CN116953786A - Multiple prediction method, device, computing equipment and storage medium - Google Patents

Multiple prediction method, device, computing equipment and storage medium Download PDF

Info

Publication number
CN116953786A
CN116953786A CN202310926718.2A CN202310926718A CN116953786A CN 116953786 A CN116953786 A CN 116953786A CN 202310926718 A CN202310926718 A CN 202310926718A CN 116953786 A CN116953786 A CN 116953786A
Authority
CN
China
Prior art keywords
matrix
domain data
seismic
frequency domain
data
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202310926718.2A
Other languages
Chinese (zh)
Inventor
徐强
焦叙明
王炜
王海昆
孙雷鸣
马德志
张明强
陈磅
周秘
李春雷
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
China Oilfield Services Ltd
Original Assignee
China Oilfield Services Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by China Oilfield Services Ltd filed Critical China Oilfield Services Ltd
Priority to CN202310926718.2A priority Critical patent/CN116953786A/en
Publication of CN116953786A publication Critical patent/CN116953786A/en
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/36—Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Remote Sensing (AREA)
  • Mathematical Physics (AREA)
  • Mathematical Analysis (AREA)
  • Acoustics & Sound (AREA)
  • Pure & Applied Mathematics (AREA)
  • Geophysics (AREA)
  • Data Mining & Analysis (AREA)
  • Mathematical Optimization (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • Geology (AREA)
  • Theoretical Computer Science (AREA)
  • Environmental & Geological Engineering (AREA)
  • Computational Mathematics (AREA)
  • Databases & Information Systems (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Software Systems (AREA)
  • Algebra (AREA)
  • Geophysics And Detection Of Objects (AREA)

Abstract

The invention discloses a multiple prediction method, a multiple prediction device, computing equipment and a storage medium. The method comprises the following steps: acquiring frequency domain data of any shot point in any seismic channel; generating a seismic data matrix according to the frequency domain data, wherein the seismic data matrix is an upper triangular matrix with a main diagonal of 0, and any non-first column of the seismic data matrix contains the frequency domain data of at least one seismic channel of the same shot point; constructing a multiple matrix, wherein the multiple matrix is an upper triangular matrix with a main diagonal of 0, and any non-first column of the multiple matrix comprises multiple models of at least one seismic channel of the same shot point; establishing a functional relation between the multiple matrix and the seismic data matrix; and obtaining model frequency domain data corresponding to any multiple model in the multiple matrix according to the functional relation. By adopting the scheme, the prediction efficiency of the multiple wave can be improved on the premise of ensuring the prediction precision of the multiple wave.

