A method, system, device, medium, and program for deconvolution of seismic data

Abstract

This disclosure relates to a method, system, device, medium, and procedure for deconvolution of seismic data. The method includes: performing multi-level noise suppression on seismic data to obtain denoised seismic data; performing geometric correction and data realignment on the denoised seismic data to obtain a common centroid gather, and dividing the common centroid gather into time windows to obtain a stratigraphic time window group; performing seismic trace analysis on the denoised seismic data to obtain a seismic trace energy spectrum matrix; performing spectral factor decomposition on the seismic trace energy spectrum matrix based on the denoised seismic data to obtain a spectral component group; and performing dual-window deconvolution on the denoised seismic data based on the spectral component group and the stratigraphic time window group to obtain standard seismic data. This improves the resolution and accuracy of the seismic data after deconvolution.

Description

Technical Field

[0001] This disclosure relates to the field of seismic data processing technology, and in particular to a method, system, device, medium, and program for deconvolution of seismic data. Background Technology

[0002] Seismic data is geophysical data obtained by artificially generating seismic waves and recording their propagation and reflection information underground. This data reflects the structure and properties of underground geological bodies and is an important basis for analyzing underground structures in seismic exploration. However, the source wavelet in seismic data can interfere with underground reflection signals, making the seismic data unclear. Furthermore, the broadband nature of the source wavelet reduces the resolution of the seismic signal, which is not conducive to detailed analysis. Therefore, it is necessary to perform deconvolution processing on seismic data to remove the influence of the source wavelet.

[0003] Existing deconvolution methods for seismic data are mostly based on a single time window. This means that actual denoised seismic data is analyzed and calculated using a single time window for the target layer. Finally, the operator is applied to the seismic data through testing to improve resolution. However, in actual processing, seismic data requires fidelity and is denoised to a relatively low degree, which introduces a lot of noise after analysis and application. Furthermore, deconvolution methods based on a single time window cannot simultaneously meet the resolution requirements of terrestrial and marine strata, which may lead to low accuracy when deconvolving seismic data. Summary of the Invention

[0004] One objective of this invention is to provide a method for determining the maturity of shale bitumen. This method involves performing strong denoising on the seismic data used for analysis while preserving its frequency components. Furthermore, it employs two time windows for terrestrial and marine strata during the analysis process. Finally, it performs deconvolution on the parameters applied to different time windows through testing, thereby improving the resolution and accuracy of the seismic data.

[0005] In a first aspect, this disclosure provides a deconvolution method for seismic data, comprising: performing multi-level noise suppression on the seismic data to obtain denoised seismic data; performing geometric correction and data realignment on the denoised seismic data to obtain a common centroid gather, and dividing the common centroid gather into time windows to obtain a stratigraphic time window group; performing seismic trace analysis on the denoised seismic data to obtain a seismic trace energy spectrum matrix; performing spectral factor decomposition on the seismic trace energy spectrum matrix based on the denoised seismic data to obtain a spectral component group; and performing dual-window deconvolution on the denoised seismic data based on the spectral component group and the stratigraphic time window group to obtain standard seismic data.

[0006] In some embodiments, the multi-level noise suppression of seismic data to obtain denoised seismic data includes: performing spectral calculation and dominant frequency filtering on the seismic data to obtain dominant frequency intervals; performing bandpass filtering on the seismic data according to the dominant frequency intervals to obtain bandpass denoised seismic data; performing frequency domain transformation on the bandpass denoised seismic data to obtain the seismic signal frequency domain; performing wavenumber filtering on the seismic data frequency domain to obtain the denoised seismic signal frequency domain; performing time domain transformation on the denoised seismic signal frequency domain to obtain directional denoised seismic data; performing wavelet decomposition on the directional denoised seismic data to obtain seismic wavelet coefficients; performing noise threshold filtering on the seismic wavelet coefficients to obtain denoised wavelet coefficients; performing wavelet reconstruction on the denoised wavelet coefficients to obtain reconstructed seismic data; and performing superposition calculation on the reconstructed seismic data to obtain denoised seismic data.

[0007] In some embodiments, the step of performing geometric correction and data realignment on the denoised seismic data to obtain a common center point gather includes: extracting a shot point location set and a receiver location set from the denoised seismic data; performing geometric correction on the denoised seismic data based on the shot point location set and the receiver location set to obtain a common center point location set; performing positional segmentation on the common center point location set to obtain a primary common center point gather; and performing migration correction on the primary common center point gather to obtain a common center point gather.

