Series deconvolution processing method, device and equipment and storage medium

Abstract

The application provides a series deconvolution processing method and device, equipment and storage medium, and belongs to the technical field of geophysics. The method comprises the following steps: performing surface consistency deconvolution on the to-be-processed seismic data to obtain first seismic data; performing predictive deconvolution processing on the first seismic data based on a virtual fixed surface to obtain second seismic data; and performing reverse static correction on the second seismic data to correct the second seismic data to a trend floating surface corresponding to a stable stratum to obtain series deconvolution data. By adopting the series deconvolution method based on geological guidance, the resolution of data is improved, the periodic multiple waves are suppressed, the wave group level relationship is improved, the amplitude and phase consistency are improved, the phase inversion and layering problems in the traditional series deconvolution processing process are solved, powerful technical support is provided for the deconvolution processing of seismic data of complex geological structures, and the demand for fine processing of seismic data is met.

Description

Technical Field

[0001] This application relates to the field of geophysical technology, and in particular to a tandem deconvolution processing method, apparatus, device, and storage medium. Background Technology

[0002] In seismic data processing, deconvolution is a technique that widens the frequency band of seismic data by compressing the seismic wavelet, thereby improving seismic data resolution and thus improving the contact relationships and characteristic morphology of stratigraphic wave groups. However, due to operator instability during deconvolution processing, certain signal-to-noise ratio (SNR) assumptions are required. Therefore, if there are laterally unevenly distributed gypsum or igneous rocks with significant variations in dip and attitude, the SNR of the seismic data will decrease, and the amplitude and frequency will also change significantly. Furthermore, the layered media encountered in seismic acquisition can produce reflections and transmissions at any layer, inevitably resulting in multiple interlayer reflections. These strata are usually relatively thin, and the two-way travel time within each layer is much shorter than the wavelet's duration. Therefore, multiple interlayer reflections alter the wavelet shape and reduce its resolution.

[0003] Currently, technicians typically use surface-consistent deconvolution to address the inconsistency of lateral wavelets in different record traces caused by lateral variations in surface thickness and velocity. They then perform cascaded predictive deconvolution to improve resolution and suppress periodic interlayer multiples, thus improving the formation wave group morphology. However, combining these two methods cannot completely eliminate the influence of large lateral variations in subsurface lithology and dip angle, as well as multiple interference effects, on the stability operator obtained during deconvolution. This can lead to formation phase reversal and layer crossover, resulting in lower accuracy of the deconvolution results. Summary of the Invention

[0004] This application provides a method, apparatus, device, and storage medium for serial deconvolution processing, which can solve the problems of phase inversion and layer crossover in traditional serial deconvolution processing. The technical solution is as follows:

[0005] On one hand, embodiments of this application provide a serial deconvolution processing method, including:

[0006] The surface-consistent deconvolution is performed on the seismic data to be processed to obtain the first seismic data.

[0007] Based on the virtual fixed surface, the first seismic data is subjected to predictive deconvolution processing to obtain the second seismic data. The virtual fixed surface is a plane obtained by correcting stable strata. Stable strata refer to strata with a reflection coefficient greater than the coefficient threshold and a continuous reflection interface.

[0008] The second seismic data is subjected to reverse static correction to correct it to the trend floating surface corresponding to the stable strata, thus obtaining cascaded deconvolution data.

[0009] On the other hand, embodiments of this application provide a serial deconvolution processing apparatus, including:

[0010] The first processing module is used to perform surface-consistent deconvolution on the seismic data to be processed to obtain the first seismic data.

[0011] The second processing module is used to perform predictive deconvolution processing on the first seismic data based on a virtual fixed surface to obtain the second seismic data. The virtual fixed surface is a plane obtained by correcting stable strata. Stable strata refer to strata with a reflection coefficient greater than the coefficient threshold and a continuous reflection interface.

[0012] The correction module is used to perform reverse static correction on the second seismic data, correcting the second seismic data to the trend floating surface corresponding to the stable strata, and obtaining cascaded deconvolution data.

[0013] On the other hand, embodiments of this application provide an electronic device, which includes a memory and a processor; the memory stores a computer program, which, when executed by the processor, implements the methods described above.

[0014] On the other hand, embodiments of this application provide a computer-readable storage medium storing a computer program that is loaded and executed by a processor to implement the methods described above.

