Bubble response suppression method, apparatus, medium, and product based on seismic oceanography

Abstract

This disclosure relates to the field of geophysical exploration technology, and particularly to a bubble response suppression method, apparatus, medium, and product based on seismic oceanography. The method includes: acquiring raw marine seismic data, which exhibits a bubble response; simulating the raw marine seismic data using a far-field wavelet to obtain a first wavelet; extracting a second wavelet from the raw marine seismic data using statistical methods; performing high-order autocorrelation fusion processing on the low-frequency signal of the second wavelet and the high-frequency signal of the first wavelet to obtain a fused desired wavelet; and applying the fused desired wavelet to the raw marine seismic data to perform bubble response suppression processing, thereby obtaining target marine seismic data. Therefore, the bubble response suppression method based on seismic oceanography provided in the exemplary embodiment of this disclosure not only effectively suppresses the bubble response but also avoids the influence on high-frequency components in the data.

Description

Technical Field

[0001] This disclosure relates to the field of geophysical exploration technology, and in particular to a bubble response suppression method, device, medium, and product based on seismic oceanography. Background Technology

[0002] In the field of marine seismic exploration, bubble response is a common and significant problem. Bubble response refers to the formation of bubbles underwater when an air gun is used as a seismic source. These bubbles not only generate elastic wave energy but also trigger secondary sound waves, thus interfering with seismic wave signals.

[0003] In related techniques, far-field wavelets are typically used to suppress bubble responses. However, since far-field wavelets are theoretical wavelets, assuming the influence of bubbles is negligible, their use in processing to eliminate bubble responses differs from that of airgun wavelets used in actual data acquisition. When seismic data is complex, the effectiveness of using far-field wavelets to suppress bubble responses is limited, lacking adaptability and proving difficult to effectively eliminate bubble responses. Summary of the Invention

[0004] This disclosure provides a bubble response suppression method, device, medium, and product based on seismic oceanography, thereby effectively eliminating bubble response in situations where seismic data is complex.

[0005] In a first aspect, this disclosure provides a bubble response suppression method based on seismic oceanography, including:

[0006] Acquire raw marine seismic data, which exhibits bubble response;

[0007] The first wavelet was obtained by simulating the raw marine seismic data using far-field wavelets;

[0008] The second wavelet was obtained by extracting wavelets from the raw marine seismic data using statistical methods.

[0009] The low-frequency signal of the second wavelet is fused with the high-frequency signal of the first wavelet using a high-order autocorrelation process to obtain the desired fused wavelet.

[0010] The fused desired wavelet is applied to the raw marine seismic data, and bubble response suppression processing is performed to obtain the target marine seismic data.

[0011] Secondly, this disclosure provides a bubble response suppression device based on seismic oceanography, comprising:

[0012] The data acquisition module is used to acquire raw marine seismic data, which exhibits bubble response.

[0013] The data processing module is used to simulate the raw marine seismic data using far-field wavelets to obtain the first wavelet;

[0014] The data processing module is also used to extract wavelets from the raw marine seismic data using data statistical methods to obtain a second wavelet;

[0015] The data processing module is further configured to perform high-order autocorrelation fusion processing on the low-frequency signal of the second wavelet and the high-frequency signal of the first wavelet to obtain the fused desired wavelet;

[0016] The data processing module is also used to apply the fused desired wavelet to the original marine seismic data, perform bubble response suppression processing, and obtain the target marine seismic data.

[0017] Thirdly, this disclosure provides a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the foregoing aspects.

[0018] Fourthly, this disclosure provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the methods described in the above aspects.

[0019] Fifthly, this disclosure provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the methods described in the foregoing aspects.

[0020] This disclosure provides a bubble response suppression method, device, medium, and product based on seismic oceanography. The method involves acquiring raw marine seismic data, which exhibits a bubble response; simulating the raw marine seismic data using a far-field wavelet to obtain a first wavelet; extracting a second wavelet from the raw marine seismic data using statistical methods; fusing the low-frequency signal of the second wavelet with the high-frequency signal of the first wavelet using high-order autocorrelation to obtain a fused desired wavelet; and applying the fused desired wavelet to the raw marine seismic data for bubble response suppression to obtain the target marine seismic data.

[0021] Among them, the technical feature is that the first wavelet is obtained by simulating the source signal under far-field conditions.

[0022] Technical features: By using statistical data extraction methods to obtain the second wavelet, a more representative second wavelet can be extracted from actual seismic data, which is closer to the characteristics of actual seismic signals.

[0023] Technical features: The low-frequency signal of the second wavelet is fused with the high-frequency signal of the first wavelet using high-order autocorrelation. By fusing wavelets with different frequency characteristics, a fused desired wavelet is obtained, which preserves the core features of the seismic data and enhances the frequency coverage and feature expression of the signal.

[0024] Technical features: By applying the fused expectation wavelet to the raw marine seismic data and performing bubble response suppression processing, not only can the bubble response be effectively suppressed, improving the signal-to-noise ratio and resolution of the seismic data, but also the influence on the high-frequency components in the data can be avoided, thus maintaining the fidelity of the data. Attached Figure Description

[0025] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings:

[0026] Figure 1 A flowchart illustrating the principle of a bubble response suppression method based on seismic oceanography is provided as an example of this disclosure.

