Sectional multiple suppression method and device based on spatial distribution of strong reflecting layer

Abstract

The invention discloses a segmented multiple suppression method and device based on spatial distribution of a strong reflecting layer. The method comprises the following steps: on the basis of pre-stack gather data, predicting and attenuating whole-course multiple waves generated due to development of a single strong reflecting layer and interlayer multiple waves generated due to development of multiple strong reflecting layers; after offset processing, an offset gather is divided into near offset gather data and middle and far offset gather data; the method comprises the following steps: transforming near-offset gather data to enable the near-offset gather data to meet prediction deconvolution application conditions, performing multiple prediction and suppression through prediction deconvolution, and predicting and suppressing multiple waves in medium-and far-offset gather data by applying bunching filtering, and the above processing is time sectional processing. According to the method, spatial distribution characteristics of a strong reflecting layer are considered, a horizon interpretation result is used as a constraint, near-offset multiple energy which is not greatly different from primary waves can be attenuated to the maximum extent through data transformation, effective information is protected, and seismic data imaging precision is improved.

Description

Technical Field

[0001] This invention relates to the field of seismic data processing technology in petroleum seismic exploration, and in particular to a segmented multiple wave suppression method and apparatus based on the spatial distribution of strong reflective layers. Background Technology

[0002] In seismic data processing, artifacts caused by multiples can reduce the realism and reliability of subsurface structural imaging, leading to serious geological biases. Therefore, multiple suppression is a crucial step in seismic data processing. Identifying and suppressing multiple reflections under complex geological conditions is a common challenge in seismic data processing. Improving the accuracy of multiple suppression is of significant research importance for enhancing data imaging quality and overall seismic data processing quality.

[0003] After years of technological development, multiple suppression methods can be divided into three categories. The first category is based on the spatial differences in characteristics between primary and multiple waves; common methods include Radon transform, FK transform, and cluster filtering. These methods are based on the separability of primary and multiple waves, meaning that multiples and primary waves have certain characteristic differences, which are used to identify and suppress multiples in seismic data. The second category is based on the periodicity and predictability of multiples. The most typical technique is predictive deconvolution, which uses filters to attenuate this repetitive seismic signal. This method is particularly effective for suppressing shallow water multiples. This type of technique is entirely based on the wave equation, overcoming the limitations of signal domain filtering methods. Based on the kinematics and dynamics of wave field propagation, it requires only a small amount of information, or even none at all, to achieve a significant multiple suppression effect. In the processing of multiples in terrestrial seismic data, this type of technique, such as SRME, XIMP, and the inverse scattering series method, has been widely used. In the process of multiple wave modeling, these techniques all require the physical element of a strong reflecting interface that generates multiple waves. SRME and XIMP also require a high-precision primary wave velocity model as prior information. The inverse scattering series law does not require any prior information, but its massive computational load limits its large-scale application in production. The third category is intelligent multiple wave attenuation technology. In recent years, thanks to the development of computer hardware and software, intelligent technologies based on deep learning have been applied to various fields, driving rapid industrial innovation. The essence of artificial intelligence is to use machines to simulate human thinking to make judgments, and deep learning is the process of making machines "smarter." Learning from massive amounts of data can even allow artificial intelligence to surpass "human" intelligence. However, due to the difficulty in obtaining multiple wave suppression labels, intelligent multiple wave attenuation technology is difficult to effectively promote and apply. Summary of the Invention

[0004] The inventors discovered that existing multiple wave suppression methods based on the spatial differences in characteristics between primary and multiple waves exhibit significant velocities between the multiple and primary waves at medium to long offsets, and their curvatures differ markedly in the τ-p domain. While these methods can identify and suppress multiple wave energy, they are less effective at short offsets where the time difference between the multiple and primary waves is small. This limitation makes it difficult to effectively suppress multiple waves when there are significant lateral variations in the strata or when the velocity difference between the primary and multiple waves is small. Existing multiple wave suppression methods based on the periodicity and predictability of multiple waves rely on predictive deconvolution techniques, which depend on the strict periodicity of the multiple waves. This condition is only met under horizontally layered media conditions, and can lead to significant errors for large-offset seismic traces. Therefore, existing methods for suppressing multiple waves do not achieve ideal results.