Description

Multiple prediction method, device, computing equipment and storage medium
Technical Field
The invention relates to the technical field of exploration, in particular to a multiple prediction method, a device, computing equipment and a storage medium.
Background
The processing of the seismic data is an important link in oil and gas exploration, and the processing result of the seismic data can directly influence the oil and gas exploration result. The multiple wave data is interference wave data common in seismic data, and has certain influence on migration velocity analysis, complex geological structure imaging, wave group characteristic fine characterization, small fault imaging and the like, so that multiple wave prediction is a key link in seismic data processing.
However, the inventors found in practice that the following drawbacks exist in the prior art: the existing multiple prediction method has the defects of complex implementation process and low prediction efficiency.
Disclosure of Invention
The present invention has been made in view of the above problems, and provides a multiple prediction method, apparatus, computing device, and storage medium that overcome or at least partially solve the above problems.
According to an aspect of the present invention, there is provided a multiple prediction method including:
acquiring frequency domain data of any shot point in any seismic channel;
generating a seismic data matrix according to the frequency domain data; wherein the seismic data matrix is an upper triangular matrix with a main diagonal of 0, and any non-first column of the seismic data matrix contains frequency domain data of at least one seismic channel of the same shot point;
constructing a multiple matrix; the multi-wave matrix is an upper triangular matrix with a main diagonal of 0, and any non-first column of the multi-wave matrix comprises a multi-wave model of at least one seismic channel of the same shot point;
establishing a functional relation between the multiple matrix and the seismic data matrix;
and obtaining model frequency domain data corresponding to any multiple model in the multiple matrix according to the functional relation.
In an alternative embodiment, the seismic data matrix is specifically:
wherein T is (,) D is the element of the ith row and jth column in the seismic data matrix (j-n,j-i) Frequency domain data of j-i seismic traces for j-n th shot point, D (1,-i) The frequency domain data of the 1 st shot in j-i seismic channels is obtained, and n is the total number of the seismic channels.
In an alternative embodiment, the multiple matrix is specifically:
wherein P is (i,j) M is the element of the ith row and the jth column in the multiple matrix (j-n,j-i) Multiple model of the j-i seismic trace for the j-n shot, M (1,-i) The method is a multiple wave model of the 1 st shot in j-i seismic traces, and n is the total number of the seismic traces.
In an alternative embodiment, said establishing a functional relationship between said multiple matrix and said seismic data matrix further comprises:
the following functional relationship is established:
＝T×T
wherein P is a multiple matrix, and T is a seismic data matrix.
In an alternative embodiment, the acquiring the frequency domain data of any shot at any seismic trace further includes:
acquiring time domain data of any shot point in any seismic channel;
and carrying out Fourier transform processing on the time domain data to obtain the frequency domain data.
In an alternative embodiment, after the acquiring the time domain data of any shot at any seismic trace, the method further includes: for any time domain data, acquiring time domain data of a plurality of adjacent sampling points of the time domain data in the longitudinal direction, calculating corresponding L2 norms according to the time domain data and the time domain data of the plurality of adjacent sampling points, and taking the product of the L2 norms and the time domain data as corrected time domain data;
the obtaining the frequency domain data after performing fourier transform processing on the time domain data further includes: and carrying out Fourier transform processing on the corrected time domain data to obtain the frequency domain data.
In an optional implementation manner, after the model frequency domain data corresponding to any multiple model in the multiple matrix is obtained according to the functional relation, the method further includes:
and carrying out Fourier inverse transformation on model frequency domain data corresponding to any multiple wave model to obtain model time domain data.
According to another aspect of an embodiment of the present invention, there is provided a multiple prediction apparatus including:
the acquisition module is used for acquiring frequency domain data of any shot point in any seismic channel;
the processing module is used for generating a seismic data matrix according to the frequency domain data; wherein the seismic data matrix is an upper triangular matrix with a main diagonal of 0, and any non-first column of the seismic data matrix contains frequency domain data of at least one seismic channel of the same shot point; constructing a multiple matrix; the multi-wave matrix is an upper triangular matrix with a main diagonal of 0, and any non-first column of the multi-wave matrix comprises a multi-wave model of at least one seismic channel of the same shot point; and establishing a functional relationship between the multiple matrix and the seismic data matrix;
and the execution module is used for obtaining model frequency domain data corresponding to any multiple model in the multiple matrix according to the functional relation.
In an alternative embodiment, the seismic data matrix is specifically:
wherein T is (,) D is the element of the ith row and jth column in the seismic data matrix (j-n,j-i) Frequency domain data of j-i seismic traces for j-n th shot point, D (1,-i) The frequency domain data of the 1 st shot in j-i seismic channels is obtained, and n is the total number of the seismic channels.
In an alternative embodiment, the multiple matrix is specifically:
wherein P is (i,j) M is the element of the ith row and the jth column in the multiple matrix (j-n,j-i) Multiple model of the j-i seismic trace for the j-n shot, M (1,-i) The method is a multiple wave model of the 1 st shot in j-i seismic traces, and n is the total number of the seismic traces.
In an alternative embodiment, the processing module is configured to: the following functional relationship is established:
＝T×T
wherein P is a multiple matrix, and T is a seismic data matrix.
In an alternative embodiment, the obtaining module is configured to: acquiring time domain data of any shot point in any seismic channel;
and carrying out Fourier transform processing on the time domain data to obtain the frequency domain data.
