CN114428278A - Seismic data reconstruction method and device based on compressed sensing, electronic equipment and medium - Google Patents
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Abstract
The application discloses a seismic data reconstruction method and device based on compressed sensing, electronic equipment and a medium. The method can comprise the following steps: establishing an initial relational expression of unknown seismic data and a linear measurement vector; converting the initial relation into a sparse domain relation; and calculating the sparse solution of the sparse domain relational expression, namely the unknown seismic data. The method effectively suppresses the interference of multiple waves, improves the surface element property, improves the migration imaging quality technology, lacks the research significance of seismic information reconstruction on the subsequent interpretation processing work of seismic data, and contributes to further seismic data processing by reconstructing the seismic data, thereby serving the exploration and development of conventional oil gas and coal bed gas.
Description
Technical Field
The invention relates to the field of seismic data acquisition and processing, in particular to a seismic data reconstruction method and device based on compressed sensing, electronic equipment and a medium.
Background
Along with the continuous deepening of exploration and development of unconventional energy sources such as coal bed gas, shale gas and the like, seismic data reconstruction has important significance on seismic data acquisition and processing and imaging. The rapid development of high-precision exploration and development technology puts higher requirements on the integrity and regularity of seismic data.
However, in the process of seismic data acquisition, due to the limitation of complex geological structures (such as mountain front zones) and environmental factors (such as noise), the phenomena of incomplete seismic data acquisition, high-frequency loss and the like are caused, and the post-processing is greatly influenced, so that the actual production requirements cannot be met.
The prior art generally carries out reconstruction based on two theories, namely a prediction filter theory and a wave equation theory.
However, the prediction filter theory is mainly applied to trace encryption interpolation and irregular missing trace interpolation reconstruction of sparse seismic traces, but the interpolation reconstruction problem of irregular sampling data is not suitable. And an underground structure velocity model, a superposition velocity and the like are needed on the basis of a wave equation theory, and when prior information cannot be obtained, a reconstruction result is seriously influenced.
Therefore, it is necessary to develop a method, an apparatus, an electronic device and a medium for reconstructing seismic data based on compressed sensing.
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The invention provides a seismic data reconstruction method, a device, electronic equipment and a medium based on compressive sensing, which can effectively suppress the interference of multiple waves, improve the surface element property and improve the migration imaging quality technology, and lack the research significance of seismic information reconstruction on the subsequent interpretation processing work of seismic data, and the reconstructed seismic data is beneficial to further seismic data processing and serves the exploration and development of conventional oil gas and coal bed gas.
In a first aspect, an embodiment of the present disclosure provides a compressed sensing-based seismic data reconstruction method, including:
establishing an initial relational expression of unknown seismic data and a linear measurement vector;
converting the initial relation into a sparse domain relation;
and calculating the sparse solution of the sparse domain relational expression, namely the unknown seismic data.
Preferably, the initial relationship is:
y＝Ax+z (1)
wherein x is a one-dimensional finite-length discrete-time signal of unknown seismic data comprising N elements, y is a linear measurement vector, A is a random or deterministic measurement matrix of size M N, and z is an unknown error term.
Preferably, the condition for completely recovering x includes:
x is a p-sparse vector, p is less than or equal to M/2;
the matrix a satisfies the restricted isometry condition.
Preferably, the limiting equidistance condition is:
there is a constant deltasE (0,1), such that there are less than s elements of any signal x e RnSatisfies formula (2):
preferably, the sparse domain relation is:
y＝sΨΦ+z (3)
where Φ is the measurement matrix, Φ ∈ RN×MΨ is a sparse dictionary of seismic data, Ψ ∈ RD×NN, M, D are the number of sensors, the compressed signal dimension, and the sparse domain dimension, respectively.
Preferably, the conditions for complete recovery include:
sparsity of each row in s is less than M/2;
the Ψ Φ matrix satisfies the RIP condition.
As a specific implementation of the embodiments of the present disclosure,
in a second aspect, an embodiment of the present disclosure further provides a compressed sensing-based seismic data reconstruction apparatus, including:
the initial relational expression establishing module is used for establishing an initial relational expression of unknown seismic data and a linear measurement vector;
the sparse domain relational expression establishing module is used for converting the initial relational expression into a sparse domain relational expression;
and the calculation module is used for calculating the sparse solution of the sparse domain relational expression, namely the unknown seismic data.
