CN115903014A

CN115903014A - Post-stack seismic data processing method and device, electronic equipment and medium

Info

Publication number: CN115903014A
Application number: CN202110955778.8A
Authority: CN
Inventors: 张珊; 于宝利; 邓勇; 钟厚财; 朱哲; 刘鑫铭
Original assignee: China National Petroleum Corp; BGP Inc
Current assignee: China National Petroleum Corp; BGP Inc
Priority date: 2021-08-19
Filing date: 2021-08-19
Publication date: 2023-04-04

Abstract

The embodiment of the invention provides a post-stack seismic data processing method, a post-stack seismic data processing device, electronic equipment and a medium, wherein the method comprises the following steps: acquiring data to be processed; the data to be processed comprises post-stack seismic data corresponding to at least one stratum; determining a target stratum; carrying out layer leveling treatment along the target stratum to obtain leveling volume data; carrying out continuous wavelet transform frequency extension on the flattened volume data to obtain frequency extension data; performing reverse leveling processing and singular value decomposition denoising processing on the topology data to obtain denoised data; and carrying out anisotropic Laplace filtering processing on the de-noised data to obtain optimized data. The embodiment of the invention can optimize the post-stack seismic data so as to improve the signal-to-noise ratio and the resolution of the data.

Description

Post-stack seismic data processing method and device, electronic equipment and medium

Technical Field

The invention relates to the technical field of petroleum and natural gas exploration and development, in particular to a post-stack seismic data processing method, a post-stack seismic data processing device, electronic equipment and a medium.

Background

The seismic data analysis is carried out on the region, which is beneficial to promoting the exploration of resources such as petroleum and the like. But the earth surface type of part of work areas is complex, the integral signal-to-noise ratio and the resolution ratio of earthquake acquisition data are influenced, in addition, in a slope area, the stratum attitude is steeper, and the steep attitude can influence the data imaging effect.

In the prior art, aiming at a special earth surface environment and complex geological structure characteristics of a work area, the resolution and the signal-to-noise ratio of seismic data are improved from the following two aspects: (1) in the collection, a mode of combining a well gun, an air gun and a seismic source for high-density collection is adopted; (2) in the aspect of processing, the difference of wavelets in frequency, waveform and phase is eliminated through wavelet consistency processing, the consistency of the wavelets is improved, and the resolution and the signal-to-noise ratio of seismic data are guaranteed. Although the quality of the seismic data after the acquisition and processing combined attack and shut-off is improved according to the method, the original seismic data are influenced by complex acquisition ground surface excitation receiving conditions and stratum structures, so that the overall signal-to-noise ratio and the overall resolution ratio are low, and the development of fine structure interpretation and reservoir prediction work cannot be satisfied.

Therefore, the method still has certain limitations to a certain extent and is not suitable for general use.

Disclosure of Invention

In view of the above, embodiments of the present invention are proposed in order to provide a post-stack seismic data processing method, apparatus, electronic device and medium that overcome the above problems or at least partially solve the above problems.

In order to solve the above problems, an embodiment of the present invention discloses a post-stack seismic data processing method, including:

acquiring data to be processed; the data to be processed comprises post-stack seismic data corresponding to at least one stratum;

determining a target stratum;

carrying out layer leveling treatment along the target stratum to obtain leveling volume data;

frequency extension is carried out on the flattened volume data based on continuous wavelet transformation to obtain frequency extension data;

carrying out reverse flattening processing and singular value decomposition denoising processing on the topology data to obtain denoised data;

and carrying out anisotropic Laplace filtering processing on the de-noising data to obtain optimized data.

Optionally, the method further comprises:

performing inversion based on the optimized data to obtain inversion data;

and comparing the inversion data with preset reference data to determine the coincidence rate of the optimized data.

Optionally, the frequency spreading the flattened volume data to obtain the frequency spreading data includes:

performing continuous wavelet transform decomposition on the flattened volume data to obtain decomposed data; the decomposed data comprises seismic signals corresponding to a plurality of frequencies;

determining a frequency extension parameter; the frequency extension parameters comprise a frequency extension range and a reference frequency;

and reconstructing based on the frequency extension parameters and the seismic signals to obtain frequency extension data.