[0008] In some embodiments, dividing the common center point gather into time windows to obtain a formation time window group includes: performing velocity spectrum analysis on the common center point gather to obtain dynamic correction velocity; performing offset detection on the common center point gather to obtain zero offset position; performing start-end delay analysis on the common center point gather to obtain start-end delay time and obtaining special delay time corresponding to the start-end delay time; obtaining the zero offset start-end time corresponding to the zero offset position; calculating the start-end time of the common center point gather based on the dynamic correction velocity, the start-end delay time, the special delay time, and the zero offset start-end time to obtain offset start-end time; and dividing the common center point gather into time windows based on the offset start-end time and the dynamic correction velocity to obtain a formation time window group.

[0009] In some embodiments, the step of performing seismic trace analysis on the denoised seismic data to obtain a seismic trace energy spectrum matrix includes: extracting a seismic trace dataset from the denoised seismic data; performing a frequency domain transformation on the seismic trace dataset to obtain a seismic trace frequency domain set; performing energy spectrum calculation on the seismic trace frequency domain set to obtain a primary energy spectrum set; performing a logarithmic transformation on the primary energy spectrum set to obtain a seismic trace energy spectrum set; and performing a matrix transformation on the seismic trace energy spectrum set to obtain a seismic trace energy spectrum matrix.

[0010] In some embodiments, the step of performing dual-window deconvolution on the denoised seismic data based on the spectral component group and the stratigraphic time window group to obtain standard seismic data includes: performing wavelet estimation on the denoised seismic data based on the spectral component group to obtain a seismic wavelet spectrum; selecting stratigraphic time windows in the stratigraphic time window group one by one as target stratigraphic time windows, and extracting target time window data corresponding to the target stratigraphic time windows from the denoised seismic data; performing deconvolution on the target time window data based on the seismic wavelet spectrum to obtain deconvolutioned seismic data; aggregating the deconvolutioned seismic data corresponding to all target stratigraphic time windows in the stratigraphic time window group into a deconvolutioned seismic data set; and stitching the deconvolutioned seismic data set to obtain standard seismic data.

[0011] Secondly, this disclosure provides a deconvolution system for seismic data, comprising: a noise suppression module for performing multi-level noise suppression on seismic data to obtain denoised seismic data; a time window partitioning module for performing geometric correction and data realignment on the denoised seismic data to obtain a common midpoint gather, and partitioning the common midpoint gather into time windows to obtain a stratigraphic time window group; a seismic trace analysis module for performing seismic trace analysis on the denoised seismic data to obtain a seismic trace energy spectrum matrix; a spectral factor decomposition module for performing spectral factor decomposition on the seismic trace energy spectrum matrix based on the denoised seismic data to obtain a spectral component group; and a dual-window deconvolution module for performing dual-window deconvolution on the denoised seismic data based on the spectral component group and the stratigraphic time window group to obtain standard seismic data.

[0012] 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 deconvolution method for seismic data described in the above aspects.

[0013] Fourthly, this disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the deconvolution method for seismic data described above.

[0014] Fifthly, this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the deconvolution method for seismic data described in the above aspects.

[0015] This disclosure provides a method, system, device, medium, and program for deconvolution of seismic data. By performing multi-level noise suppression on the seismic data, denoised seismic data is obtained, which can suppress anomalous noise in common midpoint gathers, thus obtaining seismic data with a higher signal-to-noise ratio. Through geometric correction and data realignment of the denoised seismic data, common midpoint gathers are obtained, and time windows are divided into stratigraphic time window groups. This allows for the use of two time windows for terrestrial and marine strata in the seismic data analysis room, thus meeting different stratigraphic designs and improving the accuracy of subsequent deconvolution. Through seismic trace analysis of the denoised seismic data, a seismic trace energy spectrum matrix is ​​obtained, which can analyze frequency distribution characteristics and extract the frequency characteristics of components such as shot points, receiver points, offsets, and common midpoint gathers in the energy spectrum, thereby improving the accuracy of subsequent spectral factor decomposition.

