CN108680950B - A kind of desert seismic signal method for detecting position based on Self-adaptive Block Matching - Google Patents
A kind of desert seismic signal method for detecting position based on Self-adaptive Block Matching Download PDFInfo
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- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
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- G—PHYSICS
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- 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
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Abstract
The present invention relates to a kind of desert seismic signal method for detecting position based on Self-adaptive Block Matching belongs to the method for detecting position of useful signal under actual seismic exploration environment.It regards seismic signal as high dimensional data, the region of useful signal is substantially determined by spectrum multiple manifold clustering algorithm, in the region selection standard block；Original seismic signal is divided into various sizes of data block, and calculates its energy differences between calibrated bolck, obtains energy differences matrix, the match block of adaptive reservation optimal size realizes signal location detection.The present invention can accurately detection low signal-to-noise ratio under desert seismic signal position while stick signal feature, to seismic prospecting have biggish application value.
Description
Technical field
The present invention relates to the method for detecting position that actual seismic explores useful signal under environment, pass through seismic signal
Seismic signal is extracted from observation data, and is further explained and handles by position detection.
Background technique
Seismic signal position detection is the basis of seismic signal analysis and processing.It, will by the position detection of seismic signal
Seismic signal is extracted from observation data, is further explained and is handled.In recent years, the complicated earth surfaces geology such as desert
Under the conditions of seismic prospecting ever more important, signal location detect to desert region deep seism processing and reservoir of oil and gas
Identification etc. have important practical significance.
Spectrum multiple manifold cluster is introduced into the detection of seismic prospecting signal location with Block- matching theory, compensates for traditional lineups
The deficiency with complex nonlinear structured data cannot be effectively treated in localization method.
Traditional clustering method assumes that data are gathered near some prototypes, using the Euclidean distance between data point come
Its " similitude " is measured, and has ignored the potential geometry of data point formation.However the distribution of many truthful datas is usually certainly
It so is gathered near the manifold of some low-dimensionals.Spectrum multiple manifold cluster (SMMC) be a kind of local similarity for containing manifold and
The clustering method of two kinds of geometric similarity constraints.By the accuracy for improving cluster result to the change of similarity matrix.
Block- matching theory can obtain the internal association between local data's block, can preferably retain the details of lineups
Information, to realize the matching and identification of seismic data useful signal.Traditional block-matching technique using artificial partitioned mode and
The size of block immobilizes, and ignores sub-block and includes characteristic information, and Block- matching theory bind profile multiple manifold clustering technique, sufficiently examines
Consider the space-time characteristic difference and its local tangent space structure of useful signal and noise, building includes the Nonlinear Dimension Reduction of block feature
Model, according to characteristic similarity matching criterior, being effectively matched between realization set of metadata of similar data sub-block, thus in low-quality seismic data
In detect effective signal location, it is accurately positioned.
Summary of the invention
The present invention provides a kind of desert seismic signal method for detecting position based on Self-adaptive Block Matching.By composing multiple manifold
The natural local geometry information that clustering algorithm makes full use of manifold sampled point to be included carrys out the more suitable phase of auxiliary construction
Like property matrix, and the approximate region of signal location is finally obtained, in this region selection standard block, is calculated by adaptive Block- matching
Method can obtain the internal association between local data's block, according to characteristic similarity matching criterior, realize between set of metadata of similar data sub-block
Be effectively matched, the preferable detailed information for retaining lineups, to realize the detection of desert seismic data useful signal position.