[0005] In order to at least partially solve the technical problems existing in the prior art, the inventors made this invention, which, through specific implementation methods, provides a segmented multiple wave suppression method and device based on the spatial distribution of strong reflection layers, which can quickly and effectively suppress multiple waves and improve the imaging accuracy of seismic data.

[0006] In a first aspect, embodiments of the present invention provide a segmented multiple suppression method based on the spatial distribution of a strong reflective layer, comprising:

[0007] Based on pre-stack gather data, predict the full-length multiple wave model caused by the development of a single strong reflector layer and the inter-layer multiple wave model caused by the development of multiple strong reflectors, respectively, and subtract the full-length multiple wave model and the inter-layer multiple wave model from the pre-stack gather data.

[0008] The gather data obtained after pre-stack multiple attenuation are offset and divided into near offset gather data and medium-to-far offset gather data.

[0009] The near-offset gather data is modified to meet the conditions for deconvolution application. A time-segmented processing method is adopted, and multiple wave suppression is performed on the modified near-offset gather data through deconvolution processing.

[0010] Multiple waves of the mid-to-long offset gather data are suppressed by using a time-segmented processing method.

[0011] Secondly, embodiments of the present invention provide a segmented multiple wave suppression device based on the spatial distribution of a strong reflective layer, comprising:

[0012] The multiple wave initial suppression module is used to predict, based on pre-stack gather data, the full-length multiple wave model caused by the development of a single strong reflective layer region and the inter-layer multiple wave model caused by the development of multiple strong reflective layers, respectively, and to subtract the full-length multiple wave model and the inter-layer multiple wave model from the pre-stack gather data.

[0013] The offset gather data grouping module is used to perform offset processing on the gather data obtained after pre-stack multiple attenuation, and divide the offset gather data into near offset gather data and medium-to-far offset gather data.

[0014] The segmented multiple suppression module is used to modify near-offset gather data to meet the conditions for deconvolution. It adopts a time-segmented processing method to suppress multiples in the modified near-offset gather data through deconvolution processing, and suppresses multiples in the mid-to-far offset gather data through a time-segmented processing method.

[0015] Thirdly, embodiments of the present invention provide a computer storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-mentioned segmented multiple wave suppression method based on the spatial distribution of strong reflective layers.

[0016] Fourthly, embodiments of this disclosure provide a server, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described segmented multiple wave suppression method based on the spatial distribution of strong reflective layers.

[0017] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0018] The segmented multiple suppression method based on the spatial distribution of strong reflective layers provided in this invention completes pre-stack multiple attenuation processing in two steps, before and after migration. First, before migration, multiple development areas are effectively identified and distinguished into single-strong-reflection layer regions and multi-strong-reflection layer regions. Different methods are used to obtain different multiple models to suppress multiples. However, it is difficult to completely predict the correct model of multiples before migration, and it is also difficult to distinguish between near-offset and primary wave energy after migration. Therefore, after migration, the gather data is divided into near-offset and mid-to-far-offset gather data. The near-offset gather data is modified to meet the conditions for deconvolution application. The gather data is then processed segmentally according to the characteristics of multiple development. This segmented comprehensive multiple suppression method based on the spatial distribution of strong reflective layers can maximize the attenuation of multiple energy while avoiding damage to the effective reflection energy in areas where multiples are not developed, protecting the effective information of the original data, and improving the imaging accuracy of seismic data. Furthermore, spatial partitioning and temporal segmentation processing can effectively utilize resources and improve processing efficiency. This method can suppress multiple reflected waves with high quality while optimizing the preservation of effective signals. In particular, high-frequency multiple waves with near offset can be reasonably attenuated. This method is easy to implement and has high computational efficiency.

[0019] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0020] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0021] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0022] Figure 1 This is a flowchart of a segmented multiple wave suppression method based on the spatial distribution of a strong reflective layer in an embodiment of the present invention;

[0023] Figure 2 This is a schematic diagram illustrating the seismic velocity analysis at different locations in the test area in this embodiment of the invention;

[0024] Figure 3 This is a schematic diagram of the predicted thickness of the strong reflective layer in the test area in an embodiment of the present invention;

[0025] Figure 4 This is a schematic diagram of the data offset profile without multiple attenuation steps.