[0016] By performing spectral factor decomposition on the seismic trace energy spectrum matrix based on the denoised seismic data, spectral component groups are obtained. This allows for the acquisition of data components with different frequency characteristics in the seismic data, thereby improving the accuracy of subsequent deconvolution operations. By performing dual-window deconvolution on the denoised seismic data based on the spectral component groups and the stratigraphic time window groups, the difficulty of noise suppression can be reduced while improving resolution, and the resolution requirements of different stratigraphic layers can be met. This is beneficial for improving the resolution and accuracy of seismic data, thereby improving the accuracy of seismic data deconvolution. Attached Figure Description

[0017] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings:

[0018] Figure 1 A flowchart illustrating the deconvolution method for seismic data according to Embodiment 1 of this disclosure is shown.

[0019] Figure 2 The image shows a comparison of seismic data before and after multi-level noise suppression in Embodiment 1 of this disclosure.

[0020] Figure 3 The image shows a comparison of seismic data before and after multi-level noise suppression in Embodiment 1 of this disclosure.

[0021] Figure 4 A schematic diagram of the formation time window group in Embodiment 1 of this disclosure is shown.

[0022] Figure 5 A schematic diagram of the formation time window group in Embodiment 1 of this disclosure is shown.

[0023] Figure 6 A cross-sectional view of the noise-reduced seismic data in Embodiment 1 of this disclosure is shown.

[0024] Figure 7 This shows a cross-sectional view of standard seismic data in Embodiment 1 of this disclosure.

[0025] Figure 8 This diagram shows a spectral comparison between standard seismic data and noise-reduced seismic data in Embodiment 1 of this disclosure.

[0026] Figure 9 A functional block diagram of the seismic data deconvolution system according to Embodiment 2 of this disclosure is shown.

[0027] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Example 1

[0032] Figure 1 This is a schematic flowchart illustrating a method for deconvolution of seismic data provided in an embodiment of this disclosure. Figure 1 As shown, a deconvolution method for seismic data includes:

[0033] S1. Perform multi-level noise suppression on the seismic data to obtain denoised seismic data.

[0034] In detail, seismic data is geophysical data obtained by artificially generating seismic waves and recording their propagation and reflection information underground. These data reflect the structure and properties of underground geological bodies and are an important basis for analyzing underground structures in seismic exploration. The seismic data includes seismic trace data, source wavelet data, reflection signals, and background noise.

[0035] Specifically, the seismic trace data is the seismic wave signal recorded by a single receiver point, the source wavelet data refers to the waveform signal excited by the source, the reflection signal refers to the reflected echo generated when the seismic wave encounters the interface of different underground media, the background signal refers to irrelevant signals introduced by natural or artificial interference, and the noise-reduced seismic data refers to the seismic data obtained by suppressing irrelevant signals in the seismic data while ensuring the frequency components.

[0036] In this embodiment of the invention, the step of performing multi-level noise suppression on seismic data to obtain denoised seismic data includes:

[0037] The seismic data is subjected to spectral calculation and dominant frequency screening to obtain the dominant frequency interval;

[0038] Bandpass filtering is performed on the seismic data according to the dominant frequency range to obtain bandpass denoised seismic data;

[0039] The bandpass noise-reduced seismic data is subjected to frequency domain transformation to obtain the frequency domain of the seismic signal;

[0040] Wavenumber filtering is performed on the frequency domain of the seismic data to obtain the denoised seismic signal in the frequency domain;

[0041] The frequency domain of the denoised seismic signal is transformed into the time domain to obtain directional denoised seismic data;

[0042] Wavelet decomposition was performed on the directional denoised seismic data to obtain seismic wavelet coefficients;

[0043] The seismic wavelet coefficients are subjected to noise threshold filtering to obtain denoised wavelet coefficients;

[0044] Wavelet reconstruction is performed on the denoised wavelet coefficients to obtain reconstructed seismic data;

[0045] The reconstructed seismic data are overlaid to obtain denoised seismic data.

[0046] In detail, the spectrum calculation refers to calculating the spectrum of each seismic trace in the seismic data, and the dominant frequency screening refers to determining the effective range of the dominant frequency based on the spectrum of each seismic trace, for example, the dominant frequency range is between 10Hz and 80Hz.

[0047] Specifically, bandpass filtering can be performed using a Butterworth filter. The bandpass denoised seismic data refers to seismic data from which irrelevant high-frequency and low-frequency signals have been removed.