Preferably, the initial relationship is:
y＝Ax+z (1)
wherein x is a one-dimensional finite-length discrete-time signal of unknown seismic data comprising N elements, y is a linear measurement vector, A is a random or deterministic measurement matrix of size M N, and z is an unknown error term.
Preferably, the condition for completely recovering x includes:
x is a p-sparse vector, p is less than or equal to M/2;
the matrix a satisfies the restricted isometry condition.
Preferably, the limiting equidistance condition is:
there is a constant deltasE (0,1), such that there are less than s elements of any signal x e RnSatisfies formula (2):
preferably, the sparse domain relation is:
y＝sΨΦ+z (3)
where Φ is the measurement matrix, Φ ∈ RN×MΨ is a sparse dictionary of seismic data, Ψ ∈ RD×NN, M, D are the number of sensors, the compressed signal dimension, and the sparse domain dimension, respectively.
Preferably, the conditions for complete recovery include:
sparsity of each row in s is less than M/2;
the Ψ Φ matrix satisfies the RIP condition.
In a third aspect, an embodiment of the present disclosure further provides an electronic device, where the electronic device includes:
a memory storing executable instructions;
a processor executing the executable instructions in the memory to implement the compressed sensing-based seismic data reconstruction method.
In a fourth aspect, the disclosed embodiments also provide a computer-readable storage medium, where a computer program is stored, and when executed by a processor, the computer program implements the compressed sensing-based seismic data reconstruction method.
The method and apparatus of the present invention have other features and advantages which will be apparent from or are set forth in detail in the accompanying drawings and the following detailed description, which are incorporated herein, and which together serve to explain certain principles of the invention.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent by describing in more detail exemplary embodiments thereof with reference to the attached drawings, in which like reference numerals generally represent like parts.
FIG. 1 shows a flow diagram of the steps of a compressed sensing-based seismic data reconstruction method according to one embodiment of the invention.
Fig. 2a, 2b, 2c and 2d show schematic diagrams of original Ricker wavelet signals and reconstruction of randomly sampled signals with sampling rates of 50%, 30% and 10%, respectively, according to an embodiment of the present invention.
FIGS. 3a, 3b, and 3c show schematic diagrams of raw single shot signal data with 20% random gather missing data and its compressed perceptual reconstruction data, respectively, according to one embodiment of the invention.
Fig. 4a, 4b and 4c show schematic diagrams of raw offset profile data and 50% random missing profile data and its compressed perceptual reconstruction data, respectively, according to an embodiment of the present invention.
FIG. 5 shows a block diagram of a compressed sensing-based seismic data reconstruction apparatus according to an embodiment of the invention.
Description of reference numerals:
201. an initial relational expression establishing module; 202. a sparse domain relational expression establishing module; 203. and a calculation module.
Detailed Description
Preferred embodiments of the present invention will be described in more detail below. While the following describes preferred embodiments of the present invention, it should be understood that the present invention may be embodied in various forms and should not be limited by the embodiments set forth herein.
The invention provides a seismic data reconstruction method based on compressed sensing, which comprises the following steps:
establishing an initial relational expression of unknown seismic data and a linear measurement vector;
converting the initial relation into a sparse domain relation;
and calculating the sparse solution of the sparse domain relational expression, namely the unknown seismic data.
In one example, the initial relationship is:
y＝Ax+z (1)
wherein x is a one-dimensional finite-length discrete-time signal of unknown seismic data comprising N elements, y is a linear measurement vector, A is a random or deterministic measurement matrix of size M N, and z is an unknown error term.
In one example, the condition to fully recover x includes:
x is a p-sparse vector, p is less than or equal to M/2;
the matrix a satisfies the restricted isometry condition.
In one example, the limiting isometry condition is:
exist asA constant deltasE (0,1), such that there are less than s elements of any signal x e RnSatisfies formula (2):
in one example, the sparse domain relationship is:
y＝sΨΦ+z (3)
where Φ is the measurement matrix, Φ ∈ RN×MΨ is a sparse dictionary of seismic data, Ψ ∈ RD×NN, M, D are the number of sensors, the compressed signal dimension, and the sparse domain dimension, respectively.