Optionally, the performing a reverse-level processing and a denoising processing on the topology data to obtain denoised data includes:

performing reverse flattening processing on the topology data to obtain reverse flattening data;

and carrying out singular value decomposition denoising processing on the inverse flattening data to obtain denoised data.

Optionally, the performing singular value decomposition denoising processing on the inverse-flattened data to obtain denoised data includes:

generating a Hankel matrix matched with the anti-leveling data;

singular value decomposition is carried out on the Hankel matrix to obtain one or more singular values and singular value vectors which correspond to each other;

determining a target singular value according to a preset rule;

and generating de-noising data by adopting the target singular value and the singular value vector corresponding to the target singular value.

Optionally, the performing filtering processing on the denoised data to obtain optimized data includes:

calculating a gradient structure tensor based on the de-noising data;

determining seismic section confidence information according to the gradient structure tensor;

according to the gradient structure tensor and the stratum inclination angle;

constructing an anisotropic Laplace filter according to the confidence coefficient information and the stratum inclination angle;

processing the de-noising data by adopting the anisotropic Laplace filter to obtain optimized data;

wherein a size and a shape of a filtering window in the anisotropic laplacian filter are determined by the confidence information, and a window direction in the anisotropic laplacian filter is determined by the stratigraphic dip; the confidence information includes line-type structural feature confidence and lateral discontinuity structural feature confidence.

The embodiment of the invention also discloses a post-stack seismic data processing device, which comprises:

the data to be processed acquisition module is used for acquiring data to be processed; the data to be processed comprises post-stack seismic data corresponding to at least one formation;

the target stratum determining module is used for determining a target stratum;

the leveling body data generation module is used for carrying out layer leveling processing along the target stratum to obtain leveling body data;

the topology data generation module is used for carrying out continuous wavelet transform and frequency expansion on the flattened volume data to obtain frequency expansion data;

the de-noising data generation module is used for carrying out reverse flattening processing and singular value decomposition de-noising processing on the rubbing data to obtain de-noising data;

and the filtering processing module is used for carrying out anisotropic Laplace filtering processing on the de-noised data to obtain optimized data.

Optionally, the method further comprises:

the inversion module is used for performing inversion based on the optimized data to obtain inversion data;

and the comparison module is used for comparing the inversion data with preset reference data and determining the coincidence rate of the optimized data.

The embodiment of the invention also discloses an electronic device, which comprises: a processor, a memory and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the post-stack seismic data processing method as described above.

The embodiment of the invention also discloses a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and when the computer program is executed by a processor, the steps of the post-stack seismic data processing method are realized.

The embodiment of the invention has the following advantages:

carrying out continuous wavelet transformation frequency expansion processing on the stacked seismic data after the stacked seismic data are leveled along a target stratum to obtain frequency expansion data so as to expand the frequency band of the seismic data; performing reverse-drawing and singular value decomposition denoising processing on the topology data so as to effectively remove random noise from the topology data to obtain denoised data; the anisotropic Laplace filtering processing is carried out on the denoised data, random noise is effectively attenuated, the transverse consistency of reflection homophase axes is enhanced, meanwhile, stratum edges and fine structures in a section can be kept, and compared with initial post-stack seismic data, the optimized data obtained through the processing has better signal-to-noise ratio and resolution ratio.

Furthermore, inversion processing is carried out according to the optimized data, more accurate explanatory data can be obtained, and the accuracy of stratum prediction of the detection area is improved.