The technical solution adopted by the present invention is that including the following steps:
(1) when seismic prospecting field data collection, it is equally spaced Z geophone group along seismic survey lines, geophone group
Quantity 20~2000, after exciting shot point, each available one of seismic prospecting record of wave detector, Z trace record forms a width
This two-dimensional seismic prospecting record matrixing is indicated, forms the matrix of a M × N by two-dimensional seismic prospecting record,
In, M ∈ [40,1200], N ∈ [20,100]；
(2) Position Approximate of desert seismic signal is extracted with spectrum multiple manifold clustering algorithm to the matrix of M × N of formation
Region determines calibrated bolck, calculates its energy value E；
1) similarity matrix W is constructed:
Calculate the matrix midpoint x of M × NiWith point xjBetween similitude weight wij, by similitude weight wijConstitute similitude
Matrix W={ wij, W ∈ RM×N,i∈[1,M],j∈[1,N]；
2) diagonal matrix D={ d is calculatedii, wherein
3) matrix L is defined, L=D-W carries out spectral factorization to it, obtains the corresponding feature vector of minimum k characteristic value
μ1,…,μk；
4) remember U=[μ1…μk]∈RN×k, U is clustered, k classification, i.e. useful signal region and noise range are obtained
Domain；
5) by the available useful signal region of result after cluster, choosing size in this region is m0×n0Standard
Block, and calculate the energy value E of calibrated bolck:
Wherein, m0、n0Respectively represent the length and width of calibrated bolck, m0∈ [3,24], n0It is representated by ∈ [3,24], A
Data matrix corresponding to calibrated bolck centered on point (i, j)；
(3) it centered on point (i, j), is spread to its surrounding, is respectively formed m1×n1、m2×n2、m3×n3The matching of size
Block calculates separately its energy value according to formula (1), obtains Ei1、Ei2、Ei3, wherein i ∈ [1, M], j ∈ [1, N], m1、m2、m3Respectively
For the length of match block under different sizes, n1、n2、n3The width of match block, m under respectively different sizes1,m2,m3∈ [3,24],
n1,n2,n3∈[3,24]；
(4) match block ENERGY E is calculated separatelyi1、Ei2、Ei3With the difference of calibrated bolck ENERGY E, and result is put into new square
In battle array Q:
Wherein, [1, M] i ∈；
(5) the smallest S value, S ∈ [1,10] are found in matrix Q, and determines that S is worth corresponding match block position, by S
The data and location information of a value Corresponding matching block are put into the size of new matrix Y, Y matrix and the size of formed M × N matrix
Unanimously, at this point, can determine signal location in two-dimensional seismic survey record, S value corresponding matching block energy and calibrated bolck
The difference of energy is smaller, illustrates that the similitude of these match blocks and calibrated bolck is stronger, and the useful signal contained in match block is more,
The preferable detailed information for retaining lineups establishes solid foundation for subsequent determining signal location.
The present invention has the advantages that the method for for the first time combining spectrum multiple manifold Clustering Theory with Self-adaptive Block Matching theory
It is introduced into the seismic signal position detection of desert, constructs similarity matrix using the structural information of local tangent space, establish feature
It extracts model to be classified accordingly based on different classes of data characteristics difference, substantially determines the position of useful signal,
According to the feature difference between the characteristic similarity and signal and noise between useful signal, combining adaptive Block- matching is theoretical,
It may finally identify that useful signal lineups have and are accurately positioned to it from low-quality desert seismic data.The present invention
Traditional lineups localization method that complex nonlinear structured data cannot be effectively treated is improved well.
Detailed description of the invention
Fig. 1 is one 24, the artificial synthesized desert earthquake record of 512 sampled points of per pass；
Fig. 2 is artificial desert seismic signal width detection column comparison diagram；
Fig. 3 is the practical exploration record of Tarim Basin desert earthquake, chooses at 500 points；
Fig. 4 a is the practical desert signal location testing result obtained using traditional block matching method；
Fig. 4 b is the practical desert seismic signal position detection result obtained using the present invention；
Fig. 5 a is directly to carry out shearlet denoising result figure to practical desert seismic data；
Fig. 5 b is to carry out shearlet denoising to detection signal area to after practical desert seismic signal position detection.
