Signal deconvolution method and device, computer device and storage medium

Abstract

The application provides a signal deconvolution method and device, computer equipment and storage medium, and belongs to the technical field of seismic data processing. The method comprises the following steps: determining initial first seismic data of a seismic gather; determining second seismic data of the seismic gather and azimuth and emergence angle of a plurality of seismic traces; determining an angle combination corresponding to the seismic trace based on the azimuth and emergence angle of the seismic trace; for each preset angle combination in a plurality of preset angle combinations, determining a target seismic trace based on the angle combination corresponding to the plurality of seismic traces; stacking ray parameters of a plurality of target seismic traces corresponding to the preset angle combination to obtain unit wavelet data corresponding to the preset angle combination; determining far-field wavelet data of the seismic gather based on the unit wavelet data; generating a deconvolution operator of the seismic gather based on the far-field wavelet data; and processing the first seismic data through the deconvolution operator to obtain deconvolution seismic data of the seismic gather. The method improves the accuracy of the deconvolution seismic data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of seismic data processing, and particularly relates to a signal deconvolution method and device, computer equipment and a storage medium. BACKGROUND

[0002] In the process of processing seismic data by the signal deconvolution method, the method can effectively process seismic wavelet data of each seismic trace in the seismic data to obtain a spike signal and a reflection coefficient; and based on the spike signal and the reflection coefficient, high-resolution seismic data can be obtained.

[0003] In the related art, the signal deconvolution method mainly adopts a one-dimensional signal deconvolution method to process seismic wavelet data. The one-dimensional signal deconvolution method only processes seismic wavelet data in the vertical direction. Since different seismic wavelets have differences in azimuth and emergence angle in the excitation direction, the one-dimensional signal deconvolution method for processing seismic wavelet data of multiple different azimuths and emergence angles will reduce the accuracy of the signal deconvolution method for processing seismic wavelet data. SUMMARY

[0004] Embodiments of the present application provide a signal deconvolution method, device, computer equipment and storage medium, which can improve the accuracy of the signal deconvolution method for processing seismic data. The technical solution is as follows:

[0005] In one aspect, a signal deconvolution method is provided, which includes:

[0006] determining initial first seismic data of a seismic trace set, the seismic trace set including a plurality of seismic traces;

[0007] based on the first seismic data, determining second seismic data of the seismic trace set, and determining azimuth and emergence angle of the plurality of seismic traces from the first seismic data, the second seismic data including a corresponding ray parameter of each seismic trace;

[0008] for each seismic trace, based on the azimuth and emergence angle of the seismic trace, determining a corresponding angle combination of the seismic trace, the angle combination including the azimuth and the emergence angle;

[0009] for each preset angle combination in a plurality of preset angle combinations, based on the corresponding angle combination of the plurality of seismic traces, determining a target seismic trace, the angle combination of the target seismic trace belonging to the preset angle combination;

[0010] for each preset angle combination, stacking the ray parameters of a plurality of target seismic traces corresponding to the preset angle combination to obtain unit wavelet data corresponding to the preset angle combination;

[0011] determine far-field wavelet data of the seismic gather based on the unit wavelet data corresponding to the plurality of preset angle combinations;

[0012] generate a deconvolution operator of the seismic gather based on the far-field wavelet data;

[0013] process the first seismic data through the deconvolution operator to obtain deconvolution seismic data of the seismic gather.

[0014] In a possible implementation, the generating the deconvolution operator of the seismic gather based on the far-field wavelet data comprises:

[0015] performing bubble elimination processing, ghost wave suppression processing and zero-phase processing on the far-field wavelet data to obtain expected wavelet data;

[0016] determining the deconvolution operator used for converting the far-field wavelet data into the expected wavelet data.

[0017] In a possible implementation, the determining the first seismic data of the seismic gather comprises:

[0018] obtaining original seismic data of the seismic gather;

[0019] performing preprocessing on the original seismic data to obtain third seismic data;

[0020] performing τ-p domain positive transformation processing on the third seismic data to obtain the first seismic data of the seismic gather.

[0021] In a possible implementation, the performing preprocessing on the original seismic data to obtain third seismic data comprises:

[0022] performing regularization processing and interpolation processing on the original seismic data to obtain fourth seismic data;

[0023] determining target seismic data from the fourth seismic data, the target seismic data being data in which waveforms occur preset fluctuation;

[0024] determining downgoing wave data in the target seismic data;

[0025] suppressing multiple wave data in the downgoing wave data to obtain the third seismic data.

[0026] In a possible implementation, the determining the downgoing wave data in the target node data comprises:

[0027] performing wave field separation processing on the target seismic data to obtain the downgoing wave data in the target seismic data.

[0028] In one possible implementation, determining the second seismic data of the seismic gather based on the first seismic data includes:

[0029] The first seismic data is subjected to noise suppression processing to obtain the first seismic data after noise suppression.

[0030] The first seismic data after noise suppression is subjected to phase axis flattening to obtain the first seismic data after phase axis flattening.

[0031] The first seismic data after the phase axis is flattened is subjected to distortion removal processing to obtain the first seismic data after distortion removal.

[0032] The first seismic data after distortion removal is subjected to boundary extrapolation to obtain the second seismic data of the seismic gather.

[0033] In one possible implementation, processing the first seismic data using the deconvolution operator to obtain the deconvolutioned seismic data of the seismic gather includes:

[0034] The first seismic data is processed by the deconvolution operator to obtain the fifth seismic data;

[0035] The fifth seismic data is subjected to τ-p domain inverse transformation to obtain the deconvolution seismic data.

[0036] On the other hand, a signal deconvolution device is provided, the device comprising:

[0037] The first determining module is used to determine the initial first seismic data of the seismic gather, which includes multiple seismic traces;

[0038] The second determining module is used to determine the second seismic data of the seismic trace set based on the first seismic data, and to determine the azimuth and emission angle of the plurality of seismic traces from the first seismic data, wherein the second seismic data includes the ray parameters corresponding to each seismic trace;

[0039] The third determining module is used to determine, for each seismic trace, an angle combination corresponding to the seismic trace based on the azimuth and emission angle of the seismic trace, wherein the angle combination includes the azimuth and the emission angle.

[0040] The fourth determining module is used to determine a target seismic trace for each of the multiple preset angle combinations based on the angle combinations corresponding to the multiple seismic traces, wherein the angle combination of the target seismic trace belongs to the preset angle combination.

[0041] The superposition module is configured to superimpose the ray parameters of a plurality of target seismic traces corresponding to each preset angle combination to obtain unit wavelet data corresponding to the preset angle combination.