Drawings

FIG. 1 is a flow chart of the steps of one embodiment of a method of post-stack seismic data processing of the present invention;

FIG. 2 is a schematic diagram of a noise reduction process provided by an embodiment of the present invention;

fig. 3a is a schematic cross-sectional view of a partial band before frequency spreading according to an embodiment of the present invention;

FIG. 3b is a schematic cross-sectional diagram illustrating a partial band spreading according to an embodiment of the present invention;

fig. 4a is a schematic cross-sectional view before denoising processing and filtering processing according to an embodiment of the present invention;

FIG. 4b is a schematic cross-sectional diagram of the denoising process and the filtering process according to the embodiment of the present invention;

FIG. 5a is a schematic diagram of an inversion profile of data to be processed according to an embodiment of the present invention;

FIG. 5b is a schematic diagram of an inversion profile of optimized data provided by an embodiment of the present invention;

FIG. 6a is a schematic diagram of data inversion before frequency spreading according to an embodiment of the present invention;

FIG. 6b is a schematic diagram of data inversion after frequency broadening according to an embodiment of the present invention;

FIG. 7 is a block diagram of a post-stack seismic data processing apparatus embodiment of the invention.

Detailed Description

In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, the present invention is described in detail with reference to the accompanying drawings and the detailed description thereof.

Referring to fig. 1, a flowchart illustrating steps of an embodiment of a post-stack seismic data processing method according to the present invention is shown, which may specifically include the following steps:

step 101, acquiring data to be processed; the data to be processed comprises post-stack seismic data corresponding to at least one stratum;

the post-stack seismic data consists of one or more seismic related time series information.

The method comprises the steps of acquiring a reflection signal of a designated position or a designated area through detection equipment to obtain initial seismic data, and performing one-time or multiple-time stacking and pressing on the initial seismic data according to a certain rule to obtain post-stack seismic data.

The embodiment of the invention mainly aims at processing post-stack seismic data, the generation process of the post-stack data is not limited by the embodiment of the invention, and post-stack seismic data obtained by different modes by a person skilled in the art do not influence the embodiment of the invention.

Step 102, determining a target stratum;

the post-stack seismic data may include data corresponding to different strata, and since the post-stack seismic data is generally analyzed for a specific stratum or a plurality of strata, a target stratum requiring targeted optimization processing may be determined according to actual needs or research purposes.

103, carrying out layer leveling treatment along the target stratum to obtain leveling volume data;

the horizon leveling processing technology is mainly used for ancient landform restoration in the explanation research of conventional seismic related data, and is a method formed by combining multiple factors on the basis of the theory of stratigraphic position of continental facies sequence, the basis of geological data and seismic data. The method can perform isochronous contrast on the stratum, has high stratum contrast precision, and can better reflect the original ancient landform characteristics.

Because the post-stack data may have a situation that the stratum occurrence is too steep, in order to avoid the problem that the processing result is not ideal due to longitudinal time variation in the post-stack seismic data, the target stratum is determined, and then the layer leveling processing is performed along the target stratum to obtain the leveling volume data.

By utilizing the isochronism of leveling along the target stratum, the longitudinal time window of the data is greatly shortened, the operation error caused by time variation is reduced, and the generated leveling volume data is favorable for selecting reasonable seismic wavelets to carry out subsequent frequency extension work.

104, performing continuous wavelet transform frequency extension on the flattened volume data to obtain frequency extension data;

the continuous wavelet transform frequency expansion technical principle is based on the assumption of approximate level of amplitude spectrum energy of broadband high-resolution seismic signals, high-frequency energy lost due to propagation is compensated in a continuous wavelet domain to recover the characteristic of high resolution of seismic data.

By carrying out continuous wavelet transform frequency broadening on the obtained flattened volume data, data with wider frequency band can be obtained relative to the flattened volume data, so that more high-resolution contents are contained in the frequency broadening data relative to the flattened volume data.

105, performing reverse leveling processing and singular value decomposition denoising processing on the topology data to obtain denoised data;

the reverse leveling processing is the reverse processing of the layer leveling processing, the data after stacking is restored by performing the reverse leveling processing on the leveling data obtained after frequency leveling, and as the data obtained after the reverse leveling processing generally keeps more random noise, the random noise in the data can be effectively reduced by performing singular value decomposition denoising processing, and the denoised data has higher signal-to-noise ratio relative to the data to be processed.

And 106, performing anisotropic Laplace filtering processing on the de-noised data to obtain optimized data.

