A strong reflection layer separation method, device, equipment, medium and program

Abstract

The present application relates to seismic interpretation processing technology, disclose a kind of strong reflection layer separation method, device, equipment, medium and procedure, wherein the method comprises: using pre-constructed geological model carries out seismic forward modeling, obtains seismic section;According to the frequency space variation characteristics of overlying strata in seismic section of original seismic data of seismic section, obtain matching wavelet;Based on matching wavelet, original seismic data is separated by strong reflection, and separated seismic record is obtained.The present application can optimize frequency parameter, improve the effect of strong reflection separation, enhance the reflection characteristics of underlying small-scale fracture-cavity, so as to more accurately represent and separate strong reflection event, improve separation accuracy.

Description

Technical Field

[0001] This invention relates to the field of earthquake interpretation processing technology, and in particular to a method, apparatus, equipment, medium, and procedure for separating strong reflective layers. Background Technology

[0002] This section is intended to provide background or context for the embodiments set forth in the claims. The description herein is not an admission that it is prior art simply because it is included in this section.

[0003] For the T74 unconformity in the Northwest exploration area, there is a significant difference in wave impedance between the strata above and below the interface, forming a distinct strong reflection phase axis on the seismic profile. This strong reflection energy masks the weak reflection characteristics of the underlying small-scale fractured-cavitary reservoirs, making accurate identification of these reservoirs extremely difficult. Therefore, separating the T74 strong reflection phase axis can highlight the weak reflection information of the small-scale fractured-cavitary reservoirs, improving prediction accuracy. Thus, accurate identification and separation of the strong reflection phase axis is crucial. The matching pursuit algorithm is a commonly used method for separating strong reflection layers. It uses the instantaneous frequency of the strong reflection phase axis as a frequency parameter and obtains a matching wavelet characterizing the strong reflection phase axis through optimized calculation of the frequency parameter controlled by a wavelet. This method yields a matching wavelet that includes not only the frequency characteristics of the strong reflection layer itself but also the frequency variations caused by the underlying small-scale fractured-cavitary reservoirs. However, it also introduces significant prediction errors. In conclusion, how to optimize the frequency parameter to more accurately characterize and separate the strong reflection phase axis, improve separation accuracy, and highlight weak reflection characteristics has become an urgent problem to be solved. Summary of the Invention

[0004] To address the above problems, embodiments of the present invention provide a method, apparatus, device, medium, and procedure for separating strong reflective layers.

[0005] In a first aspect, embodiments of the present invention provide a method for separating a strong reflective layer, comprising:

[0006] Seismic forward modeling was performed using a pre-constructed geological model to obtain seismic profiles;

[0007] The frequency spatial variation characteristics of the overlying strata in the seismic profile are calculated based on the original seismic data of the seismic profile to obtain the matched wavelet;

[0008] Based on the matched wavelet, strong reflection separation is performed on the original seismic data to obtain the separated seismic record.

[0009] According to an embodiment of the present invention, the step of performing seismic forward modeling using a pre-constructed geological model to obtain a seismic profile includes:

[0010] The geological model is subjected to self-excitation and self-absorption forward modeling using a preset wavelet function to obtain the wavelet morphology and frequency characteristics.

[0011] Seismic profiles are generated based on the wavelet morphology and frequency characteristics.

[0012] According to an embodiment of the present invention, the step of calculating the frequency spatial variation characteristics of the overlying strata in the seismic profile based on the original seismic data of the seismic profile to obtain the matched wavelet includes:

[0013] The three instantaneous attributes are calculated based on the original seismic data of the seismic profile using a preset complex seismic trace analysis technique.

[0014] The initial matching wavelet is calculated based on the three instantaneous attributes and the pre-acquired scale factor, and the initial matching wavelet is optimized based on the formation frequency of the overlying strata to obtain the optimized initial matching wavelet.

[0015] The wavelet amplitude is calculated based on the optimized initial matched wavelet and the target signal in the original seismic data.

[0016] The wavelet amplitude is calculated using the following formula:

[0017]

[0018] Where a represents the wavelet amplitude, g represents the initial matched wavelet, and R represents the target signal in the original seismic data;

[0019] The matched wavelet is obtained by multiplying the wavelet amplitude and the optimized initial matched wavelet.

[0020] The matched wavelet is calculated using the following formula:

[0021] g = a × g

[0022] Wherein, g′ represents the matched wavelet, a represents the wavelet amplitude, and g represents the initial matched wavelet.

