In the above technical solution, obtaining the second receiving point corresponding to the first receiving point in the regular observation system in the irregular observation system, and obtaining the value of the element in the three-dimensional array based on the first receiving point, the second receiving point, and seismic data, specifically includes: obtaining the third receiving line where the first receiving point is located in the regular observation system; obtaining the fourth receiving line corresponding to the third receiving line in the irregular observation system; obtaining the second receiving point on the fourth receiving line that is closest to the first receiving point; obtaining the first distance between the first receiving point and the second receiving point; setting the value of the element in the three-dimensional array corresponding to the first receiving point to 0 based on the first distance being greater than a first threshold; and setting the value of the element in the three-dimensional array corresponding to the first receiving point to the seismic data collected by the second receiving point based on the first distance being less than or equal to the first threshold.

[0012] In this technical solution, the numerical values ​​of elements in the three-dimensional array are assigned by a first distance and a first threshold, which can improve the accuracy of subsequent seismic data reconstruction through a rule-based observation system.

[0013] In the above technical solution, the set of receiving points to be reconstructed in the rule observation system is obtained based on the values ​​of the elements in the three-dimensional array. Specifically, this includes: obtaining the values ​​of the elements in the three-dimensional array; and setting the set of receiving points corresponding to the elements in the three-dimensional array as the set of receiving points to be reconstructed in the rule observation system based on the value of 0.

[0014] In this technical solution, the receiving point and receiving line to be reconstructed are obtained by judging the numerical values ​​of the elements in the three-dimensional array, thereby enabling subsequent seismic data reconstruction and improving the accuracy of the reconstructed seismic data.

[0015] In the above technical solution, a two-dimensional curvelet transform is performed on each receiving line in the regular observation system to obtain the first value after reconstruction of the receiving point to be reconstructed. The first value is then used to update the set of receiving points to be reconstructed to obtain a three-dimensional array after the first reconstruction. Specifically, this includes: performing a first two-dimensional curvelet transform on each receiving line in the regular observation system to obtain multiple first curvelet coefficients; obtaining the maximum and minimum values ​​among the first curvelet coefficients, which are respectively the maximum and minimum values ​​of the first curvelet coefficients; obtaining the average value of the first curvelet coefficients, i.e., the average value of the first curvelet coefficients; constructing a first threshold function based on the maximum, minimum, and average values ​​of the first curvelet coefficients; and iteratively calculating the two-dimensional curvelet transform for each receiving line based on the first threshold function until a first stopping condition is reached to obtain the first value after reconstruction of each receiving line. The first value is then used to update the set of receiving points to be reconstructed to obtain a three-dimensional array after the first reconstruction.

[0016] In this technical solution, a two-dimensional curvelet transform is used to reconstruct three-dimensional data, providing basic data for the three-dimensional curvelet transform and reducing the generation of random noise during the reconstruction process to a certain extent.

[0017] In the above technical solution, a first threshold function is constructed based on the maximum value, minimum value, and average value of the first curvature coefficient, specifically including:

[0018]

[0019] Where σ1 represents the first threshold function, Indicates calculation The value of λ1 is raised to the power of λ1, where λ1 represents the power factor, with a value range greater than 0 and less than or equal to 1. k1 and k2 represent adjustment coefficients, both with a value range greater than 0 and less than or equal to 1. D1 represents the iteration number, and d1 represents the d1th iteration.

[0020] In this technical solution, a first threshold function is constructed based on the maximum, minimum and average values ​​of the first curve coefficients. This suppresses noise while minimizing damage to the effective signal, thereby improving the signal-to-noise ratio of the reconstructed data.

[0021] In the above technical solution, a three-dimensional curvelet transform is performed on the element values ​​in the three-dimensional array after the first reconstruction to obtain the three-dimensional array after the second reconstruction. Specifically, this includes: performing a first three-dimensional curvelet transform on the element values ​​in the three-dimensional array after the first reconstruction to obtain multiple second curvelet coefficients; obtaining the maximum and minimum values ​​among the second curvelet coefficients, which are respectively the maximum and minimum values ​​of the second curvelet coefficients; obtaining the average value of the second curvelet coefficients, i.e., the average value of the second curvelet coefficients; constructing a second threshold function based on the maximum value, minimum value, and average value of the second curvelet coefficients; and performing a three-dimensional curvelet transform on the second threshold function. The iterative calculation of the curvelet transform involves setting the second curvelet coefficient of the three-dimensional curvelet transform that satisfies the first condition to 0, and performing the three-dimensional inverse curvelet transform. The first root mean square amplitude of the receiver points that do not require reconstruction in the regular observation system before the first reconstruction is obtained. The second root mean square amplitude of the element values ​​in the three-dimensional array after the three-dimensional inverse curvelet transform is obtained. Based on the first and second root mean square amplitudes, a threshold parameter is obtained. Based on the threshold parameter and the number of iterations, it is determined whether the second stopping condition has been met. Based on the achievement of the second stopping condition, the values ​​of the corresponding elements in the three-dimensional array are updated using the results of the iterative three-dimensional inverse curvelet transform to obtain the three-dimensional array after the second reconstruction.

[0022] In this technical solution, three-dimensional reconstruction is performed on the data reconstructed by two-dimensional curvelet transform, which reduces the number of iterations of three-dimensional curvelet transform and improves the computational efficiency of the method.

[0023] In the above technical solution, the threshold parameter is obtained based on the first root mean square amplitude and the second root mean square amplitude, specifically including:

[0024]

[0025] Where η represents the threshold parameter, P d P0 represents the second root mean square amplitude, and P0 represents the first root mean square amplitude.

[0026] In this technical solution, the relationship between the first root mean square amplitude of all traces in the original trace set that does not need to be reconstructed and the second root mean square amplitude of the reconstructed new data, as well as the number of iterations, is used to determine whether the second reconstruction is complete, which can ensure the fidelity and quality of the data in the reconstructed three-dimensional array.

[0027] Secondly, the technical solution of this application provides a multi-sampling-rate seismic data reconstruction device, comprising: a first establishment module for establishing a regular observation system based on an irregular observation system; a second establishment module for establishing a three-dimensional array based on the regular observation system; a first acquisition module for acquiring the second receiving point corresponding to the first receiving point in the regular observation system in the irregular observation system, and acquiring the values ​​of the elements in the three-dimensional array based on the first receiving point, the second receiving point, and the seismic data; a second acquisition module for acquiring the set of receiving points to be reconstructed in the regular observation system based on the values ​​of the elements in the three-dimensional array; a third acquisition module for performing a two-dimensional curvelet transform on each receiving line in the regular observation system to obtain the first value of the reconstructed receiving point, updating the set of receiving points to be reconstructed using the first value to obtain the three-dimensional array after the first reconstruction; and a fourth acquisition module for performing a three-dimensional curvelet transform on the element values ​​in the three-dimensional array after the first reconstruction to obtain the three-dimensional array after the second reconstruction, wherein the element values ​​in the three-dimensional array after the second reconstruction are the reconstructed seismic data.