[0026] Figure 5 This is a schematic diagram of the imaging profile after multiple wave attenuation through SRME and mid-to-long offset beam filtering in an embodiment of the present invention.

[0027] Figure 6 For CRP gathers migrated to the multiple wave development region;

[0028] Figure 7 This is a CRP gather for the multiple wave development region in this embodiment of the invention;

[0029] Figure 8 This is a schematic diagram of the cross-section of the results before near-offset multiple wave suppression;

[0030] Figure 9 This is a schematic diagram of the cross-sectional view of the result after near-offset multiple wave suppression in an embodiment of the present invention;

[0031] Figure 10 This is a schematic diagram of the segmented multiple wave suppression device based on the spatial distribution of a strong reflective layer in an embodiment of the present invention. Detailed Implementation

[0032] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0033] It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Furthermore, with respect to numerical ranges in this invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Every smaller range between any stated value or intermediate value within a stated range, and any other stated value or intermediate value within said range, is also included in this invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.

[0034] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. While only preferred methods and materials have been described herein, any methods and materials similar or equivalent to those described herein may be used in the implementation or testing of this invention. All references to this specification are incorporated by way of citation to disclose and describe methods and / or materials associated with those references. In the event of any conflict with any incorporated reference, the content of this specification shall prevail.

[0035] Example

[0036] This invention provides a segmented multiple suppression method based on the spatial distribution of strong reflective layers, the process of which is as follows: Figure 1 As shown, it includes the following steps:

[0037] Step S11: Based on pre-stack gather data, predict the full-length multiple wave model caused by the development of a single strong reflector layer and the inter-layer multiple wave model caused by the development of multiple strong reflectors, respectively, and subtract the full-length multiple wave model and the inter-layer multiple wave model from the pre-stack gather data.

[0038] First, static correction, conventional noise attenuation, and surface consistency processing were completed for the pre-stack gather data. Combining previous geological understanding and seismic interpretation stratigraphic results, the development characteristics (spatial location and number of layers) of spatially strong reflective layers were clarified. Starting from the multiple development mechanism, possible multiple development scenarios under different geological conditions were analyzed. Based on this analysis, velocity spectra and velocity analysis super-gathers were obtained using the pre-stack gather data. According to the distribution of energy clusters related to the velocity spectrum, the flattening status of the velocity analysis super-gathers, and regional geological understanding, regions with single-strong-reflective-layer development and regions with multiple-strong-reflective-layer development were identified from the pre-stack gather data. Full-length multiple models generated in regions with single-strong-reflective-layer development and inter-layer multiple models generated in regions with multiple-strong-reflective-layer development were predicted, respectively.

[0039] Furthermore, SRME technology can be used to predict full-length multiple wave models caused by the development of a single strong reflective layer, and SRME and XIMP technologies can be used to predict interlayer multiple wave models caused by the development of multiple strong reflective layers.

[0040] Specifically, for regions with a single strong reflective layer, the terrestrial SRME technique is used to predict a multiple reflection model similar to a free surface generated between the strong reflective layer and the surface, and the multiple wave model is subtracted from the gather data using the matching subtraction technique. For regions with multiple (two or more) strong reflective layers, the terrestrial SRME+XIMP technique is used to predict a complex multiple wave model generated by multiple reflections between multiple strong reflective layers and the surface, and between multiple strong reflective layers, and the multiple wave model is subtracted from the gather data using the matching subtraction technique.

[0041] In some embodiments, during the prediction process of the full-length multiple wave model generated by the development of a single strong reflective layer and the inter-layer multiple wave model generated by the development of multiple strong reflective layers, the layer interpretation data can also be used as a constraint, mainly including the interpretation data of strong reflective layers that can generate multiple waves as a constraint.

[0042] Step S12: Perform offset processing on the gather data after pre-stack multiple attenuation, and divide the offset gather data into near offset gather data and medium-to-far offset gather data.