[0048] In detail, frequency domain transformation can be performed using the Fast Fourier Transform (FFT) method, and time domain transformation can be performed using the Inverse Fourier Transform (IFT) method. The frequency domain transformation refers to converting the seismic wave signal in the time domain into a time domain signal, and the time domain transformation refers to converting the frequency domain signal of the noise-reduced seismic data into a time domain signal.

[0049] In this embodiment of the invention, wavenumber filtering refers to identifying the wavenumber regions of noise distribution and filtering the corresponding wavenumber regions. For example, filtering to remove surface waves and low-speed noise in the low wavenumber region and filtering to remove high-frequency interference noise in the high wavenumber region.

[0050] Specifically, wavelet decomposition is the process of decomposing a signal into sub-signals of different frequencies and time-domain resolutions for analyzing the time-frequency characteristics of the signal. Wavelet decomposition can be performed using wavelet basis functions such as Daubechies, Haar, and Symlets. The seismic wavelet coefficients refer to the characteristic components of the directional denoised seismic data in different frequency bands. Wavelet reconstruction is the process of combining the decomposed sub-signals to reconstruct the original signal.

[0051] In detail, the superposition calculation refers to using the signal consistency of the same reflection point to superimpose and strengthen the reflected wave, thereby reducing the inconsistent noise part in the superposition process.

[0052] Specifically, refer to Figure 2 and Figure 3 The image shown is a comparison chart of the seismic data before and after multi-level noise suppression and the denoised seismic data. Figure 2 and Figure 3 The portion at point a is the aforementioned seismic data. Figure 2 and Figure 3 The portion at point b is the denoised seismic data.

[0053] In this embodiment of the invention, by performing multi-level noise suppression on the seismic data, denoised seismic data is obtained, which can suppress the abnormal noise in the common center point gather, thereby obtaining seismic data with a higher signal-to-noise ratio.

[0054] S2. Perform geometric correction and data realignment on the denoised seismic data to obtain common center point gathers, and divide the common center point gathers into time windows to obtain stratigraphic time window groups.

[0055] In detail, the common midpoint gather refers to multiple seismic traces collected at the same common midpoint (CMP). These seismic traces come from different shot-receiver distances, but they reflect the geological interface at the same location underground.

[0056] Specifically, the geometric correction and data realignment of the denoised seismic data to obtain the common centroid gather includes:

[0057] Extract the shot location set and receiver location set from the denoised seismic data;

[0058] Geometric correction is performed on the denoised seismic data based on the shot point location set and the receiver location set to obtain the common center point location set.

[0059] The common center point location set is divided into position channels to obtain the primary common center point channel set;

[0060] The primary common center point gather is offset-corrected to obtain the common center point gather.

[0061] In detail, the shot point locations in the shot point location set refer to the starting points of artificially generated seismic waves in seismic exploration, and the geophone locations in the geophone location set refer to the specific locations on the ground where geophones are placed to record the vibration signals when the seismic waves arrive.

[0062] Specifically, the geometric correction refers to taking the midpoint between the shot point and the receiver point of each seismic trace as the common center point position. The positional trace segmentation refers to segmenting and numbering according to the distance of the seismic survey line. For example, according to the seismic survey line, a common center point trace is defined at fixed intervals. The offset correction includes static correction and dynamic correction.

[0063] Specifically, the step of dividing the common center point gather into time windows to obtain a formation time window group includes:

[0064] Velocity spectrum analysis was performed on the common center point gather to obtain the dynamic correction velocity;

[0065] Offset detection is performed on the common center point gather to obtain the zero offset position;

[0066] Perform start-end delay analysis on the common center point gather to obtain the start-end delay time, and obtain the special delay time corresponding to the start-end delay time;

[0067] Obtain the start and end times of the zero offset position;

[0068] The start and end times of the common center point gather are calculated based on the dynamic correction speed, the start and end delay times, the special delay times, and the zero offset start and end times to obtain the offset start and end times.

[0069] The common center point gather is divided into time windows based on the offset start and end times and the dynamic correction rate to obtain a formation time window group.

[0070] Specifically, the dynamic correction velocity is the speed at which seismic waves propagate underground, used to eliminate time differences in reflection events caused by different offset distances. The velocity spectrum analysis is a technique used in seismic data processing to estimate the velocity of the subsurface medium. It generates a velocity spectrum chart by measuring the energy changes of reflected signals at different offset distances, thereby obtaining the optimal velocity for dynamic correction and stacking processing.