[0028] This application uses two-dimensional curvelet transform to reconstruct three-dimensional data, providing basic data for three-dimensional curvelet transform, reducing the generation of random noise during the reconstruction process. At the same time, three-dimensional reconstruction is performed on the data reconstructed by two-dimensional curvelet transform, reducing the number of iterations of three-dimensional curvelet transform, improving computational efficiency, and having good reliability and practicality, meeting the needs of exploration and production. Attached Figure Description

[0029] Figure 1 One of the flowcharts of the multi-sampling-rate seismic data reconstruction method provided in this application is shown;

[0030] Figure 2 The second schematic flowchart of the multi-sampling-rate seismic data reconstruction method provided in the embodiments of this application is shown;

[0031] Figure 3 The third schematic flowchart of the multi-sampling-rate seismic data reconstruction method provided in this application embodiment is shown;

[0032] Figure 4 The fourth schematic flowchart of the multi-sampling-rate seismic data reconstruction method provided in this application embodiment is shown;

[0033] Figure 5 The fifth illustration shows a flowchart of the multi-sampling-rate seismic data reconstruction method provided in the embodiments of this application;

[0034] Figure 6 The sixth illustration shows a flowchart of the multi-sampling-rate seismic data reconstruction method provided in this application embodiment;

[0035] Figure 7The seventh illustration shows a flowchart of the multi-sampling-rate seismic data reconstruction method provided in the embodiments of this application;

[0036] Figure 8 A schematic diagram of an irregular observation system provided in an embodiment of this application is shown;

[0037] Figure 9 A schematic diagram of gun collection data provided in an embodiment of this application is shown;

[0038] Figure 10 A schematic diagram of the rule-based observation system provided in an embodiment of this application is shown;

[0039] Figure 11 This illustration shows a shot gather data reconstructed using a multi-sampling-rate seismic data reconstruction method according to an embodiment of this application.

[0040] Figure 12 A schematic diagram of shot gather data reconstructed directly using three-dimensional curvelet transform is shown in the relevant technology;

[0041] Figure 13 A structural block diagram of the multi-sampling-rate seismic data reconstruction device provided in an embodiment of this application is shown;

[0042] Figure 14 A structural block diagram of the electronic device provided in an embodiment of this application is shown;

[0043] Figure 15 A schematic diagram of the hardware structure of an electronic device according to an embodiment of this application is shown.

[0044] in, Figures 13 to 15 The correspondence between the reference numerals and component names in the attached drawings is as follows:

[0045] 100: Multi-sampling-rate seismic data reconstruction device; 110: First establishment module; 120: Second establishment module; 130: First acquisition module; 140: Second acquisition module; 150: Third acquisition module; 160: Fourth acquisition module; 1000: Electronic device; 1002: Processor; 1004: Memory; 1100: Electronic device; 1101: Radio frequency unit; 1102: Network module; 1103: Audio output unit; 1104: Input unit; 11041: Graphics processor; 11042: Microphone; 1105: Sensor; 1106: Display unit; 11061: Display panel; 1107: User input unit; 11071: Touch panel; 11072: Other input devices; 1108: Interface unit; 1109: Memory; 1110: Processor. Detailed Implementation

[0046] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0047] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0048] The following is in conjunction with the appendix Figures 1 to 15 The present application provides a detailed description of the multi-sampling-rate seismic data reconstruction method and apparatus, electronic equipment and readable storage medium provided in the embodiments of this application through specific implementations and application scenarios.

[0049] This application provides a method for reconstructing seismic data with multiple sampling rates. Figure 1 This document illustrates one of the flowcharts of a multi-sampling-rate seismic data reconstruction method provided in an embodiment of this application, as shown below. Figure 1 As shown, multi-sampling-rate seismic data reconstruction methods include:

[0050] Step 102: Establish a regular observation system based on the irregular observation system.

[0051] Step 104: Establish a three-dimensional array based on the rule-based observation system.

[0052] Step 106: Obtain the second receiving point corresponding to the first receiving point in the regular observation system in the irregular observation system. Based on the first receiving point, the second receiving point, and the seismic data, obtain the values ​​of the elements in the three-dimensional array.

[0053] Step 108: Based on the numerical values ​​of the elements in the three-dimensional array, obtain the set of receiving points to be reconstructed in the rule-based observation system.

[0054] Step 110: Perform a two-dimensional curvelet transform on each receiving line in the regular observation system to obtain the first value of the reconstructed receiving point. Use the first value to update the set of receiving points to be reconstructed to obtain the three-dimensional array after the first reconstruction.

[0055] Step 112: Perform a three-dimensional curvelet transform on the element values ​​in the three-dimensional array after the first reconstruction to obtain the three-dimensional array after the second reconstruction. The element values ​​in the three-dimensional array after the second reconstruction are the reconstructed seismic data.

[0056] In practical work, there are three simple methods for processing multi-rate seismic data: First, copying adjacent traces or using linear interpolation to obtain missing traces from adjacent traces. Second, ignoring missing traces or retaining bad traces. Third, disregarding multi-rate sampling points and using a stacking method to place different trace gathers onto a single element. These simple methods often lead to significant errors, which can greatly affect subsequent processing methods and make it difficult to achieve ideal results. Therefore, over the past thirty years, many scholars both domestically and internationally have conducted extensive research on seismic data reconstruction methods. A number of seismic data reconstruction methods have been explored and developed, which can be roughly divided into three categories: filter-based methods, wavefield extension operator-based methods, and transform domain-based methods.

[0057] Currently, seismic data regularization reconstruction methods based on sparse transform are a research hotspot in seismic data reconstruction. The basic idea is to transform seismic data into various data domains, then utilize the sparse nature of the data in the transform domain for data interpolation and regularization processing, and finally obtain ideal, regularized seismic data through inverse transform. With the development of compressed sensing theory, which breaks the constraints of the Nyquist sampling theorem, high-precision complete signal reconstruction can be achieved with less data by relying on the sparsity of the signal itself and the non-correlation of observation methods. In recent years, seismic data processing techniques based on compressed sensing theory have continued to develop. Among them, three-dimensional curvelet transform (3D-CUT) data reconstruction methods have been increasingly applied in recent years due to its highly sparse representation of wavefield data and reliable numerical performance. Related techniques include introducing convex set projection algorithms into curvelet transform-based interpolation methods to achieve data reconstruction; using joint iterative methods for data reconstruction in the curvelet domain; performing two-dimensional reconstruction of effective frequency slices in the frequency domain to ultimately achieve three-dimensional seismic data reconstruction; and proposing a three-dimensional low-redundancy curvelet transform seismic data reconstruction method.