Specific embodiment
Include the following steps:
(1) when seismic prospecting field data collection, it is equally spaced Z geophone group along seismic survey lines, geophone group
Quantity 20~2000, after exciting shot point, each available one of seismic prospecting record of wave detector, Z trace record forms a width
This two-dimensional seismic prospecting record matrixing is indicated, forms the matrix of a M × N by two-dimensional seismic prospecting record,
In, M ∈ [40,1200], N ∈ [20,100]；
(2) Position Approximate of desert seismic signal is extracted with spectrum multiple manifold clustering algorithm to the matrix of M × N of formation
Region determines calibrated bolck, calculates its energy value E；
1) spectrum multiple manifold cluster (SMMC) is a kind of local similarity for containing manifold and two kinds of geometric similarity constraints
Clustering method, by improving the accuracy of cluster result to the change of similarity matrix, firstly, construction similarity matrix W:
Calculate the matrix midpoint x of M × NiWith point xjBetween similitude weight wij, by similitude weight wijConstitute similitude
Matrix W={ wij, W ∈ RM×N,i∈[1,M],j∈[1,N]；
2) diagonal matrix D={ d is calculatedii, wherein
3) matrix L is defined, L=D-W carries out spectral factorization to it, obtains the corresponding feature vector of minimum k characteristic value
μ1,…,μk；
4) remember U=[μ1…μk]∈RN×k, U is clustered, k classification, i.e. useful signal region and noise range are obtained
Domain；
5) by the available useful signal region of result after cluster, choosing size in this region is m0×n0Standard
Block, and calculate the energy value E of calibrated bolck:
Wherein, m0、n0Respectively represent the length and width of calibrated bolck, m0∈ [3,24], n0It is representated by ∈ [3,24], A
Data matrix corresponding to calibrated bolck centered on point (i, j)；
(3) it centered on point (i, j), is spread to its surrounding, is respectively formed m1×n1、m2×n2、m3×n3The matching of size
Block calculates separately its energy value according to formula (1), obtains Ei1、Ei2、Ei3, wherein i ∈ [1, M], j ∈ [1, N], m1、m2、m3Respectively
For the length of match block under different sizes, n1、n2、n3The width of match block, m under respectively different sizes1,m2,m3∈ [3,24],
n1,n2,n3∈[3,24]；
(4) match block ENERGY E is calculated separatelyi1、Ei2、Ei3With the difference of calibrated bolck ENERGY E, and result is put into new square
In battle array Q:
Wherein, [1, M] i ∈；
(5) the smallest S value is found in matrix Q, S takes 3, and determines that 3 are worth corresponding match block position, and 3 are worth
The data and location information of Corresponding matching block are put into the size of new matrix Y, Y matrix and the size one of formed M × N matrix
It causes, at this point, can determine signal location in two-dimensional seismic survey record, this 3 values corresponding matching block energy and calibrated bolck
The difference of energy is smaller, illustrates that the similitude of these match blocks and calibrated bolck is stronger, and the useful signal contained in match block is more,
The preferable detailed information for retaining lineups establishes solid foundation for subsequent determining signal location.
Applicating example: Tarim Basin desert seismic signal position detection
Fig. 1 is one 24, the artificial synthesized desert earthquake record of 512 sampled points of per pass.
The comparison of the artificial synthesized desert Earthquake signal detection of table 1
| Title | Detect sampling number | Accuracy rate |
| Traditional Block- matching | 391 | 50.19% |
| The present invention | 685 | 87.93% |
In order to prove superiority of the invention, traditional block matching algorithm will be selected to test as a comparison, traditional Block- matching is calculated
Method is fixed block mode, and whole picture record piecemeal size immobilizes, and block size selects 7 × 3 respectively；It is proposed by the present invention to be based on
Self-adaptive Block Matching method, calibrated bolck block size are 15 × 13, and match block block size is 7 × 3,9 × 5,11 × 7；
Fig. 2 is artificial desert seismic signal width detection column comparison diagram, is found out in figure, and it is bright that the present invention detects signal width
It is aobvious to be better than traditional block matching method, signal is not detected in the 1st, the 8th tradition block matching algorithm；
Fig. 3 is the practical exploration record of Tarim Basin desert earthquake, chooses at 500 points；
To the processing of practical desert earthquake record, Fig. 4 a is the practical desert signal location detection that traditional block matching method obtains
As a result, Fig. 4 b is the practical desert seismic signal position detection obtained using the present invention as a result, it can be seen from the figure that the present invention
Method can accurately detect signal location under practical desert seismic data, effect is obvious, and traditional block matching algorithm pair
The testing result inaccuracy in practical desert, effect are very undesirable.
