Source-by-source wavelet optimization processing method based on near-field measurement wavelet, computer program product and equipment

Abstract

The invention relates to a source-by-source wavelet optimization processing method based on near-field measurement wavelets, a computer program product and equipment. The method comprises the following steps: acquiring corresponding near-field measurement wavelets emitted by a plurality of seismic sources in a target work area each time, wherein each seismic source emits a plurality of corresponding near-field measurement wavelets in different directions each time; the multiple near-field measurement wavelets emitted each time are synthesized, and corresponding near-field wavelets irrelevant to the direction are obtained; interference waves in each near-field wavelet are removed, and corresponding seismic source wavelets are obtained; standard wavelets are established according to all the seismic source wavelets; carrying out filtering matching on the source wavelet corresponding to each emission and the standard wavelet to obtain a corresponding matched filtering operator; and carrying out optimization processing on the seismic data of the target work area based on the matched filtering operator emitted by each seismic source each time. The method can eliminate the adverse effects of the changes of different seismic source wavelets, improves the interpretability, amplitude preservation and resolution of an optimization result, and provides reliable basic data for the fine prediction of reservoir interpretation of data.

Description

Technical Field

[0001] The present invention relates to the field of marine seismic exploration, and particularly to a source-by-source wavelet optimization processing method, a computer program product and a device based on near-field measured wavelets. Background Art

[0002] With the continuous deepening of offshore seismic exploration and development work, the requirements for the vertical resolution of seismic data for reservoir prediction and the amplitude preservation of reflected wave groups are getting higher and higher. The wavelet optimization processing of offshore seismic exploration data plays a very important role in improving the data processing effect. In order to improve the construction efficiency, the construction method of multiple seismic sources is usually adopted in the acquisition of offshore seismic data. The existing wavelet optimization processing methods are all carried out based on the assumption that the seismic source wavelets are constant during the acquisition process, and the same seismic source wavelet is used to optimize all seismic sources. For example, the same far-field wavelet is used to optimize the seismic data corresponding to all seismic sources.

[0003] However, in the actual acquisition process, due to the influence of objective factors such as air gun machinery and sea conditions, the seismic source wavelets of different seismic sources often have certain changes. During the processing of seismic data in a certain sea area exploration area recently, it was found that there are obvious differences in the characteristics of seismic source wavelets of different seismic sources, especially the large differences in the bubble response characteristics of seismic source wavelets of different seismic sources. If the conventional method of using the same seismic source wavelet to optimize the wavelets of the entire three-dimensional data (or the entire survey line) is still adopted, the adverse effects of poor amplitude preservation and low resolution of the final seismic data processing results caused by the changes of seismic source wavelets of different seismic sources cannot be eliminated. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a source-by-source wavelet optimization processing method, a computer program product and a device based on near-field measured wavelets.

[0005] The technical solution adopted by the present invention to solve its technical problems is to construct a source-by-source wavelet optimization processing method based on near-field measured wavelets, including the following steps: S1, obtaining the near-field measured wavelets corresponding to each emission of multiple seismic sources in a target work area, wherein each emission of each seismic source corresponds to multiple near-field measured wavelets in different directions; S2, respectively synthesizing the multiple near-field measured wavelets emitted each time to obtain corresponding near-field wavelets independent of direction; S3, respectively removing the interfering waves in each near-field wavelet to obtain the corresponding seismic source wavelets; S4, establishing a standard wavelet according to all seismic source wavelets; S5, respectively performing filter matching between the seismic source wavelets corresponding to each emission and the standard wavelet to obtain the corresponding matching filter operators; S6, optimizing the seismic data in the target work area based on the matching filter operators emitted by each seismic source each time.

[0006] Further, the removing of the interference waves in each of the near-field wavelets in step S3 includes: performing deghosting processing on each of the near-field wavelets respectively.

[0007] Further, when the seafloor reflection time of the target work area is less than or equal to the length of the source wavelet, after performing the deghosting processing on each of the near-field wavelets respectively, the method further includes: performing seafloor reflection removal processing on each of the near-field wavelets respectively.