[0027] Figure 2 A schematic flowchart illustrating an exemplary bubble response suppression method based on seismic oceanography provided in this disclosure;

[0028] Figure 3 This is a schematic diagram of a wavelet extracted by statistical data extraction, provided as an example of this disclosure.

[0029] Figure 4 This is a schematic diagram of a far-field wavelet after debubbling, provided as an example of this disclosure.

[0030] Figure 5 This is an exemplary schematic diagram of a wavelet after high-order autocorrelation processing provided in this disclosure;

[0031] Figure 6 This is an exemplary schematic diagram of a single cannon before and after bubble compression provided in this disclosure;

[0032] Figure 7 This is an exemplary schematic diagram comparing the spectrum before and after bubble compression provided in this disclosure;

[0033] Figure 8 This is a schematic block diagram of the functional modules of a bubble response suppression device based on seismic oceanography provided as an example of this disclosure. Detailed Implementation

[0034] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort should fall within the protection scope of this disclosure.

[0035] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0036] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0037] Example 1

[0038] Figure 1 The present disclosure provides an exemplary flowchart of a bubble response suppression method based on seismic oceanography. By fusing far-field wavelets with wavelets extracted by statistical data methods, and employing multidimensional wavelet transform and autocorrelation techniques, a fused desired wavelet is synthesized to effectively suppress bubble interference in marine seismic data.

[0039] like Figure 1 As shown, a bubble response suppression method based on seismic oceanography may include the following steps:

[0040] Step S110: Obtain raw marine seismic data. The raw marine seismic data contains bubble responses.

[0041] In this embodiment, raw marine seismic data was acquired, which exhibited bubble response. The bubble response is due to the secondary sound waves generated by bubbles formed in the water after the air gun is fired, interfering with the seismic wave signal.

[0042] Step S120: Simulate the original marine seismic data using far-field wavelet to obtain the first wavelet.

[0043] In this embodiment, the original marine seismic data can be simulated using a far-field wavelet to obtain the first wavelet. The far-field wavelet is characterized by stable source signal properties under far-field conditions, where the waveform no longer changes with distance, and only the pressure value attenuates with distance. In this case, the source array can be considered a point source.

[0044] Step S130: Use statistical methods to extract the second wavelet from the raw marine seismic data.

[0045] In this embodiment, the original marine seismic data can be simulated using a far-field wavelet to obtain the first wavelet. The far-field wavelet is generated under far-field conditions where the source signal characteristics are stable, the waveform no longer changes with distance, and only the pressure value attenuates with distance. In this case, the source array can be considered a point source, and its phase spectrum is independent of the source-receiver distance.

[0046] Step S140: Perform high-order autocorrelation fusion processing on the low-frequency signal of the second wavelet and the high-frequency signal of the first wavelet to obtain the desired fused wavelet.

[0047] In this embodiment, higher-order autocorrelation is a statistical analysis method that can reveal the correlation between signals. Through higher-order autocorrelation, the signals of two wavelets can be fused to obtain a desired wavelet that contains both low-frequency information and retains high-frequency characteristics. Specifically, higher-order autocorrelation functions of the two signals, such as fourth-order or sixth-order autocorrelation, can be calculated, and then the signals can be fused based on the autocorrelation results.

[0048] By processing high-order autocorrelation, the signals of the two wavelets are fused into a single desired wavelet. This desired wavelet contains both the low-frequency information of the wavelet extracted based on data statistics and retains the high-frequency characteristics of the far-field wavelet. Therefore, the desired wavelet can more comprehensively reflect the characteristics of the seismic signal and provide a more accurate basis for subsequent bubble response suppression.

[0049] Step S150: Apply the fused desired wavelet to the raw marine seismic data and perform bubble response suppression processing to obtain the target marine seismic data.

[0050] In this embodiment, the fused desired wavelet is applied to the raw marine seismic data to perform bubble response suppression processing, thereby obtaining the target marine seismic data. This step effectively suppresses the bubble response by combining the fused desired wavelet with the raw data, improving the signal-to-noise ratio and resolution of the seismic data while maintaining data fidelity.

[0051] Based on this, a first wavelet is obtained by simulating the source signal under far-field conditions, and a second wavelet is extracted from actual seismic data. High-order autocorrelation fusion processing is performed on the low-frequency signal of the second wavelet and the high-frequency signal of the first wavelet to fuse wavelets with different frequency characteristics, resulting in a fused desired wavelet, which enhances the frequency coverage and feature representation of the signal. Finally, the fused desired wavelet is applied to the original marine seismic data to effectively suppress bubble interference in complex marine seismic data, improve the signal-to-noise ratio and resolution of the seismic data, and maintain data fidelity.

[0052] Example 2

[0053] Based on the above embodiments, in another embodiment provided in this disclosure, the above-mentioned bubble response suppression method based on seismic oceanography may further include:

[0054] Multidimensional wavelet transforms were performed on the first and second wavelets respectively to obtain wavelet coefficients at different scales.

[0055] Multi-scale constraint processing is applied to the wavelet coefficients to obtain optimized wavelet coefficients.

[0056] In this embodiment, multidimensional wavelet transforms are performed on the first wavelet obtained from far-field wavelet simulation and the second wavelet extracted by statistical data method. Multidimensional wavelet transform can decompose a signal into different time and frequency domains. It decomposes the signal at multiple levels through wavelet functions, obtaining wavelet coefficients at different scales, thereby achieving local decomposition of the signal. In practice, suitable wavelet basis functions, such as Daubechies wavelets or Symlets wavelets, can be selected, and the number of wavelet transform levels and scales are determined according to the characteristics of the signal and processing requirements.

