CN115204317A - Sound velocity profile continuation method and system based on orthogonal empirical function decomposition - Google Patents
Sound velocity profile continuation method and system based on orthogonal empirical function decomposition Download PDFInfo
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Abstract
The invention provides a sound velocity profile extension method and system based on orthogonal empirical function decomposition, which relate to the technical field of ocean acoustic application and are used for solving the problem that a deep sea part extended by the existing method does not accord with the real sound velocity distribution rule of the deep sea in a region; and constructing an extended full-sea-depth sound velocity profile according to the characteristic vector coefficients and the sample data of the full-sea-depth historical sound velocity profile, and by the mode, matching the actually measured sound velocity value part of the sound velocity profile to be extended with high precision and ensuring that the sound velocity distribution of the extended part accords with the actual sound velocity distribution rule of the region.
Description
Technical Field
The invention belongs to the technical field of ocean acoustic application, and particularly relates to a sound velocity profile continuation method and system based on orthogonal empirical function decomposition.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art that is already known to a person of ordinary skill in the art.
With the development of the underwater sound velocity field construction technology, sound velocity profile extension can be widely applied to application systems which take sound waves as signal carriers, such as underwater communication, positioning, navigation and the like. Under the condition that shallow sea and deep sea sound velocity profile samples coexist, deep sea partial continuation under the condition of high-precision matching of shallow sea actual measurement sound velocity values is carried out on the shallow sea sound velocity profile samples, the number of full-sea deep sound velocity profile reference samples can be increased, and the real-time estimation precision of the regional sound velocity field is improved.
The existing underwater sound velocity profile continuation method comprises the following steps: liu Fu Chen and the like propose a sound velocity profile continuation method based on polynomial fitting, fitting an actually measured shallow sea part sound velocity profile by using a polynomial, and extending in a deep sea area; the method comprises the steps of matching a full-sea deep sound velocity profile sample and a sound velocity profile sample to be extended at a common part of a deep-sea isothermal layer, calculating a structural coefficient of the sound velocity profile to be extended at the deep-sea isothermal layer by combining an empirical orthogonal function reconstruction process of the sound velocity profile, and constructing the distribution condition of the sound velocity profile in the deep-sea isothermal layer; the Drynaudian provides a sound velocity profile complementing method and device based on historical data and machine learning, the historical data is subjected to experience orthogonal function analysis to obtain characteristic vectors and characteristic vector coefficients, neurons representing different reference classification information are obtained through unsupervised learning method training, the association degree between an actually measured sound velocity profile and each neuron is calculated according to Euclidean distance, and the sound velocity profile is reconstructed by using the characteristic vector coefficients corresponding to the neurons with the highest association degree; a sound velocity profile continuation method based on low-rank matrix completion is provided by CaiKujg et al, a to-be-supplemented matrix is constructed by utilizing a to-be-extended sound velocity profile and a historical full-sea deep sound velocity profile, and the to-be-supplemented matrix is supplemented by applying a low-rank matrix completion algorithm, so that sound velocity profile continuation is completed.
Although there are various sound velocity profile continuation methods, the following problems still exist: firstly, the matching precision of the actually measured part of the sound velocity profile to be extended is not high enough; and secondly, the actually measured part of the sound velocity profile is excessively matched, so that the extended deep sea part does not accord with the real sound velocity distribution rule of the regional deep sea.
Disclosure of Invention
In order to solve the problems, the invention provides a sound velocity profile continuation method and a sound velocity profile continuation system based on orthogonal empirical function decomposition, which are used for matching the actually measured sound velocity value part of the sound velocity profile to be continued with high precision, ensuring that the sound velocity distribution of the continuation part accords with the actual sound velocity distribution rule of the region, and providing more and more reliable reference samples for the sound velocity profile inversion.