[0058] A comprehensive review of recent 3D curvelet transform-based reconstruction algorithms reveals that these algorithms involve a large number of iterations, resulting in low computational efficiency. Furthermore, they generate noise interference during the 3D curvelet transform-based data reconstruction process. Therefore, it is essential to propose an algorithm that can reduce the number of iterations in 3D curvelet transform reconstruction and also reduce the generation of random noise during the reconstruction process, thereby obtaining higher-quality reconstructed data.

[0059] This embodiment uses two-dimensional curvelet transform to reconstruct three-dimensional data, providing basic data for three-dimensional curvelet transform, reducing the generation of random noise during the reconstruction process, and performing three-dimensional reconstruction on the data reconstructed by two-dimensional curvelet transform, reducing the number of iterations of three-dimensional curvelet transform and improving computational efficiency.

[0060] Practical testing has shown that the multi-sampling rate seismic data reconstruction method in this embodiment has good reliability and practicality, meeting the needs of exploration and production.

[0061] In some embodiments of this application, Figure 2 This is a second schematic flowchart of the multi-sampling-rate seismic data reconstruction method provided in an embodiment of this application. Figure 2 As shown, a regular observation system is established based on the irregular observation system, specifically including:

[0062] Step 202: Obtain the first receiver line spacing, the first number of receiver lines, the first number of sampling points at each receiver point, and the collected seismic data of the irregular observation system.

[0063] Step 204: Determine the second receiving line spacing, the second number of receiving lines, the distance between two receiving points, and the second number of sampling points for each receiving point in the rule observation system. The second receiving line spacing is the same as the first receiving line spacing, the second number of receiving lines is the same as the first number of receiving lines, and the second number of sampling points is the same as the first number of sampling points.

[0064] Understandably, based on the geological task, an irregular observation system W0 is designed with a first receiver line spacing of L meters and a first receiver line number of S. Single-shot seismic data is excited, collected, and recorded, and the first sampling point number for each receiver point (per trace) is N.

[0065] Furthermore, based on the irregular observation system W0 designed above, a new regular observation system W1 is designed. The second receiving line spacing of the regular observation system W1 is also L meters, the number of second receiving lines is S, the distance between two receiving points, i.e., the channel spacing, is R meters, each second receiving line has M channels, and the number of second sampling points for each receiving point (each channel) is N.

[0066] In this embodiment, an irregular observation system is designed based on the geological task, and a regular observation system is obtained through the irregular observation system, which provides the possibility for subsequent seismic data reconstruction through the regular observation system.

[0067] In some embodiments of this application, Figure 3 The third schematic diagram of the multi-sampling-rate seismic data reconstruction method provided in this application embodiment is shown. Figure 3 As shown, a three-dimensional array is established based on the rule-based observation system, specifically including:

[0068] Step 302: Based on the second receiving line spacing, the second number of receiving lines, and the second number of sampling points of the rule-based observation system, establish a three-dimensional array.

[0069] In this embodiment, each data point in the rule observation system W1 is used to construct a three-dimensional array A[s][m][n], where s represents the s-th second receiving line, s is greater than or equal to 1 and less than or equal to S, m is the m-th receiving point on the s-th second receiving line, m is greater than or equal to 1 and less than or equal to M, and n is the number of sample points (i.e., the number of second sampling points) collected by the m-th receiving point on the s-th second receiving line.

[0070] In this embodiment, a three-dimensional array is established for each data point. By updating the values ​​in the three-dimensional array, the reconstructed seismic data is finally obtained, which can effectively improve the computational efficiency of the method.

[0071] In some embodiments of this application, Figure 4 The fourth schematic flowchart of the multi-sampling-rate seismic data reconstruction method provided in this application embodiment is shown. Figure 4 As shown, the second receiving point corresponding to the first receiving point in the regular observation system is obtained in the irregular observation system. Based on the first receiving point, the second receiving point, and seismic data, the values ​​of elements in the three-dimensional array are obtained, specifically including:

[0072] Step 402: Obtain the third receiving line where the first receiving point is located in the rule observation system.

[0073] Step 404: Obtain the fourth receiving line corresponding to the third receiving line in the irregular observation system.

[0074] Step 406: Obtain the second receiving point on the fourth receiving line that is closest to the first receiving point.

[0075] Step 408: Obtain the first distance between the first receiving point and the second receiving point.

[0076] Step 410: Based on the fact that the first distance is greater than the first threshold, set the value of the element in the three-dimensional array corresponding to the first receiving point to 0.

[0077] Step 412: Based on the first distance being less than or equal to the first threshold, set the value of the element in the three-dimensional array corresponding to the first receiving point to the seismic data collected by the second receiving point.

[0078] In this embodiment, for any receiving line (i.e., the third receiving line) in the regular observation system W1, a receiving line (i.e., the fourth receiving line) corresponding to the actual coordinate position is found in the irregular observation system W0. For any receiving line (i.e., the first receiving point) in the regular observation system W1, the nearest receiving point (i.e., the second receiving point) is found in the corresponding receiving line in the irregular observation system W0, and the distance between the first receiving point and the second receiving point is calculated as δ. If δ is less than or equal to a first threshold, the value of the corresponding second receiving point in the irregular observation system W0 is assigned to the corresponding first receiving point in the regular observation system W1; if δ is greater than the first threshold, the value of the corresponding first receiving point in the regular observation system W1 is assigned to 0. At this time, the three-dimensional array A[s][m][n] corresponding to each receiving point in the regular observation system W1 is assigned a value.

[0079] In this embodiment, the first threshold is ε·R, where ε represents the coefficient for determining whether to assign the value of the corresponding receiver point in the irregular observation system W0 to the corresponding receiver point in the regular observation system W1, and R represents the distance between two receiver points in the regular observation system (i.e., the track spacing after regularization). The larger the value of ε, the more receiver points in the regular observation system W1 can be assigned values, but the lower the reliability of the reconstructed data. For example, the value of ε can be greater than 0 and less than or equal to 1; as another example, ε can be 0.5.

[0080] In this embodiment, assigning values ​​to elements in the three-dimensional array using a first distance and a first threshold can improve the accuracy of subsequent earthquake data reconstruction using a rule-based observation system.

[0081] In some embodiments of this application, Figure 5 The fifth schematic diagram of the multi-sampling-rate seismic data reconstruction method provided in this application embodiment is shown. Figure 5 As shown, based on the numerical values ​​of elements in the three-dimensional array, the set of receiving points to be reconstructed in the rule-based observation system is obtained, specifically including:

[0082] Step 502: Obtain the values ​​of the elements in the three-dimensional array.

[0083] Step 504: Based on the value of 0, set the set of receiving points corresponding to the elements in the three-dimensional array as the set of receiving points to be reconstructed in the rule observation system.

[0084] In this embodiment, if the three-dimensional array A[s][m][n] is 0, that is, the m-th receiving point on the s-th second receiving line is to be reconstructed, then the receiving line to be reconstructed and the corresponding channel to be reconstructed in the regular observation system can be obtained.