[0023] According to an embodiment of the present invention, the calculation of the initial matched wavelet based on the three instantaneous properties and the pre-acquired scale factor includes:

[0024] The initial matched wavelet is calculated using the following formula:

[0025]

[0026] Wherein, g represents the initial matched wavelet, f represents the frequency in the three instantaneous attributes, u represents the center delay time in the three instantaneous attributes, φ represents the phase in the three instantaneous attributes, k represents the scale factor, t represents the preset time parameter, and π represents the preset calculation parameter.

[0027] According to an embodiment of the present invention, optimizing the initial matching wavelet based on the formation frequency of the overlying strata to obtain an optimized initial matching wavelet includes:

[0028] Set the perturbation amount of the initial matched wavelet, and determine the search range in a preset search dictionary based on the perturbation amount;

[0029] The parameters of the initial matching wavelet are updated according to the search range and the formation frequency to obtain the optimized initial matching wavelet.

[0030] According to an embodiment of the present invention, the step of performing strong reflection separation on the original seismic data based on the matched wavelet to obtain a separated seismic record includes:

[0031] The separated seismic records are obtained by performing separation calculations based on the original seismic data and the matched wavelet using a preset matched tracking strong reflection separation method.

[0032] The matching pursuit strong reflection separation method is expressed as follows:

[0033] S=S0-g′

[0034] Wherein, S represents the separated seismic record, S0 represents the original seismic record, and g′ represents the matched wavelet.

[0035] Secondly, embodiments of the present invention provide a strong reflective layer separation device, characterized in that it includes:

[0036] The earthquake forward modeling module is used to perform earthquake forward modeling using pre-built geological models to obtain earthquake profiles;

[0037] The matching wavelet calculation module is used to calculate the frequency spatial variation characteristics of the overlying strata in the seismic profile based on the original seismic data of the seismic profile, and to obtain the matching wavelet;

[0038] The strong reflection separation module is used to perform strong reflection separation on the original seismic data based on the matched wavelet to obtain separated seismic records.

[0039] Thirdly, embodiments of the present invention provide an electronic device, which includes:

[0040] processor;

[0041] Memory used to store the processor's executable instructions;

[0042] The processor is configured to execute the instructions to implement a strong reflection layer separation method as described in the first aspect above.

[0043] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a strong reflection layer separation method as described in the first aspect above.

[0044] Fifthly, embodiments of the present invention provide a computer program, characterized in that, when executed by a processor, the program implements a strong reflection layer separation method as described in the first aspect above.

[0045] Compared with the prior art, the above-mentioned technical solution of the present invention has the following beneficial effects:

[0046] The embodiments of the present invention perform seismic forward modeling using a pre-constructed geological model, which can accurately obtain seismic profiles and verify the reliability and necessity of the overlying strata frequency-constrained strong reflection separation method. By calculating the frequency spatial variation characteristics of the seismic profile using the original seismic data, the matching wavelet can be accurately obtained, highlighting and improving the weak reflection characteristics of small-scale fracture-cavity bodies. By performing strong reflection separation on the original seismic data using the matching wavelet, the separated seismic records can be accurately obtained, and the strong reflection phase axes can be more accurately characterized and separated, thus improving the separation accuracy. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 A flowchart illustrating the process of the strong reflection layer separation method according to Embodiment 1 of the present invention is shown;

[0049] Figure 2 A schematic diagram of the specific structure of the geological model of Embodiment 1 of the present invention is shown;

[0050] Figure 3 A schematic diagram showing the wavelet morphology and frequency characteristics of the geological model after forward modeling according to Embodiment 1 of the present invention;

[0051] Figure 4 This shows a schematic diagram of the forward modeling records of a geological model with and without a strong reflective layer, based on an embodiment of the present invention.

[0052] Figure 5 The diagram shows the instantaneous frequency of the strong reflection in-phase axis and the strong reflection separation cross-sectional structure corresponding to the conventional method of Embodiment 1 of the present invention.

[0053] Figure 6This shows a schematic diagram of the structure of the overlying strata frequency parameters and the strong reflection separation profile after the strong reflection layer is separated by matching and tracking the strong reflection phase axis according to Embodiment 1 of the present invention.

[0054] Figure 7 The image shows a comparison of the effects of separating the seismic waveform, the standard reservoir reflection waveform, and the conventional reflection waveform according to Embodiment 1 of the present invention.