In order to achieve the above object, the present invention mainly includes the following aspects:
in a first aspect, an embodiment of the present invention provides a sound velocity profile continuation method based on orthogonal empirical function decomposition, including:
acquiring sample data of a sound velocity profile to be extended, the maximum depth of the actually measured sound velocity value of the sound velocity profile to be extended and historical sound velocity profiles of the whole sea depth;
according to the maximum depth of the actually measured sound velocity value of the sound velocity profile to be extended, carrying out depth interception on the sample data of the full-sea-depth historical sound velocity profile to obtain the intercepted data of the historical sound velocity profile sample;
performing empirical orthogonal function decomposition on the sample truncation data of the historical sound velocity profile to obtain a first feature vector of a preset order of the sample truncation data of the historical sound velocity profile, and performing matching field processing on the sound velocity profile to be extended by using the first feature vector to obtain a feature vector coefficient corresponding to the sound velocity profile to be extended in the first feature vector;
and constructing an extended full-sea-depth sound velocity profile according to the characteristic vector coefficient and the sample data of the full-sea-depth historical sound velocity profile.
In a possible implementation manner, the obtaining manner of the first feature vector includes:
determining an average sound velocity profile of the truncated data of the historical sound velocity profile sample according to the truncated data of the historical sound velocity profile sample, and constructing a first residual error matrix;
calculating an autocovariance matrix of the first residual error matrix, and solving an eigenvalue of the autocovariance matrix and a corresponding eigenvector;
and arranging the obtained eigenvalues from big to small, and selecting the eigenvector corresponding to the eigenvalue of the preset order arranged at the front as the first eigenvector.
In a possible implementation manner, the performing matching field processing on the sound velocity profile to be extended by using the first eigenvector to obtain an eigenvector coefficient of the sound velocity profile to be extended corresponding to the first eigenvector includes:
determining a residual vector between the sound velocity profile to be extended and an average sound velocity profile of sample truncation data of the historical sound velocity profile;
and determining a characteristic vector coefficient of the sound velocity profile to be extended corresponding to the first characteristic vector according to the residual vector and the first characteristic vector.
In one possible embodiment, the feature vector coefficients are the product of a transpose of the first feature vector and the residual vector.
In a possible implementation manner, the constructing an extended full-sea-depth acoustic velocity profile according to the eigenvector coefficients and sample data of the full-sea-depth historical acoustic velocity profile includes:
performing empirical orthogonal function decomposition on the sample data of the full-sea-depth historical sound velocity profile to obtain an average sound velocity profile of the full-sea-depth historical sound velocity profile and a second eigenvector of a preset order;
and constructing an extended full-sea-depth sound velocity profile according to the average sound velocity profile, the characteristic vector coefficient and the second characteristic vector of the full-sea-depth historical sound velocity profile.
In a possible implementation manner, the constructing a extended full-sea-depth sound velocity profile according to an average sound velocity profile, a feature vector coefficient, and a second feature vector of the full-sea-depth historical sound velocity profile includes:
and calculating the product of the feature vector coefficient and the second feature vector, and determining the sum of the product and the average sound velocity profile of the whole-sea-depth historical sound velocity profile as the extended whole-sea-depth sound velocity profile.
In a possible implementation manner, the obtaining manner of the second feature vector includes:
determining an average sound velocity profile of the full-sea-depth historical sound velocity profile according to the sample data of the full-sea-depth historical sound velocity profile, and constructing a second residual matrix;
calculating an autocovariance matrix of the second residual error matrix, and solving an eigenvalue of the autocovariance matrix and a corresponding eigenvector;
and arranging the obtained eigenvalues from big to small, and selecting the eigenvector corresponding to the eigenvalue of the preset order arranged at the front as a second eigenvector.
In a possible implementation manner, the method for obtaining the full-sea-depth historical sound velocity profile sample data by preprocessing an acquired full-sea-depth historical sound velocity profile data set specifically includes: and performing depth interception on the full-sea-depth historical sound velocity profile data set according to the maximum depth of the actually measured sound velocity value of the full-sea-depth sound velocity profile to obtain full-sea-depth historical sound velocity profile sample data.