The anisotropic Laplace filter is adopted to filter the de-noising data, and the obtained optimized data not only effectively attenuates random noise and enhances the transverse consistency of reflection in-phase axes, but also can keep stratum edges and fine structures in a section and is beneficial to improving the reliability of subsequent seismic data interpretation.

In the embodiment of the invention, the frequency extension data is obtained by carrying out continuous wavelet transformation frequency extension processing on the stacked seismic data after the stacked seismic data are leveled along a target stratum, so as to widen the frequency band of the seismic data; performing reverse-drawing and singular value decomposition denoising processing on the topology data so as to effectively remove random noise from the topology data to obtain denoised data; the anisotropic Laplace filtering processing is carried out on the denoised data, random noise is effectively attenuated, the transverse consistency of reflection in-phase axes is enhanced, meanwhile, stratum edges and fine structures in a section can be kept, optimized data obtained after the processing have better signal-to-noise ratio and resolution ratio compared with initial post-stack seismic data, and inversion processing and stratum related prediction on the seismic data are facilitated.

In an optional embodiment of the invention, the method further comprises:

performing inversion based on the optimized data to obtain inversion data;

Inversion processing is carried out based on the optimized data, wherein the inversion processing comprises but is not limited to a statistical inversion mode and a deterministic inversion mode, inversion data are generated, and the coincidence rate of the optimized data relative to the reference data can be determined by comparing the inversion data with the reference data.

The embodiment of the invention does not limit the content, form and generation method of the reference data, and only needs to determine the coincidence rate of the inversion data through the reference data.

In an alternative embodiment of the present invention, the step 104 includes:

step S11, carrying out continuous wavelet transform decomposition on the flattened volume data to obtain decomposed data; the decomposed data comprises seismic signals corresponding to a plurality of frequencies;

the decomposed data may be divided into a plurality of frequency band intervals for subsequent processing, for example: the frequency bands are divided into five frequency bands of less than 10Hz (Hertz ), 10-20 Hz, 20-40 Hz, 40-60 Hz and 60-70 Hz. It may be understood that the frequency bands may be divided into other numbers of frequency bands in other manners, which is not limited in the embodiment of the present invention.

A substep S12 of determining a frequency spreading parameter; the frequency extension parameter comprises a frequency extension range and a reference frequency;

and determining a frequency extension range and a reference frequency of the frequency extension. Due to the optimization purposes of the data to be processed and different data, different reference frequencies can be obtained, the reference frequencies are analyzed towards high and low frequencies to determine the frequency range containing effective data, and the frequency extension range is determined according to the frequency range containing effective information.

And a substep S13, reconstructing based on the frequency extension parameters and the seismic signals to obtain frequency extension data.

And on the premise of keeping the phase information in the seismic signals unchanged, reconstructing the seismic signals by using the widened frequency spectrum to obtain frequency extension data, wherein the frequency extension data has a wider frequency band relative to the flattening data. The spread data is substantially a time series of seismic traces.

In an alternative embodiment of the present invention, the step 105 may include:

step S21, performing reverse leveling processing on the topology data to obtain reverse leveling data;

and a substep S22, performing singular value decomposition denoising processing on the inverse flattening data to obtain denoised data.

And performing reverse leveling processing on the topology data to obtain reverse leveling data, wherein the reverse leveling is reverse processing of the layer leveling processing and is not described herein again.

And restoring the stratum attitude of the data through the reverse leveling processing. Because the random noise in the data to be processed still remains in the reverse leveling data, the random noise in the reverse leveling data can be effectively removed by performing singular value decomposition denoising processing on the reverse leveling data, and the denoised data is obtained.

In an alternative embodiment of the present invention, the substep S22 may comprise:

a substep S221 of generating a Hankel matrix matched with the inverse Laplace data;

substep S222, performing singular value decomposition on the Hankel matrix to obtain one or more singular values and singular value vectors which correspond to each other;

the singular values are non-zero singular values. The substep S222 may obtain a left singular matrix, a diagonal matrix with singular values as diagonal elements, and a right singular matrix.

