Multiple wave prediction method and device for sparse seabed nodes and computing equipment

Abstract

The invention discloses a multiple prediction method and device for sparse seabed nodes and computing equipment. The method comprises the following steps: acquiring original data of any common midpoint gather; performing low-speed dynamic time difference correction on the common midpoint gather original data to generate common midpoint gather correction data; extracting a plurality of near-trace data of the common midpoint gather correction data; performing superposition processing on the plurality of near-channel data to generate near-channel superposition data; performing convolution on the near-channel superposition data and the common midpoint gather correction data to generate initial multiple data; and performing low-speed inverse dynamic time difference correction on the initial multiple data to generate multiple prediction data. By adopting the scheme, the multiple can be predicted in the sparse seabed node scene, the multiple prediction precision in the sparse seabed node scene is improved, and the scheme is simple and easy to implement and high in multiple prediction efficiency.

Description

Technical Field

[0001] This application relates to the field of exploration technology, and in particular, to a multiple wave prediction method, device, computing device, computer storage medium, and computer program product for sparse seafloor nodes. Background Art

[0002] Sparse seafloor nodes refer to seismic data acquisition nodes arranged at relatively large intervals on the seafloor. Compared with the traditional relatively dense acquisition method, the node distribution is sparser, which can reduce the acquisition cost, reduce the difficulty of node laying, and improve the applicable range.

[0003] However, the inventors found in the implementation process that there are the following defects in the prior art: Due to the sparse distribution of sparse seafloor nodes and few spatial sampling points, conventional multiple wave prediction methods based on dense sampling data (such as the SRME method, etc.) cannot be applied to the multiple wave prediction scenario of sparse seafloor nodes. And the prior art has insufficient multiple wave prediction accuracy for the sparse seafloor node scenario, with complex prediction algorithms and low preset efficiency. Summary of the Invention

[0004] In view of the above problems, this application is proposed to provide a multiple wave prediction method, device, computing device, computer storage medium, and computer program product for sparse seafloor nodes that overcome the above problems or at least partially solve the above problems.

[0005] According to the first aspect of this application, there is provided a multiple wave prediction method for sparse seafloor nodes, including:

[0006] Obtain the original data of any common midpoint gather;

[0007] Perform low-velocity dynamic time difference correction on the original data of the common midpoint gather to generate corrected data of the common midpoint gather;

[0008] Extract multiple near-trace data from the corrected data of the common midpoint gather;

[0009] Perform stacking processing on the multiple near-trace data to generate near-trace stacked data;

[0010] Convolve the near-trace stacked data with the corrected data of the common midpoint gather to generate initial multiple wave data;

[0011] Perform low-velocity inverse dynamic time difference correction on the initial multiple wave data to generate multiple wave prediction data.

[0012] In an alternative embodiment, the extracting multiple near-trace data from the corrected data of the common midpoint gather includes:

[0013] Calculate the offset of each seismic trace in the common midpoint gather correction data;

[0014] Sort each seismic trace in ascending order of offset;

[0015] Select the first preset number of seismic traces as the near traces of the common midpoint gather and obtain the near trace data.

[0016] In an alternative embodiment, the method further includes: assigning corresponding weights to each near trace data;

[0017] The superimposing process of the multiple near trace data includes: superimposing the multiple near trace data according to the weights corresponding to the multiple near trace data.

[0018] In an alternative embodiment, the assigning corresponding weights to each near trace data includes:

[0019] Calculate the offset of each near trace data, and determine the first weight of each near trace data according to the offset;

[0020] Calculate the signal-to-noise ratio of each near trace data, and determine the second weight of each near trace data according to the signal-to-noise ratio;

[0021] For any near trace data, determine the comprehensive weight of the near trace data according to the first weight and the second weight of the near trace data;

[0022] Determine the weights assigned to each near trace data according to the comprehensive weights.

[0023] In an alternative embodiment, the correction speed of the low-velocity dynamic moveout correction is lower than the root-mean-square speed.

[0024] In an alternative embodiment, the convolving the near trace superimposed data with the common midpoint gather correction data includes:

[0025] Perform Fourier transform on the near trace superimposed data to obtain near trace superimposed frequency domain data, and perform Fourier transform on the common midpoint gather correction data to obtain common midpoint gather frequency domain data;

[0026] Perform dot product processing on the near trace superimposed frequency domain data and the common midpoint gather frequency domain data to obtain multiple wave frequency domain data;

[0027] Perform inverse Fourier transform on the multiple wave frequency domain data to obtain the initial multiple wave data.