[0042] The fifth determination module is configured to determine far-field wavelet data of the seismic trace set based on the unit wavelet data corresponding to the plurality of preset angle combinations.

[0043] The generation module is configured to generate a deconvolution operator of the seismic trace set based on the far-field wavelet data.

[0044] The processing module is configured to process the first seismic data by using the deconvolution operator to obtain deconvolution seismic data of the seismic trace set.

[0045] In a possible implementation, the generation module is configured to:

[0046] perform bubble elimination processing, ghost wave suppression processing, and zero-phase processing on the far-field wavelet data to obtain desired wavelet data;

[0047] determine the deconvolution operator used for converting the far-field wavelet data into the desired wavelet data.

[0048] In a possible implementation, the first determination module includes:

[0049] The acquisition unit is configured to acquire original seismic data of the seismic trace set.

[0050] The first processing unit is configured to perform preprocessing on the original seismic data to obtain third seismic data.

[0051] The second processing unit is configured to perform τ-p domain positive transformation processing on the third seismic data to obtain the first seismic data of the seismic trace set.

[0052] In a possible implementation, the first processing unit includes:

[0053] The processing subunit is configured to perform regularization processing and interpolation processing on the original seismic data to obtain fourth seismic data.

[0054] The first determination subunit is configured to determine target seismic data from the fourth seismic data, the target seismic data being data in which waveforms occur preset fluctuations.

[0055] The second determination subunit is configured to determine downgoing wave data in the target seismic data.

[0056] The suppression unit is configured to suppress multiple wave data in the downgoing wave data to obtain the third seismic data.

[0057] In a possible implementation, the second determining subunit is configured to:

[0058] perform wave field separation processing on the target seismic data to obtain downgoing wave data in the target seismic data.

[0059] In a possible implementation, the second determining module is configured to:

[0060] perform noise suppression processing on the first seismic data to obtain first seismic data after noise suppression;

[0061] perform trace flattening processing on the first seismic data after noise suppression to obtain first seismic data after trace flattening;

[0062] perform distortion cutting processing on the first seismic data after trace flattening to obtain first seismic data after cutting of distortion;

[0063] perform boundary extrapolation processing on the first seismic data after cutting of distortion to obtain second seismic data of the seismic trace gather.

[0064] In a possible implementation, the processing module is configured to:

[0065] perform processing on the first seismic data by using the deconvolution operator to obtain fifth seismic data;

[0066] perform τ-p domain inverse transformation processing on the fifth seismic data to obtain the deconvolution seismic data.

[0067] In another aspect, a computer device is provided, which includes one or more processors and one or more memories, and the one or more memories store at least one instruction, which is loaded and executed by the one or more processors to implement operations performed by the signal deconvolution method according to any of the foregoing implementation manners.

[0068] In another aspect, a computer readable storage medium is provided, which stores at least one instruction, which is loaded and executed by a processor to implement operations performed by the signal deconvolution method according to any of the foregoing implementation manners.

[0069] In another aspect, a computer program product or a computer program is provided, which includes computer program code stored in a computer readable storage medium. A processor of a computer device reads the computer program code from the computer readable storage medium, and the processor executes the computer program code, so that the computer device performs operations performed by the signal deconvolution method according to the foregoing description.

[0070] The technical scheme provided by the embodiments of the present application has at least the following beneficial effects:

[0071] The signal deconvolution method provided by the embodiments of the present application divides the seismic traces based on the azimuth and emergence angle of the seismic traces, and then determines the unit wavelet data and the far-field wavelet data based on the seismic traces with divided azimuth and emergence angle, so that the determined far-field wavelet data fully considers the factors of the azimuth and emergence angle, and further makes the deconvolution operator determined based on the far-field wavelet data related to the azimuth and emergence angle, so that when the first seismic data is processed based on the deconvolution operator, the differences caused by the azimuth and emergence angle can be effectively compensated, thereby making the deconvolution seismic data determined based on the deconvolution operator more accurate. BRIEF DESCRIPTION OF DRAWINGS

[0072] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0073] Figure 1 is a flowchart of a signal deconvolution method provided by the embodiments of the present application;

[0074] Figure 2 is a schematic diagram of a seismic trace set provided by the embodiments of the present application;

[0075] Figure 3 is a schematic diagram of a seismic trace set provided by the embodiments of the present application;

[0076] Figure 4 is a schematic diagram of a seismic trace set provided by the embodiments of the present application;

[0077] Figure 5 is a division diagram of a ray parameter provided by the embodiments of the present application;

[0078] Figure 6 is a schematic diagram of a unit wavelet provided by the embodiments of the present application;

[0079] Figure 7 is a schematic diagram of a far-field wavelet provided by the embodiments of the present application;

[0080] Figure 8 is a spectrum diagram of a far-field wavelet provided by the embodiments of the present application;

[0081] Figure 9 is a spectrum diagram of an expected wavelet provided by the embodiments of the present application;

[0082] Figure 10 is a phase spectrum of a far-field wavelet provided by an embodiment of the present application;

[0083] Figure 11 is a phase spectrum of a desired wavelet provided by an embodiment of the present application;

[0084] Figure 12 is a schematic diagram of a desired wavelet provided by an embodiment of the present application;

[0085] Figure 13 is a schematic diagram of a seismic gather provided by an embodiment of the present application;

[0086] Figure 14 is a schematic diagram of a seismic gather provided by an embodiment of the present application;

[0087] Figure 15 is a schematic diagram of a seismic wavelet provided by an embodiment of the present application;

[0088] Figure 16 is a superposition diagram of a seismic wavelet provided by an embodiment of the present application;

[0089] Figure 17 is a frequency spectrum diagram of seismic data provided by an embodiment of the present application;

[0090] Figure 18 is a profile diagram of seismic data provided by an embodiment of the present application;

[0091] Figure 19 is a block diagram of a signal deconvolution device provided by an embodiment of the present application;

[0092] Figure 20 is a block diagram of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0093] In order to make the objectives, technical solutions and advantages of the present application clearer, the following will further describe the embodiments of the present application in detail with reference to the accompanying drawings.

[0094] The terms "first", "second", "third", and "fourth" and the like in the description and claims of the present application and the accompanying drawings are used to distinguish different objects, and are not used to describe a particular order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but can optionally include other steps or units not listed or can optionally include other steps or units inherent to the process, method, product or device.

[0095] The embodiment of the present application provides a signal deconvolution method, referring to Figure 1 , the method comprises:

[0096] Step 101: The computer device determines the initial first seismic data of a seismic trace set.