In a second aspect, an embodiment of the present invention provides a sound velocity profile continuation system based on orthogonal empirical function decomposition, including:
the data acquisition module is used for acquiring a sound velocity profile to be extended, the maximum depth of the actually measured sound velocity value of the sound velocity profile to be extended and historical sound velocity profile sample data of the whole sea depth;
the depth interception module is used for carrying out depth interception on the sample data of the full-sea-depth historical sound velocity profile according to the maximum depth of the actually-measured sound velocity value of the sound velocity profile to be extended to obtain the intercepted data of the historical sound velocity profile sample;
the processing module is used for performing empirical orthogonal function decomposition on the truncated data of the historical sound velocity profile sample to obtain a first feature vector of a preset order of the truncated data of the historical sound velocity profile sample, and performing matching field processing on the sound velocity profile to be extended by using the first feature vector to obtain a feature vector coefficient of the sound velocity profile to be extended corresponding to the first feature vector;
and the construction module is used for constructing the extended full-sea-depth sound velocity profile according to the characteristic vector coefficient and the sample data of the full-sea-depth historical sound velocity profile.
In a possible implementation, the building module is specifically configured to: performing empirical orthogonal function decomposition on the sample data of the full-sea-depth historical sound velocity profile to obtain an average sound velocity profile of the full-sea-depth historical sound velocity profile and a second eigenvector of a preset order;
and constructing the extended full-sea-depth sound velocity profile according to the average sound velocity profile, the characteristic vector coefficient and the second characteristic vector of the full-sea-depth historical sound velocity profile.
The above one or more technical solutions have the following beneficial effects:
according to the method, empirical orthogonal function decomposition is carried out on the truncated data of a historical sound velocity profile sample to obtain a first feature vector of a preset order of the truncated data of the historical sound velocity profile sample, and matching field processing is carried out on the sound velocity profile to be extended by utilizing the first feature vector to obtain a feature vector coefficient of the sound velocity profile to be extended corresponding to the first feature vector; and constructing a prolonged full-sea deep sound velocity profile according to the characteristic vector coefficient and the sample data of the full-sea deep historical sound velocity profile. By the method, the main characteristics of the sound velocity profile sample to be extended in the cut-off depth of the area in which the sound velocity profile sample is located can be fully captured, the characteristic vector coefficient and the characteristic vector can be used for high-precision matching reconstruction of the sound velocity profile sample to be extended in the cut-off depth area (shallow sea) with obvious sound velocity change, meanwhile, the data of the depth part of the historical full-sea sound velocity profile sample exceeding the sound velocity profile sample to be extended is used as reference constraint, the sound velocity distribution of the extended part is guaranteed to accord with the actual sound velocity distribution rule of the area, and more reliable reference samples are provided for sound velocity profile inversion.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a flow chart of a method for extending a sound velocity profile based on orthogonal empirical function decomposition according to an embodiment of the present invention;
FIG. 2 is an overall framework diagram of a sound velocity profile continuation method based on orthogonal empirical function decomposition according to an embodiment of the present invention;
fig. 3 is a diagram illustrating a comparison between the sound velocity profile extension and the sound velocity profile extension according to an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
Referring to fig. 1, the present embodiment provides a sound velocity profile continuation method based on orthogonal empirical function decomposition, which specifically includes the following steps:
s101: and obtaining sample data of the sound velocity profile to be extended, the maximum depth of the actually measured sound velocity value of the sound velocity profile to be extended and the historical sound velocity profile of the whole sea depth.
In the specific implementation, the maximum depth of the actually measured sound velocity value of the sound velocity profile to be extended is shallow seawater sound velocity profile data, and the historical sound velocity profile sample data of the full-sea depth comprises complete seawater sound velocity profile data obtained through multiple measurementsOf 1 atiSample data S of sound velocity profile of whole sea i Is represented as follows:
wherein the content of the first and second substances,sin order to be the value of the speed of sound,dfor the purpose of the depth value,d D is the maximum depth of the measured sound velocity value of the full-sea-depth sound velocity profile,s D is the sound velocity value of the sound velocity profile at full depth of sea,Iis the number of samples.