The singular value vectors may include a left singular value vector derived from the left singular matrix column vector and a right singular value vector derived from the right singular matrix column vector.

A substep S223 of determining a target singular value according to a preset rule;

and screening out a specific singular value as a target singular value through a preset rule.

And a substep S224, generating de-noising data by adopting the target singular value and a singular value vector corresponding to the target singular value.

And generating de-noising data by adopting the target singular value and the singular value vector corresponding to the target singular value. Due to the fact that the part, corresponding to the non-target singular value, in the Hankel matrix is removed, random noise in the anti-leveling data can be effectively reduced.

Referring to fig. 2, which shows a process diagram provided by the embodiment of the present invention, in an example, data before singular value denoising is shown as (a) in fig. 2, which is a black-and-white image including 15 × 25 pixels, and as (b) in fig. 2, the black-and-white image includes three columns, and as (c) in fig. 2, the black-and-white image may be represented as a 15 × 25 matrix M, which has 375 elements in total, and the matrix M is subjected to singular value decomposition to obtain 3 non-zero singular values:

σ ₁ ＝14.72，σ ₂ ＝5.22，σ ₃ ＝3.31，

the matrix M can be expressed as:

this is the ideal data, the actual data contains much noise in the useful signal as shown in (d) of FIG. 2, and when the singular value decomposition is applied, the corresponding matrix M' is obtained, and a plurality of non-zero singular values σ _i The matrix M' can be expressed as:

wherein u is _i As vectors of left singular values、

Is the right singular vector.

The non-zero singular values may include (in order from large to small): sigma ₁ ＝14.15，σ ₂ ＝4.67，σ ₃ ＝3.00，σ ₄ ＝0.21，σ ₅ ＝0.19，...，σ ₁₅ ＝0.05。

Obviously, the first three singular values are much larger than the others, indicating that most of the information is included, namely:

the first three singular values can be taken as target singular values and singular value vectors corresponding to the target singular values to generate de-noising data.

In an alternative embodiment of the present invention, the step 106 includes: calculating a gradient structure tensor based on the de-noising data; determining seismic section confidence information according to the gradient structure tensor; according to the gradient structure tensor and the stratum inclination angle; constructing an anisotropic Laplace filter according to the confidence coefficient information and the stratum inclination angle; processing the de-noising data by adopting the anisotropic Laplace filter to obtain optimized data; wherein a size and a shape of a filtering window in the anisotropic laplacian filter are determined by the confidence information, and a window direction in the anisotropic laplacian filter is determined by the dip angle; the confidence information includes line type structure feature confidence and lateral discontinuity feature confidence.

By the filtering mode, stratum inclination is estimated by adopting a gradient structure tensor, the rule degree of a stratum structure is analyzed, the confidence measurement of two structure characteristics of linear and transverse discontinuity in a seismic section is introduced on the basis, the scale and the shape of a filtering window of a structure self-adaptive median filter are regulated and controlled by adopting the two confidence values, and the direction of a filter window function is regulated by utilizing a stratum inclination angle, so that the filtering operation window can optimally match and process the stratum structure characteristics in the adjacent domain, the problems of random noise attenuation and effective signal fidelity of the seismic section are well solved, not only is random noise effectively attenuated, the transverse consistency of reflection homophase axes is enhanced, but also stratum edges and fine structures in the section can be kept, and the reliability of subsequent seismic data interpretation is improved.

The embodiment of the invention processes the post-stack seismic data, thereby realizing the following effects including but not limited to:

1) Imaging effect of seismic data:

referring to fig. 3a, a schematic cross-sectional view before frequency spreading of a partial frequency band provided in an embodiment of the present invention is shown; referring to fig. 3b, a schematic cross-sectional view of the frequency-extended partial band provided in the embodiment of the present invention is shown.

As can be seen from fig. 3a and fig. 3b, the main frequency band is significantly increased, and the effective information of low frequency and high frequency is richer than the original information.