[0057] Furthermore, optimized wavelet coefficients can be obtained by performing multi-scale constraint processing on wavelet coefficients at different scales.

[0058] Based on this, seismic signals can be decomposed into wavelet coefficients at different scales through multidimensional wavelet transform. These wavelet coefficients at different scales describe the signal's characteristics at different frequencies and times. Specifically, wavelet decomposition divides the signal into low-frequency components (approximation coefficients) and high-frequency components (detail coefficients), with each level of detail coefficients corresponding to different frequency bands of the signal.

[0059] Furthermore, wavelet coefficients can capture local features of a signal, such as abrupt changes, edges, and textures. In seismic signal processing, local features are crucial for identifying and analyzing wavelet characteristics. For example, wavelet coefficients for low-frequency signals can highlight the main shape and energy distribution of the wavelet, while wavelet coefficients for high-frequency signals can reveal detailed changes and noise components in the wavelet.

[0060] Example 3

[0061] Based on the above embodiments, in another embodiment provided in this disclosure, the above-mentioned multi-scale constraint processing of wavelet coefficients to obtain optimized wavelet coefficients may include:

[0062] Multi-scale constrained convolution processing is applied to wavelet coefficients;

[0063] Obtain a preset threshold and edit outliers for wavelet coefficients that do not meet the preset threshold;

[0064] Phase adjustment is performed on the wavelet coefficients to obtain optimized wavelet coefficients.

[0065] In the embodiments, the optimization process for multi-scale constraints may include convolution processing, outlier editing, and phase adjustment.

[0066] Specifically, convolution enhances the characteristics of a signal by performing a convolution operation with a known filter. Under multi-scale constraints, wavelet coefficients at different scales can be convolved to enhance the local features of the signal. For example, a high-pass filter can be used to convolve high-frequency wavelet coefficients to highlight the high-frequency components of the signal; a low-pass filter can be used to convolve low-frequency wavelet coefficients to smooth the low-frequency components of the signal.

[0067] Outliers are extreme values ​​in wavelet coefficients that deviate from the normal range, possibly caused by noise, interference, or other abnormal factors. Under multi-scale constraints, outlier detection and editing can be performed on wavelet coefficients at different scales. For example, a threshold can be set, and wavelet coefficients exceeding the threshold can be considered outliers, and they can be replaced, smoothed, or removed to eliminate the influence of noise and interference.

[0068] Therefore, convolution processing can enhance the signal characteristics represented by wavelet coefficients at different scales. For example, using a high-pass filter to convolve high-frequency wavelet coefficients can highlight the high-frequency components of the signal; while using a low-pass filter to convolve low-frequency wavelet coefficients helps smooth the low-frequency components of the signal, thereby improving the resolution and clarity of seismic data. Furthermore, by setting thresholds to identify and process extreme wavelet coefficients that deviate from the normal range, the negative impact of noise on seismic data can be effectively reduced, thus improving data quality. And by correcting the phase of the wavelet coefficients, phase errors and inconsistencies can be eliminated, improving signal coherence and making the seismic data more realistically reflect the subsurface structure.

[0069] Example 4

[0070] Based on the above embodiments, in another embodiment provided in this disclosure, step S120 may include:

[0071] The raw marine seismic data is simulated to construct a far-field wavelet, and then deconvolution is performed on the far-field wavelet.

[0072] Add white noise to the far-field wavelet after deconvolution;

[0073] The far-field wavelet after adding white noise is time-difference adjusted so that the dominant frequency of the far-field wavelet matches the dominant frequency of the seismic signal, and the time-difference adjusted far-field wavelet is determined as the first wavelet.

[0074] In this embodiment, in seismic oceanography, a wavelet refers to the fundamental waveform generated by the seismic source in a seismic signal. It is a time-domain signal that contains the amplitude and phase information of the seismic wave, and the shape and characteristics of the wavelet affect the resolution accuracy of seismic data.

[0075] Far-field wavelet refers to the characteristic of the source signal tending to stabilize under far-field conditions (i.e., the receiver is far enough away from the source), where the waveform no longer changes with distance, and only the pressure value attenuates with distance. In this case, the source array can be regarded as a point source, and its phase spectrum is independent of the source-receiver distance.

[0076] For example, a simulated wavelet can be obtained from a far-field wavelet using deconvolution, a white noise operator, and time difference adjustment.

[0077] First, deconvolution can be performed on the far-field wavelet. Deconvolution is a signal processing technique that eliminates the aliasing effect of the wavelet, making the reflection interface in the seismic signal clearer and more identifiable. In practice, the Wiener-Levinson deconvolution algorithm can be used. This algorithm is based on the minimum mean square error criterion and determines the deconvolution operator by solving the Wiener-Hopf equation, thereby achieving deconvolution of the wavelet.

[0078] Furthermore, white noise is added to the wavelet after deconvolution to simulate the noise environment in actual seismic data. White noise has a uniform power spectral density; by adding white noise, the processed wavelet can be made closer to the actual seismic signal, improving the adaptability and accuracy of subsequent processing. Typically, the amount of white noise added can be determined based on the signal-to-noise ratio of the actual data to achieve the goal of simulating the real environment.