S102: and performing depth interception on the full-sea-depth historical sound velocity profile sample data according to the maximum depth of the actually measured sound velocity value of the sound velocity profile to be extended to obtain historical sound velocity profile sample interception data.
In specific implementation, the sound velocity profile sample data S of the whole sea depth history i Performing partial depth 0-d truncation:d d the maximum depth of the actually measured sound velocity value of the sound velocity profile to be extended is reserved for the part, the 0-d depth interval part of each historical sound velocity profile sample is reserved, and the truncated data of the historical sound velocity profile sample
Expressed as:
。
s103: and performing empirical orthogonal function decomposition on the truncated data of the historical sound velocity profile sample to obtain a first feature vector of a preset order of the truncated data of the historical sound velocity profile sample, and performing matching field processing on the sound velocity profile to be extended by using the first feature vector to obtain a feature vector coefficient of the sound velocity profile to be extended corresponding to the first feature vector.
In specific implementation, the empirical orthogonal function analysis method is utilized to cut off data of historical sound velocity profile samples
Performing empirical orthogonal function decomposition to obtain historical sound velocity profileThe method comprises the steps of truncating a first feature vector of a preset order of data by a surface sample, and then carrying out matching field processing on a sound velocity profile to be extended by utilizing the first feature vector to obtain a feature vector coefficient of the sound velocity profile to be extended corresponding to the first feature vector.
The obtaining mode of the first feature vector comprises the following steps: determining an average sound velocity profile of the sample truncation data of the historical sound velocity profile according to the sample truncation data of the historical sound velocity profile, and constructing a first residual matrix; calculating an autocovariance matrix of the first residual error matrix, and solving an eigenvalue of the autocovariance matrix and a corresponding eigenvector; and arranging the obtained eigenvalues from big to small, and selecting the eigenvector corresponding to the eigenvalue of the preset order arranged at the front as the first eigenvector.
Specifically, the average sound velocity profile of the truncated data of the historical sound velocity profile sample is as follows:
constructing a first residual matrix:
the autocovariance matrix of the first residual matrix is:
wherein, the first and the second end of the pipe are connected with each other,C S,d for a matrix of (d + 1) × (d + 1) order, calculateC S,d Eigenvalue matrix of
And eigenvector matrix V d And satisfies the following conditions:
wherein the content of the first and second substances,
for the diagonal matrix of order N:
n is the order of the characteristic value, V d Is a (d + 1) x N order matrix, each column of which is a feature vector, V d Can be expressed as
Characteristic value
And feature vector
And (7) corresponding.
Arranging the obtained eigenvalues from big to small, and selecting eigenvectors corresponding to the top n eigenvalues
The first eigenvector of a preset order of the data is truncated for the historical sound velocity profile samples, where the order value is typically an integer between 3-5.
The matching field processing is performed on the sound velocity profile to be extended by using the first feature vector to obtain a feature vector coefficient of the sound velocity profile to be extended corresponding to the first feature vector, and the method comprises the following steps: determining a residual vector between the sound velocity profile to be extended and an average sound velocity profile of sample truncation data of the historical sound velocity profile; and determining a characteristic vector coefficient of the sound velocity profile to be extended corresponding to the first characteristic vector according to the residual vector and the first characteristic vector. Optionally, the feature vector coefficient is a product of a transpose of the first feature vector and the residual vector.