Fig. 4a is a schematic cross-sectional view illustrating denoising processing and filtering processing according to an embodiment of the present invention; fig. 4b shows a schematic cross-sectional view after denoising and filtering provided by the embodiment of the present invention;

aiming at the further singular value decomposition denoising and anisotropic Laplace filtering processing of the spread data, the comparison between the graph 4a and the graph 4b shows that the signal-to-noise ratio and the resolution of the seismic data after the singular value decomposition denoising and the anisotropic Laplace filtering processing are improved, the correlation of the synthetic record calibration result of the well drilling is better, the sand body superposition relationship features are clear and obvious, and the corresponding relationship with the well drilling is accurate.

2) Reservoir prediction effect:

referring to fig. 5a, a schematic diagram of an inversion profile of data to be processed according to an embodiment of the present invention is shown; referring to fig. 5b, a schematic diagram of an inversion profile of optimized data provided by an embodiment of the present invention is shown;

by comparing fig. 5a and fig. 5b, it can be known that the frequency extended high-resolution seismic data is used for carrying out the prediction research on the specific reservoir in the research area, and the inversion result shows that the seismic data impedance inversion result processed by the embodiment of the invention has obviously improved resolution and clear sand body distribution characteristics.

3) The popularization and application effects are as follows:

referring to fig. 6a, a schematic diagram of data inversion before frequency broadening provided by an embodiment of the present invention is shown; referring to fig. 6b, a schematic diagram of data inversion after frequency broadening provided by the embodiment of the present invention is shown.

By applying the method to the area outside the research area, as can be seen by comparing fig. 6a and fig. 6b, the signal-to-noise ratio and the resolution of the seismic data processed by the method recorded in the embodiment of the invention are obviously improved, the main frequency is improved, the superposition and the transverse distribution characteristics of the longitudinal sand bodies are clear, and the data foundation is laid for developing the reservoir prediction in the later period.

The following examples further illustrate embodiments of the invention.

The surface types of the target detection areas are complex, and comprise Gobi and wind erosion hills as main types, and water areas, swamp reeds, farmlands and the like. The overall signal-to-noise ratio and the resolution of the seismic acquisition data are influenced by various earth surface types, and in addition, in a slope area, the stratum attitude is steeper, and the steep attitude influences the data imaging effect. The specific implementation mode of improving the seismic data resolution and the signal-to-noise ratio in the area by using the invention is as follows:

1) Exporting target layer data along the layer, and flattening the data body along the target layer to generate a flattened body;

in order to ensure timeliness of data operation, firstly, a 300ms time window is given up and down along a target layer (target stratum), target layer data is exported, the data body is flattened to generate a flattened body, the longitudinal time window of the data body is reduced to 2000ms from original 4300ms, and operation errors caused by unreasonable wavelet selection due to longitudinal time variation are reduced.

2) Frequency extension processing by using a continuous wavelet transform algorithm is carried out on the flattening body;

by frequency analysis of seismic data in a research area, if-20 db is taken as a standard for dividing the bandwidth, the bandwidth of a target layer is 5-60Hz, and the dominant frequency is 30Hz, but actually, seismic signals between-20 db and-30 db still have a certain signal-to-noise ratio, and the frequency extraction basis is provided. In order to further determine the distribution range of effective signals, seismic data are subjected to frequency division scanning by a series of parameters such as LP10Hz, BP (10-20) Hz, BP (20-40) Hz, BP (40-60) Hz, BP (60-80) Hz and the like, and according to the scanning result, effective information is still hidden and visible and random noise is not obvious at 60Hz, so that a high-frequency expanding end is set to be 80Hz, and a low-frequency end utilizes the low-frequency effective information to the maximum extent. The data before and after the frequency expansion shows that the main frequency range is obviously improved, and the effective information of low frequency and high frequency is richer than the original data. And after determining the frequency extending parameters, performing operation to generate a frequency extending data volume of the flattening volume.