[0079] Finally, the wavelet after adding white noise is time-difference adjusted to align with the time axes of different signals, and the time-difference-adjusted far-field wavelet is determined as the first wavelet. The purpose of time-difference adjustment is to match the dominant frequency of the wavelet with the dominant frequency of the seismic signal, thereby improving the effect of subsequent processing. In practice, the time difference can be determined by calculating the cross-correlation function between the wavelet and the seismic signal, and the wavelet can be shifted according to the time difference to align it with the time axis of the seismic signal.

[0080] Based on this, the first wavelet obtained from the far-field wavelet can more accurately reflect the characteristics of the seismic signal, providing accurate basic data for subsequent bubble response suppression processing.

[0081] Example 5

[0082] Based on the above embodiments, in another embodiment provided in this disclosure, step S130 may include:

[0083] The target area for wavelet extraction was determined based on the raw marine seismic data.

[0084] By using a preset time window and a combination of target parameters, wavelet statistics are performed in the target region to obtain the second wavelet.

[0085] In this embodiment, multichannel statistical analysis of seismic data can be performed to calculate the amplitude spectrum of each channel. Then, the logarithmic spectrum of the wavelet can be determined using methods such as geometric averaging. Finally, the time-domain waveform of the wavelet can be retrieved through steps such as Fourier transform. The wavelet extracted using statistical data methods can better reflect the characteristics of the actual seismic signal.

[0086] Specifically, in the seismic data's shot profile, regions with significant bubble interference are first selected for wavelet extraction. Bubble interference typically exhibits low-frequency, long-period signal characteristics. By observing the profile, these regions can be identified and used as target areas for wavelet extraction, thereby more accurately extracting wavelets containing bubble responses.

[0087] Then, within the selected target region, an appropriate time window is chosen for wavelet statistics to obtain the second wavelet. The statistical time window should cover the low-frequency bubble cycle to ensure the accuracy of the statistical results. The length of the time window can be determined based on the periodic characteristics of the bubble response, and it typically needs to include multiple bubble response cycles for sufficient statistical analysis.

[0088] Finally, spectral analysis is performed on the extracted wavelet to understand its frequency component distribution. Spectral analysis can employ methods such as Fast Fourier Transform (FFT) to convert the time-domain signal into a frequency-domain signal, thereby observing the frequency characteristics of the wavelet. Through spectral analysis, the main frequency components in the wavelet can be identified.

[0089] Example 6

[0090] Based on the above embodiments, in another embodiment provided in this disclosure, it may further include:

[0091] Multiple candidate parameter combinations are obtained. The parameters in the candidate parameter combinations include statistical range, time window, number of iterations, filter length, and white noise factor.

[0092] Numerical simulation was used to determine the evaluation index of the wavelet corresponding to multiple candidate parameter combinations;

[0093] If the evaluation indicators meet the preset conditions, the candidate parameter combination will be determined as the target parameter combination.

[0094] In the embodiments, the process of extracting wavelets using the data statistical method is affected by multiple parameters, such as statistical range, time window, number of iterations, filter length, and white noise factor.

[0095] The statistical range determines the spatial range of wavelet extraction. If the range is too small, the statistical results may be highly random, while if the range is too large, it may contain too much noise and interference.

[0096] The time window affects the frequency resolution and time domain resolution of the wavelet, and the window length can be determined based on the frequency characteristics of the wavelet and the sampling rate of the seismic data.

[0097] In some iterative algorithms, the number of iterations determines the degree of convergence and accuracy of the algorithm. Too many or too few iterations may affect the effect of wavelet extraction.

[0098] The length of a filter affects the filtering effect and the smoothness of the wavelet. If the length is too short, the filtering may be insufficient, while if the length is too long, the wavelet may be distorted.

[0099] To improve the signal-to-noise ratio, a certain amount of white noise needs to be added to the data. The magnitude of the white noise factor will affect the signal-to-noise ratio and the accuracy of wavelet extraction.

[0100] Therefore, to obtain the best wavelet extraction results, the above parameters need to be optimized. Optimization methods may include:

[0101] Parameter testing: The wavelet extraction performance under different parameter combinations is evaluated through experiments or numerical simulations. Evaluation metrics may include the wavelet's signal-to-noise ratio, frequency characteristics, and matching degree with the actual signal. By comparing the evaluation results under different parameter combinations, the target parameter combination is determined.

[0102] Sensitivity analysis: Analyze the sensitivity of each parameter to the wavelet extraction effect, identify key parameters and insensitive parameters, and prioritize the optimization of key parameters.

[0103] Adaptive adjustment: Based on the characteristics of seismic data and feedback information during the wavelet extraction process, the parameter values ​​are dynamically adjusted to adapt to different data conditions.

[0104] After determining the target parameter combination, the extracted wavelet can be appropriately processed to improve its quality. Processing methods can include filtering, smoothing, and normalization. Filtering can remove noise and interference components from the wavelet, smoothing can make the wavelet smoother and more continuous, and normalization can adjust the amplitude of the wavelet to a uniform range, facilitating subsequent processing.

