CN115407393A - Multiple suppression method based on mutual information - Google Patents
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Abstract
The invention discloses a multiple suppression method based on mutual information, which comprises the steps of collecting seismic data and predicted multiple signal data, and preprocessing the data; constructing a plane wave basis function according to the preprocessed parameters, and converting frequency space domain data into time plane wave domain data; introducing mutual information factors, and extracting multiple components from the time plane wave domain data to construct a plane wave domain filter; and converting the data output by the filter back to a time-space domain to obtain complete multi-wave-removing data. The method is based on the mutual information minimization criterion, forms the multiple wave self-adaptive subtraction factor in the sparse transform domain of the seismic data, can effectively suppress the multiple waves and protect the primary waves in the original data, decomposes the plane waves to be used as the sparse transform of the seismic signals, can extract the mutual information energy in the original data and the multiple wave data with high resolution, namely extracts the multiple wave components in the original data, and can effectively suppress the multiple waves in the original data.
Description
Technical Field
The invention relates to the technical field of geophysical exploration, in particular to a multiple suppression method based on mutual information.
Background
In seismic exploration, due to the existence of strong reflection interfaces such as a free surface and a seabed, multiple reflection waves in seismic data widely develop, but the existing seismic data processing flow and imaging method mainly aim at the quality of velocity analysis and imaging which are influenced by the existence of the multiple reflection waves, so that multiple wave signals in the seismic data are generally required to be suppressed before processing such as velocity analysis and imaging, and multiple wave suppression is an important issue of seismic data processing.
The multiple suppression method can be divided into a filtering method and a prediction method, the filtering method suppresses the multiple by using the spatial difference of the primary wave and the multiple in a transform domain, common transforms include F-K transform, radon transform and the like, and the method is simple, but is greatly influenced by factors such as data quality, geological structure conditions and the like, and has limitation in use.
The prediction method is based on the predictability of multiples, and a high-dimensional convolution model is established according to a fluctuation theory to describe the prediction relation between the multiples and the primary waves or the observed wave fields, so that the suppression of the multiples is realized. The prediction-type multiple suppression method is generally divided into two steps of prediction and subtraction, wherein the prediction stage mainly aims at predicting the kinematic information of multiples, and the subtraction stage is matched with the dynamic information of the multiples. Prediction-like methods can be further classified into data-driven and model-driven methods. It can be seen that, in the industry, the multiple prediction method has been discussed and developed sufficiently, but the multiple subtraction method has less discussion, and the adaptive multiple subtraction is generally regarded as a data matching problem based on the minimum energy criterion. The minimum energy criterion has two assumptions: the first is orthogonality assumption, i.e. the signals of the primary and the multiple are assumed to be orthogonal to each other, and the second is the assumption of minimum energy of the primary wave. When these two assumptions are not satisfied, conventional multiple subtraction methods cannot sufficiently suppress multiples and can even damage the primary effective wave.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and title of the application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the invention provides a multiple suppressing method based on mutual information, which can solve the problem that the traditional multiple self-adaptive subtraction technology cannot sufficiently suppress multiples and protect effective primaries.
In order to solve the above technical problem, the present invention provides the following technical solution, a multiple suppression method based on mutual information, including:
collecting seismic data and predicted multiple signal data, and preprocessing the data;
constructing a plane wave basis function according to the preprocessed parameters, and converting frequency space domain data into time plane wave domain data;
introducing mutual information factors, and extracting multiple components from the time plane wave domain data to construct a plane wave domain filter;
and converting the data output by the filter back to a time-space domain to obtain complete multi-wave-removal data.
As a preferable scheme of the multiple suppressing method based on mutual information according to the present invention, wherein: the preprocessing comprises inputting the collected data into a channel head keyword extraction program, and acquiring observation system information and data sampling parameters.
As a preferable embodiment of the multiple compression method based on mutual information according to the present invention, wherein: the observation system information and data sampling parameters comprise the number of time sampling points, time sampling intervals, and the spatial positions of shot points and demodulator probes relative to the observation system.