[0055] Figure 8 This shows a schematic diagram of the T74 instantaneous frequency plane and the overlying stratum frequency plane according to Embodiment 1 of the present invention;

[0056] Figure 9 A comparison graph showing the root mean square amplitude of the results before and after the improvement of Embodiment 1 of the present invention is displayed;

[0057] Figure 10 This diagram shows the functional block diagram of the strong reflection layer separation device according to Embodiment 3 of the present invention;

[0058] Figure 11 This diagram shows the structural composition of an electronic device that implements the strong reflection layer separation method according to Embodiment 4 of the present invention. Detailed Implementation

[0059] The present disclosure will be further described below with reference to the embodiments shown in the accompanying drawings.

[0060] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0061] This invention proposes a method for separating strong reflection layers. Based on a pre-constructed geological model, seismic profiles are obtained through seismic forward modeling. The frequency spatial variation characteristics of the seismic profiles are calculated based on the original seismic data to obtain a matched wavelet. Strong reflection separation is then performed on the original seismic data based on the matched wavelet to obtain the separated seismic records. Compared to traditional methods, this method can accurately identify the T74 strong reflection phase axis, optimize frequency parameters, improve the separation effect, enhance the reflection characteristics of underlying small-scale fissures and cavities, and thus more accurately characterize and separate strong reflection phase axes, improving separation accuracy.

[0062] Example 1

[0063] like Figure 1 As shown, this invention proposes a method for separating strong reflective layers, comprising the following steps:

[0064] S1. Use a pre-built geological model to perform forward seismic simulation to obtain seismic profiles.

[0065] In this embodiment of the invention, the study can be conducted on the Ordovician carbonate fracture-vuggy reservoir in the Tarim Basin. There is a significant influence of the T74 strong reflection layer. Since the strong reflection shields the weak reflection signal of the underlying small-scale fracture-vuggy body, it brings great difficulties to reservoir prediction. Therefore, it is necessary to carry out research on strong reflection separation methods to highlight the underlying weak reflection signal.

[0066] Furthermore, the geological model refers to an observation system, such as... Figure 2 The diagram shows the specific structure of the geological model. The model is set to have a length of 800m, a shot spacing of 10m, and a receiver spacing of 10m. The geological model contains four strata, with reflection interfaces A, B, and C from top to bottom. The velocity and density parameters of these interfaces increase progressively, and all reflection coefficients are positive. Reflection interface B is a strong reflection interface. Below reflection interface B, a reservoir of a certain size is simulated, with a maximum thickness of 20m and an impedance greater than that of the surrounding rock. Compared to reflection interface B, the reservoir's reflection is relatively weak. Specific reservoir parameters are as follows: Figure 2 As shown.

[0067] In this embodiment of the invention, the step of using a pre-constructed geological model to perform seismic forward modeling to obtain a seismic profile includes:

[0068] The geological model is subjected to self-excitation and self-absorption forward modeling using a preset wavelet function to obtain the wavelet morphology and frequency characteristics.

[0069] Seismic profiles are generated based on the wavelet morphology and frequency characteristics.

[0070] In this embodiment of the invention, the wavelet function refers to the Morlet wavelet function. The frequency of the wavelet function is set to 35Hz. Detectors are placed at the shot points of the geological model, and the corresponding signal data is received using the 35Hz Morlet wavelet function. A forward modeling algorithm is used to analyze the corresponding wavelet morphology and frequency characteristics based on the signal data. The signal data includes signal, amplitude, frequency, and trace number data, etc. Specifically, for example... Figure 3 As shown in Figure a, the wavelet shape after forward modeling of the geological model is represented, as follows: Figure 3 As shown in b, the frequency characteristics of the geological model after forward modeling are represented, where the horizontal dominant frequency is 35Hz.

[0071] Furthermore, generating a seismic profile based on the wavelet morphology and frequency characteristics refers to extracting features from the wavelet morphology to obtain wavelet variation features, identifying strong reflection features based on the wavelet variation features and frequency characteristics to obtain strong reflection features; obtaining the seismic interface, removing the strong reflection features from the seismic interface to obtain the target seismic interface, and performing forward modeling on the target seismic interface to obtain the seismic profile.