[0097] The seismic trace set comprises a plurality of seismic traces, and the seismic trace set is a trace set formed by all seismic traces received by any geophone of a target region to be studied. The target region is a region of seismic data to be processed, and can be a marine region. The target region comprises a plurality of geophones, that is, the seismic data of the target region comprises the first seismic data of a plurality of seismic trace sets. The seismic data of the target region can be data obtained by receiving, by a geophone, a seismic wave signal generated by an air gun source after the seismic wave signal propagates in the target region.

[0098] The step can be implemented through the following steps (1)-(3):

[0099] (1) The computer device acquires original seismic data of the seismic trace set.

[0100] The original seismic data comprises travel times and lateral travel distances of a plurality of seismic traces of the seismic trace set.

[0101] (2) The computer device pre-processes the original seismic data to obtain third seismic data.

[0102] The step can be implemented through the following steps A1-A4:

[0103] A1: The computer device performs regularization processing and interpolation processing on the original seismic data to obtain fourth seismic data.

[0104] It should be noted that, due to factors such as obstacles, restricted areas, marine streamer feather drift and economic costs in the original seismic data acquisition process, the original seismic data is usually irregularly or sparsely sampled along the spatial direction, and irregular seismic data can also be caused by trace loss and bad trace rejection. Spatial aliasing can be introduced in the process of processing irregular seismic data; therefore, the original seismic data needs to be subjected to regularization processing so as to obtain a regular and complete seismic trace set; and the original seismic data needs to be subjected to interpolation processing to fill in the missing data in the original seismic data and increase the density of the original seismic data, so as to obtain complete seismic data and improve the precision of the original seismic data. In this way, through the regularization processing and the interpolation processing, spatial aliasing and other artifacts after the original seismic data is subjected to τ-p domain forward transformation can be avoided.

[0105] Referring to Figure 2 , Figure 2 The left graph of FIG. 1 is a seismic trace set after the original seismic data is subjected to τ-p domain forward transformation, and spatial aliasing phenomenon exists.Figure 2 The right graph of FIG. 1 is a seismic gather of the fourth seismic data obtained by τ-p domain positive transformation after the regularizing and interpolating of the original seismic data. As can be seen from the graph, the spatial aliasing phenomenon is effectively eliminated after the regularizing and interpolating.

[0106] A2: The computer device determines target seismic data from the fourth seismic data, and the target seismic data is data with a preset wave fluctuation.

[0107] The target seismic data with the preset wave fluctuation is data with weak noise and strong bubble energy in the fourth seismic data.

[0108] A3: The computer device determines downgoing wave data in the target seismic data.

[0109] In this step, the computer device performs wave field separation processing on the target seismic data to obtain the downgoing wave data in the target seismic data. The computer device performs cross-ghosting processing on the target seismic data based on the water detection data and the land detection data in the original seismic data to obtain the separated downgoing wave data.

[0110] In the embodiments of the present application, the bubble effect of the downgoing wave data is relatively stronger, so that by determining the downgoing wave data, the third seismic data subjected to τ-p domain positive transformation can be more reasonable.

[0111] A4: The computer device suppresses multiple wave data in the downgoing wave data to obtain third seismic data.

[0112] The method for suppressing the multiple wave data can be common center point stacking method, Radon transform method, etc.

[0113] It should be noted that the length of the seismic wavelet of the seismic trace has a greater impact on the processing effect of the signal deconvolution method. The longer the seismic wavelet, the richer the bubble information it contains, and thus the better the effect of suppressing bubble information by the signal deconvolution method. However, the length of the seismic wavelet is restricted by the multiple wave, so after suppressing the multiple wave data, the signal deconvolution method can better suppress the bubble information in the first seismic data.

[0114] (3) The computer device performs τ-p domain positive transformation processing on the third seismic data to obtain the first seismic data of the seismic gather.

[0115] Referring to Figure 3 , Figure 3 The left graph of FIG. 1 is a seismic gather of the third seismic data, and the vertical coordinate is the travel time and the horizontal coordinate is the lateral travel distance. Figure 3 The right graph of FIG. 1 is a seismic gather of the first seismic data obtained by τ-p domain positive transformation, and the vertical coordinate is the intercept of the seismic trace and the horizontal coordinate is the ray parameter.

[0116] It should be noted that since the seismic trace set is a three-dimensional seismic trace set, including a lateral seismic trace set and a vertical seismic trace set, the τ-p domain forward transformation is a three-dimensional τ-p domain forward transformation, and the first seismic data is located in a three-dimensional τ-p domain. x -p y domain.

[0117] In the embodiment of the present application, by performing three-dimensional τ-p domain forward transformation processing on the third seismic data, the third seismic data in the time-space domain is converted to the three-dimensional τ-p domain, and most of the signal deconvolution is performed in the three-dimensional τ-p domain. The three-dimensional τ-p domain transformation is the basic work of signal deconvolution, and is the key to the success or failure of signal deconvolution, and then the first seismic data in the three-dimensional τ-p domain is processed.

[0118] Step 102: The computer device determines the second seismic data of the seismic trace set based on the first seismic data, and determines the azimuth and emergence angle of the plurality of seismic traces from the first seismic data,

[0119] The second seismic data includes the ray parameter corresponding to each seismic trace, and the ray parameter of each seismic trace includes the ray parameter corresponding to each intercept as shown in the right side of the figure. Figure 3

[0120] The computer device determines the second seismic data of the seismic trace set by the following steps (1)-(4):

[0121] (1) The computer device performs noise suppression processing on the first seismic data to obtain the first seismic data after noise suppression.

[0122] It should be noted that in seismic exploration, the geophone records various interference wave noises from the ground and the underground that are irrelevant to the underground formation while collecting effective wave data from the underground formation. The interference wave noises confuse the effective wave, making it difficult to extract effective seismic data. The first seismic data includes linear interference wave noises, abnormal amplitude interference wave noises and the like. In the embodiment of the present application, a three-dimensional F-K (frequency-wave number) filtering method can be used to suppress linear interference wave noises, and a median filtering method can be used to suppress abnormal amplitude noises. In the embodiment of the present application, the first seismic data is subjected to noise suppression processing, which can effectively highlight the effective wave data, and thus effective first seismic data can be obtained.

[0123] (2) The computer device performs a trace flattening process on the first seismic data after noise suppression to obtain the first seismic data after trace flattening.

[0124] ​It should be noted that the non-flatness of the seismic trace gathers will affect the subsequent processing of the seismic trace gathers, therefore, the first seismic data needs to be processed for the flattening of the event, and the axes of multiple seismic traces are pulled to the same time line to obtain the flattened seismic trace gathers. If multiple first seismic data are processed at the same time, the seismic trace gathers corresponding to the multiple first seismic data can be flattened.