[0008] Further, the performing of the seafloor reflection removal processing on each of the near-field wavelets respectively includes: acquiring the near-offset seismic data of each shot, thereby establishing a corresponding noise model; respectively removing the seafloor reflection from each near-field wavelet based on the corresponding noise model by an adaptive subtraction method.

[0009] Further, step S4 includes: calculating the average wavelet of all the source wavelets, and obtaining the standard wavelet after de-bubbling and zero-phase processing.

[0010] Further, step S6 includes: performing de-bubbling processing on the seismic data based on each of the matched filtering operators.

[0011] Further, after the de-bubbling processing, the method further includes: performing zero-phase processing on the seismic data based on each of the matched filtering operators.

[0012] Further, after step S3, the method further includes: synthesizing the far-field wavelet of each shot by using the source wavelet and the source ghost wave of each shot.

[0013] The present invention also constructs a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the method for optimizing the source wavelet one by one based on the near-field measured wavelet as described in any one of the above are implemented.

[0014] The present invention also constructs a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the method for optimizing the source wavelet one by one based on the near-field measured wavelet as described in any one of the above are implemented.

[0015] Implementing the present invention has the following beneficial effects: By collecting the near-field measured wavelets of each shot of each source, synthesizing the near-field wavelets and then removing the interference waves to obtain the source wavelet of each shot, after filtering and matching the source wavelet of each shot with the standard wavelet, using them one by one on the seismic data for optimization processing, eliminating the adverse effects of the wavelet variations between different sources and multiple shots of the same source, obtaining seismic data that can accurately reflect the spatial variation of the source wavelet, improving the interpretability, amplitude preservation and resolution of the optimization processing result, and thus providing reliable basic data for fine prediction and reservoir interpretation of data. Description of the Drawings

[0016] The present invention will be further described below in conjunction with the accompanying drawings and embodiments. In the drawings:

[0017] Figure 1 is the superimposed display diagram of the near-field measurement wavelets of a dual-vibrator arranged in the construction order of a certain survey line;

[0018] Figure 2a is the superimposed display diagram of the near-field measurement wavelets of vibrator 1 of a certain survey line;

[0019] Figure 2b is the superimposed display diagram of the near-field measurement wavelets of vibrator 2 of a certain survey line;

[0020] Figure 3a is the near-trace profile display diagram of the seismic data of vibrator 1 of a certain survey line;

[0021] Figure 3b is the near-trace profile display diagram of the seismic data of vibrator 2 of a certain survey line;

[0022] Figure 4 is the flow schematic diagram of an embodiment of the source-by-source wavelet optimization processing method based on near-field measurement wavelets of the present invention;

[0023] Figure 5 is the display diagram of multiple near-field measurement wavelets of the original measurement;

[0024] Figure 6 is the display diagram of the near-field wavelets synthesized using near-field measurement wavelets;

[0025] Figure 7 is the display diagram of the near-field wavelets after de-ghosting;

[0026] Figure 8a is the display diagram of the near-field wavelets before removing the sea-bottom reflection;

[0027] Figure 8b is the display diagram of the near-field wavelets after removing the sea-bottom reflection;

[0028] Figure 9 is the display diagram of the standard wavelet;

[0029] Figure 10 is the display diagram of the matched filtering operator of a certain vibrator;

[0030] Figure 11a is the display diagram of the seismic data before the optimization processing;

[0031] Figure 11b is the display diagram of the seismic data after the optimization processing;

[0032] Figure 12a is the autocorrelation schematic diagram of the seismic data before the de-bubbling and zero-phase processing;

[0033] Figure 12b It is the autocorrelation schematic diagram of the seismic data after de-bubbling and zero-phase processing;

[0034] Figure 13a It is the stacked section of the seismic data obtained by using the conventional far-field wavelet optimization processing;

[0035] Figure 13b It is the stacked section of the seismic data obtained by using the technical solution of the present invention for optimization processing;

[0036] Figure 14 It is the spectrum comparison diagram of the technical solution of the present invention and the conventional far-field wavelet optimization processing;

[0037] Figure 15 It is the remaining phase comparison diagram of the technical solution of the present invention and the conventional far-field wavelet optimization processing;

[0038] Figure 16 It is the mosaic display diagram of the migrated section and the synthetic seismogram obtained by using the technical solution of the present invention for optimization processing;