[0015] The technical solution provided in this application includes at least the following beneficial effects:

[0016] The tandem deconvolution processing method, apparatus, equipment, and storage medium provided in this application improve data resolution, suppress periodic multiple waves, improve wave group hierarchy, and enhance amplitude and phase consistency by employing a geologically guided tandem deconvolution method. This solves the phase inversion and layer crossover problems in traditional tandem deconvolution processing, providing strong technical support for deconvolution processing of seismic data with complex geological structures and meeting the needs of fine seismic data processing. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0018] Figure 1 This is a flowchart of a serial deconvolution processing method provided in an exemplary embodiment of this application;

[0019] Figure 2 This is a flowchart of a serial deconvolution processing method provided in another exemplary embodiment of this application;

[0020] Figure 3 This is a schematic diagram of a smooth trend floating surface and a virtual fixed surface provided in an exemplary embodiment of this application;

[0021] Figure 4 This is a schematic diagram of a deconvolution analysis time window provided in an exemplary embodiment of this application;

[0022] Figure 5 This is a comparison chart of seismic data effects provided in an exemplary embodiment of this application;

[0023] Figure 6 This is a schematic diagram of a north-south survey line provided in an exemplary embodiment of this application;

[0024] Figure 7 This is a schematic diagram of an east-west survey line provided in an exemplary embodiment of this application;

[0025] Figure 8 This is a flowchart of a serial deconvolution processing method provided in another exemplary embodiment of this application;

[0026] Figure 9 This is a wavelet curve quality control plot provided in an exemplary embodiment of this application;

[0027] Figure 10 This is a curve comparison diagram of amplitude spectrum and phase spectrum provided in an exemplary embodiment of this application;

[0028] Figure 11 This is a structural block diagram of a serial deconvolution processing apparatus provided in an exemplary embodiment of this application;

[0029] Figure 12 This is a structural block diagram of an electronic device provided in an exemplary embodiment of this application. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0031] Example 1

[0032] Please refer to Figure 1 The diagram illustrates a flowchart of a concatenated deconvolution processing method provided in an exemplary embodiment of this application. The method includes the following steps:

[0033] Step 101: Perform surface-consistent deconvolution on the seismic data to be processed to obtain the first seismic data.

[0034] Deconvolution, also known as inverse filtering or deconvolution, is a processing method that eliminates the effects of a previous filtering process. This method improves the vertical resolution of seismic data by compressing the fundamental wavelet. Surface consistency deconvolution is a deconvolution process used in seismic data processing to eliminate the influence of surface factors such as shot points and receiver points on the seismic record. This method is one of the more commonly used deconvolution methods in seismic data processing in areas with complex surfaces.

[0035] The equipment first performs surface-consistent deconvolution on the seismic data to be processed, eliminating the influence of the near-surface on the deconvolution operator, and appropriately increasing the data resolution without reducing the signal-to-noise ratio, thus obtaining the first seismic data.

[0036] Step 102: Based on the virtual fixed surface, perform predictive deconvolution processing on the first seismic data to obtain the second seismic data. The virtual fixed surface is a plane obtained by correcting stable strata. Stable strata refer to strata with a reflection coefficient greater than the coefficient threshold and a continuous reflection interface.

[0037] Predictive deconvolution is a process of filtering seismic traces using a prediction error filter. Because the prediction step size can be appropriately selected to suppress and eliminate repetitive vibrations at that scale, it is often used to eliminate predictable interference waves such as marine rumbling reverberation and multiple waves.

[0038] Since improving resolution is a process of repeated trials and gradual improvement, technicians typically use tandem deconvolution to process seismic data. Surface consistency deconvolution and predictive deconvolution are a common tandem deconvolution combination. Surface consistency deconvolution can resolve the lateral wavelet inconsistencies caused by variations in surface thickness and velocity in the lateral direction, and tandem predictive deconvolution further improves resolution while suppressing periodic interlayer multiples and improving the formation wave group morphology. However, tandem surface consistency deconvolution and predictive deconvolution cannot completely eliminate the influence of large lateral variations in subsurface lithology and dip angle, as well as the effects of multiple interference, on the stability operator obtained during deconvolution, thus leading to formation phase reversal and layer crossover phenomena.