[0125] (3) The computer device performs distortion cut processing on the first seismic data after the flattening of the event, and obtains the first seismic data after the cut of the distortion.

[0126] It should be noted that there are boundary information and deep information and other distortion data in the first seismic data that affect the effective wave data, therefore, the first seismic data needs to be processed for the cut of the distortion to obtain effective first seismic data.

[0127] (4) The computer device performs boundary extrapolation processing on the first seismic data after the cut of the distortion, and obtains the second seismic data of the seismic trace gather.

[0128] The computer device performs extrapolation processing on the seismic trace gather of the first seismic data after the cut of the distortion along the cut boundary to the two sides, and further realizes the effective removal of the distortion data in the first seismic data.

[0129] Referring to Figure 4 , Figure 4 The left graph of FIG. 1 is the seismic trace gather of the first seismic data obtained by τ-p domain positive transformation, Figure 4 The right graph of FIG. 1 is the seismic trace gather of the second seismic data obtained after the first seismic data is processed. It can be seen that the seismic trace gather of the second seismic data is regular and smooth, and the noise data, distortion data and other information unrelated to the bubble information are removed, and the seismic trace gather with rich bubble information is obtained. The azimuth and emergence angle of multiple seismic traces can be extracted from the angle data of each seismic trace in the seismic trace gather shown in the right graph of FIG. 1. Figure 4

[0130] Step 103: The computer device determines the angle combination corresponding to the seismic trace based on the azimuth and emergence angle of the seismic trace for each seismic trace.

[0131] It should be noted that the seismic wavelet of the seismic trace will have differences in azimuth and emergence angle in the emission direction, and different azimuth and emergence angles have different effects on the seismic trace, therefore, the factors of azimuth and emergence angle need to be fully considered when processing the seismic data.

[0132] ​The angle combination includes the azimuth and emission angle of the seismic trace. The azimuth ranges from 0° to 360°, and the emission angle ranges from 0° to 90°. The angle combination is obtained by combining the azimuth and emission angle of the seismic trace. For example, if the azimuth of the seismic trace is 35° and the emission angle is 70°, then the angle combination of the seismic trace is 35° × 70°; if the azimuth of the seismic trace is 30.2° and the emission angle is 25.9°, then the angle combination of the seismic trace is 30.2° × 25.9°.

[0133] Step 104: For each of the multiple preset angle combinations, the computer device determines the target seismic trace based on the angle combinations corresponding to the multiple seismic traces.

[0134] Among them, the angle combination of the target seismic trace belongs to the preset angle combination.

[0135] Multiple preset angle combinations include azimuth angles within a 360° range and emission angles within a 90° range. These preset angle combinations include 0°×0°, 0°×1°, 0°×2°, 1°×0°, 2°×0°, 1°×1°, etc. Each preset angle combination includes multiple azimuth angles within an angle range and multiple emission angles within the same angle range. For example, for the preset angle combination 1°×1°, this combination includes azimuth angles within the interval [1,2) and emission angles within the interval [1,2), such as an azimuth angle of 1.25° and an emission angle of 1.36°. Seismic traces with angle combinations of 1.1°×1.5°, 1.38°×1.69°, etc., are the target seismic traces for this preset angle combination.

[0136] See Figure 5 , Figure 5 The upper part is a diagram showing the division of ray parameters for seismic traces corresponding to multiple azimuth angles in sequence. Figure 5 The lower half of the diagram shows the ray parameters of seismic traces corresponding to multiple exit angles, arranged sequentially. As can be seen from the diagram, each azimuth and exit angle range includes ray parameters from multiple seismic traces.

[0137] In this embodiment, the seismic trace is divided based on its azimuth and emission angle, which fully considers the influence of emission angle and azimuth on the seismic trace, thereby improving the accuracy of subsequent seismic trace processing.

[0138] Step 105: For each preset angle combination, the computer device superimposes the ray parameters of multiple target seismic traces corresponding to that preset angle combination to obtain the unit wavelet data corresponding to that preset angle combination.

[0139] Each preset angle combination corresponds to a unit wavelet; Figure 6A schematic diagram of the multiple unit wavelets generated for the multiple unit wavelet data corresponding to the multiple preset angle combinations, from which it can be seen that the multiple unit wavelets are regularly and orderly arranged.

[0140] Step 106: The computer device determines the far-field wavelet data of the seismic trace set based on the unit wavelet data corresponding to the multiple preset angle combinations.

[0141] Continuing to refer to Figure 6 , the computer device superimposes the unit wavelet data of the multiple unit wavelets as shown in Figure 6 to obtain a far-field wavelet data, and the far-field wavelet corresponding to the far-field wavelet data is as shown in Figure 7 .

[0142] Step 107: The computer device generates a deconvolution operator of the seismic trace set based on the far-field wavelet data.

[0143] This step can be implemented through the following steps (1)-(2):

[0144] (1) The computer device performs bubble elimination processing, ghost wave suppression processing, and zero-phase processing on the far-field wavelet data to obtain desired wavelet data.

[0145] Among them, the computer device can use the predictive deconvolution method to perform bubble elimination processing, use the simulated shot point virtual reflection method to perform ghost wave suppression processing, and perform phase adjustment to realize zero-phase processing.

[0146] Figure 8 A spectrum diagram of the far-field wavelet, from which it can be seen that there is jitter in both the low-frequency and high-frequency parts, the jitter in the low-frequency part represents the influence of bubbles, and the jitter in the high-frequency part represents the influence of ghost waves, indicating that the far-field wavelet has bubble effect and ghost wave influence. Figure 9 A spectrum diagram of the desired wavelet, from which it can be seen that bubbles are effectively eliminated and ghost waves are effectively suppressed. Figure 10 A phase spectrum of the far-field wavelet, from which it can be seen that the phase of the far-field wavelet is not shifted to zero. Figure 11 A phase spectrum of the desired wavelet, from which it can be seen that the phase of the desired wavelet is shifted to zero. Figure 12 A desired wavelet corresponding to the desired wavelet data, from which it can be seen that the bubble of the desired wavelet is eliminated, the ghost wave is suppressed, and the phase is zero phase, which is the ideal seismic wavelet to be obtained.

[0147] (2) The computer device determines the deconvolution operator used to convert the far-field wavelet data into the desired wavelet data.

[0148] It should be noted that there is a certain gap between the far-field wavelet data and the expected wavelet data, and by matching the far-field wavelet data and the expected wavelet data, a factor that can make the far-field wavelet data reach the expected wavelet data, that is, the deconvolution operator, is obtained. The following will be illustrated by taking formula one as an example.