[0028] According to the second aspect of the present application, there is provided a multiple wave prediction device for sparse seafloor nodes, including:

[0029] A data acquisition module for acquiring original data of any common midpoint gather;

[0030] A first correction module for performing low-velocity dynamic time difference correction on the original data of the common midpoint gather to generate corrected data of the common midpoint gather;

[0031] An extraction module for extracting a plurality of near-trace data from the corrected data of the common midpoint gather;

[0032] A stacking module for performing stacking processing on the plurality of near-trace data to generate near-trace stacked data;

[0033] A generation module for convolving the near-trace stacked data with the corrected data of the common midpoint gather to generate initial multiple wave data;

[0034] A second correction module for performing low-velocity inverse dynamic time difference correction on the initial multiple wave data to generate multiple wave prediction data.

[0035] In an optional implementation manner, the extraction module is configured to: calculate the offset of each seismic trace in the corrected data of the common midpoint gather;

[0036] Sort each seismic trace in ascending order of offset;

[0037] Select the first preset number of seismic traces as the near traces of the common midpoint gather and obtain near-trace data.

[0038] In an optional implementation manner, the stacking module is configured to: assign corresponding weights to each near-trace data;

[0039] Perform stacking processing on the plurality of near-trace data according to the weights corresponding to the plurality of near-trace data.

[0040] In an optional implementation manner, the stacking module is configured to: calculate the offset of each near-trace data, and determine the first weight of each near-trace data according to the offset;

[0041] Calculate the signal-to-noise ratio of each near-trace data, and determine the second weight of each near-trace data according to the signal-to-noise ratio;

[0042] For any near-trace data, determine the comprehensive weight of the near-trace data according to the first weight and the second weight of the near-trace data;

[0043] Determine the weights assigned to each near-trace data according to the comprehensive weights.

[0044] In an optional implementation manner, the correction speed of the low-velocity dynamic time difference correction is lower than the root mean square speed.

[0045] In an alternative embodiment, the generating module is configured to: perform a Fourier transform on the near-offset stack data to obtain near-offset stack frequency-domain data, and perform a Fourier transform on the common midpoint gather correction data to obtain common midpoint gather frequency-domain data;

[0046] perform a dot product operation on the near-offset stack frequency-domain data and the common midpoint gather frequency-domain data to obtain multiple-trip frequency-domain data;

[0047] perform an inverse Fourier transform on the multiple-trip frequency-domain data to obtain the initial multiple-trip data.

[0048] According to a third aspect of the present application, there is provided a computing device, including: a processor, a memory, a communication interface, and a communication bus, where the processor, the memory, and the communication interface complete communication with each other through the communication bus;

[0049] The memory is used to store at least one executable instruction, and the executable instruction causes the processor to perform operations corresponding to the multiple-trip prediction method for sparse seafloor nodes described above.

[0050] According to a fourth aspect of the present application, there is provided a computer storage medium, where at least one executable instruction is stored in the storage medium, and the executable instruction causes a processor to perform operations corresponding to the multiple-trip prediction method for sparse seafloor nodes described above.

[0051] According to a fifth aspect of the present application, there is provided a computer program product, including at least one executable instruction, and the executable instruction causes a processor to perform operations corresponding to the multiple-trip prediction method for sparse seafloor nodes described above.

[0052] The multiple-trip prediction method, apparatus, computing device, computer storage medium, and computer program product provided by the present application obtain any common midpoint gather raw data; perform low-velocity dynamic time difference correction on the common midpoint gather raw data to generate common midpoint gather correction data; extract multiple near-offset data from the common midpoint gather correction data; perform stacking processing on the multiple near-offset data to generate near-offset stack data; convolve the near-offset stack data with the common midpoint gather correction data to generate initial multiple-trip data; perform low-velocity inverse dynamic time difference correction on the initial multiple-trip data to generate multiple-trip prediction data. By adopting this solution, it is possible to realize the prediction of multiple-trip waves in the sparse seafloor node scenario, improve the prediction accuracy of multiple-trip waves in the sparse seafloor node scenario, and this solution is simple and easy to implement, and has a high multiple-trip prediction efficiency.