[0071] In detail, the zero offset position refers to the seismic record position under the special case where the shot point and the receiver point coincide; the zero offset start and end time refers to the two-way propagation time corresponding to the zero offset position; the start and end delay time refers to a time window artificially set in advance within the propagation time range of seismic waves in seismic exploration to accurately capture the signal of the reflected event; and the special delay time refers to a special start and end delay time artificially set in advance.

[0072] Specifically, the hyperbolic correction algorithm or linear correction algorithm can be used to calculate the start and end times of the common center point gather based on the dynamic correction speed, the start and end delay times, the special delay times, and the zero offset start and end times, so as to obtain the offset start and end times.

[0073] The formula for the linear correction algorithm is as follows:

[0074] T(X)=D+(T(0)+L)+X*1000 / VV≠0

[0075] T(X)=D+T(0)+LV=0

[0076] Wherein, T(X) refers to the offset start and end time when the offset distance is X, D refers to the start and end delay time, T(0) refers to the offset start and end time when the offset distance is X, that is, the zero offset start and end time, L is the special delay time, X is the offset distance, and V refers to the dynamic correction speed.

[0077] The hyperbolic correction algorithm formula is as follows:

[0078]

[0079] T(X)=D+T(0)+LV=0

[0080] Wherein, T(X) refers to the offset start and end time when the offset distance is X, D refers to the start and end delay time, T(0) refers to the offset start and end time when the offset distance is X, that is, the zero offset start and end time, L is the special delay time, X is the offset distance, and V refers to the dynamic correction speed.

[0081] For details, refer to Figure 4 and Figure 5 The diagram shown is a schematic diagram of the formation time window group, in which... Figure 4 This is the first type of stratigraphic time window. Figure 5 The second type of formation time window is a combination of two or more formation time window groups. The start and end times of the offset can be two, so there are two corresponding time window groups. The step of dividing the common center point gather into time windows according to the start and end times of the offset and the dynamic correction rate to obtain the formation time window group includes determining the time window corresponding to the zero offset and calculating the corrected formation time window according to the dynamic correction formula.

[0082] In this embodiment of the invention, by performing geometric correction and data realignment on the denoised seismic data, a common center point gather is obtained, and the common center point gather is divided into time windows to obtain a stratigraphic time window group. This allows for the use of two time windows for terrestrial strata and marine strata in the seismic data analysis room, thereby meeting different stratigraphic designs and improving the accuracy of subsequent deconvolution.

[0083] S3. Perform seismic trace analysis on the denoised seismic data to obtain the seismic trace energy spectrum matrix.

[0084] In detail, the seismic trace energy spectrum matrix is ​​a matrix composed of the energy spectrum data of each seismic trace in the denoised seismic data.

[0085] In this embodiment of the invention, the step of performing seismic trace analysis on the denoised seismic data to obtain the seismic trace energy spectrum matrix includes:

[0086] Seismic trace datasets are extracted from the denoised seismic data;

[0087] The seismic trace dataset is transformed into a frequency domain to obtain a seismic trace frequency domain set;

[0088] Energy spectrum calculations are performed on the frequency domain set of the seismic traces to obtain a primary energy spectrum set;

[0089] Logarithmic transformation is performed on the primary energy spectrum set to obtain the seismic trace energy spectrum set;

[0090] The seismic trace energy spectrum set is transformed into a matrix to obtain the seismic trace energy spectrum matrix.

[0091] In detail, the frequency domain conversion can be performed using the Fourier transform method. The energy spectrum calculation includes calculating the amplitude spectrum and calculating the primary energy spectrum based on the square of the amplitude spectrum.

[0092] Specifically, each row in the seismic trace energy spectrum matrix corresponds to a seismic trace energy spectrum in the seismic trace energy spectrum set, and each column in the seismic trace energy spectrum matrix corresponds to a frequency.

[0093] In this embodiment of the invention, by performing seismic trace analysis on the denoised seismic data, a seismic trace energy spectrum matrix is ​​obtained. This allows for the analysis of frequency distribution characteristics and the extraction of frequency characteristics of components such as shot points, receiver points, offsets, and common center point gathers in the energy spectrum, thereby improving the accuracy of subsequent spectral factor decomposition.