As a preferable embodiment of the multiple compression method based on mutual information according to the present invention, wherein: the preprocessing further comprises segmenting the seismic data and the predicted multiples signal data into small blocks according to spatial data traces, wherein each data block comprises a certain number of complete seismic traces, and the segmentation of the data blocks requires that the spatial distribution of the event axis meets linearity.
As a preferable scheme of the multiple suppressing method based on mutual information according to the present invention, wherein: the preprocessing also comprises that plane wave decomposition is carried out by using an inversion method for amplitude preservation of seismic data processing, wherein the expression of the inversion method is as follows:
s(p,ω)＝(L H L+λI) -1 L H ·d(x,ω)
the inversion method needs to solve an inverse matrix, and the expression is as follows:
inverse matrix (L) H L+λI) -1
Wherein p represents a ray parameter, ω represents a wave number of a signal after Fourier transform, s (p, ω) represents a signal of a frequency plane wave domain, L represents a plane wave basis function matrix H Represents the conjugate transpose of the matrix L, d (x, ω) represents the signal in the frequency-space domain, x represents the spatial position of the signal detector, I represents the unit diagonal matrix, and λ represents the stability factor for the solution of the stability matrix equation.
As a preferable embodiment of the multiple compression method based on mutual information according to the present invention, wherein: the form of the plane wave basis function is:
where i denotes the imaginary unit of the complex signal, p x,1 Representing the 1 st ray parameter, x, in the x-direction component 1 Representing the 1 st spatial coordinate position in the x-direction,
a natural exponential function representing the complex signal (which describes the time delay of each spatial channel signal relative to the reference channel).
As a preferable embodiment of the multiple compression method based on mutual information according to the present invention, wherein: the mutual information factor includes at least one of,
in the plane wave domain, sparse energy corresponding to a linear same-phase axis has symmetrical correlation, and a mutual information factor is extracted through the following formula:
wherein p is i Representing the ith plane wave ray parameter in the data, p' representing p i Ray parameters, τ, within a centered local data window j Denotes the jth time sample point, τ' denotes τ j Time sampling points in a local data window as a center, u and m respectively represent original data and multiple data of a plane wave domain, and w i,j And (u, m) is the weight of the filter extracted at the point i and the point j, the size of the weight indicates the size of the possibility that multiples exist in the transform domain, and mu and eta respectively represent the range of a data window used for constructing the mutual information factor in the plane wave domain.
As a preferable embodiment of the multiple compression method based on mutual information according to the present invention, wherein: the time-plane wave-domain data includes,
and (3) constructing a multiple suppression filter according to the mutual information factor to subtract the predicted multiples, namely the time plane wave domain data for removing the multiples, wherein the expression is as follows:
s′(p,τ)＝s(p,τ)-f(p,τ)*s(p,τ)
where s (p, τ) represents a signal of a plane wave domain including multiples, and s' (p, τ) represents plane wave domain data after the multiples are suppressed.
As a preferable embodiment of the multiple compression method based on mutual information according to the present invention, wherein: the filter comprises the following multi-wave adaptive reduction filter constructed by a suppressing process according to w (u, m):
p(τ,p)＝u(τ,p)-f(τ,p)·u(τ,p)
wherein p (tau, p) represents the plane wave domain data after multiple wave suppression, u (tau, p) represents the signal of the plane wave domain containing multiple waves, and f (tau, p) is the constructed multiple wave adaptive subtraction filter; epsilon is adjusted according to the quality of the seismic data, a nonzero number between 0 and 1 is taken, and the larger the value is, the higher the signal-to-noise ratio of the in-situ seismic data is; n is usually a number between 2 and 10, and p (τ, p) is the resulting multiple-suppressed data.
As a preferable embodiment of the multiple compression method based on mutual information according to the present invention, wherein: the transition back to the time-space domain includes,
one-dimensional fast Fourier transform is carried out on the time plane wave domain data with the multiples removed channel by channel to convert the time plane wave domain data into a frequency plane wave domain;
converting the plane wave domain data back to a space domain according to the constructed plane wave basis function;
and performing one-dimensional inverse Fourier transform on the frequency space domain data channel by channel so as to obtain time-space domain data from which multiple wave waves are removed.