[0105] One or more technical solutions provided in the exemplary embodiments of this disclosure simulate wavelets using far-field wavelets, extract wavelets using statistical data methods, and perform multidimensional wavelet transform and multi-scale constraint processing on the two wavelets respectively. A high-order autocorrelation is performed between the low-frequency signal of the wavelet extracted based on statistical data methods and the high-frequency signal of the far-field wavelet to obtain a fused desired wavelet, which is then applied to subsequent bubble response suppression.

[0106] Among these methods, the use of statistical data to extract low-frequency signals from the wavelet containing bubble responses can better suppress bubble responses in complex data, solving the problem of unsatisfactory far-field wavelet suppression. Furthermore, the statistical data method extracts the influence of wavelets on high-frequency components in the data, providing high-fidelity, wideband data for subsequent processing.

[0107] Therefore, the bubble response suppression method based on seismic oceanography provided in the exemplary embodiments of this disclosure can effectively eliminate bubble response when seismic data is complex.

[0108] Example 7

[0109] Based on the above embodiments, this embodiment provides an application example.

[0110] In the field of marine seismic exploration, bubble response is a common and significant problem. Bubble response refers to the formation of bubbles underwater when an air gun is used as a seismic source. These bubbles not only generate elastic wave energy but also trigger secondary sound waves, thus interfering with seismic wave signals.

[0111] In related techniques, far-field wavelets are typically used to suppress bubble responses. However, since far-field wavelets are theoretical wavelets, assuming the influence of bubbles is negligible, their use in processing to eliminate bubble responses differs from that of airgun wavelets used in actual data acquisition. When seismic data is complex, the effectiveness of using far-field wavelets to suppress bubble responses is limited, lacking adaptability and proving difficult to effectively eliminate bubble responses.

[0112] For example, Figure 2 This disclosure provides an exemplary flowchart of a bubble response suppression method based on seismic oceanography, as shown below. Figure 2 As shown, the bubble response suppression method based on seismic oceanography may include the following steps:

[0113] Step S210: Simulate wavelet using far-field wavelet.

[0114] In seismic oceanography, a wavelet refers to the fundamental waveform generated by the seismic source in a seismic signal. It is a time-domain signal that contains the amplitude and phase information of the seismic wave, and the shape and characteristics of the wavelet affect the resolution and accuracy of seismic data.

[0115] Far-field wavelet refers to the characteristic of the source signal tending to stabilize under far-field conditions (i.e., the receiver is far enough away from the source), where the waveform no longer changes with distance, and only the pressure value attenuates with distance. In this case, the source array can be regarded as a point source, and its phase spectrum is independent of the source-receiver distance.

[0116] For example, a simulated wavelet can be obtained from a far-field wavelet using deconvolution, a white noise operator, and time difference adjustment.

[0117] First, deconvolution can be performed on the far-field wavelet. Deconvolution is a signal processing technique that eliminates the aliasing effect of the wavelet, making the reflection interface in the seismic signal clearer and more identifiable. In practice, the Wiener-Levinson deconvolution algorithm can be used. This algorithm is based on the minimum mean square error criterion and determines the deconvolution operator by solving the Wiener-Hopf equation, thereby achieving deconvolution of the wavelet.

[0118] Furthermore, white noise is added to the wavelet after deconvolution to simulate the noise environment in actual seismic data. White noise has a uniform power spectral density; by adding white noise, the processed wavelet can be made closer to the actual seismic signal, improving the adaptability and accuracy of subsequent processing. Typically, the amount of white noise added can be determined based on the signal-to-noise ratio of the actual data to achieve the goal of simulating the real environment.

[0119] Finally, time difference adjustment is performed on the wavelet after adding white noise to align it with the time axis of the different signals. The purpose of time difference adjustment is to match the dominant frequency of the wavelet with the dominant frequency of the seismic signal, thereby improving the effect of subsequent processing. In practice, the time difference can be determined by calculating the cross-correlation function between the wavelet and the seismic signal, and the wavelet can be shifted according to the time difference to align it with the time axis of the seismic signal.

[0120] Step S220: Extract wavelets using statistical methods.

[0121] For example, multichannel statistical analysis of seismic data can be performed to calculate the amplitude spectrum of each channel. Then, the logarithmic spectrum of the wavelet can be determined using methods such as geometric averaging. Finally, the time-domain waveform of the wavelet can be retrieved through steps such as Fourier transform. The wavelet extracted using statistical methods can better reflect the characteristics of the actual seismic signal.

[0122] Specifically, in the seismic data's shot profile, regions with significant bubble interference are first selected for wavelet extraction. Bubble interference typically exhibits low-frequency, long-period signal characteristics. By observing the profile, these regions can be identified and used as target areas for wavelet extraction, thereby more accurately extracting wavelets containing bubble responses.

[0123] Then, within the selected target region, an appropriate time window is chosen for wavelet statistics. The statistical time window should cover the low-frequency bubble cycle to ensure the accuracy of the statistical results. The length of the time window can be determined based on the periodic characteristics of the bubble response, and typically needs to include multiple bubble response cycles for sufficient statistical analysis.

[0124] Finally, spectral analysis is performed on the extracted wavelet to understand its frequency component distribution. Spectral analysis can employ methods such as Fast Fourier Transform (FFT) to convert the time-domain signal into a frequency-domain signal, thereby observing the frequency characteristics of the wavelet. Through spectral analysis, the main frequency components in the wavelet can be identified.

[0125] For example, Figure 3 This is a schematic diagram of a wavelet extracted by a statistical method provided as an example of this disclosure.