[0085] In this embodiment, the channels (receiving points) to be reconstructed in the rule observation system W1 can be grouped into a set Π0, and the channels (receiving points) that do not need to be reconstructed can be grouped into a set Π1. The root mean square amplitude of all channels (receiving points) in the set Π1 can be calculated and denoted as P0.

[0086] In this embodiment, by judging the values ​​of the elements in the three-dimensional array, the receiving point to be reconstructed and the receiving line to be reconstructed are obtained, thereby enabling subsequent seismic data reconstruction and improving the accuracy of seismic data reconstruction.

[0087] In some embodiments of this application, Figure 6 The sixth schematic diagram of the multi-sampling-rate seismic data reconstruction method provided in this application embodiment is shown. Figure 6 As shown, a two-dimensional curvelet transform is performed on each receiving line in the regular observation system to obtain the first value of the reconstructed receiving point. The set of receiving points to be reconstructed is updated using the first value to obtain the three-dimensional array after the first reconstruction, specifically including:

[0088] Step 602: Perform the first two-dimensional curvilinear transformation on each receiving line in the regular observation system to obtain multiple first curvilinear coefficients.

[0089] Step 604: Obtain the maximum and minimum values ​​of the first curved wave coefficient, which are the maximum and minimum values ​​of the first curved wave coefficient, respectively.

[0090] Step 606: Obtain the average value of the first curvature coefficient, i.e., the average value of the first curvature coefficient.

[0091] Step 608: Construct a first threshold function based on the maximum value, minimum value, and average value of the first curvature coefficient;

[0092] Step 610: Based on the first threshold function, perform iterative calculation of two-dimensional curve transform for each receiving line until the first stopping condition is reached, and obtain the first value after reconstruction of each receiving line. Use the first value to update the set of receiving points to be reconstructed to obtain the three-dimensional array after the first reconstruction.

[0093] In this embodiment, a first two-dimensional curvelet transform is performed on each of the S receiving lines in the regular observation system, and a corresponding first curvelet coefficient is obtained for each receiving line. The average value of the first curvelet coefficients is then obtained from multiple first curvelet coefficients. Using the maximum value of the first curvature coefficient C max1 The minimum value of the first curvature coefficient is C. min1 Construct the first threshold function.

[0094] In this embodiment, the first threshold function is used for iterative calculation until the first stopping condition is reached. The first stopping condition is the preset number of iterations. The data after reconstruction of each receiving line is obtained, the channel in the set Π0 gets a new value, and it is assigned to the corresponding three-dimensional array A[s][m][n] to obtain the three-dimensional array after the first reconstruction.

[0095] In this embodiment, the channels in set Π0 are reassigned and are no longer 0. This reduces the generation of random noise to a certain extent and improves the accuracy of the method during the subsequent reconstruction using three-dimensional curve transform.

[0096] In this embodiment, a two-dimensional curvelet transform is used to reconstruct the three-dimensional data, providing basic data for the three-dimensional curvelet transform and reducing the generation of random noise during the reconstruction process to a certain extent.

[0097] In some embodiments of this application, a first threshold function is constructed based on the maximum value of the first curvature coefficient, the minimum value of the first curvature coefficient, and the average value of the first curvature coefficient, specifically including:

[0098]

[0099] Where σ1 represents the first threshold function, and pow(x,y) represents calculating x raised to the power of y. In this embodiment, Indicates calculation The power of λ1 is where λ1 represents the power factor, with a value range greater than 0 and less than or equal to 1; k1 and k2 represent adjustment coefficients, both with a value range greater than 0 and less than or equal to 1; D1 represents the number of iterations of the two-dimensional curvelet transform (i.e., the total number of iterations of the two-dimensional curvelet transform); and d1 represents the d1th iteration.

[0100] Furthermore, λ1 is a power factor. For data with low signal-to-noise ratio, λ1 is taken to be smaller, and vice versa. For example, it can be taken to be 0.5.

[0101] Furthermore, k1 and k2 are adjustment coefficients, with the maximum number of curves that can be eliminated set as k1·C. max1 The maximum curvelet coefficient that can be removed in the last iteration is set to (k2+1)·C. min1 The values ​​of k1 and k2 are both greater than 0 and less than or equal to 1. For seismic data with low signal-to-noise ratio, k1 and k2 are both taken to be smaller. For example, the values ​​of k1 and k2 are 0.8 and 0.2, respectively.

[0102] Furthermore, if Then Assigned to (k2+1)·C min1 .

[0103] Furthermore, D1 is the total number of iterations; for example, D1 can be 15.

[0104] In this embodiment, a first threshold function is constructed based on the maximum, minimum and average values ​​of the first curve coefficients to suppress noise while minimizing damage to the effective signal, thereby improving the signal-to-noise ratio of the reconstructed data.

[0105] In some embodiments of this application, Figure 7 The seventh flowchart illustrates the multi-sampling-rate seismic data reconstruction method provided in this application embodiment. Figure 7 As shown, a three-dimensional curvelet transform is performed on the element values ​​in the first reconstructed three-dimensional array to obtain the second reconstructed three-dimensional array, specifically including:

[0106] Step 702: Perform a first three-dimensional curvilinear transformation on the element values ​​in the first reconstructed three-dimensional array to obtain multiple second curvilinear coefficients.

[0107] Step 704: Obtain the maximum and minimum values ​​of the second curvature coefficient, which are the maximum and minimum values ​​of the second curvature coefficient, respectively.

[0108] Step 706: Obtain the average value of the second curvature coefficient, i.e., the average value of the second curvature coefficient;

[0109] Step 708: Construct a second threshold function based on the maximum value of the second curvature coefficient, the minimum value of the second curvature coefficient, and the average value of the second curvature coefficient;

[0110] Step 710: Based on the second threshold function, perform iterative calculation of the three-dimensional curvelet transform, set the second curvelet coefficient of the three-dimensional curvelet transform that satisfies the first condition to 0, and perform the three-dimensional curvelet inverse transform.

[0111] Step 712: Obtain the first root mean square amplitude of the receiver points that do not need to be reconstructed in the rule observation system before the first reconstruction.

[0112] Step 714: Obtain the second root mean square amplitude of the element values ​​in the three-dimensional array after the three-dimensional inverse curve transform;

[0113] Step 716: Obtain the threshold parameter based on the first root mean square amplitude and the second root mean square amplitude;

[0114] Step 718: Based on the threshold parameter and the number of iterations, determine whether the second stopping condition has been reached. Based on the second stopping condition, update the values ​​of the corresponding elements in the three-dimensional array using the result of the three-dimensional inverse curve transform after iteration, and obtain the three-dimensional array after the second reconstruction.

[0115] In this embodiment, a three-dimensional curvelet transform is performed on the three-dimensional volume data A[s][m][n] after the first reconstruction. The maximum, minimum and average values ​​of the second curvelet coefficients of the first three-dimensional curvelet transform are used to construct the second threshold function σ2.