The invention has the beneficial effects that: the method is based on mutual information minimization criterion, and constructs the multi-order wave self-adaptive subtraction factor in the sparse transform domain of the seismic data, so that the multi-order wave can be effectively suppressed and the primary wave in the original data can be protected. The method takes plane wave decomposition as sparse transformation of seismic signals, and seismic signals in a local window are always approximately linearly distributed on the same phase axis, so the method has universality; in addition, in a plane wave domain, the energy spectrum corresponding to the linear same-phase axis has symmetrical correlation, and the mutual information energy in the original data and the multi-wave data can be extracted with high resolution, namely, the multi-wave components in the original data are extracted, so that the multi-wave in the original data can be effectively suppressed.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a schematic flow chart of a multiple suppressing method based on mutual information according to an embodiment of the present invention;
FIG. 2 is a graph of test results in a synthetic linear event of a multiple-order-compression method based on mutual information according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating simulation data application results of a multiple suppression method based on mutual information according to an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating the result of three-dimensional data application of a multiple compression method based on mutual information according to an embodiment of the present invention;
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, the references herein to "one embodiment" or "an embodiment" refer to a particular feature, structure, or characteristic that may be included in at least one implementation of the present invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not necessarily enlarged to scale, and are merely exemplary, which should not limit the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected" and "connected" in the present invention are to be construed broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1, a first embodiment of the present invention provides a method for multiple compression based on mutual information, including:
s1: collecting seismic data and predicted multiple signal data, and preprocessing the data;
furthermore, the seismic data in the SEG-Y standard format and the predicted multiple signals in the SEG-Y standard format are input into a channel head keyword program, and the program acquires observation system information and data sampling parameters from the channel head keyword of the seismic data in the SEG-Y standard format.
It should be noted that the seismic data in the SEG-Y standard format and the predicted multiples signal in the SEG-Y standard format need to be input simultaneously, and the required observation system parameters can be extracted from the seismic data in the SEG-Y format.
It should also be noted that the SEG-Y format is one of the standard tape data formats proposed by SEG (Society of Exploration geographitics), which is one of the most common formats of seismic data in the oil Exploration industry.
Furthermore, the original data and the predicted multiples are segmented into small blocks according to spatial data channels, namely a certain number of channels of seismic data are taken out from the original data every time according to the spatial sequence, and the data complexity can be reduced by performing data processing on a small-scale seismic data, so that the distribution of the same-phase axis in the spatial direction in a small data block meets the linear assumption required by the invention.
It should be noted that, the input original seismic data and the multiple data are divided into small data blocks, each data block contains a certain number of complete seismic channels, the data block division requirement enables the spatial distribution of the same phase axis to meet linearity, 50 to 100 channels of data can be taken as a data block, the multiple is compressed block by block and then combined into complete data, and the number of the data blocks to be divided is 50.
It should also be noted that the observation system parameters are: the number of time sampling points, the time sampling interval, and the spatial positions of the shot point and the demodulator probe relative to the observation system.
S2: constructing a plane wave basis function according to the preprocessed parameters, and converting frequency space domain data into time plane wave domain data;
further, a one-dimensional fast fourier transform is performed per data trace to convert the time (t) -space (x) domain seismic data d (x, t) into the frequency-space domain d (x, ω). Constructing a plane wave basis function according to the observation system parameters, wherein the form of the two-dimensional plane wave basis function is as follows:
where i denotes the imaginary unit of the complex signal, p x,1 Representing the 1 st ray parameter, x, in the x-direction component 1 Representing the 1 st spatial coordinate position in the x-direction,
a natural exponential function representing the complex signal, which describes the time delay of each spatial channel signal relative to the reference channel.
It should be noted that the plane wave basis function described above can be easily extended to any high dimension by increasing the matrix dimension, thereby accommodating higher dimensionsProcessing of seismic data, which can decompose the frequency-space domain data d (x, omega) plane wave according to the constructed plane wave basis function, i.e. s (p, t) = (L) H L+λI) -1 L H D (x, ω), converted to plane wave domain data s (p, ω), and then inverse fourier transformed back to time domain s (p, t) channel by channel.