[0072] Specifically, after performing forward modeling on interfaces A, B, and C using the geological model, three types of wave crest reflection interfaces were obtained, such as... Figure 4 Figure a shows a schematic diagram of the forward modeling record of the strongly reflective layer. In this diagram, phase axis B exhibits significant waveform variations at the reservoir development site. However, since phase axis B is a set of strong reflectors while the reservoir is a weak reflector, the weak reflection characteristics of the reservoir are masked by the strong reflections, making feature identification difficult. Therefore, it is necessary to use the geological model to remove the strong reflection characteristics of the B interface before performing forward modeling to obtain the seismic profile, as shown below. Figure 4 Figure b shows a schematic diagram of the forward modeling record of the geological model for a reservoir without a strong reflection layer. In this case, the reservoir characteristics are significantly enhanced, which is the effect of ideal strong reflection separation (weak reflection characteristics of the reservoir).

[0073] In this embodiment of the invention, seismic forward modeling using a geological model can accurately obtain seismic profiles, thereby accurately acquiring the original seismic data corresponding to the seismic profiles and improving the accuracy of data processing.

[0074] S2. Calculate the frequency spatial variation characteristics of the overlying strata in the seismic profile based on the original seismic data of the seismic profile, and obtain the matched wavelet.

[0075] In this embodiment of the invention, the raw seismic data includes signal data, waveform data, time data, and scale data, etc.

[0076] In this embodiment of the invention, the step of calculating the frequency spatial variation characteristics of the overlying strata in the seismic profile based on the original seismic data of the seismic profile to obtain the matched wavelet includes:

[0077] The three instantaneous attributes are calculated based on the original seismic data of the seismic profile using a preset complex seismic trace analysis technique.

[0078] The initial matching wavelet is calculated based on the three instantaneous attributes and the pre-acquired scale factor, and the initial matching wavelet is optimized based on the formation frequency of the overlying strata to obtain the optimized initial matching wavelet.

[0079] The wavelet amplitude is calculated based on the optimized initial matched wavelet and the target signal in the original seismic data.

[0080] The matched wavelet is obtained by multiplying the wavelet amplitude and the optimized initial matched wavelet.

[0081] In this embodiment of the invention, the complex seismic trace analysis technique refers to transforming the actual seismic trace corresponding to the seismic profile into a complex seismic trace through Hilbert transformation, and then separating three instantaneous attributes from the complex signal in the seismic data corresponding to the complex seismic trace. The three instantaneous attributes include frequency, center delay time, and phase. The scale factor refers to a pre-set standard scaling factor.

[0082] In this embodiment of the invention, the initial matched wavelet is calculated using the following formula:

[0083]

[0084] Wherein, g represents the initial matched wavelet, f represents the frequency in the three instantaneous attributes, u represents the center delay time in the three instantaneous attributes, φ represents the phase in the three instantaneous attributes, k represents the scale factor, t represents the preset time parameter, and π represents the preset calculation parameter.

[0085] In this embodiment of the invention, optimizing the initial matching wavelet based on the formation frequency of the overlying strata to obtain the optimized initial matching wavelet includes:

[0086] Set the perturbation amount of the initial matched wavelet, and determine the search range in a preset search dictionary based on the perturbation amount;

[0087] The parameters of the initial matching wavelet are updated according to the search range and the formation frequency to obtain the optimized initial matching wavelet.

[0088] In this embodiment of the invention, based on the initial value of the initial matched wavelet calculated in the above steps, a perturbation amount is set, wherein the perturbation amount refers to several influencing factors; based on the influence value of the perturbation amount on the three instantaneous attributes and the scale factor in the initial matched wavelet, the fluctuation range of the three instantaneous attributes and the scale factor is found in the search dictionary, i.e., the search range. For example, the search range of the center delay time u in the three instantaneous attributes is [u-Δu, u+Δu], where Δu represents the time interval. The search dictionary contains several parameters and the interval size affected by the corresponding influencing factors.

[0089] In this embodiment of the invention, the frequency in the initial matched wavelet is replaced by the formation frequency, and the optimal values ​​of the three instantaneous attributes and the scale factor are found within the search range. The initial matched wavelet is recalculated based on the formation frequency, the optimal attribute value, and the optimal scale value to update the initial matched wavelet and obtain the optimized initial matched wavelet.

[0090] In this embodiment of the invention, the wavelet amplitude is calculated using the following formula:

[0091]

[0092] Where a represents the wavelet amplitude, g represents the initial matched wavelet, and R represents the target signal in the original seismic data.

[0093] In this embodiment of the invention, the matched wavelet is calculated using the following formula:

[0094] g′=a×g

[0095] Wherein, g′ represents the matched wavelet, a represents the wavelet amplitude, and g represents the initial matched wavelet.