In an alternative embodiment, the obtaining module is configured to: for any time domain data, acquiring time domain data of a plurality of adjacent sampling points of the time domain data in the longitudinal direction, calculating corresponding L2 norms according to the time domain data and the time domain data of the plurality of adjacent sampling points, and taking the product of the L2 norms and the time domain data as corrected time domain data;
and carrying out Fourier transform processing on the corrected time domain data to obtain the frequency domain data.
In an alternative embodiment, the execution module is configured to: and carrying out Fourier inverse transformation on model frequency domain data corresponding to any multiple wave model to obtain model time domain data.
According to yet another aspect of an embodiment of the present invention, there is provided a computing device including: the device comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete communication with each other through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the multiple wave prediction method.
According to still another aspect of the embodiments of the present invention, there is provided a computer storage medium having at least one executable instruction stored therein, the executable instruction causing a processor to perform operations corresponding to the multiple prediction method described above.
In the multiple prediction method, the device, the computing equipment and the storage medium provided by the invention, frequency domain data of any shot point in any seismic channel is constructed as the upper triangular matrix with the main diagonal of 0, and multiple matrixes with the same structure are constructed, so that multiple prediction is converted into matrix operation, and the prediction efficiency of multiple is improved on the premise of ensuring the multiple prediction precision.
The foregoing description is only an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present invention more readily apparent.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
fig. 1 shows a schematic flow chart of a multiple prediction method according to an embodiment of the present invention;
FIG. 2 shows a schematic diagram of a seismic data matrix provided by an embodiment of the invention;
FIG. 3 shows a schematic representation of yet another seismic data matrix provided by an embodiment of the invention;
FIG. 4 is a schematic diagram of a multiple matrix according to an embodiment of the present invention;
FIG. 5 shows a schematic diagram of yet another multiple matrix provided by an embodiment of the present invention;
FIG. 6 is a schematic flow chart of another multiple prediction method according to an embodiment of the present invention;
FIG. 7 shows a forward model provided by an embodiment of the present invention versus a multiple predicted by an embodiment of the present invention;
FIG. 8 shows a graph of actual shot gather data versus the multiples predicted by an embodiment of the present invention;
fig. 9 shows a schematic structural diagram of a multiple prediction apparatus according to an embodiment of the present invention;
FIG. 10 illustrates a schematic diagram of a computing device provided by an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Fig. 1 shows a flow chart of a multiple prediction method according to an embodiment of the present invention. As shown in fig. 1, the method specifically includes the following steps:
step S110, frequency domain data of any shot point in any seismic channel is obtained.
Acquiring the acquired seismic data, and acquiring the seismic data of each shot point in each seismic channel from the acquired seismic data, wherein the representation of the seismic data in the frequency domain is the frequency domain data of the corresponding shot point in the corresponding seismic channel.
Step S120, generating a seismic data matrix according to the frequency domain data.
In the embodiment of the invention, the multiple is predicted by the frequency domain data, so that the multiple prediction process can be simplified, and the multiple prediction efficiency can be improved. And the multiple prediction process is converted into a matrix operation process of a seismic data matrix and a multiple matrix, so that the prediction efficiency of the multiple is further improved.
Specifically, in the embodiment of the invention, a seismic data matrix and a multiple matrix are constructed based on the following formula 1.
M(s x ,g y ,f)＝∫ k D(s x ,g k ,f)D(s x-k ,g y-k F) dk (equation 1)
Wherein M(s) x ,g y F) represents the predicted shot point at s x Position, detector point g y A multiple model of (2); d(s) x ,g k F) denotes the shot point at s x Position, detector point g k Is a piece of seismic data; d(s) x-k ,g y-k F) denotes the shot point at s x-k Position, detector point g y-k Is a single line of seismic data. F in formula 1 represents that the data is frequency domain data.
The constructed seismic data matrix contains frequency domain data of each shot point in each seismic channel. The seismic data matrix is an upper triangular matrix with a main diagonal of 0, and any non-first column of the seismic data matrix contains frequency domain data of at least one seismic channel of the same shot point, so as to improve the multiple prediction efficiency.
In an alternative embodiment, the seismic data matrix may be expressed specifically as the following equation 2:
wherein T is a seismic data matrix, T (,) D is the element of the ith row and jth column in the seismic data matrix (j-n,j-i) Frequency domain data of j-i seismic traces for j-n th shot point, D (1,-i) The frequency domain data of the 1 st shot in j-i seismic channels is obtained, and n is the total number of the seismic channels.
Specifically, the total number of rows and total columns in the seismic data matrix are the same, and the total number of rows and total columns is equal to the sum of the total number of shots and the total number of seismic traces. For example, if m shots and n seismic traces are included, the total number of rows and columns of the seismic data matrix is m+n.
Taking fig. 2 as an example, T (m_n) in fig. 2 represents a seismic data matrix corresponding to m shots and n seismic traces, D s1g1 Frequency domain data representing the 1 st seismic trace of the 1 st shot, D s4gn Frequency domain data representing the nth seismic trace of the 4 th shot, D smgn Frequency domain data … … representing the mth seismic trace of the mth shot. It can be seen that when i is greater than or equal to j, the element in the seismic data matrix is 0, so that the seismic data matrix is an upper triangular matrix with a main diagonal of 0; in the upper triangle area of the seismic data matrix, one shot point is arranged in each column from the n+1st column, the n+1st column is arranged with the 1 st shot point in each seismic channel frequency domain data, the 2 nd shot point is arranged in each seismic channel frequency domain data, the … … n+m column is arranged with the m shot point in each seismic channel frequency domain data, and the corresponding shot points are arranged in a column in a manner of arranging the seismic channels in reverse order when the corresponding shot points are arranged in each seismic channel frequency domain data, namely, the non-zero elements of the column are arranged from bottom to topThe trace numbers of the seismic traces are successively incremented, e.g. column n+1 is successively D from top to bottom s1gn ，……D s1g4 ，D s1g3 ，D s1g2 ，D s1g1 Namely, the frequency domain data of the 1 st shot point in the nth seismic channel and the (n-1) th seismic channel … … and the 1 st seismic channel are arranged. In the 2 nd to nth columns, the frequency domain data of the 1 st shot point is arranged in each column, and the number of columns is increased, and the number of frequency domain data arranged in the column is increased.