[0094] S4. Perform spectral factor decomposition on the seismic trace energy spectrum matrix based on the denoised seismic data to obtain spectral component groups.

[0095] In detail, the spectral component group includes multiple spectral components, wherein the spectral component refers to the logarithmic spectrum of each component of the data shot point item, receiver item, common center point item, offset item, etc. in the seismic trace data.

[0096] In this embodiment of the invention, the step of performing spectral factor decomposition on the seismic trace energy spectral matrix based on the denoised seismic data to obtain spectral component sets includes:

[0097] Extract the spectral factor coefficient matrix from the denoised seismic data;

[0098] Construct spectral component equations based on the spectral factor coefficient matrix and the seismic trace energy spectrum matrix;

[0099] The spectral component equations are solved by least squares to obtain the spectral component set.

[0100] Specifically, the spectral factor coefficient matrix is ​​a coefficient matrix corresponding to the indicative relationship between parameters such as shot point, receiver point, common center point, and offset in the denoised seismic data.

[0101] In detail, the spectral component equation is an equation indicating the relationship between the spectral factor coefficient matrix, the spectral component group, and the seismic trace energy spectrum matrix, that is, the seismic trace energy spectrum matrix is ​​equal to the spectral factor coefficient matrix multiplied by the spectral component group.

[0102] In this embodiment of the invention, by performing spectral factor decomposition on the seismic trace energy spectrum matrix based on the denoised seismic data to obtain spectral component groups, it is possible to obtain the components of data with different frequency characteristics in the seismic data, thereby improving the accuracy of subsequent deconvolution operations.

[0103] S5. Perform dual-window deconvolution on the denoised seismic data according to the spectral component group and the stratigraphic time window group to obtain standard seismic data.

[0104] Specifically, the standard seismic data refers to the high-resolution, denoised seismic data after deconvolution.

[0105] In this embodiment of the invention, the step of performing dual-window deconvolution on the denoised seismic data based on the spectral component group and the stratigraphic time window group to obtain standard seismic data includes:

[0106] Based on the spectral component group, wavelet estimation is performed on the denoised seismic data to obtain the seismic wavelet spectrum;

[0107] Each stratigraphic time window in the stratigraphic time window group is selected as the target stratigraphic time window, and the target time window data corresponding to the target stratigraphic time window is extracted from the denoised seismic data;

[0108] The target time window data is deconvolved based on the seismic wavelet spectrum to obtain deconvolved seismic data;

[0109] The deconvolution seismic data corresponding to all target strata time windows in the aforementioned strata time window group are aggregated into a deconvolution seismic data group;

[0110] The deconvolution seismic data set was stitched together to obtain standard seismic data.

[0111] In detail, the wavelet estimation refers to fitting the spectral components of the seismic signal using the shot point, receiver point, common center point, and offset in the spectral component group to estimate the spectrum of the wavelet.

[0112] Specifically, the deconvolution refers to designing an inverse filter based on the seismic wavelet spectrum, deconvolving the frequency-domain converted denoised seismic data through the inverse filter, and converting the deconvolution result into deconvolved seismic data in the time domain.

[0113] For details, refer to Figure 6 as well as Figure 7 The image shown is a cross-sectional comparison diagram of the standard seismic data and the denoised seismic data. Figure 6 This is a cross-sectional view of the denoised seismic data. Figure 7 This is a cross-sectional view of the standard seismic data.

[0114] Specifically, refer to Figure 8 The figure shows a comparison of the spectra of the standard seismic data and the denoised seismic data. In the figure, a is the spectrum of the standard seismic data, and b is the spectrum of the denoised seismic data.

[0115] In this embodiment of the invention, by performing dual-window deconvolution on the denoised seismic data according to the spectral component group and the stratigraphic time window group, the difficulty of noise suppression can be reduced while improving the resolution, and the resolution requirements of different stratigraphic layers can be met, which is conducive to improving the resolution and accuracy of seismic data.

[0116] This disclosure provides a deconvolution method for seismic data. By performing multi-level noise suppression on the seismic data, denoised seismic data is obtained. This suppresses anomalous noise in common midpoint gathers, resulting in seismic data with a higher signal-to-noise ratio. Geometric correction and data realignment are performed on the denoised seismic data to obtain common midpoint gathers. These gathers are then divided into time windows to create stratigraphic time window groups. This allows for the use of two time windows for terrestrial and marine strata in the seismic data analysis room, satisfying different stratigraphic designs and improving the accuracy of subsequent deconvolution. Seismic trace analysis is performed on the denoised seismic data to obtain a seismic trace energy spectrum matrix. This allows for the analysis of frequency distribution characteristics and the extraction of frequency characteristics from components such as shot points, receivers, offsets, and common midpoint gathers, thereby improving the accuracy of subsequent spectral factor decomposition.