[0043] Typically, based on the difference in time difference between multiples and primary waves, migrated gather data are divided into near-offset gather data and mid-to-far-offset gather data. In near-offset gather data, the difference in time difference between multiples and primary waves in seismic traces is less than a set threshold, while in mid-to-far-offset gather data, the difference in time difference between multiples and primary waves in seismic traces is not less than the aforementioned threshold.

[0044] Furthermore, the threshold here can be set to 50ms. For example, gather data with a significant downtilt time difference (downtilt time difference greater than 50ms) on the same phase axis can be identified as mid-to-long offset gather data.

[0045] Step S13: Modify the near-offset gather data to meet the conditions for deconvolution application. Use a time-segmented processing method to perform multiple wave suppressions on the modified near-offset gather data through deconvolution processing.

[0046] The near-offset gather data is modified to meet the conditions for predictive deconvolution application. The development period of multiples is estimated in different time periods. The development period of multiples is used as the prediction step size for multi-channel predictive deconvolution processing to attenuate the energy of multiples and primarys in near-offset gathers that have no significant time difference. The data modification method and the determination of the prediction step size will be described in detail later.

[0047] Step S14: Use a time-segmented processing method to suppress multiple waves of mid-to-long offset gather data.

[0048] For medium- to long-range offset gather data, cluster filtering can be applied to predict multiple wave models. For multiple waves with significant attenuation and time difference from the primary wave, this processing involves applying different model parameters to predict the multiple wave model in different time periods. This segmented processing is mainly along time segments. The time difference between the phase axis of the multiple reflection CRP gather and the phase axis of the primary wave increases with time. Based on the medium- to long-range offset gather data, multiple time periods can be divided according to the temporal and positional differences in the energy distribution of the multiple wave and the time difference between the multiple wave and the primary wave. Different parameters are applied to different time periods to complete the prediction of the multiple wave model.

[0049] The above processing was completed by combining the low-velocity energy clusters in the velocity spectrum and the partitioning and segmentation of the super gather flattening based on velocity analysis, and by merging the near-, mid- and far-offset CRP gather data to complete the pre-stack multiple suppression processing.

[0050] The segmented multiple suppression method based on the spatial distribution of strong reflective layers provided in this invention completes pre-stack multiple attenuation processing in two steps, before and after migration. First, before migration, multiple development areas are effectively identified and distinguished into single-strong-reflection layer regions and multi-strong-reflection layer regions. Different methods are used to obtain different multiple models to suppress multiples. However, it is difficult to completely predict the correct model of multiples before migration, and it is also difficult to distinguish between near-offset and primary wave energy after migration. Therefore, after migration, the gather data is divided into near-offset and mid-to-far-offset gather data. The near-offset gather data is modified to meet the conditions for deconvolution application. The gather data is then processed segmentally according to the characteristics of multiple development. This segmented comprehensive multiple suppression method based on the spatial distribution of strong reflective layers maximizes the attenuation of multiple energy while avoiding damage to the effective reflection energy in areas where multiples are not developed, thus protecting the effective information of the original data and improving the imaging accuracy of seismic data. This method can suppress multiple reflections with high quality while optimally preserving effective signals, especially achieving reasonable attenuation of high-frequency multiples at near-offset. This technology is convenient to implement and computationally efficient.

[0051] The test blocks used in this embodiment of the invention exhibit both free-surface multiple reflections caused by a single strong reflective layer and interlayer multiple reflections caused by multiple (two or more) strong reflective layers. It is difficult to fully predict the correct model of this type of multiple reflection before the offset, and it is also difficult to distinguish between the near offset and the primary wave energy after the offset.

[0052] Seismic records are considered to be the convolution of the excitation wavelet and the reflection coefficient, i.e.:

[0053]

[0054] Where s(t) is the seismic wavelet, which is generally assumed to be in minimum phase, and ξ(t) is the reflection coefficient, which is assumed to be white noise. When the input is u(t), the expected predicted output is u(t+a) at a later time t+a, but the actual output... If l(t) is the prediction factor, then the filtered result, which is the prediction error, is... The output error energy is e t =∑ε t 2 Using the least squares criterion as a constraint, an algorithm for predicting deconvolution is obtained:

[0055]

[0056] r xx (τ) is the autocorrelation function, obtained from the input seismic record u(t). The matrix equation is then solved to obtain the prediction factor c(l). The result c(l) is then used for convolution to obtain the prediction result. This is then subtracted from the actual seismic record at time t+a as multiple wave interference, thus completing the multiple wave attenuation.