Shearlet denoising is directly carried out to practical desert seismic data, as a result as shown in Figure 5 a, to detection signal area
Shearlet denoising is carried out, as a result as shown in Figure 5 b, comparison diagram 5a, Fig. 5 b, which can be seen that, directly denoises desert seismic data,
Effect is unobvious, and noise residual is serious, and is denoised again after detecting signal location, can removal noise more efficiently, go
Effect of making an uproar is substantially better than direct denoising.
Claims (1)
1. a kind of desert seismic signal method for detecting position based on Self-adaptive Block Matching, includes the following steps:
(1) when seismic prospecting field data collection, Z geophone group, the quantity of geophone group are equally spaced along seismic survey lines
20~2000, after exciting shot point, each available one of seismic prospecting record of wave detector, Z trace record forms width two dimension
Seismic prospecting record, this two-dimensional seismic prospecting record matrixing is indicated, forms the matrix of a M × N, wherein M ∈
[40,1200], N ∈ [20,100]；
(2) the Position Approximate region of desert seismic signal is extracted with spectrum multiple manifold clustering algorithm to the matrix of M × N of formation,
It determines calibrated bolck, calculates its energy value E；
1) similarity matrix W is constructed:
Calculate the matrix midpoint x of M × NiWith point xjBetween similitude weight wij, by similitude weight wijConstitute similarity matrix
W={ wij, W ∈ RM×N,i∈[1,M],j∈[1,N]；
2) diagonal matrix D={ d is calculatedii, wherein
3) matrix L is defined, L=D-W carries out spectral factorization to it, obtains the corresponding feature vector of minimum k characteristic value
μ1,…,μk；
4) remember U=[μ1…μk]∈RN×k, U is clustered, k classification, i.e. useful signal region and noise region are obtained；
5) by the available useful signal region of result after cluster, choosing size in this region is m0×n0Calibrated bolck, and
Calculate the energy value E of calibrated bolck:
Wherein, m0、n0Respectively represent the length and width of calibrated bolck, m0∈ [3,24], n0It is with point representated by ∈ [3,24], A
Data matrix corresponding to calibrated bolck centered on (i, j)；
(3) it centered on point (i, j), is spread to its surrounding, is respectively formed m1×n1、m2×n2、m3×n3The match block of size, according to
Its energy value is calculated separately according to formula (1), obtains Ei1、Ei2、Ei3, wherein i ∈ [1, M], j ∈ [1, N], m1、m2、m3Respectively not
With the length of match block under size, n1、n2、n3The width of match block, m under respectively different sizes1, m2, m3∈ [3,24], n1,
n2, n3∈[3,24]；
(4) match block ENERGY E is calculated separatelyi1、Ei2、Ei3With the difference of calibrated bolck ENERGY E, and result is put into new matrix Q
In:
αi=| Ei1-E|、βi=| Ei2-E|、γi=| Ei3-E|、
Wherein, [1, M] i ∈；
(5) the smallest S value, S ∈ [1,10] are found in matrix Q, and determine that S is worth corresponding match block position, and S is worth
The data and location information of Corresponding matching block are put into the size of new matrix Y, Y matrix and the size one of formed M × N matrix
It causes, at this time, it is determined that signal location in two-dimensional seismic survey record.
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| CN114185080B (en) * | 2020-09-15 | 2024-09-27 | 中国石油化工股份有限公司 | Three-dimensional seismic acquisition detector layout quality detection method and detection system |
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