[0117] By performing spectral factor decomposition on the seismic trace energy spectrum matrix based on the denoised seismic data, spectral component groups are obtained. This allows for the acquisition of data components with different frequency characteristics in the seismic data, thereby improving the accuracy of subsequent deconvolution operations. By performing dual-window deconvolution on the denoised seismic data based on the spectral component groups and the stratigraphic time window groups, the difficulty of noise suppression can be reduced while improving resolution, and the resolution requirements of different stratigraphic layers can be met. This is beneficial for improving the resolution and accuracy of seismic data, thereby improving the accuracy of seismic data deconvolution.

[0118] Example 2

[0119] Based on the above embodiments, Figure 9 This is a functional block diagram of a seismic data deconvolution system provided in an embodiment of this disclosure. Figure 9 As shown, a deconvolution system for seismic data includes:

[0120] The seismic data deconvolution system 900 described in this embodiment can be installed in an electronic device. Depending on the functions implemented, the seismic data deconvolution system 900 may include a noise suppression module 901, a time window division module 902, a seismic trace analysis module 903, a spectral factor decomposition module 904, and a dual-window deconvolution module 905. The modules described in this disclosure can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0121] In this embodiment, the functions of each module / unit are as follows:

[0122] The noise suppression module 901 is used to perform multi-level noise suppression on seismic data to obtain noise-reduced seismic data.

[0123] The time window division module 902 is used to perform geometric correction and data realignment on the denoised seismic data to obtain common center point gathers, and to divide the common center point gathers into time windows to obtain stratigraphic time window groups.

[0124] The seismic trace analysis module 903 is used to perform seismic trace analysis on the denoised seismic data to obtain the seismic trace energy spectrum matrix.

[0125] The spectral factor decomposition module 904 is used to perform spectral factor decomposition on the seismic trace energy spectrum matrix based on the noise-reduced seismic data to obtain spectral component groups.

[0126] The dual-window deconvolution module 905 is used to perform dual-window deconvolution on the denoised seismic data according to the spectral component group and the stratigraphic time window group to obtain standard seismic data.

[0127] In detail, each module in the seismic data deconvolution system 900 described in this embodiment of the present disclosure uses the same technical means as the seismic data deconvolution method described in Embodiment 1, and can produce the same technical effect, which will not be repeated here.

[0128] Example 3

[0129] 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.

[0130] In some embodiments of this example, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the steps of the method described in the above embodiments.

[0131] In some embodiments of this example, a computer program product is provided, including a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the method described in the above embodiments.

[0132] 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.

[0133] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof, including but 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.).

[0134] Computer-readable storage media may also store at least one computer-executable program, 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.

[0135] 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.).

[0136] The processor can communicate with external devices via the I / O bus through wired or wireless networks.

[0137] 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.

[0138] In the embodiments provided in this disclosure, it should be understood that the disclosed systems and methods can also be implemented in other ways. The system 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 systems, 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.

[0139] 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.

[0140] 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 for deconvolution of seismic data, characterized in that, include: Multi-level noise suppression is applied to seismic data to obtain denoised seismic data; The denoised seismic data is geometrically corrected and data is reallocated to obtain a common center point gather. The common center point gather is then divided into time windows to obtain a stratigraphic time window group. Seismic trace analysis was performed on the denoised seismic data to obtain the seismic trace energy spectrum matrix; Based on the denoised seismic data, the energy spectrum matrix of the seismic trace is decomposed by spectral factorization to obtain spectral component groups; Standard seismic data is obtained by performing dual-window deconvolution on the denoised seismic data based on the spectral component group and the stratigraphic time window group.