[0149] Formula one: Y = a * X.

[0150] Wherein, Y represents the expected wavelet data, X represents the far-field wavelet data, and a represents the deconvolution operator. Then the deconvolution operator can be obtained by dividing the expected wavelet data by the far-field wavelet data, that is, the deconvolution operator is obtained, and the matching processing is realized.

[0151] In the embodiment of the present application, since the seismic traces are divided based on the azimuth and the emergence angle of the seismic traces, the unit wavelet data and the far-field wavelet data are sequentially determined based on the divided seismic traces with the azimuth and the emergence angle, so that the determined far-field wavelet data fully considers the factors of the azimuth and the emergence angle, so that the deconvolution operator obtained based on the far-field wavelet data is related to the azimuth and the emergence angle, and the deconvolution operator effectively compensates for the differences of the seismic wavelets of the seismic traces in the azimuth and the emergence angle. Moreover, since the expected wavelet for determining the deconvolution operator is a wavelet that has been subjected to bubble elimination processing, ghost wave suppression processing and zero-phase processing, the deconvolution operator can effectively eliminate bubble information in the seismic data, suppress ghost wave data and make the seismic data zero-phase when processing the seismic data.

[0152] Step 108: The computer device processes the first seismic data by the deconvolution operator to obtain deconvolution seismic data of the seismic trace set.

[0153] This step can be realized by the following steps (1)-(2):

[0154] (1) The computer device processes the first seismic data by the deconvolution operator to obtain the fifth seismic data.

[0155] It should be noted that since the deconvolution operator is an operator related to the azimuth and the emergence angle, processing the first seismic data based on the deconvolution operator can effectively compensate for the differences of the seismic wavelets of the seismic traces in the azimuth and the emergence angle, so that the final seismic wavelets of the entire fifth seismic data tend to be consistent, thereby improving the accuracy of the fifth seismic data, realizing the leap of the signal deconvolution method from one dimension to three dimensions, and making the three-dimensional signal deconvolution method process the seismic data in the wide frequency field more ideal.

[0156] (2) The computer device performs τ-p domain inverse transformation processing on the fifth seismic data to obtain the deconvolution seismic data.

[0157] Since the fifth seismic data is seismic data in the τ-p domain, the fifth seismic data needs to be processed by τ-p domain inverse transformation to obtain original spatial and temporal domain seismic data, i.e., deconvolution seismic data.

[0158] It should be noted that the target area to which the seismic gather belongs includes multiple seismic gathers, and the multiple seismic gathers are processed according to the deconvolution method to obtain deconvolution seismic data corresponding to each seismic gather.

[0159] Referring to Figure 13 , Figure 13 From top to bottom, the seismic gather of seismic data before signal deconvolution, the seismic gather of seismic data processed by the one-dimensional signal deconvolution method, and the seismic gather of seismic data processed by the signal deconvolution method provided by the embodiment of the present application. Referring to Figure 14 , Figure 14 From left to right, the seismic gather of seismic data before signal deconvolution, the seismic gather of seismic data processed by the one-dimensional signal deconvolution method, and the local enlarged view of the seismic gather of seismic data processed by the signal deconvolution method provided by the embodiment of the present application. From Figure 13 and Figure 14 It can be seen that the bubbles of the seismic gather of seismic data processed by the signal deconvolution method provided by the embodiment of the present application are obviously suppressed, and the effect of suppressing ghost waves is more ideal.

[0160] Referring to Figure 15 , Figure 15 From top to bottom, the schematic diagram of the seismic wavelet corresponding to the seismic data processed by the one-dimensional signal deconvolution method, and the schematic diagram of the seismic wavelet corresponding to the seismic data processed by the signal deconvolution method provided by the embodiment of the present application. Figure 16 is a superimposed view of the seismic wavelet corresponding to the seismic data processed by the one-dimensional signal deconvolution method and the seismic wavelet corresponding to the seismic data processed by the signal deconvolution method provided by the embodiment of the present application. Referring to Figure 17 , Figure 17 includes the spectrum of seismic data before signal deconvolution, the spectrum of seismic data processed by the one-dimensional signal deconvolution method, and the spectrum of seismic data processed by the signal deconvolution method provided by the embodiment of the present application. From Figure 15 , 16 and 17 can be seen that the sidelobes of the seismic wavelet obtained by the method provided by the embodiment of the present application are smaller, the peak is sharper, the frequency information is more abundant, and the low-frequency information is stronger. It is shown that the seismic wavelet obtained by processing seismic data by the method provided by the embodiment of the present application is more ideal.

[0161] Referring to 18, Figure 18The stratum profile of the seismic data processed by the one-dimensional signal deconvolution method from left to right is a stratum profile of the seismic data processed by the signal deconvolution method provided by the embodiment of the present application. It can be seen from the comparison that the bubbles are effectively eliminated and the ghost waves are suppressed in the stratum profile of the right drawing, and each phase axis is more fine, thereby improving the resolution of the stratum profile corresponding to the seismic data.

[0162] The signal deconvolution method provided by the embodiment of the present application divides the seismic traces based on the azimuth and the emergence angle of the seismic traces, and then sequentially determines the unit wave data and the far-field wave data based on the seismic traces divided based on the azimuth and the emergence angle, so that the determined far-field wave data sufficiently considers the factors of the azimuth and the emergence angle. Furthermore, the deconvolution operator determined based on the far-field wave data is related to the azimuth and the emergence angle, so that when the first seismic data is processed based on the deconvolution operator, the differences caused by the azimuth and the emergence angle can be effectively compensated, thereby making the deconvolution seismic data determined based on the deconvolution operator more accurate.

[0163] The signal deconvolution device provided by the embodiment of the present application also provides a signal deconvolution device, referring to Figure 19 , the device comprises:

[0164] The first determination module 1901 is configured to determine the first seismic data of the seismic trace set, and the seismic trace set comprises a plurality of seismic traces.

[0165] The second determination module 1902 is configured to determine the second seismic data of the seismic trace set based on the first seismic data, and determine the azimuth and the emergence angle of the plurality of seismic traces from the first seismic data, and the second seismic data comprises the ray parameter corresponding to each seismic trace.

[0166] The third determination module 1903 is configured to, for each seismic trace, determine the angle combination corresponding to the seismic trace based on the azimuth and the emergence angle of the seismic trace, and the angle combination comprises the azimuth and the emergence angle.

[0167] The fourth determination module 1904 is configured to, for each preset angle combination in the plurality of preset angle combinations, determine the target seismic trace based on the angle combination corresponding to the plurality of seismic traces, and the angle combination of the target seismic trace belongs to the preset angle combination.