[0096] In this embodiment of the invention, the frequency spatial variation characteristics of the seismic profile can be accurately calculated based on the original seismic data of the seismic profile, thereby accurately obtaining the matching wavelet and thus accurately achieving strong reflection separation.

[0097] S3. Based on the matched wavelet, perform strong reflection separation on the original seismic data to obtain the separated seismic record.

[0098] In this embodiment of the invention, the step of performing strong reflection separation on the original seismic data based on the matched wavelet to obtain the separated seismic record includes:

[0099] The separated seismic records are obtained by performing separation calculations based on the original seismic data and the matched wavelet using a preset matched tracking strong reflection separation method.

[0100] The matching pursuit strong reflection separation method is expressed as follows:

[0101] S=S0-g′

[0102] Wherein, S represents the separated seismic record, S0 represents the original seismic record, and g′ represents the matched wavelet.

[0103] In this embodiment of the invention, the matched pursuit strong reflection separation method is used to identify and remove the strong reflection in-phase axis B in the post-stack profile. The conventional method uses the calculated instantaneous frequency of the strong reflection in-phase axis as the initial frequency input for matched pursuit to obtain the matched wavelet of the strong reflection in-phase axis, and then performs strong reflection separation to obtain the conventional result, such as... Figure 5 Figure a shows the instantaneous frequency of the strong reflection in-phase axis B in the conventional method. It can be seen that the frequency exhibits a significant lateral variation. This variation originates from the superposition of the strong reflection interface B and the reservoir characteristics, and not merely from the frequency characteristics of the strong reflection interface B. The strong reflection separation profile in the conventional method is shown below. Figure 5As shown in b, it can be seen that due to the influence of frequency parameters, the entire set of strong reflection in-phase axes B (including individual reflection interfaces B and reservoir response characteristics) is completely separated. The processed profile retains some weaker reservoir response characteristics, but these are consistent with the theoretical results. Figure 4 b) The difference is too great, and the goal of high-precision strong reflection identification and separation has not been achieved. The weak reflection characteristics of the reservoir have not been well recovered.

[0104] Furthermore, to avoid interference from the reservoir mass in frequency calculations, the calculation is not performed directly on the strong reflection phase axis B, but instead on the phase axis A overlying B. According to seismic wave propagation theory, the frequency of the seismic wave before reaching the reflection interface B is the true frequency characteristic of the strong reflection phase axis B (i.e., unaffected by the reservoir mass beneath the reflection interface B). For example, the instantaneous frequency of phase axis A is as follows: Figure 6 As shown in Figure a, the horizontal frequency is 35Hz. Using the 35Hz frequency as a constraint, matching pursuit is performed to identify and separate the strong reflection layer. The results are as follows. Figure 6 As shown in b, the result of matching and tracking the strong reflection layer separation on the strong reflection in-phase axis B is shown. The strong reflection separation result has a high degree of matching with the weak reflection characteristics of the theoretical reservoir.

[0105] In this embodiment of the invention, after performing strong reflection separation on the original seismic data based on the matched wavelet to obtain the separated seismic record, the method further includes:

[0106] The separated seismic records are waveform converted to obtain separated seismic waveforms;

[0107] Obtain standard reservoir reflection waveforms and conventional reflection waveforms; perform separation effect analysis based on the standard reservoir reflection waveforms, conventional reflection waveforms, and separated seismic waveforms; and determine whether the results of the separation effect analysis are within the preset target variation range.

[0108] If the result is not within the target variation range, the separated seismic record is deemed unqualified.

[0109] When the result falls within the target variation range, the separated seismic record is deemed qualified.

[0110] In this embodiment of the invention, the waveform conversion of the separated seismic records can be achieved by inversion based on the separated seismic records to obtain the separated seismic waveforms; the standard reservoir reflection waveform refers to the theoretical weak reflection waveform, for example, it can be the 43rd trace (reservoir location) seismic waveform, and the conventional reflection waveform refers to the general reflection waveform.

[0111] Specifically, the seismic waveform of trace 43 (reservoir location) was extracted for comparison, such as... Figure 7 As shown, it can be observed that in the storage group location, the conventional results... Figure 7 b and the theoretical weak reflection waveform Figure 7 There are significant differences, the separation effect is poor, and it cannot effectively characterize the reservoir reflection characteristics. The frequency-constrained separation results of the overlying strata are also poor. Figure 7 c has a better similarity to the theoretical weak reflection waveform, which can effectively characterize the reservoir reflection characteristics and verify the adaptability and effectiveness of the method. Therefore, the strong reflection separation method based on the frequency constraint of the overlying strata can better identify the strong reflection phase axis and achieve better separation results, which is helpful for the subsequent identification and prediction of the weak reflection characteristics of the reservoir.