Taking fig. 3 as an example, T (4_3) in fig. 3 represents a seismic data matrix corresponding to 4 shots and 3 seismic traces. D (D) s1g1 Frequency domain data representing the 1 st seismic trace of the 1 st shot, D s4g3 Frequency domain data … … representing the 4 th shot at the 3 rd seismic trace. From this, the main diagonal and the lower triangle area in the seismic data matrix are all 0; and its corresponding element is also 0 when j-i is greater than 3. The 2 nd and 3 rd columns are arranged with partial frequency domain data of the 1 st shot point; the 4 th to 7 th columns sequentially arrange all the frequency domain data of the 1 st to 4 th shots.
Step S130, constructing a multiple matrix.
The built multiple matrix comprises multiple models of all shot points in all seismic channels, and the multiple models can be understood as corresponding multiple parameters. The multiple matrix is an upper triangular matrix with a main diagonal of 0, and any non-first column of the multiple matrix comprises multiple models of at least one seismic channel of the same shot point.
In an alternative embodiment, the multiple matrix may be specifically expressed as the following formula 3:
wherein P is a multiple matrix, P (i,j) M is the element of the ith row and the jth column in the multiple matrix (j-n,j-i) Multiple model of the j-i seismic trace for the j-n shot, M (1,-i) The method is a multiple wave model of the 1 st shot in j-i seismic traces, and n is the total number of the seismic traces.
Specifically, the total number of rows and total columns in the seismic data matrix are the same, and the total number of rows and total columns is equal to the sum of the total number of shots and the total number of seismic traces. For example, if m shots and n seismic traces are included, the total number of rows and columns of the seismic data matrix is m+n.
Taking fig. 4 as an example, P (m_n) in fig. 4 represents a multiple matrix corresponding to M shots and n seismic traces, M s1g1 Multiple model representing 1 st seismic trace of 1 st shot, M s4gn Multiple model representing the nth seismic trace of the 4 th shot, M smgn A multiple model … … representing the mth seismic trace of the mth shot. From the above, when i is not less than j, the element in the multiple matrix is 0, so that the multiple matrix is an upper triangular matrix with a main diagonal of 0; in the upper triangle area of the multiple matrix, one shot point is arranged in the multiple model of each seismic channel from the n+1st row, the 1 st shot point is arranged in the multiple model of each seismic channel from the n+1st row, the 2 nd shot point is arranged in the multiple model of each seismic channel from the n+2nd row, the M th shot point is arranged in the multiple model of each seismic channel … … n+m th row, and the corresponding shot points are arranged in a row in a manner of arranging the seismic channels in reverse order when the multiple models of each seismic channel are arranged in one row, namely, the channel numbers of the seismic channels from bottom to top in the non-zero elements of the row are sequentially increased, for example, the n+1st row is sequentially M from top to bottom s1gn ，……M s1g4 ，M s1g3 ，M s1g2 ，M s1g1 Namely, arranging multiple wave models of the 1 st shot point in the nth seismic channel and the (n-1) th seismic channel … … and the 1 st seismic channel. In the 2 nd to nth columns, the multiple models of the 1 st shot are arranged in each column, and the number of columns is increased, and the number of the multiple models arranged in the column is increased.
Taking fig. 5 as an example, T (4_3) in fig. 5 represents a multiple matrix corresponding to 4 shots and 3 seismic traces. M is M s1g1 Multiple model representing 1 st seismic trace of 1 st shot, M s4g3 A multiple model … … representing the 4 th shot at the 3 rd seismic trace. From the above, the main diagonal and the lower triangle in the multiple model matrix are all 0; and its corresponding element is also 0 when j-i is greater than 3. The 2 nd row and the 3 rd row are arranged as part of the 1 st cannonA multiple model of the point; the 4 th to 7 th columns sequentially arrange all the multiple models of the 1 st to 4 th shots.
And it can be seen that the multiple matrix is identical to the seismic data matrix in terms of the element arrangement.
Step S140, establishing a functional relation between the multiple matrix and the seismic data matrix.
And establishing a functional relation between the multiple matrix and the seismic data matrix so that each multiple model in the multiple matrix meets the formula 1, thereby guaranteeing the accuracy of multiple prediction.
In an alternative embodiment, the multiple matrix is as shown in equation 4 as a function of the seismic data matrix:
p=t×t (formula 4)
Wherein P is a multiple matrix, and T is a seismic data matrix.
Therefore, the prediction process of the multiple waves is converted into matrix operation, so that model data of each multiple wave model can be solved by using a simple algorithm of the matrix operation.
And step S150, obtaining model frequency domain data corresponding to any multiple model in the multiple matrix according to the functional relation.
Model frequency domain data corresponding to any multiple model can be obtained through matrix operation, namely, each multiple model is represented by using the frequency domain data of shot points in the seismic channel. Taking the model data of the multiple model of the 3 rd shot at the 3 rd seismic trace as an example, M can be obtained by the above formula 1 s3g3 ＝D s3g1 *D s2g2+ D s3g2 *D s1g1 . M can be obtained by the formulas 4, 3 and 5 s3g3 ＝(0，0，0，D s1g1 ，D s2g2 ，D s3g3 )╳(0，0，D s3g3 ，D s3g2 ，D s3g1 ，0，0) T ＝D s3g1 *D s2g2+ D s3g2 *D s1g1 . The prediction results of the two are the same, so that the prediction efficiency of the multiple is improved on the premise of ensuring the prediction precision of the multiple in the method.
Therefore, the frequency domain data of any shot point in any seismic channel is constructed as the upper triangular matrix with the main diagonal of 0, and the multiple matrix with the same structure is constructed, so that the multiple prediction is converted into matrix operation, and the prediction efficiency of the multiple is improved on the premise of ensuring the multiple prediction precision.