Where s (p, τ) represents a signal of a plane wave domain including multiples, and s' (p, τ) represents plane wave domain data after the multiples are suppressed.
Furthermore, the time-space domain data is subjected to fast Fourier transform according to channels, multi-thread parallel computing is used for improving the processing efficiency, and then the data is subjected to plane wave decomposition and is converted into a plane wave domain.
It should be noted that, for amplitude preservation of seismic data processing, an inversion method (s (p, t) = (L) = is required H L+λI) - 1 L H D (x, ω)) for plane wave decomposition, which requires solving the inverse matrix (L) H L+λI) -1 The matrix is also obtained by the constructed plane wave basis function, and the solution of the inverse matrix can refer to a general linear matrix equation numerical solution method.
It should be noted that, in the formula, p represents a ray parameter, ω represents a signal wave number after fourier transform, s (p, ω) represents a signal of a frequency plane wave domain, L represents a plane wave basis function matrix, and L represents H The method comprises the steps of representing the conjugate transpose of a matrix L, representing signals of a frequency space domain by d (x, omega), representing the space position of a signal detector by x, representing a unit diagonal matrix by I, and representing a stability factor by lambda to solve a stability matrix equation, wherein the stability factor is usually 0.1 and can be actually adjusted according to data quality and signal-to-noise ratio.
S3: introducing mutual information factors, and extracting multiple components from the time plane wave domain data to construct a plane wave domain filter;
furthermore, a mutual information factor w is introduced, multiple components in the original data are extracted from the time plane wave domain data s (p, t), and a multiple suppression filter is constructed according to the mutual information factor w to subtract the predicted multiple s '(p, t) = s (p, t) -w × s (p, t), and s' (p, t) is the time plane wave domain data from which the multiple is removed.
It should be noted that the mutual information factor w indicates a multiple component in the original data, which is extracted from the correlation of the predicted multiple and the original data in the sparse transform domain. The method takes plane wave decomposition as sparse transformation, and in a plane wave domain, sparse energy corresponding to a linear same-phase axis has symmetrical correlation, so that mutual information factors can be extracted through the following formula:
wherein p is i Representing the ith plane wave ray parameter in the data, p' representing p i Ray parameters, τ, within a centered local data window j Denotes the jth time sample point, τ' denotes τ j Time sampling points in a local data window as a center, u and m respectively represent original data and multiple data of a plane wave domain, and w i,j And (u, m) is the weight of the filter extracted at the point i and the point j, the size of the weight indicates the size of the possibility that multiples exist in the transform domain, and mu and eta respectively represent the range of a data window used for constructing the mutual information factor in the plane wave domain.
It should be noted that the above formula is one of the main innovative points of the present invention, and the multiple signals can be accurately identified in the plane wave domain with high resolution by using the formula, so as to remove the multiple components in the original data while protecting the other effective signals from being damaged.
Further, the squashing process is implemented according to a multi-wave adaptive subtraction filter constructed by w (u, m):
p(τ,p)＝u(τ,p)-f(τ,p)·u(τ,p)
wherein p (tau, p) represents the plane wave domain data after multiple wave suppression, u (tau, p) represents the signal of the plane wave domain containing multiple waves, and f (tau, p) is the constructed multiple wave adaptive subtraction filter; adjusting epsilon according to the quality of the seismic data, taking a nonzero number between 0 and 1, wherein the larger the value is, the higher the signal-to-noise ratio of the in-situ seismic data is; n is usually a number between 2 and 10, and p (τ, p) is the resulting multiple-suppressed data.
S4: and converting the data output by the filter back to a time-space domain to obtain complete multi-wave-removal data.
Furthermore, the time plane wave domain data S ' (p, t) from which the multiples are removed is subjected to one-dimensional fast fourier transform channel by channel to be converted into a frequency plane wave domain S ' (p, ω), then the plane wave domain data is converted back into a spatial domain according to a plane wave basis function constructed in S2, namely d ' (x, ω) = L · S ' (p, ω), and then the frequency-spatial domain data is subjected to one-dimensional inverse fourier transform channel by channel to obtain the time-spatial domain data d ' (x, t) from which the multiples are removed.