[0126] To further improve the accuracy of wavelet extraction, this embodiment also proposes a parameter optimization method for wavelet extraction using data statistics.

[0127] The process of extracting wavelets using statistical methods is affected by multiple parameters, such as statistical range, time window, number of iterations, filter length, and white noise factor.

[0128] The statistical range determines the spatial range of wavelet extraction. If the range is too small, the statistical results may be highly random, while if the range is too large, it may contain too much noise and interference.

[0129] The time window affects the frequency resolution and time domain resolution of the wavelet, and the window length can be determined based on the frequency characteristics of the wavelet and the sampling rate of the seismic data.

[0130] In some iterative algorithms, the number of iterations determines the degree of convergence and accuracy of the algorithm. Too many or too few iterations may affect the effect of wavelet extraction.

[0131] The length of a filter affects the filtering effect and the smoothness of the wavelet. If the length is too short, the filtering may be insufficient, while if the length is too long, the wavelet may be distorted.

[0132] To improve the signal-to-noise ratio, a certain amount of white noise needs to be added to the data. The magnitude of the white noise factor will affect the signal-to-noise ratio and the accuracy of wavelet extraction.

[0133] Therefore, to obtain the best wavelet extraction results, the above parameters need to be optimized. Optimization methods may include:

[0134] Parameter testing: The wavelet extraction performance under different parameter combinations is evaluated through experiments or numerical simulations. Evaluation metrics may include the wavelet's signal-to-noise ratio, frequency characteristics, and matching degree with the actual signal. By comparing the evaluation results under different parameter combinations, the optimal parameter combination is determined.

[0135] Sensitivity analysis: Analyze the sensitivity of each parameter to the wavelet extraction effect, identify key parameters and insensitive parameters, and prioritize the optimization of key parameters.

[0136] Adaptive adjustment: Based on the characteristics of seismic data and feedback information during the wavelet extraction process, the parameter values ​​are dynamically adjusted to adapt to different data conditions.

[0137] After determining the optimal parameter combination, the extracted wavelet can be appropriately processed to improve its quality. Processing methods can include filtering, smoothing, and normalization. Filtering can remove noise and interference components from the wavelet, smoothing can make the wavelet smoother and more continuous, and normalization can adjust the amplitude of the wavelet to a uniform range, facilitating subsequent processing.

[0138] Step S230: Perform multidimensional wavelet transform on the two types of wavelets respectively.

[0139] Multidimensional wavelet transforms were performed on the wavelets obtained from far-field wavelet simulation and those extracted using statistical data methods. Multidimensional wavelet transform decomposes a signal into different time and frequency domains. It uses wavelet functions to perform multi-level decomposition of the signal, obtaining wavelet coefficients at different scales, thus achieving local decomposition of the signal. In practice, appropriate wavelet basis functions, such as Daubechies wavelets or Symlets wavelets, can be selected, and the number of wavelet transform levels and scales are determined based on the signal characteristics and processing requirements.

[0140] Based on this, seismic signals can be decomposed into wavelet coefficients at different scales through multidimensional wavelet transform. These wavelet coefficients at different scales describe the signal's characteristics at different frequencies and times. Specifically, wavelet decomposition divides the signal into low-frequency components (approximation coefficients) and high-frequency components (detail coefficients), with each level of detail coefficients corresponding to different frequency bands of the signal.

[0141] Furthermore, wavelet coefficients can capture local features of a signal, such as abrupt changes, edges, and textures. In seismic signal processing, local features are crucial for identifying and analyzing wavelet characteristics. For example, wavelet coefficients for low-frequency signals can highlight the main shape and energy distribution of the wavelet, while wavelet coefficients for high-frequency signals can reveal detailed changes and noise components in the wavelet.

[0142] Step S240: Perform multi-scale constraint optimization on the two types of wavelets respectively.

[0143] For example, optimization processing for multi-scale constraints may include convolution processing, outlier editing, and phase adjustment.

[0144] Specifically, convolution enhances the characteristics of a signal by performing a convolution operation with a known filter. Under multi-scale constraints, wavelet coefficients at different scales can be convolved to enhance the local features of the signal. For example, a high-pass filter can be used to convolve high-frequency wavelet coefficients to highlight the high-frequency components of the signal; a low-pass filter can be used to convolve low-frequency wavelet coefficients to smooth the low-frequency components of the signal.

[0145] Outliers are extreme values ​​in wavelet coefficients that deviate from the normal range, possibly caused by noise, interference, or other abnormal factors. Under multi-scale constraints, outlier detection and editing can be performed on wavelet coefficients at different scales. For example, a threshold can be set, and wavelet coefficients exceeding the threshold can be considered outliers, and they can be replaced, smoothed, or removed to eliminate the influence of noise and interference.

[0146] For example, Figure 4 This is an exemplary schematic diagram of a far-field wavelet after debubbling, provided in this disclosure, wherein the blue line represents the wavelet before debubbling and the green line represents the wavelet after debubbling.

[0147] By adjusting the phase of wavelet coefficients, signal consistency and accuracy can be ensured. For example, the instantaneous phase of wavelet coefficients can be calculated and smoothed or corrected to eliminate phase errors and inconsistencies, which helps improve signal coherence and consistency.

[0148] Step S250: Perform high-order autocorrelation processing on the low-frequency signal of the wavelet extracted based on the data statistics method and the high-frequency signal of the far-field wavelet.