In one example, the conditions for full recovery include:
sparsity of each row in s is less than M/2;
the Ψ Φ matrix satisfies the RIP condition.
Specifically, the seismic data for each trace is represented by a discrete-time signal x of substantially one-dimensional finite length, N elements. According to the compressed sensing theory, the vector x belongs to RnA linear measurement vector y ∈ R that can be determined from insufficientmIs conditionally recovered and is described as equation (1).
There are two conditions for full recovery x: firstly, x is a p-sparse vector, and p is less than or equal to M/2; second, the matrix a satisfies the Restricted Isometry Property (RIP) condition. I.e. there is a constant deltasE (0,1), such that there are less than s elements of any signal x e RnSatisfying the formula (2).
In most cases, seismic signals do not directly satisfy the sparsity required by compressed sensing theory. I.e. the columns of the sampling matrix a cannot be sparsely represented by the rows of coefficient bases. Incoherency is defined as:
the coherence value range is
Since Φ ═ a Ψ, equation (4) can be converted to:
i.e. the maximum absolute value obtained by performing inner product and normalization on all columns in phi.
However, the undersampling method may be applied to the seismic data after the seismic data is converted into a sparse domain. Equation (1) can be converted to equation (3) and the compressed seismic data is expressed as y (y ∈ R)T×M). The original seismic data is called x (x is equal to R)T×NX ═ s Ψ), where T is the duration of the seismic data and s ∈ RT×DAre the corresponding coefficients. Two recovery conditions were as follows: firstly, sparsity of each row in s is less than M/2; second, the Ψ Φ matrix satisfies the RIP condition.
Let Ψ be a dictionary with x ∈ s Ψ with a coherence coefficient μ, if the signal x ∈ RNCan be decomposed into x | | | s | | | survival in the dictionary Ψ0<1/μ (Ψ), then s has a unique sparse representation in Ψ. If the simultaneous satisfaction of s | | non-calculation0<(μ (Ψ) +1)/2 μ (Ψ), then this sparse solution can be solved by solving for L1And solving a norm minimization problem.
(1) Structured matrix sampling
The form of the circulant matrix is as follows:
wherein A isi,j＝Ai+1,j+1＝ai-j。
Only one column of matrix elements needs to be accessed. And the circulant matrix coherence is greater than the diagonal matrix. The storage cost can be greatly reduced by using the circulant matrix for compressed sensing sampling.
(2) Numerical simplified sampling matrix
Original sampling matrix elements which take values randomly according to Gaussian distribution are replaced by elements which are simpler in numerical value, and the distribution randomness of the matrix elements is maintained in the distribution. The simplified sampling matrix uses a 0-1 bernoulli matrix, i.e. the elements are only 0 and 1, and the distribution pattern is a bernoulli distribution with the same distribution. For each matrix element, the probability density function f of the distribution is:
(3) solution/reconstruction method
By utilizing redundant information existing in the signal, in a proper representation domain (such as a frequency domain), the signal is not sparse, and linear measurement is carried out. In order to reconstruct the original signal from the linear measurement, the compressed sensing needs to solve a least squares problem of L1-norm regularization. Under certain conditions, this regularized least squares problem explains the coefficient solution of the original underdetermined linear system.
(4) Basis pursuit reconstruction algorithm
The signal is reconstructed using basis pursuit and converted into a mathematical optimization problem. In the form of
So that y is Hs. Where n × 1 vector on s represents the output signal, m × 1 measurement vector on y, m × n measurement matrix on H, where m<n, it is recommended to use a noise reduction basis tracking algorithm.
The invention also provides a seismic data reconstruction device based on compressed sensing, which comprises:
the initial relational expression establishing module is used for establishing an initial relational expression of unknown seismic data and a linear measurement vector;
the sparse domain relational expression establishing module is used for converting the initial relational expression into a sparse domain relational expression;
and the calculation module is used for calculating the sparse solution of the sparse domain relational expression, namely the unknown seismic data.
In one example, the initial relationship is:
y＝Ax+z (1)
wherein x is a one-dimensional finite-length discrete-time signal of unknown seismic data comprising N elements, y is a linear measurement vector, A is a random or deterministic measurement matrix of size M N, and z is an unknown error term.
In one example, the condition to fully recover x includes:
x is a p-sparse vector, p is less than or equal to M/2;
the matrix a satisfies the restricted isometry condition.