[0053] The above description is only an overview of the technical solution of the present application. In order to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specific embodiments of the present application are specifically exemplified. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present application. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0055] Figure 1 shows a schematic flow chart of a method for predicting multiple waves of a sparse seafloor node provided by an embodiment of the present application;

[0056] Figure 2 shows a schematic flow chart of a method for obtaining near-offset data provided by an embodiment of the present application;

[0057] Figure 3 shows a schematic flow chart of a method for determining weights provided by an embodiment of the present application;

[0058] Figure 4 shows a schematic flow chart of a method for generating initial multiple-wave data provided by an embodiment of the present application;

[0059] Figure 5 shows a schematic diagram of the original data of a common midpoint gather provided by an embodiment of the present application;

[0060] Figure 6 shows a schematic diagram of preset multiple-wave data provided by an embodiment of the present application;

[0061] Figure 7 shows a seismic profile before multiple-wave suppression provided by an embodiment of the present application;

[0062] Figure 8 shows a seismic profile after multiple-wave suppression provided by an embodiment of the present application;

[0063] Figure 9 shows a schematic structural diagram of a multiple-wave prediction device for a sparse seafloor node provided by an embodiment of the present application;

[0064] Figure 10 shows a schematic structural diagram of a computing device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0065] Exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present application can be more thoroughly understood and the scope of the present application can be completely conveyed to those skilled in the art.

[0066] Figure 1 The flowchart of a multiple wave prediction method for a sparse seafloor node provided by an embodiment of the present application is shown. Among them, the multiple wave prediction method provided by the embodiment of the present application is applied to a sparse seafloor node scenario.

[0067] Specifically, as Figure 1 shown, the method includes the following steps:

[0068] Step S101, obtain the original data of any common midpoint gather.

[0069] A common midpoint gather (CMP Gather) refers to a set of seismic traces where all subsurface reflection points are located at the same central position. That is, a common midpoint gather contains multiple seismic traces, and these multiple seismic traces correspond to the same central point.

[0070] In a specific implementation process, first obtain the original acquisition data. Calculate the central position of the reflection point for each shot-receiver pair. Among them, a shot-receiver pair corresponds to a shot point (source) and a geophone point (receiver), and then the average value of the horizontal coordinates of the shot point and the geophone point in the shot-receiver pair is used as the central position of the reflection point of the shot-receiver pair.

[0071] Further, data sorting is performed according to the central positions of the reflection points of each shot-receiver pair, so that the shot-receiver pairs with the same central position of the reflection point are sorted into the same set. Each set contains multiple shot-receiver pairs, and each shot-receiver pair corresponds to a seismic trace data. Thus, each set contains multiple seismic trace data, and the integration of the multiple seismic trace data in each set obtains the common midpoint gather data corresponding to the set. The common midpoint gather data obtained in this step is called the original data of the common midpoint gather.

[0072] For each set of original data of the common midpoint gather, the corresponding multiple wave prediction result can be obtained through the implementation of the subsequent steps.

[0073] Step S102, perform low-velocity dynamic time difference correction on the original data of the common midpoint gather to generate corrected data of the common midpoint gather.

[0074] To improve the prediction accuracy of multiples, the embodiments of the present application perform dynamic NMO correction on the original data of the common midpoint gather. Among them, the correction velocity of the dynamic NMO correction is lower than the corresponding normal root-mean-square velocity, that is, it satisfies the following formula 1. Therefore, the embodiments of the present application perform low-velocity dynamic NMO correction on the original data of the common midpoint gather, and the gather data obtained after the low-velocity dynamic NMO correction is called the corrected data of the common midpoint gather. Through this low-velocity dynamic NMO correction, the multiples can be flattened, which is convenient for accurately extracting the characteristics of multiples subsequently.

[0075] v c (t) < v z (t) (Formula 1)

[0076] Among them, v c (t) represents the correction velocity at the seismic data time t; v z (t) represents the normal root-mean-square velocity at the seismic data time t.

[0077] In an alternative embodiment, the correction velocity at the seismic data time t can specifically be the normal root-mean-square velocity at the seismic data time t / 2. That is, the correction velocity can be obtained by the following formula 2 to improve the correction effect and the flattening effect of the in-phase axis.

[0078] v c (t) = v z (t / 2) (Formula 2)

[0079] Taking Table 1 below as an example, the correction velocity at 200 ms = the normal root-mean-square velocity at 100 ms = 1800; the correction velocity at 2000 ms = the normal root-mean-square velocity at 1000 ms = 3000; the correction velocity at 4000 ms = the normal root-mean-square velocity at 2000 ms = 4000.

[0080] Table 1

[0081]

[0082]

[0083] After obtaining the correction velocity, the original data of the common midpoint gather is corrected using the correction velocity to obtain the corrected data of the common midpoint gather. For example, the correction can be performed through the following formula 3:

[0084]

[0085] Among them, v c(t) represents the correction speed at the seismic data time t; offset represents the offset distance of seismic track j in the original data of the common center point gather; d(j,t) represents the original data of seismic track j, that is, the data before correction; D(j,t) represents the seismic track correction data of seismic track j, that is, the data after correction.

[0086] The common-center-point gather correction data are obtained based on the seismic trace correction data of each seismic trace in the common-center-point gather.