[0039] In one possible implementation, the electronic device of this application does not directly perform predictive deconvolution processing after performing surface-consistent deconvolution on seismic data. Instead, it determines the virtual fixed surface corresponding to the stable strata and then performs predictive deconvolution processing on the first seismic data based on the virtual fixed surface.

[0040] Among them, stable strata refer to strata with a reflection coefficient greater than the coefficient threshold and a continuous reflection interface, while virtual fixed surface is a virtual plane obtained after the stable strata are corrected for height difference. The consistent height of the virtual fixed surface can make the deconvolution operator more stable in the lateral direction and eliminate the problem of instability of the deconvolution operator caused by large changes in the dip angle of the strata.

[0041] Step 103: Perform reverse static correction on the second seismic data to correct the second seismic data to the trend floating surface corresponding to the stable strata, and obtain tandem deconvolution data.

[0042] Static correction, also known as topographic relief correction, refers to the process of correcting seismic data to a unified horizontal reference surface. After the electronic equipment performs predictive deconvolution based on a virtual fixed surface, it performs reverse static correction on the obtained second seismic data, correcting the second seismic data from the horizontal fixed virtual surface back to the trend floating surface corresponding to the stable strata, thus obtaining seismic data after geological-guided tandem deconvolution processing.

[0043] In summary, the tandem deconvolution processing method provided in this application improves data resolution, suppresses periodic multiples, improves wave group hierarchy, and enhances amplitude and phase consistency by employing a geologically guided tandem deconvolution method. It solves the phase inversion and layer crossover problems in the traditional tandem deconvolution processing process, providing strong technical support for the deconvolution processing of seismic data with complex geological structures and meeting the needs of fine seismic data processing.

[0044] Example 2

[0045] Please refer to Figure 2 The diagram illustrates a flowchart of a concatenated deconvolution processing method provided in another exemplary embodiment of this application. The method includes the following steps:

[0046] Step 201: Perform surface-consistent deconvolution on the seismic data to be processed to obtain the first seismic data.

[0047] The specific implementation of step 201 can be referred to step 101 above, and will not be repeated here in the embodiments of this application.

[0048] Step 202: Determine the time correction amount corresponding to the virtual fixed surface and the trend floating surface. The time correction amount is used to indicate the time difference of the seismic wave propagation to the ground.

[0049] A trend-floating surface is a smooth curved surface that reflects the stable undulation trend of strata, while a virtual fixed surface is a horizontal plane with uniform height. Information about each point on the trend-floating surface can be represented as (X, Y, T). z ), where X and Y represent coordinates, T z This is the time it takes for the seismic wave to travel from that point to the ground. On a trend-floating surface, due to its unevenness, the time varies at different points, while on a virtual fixed surface, all points have the same height and therefore the corresponding time is the same. This can be represented as (X, Y, T). avg The time correction is the time difference ΔT = T_0 when the seismic wave reaches the ground. z -T avgIt can reflect the height difference between the trend floating surface and the virtual fixed surface. The electronic device performs static correction on the first seismic data by determining the time correction amount between the virtual fixed surface and the trend floating surface, controls the analysis time window of the predictive deconvolution on the virtual fixed surface, and then performs predictive deconvolution processing.

[0050] In one possible implementation, the electronic device first needs to determine the trend floating surface and define the virtual fixed surface. Before step 202, the method of this embodiment further includes the following steps:

[0051] Step 1: Determine the stable strata after well calibration based on well data and interpret the stratigraphic layers corresponding to the first seismic data.

[0052] Step 2: Based on the smoothing size parameters, perform formation smoothing on the stable formations after formation interpretation to obtain the trend floating surface.

[0053] Step 3: Define a virtual fixed surface based on the trend floating surface. The propagation time of each point on the virtual fixed surface is the average propagation time of each point on the trend floating surface.

[0054] After performing surface-consistent deconvolution on the seismic data to be processed, the electronic equipment outputs the first seismic data and the stack. The electronic equipment acquires well data, identifies a stable formation H01 after well calibration, and interprets the stratigraphic layers of the surface-consistent deconvolution stack. Stable formations are defined as those with a reflection coefficient greater than a threshold and continuous reflection interfaces. The electronic equipment determines formation stability by analyzing the magnitude of the reflection coefficient in the well data and the continuity of the lateral reflection interfaces (phase axes). For illustration, the threshold is 0.18. The electronic equipment can trace and interpret stable formations based on a preset grid size (e.g., 200m × 200m) and then mesh the grid.