2. The deconvolution method for seismic data according to claim 1, characterized in that, The process of performing multi-level noise suppression on seismic data to obtain denoised seismic data includes: The seismic data is subjected to spectral calculation and dominant frequency screening to obtain the dominant frequency interval; Bandpass filtering is performed on the seismic data according to the dominant frequency range to obtain bandpass denoised seismic data; The bandpass noise-reduced seismic data is subjected to frequency domain transformation to obtain the frequency domain of the seismic signal; Wavenumber filtering is performed on the frequency domain of the seismic data to obtain the denoised seismic signal in the frequency domain; The frequency domain of the denoised seismic signal is transformed into the time domain to obtain directional denoised seismic data; Wavelet decomposition was performed on the directional denoised seismic data to obtain seismic wavelet coefficients; The seismic wavelet coefficients are subjected to noise threshold filtering to obtain denoised wavelet coefficients; Wavelet reconstruction is performed on the denoised wavelet coefficients to obtain reconstructed seismic data; The reconstructed seismic data are overlaid to obtain denoised seismic data.

3. The deconvolution method for seismic data according to claim 1, characterized in that, The geometric correction and data realignment of the denoised seismic data to obtain the common centroid gather includes: Extract the shot location set and receiver location set from the denoised seismic data; Geometric correction is performed on the denoised seismic data based on the shot point location set and the receiver location set to obtain the common center point location set. The common center point location set is divided into position channels to obtain the primary common center point channel set; The primary common center point gather is offset-corrected to obtain the common center point gather.

4. The deconvolution method for seismic data according to claim 1, characterized in that, The step of dividing the common center point gather into time windows to obtain a formation time window group includes: Velocity spectrum analysis was performed on the common center point gather to obtain the dynamic correction velocity; Offset detection is performed on the common center point gather to obtain the zero offset position; Perform start-end delay analysis on the common center point gather to obtain the start-end delay time, and obtain the special delay time corresponding to the start-end delay time; Obtain the start and end times of the zero offset position; The start and end times of the common center point gather are calculated based on the dynamic correction speed, the start and end delay times, the special delay times, and the zero offset start and end times to obtain the offset start and end times. The common center point gather is divided into time windows based on the offset start and end times and the dynamic correction rate to obtain a formation time window group.

5. The deconvolution method for seismic data according to claim 1, characterized in that, The process of performing seismic trace analysis on the denoised seismic data to obtain the seismic trace energy spectrum matrix includes: Seismic trace datasets are extracted from the denoised seismic data; The seismic trace dataset is transformed into a frequency domain to obtain a seismic trace frequency domain set; Energy spectrum calculations are performed on the frequency domain set of the seismic traces to obtain a primary energy spectrum set; Logarithmic transformation is performed on the primary energy spectrum set to obtain the seismic trace energy spectrum set; The seismic trace energy spectrum set is transformed into a matrix to obtain the seismic trace energy spectrum matrix.

6. The deconvolution method for seismic data according to claim 1, characterized in that, The step of performing dual-window deconvolution on the denoised seismic data based on the spectral component group and the stratigraphic time window group to obtain standard seismic data includes: Based on the spectral component group, wavelet estimation is performed on the denoised seismic data to obtain the seismic wavelet spectrum; Each stratigraphic time window in the stratigraphic time window group is selected as the target stratigraphic time window, and the target time window data corresponding to the target stratigraphic time window is extracted from the denoised seismic data; The target time window data is deconvolved based on the seismic wavelet spectrum to obtain deconvolved seismic data; The deconvolution seismic data corresponding to all target strata time windows in the aforementioned strata time window group are aggregated into a deconvolution seismic data group; The deconvolution seismic data set was stitched together to obtain standard seismic data.

7. A deconvolution system for seismic data, characterized in that, include: The noise suppression module is used to perform multi-level noise suppression on seismic data to obtain denoised seismic data. The time window division module is used to perform geometric correction and data realignment on the denoised seismic data to obtain common center point gathers, and to divide the common center point gathers into time windows to obtain stratigraphic time window groups. The seismic trace analysis module is used to perform seismic trace analysis on the denoised seismic data to obtain the seismic trace energy spectrum matrix; The spectral factor decomposition module is used to perform spectral factor decomposition on the seismic trace energy spectrum matrix based on the denoised seismic data to obtain spectral component groups. The dual-window deconvolution module is used to perform dual-window deconvolution on the denoised seismic data based on the spectral component group and the stratigraphic time window group to obtain standard seismic data.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the deconvolution method for seismic data according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the deconvolution method for seismic data as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the deconvolution method for seismic data as described in any one of claims 1 to 6.

Images