[0087] Step S103, extracting a plurality of near-track data of the common-center point gather correction data.

[0088] The common center point gather correction data includes multiple seismic trace data, and seismic trace data with a small offset are extracted from the multiple seismic trace data. The seismic trace data with a small offset is the near-trace data. One near-trace data corresponds to one seismic trace data.

[0089] In an optional implementation, the short-cut data can be specifically obtained by Figure 2 Extraction steps shown:

[0090] S1031, calculating the offset distance of each seismic trace in the common center point gather correction data.

[0091] For each seismic trace included in the common center point gather correction data, the offset of each seismic trace is calculated. The offset refers to the horizontal distance between the source (such as the shot point) and the receiving point (such as the detector).

[0092] S1032, sorting the seismic traces in order of offset from low to high.

[0093] Arrange in ascending order according to the size of the offset, that is, the smaller the offset, the higher the sorting order.

[0094] S1033, selecting a preset number of seismic traces before the sequence as near traces of the common center point trace gather, and obtaining near trace data.

[0095] A preset number m is configured, and the first m seismic traces are extracted as near traces according to the sorting result, and the seismic trace correction data of the first m seismic traces are used as near trace data.

[0096] In an optional implementation, after selecting a preset number of seismic traces as the near traces of the common center point trace set before the position sequence, further verify whether the extracted near traces are abnormal traces; and remove the abnormal traces. The seismic trace data of the near traces after the abnormal traces are removed are used as the near trace data; or, new seismic traces can be filled in as near traces based on the sorting results again, so that the near traces finally extracted meet both the preset number requirement and the data quality requirement of the normal traces.

[0097] Among them, abnormal traces can be removed according to seismic trace data amplitudes and / or coherence parameters, etc. For example, the root mean square amplitude of the entire common midpoint gather can be calculated based on the common midpoint gather correction data, and the maximum absolute amplitude of each seismic trace can be calculated separately. The ratio of the maximum absolute amplitude to the root mean square amplitude is used as the amplitude ratio of the corresponding seismic trace. If the amplitude ratio of a certain seismic trace is within the abnormal range, then this seismic trace is determined to be an abnormal trace; and / or, for any seismic trace, the coherence parameter between this seismic trace and adjacent seismic traces is calculated. If the coherence parameter is within the abnormal range (such as the parameter value is too small), then this seismic trace is determined to be an abnormal trace.

[0098] Among them, the preset number can be a fixed value. For example, the preset number can be 3; or, different preset numbers can be configured according to different water depths, and the size of the preset number is positively correlated with the water depth. For example, the preset number in the shallow water area can be 3, and the preset number in the deep water area can be 5, etc.

[0099] Step S104: Perform stacking processing on multiple near-trace data to generate near-trace stacked data.

[0100] The data obtained by stacking the seismic trace data of multiple near traces (i.e., multiple near-trace data) extracted in step S103 is called near-trace stacked data. Among them, due to the strong interference of near-trace multiple waves, and the multiple wave event has been flattened by the low-velocity dynamic time difference correction in step S102, the near-trace stacked data after stacking the near-trace data can be used as a multiple wave feature template.

[0101] The stacking process of the near-trace data can be as shown in Formula 4:

[0102]

[0103] Among them, D(i,t) represents the i-th near-trace data; m represents the preset number; G(1,t) represents the near-trace stacked data.

[0104] In an alternative embodiment, in order to improve the preset accuracy of multiple waves, this embodiment also assigns corresponding weights to each near-trace data, and then performs stacking processing on multiple near-trace data according to the weights corresponding to the multiple near-trace data. For example, the stacking processing can be performed through the following Formula 5:

[0105]

[0106] Among them, D(i,t) represents the i-th near-trace data; m represents the preset number; G(1,t) represents the near-trace stacked data; R(i) represents the weight corresponding to the i-th near-trace data.

[0107] Further optionally, the weight of the near-trace data can be obtained through Figure 3The steps shown are determined to improve the rationality of weight configuration and the accuracy of multiple wave prediction:

[0108] S1041. Calculate the offset of each near - trace data, and determine the first weight of each near - trace data according to the offset.

[0109] Determine the first weight of each near - trace data from the offset dimension. Among them, the first weight is negatively correlated with the offset of the near - trace data, that is, the smaller the offset of the near - trace data, the higher the corresponding first weight.

[0110] S1042. Calculate the signal - to - noise ratio of each near - trace data, and determine the second weight of each near - trace data according to the signal - to - noise ratio.

[0111] Determine the second weight of each near - trace data from the signal - to - noise ratio dimension. Among them, the second weight is positively correlated with the signal - to - noise ratio of the near - trace data, that is, the higher the signal - to - noise ratio of the near - trace data, the higher the corresponding second weight.