[0116]

[0117] Where σ² represents the second threshold function, and pow² represents the calculation... The value of λ2 is raised to the power of λ, where λ2 represents the power factor, with a value range greater than 0 and less than or equal to 1. k3 and k4 represent adjustment coefficients, both with a value range greater than 0 and less than or equal to 1. D2 represents the number of iterations of the three-dimensional curvelet transform (i.e., the total number of iterations of the three-dimensional curvelet transform), and d2 represents the d2th iteration.

[0118] Furthermore, λ2 is a power factor. For data with low signal-to-noise ratio, λ2 is taken to be smaller, and vice versa. For example, it can be taken to be 0.5.

[0119] Furthermore, k3 and k4 are adjustment coefficients, with the second curvilinear coefficient that can be eliminated at most set to k3·C. max3 The maximum second curvature coefficient that can be removed in the last iteration is set to (k4+1)·C. min2 The values ​​of k3 and k4 are both greater than 0 and less than or equal to 1. For seismic data with low signal-to-noise ratio, k3 and k4 are both set to smaller values. For example, the values ​​of k3 and k4 are 0.8 and 0.2, respectively.

[0120] Furthermore, if Then Assigned to (k4+1)·C min2 .

[0121] Furthermore, D2 is the total number of iterations; for example, D2 can be 40.

[0122] Furthermore, the first root mean square amplitude of the receiver point set that does not need to be reconstructed in the rule observation system before the first reconstruction is obtained, that is, the root mean square amplitude P0 of the original set Π1.

[0123] Furthermore, based on the second threshold function, iterative calculations are performed, setting curvelet coefficients less than σ² to 0, and then a three-dimensional inverse curvelet transform is performed to calculate the second root mean square amplitude of all channels in set Π1 after the d2th reconstruction, i.e., the root mean square amplitude P of set Π1 after the d2th reconstruction. d Among them, the root mean square amplitude of all traces in set Π1 after the d2th reconstruction is P. d The root mean square amplitude mentioned above is P. dIt is calculated from the data after the d2th three-dimensional inverse curve transform, and the root mean square amplitude of all channels in the original set Π1 is P0.

[0124] Furthermore, by combining the given number of iterations and the threshold parameter, it is determined whether to stop reconstruction (i.e., whether the second stopping condition has been met). If the threshold parameter is less than the preset threshold parameter or the given number of iterations has been reached, the calculation stops; otherwise, the next three-dimensional curvelet transform continues until the three-dimensional data after the second reconstruction is obtained. Here, the second stopping condition refers to reaching the maximum number of iterations or the threshold parameter being less than the preset threshold parameter.

[0125] Furthermore, for example, the preset threshold parameter can be set to 0.1.

[0126] Furthermore, for example, the given number of iterations could be 40.

[0127] In this embodiment, three-dimensional reconstruction is performed on the data reconstructed by two-dimensional curvelet transform, which reduces the number of iterations of three-dimensional curvelet transform and improves the computational efficiency of the method.

[0128] In some embodiments of this application, a threshold parameter is obtained based on a first root mean square amplitude and a second root mean square amplitude, specifically including:

[0129]

[0130] Where η represents the threshold parameter, P d P represents the second root mean square amplitude, and P0 represents the first root mean square amplitude (i.e., P). d denoted as the second root mean square amplitude of all traces in set Π1 after the d2th reconstruction, and Ρ0 represents the first root mean square amplitude of all traces in the original set Π1.

[0131] In this embodiment, the relationship between the first root mean square amplitude of all channels in the original channel set that does not need to be reconstructed and the second root mean square amplitude of the reconstructed new data, as well as the number of iterations, is used to determine whether the second reconstruction is completed, which can ensure the fidelity and quality of the data in the reconstructed three-dimensional array. Specific implementation examples:

[0133] In some embodiments of this application, the multi-sampling-rate seismic data reconstruction method uses two-dimensional curvelet transform to reconstruct three-dimensional data, providing basic data for the three-dimensional curvelet transform and reducing the generation of random noise during the reconstruction process. Simultaneously, three-dimensional reconstruction is performed on the data reconstructed by the two-dimensional curvelet transform, reducing the number of iterations of the three-dimensional curvelet transform and improving computational efficiency. A threshold function is constructed based on the maximum, minimum, and average values ​​of the first curvelet coefficients to suppress noise while minimizing damage to the effective signal, thereby improving the quality of the reconstructed data. The method uses the relationship between the values ​​of all traces in the original trace set that do not require reconstruction and the root mean square amplitude of the reconstructed new data, along with the number of iterations, to determine whether the reconstruction is complete, ensuring the fidelity and quality of the reconstructed data.

[0134] The core of this embodiment is to first design a new regular observation system based on the irregular observation system, determining parameters such as the receiver line spacing, number of receiver lines, channel spacing, and number of receiver points per receiver line. For each receiver point on each receiver line in the new regular observation system, a three-dimensional array is constructed to store the data of each receiver point. Then, for any receiver line in the regular observation system, the corresponding receiver line in the irregular observation system is found at its actual coordinate position. For any receiver point in the regular observation system's receiver line, the nearest receiver point is found in the corresponding receiver line in the irregular observation system. Based on a given threshold parameter, it is determined whether to assign a value to the corresponding receiver point in the new observation system. Next, a two-dimensional curvelet transform is performed on each receiver line. A first threshold function is constructed based on the maximum and minimum values ​​of the first curvelet coefficients obtained from the first two-dimensional curvelet transform. Iterative calculations are performed to obtain new values ​​for the receiver points to be reconstructed and assign them to the corresponding three-dimensional array. Finally, a three-dimensional curvelet transform is performed on the new three-dimensional data volume, constructing an iterative threshold function (i.e., a second threshold function). Each time the second curvelet coefficient is less than the iterative threshold parameter...

[0135] The second curvature coefficient of the (i.e., the second threshold function) is set to 0, and then the newly set second curvature coefficient is inversely transformed to obtain the reconstructed data. Finally, the relationship between the values ​​of all channels in the original set of channels that do not need to be reconstructed and the root mean square amplitude of the reconstructed new data, as well as the number of iterations, is used to determine whether the reconstruction is over and to obtain the reconstructed data.

[0136] The main technology provided in this embodiment is as follows: designing a new regular observation system based on an irregular observation system; finding the corresponding receiving line in the irregular observation system for any receiving line in the regular observation system; finding the nearest receiving point in the irregular observation system for any receiving point in the receiving line of the regular observation system; determining whether to assign a value to the corresponding receiving point in the new observation system based on a given threshold parameter; performing a two-dimensional curvelet transform on each receiving line; constructing a threshold function based on the maximum and minimum values ​​of the first curvelet coefficients obtained from the first two-dimensional curvelet transform; iteratively calculating the new value of the channel to be reconstructed; performing a three-dimensional curvelet transform on the new three-dimensional data volume; constructing an iterative threshold function; ending the reconstruction by the number of iterations and the relationship between the existing value receiving point data and the root mean square amplitude of the receiving point data reconstructed each time; and obtaining the reconstructed data.