In particular, the acoustic velocity profile to be extended may be expressed as
The sound velocity value vector of the sound velocity profile to be extended is expressed as
Wherein, in the step (A),trepresenting the sound velocity profile to be extended as test data, and the residual vector between the test data and the average sound velocity profile of the truncation data of the 0-d depth historical sound velocity profile sampleS X,t Comprises the following steps:
performing a matching field processing process on the sound velocity profile to be extended by utilizing the first n-order eigenvector of the depth from 0 to d, calculating the projection of the sound velocity profile to be extended on the first n-order eigenvector, and acquiring the eigenvector coefficient of the projection of the first n-order eigenvector of the sound velocity profile to be extended on the depth from 0 to dCf d ：
Here, the number of the first and second electrodes,
is the first feature vector
The transposing of (1).
S104: and constructing a prolonged full-sea deep sound velocity profile according to the characteristic vector coefficient and the sample data of the full-sea deep historical sound velocity profile.
Specifically, in S104, the constructing an extended full-sea-depth sound velocity profile according to the eigenvector coefficients and the sample data of the full-sea-depth historical sound velocity profile includes: performing empirical orthogonal function decomposition on the sample data of the full-sea-depth historical sound velocity profile to obtain an average sound velocity profile of the full-sea-depth historical sound velocity profile and a second feature vector of a preset order; and constructing the extended full-sea-depth sound velocity profile according to the average sound velocity profile, the characteristic vector coefficient and the second characteristic vector of the full-sea-depth historical sound velocity profile.
As shown in fig. 2, preprocessing the acquired full-sea-depth historical sound velocity profile data set to obtain full-sea-depth historical sound velocity profile sample data, specifically including: according to the measured maximum depth of the sound velocity value of the full-sea-depth sound velocity profiled D And carrying out depth interception on the full-sea-depth historical sound velocity profile data set to obtain full-sea-depth historical sound velocity profile sample data, namely reserving 0-D full-sea-depth sound velocity profile sample S in the part i 。
The second feature vector is obtained in a manner that: determining an average sound velocity profile of the full-sea-depth historical sound velocity profile according to the sample data of the full-sea-depth historical sound velocity profile, and constructing a second residual matrix; calculating an autocovariance matrix of the second residual error matrix, and solving an eigenvalue of the autocovariance matrix and a corresponding eigenvector; and arranging the obtained eigenvalues from large to small, and selecting the eigenvector corresponding to the eigenvalue of the preset order arranged at the front as a second eigenvector.
In specific implementation, the average acoustic velocity profile of the 0-D full-sea deep acoustic velocity profile sample is:
constructing a second residual matrix of the 0-D full-sea sound velocity profile sample:
the autocovariance matrix of the second residual matrix is:
wherein, C S,D For a matrix of order (D + 1) × (D + 1), C is calculated S,D Characteristic value of (2)
And a feature vector V D And satisfies the following conditions:
wherein the characteristic value
For diagonal matrices:
where M is the order of the eigenvalues, V D Is a matrix of order (D + 1) xM, each column of which is a feature vector, V D Can be expressed as
Characteristic value of
And feature vector
And (7) correspondingly.
Arranging the diagonal elements of the eigenvalue matrix of the auto-covariance matrix corresponding to the second residual matrix in the 0-D full sea depth through empirical orthogonal function decomposition from large to small, and selecting the first m-order eigenvector
Here, the preset order m = n takes an integer of 3-5, and in the present embodiment, m = n =3 may be taken.
The method for constructing the extended full-sea-depth sound velocity profile according to the average sound velocity profile, the characteristic vector coefficient and the second characteristic vector of the full-sea-depth historical sound velocity profile comprises the following steps: and calculating the product of the characteristic vector coefficient and the second characteristic vector, and determining the sum of the product and the average sound velocity profile of the whole-sea-depth historical sound velocity profile as the extended whole-sea-depth sound velocity profile.