[0112] In the actual implementation process, calculate the signal - to - noise ratio of each near - trace data and the sum of all signal - to - noise ratios respectively. Then, obtain the signal - to - noise ratio proportion according to the ratio of the signal - to - noise ratio of each near - trace data to the sum of the signal - to - noise ratios. Further, the signal - to - noise ratio proportion can be used as the corresponding second weight.

[0113] S1043. For any near - trace data, determine the comprehensive weight of the near - trace data according to the first weight and the second weight of the near - trace data.

[0114] Obtain the comprehensive weight by integrating the first weight and the second weight. For example, the product, sum, or weighted sum result of the first weight and the second weight (that is, different weight values are assigned to the offset dimension and the signal - to - noise ratio dimension, and then weighted sum is performed according to the first weight, the second weight, and the corresponding dimension weight values) can be used as the corresponding comprehensive weight.

[0115] S1044. Determine the weights assigned to each near - trace data according to the comprehensive weight.

[0116] Through the above steps S1041 - S1043, the comprehensive weights of each near - trace data can be obtained, and further calculate the sum of the comprehensive weights. For any near - trace data, use the ratio of the comprehensive weight of the near - trace data to the sum of the comprehensive weights as the final weight of the near - trace data.

[0117] Step S105. Convolve the near - trace stack data with the common mid - point gather correction data to generate the initial multiple wave data.

[0118] Since the near - trace stacked data has strong multiple characteristics, it can be used as a multiple - characteristic template. Thus, by convolving the near - trace stacked data with the common - mid - point gather corrected data, multiple data can be obtained, and this multiple data is called the initial multiple data.

[0119] In an alternative embodiment, to improve the generation efficiency of the initial multiple data, the following Figure 4 steps can be specifically adopted to generate the initial multiple data:

[0120] S1051, perform Fourier transform on the near - trace stacked data to obtain the near - trace stacked frequency - domain data, and perform Fourier transform on the common - mid - point gather corrected data to obtain the common - mid - point gather frequency - domain data.

[0121] First, perform Fourier transform on the near - trace stacked data and the common - mid - point gather corrected data respectively to obtain the corresponding frequency - domain data. That is, the frequency - domain data obtained by performing Fourier transform on the near - trace stacked data is called the near - trace stacked frequency - domain data, and the frequency - domain data obtained by performing Fourier transform on the common - mid - point gather corrected data is called the common - mid - point gather frequency - domain data.

[0122] For example, the common - mid - point gather frequency - domain data can be generated by the following formula 6, and the near - trace stacked frequency - domain data can be generated by the following formula 7:

[0123]

[0124] where ω represents the angular frequency; N represents the number of samples in the frequency domain; D(j,t) represents the corrected seismic trace data of seismic trace j in the common - mid - point gather corrected data; FD(j,ω) represents the frequency - domain data of seismic trace j in the near - trace stacked frequency - domain data; G(1,t) represents the near - trace stacked data; FG(1,ω) represents the near - trace stacked frequency - domain data.

[0125] S1052, perform element - by - element multiplication on the near - trace stacked frequency - domain data and the common - mid - point gather frequency - domain data to obtain the multiple frequency - domain data.

[0126] Specifically, for the frequency - domain data of each seismic trace in the common - mid - point gather frequency - domain data, perform element - by - element multiplication with the near - trace stacked frequency - domain data to obtain the multiple frequency - domain data of each seismic trace.

[0127] For example, the element - by - element multiplication can be performed by the following formula 8 to obtain the multiple frequency - domain data:

[0128] FM(j,ω) = FD(j,ω) * FG(1,ω) (Formula 8)

[0129] Among them, FD(j, ω) represents the frequency-domain data of seismic trace j in the near-offset stack frequency-domain data; FG(1, ω) represents the near-offset stack frequency-domain data; FM(j, ω) represents the multiple frequency-domain data of seismic trace j.

[0130] S1053. Perform Fourier inverse transform on the multiple frequency-domain data to obtain the initial multiple data.

[0131] The data obtained by performing Fourier inverse transform on the multiple frequency-domain data in step S1052 is the initial multiple data. For example, the initial multiple data can be obtained by performing Fourier inverse transform through the following formula 9:

[0132]

[0133] Among them, M(j, t) represents the initial multiple data corresponding to seismic trace j; FM(j, ω) represents the multiple frequency-domain data of seismic trace j.

[0134] Step S106. Perform low-velocity inverse moveout correction on the initial multiple data to generate multiple prediction data.