[0057] As can be seen from the above explanation of the predictive deconvolution principle, this method can be applied to predict wavefield when the signal has a certain periodicity. It is typically used to predict signals that appear at fixed periods in post-stack profiles, shot gathers, and pre-stack CMP gathers. Since the offset CRP gather itself does not meet the conditions for predictive deconvolution, it is transformed into pre-stack shot gather data, with the offset distance of the CRP gather used as the shot-receiver distance. The time period parameter is crucial in predictive deconvolution applications. This parameter is determined as follows: A sliding window length is set. Within the sliding window, statistical autocorrelation analysis is performed on the transformed near-offset gather data (i.e., the CRP gather). The maximum correlation energy delay time within different time windows is output, and this time is used as the predictive deconvolution step size at the center of the corresponding time window. Within each sliding window, based on the corresponding predictive deconvolution step size, multiple wave suppression operations are performed on the transformed near-offset gather data through deconvolution processing.

[0058] Within the predicted time range of multiples, the optimal prediction deconvolution step size is obtained by sliding the time window at certain time intervals for different time periods. Finally, through statistical analysis, the prediction deconvolution processing parameters for different spatial locations and time periods are obtained, and the prediction and suppression of near-offset multiples of CRP gathers in different regions and segments are carried out.

[0059] In this embodiment of the invention, predictive deconvolution is applied to different times t, and different prediction time steps Δτ are selected segmentally based on the duration differences of CRP gather multiple energy, ultimately suppressing interlayer multiple energy generated by different reflector layers. Δτ can also be obtained by performing statistical autocorrelation on the CRP gather data to obtain a specific time value.

[0060] In the identification of multiples, the current application of existing seismic geological knowledge, drilling and logging data, and VSP data for multiple development identification is widespread. This invention, based on the above technologies, applies the velocity spectrum analysis results semi-quantitatively. By using seismic velocity spectra and corresponding gather characteristics, combined with geological knowledge, the spatial development of multiples is clarified, the data is partitioned, and a targeted multiple suppression technique is developed. In the multiple suppression technique, firstly, in the gather data before migration, SRME and XIMP techniques are used to predict multiple models for full-range multiples and inter-layer multiples caused by a single strong reflection interface, and subtractive attenuation multiples are matched from the data. Then, for multiple development areas with multiples at multiple strong reflection layers, the migrated CRP gathers are grouped. For mid-to-long-range migrations, cluster filtering is applied to attenuate residual multiples with significant time differences from the primary wave. For near-range migration multiples, a sliding time window scan is used, and multi-channel prediction deconvolution attenuation of multiples is applied in segments with different prediction step sizes.

[0061] See Figure 2 The diagram shown is a schematic representation of the seismic velocity analysis at different locations in the experimental area of ​​this embodiment. Figure 3 This is a schematic diagram showing the predicted thickness of the strong reflective layer in the test area.

[0062] Figure 4 This is a schematic diagram of a data offset profile without multiple attenuation steps. Figure 5 This is a schematic diagram of the imaging profile after multiple wave attenuation via SRME and mid-to-long offset beam filtering. Figure 6 For CRP gathers migrated to the multiple wave development region, Figure 7 The offset CRP gather after implementing the present invention in the multi-wave development region; Figure 8 This is a schematic diagram of the cross-section of the results before near-offset multiple wave suppression. Figure 9 This is a schematic diagram of the cross-section of the result after near-offset multiple wave suppression.

[0063] The above comparison shows that the method in this embodiment significantly suppresses multiple waves and improves the imaging accuracy of seismic data.