[0039] Figure 17a It is the display diagram of the far-field wavelet synthesized by using the near-field wavelet;

[0040] Figure 17b It is the display diagram of the far-field wavelet obtained by actual measurement;

[0041] Figure 18 It is the spectrum comparison diagram of the far-field wavelets obtained by two methods. Specific embodiments

[0042] For a clearer understanding of the technical features, objectives and effects of the present invention, the specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0043] The method for optimizing the wavelet source by source based on near-field measurement wavelets of the present invention is used to optimize the seismic data obtained from a multi-source detection scheme. The multi-source detection scheme usually uses two or more sources to simultaneously or alternately emit source wavelets to excite the target work area, and obtain seismic data from the reflected waves. In this embodiment, the source uses an air gun array, and in other embodiments, an electric spark source can also be used. At the same time, sensors (such as hydrophones) are installed at multiple geophone points near the source to measure the source wavelets of each source, which helps to correct the wave group characteristics in seismic data processing.

[0044] During the acquisition process, affected by objective factors such as air gun machinery and sea conditions, the measured source wavelets often have certain variations. Figure 1It is the superposition display of the near-field measurement wavelets of dual vibration sources arranged in the construction sequence along a certain survey line. The odd channels are the superposition of the near-field measurement wavelets of vibration source 1, and the even channels are the superposition of the near-field measurement wavelets of vibration source 2. An obvious sawtooth phenomenon can be seen, indicating that there are obvious differences in the wavelet characteristics of the two vibration sources. Figure 2a and Figure 2b are respectively the separate displays of the superposition of the near-field measurement wavelets of vibration source 1 and vibration source 2 on this survey line, that is, the separate displays of the wavelets of the two vibration sources. It can be seen that the wavelets of the same vibration source have certain changes during the construction process, and the differences between the wavelets of different vibration sources are even greater. Figure 3a and Figure 3b are respectively the separate display diagrams of the near-offset sections of the seismic data corresponding to the two vibration sources on this survey line. There are also obvious differences in the wave group characteristics of the two seismic data in the section.

[0045] The prior art uses a unified vibration source wavelet to optimize the seismic data obtained from the same emission of two vibration sources, and cannot eliminate the adverse effects of the changes in the wavelets of different vibration sources, resulting in poor amplitude preservation and low resolution of the final seismic data processing results. Therefore, the present invention provides a per-source wavelet optimization processing method based on near-field measurement wavelets to eliminate the adverse effects of the changes in the vibration source wavelets during the acquisition process on the subsequent processing.

[0046] As Figure 4 shown, in an embodiment of the per-source wavelet optimization processing method based on near-field measurement wavelets of the present invention, the following steps are included:

[0047] S1. Obtain the near-field measurement wavelets corresponding to each emission of multiple vibration sources in the target work area, where each emission of each vibration source corresponds to multiple near-field measurement wavelets in different directions.

[0048] The target work area in this embodiment refers to a certain survey line, and multiple vibration sources emit vibration source wavelets along this survey line to excite the corresponding formation. The near-field measurement wavelet refers to the wavelet directly observed near the vibration source and is measured by a sensor arranged near the vibration source. Arrange several hydrophone marine seismic exploration instruments in the near-field area of the vibration source. As Figure 5 shown, for each shot fired by one vibration source, several near-field measurement wavelets can be independently recorded at the same time, and each near-field measurement wavelet has a different direction.

[0049] S2. Synthesize the multiple near-field measurement wavelets emitted each time respectively to obtain the corresponding near-field wavelets independent of direction.

[0050] As Figure 6 shown, in this embodiment, all the near-field measurement wavelets corresponding to each shot are linearly superposed respectively, and multiple near-field measurement wavelets related to direction are superposed to obtain a near-field wavelet independent of direction. In other embodiments, other synthesis methods can also be used.

[0051] S3. Remove the interference waves from each near-field wavelet respectively to obtain the corresponding source wavelet.

[0052] Specifically, due to the influence of environmental factors such as sea conditions, there will be interference waves in the near-field measured wavelet in addition to the source wavelet emitted by the source. After removing the interference waves, it can be closer to the true source wavelet emitted by the source.