[0135] Since step S102 performs low-velocity moveout correction on the common midpoint gather original data, the initial multiple data obtained in step S105 is the data after low-velocity moveout correction. In order to restore the real multiple data, this step performs low-velocity inverse moveout correction on the initial multiple data. The multiple data obtained by low-velocity inverse moveout correction is the multiple prediction data.

[0136] For example, low-velocity moveout correction can be performed through the following formula 10:

[0137] m(j, t) = M(j, t - Δt) (formula 10)

[0138] Among them, M(j, t) represents the initial multiple data corresponding to seismic trace j; Δt can be calculated with reference to formula 3; m(j, t) represents the multiple prediction data corresponding to seismic trace j.

[0139] Adopting the above steps S101 - S106 can achieve accurate prediction of multiples in a sparse seafloor node scenario, thus facilitating accurate suppression of multiples. Refer to Figure 5 and Figure 6 , Figure 5 is the common midpoint gather original data, Figure 6 is based on Figure 5 the common midpoint gather original data, and the multiple prediction data obtained by using the method of the embodiment of the present application. By comparing Figure 5 and Figure 6 it can be seen that the method of the embodiment of the present application can accurately predict multiples; or, refer toFigure 7 and Figure 8 , Figure 7 Without multiple wave suppression, there are many multiple waves under the strong reflection layer, and the multiple waves can be effectively suppressed after being suppressed by this method.

[0140] It can be seen from this that the multiple wave prediction method for sparse seafloor nodes provided by the embodiments of the present application is based on the original data of the common midpoint gather, performs low-velocity dynamic time difference correction on the original data of the common midpoint gather to flatten the multiple wave in-phase axis, identifies multiple near-trace data, and stacks the near-trace data to obtain near-trace stacked data, so that the characteristics of multiple waves in the sparse seafloor node scenario can be accurately obtained. Furthermore, the near-trace stacked data is convolved with the corrected data of the common midpoint gather and low-velocity inverse dynamic time difference correction is performed to obtain multiple wave prediction data. The adoption of this solution can accurately predict multiple waves in the sparse seafloor node scenario, and the implementation process of this solution is simple and easy, and the multiple wave prediction efficiency is high.

[0141] Figure 9 shows a schematic structural diagram of a multiple wave prediction device for sparse seafloor nodes provided by the embodiments of the present application. Specifically, as Figure 9 shown, the multiple wave prediction device 900 includes: a data acquisition module 910, a first correction module 920, an extraction module 930, a stacking module 940, a generation module 950, and a second correction module 960.

[0142] The data acquisition module 910 is configured to acquire any original data of a common midpoint gather;

[0143] The first correction module 920 is configured to perform low-velocity dynamic time difference correction on the original data of the common midpoint gather to generate corrected data of the common midpoint gather;

[0144] The extraction module 930 is configured to extract multiple near-trace data from the corrected data of the common midpoint gather;

[0145] The stacking module 940 is configured to perform stacking processing on the multiple near-trace data to generate near-trace stacked data;

[0146] The generation module 950 is configured to convolve the near-trace stacked data with the corrected data of the common midpoint gather to generate initial multiple wave data;

[0147] The second correction module 960 is configured to perform low-velocity inverse dynamic time difference correction on the initial multiple wave data to generate multiple wave prediction data.

[0148] In an optional implementation manner, the extraction module 930 is configured to: calculate the offset of each seismic trace in the corrected data of the common midpoint gather;

[0149] Sort each seismic trace in ascending order of offset;

[0150] Select the first preset number of seismic traces as the near traces of the common midpoint gather, and obtain the near trace data.

[0151] In an alternative embodiment, the stacking module 940 is configured to: assign corresponding weights to each near trace data;

[0152] Stack the multiple near trace data according to the weights corresponding to the multiple near trace data.

[0153] In an alternative embodiment, the stacking module 940 is configured to: calculate the offset of each near trace data, and determine the first weight of each near trace data according to the offset;

[0154] Calculate the signal-to-noise ratio of each near trace data, and determine the second weight of each near trace data according to the signal-to-noise ratio;

[0155] For any near trace data, determine the comprehensive weight of the near trace data according to the first weight and the second weight of the near trace data;

[0156] Determine the weights assigned to each near trace data according to the comprehensive weights.

[0157] In an alternative embodiment, the correction speed of the low-velocity normal moveout correction is lower than the root-mean-square velocity.

[0158] In an alternative embodiment, the generation module 950 is configured to: perform Fourier transform on the near trace stacked data to obtain near trace stacked frequency domain data, and perform Fourier transform on the common midpoint gather corrected data to obtain common midpoint gather frequency domain data;

[0159] Perform dot product processing on the near trace stacked frequency domain data and the common midpoint gather frequency domain data to obtain multiple wave frequency domain data;

[0160] Perform inverse Fourier transform on the multiple wave frequency domain data to obtain the initial multiple wave data.