[0064] Based on the inventive concept of this invention, embodiments of this invention also provide a segmented multiple wave suppression device based on the spatial distribution of a strong reflective layer, the structure of which is as follows: Figure 10 As shown, it includes:

[0065] The multiple primary suppression module 101 is used to predict, based on pre-stack gather data, the full-length multiple model caused by the development of a single strong reflector layer and the inter-layer multiple model caused by the development of multiple strong reflectors layer, respectively, and to subtract the full-length multiple model and the inter-layer multiple model from the pre-stack gather data.

[0066] The offset gather data grouping module 102 is used to perform offset processing on the gather data obtained after pre-stack multiple attenuation, and divide the offset gather data into near offset gather data and medium-to-far offset gather data.

[0067] The segmented multiple suppression module 103 is used to modify near-offset gather data to meet the conditions for deconvolution application. It adopts a time-segmented processing method to suppress multiples in the modified near-offset gather data through deconvolution processing, and suppresses multiples in the mid-to-far offset gather data through a time-segmented processing method.

[0068] In some embodiments, the multiple primary suppression module 101, which predicts the full-range multiple model generated by the development of a single strong reflective layer and the interlayer multiple model generated by the development of multiple strong reflective layers, is used for:

[0069] The SRME technique was used to predict the full-length multiple wave model caused by the development of a single strong reflective layer, and the SRME and XIMP techniques were used to predict the interlayer multiple wave model caused by the development of multiple strong reflective layers.

[0070] In some embodiments, the multiple primary suppression module 101, which predicts the full-range multiple model generated by the development of a single strong reflective layer and the interlayer multiple model generated by the development of multiple strong reflective layers, is further configured to:

[0071] Using stratigraphic interpretation data as constraints, we predict full-length multiple wave models caused by the development of a single strong reflective layer and inter-layer multiple wave models caused by the development of multiple strong reflective layers.

[0072] In some embodiments, the offset gather data grouping module 102, which divides the offset gather data into near-offset gather data and mid-to-long-offset gather data, is used for:

[0073] Based on the time difference between multiples and primary waves, the migrated gather data is divided into near-offset gather data and mid-to-far-offset gather data. In the near-offset gather data, the time difference between multiples and primary waves in the seismic traces is less than a set threshold, while in the mid-to-far-offset gather data, the time difference between multiples and primary waves in the seismic traces is not less than the threshold.

[0074] In some embodiments, the segmented multiple suppression module 103, which suppresses the multiples of the mid-to-long offset gather data using a time-segmented processing method, is used for:

[0075] Based on the mid-to-long offset gather data, multiple time periods are divided according to the temporal location differences in the energy distribution of multiples and the time difference between multiples and primary waves. For each time period, different model parameters are applied, and multiple wave models are predicted through cluster filtering to suppress the multiples in the mid-to-long offset gather data.

[0076] In some embodiments, the segmented multiple suppression module 103, which modifies the near-offset gather data to meet the conditions for deconvolution applications, is used for:

[0077] The near-offset gather data was transformed into pre-stack shot gather data, with the offset of the offset CRP gather used as the shot-receiver distance.

[0078] In some embodiments, the segmented multiple suppression module 103 employs a time-segmented processing method, performing multiple suppression on the modified near-offset gather data through deconvolution processing, for the following purposes:

[0079] Set the sliding window length, perform autocorrelation analysis on the modified near-offset gather data within the sliding window, output the maximum correlation energy delay time within different time windows, and use this time as the prediction deconvolution step size at the center time of the corresponding time window; within each sliding window, perform multiple wave prediction and suppression on the modified near-offset gather data through deconvolution processing according to the corresponding prediction deconvolution step size.

[0080] In some embodiments, the multiple primary suppression module 101, which predicts, based on pre-stack gather data, the full-length multiple model caused by the development of a single strong reflector layer and the inter-layer multiple model caused by the development of multiple strong reflectors layer, is used for:

[0081] Velocity spectra and velocity analysis super gathers are obtained using pre-stack gather data. Based on the distribution of energy clusters related to the velocity spectra, the flattening of super gathers, and regional geological understanding, regions with single strong reflective layers and regions with multiple strong reflective layers are identified from the pre-stack gather data. Full-length multiple wave models generated in regions with single strong reflective layers and interlayer multiple wave models generated in regions with multiple strong reflective layers are predicted, respectively.