3) The flattening body after the frequency broadening processing is reversely flattened and then is subjected to singular value decomposition denoising processing;

and then, carrying out reverse leveling processing on the leveled volume data subjected to frequency extension processing, generating partial random interference due to the increase of the frequency of seismic data, then selecting effective components by using a singular value decomposition denoising method, reconstructing a new signal, selecting reasonable denoising parameters (including but not limited to non-zero target singular values), and removing the random interference. The method is very effective for the random noise of the overlapped data.

4) Carrying out anisotropic Laplace filtering processing on the denoised data volume;

the data volume after the random noise is removed still has the problem of poor continuity of the in-phase axis left in the original post-stack data, and for the problem, the continuity of the in-phase axis is improved by continuously performing anisotropic laplacian filtering processing. The anisotropic Laplace filtering is also called as edge preserving filtering, and the method can effectively attenuate random noise, enhance the transverse consistency of reflection in-phase axes, simultaneously can keep stratum edges and fine structures in a section and is beneficial to improving the reliability of subsequent seismic data interpretation.

5) And (5) performing post-stack inversion work, verifying the reliability of the data, and finally obtaining the post-stack seismic data with high resolution and high signal-to-noise ratio in the target detection area.

And finally, performing post-stack inversion work on different target layers respectively, and carrying out research on reservoir prediction in two modes of post-stack statistical inversion and deterministic inversion, thereby obtaining better effects. Oil-producing reservoir layers at different positions of a target detection area are counted, and the inversion result coincidence rate is greatly improved by utilizing seismic data with improved signal-to-noise ratio and resolution, and is improved to 93% from 70% originally.

It should be noted that, for simplicity of description, the method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the illustrated order of acts, as some steps may occur in other orders or concurrently in accordance with the embodiments of the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required to implement the invention.

Referring to fig. 7, a block diagram of a post-stack seismic data processing apparatus according to an embodiment of the present invention is shown, which may specifically include the following modules:

a to-be-processed data obtaining module 701, configured to obtain to-be-processed data; the data to be processed comprises post-stack seismic data corresponding to at least one stratum;

a target formation determination module 702 for determining a target formation;

a leveling volume data generating module 703, configured to perform layer leveling processing along the target stratum to obtain leveling volume data;

a frequency-extending data generating module 704, configured to perform continuous wavelet transform frequency extending on the flattened volume data to obtain frequency-extending data;

a de-noising data generating module 705, configured to perform inverse flattening processing and singular value decomposition de-noising processing on the expansion data to obtain de-noising data;

and a filtering processing module 706, configured to perform anisotropic laplacian filtering on the de-noised data to obtain optimized data.

In an optional embodiment of the invention, the apparatus further comprises:

the inversion module is used for carrying out inversion based on the optimized data to obtain inversion data;

In an optional embodiment of the present invention, the topology data generating module 704 includes:

the decomposition sub-module is used for carrying out continuous wavelet transform decomposition on the flattened volume data to obtain decomposition data; the decomposed data includes seismic signals corresponding to a plurality of frequencies;

the frequency extension parameter determining submodule is used for determining frequency extension parameters; the frequency extension parameter comprises a frequency extension range and a reference frequency;

and the signal reconstruction submodule is used for reconstructing based on the frequency extension parameters and the seismic signals to obtain frequency extension data.

In an optional embodiment of the present invention, the denoised data generating module 705 includes:

the back-leveling processing submodule is used for performing back-leveling processing on the topology data to obtain back-leveling data;

and the singular value decomposition denoising processing submodule is used for carrying out singular value decomposition denoising processing on the inverse leveling data to obtain denoising data.

In an optional embodiment of the present invention, the singular value decomposition denoising processing sub-module includes:

the matrix construction unit is used for generating a Hankel matrix matched with the reverse leveling data;

the singular value decomposition unit is used for performing singular value decomposition on the Hankel matrix to obtain one or more singular values and singular value vectors which correspond to each other;

the target singular value determining unit is used for determining a target singular value according to a preset rule;

and the de-noising data generating unit is used for generating de-noising data by adopting the target singular value and the singular value vector corresponding to the target singular value.