[0161] It can be seen that the multiple wave prediction device for sparse seafloor nodes provided by the embodiments of the present application is based on the original data of the common midpoint gather, performs low-velocity dynamic time difference correction on the original data of the common midpoint gather to flatten the multiple wave isochrones, identifies multiple near-trace data, and superimposes the near-trace data to obtain near-trace superimposed data, so as to accurately obtain the multiple wave characteristics in the sparse seafloor node scenario. Furthermore, the near-trace superimposed data and the corrected data of the common midpoint gather are convolved and low-velocity inverse dynamic time difference correction is performed to obtain multiple wave prediction data. By adopting this solution, accurate prediction of multiple waves can be carried out in the sparse seafloor node scenario, and the implementation process of this solution is simple and easy, and the multiple wave prediction efficiency is high.

[0162] The embodiments of the present application provide a non-volatile computer storage medium, and the computer storage medium stores at least one executable instruction or computer program, and the executable instruction or computer program can enable a processor to execute the operations corresponding to the multiple wave prediction method for sparse seafloor nodes in any of the above method embodiments.

[0163] The embodiments of the present application provide a computer program product, and the computer program product includes at least one executable instruction or computer program, and the executable instruction or computer program can enable a processor to execute the operations corresponding to the multiple wave prediction method for sparse seafloor nodes in any of the above method embodiments.

[0164] Figure 10 The structural schematic diagram of a computing device provided by the embodiments of the present application is shown. The specific implementation of the computing device is not limited in the specific embodiments of the present application.

[0165] As Figure 10 shown, the computing device may include: a processor 1002, a communications interface 1004, a memory 1006, and a communications bus 1008.

[0166] Among them: the processor 1002, the communications interface 1004, and the memory 1006 communicate with each other through the communications bus 1008. The communications interface 1004 is used to communicate with network elements of other devices such as clients or other servers. The processor 1002 is used to execute the program 1010, and specifically can execute the relevant steps in the embodiments of the multiple wave prediction method for sparse seafloor nodes for the computing device described above.

[0167] Specifically, the program 1010 may include program code, and the program code includes computer operation instructions.

[0168] The processor 1002 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application. One or more processors included in the computing device may be of the same type, such as one or more CPUs; or may be of different types, such as one or more CPUs and one or more ASICs.

[0169] A memory 1006 for storing a program 1010. The memory 1006 may include high-speed RAM memory and may also include non-volatile memory, such as at least one disk memory. The program 1010 may specifically be used to cause the processor 1002 to perform the operations in the above method embodiments.

[0170] In summary, according to the computing device, computer storage medium, and computer program product provided in this embodiment, based on the common midpoint gather raw data, the common midpoint gather raw data is subjected to low-velocity dynamic normal moveout correction to flatten the multiple event axis, and multiple near-offset data are identified and the near-offset data are stacked to obtain near-offset stacked data, so that the multiple wave characteristics in the sparse seafloor node scenario can be accurately obtained. Furthermore, the near-offset stacked data and the common midpoint gather corrected data are convolved and low-velocity inverse dynamic normal moveout correction is performed to obtain multiple wave prediction data. The proposed solution can accurately predict multiples in the sparse seafloor node scenario, and the implementation process of the solution is simple and easy, and the multiple wave prediction efficiency is high.

[0171] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems may also be used in conjunction with the teachings based herein. The structure required to construct such a system will be apparent from the above description. In addition, the embodiments of the present application are not directed to any specific programming language. It should be understood that the content of the present application described herein can be implemented using various programming languages, and the description of the specific language above is to disclose the best mode of the present application.

[0172] In the specification provided herein, a large number of specific details are set forth. However, it can be understood that the embodiments of the present application may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0173] Similarly, it should be understood that, in order to streamline the present application and assist in understanding one or more of the various inventive aspects, in the foregoing description of the exemplary embodiments of the present application, the various features of the embodiments of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, the disclosed method should not be construed as reflecting an intention that the claimed application requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, the inventive aspects lie in less than all the features of the single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate embodiment of the present application.

[0174] Those skilled in the art can understand that the modules in the devices in the embodiments can be adaptively changed and disposed in one or more devices different from the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and in addition, they can be divided into multiple sub-modules or sub-units or sub-components. Except that at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all the features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all the processes or units of any method or device so disclosed. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) can be replaced by an alternative feature that provides the same, equivalent, or similar purpose.