It should be noted that the result of the multiple compression described in S3 is converted back into the time-space domain, which is the inverse process corresponding to the local plane wave decomposition of S2. And splicing the recovered seismic data blocks according to the original space coordinates to obtain complete multi-wave-removing data.
Example 2
Referring to fig. 2-4, a multiple wave suppression method based on mutual information is provided as an embodiment of the present invention, and scientific demonstration is performed through comparative experiments in order to verify the beneficial effects of the present invention.
Taking the multiple seismic data gather shown in fig. 3 as an example of input (simultaneously inputting the multiple data of the left image and the multiple prediction result of the right image), the number of sampling points in the spatial direction of the data is 800, and the spatial sampling interval is 5 meters; the number of time sampling points is 5000, and the sampling interval is 0.3 millisecond; the data is marine seismic data, the included range of the inclination angle direction of the plane wave is determined according to the seawater speed, the seawater speed is 1500m/s, the maximum ray parameter of the plane wave decomposition of the data is 1/1500=0.000667s/m, the sampling interval of the ray parameter of the plane wave decomposition is set to be 0.000003s/m, and the sampling number is 601 directions. The input data is divided in the space direction by the scale of 100 data tracks of each data block.
The seismic data d (x, t) of a time-space domain are converted into a frequency-space domain d (x, omega) by using a fast Fourier transform algorithm, the number of time sampling points of the original data is zeroed to 8192 (power exponent power of 2) according to the acceleration characteristic of the fast Fourier transform algorithm, and the multithreading parallel computation is used according to the tracks. And respectively carrying out plane wave decomposition on the block data bodies of the two input data according to the observation system and the ray parameters determined in the step S1.
A multi-order wave self-adaptive filter is constructed according to the symmetrical correlation of the invention, the data windows mu and eta are respectively 10 and 50 when mutual information factors are extracted, the epsilon is 1,n is 6 when the self-adaptive filter is constructed, and the multi-order wave is suppressed according to the constructed filter.
And converting the plane wave domain data after the multiple suppression back to a time space domain.
The invention relates to a multi-wave self-adaptive suppression method based on mutual information minimization criterion, which constructs a multi-wave self-adaptive subtraction factor in a sparse transform domain of seismic data, suppresses the multi-wave and protects primary waves in original data. According to the method, plane wave decomposition is used as sparse transformation of seismic signals, in a plane wave domain, an energy spectrum corresponding to a linear homophase axis has symmetrical correlation, mutual information energy in original data and multi-time wave data can be extracted at high resolution, namely, multi-time wave components in the original data are extracted, and therefore the multi-time waves in the original data can be effectively suppressed.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application 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. The solution in the embodiment of the present application may be implemented by using various computer languages, for example, object-oriented programming language Java and transliteration scripting language JavaScript, etc.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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 apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, 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 apparatus 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 apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the preferred embodiments of the present application have been described, additional variations and modifications in those 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 alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (10)
1. A multiple suppression method based on mutual information is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
collecting seismic data and predicted multiple signal data, and preprocessing the data;
constructing a plane wave basis function according to the preprocessed parameters, and converting frequency space domain data into time plane wave domain data;
introducing mutual information factors, and extracting multiple components from the time plane wave domain data to construct a plane wave domain filter;
and converting the data output by the filter back to a time-space domain to obtain complete multi-wave-removing data.
2. The method of reciprocal information-based multiple suppression as defined in claim 1, wherein: the preprocessing comprises inputting the collected data into a channel head keyword extraction program, and acquiring observation system information and data sampling parameters.
3. The method of reciprocal information-based multiple suppression as defined in claim 2, wherein: the observation system information and data sampling parameters comprise the number of time sampling points, time sampling intervals, and the spatial positions of shot points and demodulator probes relative to the observation system.