Fig. 6 shows a flow chart of yet another multiple prediction method according to an embodiment of the present invention.
As shown in fig. 6, the method specifically includes the following steps:
step S610, time domain data of any shot point in any seismic channel is obtained.
In general, the initially acquired seismic data is time domain data, and then the time domain data of each shot in each seismic trace is acquired in this step.
Step S620, for any time domain data, pre-processing the time domain data to obtain corrected time domain data.
In the practical implementation process, as the multiple is mainly generated in the strong reflecting layer, in order to simplify the prediction process of the multiple and reduce noise interference, the embodiment of the invention further preprocesses the time domain data after obtaining the time domain data, so that the prediction can be mainly performed according to the data of the strong reflecting layer in the subsequent multiple prediction, and the prediction effect is improved.
In an optional preprocessing mode, time domain data of a plurality of adjacent sampling points in the longitudinal direction of the time domain data are obtained, corresponding L2 norms are calculated according to the time domain data and the time domain data of the plurality of adjacent sampling points, and products of the L2 norms and the time domain data are used as corrected time domain data. In the preprocessing mode, the L2 norm is used as the weight of the time domain data, so that the weight of the strong reflection layer data is improved. The corrected time domain data can be obtained specifically by the following formula 5:
x(t)＝‖c‖ 2 * d (t) (equation 5)
Wherein d (t) is original time domain data, x (t) is corrected time domain data, c is a sampling point set corresponding to d (t), c= { d (t-n), d (t-n+1), …, d (t), …, d (t+n-1), d (t+n) }, n is a weighted sampling point number, namely, n adjacent sampling points are selected in the longitudinal direction and upward with the sampling point corresponding to d (t) as the center, n adjacent sampling points are selected downward, the selected adjacent sampling points and the sampling point corresponding to d (t) are used as a c set, the L2 norm of the data in the c set is calculated, and the product of the L2 norm and d (t) is used as corrected time domain data.
In yet another alternative preprocessing mode, for any time domain data, the absolute value of the time domain data is taken as a weight, and the product of the weight and the time domain data is taken as corrected time domain data.
Step S630, carrying out Fourier transform processing on the corrected time domain data to obtain frequency domain data of any shot point in any seismic channel.
And carrying out Fourier transform processing on the time domain data to obtain corresponding frequency domain data. Specifically, for the corrected time domain data of any shot point in any seismic channel, fourier transformation processing is performed on the time domain data to obtain corresponding frequency domain data.
For example, fourier transform processing can be performed using the following equation 6:
where D (f) is frequency domain data, x (t) is corrected time domain data, f is frequency, and N is the number of frequency domain samples.
Step S640, generating a seismic data matrix according to the frequency domain data, constructing a multiple matrix, and establishing a functional relation between the multiple matrix and the seismic data matrix.
Step S650, obtaining model frequency domain data corresponding to any multiple model in the multiple matrix according to the functional relation.
The specific implementation process of step S640 to step S650 may refer to the description in the embodiment of fig. 1, and will not be described herein.
Step S660, for any multiple model, performing Fourier inverse transformation on the model frequency domain data corresponding to the multiple model to obtain model time domain data.
The multiple model obtained in step S650 is a frequency domain data representation, and in this step, the multiple model represented by the time domain data may be obtained through inverse fourier transform processing.
For example, the inverse fourier transform process may be performed by the following equation 7:
wherein M (f) is model frequency domain data, M (t) is model time domain data, f is frequency, and N is the number of frequency domain samples.
The following will illustrate the prediction effect of the embodiment of the present invention by taking fig. 7 and 8 as examples:
fig. 7 shows a comparison graph of a forward model and a multiple predicted by an embodiment of the present invention. In fig. 7, the area a is shot set data generated by adopting a forward model, the waves indicated by the arrow in the area a are the multiple waves under the effective reflection layer, and the area B is the multiple waves predicted by the method provided by the embodiment of the invention, wherein the forward model and the embodiment of the invention adopt the same original data, and as can be seen from the same original data, the multiple waves predicted by the method provided by the embodiment of the invention are very close to the multiple waves generated by the forward model, and have better prediction effect.
FIG. 8 shows a graph of actual shot gather data versus the multiples predicted by an embodiment of the present invention. In fig. 8, the area C is a schematic diagram of actual shot set data, the waves indicated by the arrows in the area C are the multiples under the effective reflection layer, and the area D is the multiples predicted by the method provided by the embodiment of the invention, wherein the actual shot set data and the embodiment of the invention adopt the same original data, and it can be seen that the multiples predicted by the method provided by the embodiment of the invention are very close to the multiples generated by the actual shot set data, and have a better prediction effect.
Therefore, in the multiple prediction method provided by the embodiment of the invention, the time domain data of any shot point in any seismic channel is collected, and the corresponding frequency domain data is obtained through Fourier transformation, so that the multiple prediction is performed on the basis of the frequency domain data, and the multiple prediction efficiency is improved; in the embodiment of the invention, the corrected time domain data can be obtained by correcting the time domain data through L2 norm and the like, and then the frequency domain data can be obtained according to the corrected time domain data, so that the prediction effect of multiple waves can be improved and the prediction efficiency can be improved mainly according to the data from the strong reflection layer during multiple wave prediction.
Fig. 9 shows a schematic structural diagram of a multiple prediction apparatus according to an embodiment of the present invention. As shown in fig. 9, the multiple prediction apparatus 900 includes: acquisition module 910, processing module 920, and execution module 930.