In one example, the limiting isometry condition is:
there is a constant deltasE (0,1), such that there are less than s elements of any signal x e RnSatisfies formula (2):
in one example, the sparse domain relationship is:
y＝sΨΦ+z (3)
where Φ is the measurement matrix, Φ ∈ RN×MΨ is a sparse dictionary of seismic data, Ψ ∈ RD×NN, M, D are the number of sensors, the compressed signal dimension, and the sparse domain dimension, respectively.
In one example, the conditions for full recovery include:
sparsity of each row in s is less than M/2;
the Ψ Φ matrix satisfies the RIP condition.
Specifically, the seismic data for each trace is represented by a discrete-time signal x of substantially one-dimensional finite length, N elements. According to the compressed sensing theory, the vector x belongs to RnA linear measurement vector y ∈ R that can be determined from insufficientmIs conditionally recovered and is described as equation (1).
There are two conditions for full recovery x: firstly, x is a p-sparse vector, and p is less than or equal to M/2; second, the matrix a satisfies the Restricted Isometry Property (RIP) condition. I.e. there is a constant deltasE (0,1), such that there are less than s elements of any signal x e RnSatisfying the formula (2).
In most cases, seismic signals do not directly satisfy the sparsity required by compressed sensing theory. I.e. the columns of the sampling matrix a cannot be sparsely represented by the rows of coefficient bases. The incoherent is defined as formula (4), and the coherent value range is
Because Φ — a Ψ, equation (4) can be transformed into equation (5), i.e., the maximum absolute value obtained by inner-product and normalization of all columns in Φ.
However, the undersampling method may be applied to the seismic data after the seismic data is converted into a sparse domain. Equation (1) can be converted to equation (3) and the compressed seismic data is expressed as y (y ∈ R)T×M). The original seismic data is called x (x is equal to R)T×NX ═ s Ψ), where T is the duration of the seismic data and s ∈ RT×DAre the corresponding coefficients. Two recovery conditions were as follows: firstly, sparsity of each row in s is less than M/2; second, the Ψ Φ matrix satisfies the RIP condition.
Let Ψ be a dictionary with x ∈ s Ψ with a coherence coefficient μ, if the signal x ∈ RNCan be decomposed into x | | | s | | | survival in the dictionary Ψ0<1/μ (Ψ), then s has a unique sparse representation in Ψ. If the simultaneous satisfaction of s | | non-calculation0<(μ (Ψ) +1)/2 μ (Ψ), then this sparse solution can be solved by solving for L1And solving a norm minimization problem.
(1) Structured matrix sampling
The circulant matrix is in the form of equation (6) where Ai,j＝Ai+1,j+1＝ai-j. Only one column of matrix elements needs to be accessed. And the circulant matrix coherence is greater than the diagonal matrix. The storage cost can be greatly reduced by using the circulant matrix for compressed sensing sampling.
(2) Numerical simplified sampling matrix
Original sampling matrix elements which take values randomly according to Gaussian distribution are replaced by elements which are simpler in numerical value, and the distribution randomness of the matrix elements is maintained in the distribution. The simplified sampling matrix uses a 0-1 bernoulli matrix, i.e. the elements are only 0 and 1, and the distribution pattern is a bernoulli distribution with the same distribution. For each matrix element, the probability density function f of the distribution is formula (7).
(3) Solution/reconstruction method
By utilizing redundant information existing in the signal, in a proper representation domain (such as a frequency domain), the signal is not sparse, and linear measurement is carried out. In order to reconstruct the original signal from the linear measurement, the compressed sensing needs to solve a least squares problem of L1-norm regularization. Under certain conditions, this regularized least squares problem explains the coefficient solution of the original underdetermined linear system.
(4) Basis pursuit reconstruction algorithm
The signal is reconstructed using basis pursuit and converted into a mathematical optimization problem. In the form of
So that y is Hs. Where n × 1 vector on s represents the output signal, m × 1 measurement vector on y, m × n measurement matrix on H, where m<n, it is recommended to use a noise reduction basis tracking algorithm.
The present invention also provides an electronic device, comprising: a memory storing executable instructions; and the processor executes executable instructions in the memory to implement the compressed sensing-based seismic data reconstruction method.
The invention also provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the compressed sensing-based seismic data reconstruction method described above.