In the practice, the method utilizesCharacteristic vector coefficient of 0-d depth front n-order characteristic vector during continuation sound velocity profile matchingCf d 0-D average sound velocity profile at full depth
And first m order eigenvectors
Combined construction of extended full-sea deep acoustic velocity profile
The method specifically comprises the following steps:
extended acoustic velocity profile
Expressed as:
Example two
The embodiment of the invention also provides a sound velocity profile continuation system based on orthogonal empirical function decomposition, which comprises:
the data acquisition module is used for acquiring a sound velocity profile to be extended, the maximum depth of the actually measured sound velocity value of the sound velocity profile to be extended and historical sound velocity profile sample data of the whole sea depth;
the depth interception module is used for carrying out depth interception on the sample data of the full-sea-depth historical sound velocity profile according to the maximum depth of the actually-measured sound velocity value of the sound velocity profile to be extended to obtain the intercepted data of the historical sound velocity profile sample;
the processing module is used for performing empirical orthogonal function decomposition on the truncated data of the historical sound velocity profile sample to obtain a first feature vector of a preset order of the truncated data of the historical sound velocity profile sample, and performing matching field processing on the sound velocity profile to be extended by using the first feature vector to obtain a feature vector coefficient of the sound velocity profile to be extended corresponding to the first feature vector;
and the construction module is used for constructing the extended full-sea-depth sound velocity profile according to the characteristic vector coefficients and the sample data of the full-sea-depth historical sound velocity profile.
As an optional implementation manner, the building module is specifically configured to: performing empirical orthogonal function decomposition on the sample data of the full-sea-depth historical sound velocity profile to obtain an average sound velocity profile of the full-sea-depth historical sound velocity profile and a second eigenvector of a preset truncation order;
and constructing the extended full-sea-depth sound velocity profile according to the average sound velocity profile, the characteristic vector coefficient and the second characteristic vector of the full-sea-depth historical sound velocity profile.
The sound velocity profile continuation system based on orthogonal empirical function decomposition provided in this embodiment is used to implement the sound velocity profile continuation method based on orthogonal empirical function decomposition, so that a specific implementation manner in the sound velocity profile continuation system based on orthogonal empirical function decomposition may be found in the foregoing example part of the sound velocity profile continuation method based on orthogonal empirical function decomposition, and details are not described here.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A sound velocity profile continuation method based on orthogonal empirical function decomposition is characterized by comprising the following steps:
acquiring sample data of a sound velocity profile to be extended, the maximum depth of the actually measured sound velocity value of the sound velocity profile to be extended and historical sound velocity profiles of the whole sea depth;
according to the maximum depth of the actually measured sound velocity value of the sound velocity profile to be extended, carrying out depth interception on the sample data of the full-sea-depth historical sound velocity profile to obtain the intercepted data of the historical sound velocity profile sample;
performing empirical orthogonal function decomposition on the sample truncation data of the historical sound velocity profile to obtain a first feature vector of a preset order of the sample truncation data of the historical sound velocity profile, and performing matching field processing on the sound velocity profile to be extended by using the first feature vector to obtain a feature vector coefficient corresponding to the sound velocity profile to be extended in the first feature vector;
and constructing a prolonged full-sea deep sound velocity profile according to the characteristic vector coefficient and the sample data of the full-sea deep historical sound velocity profile.
2. The method according to claim 1, wherein the obtaining of the first eigenvector comprises:
determining an average sound velocity profile of the truncated data of the historical sound velocity profile sample according to the truncated data of the historical sound velocity profile sample, and constructing a first residual error matrix;
calculating an autocovariance matrix of the first residual error matrix, and solving an eigenvalue of the autocovariance matrix and a corresponding eigenvector;
and arranging the obtained eigenvalues from big to small, and selecting the eigenvector corresponding to the eigenvalue of the preset order arranged at the front as the first eigenvector.
3. The sound velocity profile continuation method based on orthogonal empirical function decomposition according to claim 1, wherein the obtaining a feature vector coefficient of the sound velocity profile to be extended corresponding to the first feature vector by performing matching field processing on the sound velocity profile to be extended by using the first feature vector includes:
determining a residual vector between the sound velocity profile to be extended and an average sound velocity profile of sample truncation data of the historical sound velocity profile;
and determining a characteristic vector coefficient of the sound velocity profile to be extended corresponding to the first characteristic vector according to the residual error vector and the first characteristic vector.