4. A method of multiple compression based on mutual information as claimed in claim 3, wherein: the preprocessing further comprises the step of segmenting the seismic data and the predicted multiples signal data into small blocks according to spatial data channels, wherein each data block comprises a certain number of complete seismic channels, and the segmentation of the data blocks requires that the spatial distribution of the event axis meets the linearity.
5. The mutual information based multiple suppression method as claimed in claim 4, wherein: the preprocessing further comprises that in order to preserve amplitude of seismic data processing, plane wave decomposition is carried out by using an inversion method, and the expression of the inversion method is as follows:
s(p,ω)＝(L H L+λI) -1 L H ·d(x,ω)
the inversion method needs to solve an inverse matrix, and the expression is as follows:
inverse matrix (L) H L+λI) -1
Wherein p represents a ray parameter, ω represents a signal wave number after Fourier transform, s (p, ω) represents a signal of a frequency plane wave domain, L represents a plane wave basis function matrix, L represents a frequency plane wave domain H Represents the conjugate transpose of the matrix L, d (x, ω) represents the signal in the frequency-space domain, x represents the spatial position of the signal detector, I represents the unit diagonal matrix, and λ represents the stability factor for the solution of the stability matrix equation.
6. The mutual information-based multiple-suppression method as claimed in claim 5, wherein: the form of the plane wave basis function is:
where i denotes the imaginary unit of the complex signal, p x,1 Representing the 1 st ray parameter, x, in the x-direction component 1 Indicating the 1 st space in the x directionThe position of the inter-coordinate is,
a natural exponential function representing the complex signal, which describes the time delay of each spatial channel signal relative to the reference channel.
7. The mutual information based multiple suppression method as claimed in claim 6, wherein: the mutual information factor includes at least one of,
in a plane wave domain, sparse energy corresponding to a linear same-phase axis has symmetrical correlation, and a mutual information factor is extracted by the following formula:
wherein p is i Representing the ith plane wave ray parameter in the data, p' representing p i Ray parameters, τ, within a centered local data window j Denotes the jth time sample point, τ' denotes τ j Time sampling points in a local data window as a center, u and m respectively represent original data and multiple data of a plane wave domain, and w i,j And (u, m) is the weight of the filter extracted at the point i and the point j, the size of the weight indicates the size of the possibility that multiples exist in the transform domain, and mu and eta respectively represent the range of a data window used for constructing the mutual information factor in the plane wave domain.
8. The mutual information-based multiple-suppression method according to claim 7, wherein: the time-plane wave-domain data includes,
and (3) constructing a multiple suppression filter according to the mutual information factor to subtract the predicted multiple, namely the time plane wave domain data for removing the multiple, wherein the expression is as follows:
s′(p,τ)＝s(p,τ)-f(p,τ)*s(p,τ)
where s (p, τ) represents a signal of a plane wave domain including multiples, and s' (p, τ) represents plane wave domain data after the multiples are suppressed.
9. The mutual information based multiple suppression method as claimed in claim 8, wherein: the filter comprises the following multi-wave adaptive reduction filter constructed by a suppressing process according to w (u, m):
p(τ,p)＝u(τ,p)-f(τ,p)·u(τ,p)
wherein p (tau, p) represents the plane wave domain data after multiple wave suppression, u (tau, p) represents the signal of the plane wave domain containing multiple waves, and f (tau, p) is the constructed multiple wave adaptive subtraction filter; adjusting epsilon according to the quality of the seismic data, taking a nonzero number between 0 and 1, wherein the larger the value is, the higher the signal-to-noise ratio of the in-situ seismic data is; n is usually a number between 2 and 10, and p (τ, p) is the resulting multiple-suppressed data.
10. The method for suppressing multiples based on mutual information as claimed in claim 9, wherein: the transition back to the time-space domain includes,
one-dimensional fast Fourier transform is carried out on the time plane wave domain data without the multiples channel by channel to convert the time plane wave domain data into a frequency plane wave domain;
converting the plane wave domain data back to a space domain according to the constructed plane wave basis function;
and performing one-dimensional inverse Fourier transform on the frequency space domain data channel by channel so as to obtain time-space domain data from which multiple wave waves are removed.
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