[0082] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0083] Based on the inventive concept of the present invention, embodiments of the present invention also provide a computer storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-mentioned segmented multiple wave suppression method based on the spatial distribution of strong reflective layers.

[0084] Based on the inventive concept of this invention, this embodiment of the invention also provides a server, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the above-mentioned segmented multiple wave suppression method based on the spatial distribution of strong reflective layers.

[0085] Unless otherwise specifically stated, terms such as processing, calculation, operation, determination, display, etc., may refer to the actions and / or processes of one or more processing or computing systems or similar devices that represent the manipulation and conversion of data representing physical (e.g., electronic) quantities within the registers or memory of the processing system into other data similarly representing physical quantities within the memory, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals can be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the above description can be represented by voltage, current, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0086] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process may be rearranged without departing from the scope of this disclosure. The appended method claims provide elements of various steps in an exemplary order and are not intended to limit the scope to the specific order or hierarchy described.

[0087] In the detailed description above, various features are combined together in a single embodiment to simplify this disclosure. This approach to disclosure should not be construed as reflecting an intention that embodiments of the claimed subject matter require more features than those stated in each claim. Rather, as reflected in the appended claims, the invention is presented with fewer features than all of the features in a single disclosed embodiment. Therefore, the appended claims are hereby clearly incorporated into the detailed description, wherein each claim stands alone as a preferred embodiment of the invention.

[0088] Those skilled in the art will also understand that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments herein can be implemented as electronic hardware, computer software, or a combination thereof. To clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art can implement the described functionality in alternative ways for each specific application; however, such implementation decisions should not be construed as departing from the scope of this disclosure.

[0089] The steps of the methods or algorithms described in conjunction with the embodiments herein can be directly embodied in hardware, software modules executed by a processor, or a combination thereof. The software module can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. The ASIC can reside in the user terminal. Alternatively, the processor and storage medium can exist as discrete components in the user terminal.

[0090] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. This software code can be stored in memory units and executed by a processor. The memory units can be implemented within the processor or outside the processor; in the latter case, they are communicatively coupled to the processor via various means, as is well known in the art.

[0091] The foregoing description includes examples of one or more embodiments. It is certainly impossible to describe all possible combinations of components or methods in order to describe the above embodiments, but those skilled in the art will recognize that further combinations and arrangements of the various embodiments are possible. Therefore, the embodiments described herein are intended to cover all such changes, modifications, and variations that fall within the scope of the appended claims. Furthermore, the term “comprising” as used in the specification or claims is interpreted in a manner similar to the term “including,” as it is understood when used as a conjunction in the claims. Additionally, the use of any term “or” in the specification of the claims is intended to mean “non-exclusive or.” The terms “first” and “second” are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

Claims

1. A segmented multiple wave suppression method based on the spatial distribution of a strong reflective layer, characterized in that, include: Based on pre-stack gather data, predict the full-length multiple wave model caused by the development of a single strong reflector layer and the inter-layer multiple wave model caused by the development of multiple strong reflectors, respectively, and subtract the full-length multiple wave model and the inter-layer multiple wave model from the pre-stack gather data. The gather data obtained after pre-stack multiple attenuation are offset and divided into near offset gather data and medium-to-far offset gather data. The near-offset gather data is modified to meet the conditions for deconvolution application. A time-segmented processing method is adopted, and multiple wave suppression is performed on the modified near-offset gather data through deconvolution processing. Multiple waves of the mid-to-long offset gather data are suppressed by using a time-segmented processing method.

2. The method according to claim 1, characterized in that, The models for predicting full-course multiples caused by the development of a single strong reflector layer and interlayer multiples caused by the development of multiple strong reflectors include: The SRME technique was used to predict the full-length multiple wave model caused by the development of a single strong reflective layer, and the SRME and XIMP techniques were used to predict the interlayer multiple wave model caused by the development of multiple strong reflective layers.