[0055] Formation smoothing refers to smoothing the gridded stable formations within the interpretation system to eliminate the impact of local structural anomalies on subsequent data processing. Smoothing parameters can be determined through parametric testing; illustratively, smoothing parameters are selected within the range of 2000m to 4000m. Electronic equipment smooths the interpreted stable formations to obtain a smoothed trend surface.

[0056] Electronic devices define a virtual fixed surface based on a trend floating surface. The propagation time at each point on the virtual fixed surface is the average propagation time at all points on the trend floating surface. That is, the spatial information of the trend floating surface is (X, Y, T). z X and Y represent coordinates, T z It is time, so each X and Y corresponds to a time T. z(Time) Assuming X has 1000 sample points and Y has 1000 sample points, then there are 1 million time sample points on the trend fluctuation surface. The average of these 1 million time sample points is the average time T. avg This average time is a fixed constant, representing the time corresponding to each point on a virtual fixed surface, i.e., the time for 1 million points (X, Y, T). avg The surface formed is a virtual fixed surface. Figure 3 A smooth trend floating surface and a virtual fixed surface corresponding to a stable stratum are shown.

[0057] Step 202 specifically includes the following steps:

[0058] Step 4: Determine the time correction amount based on the propagation time of each point on the trend floating surface and the propagation time of the corresponding points on the virtual fixed surface.

[0059] The correction amount ΔT is the height difference between the smoothed trend floating surface and the virtual fixed surface, i.e., ΔT = T. z -T avg .

[0060] Step 203: Based on the time correction, perform static correction on the first seismic data, and control the analysis time window of the predicted deconvolution on a virtual fixed surface to obtain the third seismic data.

[0061] By using the time correction ΔT, static correction is performed on the seismic data after surface-consistent deconvolution. The purpose is to ensure that the time window for predictive deconvolution analysis is controlled on the virtual surface, so that the calculated deconvolution operator is more stable in the lateral direction. Figure 4 The analysis time window for deconvolution on different surfaces is shown.

[0062] Step 204: Perform predictive deconvolution processing on the third earthquake data to obtain the second earthquake data.

[0063] Predictive deconvolution processing is performed on the third earthquake data to eliminate the influence of interlayer multiple interference on the wavelet morphology and suppress periodic multiples. It also addresses the instability of the deconvolution operator caused by large variations in stratigraphic dip angle. The specific implementation process can be found in step 102 above.

[0064] Step 205: Perform reverse static correction on the second seismic data based on the time correction amount to obtain cascaded deconvolution data.

[0065] The electronic device reuses the time correction amount calculated in the above process to perform reverse static correction on the second seismic data, so that the second seismic data is corrected to a smooth trend floating surface, and finally obtains seismic data and profiles after geological-guided tandem deconvolution processing.

[0066] Figure 5The paper presents a comparison of three seismic data effects: before tandem deconvolution, after tandem deconvolution based on conventional methods, and after tandem deconvolution using the method provided in the embodiments of this application. Figure 6 and Figure 7 These are comparison diagrams of cross-sections processed by survey lines in different directions. It can be seen that in the east and south directions of the cross-section, traditional series deconvolution has the problem of inconsistent phase and amplitude. However, after processing the cross-section using the method of the embodiment of this application, the stratigraphic relationship in the southeast direction is clearer, and the above problem is better solved.

[0067] In this embodiment, the surface consistency deconvolution method is first adopted to address the influence of near-surface and static correction factors on the deconvolution operator, thereby improving the lateral consistency of the surface wavelet. Then, following the geological structure-guided approach, the predictive deconvolution method is cascaded to eliminate the influence of large lateral variations in subsurface dip angle and multiple waves generated by special lithological bodies on amplitude and phase. This ensures a more accurate predictive deconvolution analysis window and a more stable deconvolution operator, thus resolving the issues of stratigraphic phase reversal and layer cross-convolution after cascaded deconvolution.

[0068] Example 3

[0069] Please refer to Figure 8 The diagram illustrates a flowchart of a concatenated deconvolution processing method provided in another exemplary embodiment of this application. The method includes the following steps:

[0070] Step 801: Preprocess the seismic data to be processed. The preprocessing includes static correction and pre-stack noise suppression.