An acquisition module 910, configured to acquire frequency domain data of any shot at any seismic trace;
a processing module 920 configured to generate a seismic data matrix according to the frequency domain data; wherein the seismic data matrix is an upper triangular matrix with a main diagonal of 0, and any non-first column of the seismic data matrix contains frequency domain data of at least one seismic channel of the same shot point; constructing a multiple matrix; the multi-wave matrix is an upper triangular matrix with a main diagonal of 0, and any non-first column of the multi-wave matrix comprises a multi-wave model of at least one seismic channel of the same shot point; and establishing a functional relationship between the multiple matrix and the seismic data matrix;
and the execution module 930 is configured to obtain model frequency domain data corresponding to any multiple model in the multiple matrix according to the functional relationship.
In an alternative embodiment, the seismic data matrix is specifically:
wherein T is (,) D is the element of the ith row and jth column in the seismic data matrix (j-n,j-i) Frequency domain data at j-i seismic traces for j-n th shot，D (1,-i) The frequency domain data of the 1 st shot in j-i seismic channels is obtained, and n is the total number of the seismic channels.
In an alternative embodiment, the multiple matrix is specifically:
wherein P is (i,j) M is the element of the ith row and the jth column in the multiple matrix (j-n,j-i) Multiple model of the j-i seismic trace for the j-n shot, M (1,-i) The method is a multiple wave model of the 1 st shot in j-i seismic traces, and n is the total number of the seismic traces.
In an alternative embodiment, the processing module is configured to: the following functional relationship is established:
＝T×T
wherein P is a multiple matrix, and T is a seismic data matrix.
In an alternative embodiment, the obtaining module is configured to: acquiring time domain data of any shot point in any seismic channel;
and carrying out Fourier transform processing on the time domain data to obtain the frequency domain data.
In an alternative embodiment, the obtaining module is configured to: for any time domain data, acquiring time domain data of a plurality of adjacent sampling points of the time domain data in the longitudinal direction, calculating corresponding L2 norms according to the time domain data and the time domain data of the plurality of adjacent sampling points, and taking the product of the L2 norms and the time domain data as corrected time domain data;
and carrying out Fourier transform processing on the corrected time domain data to obtain the frequency domain data.
In an alternative embodiment, the execution module is configured to: and carrying out Fourier inverse transformation on model frequency domain data corresponding to any multiple wave model to obtain model time domain data.
Therefore, the multiple prediction device provided by the embodiment of the invention constructs the frequency domain data of any shot point in any seismic channel as the upper triangular matrix with the main diagonal of 0, and constructs the multiple matrix with the same structure, so that the multiple prediction is converted into matrix operation, and the prediction efficiency of the multiple is improved on the premise of ensuring the multiple prediction precision.
Embodiments of the present invention provide a non-volatile computer storage medium storing at least one executable instruction that may perform the multiple prediction method of any of the above method embodiments.
FIG. 10 illustrates a schematic diagram of a computing device provided by an embodiment of the present invention. The specific embodiments of the present invention are not limited to a particular implementation of a computing device.
As shown in fig. 10, the computing device may include: a processor 1002, a communication interface Communications Interface, a memory 1006, and a communication bus 1008.
Wherein: the processor 1002, communication interface 1004, and memory 1006 communicate with each other via a communication bus 1008. Communication interface 1004 is used for communicating with network elements of other devices, such as clients or other servers. The processor 1002 is configured to execute the program 1010, and may specifically perform the relevant steps in the above-described embodiment of the multiple prediction method.
In particular, program 1010 may include program code including computer operating instructions.
The processor 1002 may be a Central Processing Unit (CPU) or a specific integrated circuit ASIC (Application Specific Integrated Circuit) or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors included by the computing device may be the same type of processor, such as one or more CPUs; but may also be different types of processors such as one or more CPUs and one or more ASICs.
Memory 1006 for storing programs 1010. The memory 1006 may include high-speed RAM memory or may further include non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. The program 1010 is specifically operable to cause the processor 1002 to perform the operations of the method embodiments described above.
The algorithms or displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general-purpose systems may also be used with the teachings herein. The required structure for a construction of such a system is apparent from the description above. In addition, embodiments of the present invention are not directed to any particular programming language. It will be appreciated that the teachings of the present invention described herein may be implemented in a variety of programming languages, and the above description of specific languages is provided for disclosure of enablement and best mode of the present invention.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the above description of exemplary embodiments of the invention, various features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or components of the embodiments may be combined into one module or unit or component and, furthermore, they may be divided into a plurality of sub-modules or sub-units or sub-components. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.
Various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components according to embodiments of the present invention may be implemented in practice using a microprocessor or Digital Signal Processor (DSP). The present invention can also be implemented as an apparatus or device program (e.g., a computer program and a computer program product) for performing a portion or all of the methods described herein. Such a program embodying the present invention may be stored on a computer readable medium, or may have the form of one or more signals. Such signals may be downloaded from an internet website, provided on a carrier signal, or provided in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names. The steps in the above embodiments should not be construed as limiting the order of execution unless specifically stated.