[0168] The stacking module 1905 is configured to, for each preset angle combination, stack the ray parameters of the plurality of target seismic traces corresponding to the preset angle combination to obtain the unit wave data corresponding to the preset angle combination.

[0169] The fifth determination module 1906 is configured to determine the far-field wave data of the seismic trace set based on the unit wave data corresponding to the plurality of preset angle combinations.

[0170] The generating module 1907 is configured to generate, based on the far-field wavelet data, a deconvolution operator of the seismic gather.

[0171] The processing module 1908 is configured to process the first seismic data by using the deconvolution operator to obtain deconvolution seismic data of the seismic gather.

[0172] In a possible implementation, the generating module 1907 is configured to:

[0173] The far-field wavelet data is subjected to bubble elimination processing, ghost wave suppression processing, and zero-phase processing to obtain expected wavelet data.

[0174] The deconvolution operator used for converting the far-field wavelet data into the expected wavelet data is determined.

[0175] In a possible implementation, the first determining module 1901 includes:

[0176] The obtaining unit is configured to obtain original seismic data of the seismic gather.

[0177] The first processing unit is configured to perform preprocessing on the original seismic data to obtain third seismic data.

[0178] The second processing unit is configured to perform τ-p domain positive transformation processing on the third seismic data to obtain the first seismic data of the seismic gather.

[0179] In a possible implementation, the first processing unit includes:

[0180] The processing subunit is configured to perform regularization processing and interpolation processing on the original seismic data to obtain fourth seismic data.

[0181] The first determining subunit is configured to determine target seismic data from the fourth seismic data, the target seismic data being data in which waveform occurs preset fluctuation.

[0182] The second determining subunit is configured to determine downgoing wave data in the target seismic data.

[0183] The suppressing unit is configured to suppress multiple wave data in the downgoing wave data to obtain the third seismic data.

[0184] In a possible implementation, the second determining subunit is configured to:

[0185] The target seismic data is subjected to wave field separation processing to obtain the downgoing wave data in the target seismic data.

[0186] In a possible implementation, the second determining module 1902 is configured to:

[0187] The first seismic data is subjected to noise suppression processing to obtain first seismic data after noise suppression;

[0188] The first seismic data after noise suppression is subjected to event flattening processing to obtain first seismic data after event flattening;

[0189] The first seismic data after event flattening is subjected to distortion cutting processing to obtain first seismic data after cutting distortion;

[0190] The first seismic data after cutting distortion is subjected to boundary extrapolation processing to obtain second seismic data of a seismic trace gather.

[0191] In a possible implementation, the processing module 1908 is configured to:

[0192] The first seismic data is processed by using a deconvolution operator to obtain fifth seismic data;

[0193] The fifth seismic data is subjected to τ-p domain inverse transformation processing to obtain deconvolution seismic data.

[0194] Figure 20 A structural block diagram of a computer device 2000 is shown, which is provided by an example embodiment of the present application. The computer device 2000 can be a portable mobile computer device, such as a smart phone, a tablet computer, an MP3 player, an MP4 player, a notebook computer, or a desktop computer. The computer device 2000 can also be referred to as a user device, a portable computer device, a laptop computer device, a desktop computer device, or other names.

[0195] Generally, the computer device 2000 includes a processor 2001 and a memory 2002.

[0196] The processor 2001 can include one or more processing cores, such as a 4-core processor, an 8-core processor, and the like. The processor 2001 can be implemented in at least one of a hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), a PLA (Programmable Logic Array). The processor 2001 can also include a main processor and a coprocessor. The main processor is a processor for processing data in an awake state, also known as a CPU (Central Processing Unit). The coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 2001 can be integrated with a GPU (Graphics Processing Unit) for rendering and drawing content required to be displayed by the display screen. In some embodiments, the processor 2001 can further include an AI (Artificial Intelligence) processor for processing computing operations related to machine learning.

[0197] The memory 2002 can include one or more computer-readable storage media that can be non-transitory. The memory 2002 can also include high-speed random access memory and nonvolatile, computer-readable storage media such as one or more magnetic disk storage devices, flash memory devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 2002 is used to store at least one instruction for being executed by the processor 2001 to implement the signal deconvolution method provided by the method embodiments in the present application.

[0198] In some embodiments, the computer device 2000 can also optionally include a peripheral device interface 2003 and at least one peripheral device. The processor 2001, the memory 2002, and the peripheral device interface 2003 can be connected through a bus or a signal line. Each peripheral device can be connected to the peripheral device interface 2003 through a bus, a signal line, or a circuit board. Specifically, the peripheral device includes at least one of a radio frequency circuit 2004, a display screen 2005, a camera assembly 2006, an audio circuit 2007, a positioning assembly 2008, and a power supply 2009.

[0199] The peripheral interface 2003 can be used to connect at least one I / O (Input / Output) related peripheral device to the processor 2001 and the memory 2002. In some embodiments, the processor 2001, the memory 2002 and the peripheral interface 2003 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 2001, the memory 2002 and the peripheral interface 2003 can be implemented on a separate chip or circuit board, and the present embodiments are not limited in this regard.

[0200] The radio frequency circuit 2004 is used to receive and send RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 2004 communicates with a communication network and other communication devices through electromagnetic signals. The radio frequency circuit 2004 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals into electrical signals. Optionally, the radio frequency circuit 2004 includes an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a subscriber identity module card, and the like. The radio frequency circuit 2004 can communicate with other computer devices through at least one wireless communication protocol. The wireless communication protocol includes but is not limited to the World Wide Web, a metropolitan area network, an intranet, various generations of mobile communication networks (2G, 3G, 4G and 5G), a wireless local area network and / or a WiFi (Wireless Fidelity) network. In some embodiments, the radio frequency circuit 2004 can also include NFC (Near Field Communication) related circuitry, and the present application is not limited in this regard.

[0201] The display screen 2005 is configured to display a UI (User Interface). The UI can include graphics, text, icons, video, and any combination thereof. When the display screen 2005 is a touch display screen, the display screen 2005 is further configured to capture touch signals on or above the surface of the display screen 2005. The touch signals can be input to the processor 2001 as control signals for processing. In this case, the display screen 2005 can also be configured to provide virtual buttons and / or virtual keyboard, also known as soft buttons and / or soft keyboard. In some embodiments, the display screen 2005 can be one, disposed on the front panel of the computer device 2000; in other embodiments, the display screen 2005 can be at least two, respectively disposed on different surfaces of the computer device 2000 or in a folding design; in other embodiments, the display screen 2005 can be a flexible display screen, disposed on a curved surface or a folding surface of the computer device 2000. Even, the display screen 2005 can also be disposed in an irregular shape, i.e. a special-shaped screen. The display screen 2005 can be made of LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), etc.