[0053] In one embodiment, removing the interference waves from each near-field wavelet in step S3 includes: performing deghosting processing on each near-field wavelet respectively. The interference waves in this embodiment include but are not limited to source ghosts and cable ghosts. Technicians can select any suitable ghost suppression technology according to the specific situation of the exploration area data, and the processing results are as Figure 7 shown. After being affected by the ghost wave, the near-field wavelet will change. By removing the sidelobes generated by ghost wave suppression, the near-field wavelet is made closer to the true source wavelet.

[0054] It can be understood that if the seabed reflection time is greater than the length of a source wavelet, the near-field wavelet after ghost wave suppression can be used as the source wavelet. When the target work area belongs to shallow water and the seabed reflection time is less than or equal to the length of a source wavelet during the detection process, refer to Figure 8a , the near-field measured wavelet collected and recorded contains the seabed reflection wave, resulting in waveform distortion. The seabed reflection wave is the wave that the signal emitted by the source reflects back from the seabed. After suppressing the ghost wave, the seabed reflection wave still needs to be processed.

[0055] In one embodiment, when the seabed reflection time of the target work area is less than or equal to the length of the source wavelet, after performing deghosting processing on each near-field wavelet respectively, it further includes: performing deseabed reflection processing on each near-field wavelet respectively.

[0056] Specifically, performing deseabed reflection processing on each near-field wavelet respectively includes: obtaining the near-offset seismic data for each emission, thereby establishing the corresponding noise model; respectively based on the corresponding noise model, removing the seabed reflection from each near-field wavelet through the adaptive subtraction method.

[0057] The near-offset seismic data in this embodiment refers to the seismic data between the source excitation point and the near-field wavelet observation point (i.e., the sensor position) during the exploration process. Each emission of the source has corresponding near-offset seismic data. A noise model is established for each emission according to the near-offset seismic data, an adaptive filter is designed, the error between the filter output and the noise model is calculated, and the parameters of the filter are dynamically adjusted according to the error until the error reaches a satisfactory level. The adjusted filter is applied to the corresponding near-field wavelet to remove the noise component, and the processing result as Figure 8b shown is used as the source wavelet for each emission. Removing the seabed reflection processing source by source eliminates the influence of the seabed reflection on the near-field wavelet morphology and can obtain a more accurate source wavelet.

[0058] S4. Establish a standard wavelet based on all source wavelets.

[0059] Specifically, calculate the average wavelet of all source wavelets, and obtain the standard wavelet after de-bubbling and zero-phase processing.

[0060] Sum up all source wavelets and then calculate the average value to obtain an average wavelet. Then, perform de-bubbling and zero-phase processing on this average wavelet to obtain a standard wavelet, which is the expected wavelet in matched filtering. As Figure 9 shown, the phase spectrum of the standard wavelet is zero, and the amplitude spectrum curve is smooth within 4 - 8 hz without obvious jitter. In other embodiments, other processing can also be performed on the average wavelet.

[0061] S5. Respectively perform filtering and matching between the source wavelets corresponding to each emission and the standard wavelet to obtain the corresponding matched filtering operators.

[0062] The source wavelets contain bubble effects and are non-zero phase wavelets. By using the standard wavelet to perform filtering and matching on the source wavelets of each source in turn, the matched filtering operators of each source are obtained (refer to Figure 10 ), realizing the process of suppressing bubble responses and zero-phase processing. The specific filtering and matching steps can refer to the prior art.

[0063] S6. Optimize the seismic data of the target work area based on the matched filtering operators for each emission of each source.

[0064] Specifically, perform de-bubbling processing on the seismic data based on each matched filtering operator.

[0065] The seismic data belongs to seismic data, usually collected by geophones. In this embodiment, the matched filtering operators of each shot are used to perform deconvolution operations on the corresponding seismic data, thereby effectively suppressing bubble effects and improving the resolution and signal-to-noise ratio of the seismic data.