[0137] The multi-sampling-rate seismic data reconstruction method in this embodiment may specifically include:

[0138] (1) Based on the geological task, design an irregular observation system W0, such as Figure 8 As shown, the receiver line spacing is 160 meters, and the number of receiver lines is 42. The trace spacing of this observation system varies from 10 to 79 meters, with an average trace spacing of 40 meters, satisfying the irregular layout based on the μ value. Point A in the figure represents the shot point. Single-shot seismic data is excited and recorded, such as... Figure 9 As shown, this is the 21st permutation of the data from the 6135th gun set. This gun set has a total of 9538 tracks, with 1750 sampling points per track and a sampling interval of 4 milliseconds.

[0139] (2) Based on the irregular observation system W0 designed in step (1), design a new regular observation system W1, such as... Figure 10 As shown, the observation system W1 has a receiver line spacing of 160 meters, 42 receiver lines, a channel spacing of 20 meters, 480 channels per receiver line, 1750 sampling points per channel, and a sampling interval of 4 milliseconds. In the figure, point B represents the shot point.

[0140] (3) In the regular observation system W1, each data point is constructed into a three-dimensional array A[s][m][n], where s is the s-th receiving line, s is greater than or equal to 1 and less than or equal to 42, m is the m-th receiving point on the s-th receiving line, m is greater than or equal to 1 and less than or equal to 480, and n is the number of sample points collected by the m-th receiving point on the s-th receiving line, n is equal to 1750.

[0141] (4) For any receiving line in the regular observation system W1, find the receiving line corresponding to the actual coordinate position in the irregular observation system W0. For any trace in the receiving line of the regular observation system W1, find the nearest receiving point in the corresponding receiving line of the irregular observation system W0, and calculate the distance as δ. If δ is less than or equal to ε·R=10, then assign the value of the corresponding receiving point in the irregular observation system W0 to the corresponding receiving point in the regular observation system W1; if δ is greater than ε·R=10, then assign the value of the corresponding receiving point in the regular observation system W1 to 0. Then the three-dimensional array A[s][m][n] corresponding to each receiving point in the regular observation system W1 is assigned a value. If the three-dimensional array A[s][m][n] is 0, that is, the m-th receiving point on the s-th receiving line is to be reconstructed, the traces to be reconstructed in the regular observation system W1 are set as Π0, and the traces that do not need to be reconstructed are set as Π1. Calculate the root mean square amplitude of all traces in the set Π1, and record it as P0=2391.

[0142] (5) Perform the first two-dimensional curvature transformation on each of the S receiving lines in step (4), and obtain the corresponding first curvature coefficient for each receiving line. The average value of the first curvature coefficient is: The maximum value C of the first curvature coefficient is used. max =939113 and minimum value C min =0.07017 Construct the first threshold function

[0143]

[0144] The total number of iterations is D = 15.

[0145] (6) Perform iterative calculations according to the first threshold function in step (5) until the number of iterations is reached and the calculation stops, to obtain the reconstructed data for each receiving line. The channel in set Π0 obtains a new value and assigns it to the corresponding three-dimensional array A

[42]

[480]

[1750] .

[0146] (7) Perform three-dimensional curvelet transform on the three-dimensional volume data A

[42]

[480]

[1750] constructed in step (6). Construct a threshold function σ2 similar to step (5) using the maximum, minimum and average values ​​of the second curvelet coefficients of the first three-dimensional curvelet transform. Perform iterative calculation based on the threshold function, set the curvelet coefficients less than σ2 to 0, and then perform three-dimensional inverse curvelet transform. Calculate the root mean square amplitude of all channels in the set Π1 after the 8th reconstruction as P8 = 601.938.

[0147] (8) Calculate based on steps (4) and (7):

[0148]

[0149] The given number of iterations and threshold parameters are used to determine whether to stop reconstruction. When the number of iterations reaches 18, P... d =2152.1, then η=0.0999 is less than the given preset threshold parameter 0.1, so the calculation stops, and the reconstructed data is obtained. The shot gather data reconstructed after 18 iterations in this embodiment is as follows. Figure 11 As shown, the reconstructed seismic data has a high overall signal-to-noise ratio and low random noise, indicating high reconstruction quality that meets the needs of post-processing of seismic data. Figure 12 The results of directly applying three-dimensional curvelet transform to the acquired single-shot seismic data show that the random noise is relatively large and the reconstruction time is 4.2 times that of the technology in this embodiment.

[0150] The multi-sampling-rate seismic data reconstruction method provided in this application can be executed by a multi-sampling-rate seismic data reconstruction device. This application uses the example of a multi-sampling-rate seismic data reconstruction device executing the multi-sampling-rate seismic data reconstruction method to illustrate the multi-sampling-rate seismic data reconstruction device provided in this application.

[0151] In some embodiments of this application, a multi-sampling-rate seismic data reconstruction apparatus is provided. Figure 13 The diagram shows a structural block diagram of the multi-sampling-rate seismic data reconstruction device provided in an embodiment of this application, as follows: Figure 13 As shown, the multi-sampling-rate seismic data reconstruction device 100 includes a first establishment module 110, a second establishment module 120, a first acquisition module 130, a second acquisition module 140, a third acquisition module 150, and a fourth acquisition module 160.

[0152] The first module 110 is used to establish a regular observation system based on the irregular observation system.

[0153] The second module 120 is used to establish a three-dimensional array based on the rule-based observation system.

[0154] The first acquisition module 130 is used to acquire the second receiving point corresponding to the first receiving point in the regular observation system in the irregular observation system, and to acquire the values ​​of elements in the three-dimensional array based on the first receiving point, the second receiving point and seismic data.

[0155] The second acquisition module 140 is used to acquire the set of receiving points to be reconstructed in the rule observation system based on the values ​​of the elements in the three-dimensional array.

[0156] The third acquisition module 150 is used to perform two-dimensional curve transformation on each receiving line in the regular observation system to obtain the first value of the reconstructed receiving point. The first value is used to update the set of receiving points to be reconstructed to obtain the three-dimensional array after the first reconstruction.

[0157] The fourth acquisition module 160 is used to perform three-dimensional curvelet transform on the element values ​​in the three-dimensional array after the first reconstruction to obtain the three-dimensional array after the second reconstruction. The element values ​​in the three-dimensional array after the second reconstruction are the reconstructed seismic data.

[0158] This application uses two-dimensional curvelet transform to reconstruct three-dimensional data, providing basic data for three-dimensional curvelet transform, reducing the generation of random noise during the reconstruction process. At the same time, three-dimensional reconstruction is performed on the data reconstructed by two-dimensional curvelet transform, reducing the number of iterations of three-dimensional curvelet transform, improving computational efficiency, and having good reliability and practicality, meeting the needs of exploration and production.