4. The method of claim 3, wherein the eigenvector coefficients are products of transposes of the first eigenvector and residual vectors.
5. The method for extending the sound velocity profile based on the orthogonal empirical function decomposition of claim 1, wherein the constructing an extended full-sea-depth sound velocity profile according to the eigenvector coefficients and the sample data of the full-sea-depth historical sound velocity profile comprises:
performing empirical orthogonal function decomposition on the sample data of the full-sea-depth historical sound velocity profile to obtain an average sound velocity profile of the full-sea-depth historical sound velocity profile and a second feature vector of a preset order;
and constructing the extended full-sea-depth sound velocity profile according to the average sound velocity profile, the characteristic vector coefficient and the second characteristic vector of the full-sea-depth historical sound velocity profile.
6. The method for extending the sound velocity profile based on the orthogonal empirical function decomposition of claim 5, wherein the constructing the extended whole-sea-depth sound velocity profile according to the average sound velocity profile, the eigenvector coefficients and the second eigenvector of the whole-sea-depth historical sound velocity profile comprises:
and calculating the product of the characteristic vector coefficient and the second characteristic vector, and determining the sum of the product and the average sound velocity profile of the whole-sea-depth historical sound velocity profile as the extended whole-sea-depth sound velocity profile.
7. The method according to claim 5, wherein the second eigenvector is obtained by:
determining an average sound velocity profile of the full-sea-depth historical sound velocity profile according to the sample data of the full-sea-depth historical sound velocity profile, and constructing a second residual matrix;
calculating an autocovariance matrix of the second residual error matrix, and solving an eigenvalue of the autocovariance matrix and a corresponding eigenvector;
and arranging the obtained eigenvalues from large to small, and selecting the eigenvector corresponding to the eigenvalue of the preset order arranged at the front as a second eigenvector.
8. The method for extending a sound velocity profile based on orthogonal empirical function decomposition of claim 1, wherein the step of preprocessing the acquired full-sea-depth historical sound velocity profile data set to obtain sample data of the full-sea-depth historical sound velocity profile specifically comprises the steps of: and performing depth interception on the full-sea-depth historical sound velocity profile data set according to the maximum depth of the actually measured sound velocity value of the full-sea-depth sound velocity profile to obtain full-sea-depth historical sound velocity profile sample data.
9. An acoustic velocity profile continuation system based on orthogonal empirical function decomposition, comprising:
the data acquisition module is used for acquiring a sound velocity profile to be extended, the maximum depth of the actually measured sound velocity value of the sound velocity profile to be extended and historical sound velocity profile sample data of the whole sea depth;
the depth interception module is used for carrying out depth interception on the sample data of the full-sea-depth historical sound velocity profile according to the maximum depth of the actually-measured sound velocity value of the sound velocity profile to be extended to obtain the intercepted data of the historical sound velocity profile sample;
the processing module is used for performing empirical orthogonal function decomposition on the sample truncation data of the historical sound velocity profile to obtain a first feature vector of a preset order of the sample truncation data of the historical sound velocity profile, and performing matching field processing on the sound velocity profile to be extended by using the first feature vector to obtain a feature vector coefficient of the sound velocity profile to be extended corresponding to the first feature vector;
and the construction module is used for constructing the extended full-sea-depth sound velocity profile according to the characteristic vector coefficients and the sample data of the full-sea-depth historical sound velocity profile.
10. The orthogonal empirical function decomposition-based sound velocity profile continuation system of claim 9, wherein the construction module is specifically configured to: performing empirical orthogonal function decomposition on the sample data of the full-sea-depth historical sound velocity profile to obtain an average sound velocity profile of the full-sea-depth historical sound velocity profile and a second feature vector of a preset order;
and constructing an extended full-sea-depth sound velocity profile according to the average sound velocity profile, the characteristic vector coefficient and the second characteristic vector of the full-sea-depth historical sound velocity profile.
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