[0071] In one possible implementation, the seismic data to be processed is single-shot data. Electronic equipment performs static correction and pre-stack noise suppression on the seismic data to improve the signal-to-noise ratio of the pre-stack data and the accuracy of the seismic data.

[0072] Step 802: Perform velocity analysis and stacking processing on the preprocessed seismic data to be processed, and perform quality control on the seismic data to be processed based on the obtained stack.

[0073] Electronic equipment performs velocity analysis and stacking processing on the preprocessed seismic data to be processed, sorts the single-shot data into CMP gathers, performs dynamic correction, and finally stacks them to output a stack. Quality control of the single-shot data is then performed based on the stack.

[0074] Step 803: Perform surface-consistent deconvolution on the seismic data to be processed to obtain the first seismic data.

[0075] Step 804: Based on the virtual fixed surface, perform predictive deconvolution processing on the first seismic data to obtain the second seismic data. The virtual fixed surface is a plane obtained by correcting stable strata. Stable strata refer to strata with a reflection coefficient greater than the coefficient threshold and a continuous reflection interface.

[0076] The specific implementation of steps 803 to 804 can be referred to steps 101 to 102 above, and will not be repeated here in the embodiments of this application.

[0077] Step 805: Based on the relationship between the wavelet curve and the envelope corresponding to the second seismic data, quality control is performed to determine the rationality of the deconvolution operator.

[0078] Step 806: In response to the reasonableness judgment result indicating that the deconvolution operator is reasonable, reverse static correction is performed on the second seismic data to obtain cascaded deconvolution data.

[0079] Electronic devices can use amplitude spectrum and phase spectrum curves for quality control to determine the rationality of the processing effect. Figure 9 The paper presents wavelet curve quality control diagrams before deconvolution, after traditional tandem deconvolution, and after geological-guided tandem deconvolution. It can be seen that the wavelet curves after tandem processing using the method of this application embodiment exhibit a normal distribution and stable wavelet morphology. Figure 10 The graphs showing the comparison of amplitude and phase spectra after two series deconvolutions are presented.

[0080] In this embodiment, qualitative analysis and evaluation are performed using wavelet curves, amplitude spectrum curves, and phase spectrum curves to ensure the reliability and accuracy of the application effect, providing reliable basic data for velocity modeling and migration imaging, and showing good application prospects in seismic data processing of complex underground geological structures.

[0081] Example 4

[0082] Based on the above embodiments, the serial deconvolution processing flow provided in an exemplary embodiment of this application is as follows:

[0083] 1. Input the seismic data DATA1 after static correction and pre-stack noise suppression, perform a velocity analysis and stacking process, and output the stack STKVOL1. Perform quality control on the seismic data DATA1 based on the stack STKVOL1.

[0084] 2. Perform surface-consistent deconvolution on the seismic data DATA1 to eliminate the influence of the near-surface on the deconvolution operator, and appropriately improve the data resolution without reducing the signal-to-noise ratio. Output the stack STKVOL2 and the seismic data DATA2.

[0085] 3. Using well data, a stable formation H01 was identified after well calibration, and the stratigraphic interpretation of the superposition STKVOL2 after surface consistent defolding was performed.

[0086] 4. Smooth the interpreted formation H01 to obtain a smooth trend floating surface Surface1, and calculate the average time T of the trend floating surface. avg It is defined as a virtual fixed surface Surface2, and then the smooth trend floating surface Surface1 is corrected to the virtual fixed surface Surface2, and the correction amount ΔT is generated.

[0087] 5. Using the correction amount ΔT, static correction is performed on the seismic data DATA2 after surface uniform deconvolution. The purpose is to ensure that the time window of the prediction deconvolution analysis is controlled on the virtual surface. This makes the calculated deconvolution operator more stable in the lateral direction, and outputs seismic data DATA3.

[0088] 6. Perform predictive deconvolution processing on the seismic data DATA3. On the one hand, eliminate the influence of interlayer multiple interference effect on the change of wavelet shape and suppress periodic multiples. On the other hand, eliminate the instability of the deconvolution operator caused by large changes in stratum dip angle. Produce seismic data DATA4, and use the relationship between the wavelet curve and the envelope for quality control to determine the rationality of the deconvolution operator.