[0149] Higher-order autocorrelation is a statistical analysis method that can reveal the correlation between signals. Through higher-order autocorrelation, the signals of two wavelets can be fused to obtain a desired wavelet that contains both low-frequency information and retains high-frequency characteristics. Specifically, higher-order autocorrelation functions of the two signals, such as fourth-order or sixth-order autocorrelation, can be calculated, and then the signals can be fused based on the autocorrelation results.

[0150] For example, Figure 5 This is a schematic diagram of a wavelet after high-order autocorrelation processing, provided as an example of this disclosure.

[0151] By processing high-order autocorrelation, the signals of the two wavelets are fused into a single desired wavelet. This desired wavelet contains both the low-frequency information of the wavelet extracted based on data statistics and retains the high-frequency characteristics of the far-field wavelet. Therefore, the desired wavelet can more comprehensively reflect the characteristics of the seismic signal and provide a more accurate basis for subsequent bubble response suppression.

[0152] Step S260: Phase adjustment of the desired wavelet is applied to suppress the bubble response.

[0153] The desired wavelet obtained by fusion is phase-adjusted to ensure that its phase is consistent with the actual signal. The phase difference can be determined by calculating the cross-correlation function between the desired wavelet and the actual seismic signal, and the desired wavelet can be translated or rotated according to the phase difference to align its phase with the actual signal.

[0154] The phase-adjusted desired wavelet is then applied to the subsequent bubble response suppression process. In practice, the desired wavelet can be used as a reference wavelet and correlated with the actual seismic signal to identify and eliminate the bubble response. For example, the cross-correlation function between the seismic signal and the desired wavelet can be calculated. Based on the cross-correlation results, the location and intensity of the bubble response can be determined. Then, filtering, deconvolution, and other methods can be used to suppress the bubble response, resulting in a clearer seismic signal.

[0155] For example, Figure 6 This is an exemplary schematic diagram of a single burst before and after bubble compression provided in this disclosure. From the comparison of single bursts, it can be seen that low-frequency vibration is better eliminated after bubble compression. Figure 7 This is an exemplary schematic diagram comparing the spectrum before and after bubble compression provided in this disclosure, where the red line represents before compression and the green line represents after compression. Spectral analysis shows that the mid-to-high frequency components of the data are well preserved.

[0156] One or more technical solutions provided in the exemplary embodiments of this disclosure simulate wavelets using far-field wavelets, extract wavelets using statistical data methods, and perform multidimensional wavelet transform and multi-scale constraint processing on the two wavelets respectively. A high-order autocorrelation is performed between the low-frequency signal of the wavelet extracted based on statistical data methods and the high-frequency signal of the far-field wavelet to obtain a fused desired wavelet, which is then applied to subsequent bubble response suppression.

[0157] Among these methods, the use of statistical data to extract low-frequency signals from the wavelet containing bubble responses can better suppress bubble responses in complex data, solving the problem of unsatisfactory far-field wavelet suppression. Furthermore, the statistical data method extracts the influence of wavelets on high-frequency components in the data, providing high-fidelity, wideband data for subsequent processing.

[0158] Therefore, the bubble response suppression method based on seismic oceanography provided in the exemplary embodiments of this disclosure can effectively eliminate bubble response when seismic data is complex.

[0159] Example 8

[0160] Based on the above embodiments, this embodiment provides an electronic device. In the case of dividing each functional module according to its corresponding functions, the exemplary embodiment of this disclosure provides a bubble response suppression device based on seismic oceanography. The bubble response suppression device based on seismic oceanography can be a server or a chip applied to a server. Figure 8 This is a schematic block diagram of the functional modules of a bubble response suppression device based on seismic oceanography, provided as an example of this disclosure. Figure 8 As shown, the bubble response suppression device 800 based on seismic oceanography includes:

[0161] Data acquisition module 810 is used to acquire raw marine seismic data, wherein the raw marine seismic data exhibits bubble response;

[0162] Data processing module 820 is used to simulate the raw marine seismic data using far-field wavelets to obtain the first wavelet;

[0163] The data processing module 820 is also used to extract a second wavelet from the raw marine seismic data using data statistics methods.

[0164] The data processing module 820 is further configured to perform high-order autocorrelation fusion processing on the low-frequency signal of the second wavelet and the high-frequency signal of the first wavelet to obtain the fused desired wavelet.

[0165] The data processing module 820 is also used to apply the fused desired wavelet to the original marine seismic data, perform bubble response suppression processing, and obtain the target marine seismic data.

[0166] In another embodiment provided in this disclosure, the data processing module 820 is further configured to perform multidimensional wavelet transform on the first wavelet and the second wavelet respectively to obtain wavelet coefficients at different scales; and to perform multi-scale constraint processing on the wavelet coefficients to obtain optimized wavelet coefficients.

[0167] In another embodiment provided in this disclosure, the data processing module 820 is further configured to perform multi-scale constrained convolution processing on the wavelet coefficients; obtain a preset threshold and perform outlier editing on the wavelet coefficients that do not meet the preset threshold; and perform phase adjustment processing on the wavelet coefficients to obtain optimized wavelet coefficients.