[0202] The camera assembly 2006 is configured to capture images or videos. Optionally, the camera assembly 2006 includes a front-facing camera and a rear-facing camera. Typically, the front-facing camera is disposed on the front panel of the computer device, and the rear-facing camera is disposed on the back of the computer device. In some embodiments, the rear-facing camera is at least two, which are any one of a main camera, a depth-of-field camera, a wide-angle camera, and a telephoto camera, to realize the background blur function by fusing the main camera and the depth-of-field camera, the panoramic shooting and VR (Virtual Reality) shooting function by fusing the main camera and the wide-angle camera, or other fusion shooting functions. In some embodiments, the camera assembly 2006 can further include a flash. The flash can be a single-color-temperature flash or a dual-color-temperature flash. The dual-color-temperature flash refers to the combination of a warm light flash and a cold light flash, which can be used for light compensation under different color temperatures.

[0203] The audio circuit 2007 can include a microphone and a speaker. The microphone is used to collect sound waves of the user and the environment, and convert the sound waves into an electrical signal input to the processor 2001 for processing, or input to the radio frequency circuit 2004 to achieve voice communication. For the purpose of stereo sound collection or noise reduction, the microphone can be multiple, respectively arranged at different parts of the computer device 2000. The microphone can also be an array microphone or an omnidirectional collection type microphone. The speaker is used to convert the electrical signal from the processor 2001 or the radio frequency circuit 2004 into sound waves. The speaker can be a traditional diaphragm speaker, or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, not only can it convert electrical signals into sound waves that humans can hear, but it can also convert electrical signals into sound waves that humans cannot hear for ranging purposes. In some embodiments, the audio circuit 2007 can also include a headphone jack.

[0204] The positioning component 2008 is used to position the current geographic location of the computer device 2000 to achieve navigation or LBS (Location Based Service). The positioning component 2008 can be a positioning component based on the GPS (Global Positioning System) of the United States, the Beidou system of China, or the Galileo system of Russia.

[0205] The power supply 2009 is used to supply power to various components in the computer device 2000. The power supply 2009 can be alternating current, direct current, disposable battery or rechargeable battery. When the power supply 2009 includes a rechargeable battery, the rechargeable battery can be a wired charging battery or a wireless charging battery. The wired charging battery is a battery charged through a wired line, and the wireless charging battery is a battery charged through a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0206] In some embodiments, the computer device 2000 further includes one or more sensors 2010. The one or more sensors 2010 include but are not limited to: an acceleration sensor 2011, a gyroscope sensor 2012, a pressure sensor 2013, a fingerprint sensor 2014, an optical sensor 2015, and a proximity sensor 2016.

[0207] The acceleration sensor 2011 can detect the acceleration in three coordinate axes of the coordinate system established by the computer device 2000. For example, the acceleration sensor 2011 can be used to detect the components of the gravitational acceleration in three coordinate axes. The processor 2001 can control the display screen 2005 to display the user interface in a landscape view or a portrait view according to the gravitational acceleration signal collected by the acceleration sensor 2011. The acceleration sensor 2011 can also be used for gaming or user motion data collection.

[0208] The gyroscope sensor 2012 can detect the body direction and rotation angle of the computer device 2000, and the gyroscope sensor 2012 can cooperate with the acceleration sensor 2011 to collect the 3D action of the user on the computer device 2000. According to the data collected by the gyroscope sensor 2012, the processor 2001 can realize the following functions: action sensing (such as changing the UI according to the user's tilt operation), image stabilization when shooting, game control, and inertial navigation.

[0209] The pressure sensor 2013 can be arranged on the side frame of the computer device 2000 and / or the lower layer of the display screen 2005. When the pressure sensor 2013 is arranged on the side frame of the computer device 2000, the user's holding signal on the computer device 2000 can be detected, and the left and right hand recognition or shortcut operation can be performed by the processor 2001 according to the holding signal collected by the pressure sensor 2013. When the pressure sensor 2013 is arranged on the lower layer of the display screen 2005, the controllable control on the UI interface can be controlled by the processor 2001 according to the pressure operation of the user on the display screen 2005. The controllable control includes at least one of a button control, a scroll bar control, an icon control, and a menu control.

[0210] The fingerprint sensor 2014 is used to collect the fingerprint of the user, and the identity of the user can be identified by the processor 2001 according to the fingerprint collected by the fingerprint sensor 2014, or by the fingerprint sensor 2014 according to the collected fingerprint. When the identity of the user is identified as a trusted identity, the processor 2001 authorizes the user to perform related sensitive operations, including unlocking the screen, viewing encrypted information, downloading software, payment, and changing settings. The fingerprint sensor 2014 can be arranged on the front, back or side of the computer device 2000. When the computer device 2000 is provided with a physical button or a manufacturer's logo, the fingerprint sensor 2014 can be integrated with the physical button or the manufacturer's logo.

[0211] The optical sensor 2015 is used to collect the ambient light intensity. In one embodiment, the processor 2001 can control the display brightness of the display screen 2005 according to the ambient light intensity collected by the optical sensor 2015. Specifically, when the ambient light intensity is high, the display brightness of the display screen 2005 is increased; when the ambient light intensity is low, the display brightness of the display screen 2005 is decreased. In another embodiment, the processor 2001 can also dynamically adjust the shooting parameters of the camera assembly 2006 according to the ambient light intensity collected by the optical sensor 2015.

[0212] The proximity sensor 2016, also referred to as a distance sensor, is usually arranged on the front panel of the computer device 2000. The proximity sensor 2016 is used to collect the distance between the user and the front of the computer device 2000. In an embodiment, when the proximity sensor 2016 detects that the distance between the user and the front of the computer device 2000 gradually decreases, the display screen 2005 is switched from the bright screen state to the screen-off state under the control of the processor 2001; when the proximity sensor 2016 detects that the distance between the user and the front of the computer device 2000 gradually increases, the display screen 2005 is switched from the screen-off state to the bright screen state under the control of the processor 2001.

[0213] Those skilled in the art can understand that the structure shown in the foregoing embodiments is not a limitation on the computer device 2000, and the computer device 2000 can include more or fewer components than those shown in the figure, or combine certain components, or adopt a different arrangement of components. Figure 20

[0214] The embodiments of the present application further provide a computer readable storage medium, and at least one instruction is stored in the computer readable storage medium. The at least one instruction is loaded and executed by a processor, so as to implement the operations performed by the signal deconvolution method in any of the foregoing implementation manners.