To facilitate understanding of the scheme of the embodiments of the present invention and the effects thereof, four specific application examples are given below. It will be understood by those skilled in the art that this example is merely for the purpose of facilitating an understanding of the present invention and that any specific details thereof are not intended to limit the invention in any way.
Example 1
FIG. 1 shows a flow diagram of the steps of a compressed sensing-based seismic data reconstruction method according to one embodiment of the invention.
As shown in fig. 1, the compressed sensing-based seismic data reconstruction method includes: step 101, establishing an initial relational expression of unknown seismic data and a linear measurement vector; 102, converting the initial relational expression into a sparse domain relational expression; and 103, calculating a sparse solution of the sparse domain relational expression, namely the unknown seismic data.
Fig. 2a, 2b, 2c and 2d show schematic diagrams of original Ricker wavelet signals and reconstruction of randomly sampled signals with sampling rates of 50%, 30% and 10%, respectively, according to an embodiment of the present invention. The Ricker wavelet signal recovered by the method is basically consistent with the original signal, and the single-channel data recovery is relatively accurate.
FIGS. 3a, 3b, and 3c show schematic diagrams of raw single shot signal data with 20% random gather missing data and its compressed perceptual reconstruction data, respectively, according to one embodiment of the invention. The single shot data recovery is related to the data missing degree, a certain error exists between the seismic channel recovery of continuous missing data and the original data, and the reconstructed data is better under the condition of less missing channels.
Fig. 4a, 4b and 4c show schematic diagrams of raw offset profile data and 50% random missing profile data and its compressed perceptual reconstruction data, respectively, according to an embodiment of the present invention. The offset section missing data has high recoverability, the reconstructed data is better under the condition of less missing channels, and certain errors exist between the seismic channel reconstructed data of continuous missing data and the original seismic section data.
Example 2
FIG. 5 shows a block diagram of a compressed sensing-based seismic data reconstruction apparatus according to an embodiment of the invention.
As shown in fig. 5, the compressed sensing-based seismic data reconstruction apparatus includes:
an initial relational expression establishing module 201, which is used for establishing an initial relational expression of unknown seismic data and a linear measurement vector;
a sparse domain relational expression establishing module 202, which converts the initial relational expression into a sparse domain relational expression;
the calculation module 203 calculates a sparse solution of the sparse domain relational expression, which is the unknown seismic data.
Alternatively, the initial relationship is:
y＝Ax+z (1)
wherein x is a one-dimensional finite-length discrete-time signal of unknown seismic data comprising N elements, y is a linear measurement vector, A is a random or deterministic measurement matrix of size M N, and z is an unknown error term.
Alternatively, the condition for completely recovering x includes:
x is a p-sparse vector, p is less than or equal to M/2;
the matrix a satisfies the restricted isometry condition.
As an alternative, the condition limiting the isometry is:
there is a constant deltasE (0,1), such that there are less than s elements of any signal x e RnSatisfies formula (2):
alternatively, the sparse domain relation is:
y＝sΨΦ+z (3)
where Φ is the measurement matrix, Φ ∈ RN×MΨ is a sparse dictionary of seismic data, Ψ ∈ RD×NN, M, D are the number of sensors, the compressed signal dimension, and the sparse domain dimension, respectively.
Alternatively, the conditions for full recovery include:
sparsity of each row in s is less than M/2;
the Ψ Φ matrix satisfies the RIP condition.
Example 3
The present disclosure provides an electronic device including: a memory storing executable instructions; and the processor runs executable instructions in the memory to realize the compressed sensing-based seismic data reconstruction method.
An electronic device according to an embodiment of the present disclosure includes a memory and a processor.
The memory is to store non-transitory computer readable instructions. In particular, the memory may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, Random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), hard disk, flash memory, etc.
The processor may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of the disclosure, the processor is configured to execute the computer readable instructions stored in the memory.
Those skilled in the art should understand that, in order to solve the technical problem of how to obtain a good user experience, the present embodiment may also include well-known structures such as a communication bus, an interface, and the like, and these well-known structures should also be included in the protection scope of the present disclosure.
For the detailed description of the present embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, which are not repeated herein.
Example 4
The embodiment of the disclosure provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the compressed sensing-based seismic data reconstruction method.