Claims (10)

1. A multiple prediction method, comprising:
acquiring frequency domain data of any shot point in any seismic channel;
generating a seismic data matrix according to the frequency domain data; wherein the seismic data matrix is an upper triangular matrix with a main diagonal of 0, and any non-first column of the seismic data matrix contains frequency domain data of at least one seismic channel of the same shot point;
constructing a multiple matrix; the multi-wave matrix is an upper triangular matrix with a main diagonal of 0, and any non-first column of the multi-wave matrix comprises a multi-wave model of at least one seismic channel of the same shot point;
establishing a functional relation between the multiple matrix and the seismic data matrix;
and obtaining model frequency domain data corresponding to any multiple model in the multiple matrix according to the functional relation.
2. The method according to claim 1, wherein the seismic data matrix is in particular:
wherein,,T (i，j) d is the element of the ith row and jth column in the seismic data matrix (j-n，j-i) Frequency domain data of j-i seismic traces for j-n th shot point, D (1，j-i) The frequency domain data of the 1 st shot in j-i seismic channels is obtained, and n is the total number of the seismic channels.
3. The method according to claim 2, characterized in that the multiple matrix is in particular:
wherein P is (i，j) M is the element of the ith row and the jth column in the multiple matrix (j-n，j-i) Multiple model of the j-i seismic trace for the j-n shot, M (1，j-i) The method is a multiple wave model of the 1 st shot in j-i seismic traces, and n is the total number of the seismic traces.
4. The method of claim 3, wherein said establishing a functional relationship of said multiple matrix with said seismic data matrix further comprises:
the following functional relationship is established:
P＝T×T
wherein P is a multiple matrix, and T is a seismic data matrix.
5. The method of any one of claims 1-4, wherein the acquiring frequency domain data for any shot at any seismic trace further comprises:
acquiring time domain data of any shot point in any seismic channel;
and carrying out Fourier transform processing on the time domain data to obtain the frequency domain data.
6. The method of claim 5, wherein after the acquiring the time domain data of any shot at any seismic trace, the method further comprises: for any time domain data, acquiring time domain data of a plurality of adjacent sampling points of the time domain data in the longitudinal direction, calculating corresponding L2 norms according to the time domain data and the time domain data of the plurality of adjacent sampling points, and taking the product of the L2 norms and the time domain data as corrected time domain data;
the obtaining the frequency domain data after performing fourier transform processing on the time domain data further includes: and carrying out Fourier transform processing on the corrected time domain data to obtain the frequency domain data.
7. The method according to claim 5, further comprising, after the obtaining model frequency domain data corresponding to any one of the multiple models in the multiple matrix according to the functional relation:
and carrying out Fourier inverse transformation on model frequency domain data corresponding to any multiple wave model to obtain model time domain data.
8. A multiple prediction apparatus, comprising:
the acquisition module is used for acquiring frequency domain data of any shot point in any seismic channel;
the processing module is used for generating a seismic data matrix according to the frequency domain data, wherein the seismic data matrix is an upper triangular matrix with a main diagonal of 0, and any non-initial column of the seismic data matrix contains frequency domain data of at least one seismic channel of the same shot point; constructing a multiple matrix, wherein the multiple matrix is an upper triangular matrix with a main diagonal of 0, and any non-first column of the multiple matrix comprises multiple models of at least one seismic channel of the same shot point; and establishing a functional relationship between the multiple matrix and the seismic data matrix;
and the execution module is used for obtaining model frequency domain data corresponding to any multiple model in the multiple matrix according to the functional relation.
9. A computing device, comprising: the device comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete communication with each other through the communication bus;
the memory is configured to store at least one executable instruction, where the executable instruction causes the processor to perform the operations corresponding to the multiple prediction method according to any one of claims 1 to 7.
10. A computer storage medium having stored therein at least one executable instruction for causing a processor to perform operations corresponding to the multiple prediction method of any one of claims 1-7.
CN202310926718.2A 2023-07-26 2023-07-26 Multiple prediction method, device, computing equipment and storage medium Pending CN116953786A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310926718.2A CN116953786A (en) 2023-07-26 2023-07-26 Multiple prediction method, device, computing equipment and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310926718.2A CN116953786A (en) 2023-07-26 2023-07-26 Multiple prediction method, device, computing equipment and storage medium