[0168] In another embodiment provided in this disclosure, the data processing module 820 is further configured to simulate the original marine seismic data, construct a far-field wavelet, and perform deconvolution processing on the far-field wavelet; add white noise to the deconvolutioned far-field wavelet; adjust the time difference of the far-field wavelet after adding white noise so that the dominant frequency of the far-field wavelet matches the dominant frequency of the seismic signal, and determine the time-difference-adjusted far-field wavelet as the first wavelet.

[0169] In another embodiment provided in this disclosure, the data processing module 820 is further configured to determine the target area for wavelet extraction based on the original marine seismic data; and to perform wavelet statistics in the target area through a preset time window and a combination of target parameters to obtain a second wavelet.

[0170] In another embodiment provided in this disclosure, the data processing module 820 is further configured to acquire multiple candidate parameter combinations, wherein the parameters in the candidate parameter combinations include statistical range, time window, number of iterations, filter length, and white noise factor; determine the evaluation index of the corresponding wavelet under the multiple candidate parameter combinations using numerical simulation; and determine the candidate parameter combinations as target parameter combinations when the evaluation index meets preset conditions.

[0171] Based on the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the above embodiments.

[0172] In some embodiments of this example, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the method described in the above embodiments.

[0173] In some embodiments of this example, a computer program product is provided, including a computer program / instructions, which, when executed by a processor, implements the steps of the method described in the above embodiments.

[0174] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component, for executing the methods in the above embodiments.

[0175] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof. Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (e.g., hard disks, floppy disks, solid-state drives, removable disks, CD-ROMs, DVD-ROMs, Blu-ray discs, etc.).

[0176] Computer-readable storage media may also store at least one computer-executable program / instruction, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0177] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).

[0178] The processor can communicate with external devices via the I / O bus through wired or wireless networks.

[0179] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0180] In the embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0181] It should be noted that, in this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0182] While the embodiments disclosed herein are as described above, the foregoing content is merely for the purpose of facilitating understanding of this disclosure and is not intended to limit this disclosure. Any person skilled in the art to which this disclosure pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope of this disclosure; however, the scope of patent protection of this disclosure shall still be determined by the scope defined in the appended claims.

Claims

1. A method of bubble response suppression based on seagoing acoustics, characterized in that, include: Acquire raw marine seismic data, which exhibits bubble response; The first wavelet was obtained by simulating the raw marine seismic data using far-field wavelets; The second wavelet was obtained by extracting wavelets from the raw marine seismic data using statistical methods. The low-frequency signal of the second wavelet is fused with the high-frequency signal of the first wavelet using a high-order autocorrelation process to obtain the desired fused wavelet. The fused desired wavelet is applied to the raw marine seismic data, and bubble response suppression processing is performed to obtain the target marine seismic data.

2. The method of claim 1, wherein, The method further includes: Multidimensional wavelet transforms were performed on the first wavelet and the second wavelet respectively to obtain wavelet coefficients at different scales; Multi-scale constraint processing is applied to the wavelet coefficients to obtain optimized wavelet coefficients.

3. The method of claim 2, wherein, The process of performing multi-scale constraint processing on the wavelet coefficients to obtain optimized wavelet coefficients includes: Multi-scale constrained convolution processing is applied to wavelet coefficients; Obtain a preset threshold and edit outliers for wavelet coefficients that do not meet the preset threshold; Phase adjustment is performed on the wavelet coefficients to obtain optimized wavelet coefficients.

4. The method of claim 1, wherein, The simulation of the original marine seismic data using far-field wavelets to obtain the first wavelet includes: The raw marine seismic data is simulated to construct a far-field wavelet, and then deconvolution is performed on the far-field wavelet. Add white noise to the far-field wavelet after deconvolution; The far-field wavelet after adding white noise is time-difference adjusted so that the dominant frequency of the far-field wavelet matches the dominant frequency of the seismic signal, and the time-difference adjusted far-field wavelet is determined as the first wavelet.

5. The method of claim 1, wherein, The method of extracting a second wavelet from raw marine seismic data using statistical methods includes: The target area for wavelet extraction is determined based on the original marine seismic data. By using a preset time window and a combination of target parameters, wavelet statistics are performed in the target region to obtain the second wavelet.

6. The method of claim 5, wherein, The method further includes: Multiple candidate parameter combinations are obtained, wherein the parameters in the candidate parameter combinations include statistical range, time window, number of iterations, filter length, and white noise factor; Numerical simulation was used to determine the evaluation index of the wavelet corresponding to multiple candidate parameter combinations; If the evaluation index meets the preset conditions, the candidate parameter combination will be determined as the target parameter combination.

7. A bubble response suppression device based on seagoing acoustics, characterized in that, include: The data acquisition module is used to acquire raw marine seismic data, which exhibits bubble response. The data processing module is used to simulate the raw marine seismic data using far-field wavelets to obtain the first wavelet; The data processing module is also used to extract wavelets from the raw marine seismic data using data statistical methods to obtain a second wavelet; The data processing module is further configured to perform high-order autocorrelation fusion processing on the low-frequency signal of the second wavelet and the high-frequency signal of the first wavelet to obtain the fused desired wavelet; The data processing module is also used to apply the fused desired wavelet to the original marine seismic data, perform bubble response suppression processing, and obtain the target marine seismic data.

8. A computer device comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program comprises instructions that, when executed by the processor, cause the processor to perform the method of any one of claims 1-7. The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product comprising computer programs / instructions, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.

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