[0159] The multi-sampling-rate seismic data reconstruction apparatus 100 provided in this application embodiment can implement all the processes of the above-described multi-sampling-rate seismic data reconstruction method embodiment and achieve the same technical effect. To avoid repetition, it will not be described again here.

[0160] The multi-sampling-rate seismic data reconstruction device in this application embodiment can be an electronic device or a component within an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other devices besides a terminal. For example, the electronic device can be a mobile phone, tablet computer, laptop computer, PDA, in-vehicle electronic device, mobile internet device (MID), augmented reality (AR) / virtual reality (VR) device, robot, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc. It can also be a server, network attached storage (NAS), personal computer (PC), television set (TV), ATM, or self-service machine, etc. This application embodiment does not specifically limit the device.

[0161] The multi-sampling-rate seismic data reconstruction device in this application embodiment can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this application embodiment does not specifically limit it.

[0162] The multi-sampling-rate seismic data reconstruction apparatus provided in this application embodiment can realize all the processes implemented in the above method embodiments, and will not be described again here to avoid repetition.

[0163] Optionally, such as Figure 14 As shown, this application embodiment also provides an electronic device 1000, which includes a processor 1002 and a memory 1004. The memory 1004 stores a program or instructions that can run on the processor 1002. When the program or instructions are executed by the processor 1002, they implement the various steps of the above method embodiments and can achieve the same technical effect. To avoid repetition, they will not be described again here.

[0164] It should be noted that the electronic devices in the embodiments of this application include the aforementioned mobile electronic devices and non-mobile electronic devices.

[0165] Figure 15 A schematic diagram of the hardware structure of an electronic device to implement an embodiment of this application.

[0166] The electronic device 1100 includes, but is not limited to, components such as: radio frequency unit 1101, network module 1102, audio output unit 1103, input unit 1104, sensor 1105, display unit 1106, user input unit 1107, interface unit 1108, memory 1109, and processor 1110.

[0167] Those skilled in the art will understand that the electronic device 1100 may also include a power supply (such as a battery) for supplying power to various components. The power supply may be logically connected to the processor 1110 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. Figure 15 The electronic device structure shown does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here.

[0168] The processor 1110 is used to establish a regular observation system based on the irregular observation system.

[0169] Processor 1110 is used to build a three-dimensional array for a rule-based observation system.

[0170] Processor 1110 is used to obtain the second receiving point corresponding to the first receiving point in the regular observation system in the irregular observation system, and to obtain the values ​​of elements in the three-dimensional array based on the first receiving point, the second receiving point and seismic data.

[0171] Processor 1110 is used to obtain the set of receiving points to be reconstructed in a rule-based observation system based on the numerical values ​​of elements in a three-dimensional array.

[0172] Processor 1110 is used to perform two-dimensional curvelet transform on each receiving line in the regular observation system to obtain the first value of the reconstructed receiving point. The first value is used to update the set of receiving points to be reconstructed to obtain the three-dimensional array after the first reconstruction.

[0173] Processor 1110 is used to perform three-dimensional curvelet transform on the element values ​​in the three-dimensional array after the first reconstruction to obtain the three-dimensional array after the second reconstruction. The element values ​​in the three-dimensional array after the second reconstruction are the reconstructed seismic data.

[0174] This application uses two-dimensional curvelet transform to reconstruct three-dimensional data, providing basic data for three-dimensional curvelet transform, reducing the generation of random noise during the reconstruction process. At the same time, three-dimensional reconstruction is performed on the data reconstructed by two-dimensional curvelet transform, reducing the number of iterations of three-dimensional curvelet transform, improving computational efficiency, and having good reliability and practicality, meeting the needs of exploration and production.

[0175] The processor 1110 provided in this application embodiment can implement each process of the above-described multi-sampling rate seismic data reconstruction method embodiment and achieve the same technical effect. To avoid repetition, it will not be described again here.

[0176] It should be understood that, in this embodiment, the input unit 1104 may include a graphics processing unit (GPU) 11041 and a microphone 11042. The GPU 11041 processes image data of still images or videos obtained by an image capture device (such as a camera) in video capture mode or image capture mode. The display unit 1106 may include a display panel 11061, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, or the like. The user input unit 1107 includes at least one of a touch panel 11071 and other input devices 11072. The touch panel 11071 is also called a touch screen. The touch panel 11071 may include a touch detection device and a touch controller. Other input devices 11072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, power buttons, etc.), trackballs, mice, and joysticks, which will not be described in detail here.

[0177] The memory 1109 can be used to store software programs and various data. The memory 1109 may primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area may store the operating system, application programs or instructions required for at least one function (such as sound playback, image playback, etc.). Furthermore, the memory 1109 may include volatile memory or non-volatile memory, or both. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM). The memory 1109 in this embodiment includes, but is not limited to, these and any other suitable types of memory.

[0178] Processor 1110 may include one or more processing units; optionally, processor 1110 integrates an application processor and a modem processor, wherein the application processor mainly handles operations involving the operating system, user interface, and applications, and the modem processor mainly handles wireless communication signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into processor 1110.

[0179] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described multi-sampling rate seismic data reconstruction method embodiments and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0180] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0181] This application also provides a chip, which includes a processor and a communication interface. The communication interface and the processor are coupled. The processor is used to run programs or instructions to implement the various processes of the above-described multi-sampling rate seismic data reconstruction method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0182] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0183] This application provides a computer program product stored in a storage medium. The program product is executed by at least one processor to implement the various processes of the above-described multi-sampling rate seismic data reconstruction method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0184] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus 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 apparatus. 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 apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0185] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0186] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for reconstructing seismic data at multiple sampling rates, characterized in that, include: Establish a regular observation system based on an irregular observation system; Based on the aforementioned rule-based observation system, a three-dimensional array is established; Obtain the second receiving point corresponding to the first receiving point in the regular observation system in the irregular observation system; and based on the first receiving point, the second receiving point, and seismic data, obtain the values ​​of the elements in the three-dimensional array. Based on the values ​​of the elements in the three-dimensional array, obtain the set of receiving points to be reconstructed in the rule-based observation system; For each receiving line in the rule-based observation system, a two-dimensional curvelet transform is performed to obtain the first value of the reconstructed receiving point. The first value is then used to update the set of receiving points to be reconstructed, resulting in the three-dimensional array after the first reconstruction, specifically including: Perform a first two-dimensional curvilinear transformation on each receiving line in the rule-based observation system to obtain multiple first curvilinear coefficients; Obtain the maximum and minimum values ​​of the first curvature coefficient, which are the maximum and minimum values ​​of the first curvature coefficient, respectively. Obtain the average value of the first curvature coefficient, i.e., the average value of the first curvature coefficient; A first threshold function is constructed based on the maximum value of the first curvature coefficient, the minimum value of the first curvature coefficient, and the average value of the first curvature coefficient; Based on the first threshold function, iterative calculation of two-dimensional curve transform is performed on each receiving line until the first stopping condition is reached, and the first value after reconstruction of each receiving line is obtained. The first value is used to update the set of receiving points to be reconstructed to obtain the three-dimensional array after the first reconstruction. A three-dimensional curvelet transform is performed on the element values ​​in the three-dimensional array after the first reconstruction to obtain the three-dimensional array after the second reconstruction. The element values ​​in the three-dimensional array after the second reconstruction are the reconstructed seismic data.