[0089] 7. Reuse the correction amount ΔT to perform reverse static correction on DATA4, so that DATA4 is corrected to the smooth trend floating surface Surface1. Finally, obtain the seismic data DATA5 and profile after geological-guided series deconvolution processing, and use amplitude spectrum and phase spectrum curves for quality control to judge the rationality of the processing effect.

[0090] Example 5

[0091] Please refer to Figure 11 The diagram illustrates a structural block diagram of a serial deconvolution processing apparatus provided in an exemplary embodiment of this application, the apparatus comprising:

[0092] The first processing module 1101 is used to perform surface consistency deconvolution on the seismic data to be processed to obtain the first seismic data.

[0093] The second processing module 1102 is used to perform predictive deconvolution processing on the first seismic data based on a virtual fixed surface to obtain the second seismic data. The virtual fixed surface is a plane obtained by correcting stable strata. The stable strata refers to strata with a reflection coefficient greater than the coefficient threshold and a continuous reflection interface.

[0094] The correction module 1103 is used to perform reverse static correction on the second seismic data, correcting the second seismic data to the trend floating surface corresponding to the stable strata, and obtaining cascaded deconvolution data.

[0095] Optionally, the second processing module 1102 is further configured to:

[0096] Determine the time correction amount corresponding to the virtual fixed surface and the trend floating surface, the time correction amount being used to indicate the propagation time difference of seismic waves reaching the ground;

[0097] Based on the time correction amount, static correction is performed on the first seismic data, and the analysis time window for predicting deconvolution is controlled on the virtual fixed surface to obtain the third seismic data.

[0098] The third earthquake data is subjected to predictive deconvolution processing to obtain the second earthquake data.

[0099] Optionally, the correction module 1103 is further configured to:

[0100] The second seismic data is subjected to inverse static correction based on the time correction amount to obtain the cascaded deconvolution data.

[0101] Optionally, the device further includes a determining module for:

[0102] Based on well data, the stable strata after well calibration are determined, and the stratigraphic interpretation is performed on the superposition corresponding to the first seismic data.

[0103] Based on the smoothing size parameters, the stable strata after formation interpretation are smoothed to obtain the trend floating surface;

[0104] The virtual fixed surface is defined based on the trend floating surface, and the propagation time of each point on the virtual fixed surface is the average propagation time of each point on the trend floating surface.

[0105] Optionally, the second processing module 1102 is further configured to:

[0106] The time correction amount is determined based on the propagation time of each point on the trend floating surface and the propagation time of the corresponding point on the virtual fixed surface.

[0107] Optionally, the determining module is further configured to:

[0108] Quality control is performed based on the relationship between the wavelet curve and the envelope corresponding to the second seismic data to determine the rationality of the deconvolution operator;

[0109] The correction module 1103 is also used for:

[0110] In response to the reasonableness determination result indicating that the deconvolution operator is reasonable, the second seismic data is subjected to reverse static correction to obtain the cascaded deconvolution data.

[0111] Optionally, the apparatus further includes a preprocessing module for:

[0112] The seismic data to be processed is preprocessed, including static correction and pre-stack noise suppression.

[0113] Velocity analysis and stacking processing are performed on the preprocessed seismic data to be processed, and quality control is performed on the seismic data to be processed based on the obtained stack.

[0114] Example 6

[0115] This application provides an electronic device; Figure 12 This is a schematic diagram of the composition structure of the electronic device provided in the embodiments of this application, such as... Figure 12 As shown, the electronic device 1200 includes: a processor 1201, at least one communication bus 1202, a user interface 1203, at least one external communication interface 1204, and a memory 1205. The communication bus 1202 is configured to enable communication between these components. The user interface 1203 may include a display screen, and the external communication interface 1204 may include standard wired and wireless interfaces. The processor 1201 is configured to execute a program of a concatenated deconvolution processing method stored in the memory to implement the steps of the method provided in the above embodiments.

[0116] This application also provides a computer-readable storage medium storing a computer program, which is loaded and executed by a processor to implement the methods described in the above embodiments.

[0117] This application also provides a computer program product that runs on a processor of a computer device, causing the computer device to perform the methods described in the above embodiments.

[0118] It should be noted that the descriptions of the storage medium, electronic device, and remote control embodiments above are similar to the descriptions of the method embodiments above, and have similar beneficial effects. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0119] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0120] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, object, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, object, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, object, or apparatus that includes that element.