[0112] In this embodiment of the invention, the study area is located in the Tarim Basin, and the research target is the Ordovician carbonate fracture-vuggy reservoir. This type of reservoir has strong lateral heterogeneity and large differences in reservoir size. It is difficult to identify small-scale fracture-vuggy bodies. Due to the influence of the overlying strata, T74 has a strong reflection characteristic, and the reflection characteristics of the lower fracture-vuggy reservoir are easily interfered with.

[0113] Furthermore, research was conducted on the matching tracking strong reflection separation method, and the frequency selection was optimized. First, the matching frequency of the T74 strong reflection in-phase axis was calculated, such as... Figure 8 As shown in Figure a, the matching frequency of the T74 strong reflection phase axis varies drastically with the transverse direction and is correlated with the fracture characteristics and the development characteristics of the fracture cavity. This affects the calculation of the true matching frequency and reduces the accuracy of subsequent matching tracking and strong reflection separation.

[0114] Specifically, the frequency characteristics of seismic data within a 200ms range above T74 were extracted to characterize the spatial frequency characteristics of the seismic wave before it reaches the T74 reflection interface, such as... Figure 8 As shown in b, the actual frequency characteristics are relatively smooth, which effectively eliminates the influence of the fracture and cavities near T74. The result is closer to the actual spatial frequency characteristics, thus better characterizing the frequency characteristics of the strong reflection in-phase axis of T74 itself. Therefore, the frequency is used as a constraint to carry out matching tracking strong reflection separation to improve the separation results.

[0115] In this embodiment of the invention, Figure 9 To improve the comparison of the root mean square amplitude (40ms below T74) before and after the results, the conventional method (such as...) Figure 9 a) The T74 strong reflection layer was separated, which significantly reduced the impact of strong reflection, but it also caused some damage to the reservoir reflection signal, especially to the weak reflection characteristics; improved methods (such as...) Figure 9 (b) It can more accurately separate the T74 strong reflection phase axis, better preserve the weak reflection characteristics of the cavities, and make the northern river channel characteristics (shown by the black arrow) more obvious. The planar prediction effect is significantly better than the conventional method, and the application effect is significantly improved.

[0116] Example 2

[0117] To better understand the present invention, a second embodiment is provided below to further explain how the present invention uses a pre-constructed geological model to perform earthquake forward modeling and obtain earthquake profiles.

[0118] In this embodiment of the invention, the step of using a pre-constructed geological model to perform seismic forward modeling to obtain a seismic profile includes:

[0119] Numerical simulations were performed based on the geological model to obtain simulation records;

[0120] The simulated recording is analyzed to obtain the simulated velocity, and the simulated recording is corrected and superimposed with offset processing to obtain imaging data;

[0121] Forward modeling is performed based on the simulated velocity and the imaging data to obtain seismic data, and seismic profiles are generated based on the seismic data.

[0122] In this embodiment of the invention, the seismic response of the geological model under the action of an assumed excitation source is utilized. For a pre-set exploration block, the physical equations corresponding to the parameters collected by the geological model are applied to calculate and solve the simulation record. A series of identical time intervals are set to perform velocity scanning based on the simulation record. The superimposed energy or similarity coefficient is used as the criteria for velocity analysis to obtain the simulated velocity.

[0123] Furthermore, the simulated record contains data such as frequency, amplitude, and time. The simulated record is corrected and superimposed with offset processing using different filtering ranges. For example, the frequency data in the simulated record is corrected using standard frequency data to obtain corrected data. The data corresponding to the simulated record after correction and superimposition with offset processing is used as imaging data. The simulated velocity and the imaging data are calculated using the Fourier transform method to obtain seismic data. The seismic data is then transformed into a profile to obtain a seismic profile.

[0124] In this embodiment of the invention, earthquake forward modeling using a geological model can accurately obtain earthquake profiles, thereby enabling further accurate analysis and processing of the earthquake profiles.

[0125] Example 3

[0126] like Figure 10 As shown in the figure, this embodiment also provides a functional block diagram of a strong reflective layer separation device.