[0066] After de-bubbling processing, it also includes: performing zero-phase processing on the seismic data based on each matched filtering operator. In this embodiment, the matched filtering operators of each shot are used to perform convolution operations on the corresponding seismic data, converting the wavelets in the seismic data into zero-phase wavelets. Before and after the optimized processing of de-bubbling and zero-phase, the cross-sectional comparisons of the seismic data stacked by each source are respectively referred to Figure 11a and Figure 11b . It can be seen that after de-bubbling and zero-phase processing, the relative relationship of amplitude energy changes significantly, and the seismic reflection structure is clearer under strong phases.

[0067] The present invention does not require logging data and far-field wavelets. It only needs to collect the near-field measured wavelets of each shot recorded during the acquisition process. By stacking the multiple near-field measured wavelets measured source by source, an independent near-field wavelet corresponding to each shot record is obtained. Combined with ghost wave suppression, removal of seabed reflection interference, matched shaping filtering, etc., the seismic data is optimized shot by shot using the corresponding matched filtering operator, solving the wavelet characteristic differences between different sources and between multiple emissions of the same source, especially the differences between different sources. Seismic data that can accurately reflect the spatial variation of the source wavelet can be obtained, improving the consistency of the wavelets, making the bubble suppression more reasonable, the zero-phase accuracy higher, the wave group characteristics of the section better, and the resolution higher, improving the interpretability, amplitude preservation, and resolution of the seismic data, and providing reliable basic data for subsequent data processing steps.

[0068] Comparison Figure 12a and Figure 12b , it can be seen from the autocorrelation comparison that the consistency of the wave group characteristics of the seismic data has improved significantly. Comparison Figure 13a and Figure 13b , using the source-by-source wavelet optimization method based on near-field measured wavelets of the present invention, the wave group characteristics of the stacked section obtained are better than those obtained by the unified far-field wavelet optimization processing in the conventional method, and the resolution is slightly higher. According to Figure 14 , it can be seen that the energy of the frequency components at the low-frequency end of the stacked section obtained by using the technology of the present invention is slightly lower, indicating that the effect of suppressing bubbles is slightly better. According to Figure 15 , it can be seen that the residual phase curve of the stacked section obtained by using the technology of the present invention is closer to zero, indicating that the zero-phase effect is slightly better. According to Figure 16 , it can be seen that the post-stack time migration section obtained by using the present invention has a high matching degree with the synthetic seismogram, good wave group characteristics, good zero-phase effect, and high resolution, laying a good foundation for data interpretation.

[0069] In one embodiment, after step S3, it further includes: synthesizing the far-field wavelet of each emission using the source wavelet and the source ghost wave of each emission.

[0070] Based on the near-field wavelet obtained in step S3, adding the corresponding source ghost wave of the source, the far-field wavelet of this emission can be obtained. Comparing the far-field wavelet obtained using the near-field wavelet (reference Figure 17a ) with the far-field wavelet measured in deep water (reference Figure 17b ), it can be seen that the characteristics of the calculated and measured far-field wavelets are very close. According to Figure 18 , it can also be seen that the low-frequency band of the spectrum is relatively close. Without the measured far-field wavelet, the present invention can also be used to obtain the far-field wavelet.

[0071] In an embodiment of the computer program product of the present invention, the computer program product includes a computer program which, when executed by a processor, implements the method for optimizing source-by-source wavelets based on near-field measurement wavelets in any of the above embodiments.

[0072] In one implementation, the computer program product may be a tangible product containing the computer program, such as a computer-readable storage medium storing the computer program. The readable storage medium may be a storage medium based on signals such as electricity, magnetism, light, electromagnetic, infrared, etc., including but not limited to: random access memory (RAM), read-only memory (ROM), magnetic tape, floppy disk, flash memory, mechanical hard disk drive (HDD), solid state drive (SSD), and so on. Exemplarily, the computer program product may be implemented as a non-volatile storage medium storing the computer program, such as read-only memory, Nand Flash, etc. In one implementation, the computer program product may be an intangible product containing the computer program. Exemplarily, the computer program product may be implemented as a virtual digital product, such as an executable file, installation package, etc. digital files storing the computer program.

[0073] The code of the computer program can be written in one or more programming languages. Programming languages such as C language, Java, C++, etc. The program code can be executed entirely on the user's computing device, or partially on the user's computing device, or executed as an independent software package, or partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case involving a remote computing device, the remote computing device can be connected to the user's computing device through any kind of network, such as a local area network (LAN), wide area network (WAN), etc., or can be connected to an external computing device (e.g., through an Internet connection provided by an operator).