In an optional embodiment of the present invention, the filtering processing module 706 includes:

the gradient structure tensor calculation submodule is used for calculating a gradient structure tensor based on the denoising data;

the confidence coefficient information determining submodule is used for determining the confidence coefficient information of the seismic section according to the gradient structure tensor;

the stratum inclination angle determining submodule is used for determining the stratum inclination angle according to the gradient structure tensor;

the anisotropic Laplace filter constructing submodule is used for constructing an anisotropic Laplace filter according to the confidence coefficient information and the stratum inclination angle;

the filtering submodule is used for processing the de-noising data by adopting the anisotropic Laplace filter to obtain optimized data;

wherein a size and a shape of a filtering window in the anisotropic laplacian filter are determined by the confidence information, and a window direction in the anisotropic laplacian filter is determined by the stratigraphic dip; the confidence information includes line type structure feature confidence and lateral discontinuity feature confidence.

For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.

An embodiment of the present invention further provides an electronic device, including: the post-stack seismic data processing method comprises a processor, a memory and a computer program which is stored in the memory and can run on the processor, wherein when the computer program is executed by the processor, each process of the post-stack seismic data processing method embodiment is realized, the same technical effect can be achieved, and the details are not repeated here to avoid repetition.

The embodiment of the invention also provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when being executed by a processor, the computer program realizes each process of the post-stack seismic data processing method embodiment, can achieve the same technical effect, and is not repeated here to avoid repetition.

The embodiments in the present specification are all described in a progressive manner, and each embodiment focuses on differences from other embodiments, and portions that are the same and similar between the embodiments may be referred to each other.

As will be appreciated by one of skill in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

While preferred embodiments of the present invention have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.

Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "include", "including" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or terminal device including a series of elements includes not only those elements but also other elements not explicitly listed or inherent to such process, method, article, or terminal device. Without further limitation, an element defined by the phrases "comprising one of 8230; \8230;" 8230; "does not exclude the presence of additional like elements in a process, method, article, or terminal device that comprises the element.

The method, the device, the electronic equipment and the medium for processing post-stack seismic data provided by the invention are described in detail, a specific example is applied in the text to explain the principle and the implementation mode of the invention, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims

1. A method of post-stack seismic data processing, comprising:

determining a target stratum;

carrying out continuous wavelet transform frequency extension on the flattened volume data to obtain frequency extension data;

2. The method of claim 1, further comprising:

performing inversion based on the optimized data to obtain inversion data;

3. The method according to claim 1 or 2, wherein the performing continuous wavelet transform frequency spreading on the flattened volume data to obtain frequency spreading data comprises:

determining a frequency extension parameter; the frequency extension parameter comprises a frequency extension range and a reference frequency;

4. The method according to claim 1 or 2, wherein the performing the inverse-flattening processing and the singular value decomposition denoising processing on the topology data to obtain denoised data comprises:

5. The method of claim 4, wherein the performing Singular Value Decomposition (SVD) denoising processing on the inverse flattened data to obtain denoised data comprises:

generating a Hankel matrix matched with the inverse Laplacian data;

determining a target singular value according to a preset rule;

6. The method according to claim 1 or 2, wherein the performing anisotropic laplacian filtering on the de-noised data to obtain optimized data comprises:

calculating a gradient structure tensor based on the de-noising data;

according to the gradient structure tensor and the stratum inclination angle;

7. A post-stack seismic data processing apparatus, comprising:

the data to be processed acquisition module is used for acquiring data to be processed; the data to be processed comprises post-stack seismic data corresponding to at least one stratum;

the target stratum determining module is used for determining a target stratum;

the de-noising data generation module is used for carrying out reverse flattening processing and singular value decomposition de-noising processing on the topology data to obtain de-noising data;

8. The apparatus of claim 1, further comprising:

9. An electronic device, comprising: a processor, a memory and a computer program stored on the memory and executable on the processor, the computer program when executed by the processor implementing the steps of the post-stack seismic data processing method as claimed in any one of claims 1-6.

10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the post-stack seismic data processing method according to any one of claims 1 to 6.