[0127] The strong reflection layer separation device 1000 described in this embodiment can be installed in an electronic device. Depending on the functions implemented, the strong reflection layer separation device 1000 may include a seismic forward modeling module 1001, a matched wavelet calculation module 1002, and a strong reflection separation module 1003. The module described in this invention can also be called a unit, referring to a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0128] In this embodiment, the functions of each module / unit are as follows:

[0129] The earthquake forward modeling module 1001 is used to perform earthquake forward modeling using a pre-built geological model to obtain earthquake profiles.

[0130] The matching wavelet calculation module 1002 is used to calculate the frequency spatial variation characteristics of the overlying strata in the seismic profile based on the original seismic data of the seismic profile, and obtain the matching wavelet.

[0131] The strong reflection separation module 1003 is used to perform strong reflection separation on the original seismic data based on the matched wavelet to obtain a separated seismic record.

[0132] In detail, each module in the strong reflection layer separation device 1000 described in the embodiments of the present invention adopts the same technical means as the strong reflection layer separation method described in Embodiment 1 and Embodiment 2 when in use, and can produce the same technical effect, which will not be repeated here.

[0133] Example 4

[0134] like Figure 11 As shown, this embodiment also provides a computer electronic device. The electronic device 1100 may include a processor 1101, a memory 1102, a communication bus 1103, and a communication interface 1104. It may also include a computer program, such as a strong reflection layer separation program, stored in the memory 1102 and capable of running on the processor 1101.

[0135] In some embodiments, the processor 1101 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 1101 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 1102 (e.g., executing a strong reflection layer separation program) and calls data stored in the memory 1102 to perform various functions of the electronic device and process data.

[0136] The memory 1102 includes at least one type of readable storage medium, including flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 1102 can be an internal storage unit of an electronic device, such as a portable hard drive. In other embodiments, the memory 1102 can be an external storage device of the electronic device, such as a plug-in portable hard drive, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc. Furthermore, the memory 1102 can include both internal and external storage units of the electronic device. The memory 1102 can be used not only to store application software and various types of data installed on the electronic device, such as the code of a strong reflection layer separation program, but also to temporarily store data that has been output or will be output.

[0137] The communication bus 1103 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 1102 and at least one processor 1101, etc.

[0138] The communication interface 1104 is used for communication between the aforementioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, Bluetooth interface, etc.), typically used to establish communication connections between the electronic device and other electronic devices. The user interface may be a display, an input unit (such as a keyboard), or, optionally, a standard wired or wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device and to display a visual user interface.

[0139] The figure only shows an electronic device with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the electronic device and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0140] For example, although not shown, the electronic device may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 1101 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0141] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0142] The strong reflection layer separation program stored in the memory 1102 of the electronic device is a combination of multiple instructions. When run in the processor 1101, it can achieve the following:

[0143] Seismic forward modeling was performed using a pre-constructed geological model to obtain seismic profiles;

[0144] The frequency spatial variation characteristics of the overlying strata in the seismic profile are calculated based on the original seismic data of the seismic profile to obtain the matched wavelet;

[0145] Based on the matched wavelet, strong reflection separation is performed on the original seismic data to obtain the separated seismic record.

[0146] Specifically, the specific implementation method of the processor 1101 of the above instructions can be referred to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, and will not be repeated here.

[0147] Furthermore, if the modules / units integrated into the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0148] Example 5

[0149] This embodiment provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the strong reflection layer separation method described above.

[0150] This program code can also be loaded onto a computer or other programmable data processing device, causing a series of operational steps to be executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device for implementing the process. Figure 1 Steps of a specified function in one or more processes.

[0151] Storage media include permanent and non-permanent, removable and non-removable media, and can be used to store information by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by computing devices.

[0152] Example 6

[0153] A computer program, characterized in that, when executed by a processor of an electronic device, can perform:

[0154] Seismic forward modeling was performed using a pre-constructed geological model to obtain seismic profiles;

[0155] The frequency spatial variation characteristics of the overlying strata in the seismic profile are calculated based on the original seismic data of the seismic profile to obtain the matched wavelet;

[0156] Based on the matched wavelet, strong reflection separation is performed on the original seismic data to obtain the separated seismic record.

[0157] A computer program is a set of instructions that a computer can recognize and execute, running on an electronic computer. It is written in a programming language and runs on a target architecture. For a computer program to run, the computer needs to load code and data. At a low level, this involves translating high-level language code (such as Java, C / C++, C#, etc.) into machine language, which is then understood by the CPU and loaded. On most computers, operating systems such as Windows and Linux load and execute many programs. In this case, each program is a separate mapping, not all executable programs on the computer. A computer program refers to a sequence of coded instructions that can be executed by a computer or other information processing device to achieve a certain result, or a sequence of symbolic instructions or symbolic statements that can be automatically converted into coded instructions.