[0074] The computer program can be carried or transmitted by signals such as electricity, magnetism, light, electromagnetic, infrared, etc. The computing device can convert the signal carrying the computer program into a digital signal and then run the computer program. When the computer program runs on the computing device, its code is used to cause the computing device to execute (more specifically, can cause the processor of the computing device to execute) the method steps of various exemplary embodiments of the present disclosure.

[0075] In an embodiment of the computing device of the present invention, it includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the method for optimizing source-by-source wavelets based on near-field measurement wavelets disclosed in any of the above embodiments. The computing device can be, but is not limited to, a laptop computer, an edge computer, a server, a workstation, or an industrial control computer.

[0076] It can be understood that the above embodiments only represent the preferred embodiments of the present invention, and the description thereof is relatively specific and detailed. However, it should not be construed as a limitation on the scope of the patent for the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, the above technical features can be freely combined, and several modifications and improvements can also be made, all of which fall within the protection scope of the present invention. Therefore, all equivalent transformations and modifications made to the scope of the claims of the present invention shall fall within the scope covered by the claims of the present invention.

Claims

1. A method for optimizing wavelet processing source by source based on near-field measurement wavelets, characterized in that It includes the following steps: S1. Obtain the near-field measurement wavelets corresponding to each emission of multiple seismic sources in the target work area. Among them, each emission of each seismic source corresponds to multiple near-field measurement wavelets in different directions; S2. Synthesize the multiple near-field measurement wavelets emitted each time respectively to obtain the corresponding near-field wavelets independent of direction; S3. Remove the interference waves in each of the near-field wavelets respectively to obtain the corresponding seismic source wavelets; S4. Establish a standard wavelet based on all the seismic source wavelets; S5. Filter and match the seismic source wavelets corresponding to each emission with the standard wavelet respectively to obtain the corresponding matched filtering operators; S6. Optimize the seismic data in the target work area based on the matched filtering operators emitted by each seismic source each time.

2. The method for optimizing the wavelet of each source based on the near-field measurement wavelet according to claim 1, characterized in that The removing of the interference waves in each of the near-field wavelets in step S3 includes: Perform deghosting processing on each of the near-field wavelets respectively.

3. The method for optimizing the wavelet of each source based on the near-field measurement wavelet according to claim 2, wherein When the seabed reflection time in the target work area is less than or equal to the length of the seismic source wavelet, after performing deghosting processing on each of the near-field wavelets respectively, it further includes: Perform de-seabed reflection processing on each of the near-field wavelets respectively.

4. The method for optimizing and processing wavelets source by source based on near-field measurement wavelets according to claim 3, wherein The performing of de-seabed reflection processing on each of the near-field wavelets respectively includes: Obtain the near-channel seismic data emitted each time, so as to establish the corresponding noise model; Based on the corresponding noise models respectively, remove the seabed reflection from each near-field wavelet by the adaptive subtraction method.

5. The method for optimizing and processing each source wavelet based on near-field measurement wavelets according to claim 1, characterized in that Step S4 includes: Calculate the average wavelet of all the seismic source wavelets, and obtain the standard wavelet after de-bubbling and zero-phase processing.

6. The method for optimizing the wavelet of each source based on the near-field measurement wavelet according to claim 1, wherein Step S6 includes: Perform de-bubbling processing on the seismic data based on each of the matched filtering operators.

7. The method for optimizing and processing wavelets source by source based on near-field measurement wavelets according to claim 6, wherein After the de-bubbling processing, it further includes: Perform zero-phase processing on the seismic data based on each of the matched filtering operators.

8. The method for optimizing the wavelet of each source based on the near-field measurement wavelet according to claim 1, wherein After step S3, it further includes: Synthesize the seismic source wavelets and the seismic source ghost waves emitted each time to obtain the far-field wavelets emitted each time.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method for optimizing the seismic source wavelet source by source based on near-field measurement wavelets according to any one of claims 1 to 8.

10. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for optimizing the seismic source wavelet source by source based on near-field measurement wavelets according to any one of claims 1 to 8.

Images