2. The multi-sampling-rate seismic data reconstruction method according to claim 1, characterized in that, The establishment of a regular observation system based on an irregular observation system specifically includes: The first receiver line spacing, the first number of receiver lines, the first number of sampling points at each receiver point, and the collected seismic data of the irregular observation system are obtained. The second receiving line spacing, the second number of receiving lines, the distance between two receiving points, and the second number of sampling points for each receiving point are determined in the rule-based observation system, wherein the second receiving line spacing is the same as the first receiving line spacing, the second number of receiving lines is the same as the first number of receiving lines, and the second number of sampling points is the same as the first number of sampling points.

3. The multi-sampling-rate seismic data reconstruction method according to claim 1, characterized in that, The establishment of a three-dimensional array based on the rule-based observation system specifically includes: A three-dimensional array is established based on the second receiving line spacing, the second number of receiving lines, and the second number of sampling points of the rule-based observation system.

4. The multi-sampling-rate seismic data reconstruction method according to claim 1, characterized in that, The step of obtaining the second receiving point corresponding to the first receiving point in the regular observation system in the irregular observation system, and the step of obtaining the numerical values ​​of the elements in the three-dimensional array based on the first receiving point, the second receiving point, and seismic data, specifically includes: Obtain the third receiving line where the first receiving point is located in the rule observation system; Obtain the fourth receiving line corresponding to the third receiving line in the irregular observation system; Obtain the second receiving point on the fourth receiving line that is closest to the first receiving point; Obtain the first distance between the first receiving point and the second receiving point; Based on the fact that the first distance is greater than the first threshold, the value of the element in the three-dimensional array corresponding to the first receiving point is set to 0; Based on the first distance being less than or equal to the first threshold, the value of the element in the three-dimensional array corresponding to the first receiving point is set as the seismic data collected by the second receiving point.

5. The multi-sampling-rate seismic data reconstruction method according to claim 1, characterized in that, The step of obtaining the set of receiving points to be reconstructed in the rule-based observation system based on the numerical values ​​of the elements in the three-dimensional array specifically includes: Obtain the numerical values ​​of the elements in the three-dimensional array; Based on the value being 0, the set of receiving points corresponding to the elements in the three-dimensional array is set as the set of receiving points to be reconstructed in the rule-based observation system.

6. The multi-sampling-rate seismic data reconstruction method according to claim 1, characterized in that, The step of constructing a first threshold function based on the maximum value, minimum value, and average value of the first curvature coefficient specifically includes: ； in, Represents the first threshold function. Indicates calculation of Power of 1 This represents the exponential factor, with values ​​greater than 0 and less than or equal to 1. and This represents the adjustment factor, and all values ​​are greater than 0 and less than or equal to 1. Indicates the number of iterations. Indicates the first The next iteration.

7. The multi-sampling-rate seismic data reconstruction method according to claim 1, characterized in that, The process of performing a three-dimensional curvelet transform on the element values ​​in the first reconstructed three-dimensional array to obtain the second reconstructed three-dimensional array specifically includes: A first three-dimensional curvilinear transformation is performed on the element values ​​in the three-dimensional array after the first reconstruction to obtain multiple second curvilinear coefficients; Obtain the maximum and minimum values ​​of the second curvature coefficient, which are the maximum and minimum values ​​of the second curvature coefficient, respectively. Obtain the average value of the second curvature coefficient, i.e., the average value of the second curvature coefficient; A second threshold function is constructed based on the maximum value, minimum value, and average value of the second curvature coefficient. Based on the second threshold function, iterative calculation of three-dimensional curvelet transform is performed, and the second curvelet coefficient of the three-dimensional curvelet transform that satisfies the first condition is set to 0, and three-dimensional inverse curvelet transform is performed. Obtain the first root mean square amplitude of the receiver point that does not need to be reconstructed in the rule-based observation system before the first reconstruction. Obtain the second root mean square amplitude of the element values ​​in the three-dimensional array after the three-dimensional inverse curve transform; Based on the first root mean square amplitude and the second root mean square amplitude, a threshold parameter is obtained; Based on the threshold parameter and the number of iterations, it is determined whether the second stopping condition has been reached. Based on the arrival of the second stopping condition, the values ​​of the corresponding elements in the three-dimensional array are updated using the result of the three-dimensional inverse curve transform after iteration, so as to obtain the three-dimensional array after the second reconstruction.

8. The multi-sampling-rate seismic data reconstruction method according to claim 7, characterized in that, The step of obtaining the threshold parameter based on the first root mean square amplitude and the second root mean square amplitude specifically includes: ； in, Indicates the threshold parameter. This represents the second root mean square amplitude. This represents the first root mean square amplitude.

9. A multi-sampling-rate seismic data reconstruction device, characterized in that, include: The first module is used to establish a regular observation system based on the irregular observation system. The second module is used to establish a three-dimensional array based on the rule-based observation system. The first acquisition module is used to acquire the second receiving point corresponding to the first receiving point in the regular observation system in the irregular observation system, and to acquire the values ​​of the elements in the three-dimensional array based on the first receiving point, the second receiving point and the seismic data. The second acquisition module is used to acquire the set of receiving points to be reconstructed in the rule observation system based on the values ​​of the elements in the three-dimensional array. The third acquisition module is used to perform a two-dimensional curvelet transform on each receiving line in the rule-based observation system to obtain the first value of the reconstructed receiving point. The first value is then used to update the set of receiving points to be reconstructed, resulting in the three-dimensional array after the first reconstruction. Specifically, this includes: Perform a first two-dimensional curvilinear transformation on each receiving line in the rule-based observation system to obtain multiple first curvilinear coefficients; Obtain the maximum and minimum values ​​of the first curvature coefficient, which are the maximum and minimum values ​​of the first curvature coefficient, respectively. Obtain the average value of the first curvature coefficient, i.e., the average value of the first curvature coefficient; A first threshold function is constructed based on the maximum value of the first curvature coefficient, the minimum value of the first curvature coefficient, and the average value of the first curvature coefficient; Based on the first threshold function, iterative calculation of two-dimensional curve transform is performed on each receiving line until the first stopping condition is reached, and the first value after reconstruction of each receiving line is obtained. The first value is used to update the set of receiving points to be reconstructed to obtain the three-dimensional array after the first reconstruction. The fourth acquisition module is used to perform three-dimensional curvelet transform on the element values ​​in the three-dimensional array after the first reconstruction to obtain the three-dimensional array after the second reconstruction. The element values ​​in the three-dimensional array after the second reconstruction are the reconstructed seismic data.

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