A computer-readable storage medium according to an embodiment of the present disclosure has non-transitory computer-readable instructions stored thereon. The non-transitory computer readable instructions, when executed by a processor, perform all or a portion of the steps of the methods of the embodiments of the disclosure previously described.
The computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROMs and DVDs), magneto-optical storage media (e.g., MOs), magnetic storage media (e.g., magnetic tapes or removable disks), media with built-in rewritable non-volatile memory (e.g., memory cards), and media with built-in ROMs (e.g., ROM cartridges).
It will be appreciated by persons skilled in the art that the above description of embodiments of the invention is intended only to illustrate the benefits of embodiments of the invention and is not intended to limit embodiments of the invention to any examples given.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.
Claims (10)
1. A seismic data reconstruction method based on compressed sensing is characterized by comprising the following steps:
establishing an initial relational expression of unknown seismic data and a linear measurement vector;
converting the initial relation into a sparse domain relation;
and calculating the sparse solution of the sparse domain relational expression, namely the unknown seismic data.
2. The compressed sensing-based seismic data reconstruction method of claim 1, wherein the initial relation is:
y＝Ax+z (1)
wherein x is a one-dimensional finite-length discrete-time signal of unknown seismic data comprising N elements, y is a linear measurement vector, A is a random or deterministic measurement matrix of size M N, and z is an unknown error term.
3. The compressed sensing-based seismic data reconstruction method of claim 2, wherein the condition for fully recovering x comprises:
x is a p-sparse vector, p is less than or equal to M/2;
the matrix a satisfies the restricted isometry condition.
4. The compressed sensing-based seismic data reconstruction method of claim 3, wherein the limiting isometry condition is:
there is a constant deltasE (0,1), such that there are less than s elements of any signal x e RnSatisfies formula (2):
5. the compressed sensing-based seismic data reconstruction method of claim 1, wherein the sparse domain relation is:
y＝sΨΦ+z (3)
where Φ is the measurement matrix, Φ ∈ RN×MΨ is a sparse dictionary of seismic data, Ψ ∈ RD×NN, M, D are the number of sensors, the compressed signal dimension, and the sparse domain dimension, respectively.
6. The compressed sensing-based seismic data reconstruction method of claim 5, wherein the condition for full recovery comprises:
sparsity of each row in s is less than M/2;
the Ψ Φ matrix satisfies the RIP condition.
7. An apparatus for seismic data reconstruction based on compressed sensing, comprising:
the initial relational expression establishing module is used for establishing an initial relational expression of unknown seismic data and a linear measurement vector;
the sparse domain relational expression establishing module is used for converting the initial relational expression into a sparse domain relational expression;
and the calculation module is used for calculating the sparse solution of the sparse domain relational expression, namely the unknown seismic data.
8. The compressed sensing-based seismic data reconstruction apparatus of claim 7, wherein the sparse domain relation is:
y＝sΨΦ+z (3)
where Φ is the measurement matrix, Φ ∈ RN×MΨ is a sparse dictionary of seismic data, Ψ ∈ RD×NN, M, D are the number of sensors, the compressed signal dimension, and the sparse domain dimension, respectively.
9. An electronic device, characterized in that the electronic device comprises:
a memory storing executable instructions;
a processor executing the executable instructions in the memory to implement the compressed sensing-based seismic data reconstruction method of any of claims 1-6.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, implements the compressed sensing-based seismic data reconstruction method of any one of claims 1-6.
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| CN114647006A (en) * | 2022-05-18 | 2022-06-21 | 东营市京川石油技术有限公司 | Earthquake weak signal enhancement method based on multiple compressed sensing |
| CN115996144A (en) * | 2022-12-08 | 2023-04-21 | 西南石油大学 | Fracturing data transmission method and system based on compressed sensing and RSA encryption algorithm |
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2020
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114647006A (en) * | 2022-05-18 | 2022-06-21 | 东营市京川石油技术有限公司 | Earthquake weak signal enhancement method based on multiple compressed sensing |
| CN114647006B (en) * | 2022-05-18 | 2022-09-13 | 地大汇能（北京）科技有限公司 | Earthquake weak signal enhancement method based on multiple compressed sensing |
| CN115996144A (en) * | 2022-12-08 | 2023-04-21 | 西南石油大学 | Fracturing data transmission method and system based on compressed sensing and RSA encryption algorithm |
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