3. The method according to claim 2, characterized in that, The models for predicting full-course multiples caused by the development of a single strong reflector layer and interlayer multiples caused by the development of multiple strong reflectors also include: Using stratigraphic interpretation data as constraints, we predict full-length multiple wave models caused by the development of a single strong reflective layer and inter-layer multiple wave models caused by the development of multiple strong reflective layers.

4. The method according to claim 1, characterized in that, The process of dividing the offset gather data into near-offset gather data and mid-to-far-offset gather data includes: Based on the time difference between multiples and primary waves, the migrated gather data is divided into near-offset gather data and mid-to-far-offset gather data. In the near-offset gather data, the time difference between multiples and primary waves in the seismic traces is less than a set threshold, while in the mid-to-far-offset gather data, the time difference between multiples and primary waves in the seismic traces is not less than the threshold.

5. The method according to claim 1, characterized in that, The method of suppressing multiple waves of the mid-to-long offset gather data by using time-segmented processing includes: Different model parameters are applied to different time periods, and multiple wave models are predicted by cluster filtering to suppress the multiple waves of the mid-to-long offset gather data.

6. The method according to claim 1, characterized in that, The method of suppressing multiple waves of the mid-to-long offset gather data by using time-segmented processing includes: Based on the aforementioned mid-to-long-range offset gather data, multiple time periods are divided according to the temporal and positional differences in the energy distribution of multiple waves and the time difference between multiple waves and the primary wave. For each time period, different model parameters are applied, and multiple wave models are predicted by cluster filtering to suppress the multiple waves of the mid-to-long offset gather data.

7. The method according to claim 1, characterized in that, The modification of near-offset gather data to meet the conditions for deconvolution applications includes: The near-offset gather data was transformed into pre-stack shot gather data, with the offset of the offset CRP gather used as the shot-receiver distance.

8. The method according to claim 7, characterized in that, The aforementioned time-segmented processing method involves performing multiple wave compressions on the modified near-offset gather data through deconvolution, including: Set the sliding window length, perform autocorrelation analysis on the modified near offset gather data within the sliding window, output the maximum correlation energy delay time within different time windows, and use this time as the prediction deconvolution step size at the center time of the corresponding time window. Within each sliding time window, based on the corresponding predicted deconvolution step size, multiple wave predictions and suppressions are performed on the modified near-offset gather data through deconvolution processing.

9. The method according to claim 1, characterized in that, The prediction of full-length multiple wave models caused by the development of a single strong reflector layer and inter-layer multiple wave models caused by the development of multiple strong reflectors, based on pre-stack gather data, includes: Velocity spectrum and velocity analysis super gathers are obtained using pre-stack gather data; Based on the distribution of energy clusters related to the velocity spectrum, the flattening of super gathers, and the understanding of regional geology, regions with single strong reflective layers and regions with multiple strong reflective layers are identified from the prestack gather data. The full-length multiple wave model and the interlayer multiple wave model generated in the region with a single strong reflective layer are predicted respectively.

10. A segmented multiple wave suppression device based on the spatial distribution of a strong reflective layer, characterized in that, The device includes: The multiple wave initial suppression module is used to predict, based on pre-stack gather data, the full-length multiple wave model caused by the development of a single strong reflective layer region and the inter-layer multiple wave model caused by the development of multiple strong reflective layers, respectively, and to subtract the full-length multiple wave model and the inter-layer multiple wave model from the pre-stack gather data. The offset gather data grouping module is used to perform offset processing on the gather data obtained after pre-stack multiple attenuation, and divide the offset gather data into near offset gather data and medium-to-far offset gather data. The segmented multiple suppression module is used to modify near-offset gather data to meet the conditions for deconvolution. It adopts a time-segmented processing method to suppress multiples in the modified near-offset gather data through deconvolution processing, and suppresses multiples in the mid-to-far offset gather data through a time-segmented processing method.

11. A computer storage medium, characterized in that, The computer storage medium stores computer-executable instructions, which, when executed by a processor, implement the segmented multiple wave suppression method based on the spatial distribution of a strong reflective layer as described in any one of claims 1 to 9.

12. A server, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the segmented multiple wave suppression method based on the spatial distribution of a strong reflective layer as described in any one of claims 1 to 9.

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