[0175] In addition, those skilled in the art can understand that although some of the embodiments herein include certain features included in other embodiments rather than other features, the combination of the features of different embodiments means that it is within the scope of the present application and forms different embodiments. For example, in the following claims, any one of the claimed embodiments can be used in any combination.

[0176] The various component embodiments of the present application can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art should understand that a microprocessor or a digital signal processor (DSP) can be used in practice to implement some or all of the functions of some or all of the components according to the embodiments of the present application. The present application can also be implemented as a device or apparatus program (such as a computer program and a computer program product) for executing part or all of the methods described herein. Such a program for implementing the present application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0177] It should be noted that the above embodiments are illustrative of the present application rather than restrictive thereof, and those skilled in the art can design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in the claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present application can be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In a unit claim listing several devices, several of these devices may be embodied by the same item of hardware. The use of the words first, second, and third, etc. does not denote any order. These words may be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A multiple wave prediction method for sparse seafloor nodes, characterized in that Including: Obtain the original data of any common midpoint gather; Perform low-velocity dynamic time difference correction on the original data of the common midpoint gather to generate corrected data of the common midpoint gather; Extract multiple near-trace data from the corrected data of the common midpoint gather; Perform stacking processing on the multiple near-trace data to generate near-trace stacked data; Convolve the near-trace stacked data with the corrected data of the common midpoint gather to generate initial multiple wave data; Perform low-velocity inverse dynamic time difference correction on the initial multiple wave data to generate multiple wave prediction data.

2. The method according to claim 1, characterized in that, The extracting multiple near-trace data from the corrected data of the common midpoint gather includes: Calculate the offset of each seismic trace in the corrected data of the common midpoint gather; Sort each seismic trace in ascending order of offset; Select the first preset number of seismic traces as the near traces of the common midpoint gather and obtain the near-trace data.

3. The method according to claim 1, characterized in that, The method further includes: assigning corresponding weights to each near-trace data; The performing stacking processing on the multiple near-trace data includes: performing stacking processing on the multiple near-trace data according to the weights corresponding to the multiple near-trace data.

4. The method according to claim 3, wherein The assigning corresponding weights to each near-trace data includes: Calculate the offset of each near-trace data, and determine the first weight of each near-trace data according to the offset; Calculate the signal-to-noise ratio of each near-trace data, and determine the second weight of each near-trace data according to the signal-to-noise ratio; For any near-trace data, determine the comprehensive weight of the near-trace data according to the first weight and the second weight of the near-trace data; Determine the weights assigned to each near-trace data according to the comprehensive weights.

5. The method according to any one of claims 1-4, characterized in that, The correction speed of the low-velocity dynamic time difference correction is lower than the root mean square speed.

6. The method according to any one of claims 1-4, characterized in that The convolving the near-trace stacked data with the corrected data of the common midpoint gather includes: Perform Fourier transform on the near-trace stacked data to obtain near-trace stacked frequency domain data, and perform Fourier transform on the corrected data of the common midpoint gather to obtain common midpoint gather frequency domain data; Perform dot product processing on the near-trace stacked frequency domain data and the common midpoint gather frequency domain data to obtain multiple wave frequency domain data; Perform inverse Fourier transform on the multiple wave frequency domain data to obtain the initial multiple wave data.

7. A multiple wave prediction device for sparse seafloor nodes, characterized in that, Including: A data acquisition module for obtaining the original data of any common midpoint gather; A first correction module for performing low-velocity dynamic time difference correction on the original data of the common midpoint gather to generate corrected data of the common midpoint gather; An extraction module for extracting multiple near-trace data from the corrected data of the common midpoint gather; A stacking module for performing stacking processing on the multiple near-trace data to generate near-trace stacked data; A generation module for convolving the near-trace stacked data with the corrected data of the common midpoint gather to generate initial multiple wave data; A second correction module for performing low-velocity inverse dynamic time difference correction on the initial multiple wave data to generate multiple wave prediction data.

8. A computing device, characterized in that, Including: A processor, a memory, a communication interface, and a communication bus. The processor, the memory, and the communication interface complete communication with each other through the communication bus; The memory is used to store at least one executable instruction, and the executable instruction causes the processor to perform operations corresponding to the multiple wave prediction method for sparse seafloor nodes described in any one of claims 1-6.

9. A computer storage medium, characterized in that, At least one executable instruction is stored in the storage medium, and the executable instruction causes the processor to perform operations corresponding to the multiple wave prediction method for sparse seafloor nodes described in any one of claims 1-6.

10. A computer program product, characterized in that, It includes at least one executable instruction, and the executable instruction causes the processor to perform operations corresponding to the multiple wave prediction method for sparse seafloor nodes described in any one of claims 1-6.

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