[0158] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations according to this application. When the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0159] It should be understood that the terms used in this way can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in a sequence other than those illustrated or described herein.

[0160] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0161] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0162] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, 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 modules.

[0163] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0164] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the invention. No appended diagram markings in the claims should be construed as limiting the scope of the claims.

[0165] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0166] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a system claim may also be implemented by a single unit or device through software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any specific order.

[0167] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for separating a strong reflective layer, characterized in that, The method includes: Seismic forward modeling was performed using a pre-constructed geological model to obtain seismic profiles; The frequency spatial variation characteristics of the overlying strata in the seismic profile are calculated based on the original seismic data of the seismic profile to obtain the matched wavelet; Based on the matched wavelet, strong reflection separation is performed on the original seismic data to obtain separated seismic records; The step of calculating the frequency spatial variation characteristics of the overlying strata in the seismic profile based on the original seismic data of the seismic profile to obtain the matched wavelet includes: The three instantaneous attributes are calculated based on the original seismic data of the seismic profile using a preset complex seismic trace analysis technique. The initial matching wavelet is calculated based on the three instantaneous attributes and the pre-acquired scale factor, and the initial matching wavelet is optimized based on the formation frequency of the overlying strata to obtain the optimized initial matching wavelet. The wavelet amplitude is calculated based on the optimized initial matched wavelet and the target signal in the original seismic data. The wavelet amplitude is calculated using the following formula: in, Indicates the amplitude of the wavelet. This represents the initial matched subwavelet. This represents the target signal in the original seismic data; The matched wavelet is obtained by multiplying the wavelet amplitude and the optimized initial matched wavelet. The matched wavelet is calculated using the following formula: in, This indicates the matched subwavelength. Indicates the amplitude of the wavelet. This represents the initial matched subwavelet; The process of performing strong reflection separation on the original seismic data based on the matched wavelet to obtain separated seismic records includes: The separated seismic records are obtained by performing separation calculations based on the original seismic data and the matched wavelet using a preset matched tracking strong reflection separation method. The matching pursuit strong reflection separation method is expressed as follows: in, This indicates the separated seismic records. Represents the original earthquake record. This refers to the matched subwavelet.

2. The high-reflectivity layer separation method as described in claim 1, characterized in that, The process of using a pre-built geological model to perform forward seismic simulation to obtain seismic profiles includes: The geological model is subjected to self-excitation and self-absorption forward modeling using a preset wavelet function to obtain the wavelet morphology and frequency characteristics. Seismic profiles are generated based on the wavelet morphology and frequency characteristics.

3. The high-reflectivity layer separation method as described in claim 1, characterized in that, The calculation of the initial matched wavelet based on the three instantaneous attributes and the pre-acquired scale factor includes: The initial matched wavelet is calculated using the following formula: in, This represents the initial matched subwavelet. This represents the frequency among the three instantaneous attributes. This represents the center delay time among the three instantaneous attributes. This represents the phase among the three instantaneous attributes. This represents the scale factor. This indicates the preset time parameter. This indicates the preset calculation parameters.

4. The high-reflectivity layer separation method as described in claim 1, characterized in that, The optimization of the initial matching wavelet based on the formation frequency of the overlying strata to obtain the optimized initial matching wavelet includes: Set the perturbation amount of the initial matched wavelet, and determine the search range in a preset search dictionary based on the perturbation amount; The parameters of the initial matching wavelet are updated according to the search range and the formation frequency to obtain the optimized initial matching wavelet.

5. A strong reflection layer separation apparatus for implementing the strong reflection layer separation method according to any one of claims 1-4, characterized in that, The device includes: The earthquake forward modeling module is used to perform earthquake forward modeling using pre-built geological models to obtain earthquake profiles; The matching wavelet calculation module is used to calculate the frequency spatial variation characteristics of the overlying strata in the seismic profile based on the original seismic data of the seismic profile, and to obtain the matching wavelet; The strong reflection separation module is used to perform strong reflection separation on the original seismic data based on the matched wavelet to obtain separated seismic records.

6. An electronic device comprising: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the strong reflection layer separation method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the strong reflection layer separation method as described in any one of claims 1 to 4.

8. A computer program, characterized in that, When the program is executed by the processor, it implements the strong reflection layer separation method as described in any one of claims 1 to 4.

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