[0215] The embodiments of the present application further provide a computer program product or a computer program, and the computer program product or the computer program includes computer program code stored in a computer readable storage medium. A processor of a computer device reads the computer program code from the computer readable storage medium, and the processor executes the computer program code, so that the computer device performs the operations performed by the signal deconvolution method.

[0216] In some embodiments, the computer program related to the embodiments of the present application can be deployed to execute on one computer device, or execute on multiple computer devices located in one place, or execute on multiple computer devices distributed in multiple places and interconnected through a communication network, and the multiple computer devices distributed in multiple places and interconnected through a communication network can constitute a blockchain system.

[0217] The embodiments of the present application provide a signal deconvolution method. Since the method divides the seismic traces based on the azimuth and the emergence angle of the seismic traces, the unit wave data and the far-field wave data are sequentially determined based on the seismic traces with the divided azimuth and emergence angle, so that the determined far-field wave data sufficiently considers the factors of the azimuth and the emergence angle. Furthermore, the deconvolution operator determined based on the far-field wave data is related to the azimuth and the emergence angle, so that when the first seismic data is processed based on the deconvolution operator, the differences caused by the azimuth and the emergence angle can be effectively compensated, so that the deconvolution seismic data determined based on the deconvolution operator is more accurate. ​

[0218] The above merely provides the optional embodiments of the present application, but does not intend to limit the present application. Besides, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall fall into the protective scope of the present application.

Claims

1. A signal deconvolution method, characterized in that, The method includes: Determine the initial seismic data of the seismic gather, which includes multiple seismic traces; Based on the first seismic data, the second seismic data of the seismic trace set is determined, and the azimuth and emission angle of the plurality of seismic traces are determined from the first seismic data. The second seismic data includes the ray parameters corresponding to each seismic trace. For each seismic trace, based on the azimuth and emission angle of the seismic trace, a corresponding angle combination is determined, wherein the angle combination includes the azimuth and the emission angle; For each of the multiple preset angle combinations, a target seismic trace is determined based on the angle combinations corresponding to the multiple seismic traces, and the angle combination of the target seismic trace belongs to the preset angle combination. For each preset angle combination, the ray parameters of multiple target seismic traces corresponding to the preset angle combination are superimposed to obtain the unit wavelet data corresponding to the preset angle combination; Based on the unit wavelet data corresponding to multiple preset angle combinations, the far-field wavelet data of the seismic gather is determined; Based on the far-field wavelet data, the deconvolution operator for the seismic gather is generated; The first seismic data is processed by the deconvolution operator to obtain the deconvolution seismic data of the seismic gather.

2. The signal deconvolution method according to claim 1, characterized in that, The deconvolution operator for generating the seismic gather based on the far-field wavelet data includes: The far-field wavelet data is subjected to bubble elimination processing, ghost wave suppression processing, and zero-phase processing to obtain the desired wavelet data; Determine the deconvolution operator used to convert the far-field wavelet data into the desired wavelet data.

3. The signal deconvolution method according to claim 1, characterized in that, The first seismic data used to determine the initial seismic gather includes: Obtain the raw seismic data of the seismic gather; The original seismic data is preprocessed to obtain the third seismic data; The third seismic data is subjected to a τ-p domain positive transform to obtain the initial first seismic data of the seismic gather.

4. The signal deconvolution method according to claim 3, characterized in that, The preprocessing of the original seismic data to obtain third seismic data includes: The original seismic data is subjected to regularization and interpolation processing to obtain the fourth seismic data; Target seismic data is determined from the fourth seismic data, wherein the target seismic data is data whose waveform exhibits a preset fluctuation; Identify the downlink wave data in the target seismic data; The multiple wave data in the downwave data are suppressed to obtain the third seismic data.

5. The signal deconvolution method according to claim 4, characterized in that, The determination of downlink wave data in the target seismic data includes: The target seismic data is subjected to wavefield separation processing to obtain the down-current wave data from the target seismic data.

6. The signal deconvolution method according to claim 1, characterized in that, The step of determining the second seismic data of the seismic gather based on the first seismic data includes: The first seismic data is subjected to noise suppression processing to obtain the first seismic data after noise suppression. The first seismic data after noise suppression is subjected to phase axis flattening to obtain the first seismic data after phase axis flattening. The first seismic data after the phase axis is flattened is subjected to distortion removal processing to obtain the first seismic data after distortion removal. The first seismic data after distortion removal is subjected to boundary extrapolation to obtain the second seismic data of the seismic gather.

7. The signal deconvolution method according to claim 1, characterized in that, The process of processing the first seismic data using the deconvolution operator to obtain the deconvolutioned seismic data of the seismic gather includes: The first seismic data is processed by the deconvolution operator to obtain the fifth seismic data; The fifth seismic data is subjected to τ-p domain inverse transformation to obtain the deconvolution seismic data.

8. A signal deconvolution device, characterized in that, The device includes: The first determining module is used to determine the initial first seismic data of the seismic gather, which includes multiple seismic traces; The second determining module is used to determine the second seismic data of the seismic trace set based on the first seismic data, and to determine the azimuth and emission angle of the plurality of seismic traces from the first seismic data, wherein the second seismic data includes the ray parameters corresponding to each seismic trace; The third determining module is used to determine, for each seismic trace, an angle combination corresponding to the seismic trace based on the azimuth and emission angle of the seismic trace, wherein the angle combination includes the azimuth and the emission angle. The fourth determining module is used to determine a target seismic trace for each of the multiple preset angle combinations based on the angle combinations corresponding to the multiple seismic traces, wherein the angle combination of the target seismic trace belongs to the preset angle combination. The overlay module is used to overlay the ray parameters of multiple target seismic traces corresponding to each preset angle combination to obtain the unit wavelet data corresponding to the preset angle combination. The fifth determining module is used to determine the far-field wavelet data of the seismic gather based on the unit wavelet data corresponding to multiple preset angle combinations; A generation module is used to generate the deconvolution operator of the seismic gather based on the far-field wavelet data; The processing module is used to process the first seismic data through the deconvolution operator to obtain the deconvolution seismic data of the seismic gather.

9. A computer device, characterized in that, The computer device includes one or more processors and one or more memories, wherein at least one instruction is stored in the one or more memories, and the at least one instruction is loaded and executed by the one or more processors to perform the operation performed by the signal deconvolution method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The storage medium stores at least one instruction, which is loaded and executed by a processor to perform the operation of the signal deconvolution method as described in any one of claims 1 to 7.

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