[0121] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0122] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0123] In addition, each functional unit in the various embodiments of this application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0124] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks.

[0125] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a controller to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0126] The above description is merely an embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for serial deconvolution processing, characterized in that, include: The surface-consistent deconvolution is performed on the seismic data to be processed to obtain the first seismic data. Based on the virtual fixed surface, the first seismic data is subjected to predictive deconvolution processing to obtain the second seismic data. The virtual fixed surface is a plane obtained by correcting stable strata. Stable strata refer to strata with a reflection coefficient greater than the coefficient threshold and a continuous reflection interface. The second seismic data is subjected to reverse static correction to correct it to the trend floating surface corresponding to the stable strata, thus obtaining cascaded deconvolution data.

2. The method according to claim 1, characterized in that, The process of performing predictive deconvolution on the first seismic data based on a virtual fixed surface to obtain second seismic data includes: Determine the time correction amount corresponding to the virtual fixed surface and the trend floating surface, the time correction amount being used to indicate the propagation time difference of seismic waves reaching the ground; Based on the time correction amount, static correction is performed on the first seismic data, and the analysis time window for predicting deconvolution is controlled on the virtual fixed surface to obtain the third seismic data. The third earthquake data is subjected to predictive deconvolution processing to obtain the second earthquake data.

3. The method according to claim 2, characterized in that, The step of performing reverse static correction on the second seismic data, correcting the second seismic data to the trend floating surface corresponding to the stable strata, to obtain cascaded deconvolution data includes: The second seismic data is subjected to inverse static correction based on the time correction amount to obtain the cascaded deconvolution data.

4. The method according to claim 2, characterized in that, Before determining the time correction amount corresponding to the virtual fixed surface and the trend floating surface, the method further includes: Based on well data, the stable strata after well calibration are determined, and the stratigraphic interpretation is performed on the superposition corresponding to the first seismic data. Based on the smoothing size parameters, the stable strata after formation interpretation are smoothed to obtain the trend floating surface; The virtual fixed surface is defined based on the trend floating surface, and the propagation time of each point on the virtual fixed surface is the average propagation time of each point on the trend floating surface.

5. The method according to claim 4, characterized in that, Determining the time correction amount corresponding to the virtual fixed surface and the trend floating surface includes: The time correction amount is determined based on the propagation time of each point on the trend floating surface and the propagation time of the corresponding point on the virtual fixed surface.

6. The method according to any one of claims 1 to 5, characterized in that, Before performing reverse static correction on the second seismic data to correct it to the trend floating surface corresponding to the stable strata, and obtaining the cascaded deconvolution data, the method further includes: Quality control is performed based on the relationship between the wavelet curve and the envelope corresponding to the second seismic data to determine the rationality of the deconvolution operator; The step of performing reverse static correction on the second seismic data, correcting the second seismic data to the trend floating surface corresponding to the stable strata, to obtain cascaded deconvolution data includes: In response to the reasonableness determination result indicating that the deconvolution operator is reasonable, the second seismic data is subjected to reverse static correction to obtain the cascaded deconvolution data.

7. The method according to any one of claims 1 to 5, characterized in that, Before performing surface-consistent deconvolution on the seismic data to be processed to obtain the first seismic data, the method further includes: The seismic data to be processed is preprocessed, including static correction and pre-stack noise suppression. Velocity analysis and stacking processing are performed on the preprocessed seismic data to be processed, and quality control is performed on the seismic data to be processed based on the obtained stack.

8. A series deconvolution processing device, characterized in that, include: The first processing module is used to perform surface consistency deconvolution on the seismic data to be processed to obtain the first seismic data. The second processing module is used to perform predictive deconvolution processing on the first seismic data based on a virtual fixed surface to obtain the second seismic data. The virtual fixed surface is a plane obtained by correcting stable strata. Stable strata refer to strata with a reflection coefficient greater than the coefficient threshold and a continuous reflection interface. The correction module is used to perform reverse static correction on the second seismic data, correcting the second seismic data to the trend floating surface corresponding to the stable strata, and obtaining cascaded deconvolution data.

9. An electronic device, characterized in that, It includes a memory and a processor; the memory stores a computer program that, when executed by the processor, implements the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The system contains a computer program that is loaded and executed by a processor to implement the method as described in any one of claims 1 to 7.

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