Publications (1)

Publication Number Publication Date
CN116953786A true CN116953786A (en) 2023-10-27

Family

ID=88444194

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310926718.2A Pending CN116953786A (en) 2023-07-26 2023-07-26 Multiple prediction method, device, computing equipment and storage medium

Country Status (1)

Country Link
CN (1) CN116953786A (en)

Similar Documents

Publication Publication Date Title
Kalchbrenner et al. Efficient neural audio synthesis
Mathieu et al. Fast training of convolutional networks through ffts
CN111247527B (en) Method and device for determining characteristic images in convolutional neural network model
CN108197652B (en) Method and apparatus for generating information
CN104807534B (en) Equipment eigentone self study recognition methods based on on-line vibration data
CN110082823B (en) Seismic data interpolation method and device
CN110969092B (en) Pulse signal denoising method and device and terminal equipment
JP6953287B2 (en) Sound source search device, sound source search method and its program
CN113238227B (en) Improved least square phase unwrapping method and system combined with deep learning
CN110069868B (en) GNSS station nonlinear motion modeling method and device
CN112164008A (en) Training method of image data enhancement network, and training device, medium, and apparatus thereof
CN111353391B (en) Radar interference effect evaluation method and device, electronic equipment and storage medium thereof
CN115469179A (en) Submarine cable defect positioning method, device, storage medium and system
CN113945968B (en) Diffracted wave imaging method and device for discontinuous geologic body and electronic equipment
US7657587B2 (en) Multi-dimensional fast fourier transform
CN116953786A (en) Multiple prediction method, device, computing equipment and storage medium
CN110598579A (en) Hypercomplex number magnetic resonance spectrum reconstruction method based on deep learning
CN115758222A (en) Signal category identification method and device, electronic equipment and storage medium
CN115760908A (en) Insulator tracking method and device based on capsule network perception characteristics
CN114722854A (en) Method and device for reducing noise of current signal of power equipment
JPH05223860A (en) Digital spectrum analyzer
CN117970480A (en) Water layer multiple prediction method, device, computing equipment and storage medium
CN115861099B (en) Satellite cloud image restoration method introducing physical imaging priori knowledge constraint
CN117554593B (en) Real-time monitoring method for soil moisture content of farmland
CN115903041A (en) Method and device for predicting interbed multiples

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination